# document

id: f9b1b5d5c000ec54cb59539c03480fe4
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 41294
content:
"[...] of research since AR5 has focused on the development of 'storylines' \r for regional atmospheric circulation change (Shepherd,  2019). "


id: f0105639c80fc26735950ad260a17cd9
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 39615
content:
"The CMIP5 projections suggest that the response of blocking \r frequency to climate change might be quite complex (Dunn-Sigouin \r et  al., 2013; Masato et  al., 2013). An eastward shift of winter \r blocking activity in the NH is indicated (Masato et al., 2013; Kitano \r and Yamada, 2016; Lee and Ahn, 2017; Matsueda and Endo, 2017) \r while during boreal summer, blocking frequency tends to decrease \r in mid-latitudes (Matsueda and Endo, 2017), with the exception of \r the eastern Europe-western Russia region (Masato et al., 2013). The \r projected decrease of blocking in boreal summer partially contrasts \r with the observed increase in Greenland blocking (Hanna et al., 2018; \r Davini and D'Andrea, 2020). However, as shown in Woollings et al. \r (2018), the spatial distribution and the magnitude of the suggested \r changes are sensitive to the blocking detection methods (Schwierz \r et al., 2004; Barriopedro et al., 2010; Davini et al., 2012). In the SH, \r [...]"


id: a559f157d076a72af56ef1a412edf6cc
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 26319
content:
"[...] blocking frequency is projected to decrease in the Pacific sector \r during austral spring and summer. However, seasonal and regional \r changes are not totally consistent across the models (Parsons et al., \r 2016), and, as assessed in Section 3.3.3.3, model biases might affect \r their response."


id: b29cea719759d738823ca09855d148c6
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 28663
content:
"Projections with CMIP6 ESMs (Kwiatkowski et  al., 2020) show \r a surface pH decline of -0.16 +- 0.002 (+-1 standard deviation) under \r SSP1-2.6 and -0.44 +- 0.005 under SSP5-8.5 from 1870-1899 to \r 2080-2099. The high-latitude oceans, in particular the Arctic, show \r greater decline in pH and accelerated acidification (Terhaar et  al., \r 2020). For the same period, model-projected bottom-water pH \r decline is -0.018 +- 0.001 under SSP1-2.6 and -0.030 +- 0.002 under \r SSP5-8.5. The projected large-scale surface ocean acidification will be \r primarily determined by the pathway of atmospheric CO2, with weak \r dependence on change in climate (high confidence) (Section 5.3.4.1; \r Hurd et al., 2018). However, for a given atmospheric CO2 scenario, \r uncertainty in projected ocean acidification increases with ocean \r depth because of model-simulated differences in ocean circulation \r that transports anthropogenic CO2 from the surface to bottom ocean \r [...]"


id: f003e4ef23049515ac226ac3cf9a1b7d
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 39607
content:
"[...] (high confidence) (Kwiatkowski et al., 2020). For example, projected \r surface pH fully separates between SSPs scenarios before 2050, but \r some overlap across SSPs is still found for projected bottom-water pH \r in 2080 (Kwiatkowski et al., 2020)."


id: f6528aab853b80400f3671c8774976b6
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 40704
content:
"The AR5 assessed it is likely that the evolution of the SAM over the \r 21st century will be primarily determined by the interplay between \r the effects of ozone recovery and changing GHG concentrations and \r influence the SAM in opposing ways. Owing to the relative effects \r of these two drivers, CMIP5 model SAM and Southern Hemisphere \r circulation projections differed markedly across forcing scenarios \r and across seasons (Barnes and Polvani, 2013; Barnes et al., 2014). \r CMIP5 models simulated a  weak negative SAM trend in austral \r summer for RCP4.5 by the end of the century (F. Zheng et al., 2013), \r while for RCP8.5 they simulated a weak positive SAM trend in austral \r summer (F. Zheng et al., 2013). A substantial fraction of the spread in \r CMIP5 projections of the end of century SH summer jet shift under \r RCP8.5 may be attributable to differences in the simulated change in \r break-up of the stratospheric polar vortex, with models that produce \r [...]"


id: c02480e79a88d790f0ca6eefdf96df79
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
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vector_id: 31098
content:
"[...] a later break-up date showing a larger summertime poleward jet shift \r (Ceppi and Shepherd, 2019). For RCP2.6, the effect of ozone recovery \r on the SAM has been found to dominate over that of GHGs in austral \r summer (Eyring et  al., 2013). In austral winter, the poleward shift \r of the SH circulation in CMIP5 models, and the associated increase \r in the SAM index, tends to be larger, on average, in higher forcing \r scenarios though with substantial inter-model spread (Barnes et al., \r 2014). New research since the AR5 shows that the previous theory \r for the apparent relationship across models between the annual \r mean climatological SH jet position and the amplitude of forced SH \r jet shift (Kidston and Gerber, 2010) does not hold at seasonal time \r scales (Simpson and Polvani, 2016)."


id: af25ce6bc45f00e2ed1d80f45d2de126
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
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vector_id: 28058
content:
"There is no consensus on changes in amplitude of ENSO SST \r variability across CMIP iterations. The main factors driving the \r diversity of ENSO SST amplitude change in climate models are internal \r variability, SST-mean warming pattern, and model systematic biases. \r First, pronounced low-frequency modulations of ENSO exist even in \r unforced control simulations due to internal variability, which leads \r a large uncertainty in quantifying future ENSO changes (Wittenberg, \r 2009; Vega-Westhoff and Sriver, 2017; Zheng et al., 2018). Second, \r ENSO characteristics depend on the climate mean state of the tropical \r Pacific; however, ENSO can also influence the mean state through \r non-linear processes (Cai et al., 2015; Timmermann et al., 2018). The \r response of the tropical Pacific mean state to anthropogenic forcing \r is characterized by a  faster warming on the equator compared to \r the off-equatorial region, a faster warming of the eastern equatorial \r [...]"


id: b0db6ada81cd0e402db595dd29b2facf
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
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vector_id: 28343
content:
"[...] Pacific compared to the central tropical Pacific (e.g., El Nino-like mean \r SST warming, see Section  7.4.4.2), and a  weakening of the Walker \r circulation in most models. Those models with a El Nino-like warming \r tend to project a  strengthening of ENSO SST variability whereas \r models with a La Nina-like warming tend to project a weakening of \r variability (Zheng et al., 2016; Kohyama and Hartmann, 2017; J. Wang \r et  al., 2017b; Cai et  al., 2018a; Fredriksen et  al., 2020). Third, how \r to take model biases into account leads to different ENSO changes. \r Kim et  al. (2014) suggested that a  subset of CMIP5 models that "


id: 547da8f2bd0ca35bd5bf01a3d0cf6c90
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 12141
content:
"In the mid- to long-term, projected climate mean state changes in the \r tropical Indian Ocean are expected to resemble a positive IOD state, \r with faster warming in the west compared to the east (Cai et al., 2013; \r X.-T. Zheng et al., 2013). However, it was argued that this projected \r mean state change could be due to the large mean state biases in \r the simulated current climate and potentially not a realistic outcome \r (G. Li et al., 2016). Mean state biases also lead to lack of consensus \r on projected equatorial Indian Ocean SST variability and equatorial \r modes of climate variability independent of the IOD (DiNezio et al., \r 2020). If mean state change will indeed resemble a  positive IOD \r state, however, this would lead to a  reduction in the amplitude \r difference between positive and negative IOD events, but with no \r robust change in IOD frequency (Cai et al., 2013). For a small subset \r of CMIP5 models that simulate IOD events best, a  slight increase \r [...]"


id: 5e7fc339c708b55a57256ec175c8887f
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 13919
content:
"Several methodologies have been applied to estimate the spatial \r patterns of climate change associated with a given level of global \r warming. These include performing model simulations under \r stabilisation scenarios designed to achieve a  specific level of \r global warming, the analysis of epochs identified within transient \r simulations that systematically exceed different thresholds of global \r warming (e.g., Mitchell et al., 2017; Dosio et al., 2018; Hoegh-Guldberg \r et al., 2018; Kjellstrom et al., 2018), and analysis based on statistical \r methodologies that include empirical scaling relationships (ESR; \r Schleussner et al., 2017; Dosio and Fischer, 2018; Seneviratne et al., \r 2018) and statistical pattern scaling (e.g., Kharin et al., 2018). These \r different methodologies are assessed in some detail in Section 4.2.4 \r (James et  al., 2017) and generally provide qualitatively consistent \r results regarding changes in the spatial patterns of temperature and \r [...]"


id: 95d6ce537b2037b950161257981f854b
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 23560
content:
"AMV long-term changes under future warming scenarios have so \r far scarcely been investigated. A  study on the CMIP5 multi-model \r simulations under RCP8.5 scenario by (Villamayor et al., 2018) found \r no substantial differences in the simulated SST patterns (and  in \r the related tropical rainfall response) when RCP8.5, historical and \r piControl simulations are compared. Such results suggest that the \r AMV is not expected to change under global warming. A  more \r recent single-model large ensemble study (Hand et al., 2020) shows \r a  pronounced change in the AMV pattern under global warming \r linked to a strong reduction of the mean AMOC and its variability. \r However, since a  superposition of multiple processes controls the \r AMV, as extensively discussed in Annex IV (Section AIV.2.7), in \r Chapter  3 (Section  3.7.7), and in Chapter  9 (Section  9.2.3.1), the \r length of the RCP8.5 simulations might be not sufficient to properly \r [...]"


id: 6bef3a3d88618e8ba619a61ac8b926e9
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 16289
content:
"[...] evaluate the respective weight and interplay of internal components \r and influences from external forcing on AMV projections."


id: f1fe32d4236ff8b05b24d35be3bb881e
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
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"In this subsection, we present the projected patterns of climate change \r obtained following the epoch approach (also called the time-shift \r method, see Section 4.2.4) under the Tier 1 SSPs (SSP1-2.6, SSP2-4.5, \r SSP3-7.0 and SSP5-8.5). For each model simulation considered under \r each of these SSPs, 20-year moving averages of the global average \r atmospheric surface temperature are first constructed, then this time \r series is used to detect the first year during when GSAT exceeds the \r 1.5degC, 2degC, 3degC and 4degC thresholds with respect to the 1850-1900 \r (Cross-Chapter Box  11.1). The temperature thresholds are not \r exceeded in all the model simulations across the Tier 1 SSPs. That \r is, decreasing numbers of simulations are available for the analysis \r of patterns of change as the temperature threshold increases. For \r each simulation within which a  given temperature threshold is \r exceeded, a 20-year global climatology is subsequently constructed \r [...]"


id: 7026b2c11fe25fc84afa20ad490830ae
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 16983
content:
"[...] to represent that level of global warming, centred on the year for \r which the threshold was first exceeded. The composite of all such \r climatologies across the Tier 1 SSPs and model simulations constitute \r the spatial patterns of change for a  given temperature threshold. \r Some of the complexities of scaling patterns of climate change with \r different levels of global warming are also discussed in the following "


id: 78f2bbec7ecbfd8a8366f5de853d8296
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 18469
content:
"The largest increase in annual mean temperature is found in the \r high latitudes of the Northern Hemisphere across all levels of \r global warming (virtually certain) (Figure  4.31). This phenomenon \r peaks in the Arctic and is known as Arctic amplification, with the \r underlying physical mechanisms assessed in detail in Section 4.5.1 \r and Chapter  7 (Section  7.4.4.1). For the CMIP6 ensemble average \r considered here, Arctic annual mean temperatures warm by a factor \r of 2.3, 2.5, 2.4 and 2.4 for 1.5degC, 2degC, 3degC and 4degC of global warming, \r respectively. That is, Arctic warming scales approximately linearly \r with GSAT. Generally, when Arctic amplification is considered across \r individual models, warming occurs at a factor of two to four times the \r global level of warming. It is unlikely that warming in the Southern \r Hemisphere high latitudes in the 21st century will exceed the change \r in GSAT, or that it will substantially exceed warming in the tropics, \r [...]"


id: 2b500b33be277493a658cef398e5e945
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 4869
content:
"[...] for GSAT change ranging between 1.5degC and 4degC (Figure 4.31 and \r Table  4.2). Correspondingly, there is low confidence of Antarctic \r amplification occurring under transient, 21st century low mitigation \r scenarios (Table 4.2 and Section 7.4.4.1). The Antarctic continent is \r projected to warm at a higher rate than the mid-latitude Southern \r Ocean, however, at all levels of global warming (Figure  4.31). \r The relevant physical mechanisms that reduce the amplitude of polar \r amplification over Antarctica compared to the Arctic are assessed in \r detail in Section 4.5.1 and Chapter 7 (Section 7.4.4.1). In the Southern \r Hemisphere the strongest warming over land is to occur, at any given \r level of global warming, over the subtropical areas of South America, \r southern Africa and Australia (high confidence). The relatively \r strong warming in subtropical southern Africa may be attributed to \r strong soil-moisture-temperature coupling and projected increased \r [...]"


id: 93b9ed387f2943bc026fa4c9a7951c2b
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 23198
content:
"[...] dryness under enhanced subsidence (Engelbrecht et al., 2015; Vogel \r et al., 2017). Across the globe, in the tropics, subtropics, and mid\u0002to high latitudes, temperatures tend to scale linearly with the level \r of increase in GSAT and patterns of change are largely scenario \r independent (high confidence)."


id: db8d99f8dc0ef891092ef94c842dce02
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 35910
content:
"Global warming of 1.5degC implies higher mean temperatures \r compared to 1850-1900, with generally higher warming over land \r compared to ocean areas (virtually certain) and larger warming in \r high latitudes compared to low latitudes (Figure 4.31). In addition, \r global warming of 2degC versus 1.5degC results in robust increases in \r the mean temperatures in almost all locations, both on land and \r in the ocean (virtually certain), with subsequent further warming \r at almost all locations at higher levels of global warming (virtually \r certain) (Hoegh-Guldberg et al., 2018). For each particular level of \r global warming, relatively larger mean warming is projected for land \r regions (virtually certain) (see Figure 4.31; Christensen et al., 2013; \r Collins et al., 2013; Seneviratne et al., 2016). The projected changes \r at 1.5degC and 2degC global warming are consistent with observed \r historical global trends in temperature and their attribution to \r [...]"


id: 200ec66ba463f6f14abf636ec0400b03
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 2856
content:
"[...] anthropogenic forcing (Chapter 3), as well as with observed changes \r under the recent global warming of 0.5degC (Schleussner et al., 2017; \r Hoegh-Guldberg et al., 2018). That is, spatial patterns of temperature \r changes associated with the 0.5degC difference in GMST warming \r between 1991-2010 and 1960-1979 (Schleussner et  al., 2017; \r Hoegh-Guldberg et al., 2018) are consistent with projected changes \r under 1.5degC and 2degC of global warming."


id: 16e97bc01d96c275fc0f1fffc9d46246
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 1251
content:
"The SR1.5 stated low confidence regarding changes in global \r monsoons at 1.5degC versus 2degC of global warming, as well as \r differences in monsoon responses at 1.5degC versus 2degC. Generally, \r statistically significant changes in regional annual average \r precipitation are expected at a global mean warming of 2.5degC-3degC \r or more (Tebaldi et  al., 2015). Over the austral-winter rainfall \r regions of south-western South America, South Africa and Australia, \r projected decreases in mean annual rainfall show high agreement\r across models and a  strong climate change signal even under \r 1.5degC of global warming, with further amplification of the signal \r at higher levels of global warming (high confidence) (Mindlin et al., \r 2020). This is a signal evident in observed rainfall trends over these \r regions (Sections 2.3.1.3 and 8.3.1.6 ). Also, over the Asian monsoon \r regions, increases in rainfall will occur at 1.5degC and 2degC of global \r [...]"


id: 915a86beeb9ede486ba5cdf83eedb123
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 22781
content:
"[...] warming (Chevuturi et  al., 2018). At warming levels of 1.5degC and \r 2degC, the changes in global monsoons are strongly dependent on \r the modelling strategies used, such as fully coupled transient, fully \r coupled quasi-equilibrium, and atmosphere-only quasi-equilibrium "


id: 1b31fa7377f11de08b29fbcf1fff5bd7
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 1975
content:
"Figure 4.32 | Projected spatial patterns of change in annual average precipitation (expressed as a percentage change) at different levels of global \r warming. Displayed are (a-d) spatial patterns of change in annual precipitation at 1.5degC, 2degC, 3degC, and 4degC of global warming relative to the period 1850-1900. No map \r overlay indicates regions where the change is robust and likely emerges from internal variability, that is, where at least 66% of the models show a change greater than the \r internal-variability threshold (Section 4.2.6) and at least 80% of the models agree on the sign of change. Diagonal lines indicate regions with no change or no robust significant \r change, where fewer than 66% of the models show change greater than the internal-variability threshold. Crossed lines indicate areas of conflicting signals where at least \r [...]"


id: 9d8adb569750145ccf15f7b473a7984f
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 24956
content:
"[...] 66% of the models show change greater than the internal-variability threshold but fewer than 80% of all models agree on the sign of change. Values were assessed from \r a 20-year period at a given warming level, based on model simulations under the Tier-1 SSPs of CMIP6. Further details on data sources and processing are available in the \r chapter data table (Table 4.SM.1)."


id: 15cecb2b26e0b5fa0d59cbcaf81d854
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 1053
content:
"In the North Atlantic, North Pacific, and Southern Hemisphere, \r the transient response of the mid-latitude jets to forcing behaves \r non-linearly with GSAT (Grise and Polvani, 2017; Ceppi et al., 2018). \r This is a  consequence of the time-dependence of the relationship \r between radiative forcing and GSAT and the temporal evolution of \r SST patterns (Ceppi et al., 2018), with a potential seasonal component \r in the SH associated with polar stratospheric temperature changes \r (Grise and Polvani, 2017). Consequently, the epoch approach applied \r to a transient simulation of the 21st century will overestimate the \r mid-latitude circulation response in a stabilized climate. Dedicated \r time slice experiments simulating stabilized climates are therefore \r required to assess differences in mid-latitude circulation at given \r levels of global warming (Li et al., 2018). A further complication in \r the SH is the competing influences of ozone recovery and increasing \r [...]"


id: 50466ce6d8ff2aa1e4b38e0518d4d3c1
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 11408
content:
"[...] GHG concentrations on the austral-summer mid-latitude circulation \r during the first half the 21st  century (Barnes and Polvani, 2013; \r Barnes et  al., 2014). Using transient 21st  century experiments to \r diagnose changes in SH mid-latitude circulation at different levels "


id: 6249de11c5b5ae0d1def8b7d92e70f59
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 14569
content:
"The SR1.5 (IPCC, 2018b) concluded with high confidence that \r overshoot trajectories 'result in higher impacts and associated \r challenges compared to pathways that limit global warming to \r 1.5degC with no or limited overshoot'. The degree and duration of \r overshoot affects the risks and impacts likely to be experienced \r (Hoegh-Guldberg et al., 2018) and the emissions pathway required to \r achieve it (Akimoto et al., 2018). Consequences relating to ice sheets \r and climatic extremes have been found to be greater at 2degC of global \r warming than at 1.5degC (Schleussner et  al., 2016; Hoegh-Guldberg \r et al., 2018) but even on recovery to lower temperatures, these effects \r may not reverse. Overshoot has been found to lead to irreversible \r changes in thermosteric sea level (Tokarska and Zickfeld, 2015; Palter \r et al., 2018; Tachiiri et al., 2019), AMOC (Palter et al., 2018), ice sheets, \r and permafrost carbon (Sections 4.7.2 and 5.4.9) and to long-lasting \r [...]"


id: b992ca66d605e39a3e2c730006405db7
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 29857
content:
"Global temperature is expected to remain approximately constant if \r emissions of CO2 were to cease (Section 4.7.1.1), and so reductions \r in GSAT are only possible in the event of net negative global CO2\r emissions. We assess here results from an overshoot scenario \r (SSP5-3.4-OS; O'Neill et al., 2016), which explores the implications \r of a peak and decline in forcing during the 21st century. Reversibility \r under more extreme and idealized carbon dioxide removal (CDR) \r scenarios is assessed in Section 4.6.3. In SSP5-3.4-OS, CO2 peaks \r at 571 ppm in the year 2062 and reverts to 497 ppm by 2100 - \r approximately the same level as in 2040. SSP5-3.4-OS has strong \r net negative emissions of CO2, exceeding those in SSP1-2.6 \r and SSP1-1.9 from 2070 onwards and reaching -5.5  PgC  yr -1\r (-20 GtCO2 yr -1) by 2100. While this causes global mean temperature \r to decline, changes in climate have not fully reversed by 2100 under \r [...]"


id: 898c20dd786faec838031bad2d4a80f1
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 21376
content:
"[...] this reversal of CO2 concentration (Figure  4.34). Quantities are \r compared for 2081-2100 relative to a 20-year period (2034-2053) \r of the same average CO2. Differences between these two periods of \r the same CO2 are: GSAT: 0.28 +- 0.30degC (mean +- standard deviation); \r global land precipitation: 0.026 +- 0.011 mm day-1; September \r Arctic sea ice area: -0.32 +- 0.53 million km2; thermosteric sea level: \r 12 +- 0.8 cm. As assessed in Section  9.3.1.1, Arctic sea ice area \r is linearly reversible with GSAT. Although these climate quantities \r are not fully reversible, the overshoot scenario results in reduced \r climate change compared with stabilisation or continued increase \r in greenhouse gases (Tsutsui et al., 2006; Palter et al., 2018; Tachiiri \r et al., 2019) (high confidence)."


id: da31ccd6bfffa8c25bbbda9aa089f225
created_at: 2023-04-19 13:22:36
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vector_id: 35683
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"The radiative forcing labels on SSP and RCP scenarios is approximate \r and enables the multiple climate forcings within the scenario to be \r characterized by a single number. While the scenarios are similar in \r terms of the stratospheric adjusted radiative forcing (Tebaldi et  al., \r 2021), they differ more in their effective radiative forcing (ERF). The \r combination of component forcings (CO2, non-CO2 greenhouse gases, \r aerosols) within the scenario also differ (Meinshausen et  al., 2020). \r The ERF levels in the RCP and SSP scenarios have been calculated \r by sampling uncertainty in forcing from a  range of different GHG \r species and aerosols (see 7.SM.1.4 for details). Figure  4.35 shows \r the time evolution and 2081-2100 mean across the families of \r scenarios and how this affects projections of GSAT. That the ERFs differ \r between corresponding SSP and RCP scenarios makes a comparison \r between  CMIP6 and CMIP5 projections challenging (Tebaldi et  al., \r [...]"


id: d62811a9fc29a9864838cdd6fb99390
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
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index: climate_gpt_v2
vector_id: 34995
content:
"[...] 2021). Wyser et  al. (2020) find the EC-Earth3-Veg model exhibits \r stronger radiative forcing and substantially greater warming under \r SSP5-8.5 than RCP8.5, and similar, but smaller additional warmings \r for SSP2-4.5 and SSP1-2.6 compared with RCP4.5 and RCP2.6, \r respectively. In addition to the global response, climate can vary \r regionally due to non-CO2 components of forcing (Samset et al., 2016; \r Richardson et al., 2018a, b)."


id: 2458a50c2d5c7228251cd8005bc93d67
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 3583
content:
"Emulators (Cross-Chapter Box 7.1) can be used to aid understanding \r of differences between generations of scenarios. The AR5 (Collins \r et  al., 2013) explored the differences between CMIP3 and CMIP5 \r (their Figure  12.40). Here we use an emulator calibrated to AR6 \r assessed GSAT ranges, thus eliminating the effect of differences \r in the model ensembles, to analyse the differences between \r SSP and RCP scenarios. MAGICC7.5 in its WGIII-calibrated setup \r (see Cross-Chapter Box 7.1) projects differences in 2081-2100 mean \r warming between the RCP2.6 and SSP1-2.6 scenarios of around \r 0.2degC, between RCP4.5 and SSP2-4.5 of around 0.3degC and between \r RCP8.5 and SSP5-8.5 of around 0.3degC (Figure  4.35b). The SSP \r scenarios also have a wider 5-95% range simulated by MAGICC7.5 \r explaining about half of the increased range seen when comparing \r CMIP5 and CMIP6 models. Higher climate sensitivity is, though, the \r primary reason behind the upper end of the warming for SSP5-8.5 \r [...]"


id: 1618ef7553c126cf5bfb724acaf1154e
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 1101
content:
"[...] reaching 1.5degC higher than the CMIP5 results. Compared with the \r differences between the CMIP5 and CMIP6 multi-model ensembles \r for the same scenario pairs (Table A6 in Tebaldi et  al., 2021), the \r higher ERFs of the SSP scenarios contribute approximately half of \r the warmer CMIP6 SSP outcomes (medium confidence)."


id: bbe644e4a86e253ce890bc965d1ce7d5
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 30289
content:
"The transient climate response to cumulative CO2 emissions, \r TCRE, allows climate policy goals to be associated with remaining \r carbon budgets as global temperature increase is near-linear with \r cumulative emissions (Section 5.5). Research since AR5 has shown \r that the concept of near-linearity of climate change to cumulative \r carbon emissions holds for measures other than just GSAT, such as \r regional climate (Leduc et al., 2016) or extremes (Harrington et al., \r 2016; Seneviratne et  al., 2016). However, ocean heat and carbon \r uptake do exhibit path dependence, leading to deviation from the \r TCRE relationship for levels of overshoot above 300 PgC (Zickfeld \r et al., 2016; Tokarska et al., 2019). Sea level rise, loss of ice sheets, \r and permafrost carbon release may not reverse under overshoot \r and recovery of GSAT and cumulative emissions (Section 4.7). TCRE \r remains a valuable concept to assess climate policy goals and how \r [...]"


id: d4e3dfef4ae9e1245081d7b4dfa615f1
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 34771
content:
"[...] to achieve them but given the non-reversibility of different climate \r metrics with CO2 and GSAT reductions, it has limitations associated \r with evaluating the climate response under overshoot scenarios and \r CO2 removal (medium confidence)."


id: 28709b456a405be42fda746f711cba1c
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 4321
content:
"Emergence in GSAT would be delayed further, owing to the inertia in \r the climate system. Although not investigating emergence as defined \r here in AR6, Tebaldi et al. (2021) used a 20-year running-mean GSAT \r and compared pairwise either model-by-model or between CM IP6 \r ensemble means from the core set of five scenarios assessed in \r this chapter. Differences by more than 0.1degC showed up in most \r cases in the near term,  with only some of the individual models \r and the comparisons of the closest scenarios showing a delay until \r the mid-term. Taking internal variability explicitly into account, Tebaldi \r and Friedlingstein (2013) and Samset et al. (2020) found emergence \r of mitigation benefits in GSAT changes about 25-30 years after \r RCP2.6 emissions diverge from the higher-emissions trajectories \r in RCP4.5 and RCP8.5. Consistently, Marotzke (2019) found about \r one-third likelihood that a trend reduction in GSAT, over the period \r [...]"


id: e93503dd1faeba44d5c43d9106deca3c
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 38383
content:
"[...] 2021-2035 relative to 2005-2020, would be attributable to the \r emissions reductions implied by the difference between RCP2.6 and \r RCP4.5. Emergence of the GSAT response to mitigation of individual \r short-lived climate forcers (SLCFs) would likewise not occur until \r several decades after emissions trajectories diverge, owing to the \r relatively small influence of individual SLCFs on the total ERF (Samset \r et al., 2020), see also Section 4.4.4 and Figure 4.18."


id: 3f7b11aca2964abba0e7d8a3ce8abcb8
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 8356
content:
"In contrast to the earlier studies, emergence in GSAT within the \r near-term has recently been found by McKenna et  al. (2021) who \r investigated the likelihood that under the SSP scenarios GSAT trends \r will exceed the largest historical observed 20-year trends. They found \r that under scenario SSP1-1.9, the 20-year GSAT trends would likely be \r lower than in SSP3-7.0 and SSP5-8.5 within the near term. This earlier \r diagnosed time of emergence compared to Marotzke (2019), while \r using a similar statistical approach, presumably arose because of the \r longer-period trends (20 rather than 15 years) and the larger difference \r between emissions trajectories considered (medium confidence). \r Using 20-year temperature anomalies relative to 1995-2014 instead \r of 20-year trends yielded a low probability of emergence (McKenna \r et al., 2021), consistent with the AR5 (Collins et al., 2013; Kirtman \r et al., 2013; Tebaldi and Friedlingstein, 2013; Samset et al., 2020). It is \r [...]"


id: aade6823db2fcad729aca9d1c08aa0f
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 27288
content:
"[...] not yet understood why GSAT trends appear to show faster emergence \r of mitigation benefits, compared to GSAT anomalies."


id: f51ed2c48984ca791e1ae6154682084f
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 40489
content:
"Emissions pathways that limit globally averaged warming to 1.5degC \r or 2degC by the year 2100 assume the use of CDR approaches in \r combination with emissions reductions to follow net negative CO2\r emissions trajectory in the second half of this century. For instance, \r in SR1.5, all analysed pathways limiting warming to 1.5degC by 2100 \r with no or limited overshoot include the use of CDR to some extent to \r offset anthropogenic CO2 emissions and the median of CO2 removal \r across all scenarios was 730 GtCO2 in the 21st  century (Rickels \r et al., 2018; Rogelj et al., 2018b). Affordable and environmentally \r and socially acceptable CDR options at scale well before 2050 are \r an important element of 1.5degC-consistent pathways especially in \r overshoot scenarios (de Coninck et  al., 2018). The required scale \r of removal by CDR can vary from 1-2 GtCO2 yr -1 year from 2050 \r onwards to as much as 20 GtCO2 yr -1 (Waisman et  al., 2019). \r [...]"


id: 267f6d7c1fd89310ce9c4d77bf3fbdda
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 3937
content:
"[...] In the SSP class of scenarios, net CO2 emissions turn negative from \r around 2050 in SSP1-1.9 and around 2070 in SSP1-2.6 and in the \r overshoot scenario SSP5-3.4-OS (O'Neill et  al., 2016). Thus, CDR \r would play a pivotal role in limiting climate warming to 1.5degC or \r 2degC (Minx et al., 2018). In stark contrast, however, two extensive \r reviews (Lawrence et al., 2018; Nemet et al., 2018) conclude that it \r is implausible that any CDR technique can be implemented at the \r scale needed by 2050."


id: 59300b1706bb5282120a09c7c4571568
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 12934
content:
"Figure  4.37 illustrates the first results from CDRMIP (Keller et  al., \r 2018). Other studies that use similar (Zickfeld et al., 2016; Schwinger \r and Tjiputra, 2018; Jeltsch-Thommes et al., 2020) or other idealized \r scenarios (MacDougall, 2013) or more realistic net negative CO2\r emissions scenarios such as RCP2.6 (C.D. Jones et al., 2016b) and \r scenarios that limit warming to 2degC or less after different levels of \r overshoot (Tokarska and Zickfeld, 2015) arrive at similar conclusions. \r Changes in key climate variables substantially lag behind the decline \r in CO2 (Figure 4.37). The precipitation increase at the beginning of \r the ramp-down phase agrees with the increase in precipitation for an \r abrupt decline in CO2 (Cao et al., 2011). Notwithstanding a decline in \r atmospheric CO2, global mean thermosteric sea level would continue \r to rise. When atmospheric CO2 returns to the piControl level, global \r mean thermosteric sea level is higher than its value at peak CO2\r [...]"


id: a6c76d14c8ba40080eaa595743bd46dd
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 26555
content:
"[...] (Figure 4.37), and it is likely that thermosteric global sea level would \r not return to piControl levels for over 1000 years after atmospheric \r CO2 is restored to piControl concentrations (Tokarska and Zickfeld, \r 2015; Ehlert and Zickfeld, 2018). Therefore, there is high confidence \r that sea level rise will not be reversed by CDR at least for several \r centuries Chapter  9 (Section  9.6.3.5). A  comparison of different \r models shows recovery of AMOC intensity during net negative CO2\r emissions, but the results are model dependent - strengthening with \r an overshoot in most models (Jackson et al., 2014) and strengthening \r but not reaching the initial state in some models (Sgubin et al., 2015). \r The overall lag in response is qualitatively similar to the lagged \r climate system response in the overshoot scenario SSP5-34-OS where \r CO2 rises until 2062 and decreases thereafter (Figure 4.34). The lag \r in climate response to CDR causes hysteresis between key climate \r [...]"


id: d13f777fa046ca492f782cf7a8a48a7
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 34166
content:
"[...] variables such as temperature, precipitation, AMOC and sea level, \r and atmosphere CO2 with the hysteresis characteristics dependent \r on the rate of CDR and climate sensitivity (MacDougall, 2013; Jeltsch\u0002Thommes et al., 2020)."


id: 5d22c2397fbf785933e9b139bb6ced70
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 13658
content:
"When CDR is applied continuously and at scales as large as currently \r deemed possible, under RCP8.5 as the background scenario, the \r widely discussed CDR options such as afforestation, ocean iron \r fertilization and surface ocean alkalinisation are individually \r expected to be relatively ineffective, with limited (8%) warming \r reductions relative to the scenario with no CDR option (Keller et al., \r 2014). Hence, the potential role that CDR will play in lowering \r the temperature in high-emissions scenarios is limited (medium \r confidence). The challenges involved in comparing the climatic \r effects of various CDR options has also been recognized in recent \r studies (Sonntag et  al., 2018; Mengis et  al., 2019). For instance, \r due to compensating processes such as biogeophysical effects of \r afforestation (warming from albedo decrease when croplands are \r converted to forests) more carbon is expected to be removed from \r [...]"


id: 8847e84e73a88fc9ff49a69c79e1bd22
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 21136
content:
"[...] the atmosphere by afforestation than by ocean alkalinisation to \r reach the same global mean cooling."


id: 74523107ecae7cfb2dab2a44f4bf6770
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 17700
content:
"Termination of CDR refers to a sudden and sustained discontinuation \r of CDR deployment (see Section  4.6.3.3 for termination effects of \r SRM). The literature on the termination effects of CDR is limited, \r mostly considering scenarios where CDR implementation is explicit \r and does not result in net negative CO2 emissions (Keller et  al., \r 2014; Gonzalez et  al., 2018). In simulations where CDR is applied \r on the RCP8.5 scenario at scales as large as currently deemed \r possible, the increase in global mean warming rates following CDR \r termination are relatively small in comparison to SRM termination \r (Keller et al., 2014). The exception is artificial ocean upwelling where \r surface cooling is mainly caused by bringing cold water from the \r deep ocean; upon termination this causes larger rates of surface \r warming (Oschlies et al., 2010). When background emissions are as \r high as in RCP8.5, termination of a  large global-scale application \r [...]"


id: 4c1ef613086fa9cce70b2d1c4db29e96
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 10651
content:
"[...] of CDR such as ocean alkalinisation for multiple decades could also \r result in large regional warming rates (up to 0.15degC per year) that \r are comparable to those caused by termination of SRM (Gonzalez \r et al., 2018). In such cases, large amounts of CO2 would be removed \r from the atmosphere before termination, and termination would \r cause a temporal trajectory of atmospheric CO2 that is parallel to the \r high-emissions scenario but from an atmosphere with much lower \r CO2 levels. Because CO2 radiative forcing is a logarithmic function of \r CO2 concentration, large regional warming rates are simulated in such \r terminations. Thus, there is high confidence that the climate effect of "


id: 4d7f17dd1b6018bf54560095295fc145
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 10913
content:
"The climate response to CDR-caused net negative CO2 emissions \r has been studied in Earth system models by prescribing idealized \r ramp-down of CO2 concentrations (MacDougall, 2013; Zickfeld \r et al., 2016; Schwinger and Tjiputra, 2018), CO2 concentrations of \r RCP scenarios that have net negative CO2 emissions (C.D. Jones \r et al., 2016b), and idealized net negative CO2 emissions scenarios \r (Tokarska and Zickfeld, 2015). The Carbon Dioxide Removal Model \r Intercomparison Project (CDRMIP) uses multiple ESMs to explore \r the climate response, effectiveness of CO2 removal, and challenges \r of CDR options (Keller et al., 2018). Idealized CDRMIP simulations \r increase CO2 concentrations at 1% per year from the level in the \r pre-industrial control run (piControl) to 4xCO2 and subsequently \r decrease at the same rate to the piControl level. This section \r assesses the lag in climate response to CDR-caused negative \r emissions; climate 'reversibility' is assessed in Section  4.7.2. \r [...]"


id: 98f845965612b8189be645321bae44bb
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 24129
content:
"[...] The  ramp-down phase, though unrealistic, represents the 'net \r negative CO2 emissions' phase."


id: 54a0ae58f32d0c4624098eb0bab71008
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 12163
content:
"The AR5 assessed the climate response to, as well as risks and side \r effects of, several SRM options (Boucher et al., 2013) and concluded \r with high confidence that SRM, if practicable, could substantially \r offset a  global temperature rise and partially offset some other \r impacts of global warming, but the compensation for the climate \r change caused by GHGs would be imprecise. The AR5 furthermore \r concluded that models consistently suggest that SRM would generally \r reduce climate differences compared to a world with elevated GHG \r concentrations and no SRM; however, there would also be residual \r regional differences in climate (e.g., temperature and rainfall) when \r compared to a climate without elevated GHGs. The AR5 concluded \r with high confidence that scaling SRM to substantial levels would \r carry the risk that if the SRM were terminated for any reason, surface \r temperatures would increase rapidly (within a  decade or two) to \r [...]"


id: fbc64d244ce4aace7679a3b61cb610
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 41663
content:
"This subsection assesses the global and large-scale physical climate \r system response to SRM based on theoretical and modelling studies. \r There is no mature technology today to implement any of the \r SRM options assessed here. A short summary of the SRM options, \r including the proposed mechanism of each SRM approach, radiative \r forcing potential, and key climate and environmental effects, is listed \r in Table 4.7. Chapter 5 (Section 5.6.3) assesses the biogeochemical \r implications of SRM, Chapter 6 (Section 6.4.6) assesses the potential \r ERF of the aerosol-based SRM options and Chapter 8 (Section 8.6.3) \r assesses the abrupt water cycle changes in response to initiation or \r termination of SRM. The risks to human and natural systems, impacts \r of SRM, ethics, and perceptions are assessed in the WGII Report \r (Chapter  16). Governance issues associated with SRM research \r and deployment are assessed in the WGII and WGIII Reports. The \r [...]"


id: 3e1988e3f05f993cc5b461bdaf87b33e
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 8094
content:
"[...] assessment of technical feasibility and engineering aspects of SRM is \r beyond the scope of this Report."


id: be35bbf04e77e215fb612bc56389821f
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 30733
content:
"The climate response to SRM depends greatly on the characteristics of \r SRM implementation approaches. There could be substantial residual \r or overcompensating climate change at both the global and regional \r scales and seasonal time scales (Kravitz et  al., 2014; McCusker \r et al., 2015; Irvine et al., 2016; Fasullo et al., 2018; Jiang et al., 2019; \r Gertler et  al., 2020). This is because the climate response to SRM \r options is different from the response to GHG increase (Figure 4.38). \r For  instance, when global mean warming is offset by a  uniform \r reduction in incoming sunlight, there is residual warming in the high \r latitudes and overcooling in the tropics (Kravitz et al., 2013a; Kalidindi \r et al., 2015), and a reduction in tropical mean rainfall (Tilmes et al., \r 2013). In simulations of stratospheric SO2 injection, SRM diminishes \r the amplitude of the seasonal cycle of temperature at many high\u0002latitude locations, with warmer winters and cooler summers (Jiang \r [...]"


id: dddb39053bdb4044760f5ddcad671247
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 36291
content:
"[...] et  al., 2019). Further, the rates of response could differ between \r surface temperature and slow components in the climate system \r such as sea level rise (Irvine et  al., 2012; Jones et  al., 2018). SRM \r implemented at a moderate intensity, for example by offsetting half \r of the global warming, has the potential to reduce negative effects \r such as reduced precipitation that are associated with fully offsetting \r global mean warming (Irvine et al., 2019; Irvine and Keith, 2020)."


id: 42da7bc78962673bc86baf2338e0b365
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 8952
content:
"to eight W m-2) could be achieved depending on the amount and \r location of SO2 injected into the stratosphere (Aquila et  al., 2014; \r Pitari et al., 2014; Niemeier and Timmreck, 2015; Kravitz et al., 2017; \r Kleinschmitt et al., 2018; Tilmes et al., 2018a). The simulated efficacy \r of SAI by emission of SO2 (radiative forcing per mass of injection rate) \r generally decreases with the increase in injection rate because of the \r growth of larger particles (about 0.5 microns) through condensation \r and coagulation reducing the mass scattering efficiency (Niemeier \r and Timmreck, 2015; Kleinschmitt et  al., 2018). However, efficacy \r changes little for total injection rate up to about 25 Tg sulphur \r per year when SO2 is injected at multiple locations simultaneously \r (Kravitz et  al., 2017; Tilmes et  al., 2018a). Differences in model \r representation of aerosol microphysics, evolution of particle size, \r stratospheric dynamics and chemistry, and aerosol microphysics-\r [...]"


id: f035da2043f54b6efd677ea760f7358b
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
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index: climate_gpt_v2
vector_id: 39648
content:
"[...] radiation-circulation interactions all contribute to the uncertainty in \r simulated cooling efficiency of SAI. Compared to sulphate aerosols, \r injection of non-sulphate particles would result in different cooling \r efficacy, but understanding is limited (Pope et al., 2012; Weisenstein \r et al., 2015; A.C. Jones et al., 2016)."


id: e3f3d53816a812988a2eeb859683be5a
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 37393
content:
"Earlier modelling studies focused on the effect of equatorial sulphate \r injection that tends to overcool the tropics and undercool the poles. \r Compared to equatorial injection, off-equatorial injection at multiple \r locations shows a  closer resemblance to the baseline climate in \r many aspects, including temperature, precipitation, and sea ice \r coverage (Kravitz et  al., 2019). However, significant regional and \r seasonal residual and overcompensating climate change is reported, \r including regional shifts in precipitation, continued warming of \r polar oceans, and shifts in the seasonal cycle of snow depth and \r sea ice cover (Fasullo et al., 2018; Jiang et al., 2019; Simpson et al., \r 2019b). By appropriately adjusting the amount, latitude, altitude, and \r timing of the aerosol injection, modelling studies suggest that SAI is\r conceptually able to achieve some desired combination of radiative \r forcing and climate response (medium confidence) (MacMartin et al., \r [...]"


id: acfa678c47e5ab2365e8a16cec66d372
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 27672
content:
"Most SRM research has focused on stratospheric aerosol injection \r (SAI) and most SAI studies have assessed the effects of injection. Most \r research has focused on stratospheric aerosol injection (SAI): the \r injection of sulphate particles or its precursor gases such as SO2, which \r would then be oxidized to H2SO4. Injection of other types of aerosol \r particles, such as calcite (CaCO3), titanium dioxide (TiO2), aluminium \r oxide (Al2O3), and engineered nanoparticles has also been proposed \r (Keith, 2010; Ferraro et al., 2011; Pope et al., 2012; Weisenstein et al., \r 2015; A.C. Jones et  al., 2016; Keith et  al., 2016), but are much less \r studied compared to sulphate injection. The natural analogue for \r sulphate aerosol injection is major volcanic eruptions (Cross-Chapter \r Box  4.1). While volcanic eruptions are not perfect analogues for \r SAI (Robock et  al., 2013; Plazzotta et  al., 2018; Duan et  al., 2019), \r [...]"


id: dbd98e3f15886ba474c340cd8b7435ff
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 35965
content:
"[...] studies on climate impacts of past volcanic eruptions can inform on \r the potential impact of stratospheric sulphate injection. For example, \r emergent constraints (Chapters 1 and 5) that relate the climate system \r response to volcanic eruptions can be used to reduce uncertainty of \r the land surface temperature response to SAI (Plazzotta et al., 2018)."


id: 8e553e3ff05748ecd7e242ec2ac1de3e
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
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vector_id: 22230
content:
"There is large uncertainty in the stratospheric response to SAI, and \r the change in stratospheric dynamics and chemistry would depend \r on the amount, size, type, location, and timing of injection. There is \r high confidence that aerosol-induced stratospheric heating will play \r an important role in surface climate change (Simpson et al., 2019b) \r by altering the effective radiative forcing (Krishnamohan et al., 2019), \r lower stratosphere stability (Ferraro and Griffiths, 2016), quasi-biennial \r oscillation (QBO) (Aquila et  al., 2014; Niemeier and Schmidt, 2017; \r Kleinschmitt et  al., 2018), polar vortexes (Visioni et  al., 2020a), and \r North Atlantic Oscillation (Jones et  al., 2021). Model simulations \r indicate stronger polar jets and weaker storm tracks and a poleward \r shift of the tropospheric mid-latitude jets in response to stratospheric \r sulphate injections in the tropics (Ferraro et al., 2015; Richter Jadwiga \r [...]"


id: 4ea770e2b44b1f7a72b00325fda9b95c
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 11108
content:
"[...] et al., 2018), as the meridional temperature gradient is increased in the \r lower stratosphere by the aerosol-induced heating. The aerosol-induced \r warming would also offset some of the GHG-induced stratospheric \r cooling. Compared to equatorial injection, off-equatorial injection is \r likely to result in reduced change in stratospheric heating, circulation, \r and QBO (Richter Jadwiga et al., 2018; Kravitz et al., 2019). Stratospheric \r ozone response to sulphate injection is uncertain depending on the \r amount, altitude, and location of injection (WMO,  2018). It is likely"


id: 6aa8061eeb60bcf1231f4192be481804
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 16048
content:
"that sulphate injection would cause a  reduction in polar column \r ozone concentration and delay the recovery of Antarctic ozone hole \r (Pitari et al., 2014; Richter Jadwiga et al., 2018; Tilmes et al., 2018b), \r which would have implications for UV radiation and surface ozone \r (Pitari et al., 2014; Xia et al., 2017; Richter Jadwiga et al., 2018; Tilmes \r et al., 2018b). Injection of non-sulphate aerosols is likely to result in less \r stratospheric heating and ozone loss (Pope et al., 2012; Weisenstein \r et  al., 2015; Keith et  al., 2016). One side effect of SAI is increased \r sulphate deposition at surface. A  recent modelling study indicates \r that to maintain global temperature at 2020 levels under RCP 8.5, \r increased sulphate deposition from stratospheric sulphate injection \r could be globally balanced by the projected decrease in tropospheric \r anthropogenic SO2 emissions, but the spatial distribution of sulphate \r [...]"


id: affa5bfa81deb01b0d5580f5ae132ee9
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 28201
content:
"Cirrus clouds trap more outgoing thermal radiation than they \r reflect incoming solar radiation and thus have an overall warming \r effect  on the climate system (Mitchell and Finnegan, 2009). The \r aim of cirrus cloud thinning (CCT) is to reduce cirrus cloud optical \r depth by increasing the heterogeneous nucleation via seeding cirrus \r clouds with an optimal concentration of ice nucleating particles, \r which might cause larger ice crystals and rapid fallout, resulting in \r reduced lifetime and coverage of cirrus clouds (Muri et  al., 2014; \r Gasparini et al., 2017; Lohmann and Gasparini, 2017; Gruber et al., \r 2019). CCT  aims to achieve the opposite effect of contrails that \r increase cirrus cover and cause a small positive ERF (Section 7.3). \r A high-resolution modelling study of CCT over a limited area of the \r Arctic suggested that cirrus seeding causes a decrease in ice crystal \r number concentration and a reduction in mixed-phase cloud cover, \r [...]"


id: 6e586282013e6d94079a9e1cb2ec76c8
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 16678
content:
"Marine cloud brightening (MCB) involves injecting small aerosols \r such as sea salt into the base of marine stratocumulus clouds \r where the aerosols act as cloud condensation nuclei (CCN). In the \r absence of other changes, an increase in CCN would produce higher \r cloud droplet number concentration with reduced droplet sizes, \r increasing cloud albedo. Increased droplet concentration may also \r increase cloud water content and optical thickness, but recent studies \r suggest that liquid water path response to anthropogenic aerosols is \r weak due to the competing effects of suppressed precipitation and \r enhanced cloud water evaporation (Toll et al., 2019). An analogue \r for MCB are reflective, persistent 'ship tracks' observed after the \r passage of a  sea-going vessel emitting combustion aerosols into \r susceptible clouds (Christensen and Stephens, 2011; Chen et  al., \r 2012; Gryspeerdt et al., 2019). A recent study (Diamond et al., 2020) \r [...]"


id: ae6495a0fc220d54f7ad12dedd496632
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 27911
content:
"[...] found a  substantial increase in cloud reflectivity from shipping in \r south-east Atlantic basin, suggesting that a  regional-scale test of \r MCB in stratocumulus-dominated regions could be successful."


id: 32ab565c206f2deaa272de5a13904b8c
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 6179
content:
"Under present-day climate, cirrus clouds exerts a  net positive \r radiative forcing of about 5 W m-2 (Gasparini and Lohmann, 2016; \r Hong et al., 2016), indicating a maximum cooling potential of the \r same magnitude if all cirrus cloud were removed from the climate \r system. However, modelling results show a  much smaller cooling \r effect of CCT. For the optimal ice nuclei seeding concentration and \r globally non-uniform seeding strategy, a net negative cloud radiative \r forcing of about 1 to 2 W m-2 is achieved (Storelvmo and Herger, \r 2014; Gasparini et  al., 2020). A  few studies find that no seeding \r strategy could achieve a significant cooling effect, owing to complex \r microphysical mechanisms limiting robust climate responses to \r cirrus seeding (Penner et al., 2015; Gasparini and Lohmann, 2016). \r A higher than optimal concentration of ice nucleating particles could \r also result in over-seeding that increases rather than decreases cirrus \r [...]"


id: 3f99d4f24780c428af62747a1d149837
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 8383
content:
"[...] optical thickness (Storelvmo et  al., 2013; Gasparini and Lohmann, \r 2016). Thus, there is low confidence in the cooling effect of CCT, due \r to limited understanding of cirrus microphysics, its interaction with \r aerosols, and the complexity of seeding strategy."


id: a97f62a8398c0a4f5fdf341a31953a6
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 27043
content:
"A hypothetical, sudden and sustained termination of SRM in a world \r with high GHG concentrations has been simulated to cause climate \r rebound effects such as rapid increase in global temperature, \r precipitation, and sea level, and rapid reduction in sea ice area (Jones \r et al., 2013; McCusker et al., 2014; Crook et al., 2015; Muri et al., \r 2018). Model simulations also show reduced precipitation over \r land areas in the first few years following termination, indicating \r general drying that would exacerbate the effects of rapid warming \r (McCusker et al., 2014). A sudden and sustained termination of SRM \r is also expected to weaken carbon sinks, accelerating atmospheric \r CO2 accumulation and warming (Tjiputra et  al., 2016; Muri et  al., \r 2018; Plazzotta et al., 2019). A gradual phase-out of SRM combined \r with mitigation and CDR could reduce the large warming rates \r from sudden SRM termination (MacMartin et  al., 2014; Keith and \r [...]"


id: 5eead26ef6ef21d8db2da91b7f5b39e9
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 13995
content:
"[...] MacMartin, 2015; Tilmes et al., 2016), though this would be limited \r by how rapidly emission reductions can be scaled up (Ekholm and \r Korhonen, 2016)."


id: 8c7e29368eef5e3fb0688165ea9ffcd8
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 21902
content:
"Internal variability could mask the response to solar radiation \r modification (SRM)-related forcing in the near term (Section 4.6.3.1). \r A detection of the global scale climate system response to stratospheric \r sulphate aerosol injection will likely require a  forcing of the size \r produced by the 1991 Mount Pinatubo eruption (Robock et al., 2010). \r In model simulations of where 5 Tg SO2 is injected into the stratosphere \r continuously (roughly one fourth of the 1991 Pinatubo eruption per \r year) under RCP 4.5, it is shown that, relative to the high-GHG world \r without SRM, the effect of SRM on global temperature and precipitation \r is detectable after one to two decades (Burger and Cubasch, 2015; \r Lo et al., 2016) which is similar to the time scale for the emergence of \r GSAT trends due to strong mitigation (Section 4.6.3.1). The detection \r time is sensitive to detection methods and filtering techniques \r (Lo et  al., 2016). An  analysis using GLENS simulation (MacMartin \r [...]"


id: c7faed5407158c85a978c759ca1d80ab
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 32489
content:
"[...] et  al., 2019) compares response in temperature, precipitation, and \r precipitation minus evapotranspiration (P-E) between a climate state \r with GHG-induced 1.5degC global mean temperature change and that \r with the same global mean temperature but under RCP4.5 emissions \r and a limited deployment of SO2 injection. It is found that at grid-scale, \r difference in climate response between these two climate states are \r not detectable by the end of this century. However, for higher emissions \r scenarios of the RCP8.5 and correspondingly larger SRM deployment \r for maintaining the same global mean temperature change of 1.5degC, \r the regional differences are detectable before the end of the century. \r In addition to surface temperature and precipitation, observations of \r aerosol burden and temperature in the stratosphere via the deployment \r of stratospheric aerosol observing system might facilitate the detection \r of climate response to SAI."


id: ec2468980ecc5bf5e6be298af78fee0c
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 38918
content:
"This subsection assesses changes in global climate indices out to \r 2300 using extensions of the SSP scenarios (Meinshausen et al., 2020) \r and literature based on extensions to the RCP scenarios from CMIP5 \r (Meinshausen et al., 2011), which differ from the SSPs despite similar \r labelling of global radiative forcing levels (Section 4.6.2). Meinshausen \r et al. (2020) describe the extensions to the SSP scenarios, which differ \r slightly from the ScenarioMIP documentation (O'Neill et al., 2016). \r A simplified approach across scenarios reduces emissions such that \r after 2100, land-use CO2 emissions are reduced to zero by 2150; any \r net negative fossil CO2 emissions are reduced to zero by 2200, and \r positive fossil CO2 emissions are reduced to zero by 2250. Non-CO2\r fossil fuel emissions are also reduced to zero by 2250 while land\u0002use-related non-CO2 emissions are held constant at 2100 levels. The \r extensions are created up to the year 2500, but ESM simulations \r [...]"


id: 5999d7b427114702e68fa73d5c72c756
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 13003
content:
"[...] have only been requested, as part of the CMIP6 protocol, to run \r to 2300. As a  result, unlike the RCP8.5 extension, SSP5-8.5 sees \r a decline in CO2 concentration after 2250, but the radiative forcing \r level is similar, reaching approximately 12 W m-2 during most of the \r extension. Both SSP1-2.6 and SSP5-3.4-OS decrease radiative forcing \r after 2100. SSP5-3.4-OS is designed to return to the same level of \r forcing as SSP1-2.6 during the first half of the 22nd century. Because \r relatively few CMIP6 ESMs have submitted results beyond 2100, \r GSAT projections using the MAGICC7 emulator (see Cross-Chapter \r Box 7.1) are also shown here."


id: c58a1d8c99358d80b85f9aa839f6f944
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 32060
content:
"Here we draw on new simulations to provide an assessment of ZEC \r using multiple ESMs (Jones et  al., 2019) and EMICs (MacDougall \r et al., 2020). Figure 4.39 shows results from 20 models that simulate \r the evolution of CO2 and the GSAT response following cessation of \r CO2 emissions for an experiment where 1000 PgC is emitted during \r a 1% per year CO2 increase. All simulations show a strong reduction in \r atmospheric CO2 concentration following cessation of CO2 emissions \r in agreement with previous studies and basic theory that natural \r carbon sinks will persist. Therefore, there is very high confidence that \r atmospheric CO2 concentrations would decline for decades if CO2\r emissions cease. Temperature evolution in the 100 years following \r cessation of emissions varies by model and across time scales, with \r some models showing declining temperature, others having ZEC close \r to zero, and others showing continued warming following cessation \r [...]"


id: 349e9753eee197a47edcb51a935f245c
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 6494
content:
"[...] of emissions (Figure  4.39). The GSAT response depends on the \r balance of carbon sinks and ocean heat uptake (MacDougall et al., \r 2020). The 20-year average GSAT change 50 years after the cessation \r of emissions (ZEC50) is summarized in Table 4.8. The mean value of \r ZEC50 is -0.079degC, with 5-95% range -0.34degC-0.28degC. There is no \r strong relationship between ZEC50 and modelled climate sensitivity "


id: 6f682f1f93189bdaea1f8fe01b28d4aa
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 16865
content:
"Both CMIP6 and CMIP5 results show that global temperature beyond \r 2100 is strongly dependent on scenario, and the difference in GSAT \r projections between high- and low-emissions scenarios continues to \r increase (high confidence). Under the extended RCP2.6 (Caesar et al., \r 2013) and SSP1-2.6 scenarios, where CO2 concentration and radiative \r forcing continue to decline beyond 2100, GSAT stabilizes during the \r 21st century before decreasing and remaining below 2degC until 2300, \r except in some of the very high climate-sensitivity ESMs, which \r project GSAT to stay above 2degC by 2300 (Figure 4.40). Under RCP8.5, \r regional temperature changes above 20degC have been reported in \r multiple models over high-latitude land areas (Caesar et al., 2013; \r Randerson et  al., 2015). Non-CO2 forcing and feedbacks remain \r important by 2300 (high confidence). Randerson et al. (2015) found \r that 1.6degC of warming by 2300 came from non-CO2 forcing alone \r [...]"


id: 5237fb7370cf12187e4db32e4563b565
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 11737
content:
"[...] in RCP8.5, and Rind et  al. (2018) show that regional forcing  from \r aerosols can have notable effects on ocean circulation on centennial "


id: 83fb9b8b3eef626ce8616f8bd9be1e7b
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 20375
content:
"Similar to AR5 and SROCC, AR6 defines an abrupt climate change as \r a large-scale abrupt change in the climate system that takes place \r over a few decades or less, persists (or is anticipated to persist) for at \r least a few decades and causes substantial impacts in human and/or \r natural systems (Glossary). Further, AR6 considers such a perturbed \r state of a  dynamical system as irreversible on a  given time scale, \r if the recovery time scale from this state due to natural processes \r takes substantially longer than the time scale of interest (Glossary). \r The AR6 adopts the related definition of a tipping point as a critical \r threshold beyond which a system reorganizes, often abruptly and/\r or irreversibly, and a tipping element as a component of the Earth \r system that is susceptible to a tipping point (Glossary). Tipping points \r may involve global or regional climate changes from one stable state \r to another stable state or to changes that occur faster than the rate \r [...]"


id: c29f461bc94f0736378144360652b626
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 31526
content:
"[...] of change of forcing (Alley et al., 2003) and include shifts from one \r equilibrium state to another and other responses of the climate system \r to external forcing (Section  1.2.4.2). While reversibility has been \r defined alternatively in the literature with respect to the response \r specifically to idealized CO2 forcing and generally GSAT change, AR6 \r considers both definitions synonymous, because it has been widely \r demonstrated that the GSAT change is reversible in models with \r respect to CO2 with a several-year lag (Boucher et al., 2012)."


id: ce393de72c130284a07d0844e8ef1938
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 33612
content:
"Global land precipitation will continue to increase in line with GSAT \r under high emissions scenarios (medium confidence). Precipitation \r changes over land show larger variability and a  less clear signal \r than global total precipitation. Caesar et  al. (2013) showed that \r under the CMIP5 extension simulations, HadGEM2-ES projected \r global land precipitation to remain roughly the same in RCP2.6, to \r increase by about 4% in RCP4.5 and to increase by about 7% in \r RCP8.5. Their results showed global precipitation increasing linearly \r with temperature while radiative forcing increases, but then more \r quickly if forcing is stabilized or reduced. This backs up findings of an \r intensification of the hydrological cycle following CO2 decrease which \r has been attributed to a build-up of ocean heat (Wu et al., 2010), and \r to a fast atmospheric adjustment to CO2 radiative forcing (Cao et al., \r 2011). Figure  4.40 shows that global land precipitation increases \r [...]"


id: b601469999a9c72679af46b5ba71d314
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 29244
content:
"[...] in CMIP6 models until 2300 for SSP5-8.5 but stabilizes in SSP1-2.6 \r and SSP5-3.4-OS. SSP1-2.6 and SSP5-3.4-OS are not distinguishable \r in behaviour of projected global land precipitation after 2100."


id: 5cdcf85cf37a5b89e13917e3d214c314
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 13602
content:
"assessment of reversibility of Earth system components. Preliminary \r results from CDRMIP are presented in Section  4.6.3. Results from \r the SSP5-3.4-Overshoot scenario and other quantities of climate \r change at the same CO2 level before and after overshoot are \r assessed in Section  4.6.2. Forcing reversal is followed by reversal \r of ocean surface and land temperature along with land and ocean \r precipitation, snow cover, and Arctic sea ice with a lag of a few years \r to decades (Table 4.10). Other tipping elements have much longer \r time scales of reversibility from decades to millennia. Drijfhout et al. \r (2015) provided an assessment of 13 regional mechanisms of abrupt \r change, finding abrupt changes in sea ice, oceanic flows, land ice, \r and terrestrial ecosystem response, although with little consistency \r among the models. The potential for abrupt changes in ice sheets, \r the AMOC, tropical forests, and ecosystem responses to ocean \r [...]"


id: a90e0e591f6769413f0c37891f871d64
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 26955
content:
"edges (Bathiany et al., 2020) has also been clarified, as has been \r the importance of distinguishing summer from winter mechanisms \r and associated abruptness, because ice area reduces gradually in \r summer, but not necessarily in winter (Bathiany et al., 2016). For \r other tipping elements including AMOC (Section  9.2.3.1), mixed \r layer depth (Section 9.2.1.3), and sea level rise (Section 9.6.3.5), \r an increase in the diversity of model structure and sensitivity to \r multiple factors has led to a better understanding of the complexity \r of the problem, with some increase in assessed uncertainty and an \r assessed deep uncertainty (Glossary) related to projected sea level \r rise with global warming levels above 3degC (Section 9.6.3.5). In still \r other cases such as Antarctic sea ice (Section 9.3.2) and Southern \r Ocean Meridional Overturning Circulation (MOC; Section 9.2.3.1), \r uncertainty remains high. Finally, it has also been postulated that \r [...]"


id: 9cc10068a20f5d23b297453fd888859f
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 24815
content:
"[...] models may be prone to being too stable (Valdes, 2011) based on \r the limitations of models as well as other lines of evidence such \r paleo-evidence of abrupt events (Dakos et  al., 2008; Klus et  al., \r 2018; Sime et al., 2019)."


id: 4c52d740c799b7f45f9c2789aba6bd10
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 10688
content:
"This section adopts an approach suggested in Sutton (2018). Since \r changes in temperature and precipitation tend to increase with \r the level of warming (Section 4.6.1), low-likelihood, high-warming \r storylines are here illustrated for a level of warming consistent with \r the upper bound of the assessed very likely range (Section 4.3.4) and \r for a level of warming above the very likely range. ECS and TCR are \r the dominant sources of uncertainty in projections of future warming \r under moderate to strong emissions scenarios (Section 7.5.7). Thus, \r a very high level of warming may occur if ECS and TCR are close to \r or above the upper bound of the assessed very likely range, which, \r to agree with historical trends, would require a  strong historical \r aerosol cooling and/or strong SST pattern effects, combined with \r strong positive cloud feedback and substantial biases in paleoclimate \r temperature reconstructions, each of which are assessed as either \r [...]"


id: b5e061f3e04940287a113970d9ebd9e9
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 29222
content:
"For SSP1-2.6, the warming consistent with the upper bound of the \r assessed very likely range corresponds to a  warming of 1.5degC in \r 2081-2100 relative to 1995-2014 and 2.4degC relative to 1850-1900 \r (Section 4.3.4), a warming well above the 2degC warming level even in \r SSP1-2.6. Based on different lines of evidence, Figure 4.41 illustrates \r by how much such a  low-likelihood, high-warming storyline \r exceeds the warming pattern consistent with the assessed best \r estimate GSAT warming of 0.9degC relative to 1995-2014. The  first \r estimate  (Figure  4.41, second row) is based on the assumption \r that the multi-model mean temperature pattern scales linearly with \r global mean warming. While linear scaling provides an appropriate \r approximation for changes in temperatures patterns at lower levels \r of warming (Section 4.2.4), this assumption cannot easily be tested \r for an extrapolation to higher levels of warming. Thus, a  second \r [...]"


id: 7e22a139724f58531999432ca7f98fd9
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 19366
content:
"[...] estimate (Figure 4.41, third row) is based on the average of the five \r models that simulate a GSAT warming most consistent with the upper \r bound of the assessed very likely range (Section 4.3.4 and Box 4.1; \r note some of the models share components). The two estimates \r for the annual mean temperature pattern for a  low-likelihood, \r high-warming storyline consistently show a  warming pattern that \r substantially exceeds the best estimate warming pattern in most \r regions except around the North Atlantic and the parts of the Arctic. \r Pattern scaling suggests more than 50% warming above the best \r estimate, with 2degC-3degC warming over much of Eurasia and North \r America and more than 4degC warming relative to 1995-2014 over the \r Arctic (Figure 4.41c). The other approach based on five models shows \r less warming than the best estimate and even larger area of cooling \r in the North Atlantic but more warming than the best estimate over \r [...]"


id: ba133a868ab48eda893a4da661a6d444
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 29959
content:
"[...] much of the tropical Pacific, Atlantic, around Antarctica and other the \r land regions (Figure 4.41e)."


id: c06ad900981afb9e8c7325400871e43f
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 31153
content:
"Figure 4.41 | High-warming storylines for changes in annual mean temperature. (a, b) Changes in 2081-2100 relative to 1995-2014 consistent with the assessed \r best global surface air temperature (GSAT) estimate (0.9degC and 3.5degC relative to 1995-2014 for SSP1-2.6 and SSP5-8.5, respectively). The CMIP6 multi-model mean is linearly \r pattern-scaled to the best GSAT estimate. (c-h) Annual mean warming above the best estimate, relative to panels (a) and (b) respectively; note the different colour bar in a high \r and very high-warming storyline for 2081-2100. (c, d) Multi-model mean warming pattern scaled to very high GSAT level corresponding to the upper bound of the assessed \r very likely range (4.8degC for SSP5-8.5 and 1.5degC for SSP1-2.6; see Section 4.3.4). (e, f) Average of five models with high GSAT warming nearest to the upper estimate of the very \r [...]"


id: f3da5afed0deede2da023840deb159dc
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
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vector_id: 40282
content:
"[...] likely range (CESM2, CESM2-WACCM, CNRM-CM6-1, CNRM-CM6-1-HR, EC-Earth3 for SSP1-2.6 and ACCESS-CM2, CESM2, CESM2-WACCM, CNRM-CM6-1, CNRM-CM6-\r 1-HRfor SSP5-8.5); (g, h) Average of four and five models, respectively (ACCESS-CM2, HadGEM3-GC31-LL, HadGEM3-GC31-MM, UKESM1-0-LL for SSP1-2.6 and CanESM5, \r CanESM5-CanOE, HadGEM3-GC31-LL: HadGEM3-GC31-MM, UKESM1-0-LL for SSP5-8.5) projecting very high GSAT warming exceeding the very likely range. Further details \r on data sources and processing are available in the chapter data table (Table 4.SM.1)."


id: 718010c867e955cd0459093785d4d4ef
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 17216
content:
"Different alternative estimates of changes in annual mean precipitation \r patterns consistent with high-warming levels are compared here. \r The first estimate (Figure 4.42b) is based on a linear pattern scaling of \r the multi-model mean precipitation pattern for SSP5-8.5 (Figure 4.42a) \r to be consistent with the upper bound of the assessed very likely\r GSAT range (see above). This estimate is reasonably consistent with \r the average response of the five models with GSAT warming most \r consistent with the upper bound of the very likely warming range \r (Figure 4.42c) except for Australia. Both estimates show about 30-40% \r larger changes in annual mean precipitation than the response pattern \r consistent with the best GSAT estimate. In a high-warming storyline, \r widespread increases of more than 30% occur in many regions north \r of 50degN and over parts of the tropics. Around the Mediterranean and \r other parts of the subtropics, a high-warming storyline is associated \r [...]"


id: d0f98164d69fe5f010e33426bdc2def5
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 34114
content:
"While individual models project even stronger warming over \r extratropical land regions (Figure 4.41 bottom row), their projected \r GSAT warming exceeds the assessed very likely 5-95% range and \r thus correspond to an extremely unlikely (below 5% likelihood) \r storyline. While all the models consistent with such a storyline tend \r to overestimate the observed warming trend over the historical \r period (Brunner et al., 2020; Liang et al., 2020; Nijsse et al., 2020; \r Tokarska et al., 2020; Ribes et al., 2021), some of them show a good \r representation of several aspects of the present-day climate (Andrews \r et al., 2019; Sellar et al., 2019; Swart et al., 2019). Such a very high\u0002warming storyline implies widespread warming of more than 1.5degC \r and 3degC above the best-estimate warming pattern under SSP1-2.6 \r and SSP5-8.5, respectively. Under SSP1-2.6, this corresponds to \r more than 3degC warming relative to 1995-2014 over land regions in \r [...]"


id: 288805848ea1ed190cb3681d41c6c8db
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 4339
content:
"[...] the northern mid- to high latitudes and more than 6degC in the Arctic \r (Figure 4.41g). Under SSP5-8.5, such a very high-warming storyline \r implies more than 8degC warming over parts of Amazonia and more \r than 6degC over most other tropical land regions (Figure 4.41h)."


id: 97ad5cb65795760fdd173fe78a3b9a1b
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 23905
content:
"Both the multi-model mean and the pattern-scaled responses show \r a smoother pattern than in individual simulations (Tebaldi and Knutti, \r 2007; Knutti et al., 2010), because the multi-model mean filters out \r internal variability and because model differences in the location \r of the largest change tend to cancel. Individual model simulations \r show opposing signs in precipitation change such as over parts of \r Australia, the west coast of North America, parts of West Africa and \r India (Figure 4.42d), which tend to offset in the multi-model mean \r response. The spatial probability distribution of precipitation changes \r shows that areas of strong precipitation increase or decrease occur \r in all models (Figure  4.42g, see also Section  4.6.1). However, due \r to the spatial smoothing, the multi-model mean response shows \r a lower area fraction of drying than most of the individual models \r (Tebaldi and Knutti, 2007; Knutti et al., 2010). The five models with \r [...]"


id: dba36579e8beb71978ddd6ba1662b6a9
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 35935
content:
"[...] GSAT warming consistent with a high-warming storyline and the two \r models projecting GSAT warming exceeding the very likely GSAT \r warming range show a  much larger area fraction of drying and \r somewhat larger fraction of strong precipitation increases than the \r multi-model mean (Figure 4.42b-d)."


id: 1c39da06cfe73dea22bb37261b2dce6f
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 2159
content:
"Figure 4.42 (continued): (a) Estimates for annual mean precipitation changes in 2081-2100, relative to 1995-2014, consistent with the best global surface air temperature \r (GSAT) estimate derived by linearly scaling the CMIP6 multi-model mean changes to a GSAT change of 3.5degC. (b, c) Estimates for annual mean precipitation changes in \r 2081-2100 relative 1995-2014 in a storyline representing a physically plausible high-global-warming level. (b) Multi-model-mean precipitation scaled to high-global-warming \r level (corresponding to 4.8degC, the upper bound of the very likely range; see Section 4.3.4). (c) Average of five models with GSAT warming nearest to the high level of \r warming (ACCESS-CM2, CESM2, CESM2-WACCM, CNRM-CM6-1, CNRM-CM6-1-HR) (d) Annual mean precipitation changes in four of the five individual model simulations \r [...]"


id: ee8d3974b51e6be21ba4d23bb0fd87f9
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 39347
content:
"[...] averaged in (c). (e, f) Local upper estimate (95% quantile across models) and lower estimate (5% quantile across models) at each grid point. Information at individual grid \r points comes from different model simulations and illustrates local uncertainty range but should not be interpreted as a pattern. (g) Area fraction of changes in annual mean \r precipitation 2081-2100 relative to 1995-2014 for (i) all CMIP6 model simulations (thin black lines), (ii) models shown in (c) (red lines), and (iii) models showing very high \r warming above the models shown in (c) (dark red lines). The grey range illustrates the 5-95% range across CMIP6 models and the solid black line the area fraction of the \r multi-model mean pattern shown in (a). Further details on data sources and processing are available in the chapter data table (Table 4.SM.1)."


id: 6a193c7cb4d144f4d591b0bae590b1a9
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 15944
content:
"However, these expected trends will be overlain by natural climate variability (see FAQ 3.2). First, a major volcanic \r eruption might occur, such as the 1991 eruption of Mt. Pinatubo on the Philippines; such an eruption might cause \r a global surface cooling of a few tenths of a degree Celsius lasting several years. Second, both atmosphere and \r ocean show variations that occur spontaneously, without any external influence. These variations range from \r localized weather systems to continent- and ocean-wide patterns and oscillations that change over months, years, \r or decades. Over a period of twenty years, natural climate variability strongly influences many climate quantities, \r when compared to the response to the increase in greenhouse gas concentrations from human activities. The effect \r of natural variability is illustrated by the very different trajectories that individual black, red or blue lines can \r [...]"


id: ca2db16708f3fa81a907bc177e22d0cd
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 32908
content:
"[...] take in FAQ 4.1, Figure 1. Whether natural variability would amplify or attenuate the human influence cannot \r generally be predicted out to twenty years into the future. Natural climate variability over the next twenty years \r thus constitutes an uncertainty that at best can be quantified accurately but that cannot be reduced."


id: 620460583fb21bb51e99b1f25a8105b9
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 14516
content:
"The time needed to detect the effect of emissions reductions is illustrated by comparing low- and high-emissions \r scenarios (FAQ 4.2, Figure 1). In the low-emissions scenario (SSP1-2.6), CO2 emissions level off after 2015 and begin \r to fall in 2020, while they keep increasing throughout the 21st century in the high-emissions scenario (SSP3-7.0). \r The uncertainty arising from natural internal variability in the climate system is represented by simulating each \r scenario ten times with the same climate model but starting from slightly different initial states back in 1850 \r (thin lines). For each scenario, the differences between individual simulations are caused entirely by simulated \r natural internal variability. The average of all simulations represents the climate response expected for a given \r scenario. The climate history that would actually unfold under each scenario would consist of this expected \r [...]"


id: 97f6e2361dca556d57cc0c451a1f789c
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 23950
content:
"[...] response combined with the contribution from natural internal variability and the contribution from potential \r future volcanic eruptions (the latter effect is not represented here)."


id: dacc2e124d9910b777fc1efe42c8b53d
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 35789
content:
"Georgii A. Alexandrov (Russian Federation), Govindasamy Bala (India/United States of America), \r Laurent Bopp (France), Lena Boysen (Germany), Long Cao (China), Naveen Chandra (Japan/\r India), Philippe Ciais (France), Sergey N.  Denisov (Russian Federation), Frank J.  Dentener (EU, \r The Netherlands), Herve Douville (France), Amanda Fay (United States of America), Piers Forster \r (United Kingdom), Baylor Fox-Kemper (United States of America), Pierre  Friedlingstein \r (United  Kingdom), Weiwei Fu (United States of America/China), Sabine Fuss (Germany), \r Veronique  Garcon (France), Bettina Gier (Germany), Nathan P.  Gillett (Canada), Luke Gregor \r (Switzerland/South Africa), Karsten Haustein (United Kingdom/Germany), Vanessa Haverd \r (Australia), Jian He (United States of America/China), Helene T.  Hewitt (United Kingdom), \r Forrest  M.  Hoffman (United States of America), Tatiana Ilyina (Germany), Robert  B.  Jackson \r [...]"


id: 310516b42571abba7024e7f9abc7938a
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 5875
content:
"[...] (United  States of America), Christopher  Jones (United Kingdom), David P.  Keller (Germany/\r United  States of America), Lester Kwiatkowski (France/United Kingdom), Robin D.  Lamboll \r (United Kingdom/United States of America, United Kingdom), Xin Lan (United States of America/\r China), Charlotte  Laufkotter (Switzerland/Germany), Corinne Le Quere (United  Kingdom), \r Andrew  Lenton (Australia), Jared Lewis (Australia/New Zealand), Spencer  Liddicoat \r (United  Kingdom), Laura  Lorenzoni (United States of America/Venezuela), Nicole Lovenduski \r (United States of  America), Andrew  H.  MacDougall (Canada), Sabine Mathesius (Canada/\r Germany), H.  Damon Matthews (Canada), Malte  Meinshausen (Australia/Germany), \r Igor I. Mokhov (Russian Federation), Vaishali Naik (United States of America), Zebedee R. J. Nicholls \r (Australia), Intan  Suci  Nurhati (Indonesia), Michael O'Sullivan (United  Kingdom), Glen  Peters \r [...]"


id: 180009afc2c77d37a5239c9373bc9386
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 1424
content:
"It is unequivocal that the increases in atmospheric carbon dioxide \r (CO2), methane (CH4) and nitrous oxide (N2O) since the pre-industrial \r period are caused by human activities. The accumulation of GHGs in \r the atmosphere is determined by the balance between anthropogenic \r emissions, anthropogenic removals, and physical-biogeochemical \r source and sink dynamics on land and in the ocean. This chapter \r assesses how physical and biogeochemical processes of the carbon \r and nitrogen cycles affect the variability and trends  of GHGs in \r the atmosphere as well as ocean acidification and deoxygenation. \r It identifies physical and biogeochemical feedbacks that have affected \r (or could affect) future rates of GHG accumulation in the atmosphere, \r and therefore, influence climate change and its impacts. This chapter \r also assesses the remaining carbon budget to limit global warming \r within various goals, as well as the large-scale consequences of \r [...]"


id: 1ea5f03e4c3ff44638ff20e36b515c6e
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 2597
content:
"[...] carbon dioxide removal (CDR) and solar radiation modification (SRM) \r on biogeochemical cycles. {Figures 5.1, 5.2}."


id: 6755aa2ab42575b7b717c4a49fefdc8d
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 15451
content:
"Global mean concentrations for well-mixed GHGs (CO2, \r CH4  and  N2O) in 2019 correspond to increases of about \r 47%, 156%, and 23%, respectively, above the levels in 1750 \r (representative of the pre-industrial era) (high confidence).\r Current atmospheric concentrations of the three GHGs are higher \r than at any point in the last 800,000  years, and in 2019 reached \r 409.9 parts per million (ppm) of CO2, 1866.3 parts per billion (ppb) \r of CH4, and 332.1 ppb of N2O (very high confidence). Current CO2\r concentrations in the atmosphere are also unprecedented in the last \r 2 million years (high confidence). In the past 60 million years, there \r have been periods in Earth's history when CO2 concentrations were \r significantly higher than at present, but multiple lines of evidence \r show that the rate at which CO2 has increased in the atmosphere \r during 1900-2019 is at least 10 times faster than at any other \r time during the last 800,000 years (high confidence), and 4-5 times \r [...]"


id: 1cd1600b085f0a70f9559c74e0325082
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 2254
content:
"[...] faster than during the last 56 million years (low confidence). {5.1.1, \r 2.2.3; Figures 5.3, 5.4; Cross-Chapter Box 2.1}"


id: 68d9a54274b1f98b3c74a2a5b6802cbc
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 15713
content:
"Ocean acidification is strengthening as a result of the ocean \r continuing to take up CO2 from human-caused emissions (very \r high confidence). This CO2 uptake is driving changes in seawater \r chemistry that result in the decrease of pH and associated reductions \r in the saturation state of calcium carbonate, which is a constituent \r of skeletons or shells of a variety of marine organisms. These trends of \r ocean acidification are becoming clearer globally, with a very likely\r rate of decrease in pH in the ocean surface layer of 0.016 to 0.020 per \r decade in the subtropics and 0.002 to 0.026 per decade in subpolar \r and polar zones since the 1980s. Ocean acidification has spread \r deeper in the ocean, surpassing 2000 m depth in the northern North \r Atlantic and in the Southern Ocean. The greater projected pH declines \r in Coupled Model Intercomparison Project Phase 6 (CMIP6) models \r are primarily a consequence of higher atmospheric CO2 concentrations \r [...]"


id: 59bac6bd95371dc2eeee1a24bea70d6
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 13028
content:
"[...] in the Shared Socio-economic Pathways (SSPs) scenarios than \r their Coupled Model Intercomparison Project Phase  5  (CMIP5) \r Representative Concentration Pathway (RCP) analogues. {5.3.2.2, \r 5.3.3.1; 5.3.4.1; Figures 5.20, 5.21} "


id: df2dfcbb54a04d48c989387dbbc53151
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 36519
content:
"Beyond 2100, land and ocean may transition from being \r a  carbon sink to a  source under either very high emissions \r or net negative emissions scenarios, but for different reasons. \r Under very high emissions scenarios such as SSP5-8.5, ecosystem \r carbon losses due to warming lead the land to transition from \r a carbon sink to a source (medium confidence), while the ocean is \r expected to remain a sink (high confidence). For scenarios in which \r CO2 concentration stabilizes, land and ocean carbon sinks gradually \r take up less carbon as the increase in atmospheric CO2 slows down. \r In scenarios with moderate net negative CO2 emissions, and CO2\r concentrations declining during the 21st century (e.g., SSP1-2.6), the \r land sink transitions to a net source in decades to a few centuries \r after CO2 emissions become net negative, while the ocean remains \r a sink (low confidence). Under scenarios with large net negative CO2\r [...]"


id: 111b5f566bd95355e202cfe42f46eba5
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 205
content:
"[...] emissions and rapidly declining CO2 concentrations (e.g., SSP5-3.4-OS \r (overshoot)), both land and ocean switch from a sink to a transient \r source during the overshoot period (medium confidence). {5.4.10, \r 5.6.2.1.2; Figures 5.30, 5.33}"


id: ef59a45a18503e664cd11505dee0d525
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 39479
content:
"Several factors affect the precise value of remaining carbon \r budgets, including estimates of historical warming, future \r emissions from thawing permafrost, and variations in \r projected non-CO2 warming. Remaining carbon budget estimates \r can increase or decrease by 150 PgC (likely range; 150 PgC equals \r 550 GtCO2) due to uncertainties in the level of historical warming, \r and by an additional +-  60  PgC (+-220 GtCO, likely range) due to \r geophysical uncertainties surrounding the climate response to non\u0002CO2 emissions such as CH4, N2O, and aerosols. Permafrost thaw is \r included in the estimates, together with other feedbacks that are \r often not captured by models. Despite the large uncertainties \r surrounding the quantification of the effects of additional Earth \r system feedback processes, such as emissions from wetlands and \r permafrost thaw, these feedbacks represent identified additional \r amplifying risk factors that scale with additional warming and mostly \r [...]"


id: 795001be316d1912db274bb97f01fcf9
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 18537
content:
"[...] increase the challenge of limiting warming to specific temperature \r thresholds. These uncertainties do not change the basic conclusion \r that global CO2 emissions would need to decline to at least net zero \r to halt global warming. {5.4, 5.5.2} "


id: b7dd70952c8ef289f0abe34a55fbb31a
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 29568
content:
"There is a  near-linear relationship between cumulative \r CO2 emissions and the increase in global mean surface air \r temperature (GSAT) caused by CO2 over the course of this \r century for global warming levels up to at least 2degC relative to \r pre-industrial (high confidence). Halting global warming would \r thus require global net anthropogenic CO2 emissions to become zero. \r The ratio between cumulative CO2 emissions and the consequent \r GSAT increase, which is called the transient climate response to \r cumulative emissions of CO2 (TCRE), likely falls in the 1.0degC-2.3degC \r per 1000 PgC range. The narrower range compared to the IPCC Fifth \r Assessment Report (AR5) is due to a better integration of evidence \r across the science in this assessment. Beyond this century, there \r is low confidence that the TCRE remains an accurate predictor of \r temperature changes in scenarios of very low or net negative CO2 \r emissions because of uncertain Earth system feedbacks that can \r [...]"


id: d2d5b04620109910020f71e21ff91662
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 34445
content:
"[...] result in further warming or a  path-dependency of warming as \r a function of cumulative CO2 emissions. {5.4, 5.5.1}"


id: 6e1567bfd1b1acd6ca7978ac445ccba6
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 16625
content:
"the water cycle, emissions of CH4 and N2O, ocean acidification \r and marine ecosystem productivity (high confidence). These side \r effects and associated Earth system feedbacks can decrease carbon \r uptake and/or change local and regional climate, and in turn limit \r the CO2 sequestration and cooling potential of specific CDR methods \r (medium confidence). Deployment of CDR, particularly on land, \r can also affect water quality and quantity, food production and \r biodiversity, with consequences for the achievement of related \r sustainable development goals (high confidence). These effects \r are often highly dependent on local context, management regime, \r prior land use, and scale of deployment (high confidence). A wide \r range of co-benefits are obtained with methods that seek to restore \r natural ecosystems or improve soil carbon (high confidence). The \r biogeochemical effects of terminating CDR are expected to be small \r [...]"


id: 352bce088ccdd4fedec8cd0f0086561f
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 6603
content:
"Changes in the abundance of well-mixed GHGs -  carbon dioxide \r (CO2), methane (CH4) and nitrous oxide (N2O) - in the atmosphere \r play a large role in determining the Earth's radiative properties and \r its climate in the past, the present and the future (Chapters  2, 4, \r 6 and 7). Since 1950, the increase in atmospheric GHGs has been the \r dominant cause of the human-induced climate change (Section 3.3). \r While the main driver of changes in atmospheric GHGs over the \r past 200 years relates to the direct emissions from human activities, \r the net accumulation of GHGs in the atmosphere is controlled by \r biogeochemical source-sink dynamics of carbon that exchange \r between multiple reservoirs on land, oceans and atmosphere. \r The combustion of fossil fuels and land-use change for the period \r 1750-2019 released an estimated 700 +- 75 PgC (1 PgC = 1015 g of \r carbon) into the atmosphere, of which less than half remains in the \r [...]"


id: 2892ec65386c125ab8eda93cf8ecad9c
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 4349
content:
"feedbacks through radiative forcing (Figure  5.2) (Friedlingstein \r et al., 2006; C.D. Jones et al., 2013; Jones and Friedlingstein, 2020). \r Biogeochemical feedbacks follow as an outcome of both carbon and \r climate forcing on the physics and the biogeochemical processes of \r the ocean and terrestrial carbon cycles (Figure 5.2) (Katavouta et al., \r 2018; Williams et al., 2019; Jones and Friedlingstein, 2020). Together, \r these carbon-climate feedbacks can amplify or suppress climate \r change by altering the rate at which CO2 builds up in the atmosphere \r through changes in the land and ocean sources and sinks (Figure 5.2; \r C.D. Jones et al., 2013; Raupach et al., 2014; Williams et al., 2019). \r These changes depend on the, often non-linear, interaction of the \r drivers (CO2 and climate) and processes in the ocean and land as well \r as the emissions scenarios (Figure 5.2; Sections 5.4 and 5.6) (Raupach \r et al., 2014; Schwinger et al., 2014; Williams et al., 2019). There is \r [...]"


id: 5c54fde03197fc35a9c6ca3b35b86bee
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 13497
content:
"[...] high confidence that carbon-climate feedbacks and their century \r scale evolution play a critical role in two linked climate metrics that \r have significant climate and policy implications: (i) the fraction of \r anthropogenic CO2 emissions that remains in the atmosphere, the \r so-called airborne fraction of CO2 (AF; Section  5.2.1.2, Figures  5.2 \r and 5.7, and FAQ 5.1); and (ii) the quasi-linear trend characteristic \r of the transient temperature response to cumulative CO2 emissions \r (TCRE; Section 5.5; MacDougall, 2016; Williams et al., 2016; Jones \r and Friedlingstein, 2020) and other GHGs (CH4 and N2O). This chapter \r assesses the implications of these issues from the perspective \r of carbon cycle processes (Figure  5.2) in Section  5.2 (historical \r and contemporary), Section  5.3 (changing carbonate chemistry), \r Section  5.4 (future projections), Section  5.5 (remaining carbon \r budget) and Section  5.6 (response to carbon dioxide removal and \r [...]"


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updated_at: 2023-04-19 13:22:36
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vector_id: 10708
content:
"The airborne fraction is an important constraint for adjustments \r in carbon-climate feedbacks and reflects the partitioning of CO2\r emissions between reservoirs by the negative feedbacks, which \r were 31% on land and 23% in the ocean for the decade 2010-2019 \r and also dominated the historical period (Figure  5.2; Table  5.1) \r (Friedlingstein et al., 2020). During the period 1959-2019, the \r airborne fraction has largely followed the growth in anthropogenic \r CO2 emissions with a  mean of 44% and a  large interannual \r variability (Ballantyne et al., 2012; Ciais et al., 2019; Friedlingstein \r et al., 2020) (Section  5.2.1.2; Table  5.1). The negative feedback to \r CO2 concentrations is associated with its impact on the air-sea and \r air-land CO2 exchange through strengthening of partial pressure of \r CO2 (pCO2) gradients as well as the internal processes that enhance \r uptake. Two of these key processes are the buffering capacity of the \r [...]"


id: 1d833d1fb270273915dfe84fe87b6180
created_at: 2023-04-19 13:22:36
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"[...] ocean and the CO2 fertilization effect on gross primary production \r (Sections 5.4.1-5.4.4)."


id: 5ae9872e608c31bad695e05d2e980d5c
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
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index: climate_gpt_v2
vector_id: 13242
content:
"Figure 5.2 | Key compartments, processes and pathways that govern historical and future CO2 concentrations and carbon-climate feedbacks through \r the coupled Earth system. The anthropogenic CO2 emissions, including land-use change, are partitioned via negative feedbacks (turquoise dotted arrows) between the \r ocean (23%), the land (31%) and the airborne fraction (46%) of anthropogenic CO2 that sets the changing CO2 concentration in the atmosphere (2010-2019; Table 5.1). This \r regulates most of the radiative forcing that creates the heat imbalance that drives the climate feedbacks to the ocean (blue) and land (green). Positive feedbacks (red arrows) \r result from processes in the ocean and on land (red text). Positive feedbacks are influenced by both carbon-concentration and carbon-climate feedbacks simultaneously. \r [...]"


id: f23ea0bf4562e8fa591f139fe05ea6e
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
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index: climate_gpt_v2
vector_id: 40015
content:
"[...] Additional biosphere processes have been included, but these have an as-yet-uncertain feedback impact (blue-dotted arrows). CO2 removal from the atmosphere into the ocean, \r land and geological reservoirs, necessary for negative emissions, has been included (grey arrows). Although this schematic is built around CO2 (the dominant greenhouse gas), \r some of the same processes also influence the fluxes of CH4 and N2O and the strength of the positive feedbacks from the terrestrial and ocean systems."


id: be190fd26b2fedcf032809501e136b59
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
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index: climate_gpt_v2
vector_id: 30713
content:
"release to the ocean-atmosphere system (Zeebe et al., 2016; Gutjahr \r et al., 2017; Cui and Schubert, 2018; Kirtland Turner, 2018). There \r is low to medium confidence in evaluations of the total amount \r of carbon released during the PETM, as proxy data constrained \r estimates vary from around 3000 to more than 7000  PgC, with \r methane hydrates, volcanic emissions, terrestrial and/or marine \r organic carbon, or some combination thereof, as the probable \r sources of carbon (Zeebe et al., 2009; Cui et al., 2011; Gutjahr et al., \r 2017; Elling et al., 2019; S.M. Jones et al., 2019; Haynes and Honisch, \r 2020). Methane emissions related to hydrate/permafrost thawing \r and fossil carbon oxidation may have acted as positive feedbacks \r (Lunt et al., 2011; Armstrong McKay and Lenton, 2018; Lyons et al., \r 2019), as the inferred increase in atmospheric CO2 can only account \r for approximately half of the reported warming (Zeebe et al., 2009). \r [...]"


id: 5f2a29d608fd4a7e9e917f8187839204
created_at: 2023-04-19 13:22:36
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index: climate_gpt_v2
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content:
"[...] The estimated, time-integrated carbon input is broadly similar \r to  the RCP8.5 extension scenario, although CO2 emissions rates \r (0.3-1.5 Pg yr-1) and by inference the rate of CO2 accumulation in the \r atmosphere (4-42 ppm per century) during the PETM were at least \r 4-5 lower than during the modern era (from 1995 to 2014; Table 2.1; \r Zeebe et al., 2016; Gingerich, 2019)."


id: 14b20c308f2937a85edd2bdda07644de
created_at: 2023-04-19 13:22:36
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index: climate_gpt_v2
vector_id: 867
content:
"cycle dynamics were altered by glacially perturbed nutrient fluxes and \r soil dynamics, as well as largely exposed shelf areas in the tropics as \r a result of lowered sea level. Recent estimates suggest deep-sea CO2\r storage during the last ice age exceeded modern values by as much \r as 750 - 950 PgC (Skinner et al., 2015, 2017; Buchanan et al., 2016; \r Anderson et al., 2019; Gottschalk et al., 2020b). A  combination of \r increased CO2 solubility associated with 2-3degC lower mean oceanic \r temperatures (Bereiter et al., 2018), increased the oceanic residence \r time of CO2 (Skinner et al., 2017), altered oceanic alkalinity (Yu \r et al., 2010; Cartapanis et al., 2018). A generally more efficient marine \r biological carbon pump (BCP; Galbraith and Jaccard, 2015; J. Yu et al., \r 2019; Galbraith and Skinner, 2020) enhanced the partition CO2 into the \r ocean interior, (although the relative contribution of each mechanism \r [...]"


id: 90cc3289f5443641ac975b5051c51296
created_at: 2023-04-19 13:22:36
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index: climate_gpt_v2
vector_id: 22682
content:
"[...] remains a matter of debate). Recent observationally constrained ESM \r results highlight that air-sea disequilibrium amplifies the effect of \r cooling and iron fertilization on glacial carbon storage (Khatiwala \r et al., 2019)."


id: f1b502c88bf248e663c6cae808fc28b0
created_at: 2023-04-19 13:22:36
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index: climate_gpt_v2
vector_id: 39908
content:
"The gradual increase in atmospheric CO2 across the LDT was \r punctuated by three centennial 10-13 ppm increments, coeval with \r 100-200 ppb increases in CH4 (Marcott et al., 2014), reminiscent \r of similar oscillations reported for the last ice age associated with \r transient warming events (Dansgaard/Oeschger (DO) events; Ahn \r and Brook, 2014; Rhodes et al., 2017; Bauska et al., 2018) as well as \r previous deglacial transitions (Nehrbass-Ahles et al., 2020). The rate \r of change in atmospheric CO2 accumulation during these transient \r events exceeds the averaged deglacial growth rates by at least 50% \r (Table 2.1, Figure 5.4). The early deglacial release of remineralized \r carbon from the ocean abyss coincided with the  resumption of \r Southern Ocean overturning circulation (Skinner  et al., 2010; \r Schmitt et al., 2012; Ferrari et al., 2014; Gottschalk et al., 2016, \r 2020a; Jaccard et al., 2016; Rae et al., 2018; Moy et al., 2019) and \r [...]"


id: 8e5dfc9c45f9f302c7dca49d97d5b4e3
created_at: 2023-04-19 13:22:36
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index: climate_gpt_v2
vector_id: 22235
content:
"[...] the concomitant reduction in the global efficiency of the marine \r BCP, associated, in part, with dwindling iron fertilization (Hain \r et al., 2010; Martinez-Garcia et al., 2014; Jaccard et al., 2016) The \r two subsequent pulses, centred 14.8 and 12.9 ka, are associated \r with enhanced air-sea gas exchange in the Southern Ocean (T. Li \r et al., 2020), iron fertilization in the South Atlantic and North Pacific \r (Lambert et al., 2021) and rapid increase in soil respiration owing \r to the resumption of AMOC and associated southward migration \r of the ITCZ (Marcott et al., 2014; Bauska et al., 2018). Rapid \r warming of high northern latitudes contributed to thaw permafrost, \r possibly liberating labile organic carbon to the atmosphere (Kohler \r et al., 2014; Crichton et al., 2016; Winterfeld et al., 2018; Meyer \r et al., 2019). Ocean surface pH reconstructions indicate that the \r ocean was oversaturated with respect to the atmosphere during \r [...]"


id: 97e18eff69ed87e945dfbea1ac4b51df
created_at: 2023-04-19 13:22:36
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content:
"[...] the early, mid-LDT (Martinez-Boti et al., 2015b; Shao et al., 2019; \r Shuttleworth et al., 2021), suggesting that ocean sources at that \r time may have been larger than terrestrial sources. Over the course \r of the LDT, the decrease in Northern Hemisphere permafrost carbon \r stocks has been more than compensated by an increase in the \r carbon stocks of mineral soils, peatland and vegetation (Lindgren \r et al., 2018; Jeltsch-Thommes et al., 2019). The land biosphere was, \r on average, a  net sink for atmospheric carbon and accumulated \r several hundred Gt of carbon over the LDT. Detailed investigations \r reveal that Antarctic air temperatures, and more generally Southern \r Hemisphere (30degS-60degS) proxy temperature reconstructions, led \r the rise in pCO2 at the onset of the LDT, 18  ka ago, by several \r hundred years (Shakun et al., 2012; Chowdhry Beeman et al., 2019). "


id: 45e41651e761b3d8a38b82fd81d2e951
created_at: 2023-04-19 13:22:36
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index: climate_gpt_v2
vector_id: 9517
content:
"Atmospheric GHG concentrations were much less variable during \r the pre-industrial Holocene (from 11.7 ka to 1750 CE). Atmospheric \r CH4 concentrations decreased at the beginning of the Holocene, \r consistent with a general weakening of boreal sources (Yang et al., \r 2017; Beck et al., 2018) and further decline during the mid-Holocene \r owing to a reduction in Southern Hemisphere emissions concomitant \r with a  southward shift of the ITCZ (Singarayer et al., 2011; Beck \r et al., 2018). Atmospheric CH4 concentrations increased about 5 ka, \r which prompted the hypothesis of an early anthropogenic influence \r related to land-use changes in South East Asia (Ruddiman et al., \r 2016). However, stable isotope compositions on CH4 extracted from \r Greenland and Antarctic ice (Beck et al., 2018) reveal that natural \r emissions located in the southern tropics were responsible for the \r rise in atmospheric CH4 concentrations, in line with model simulations \r [...]"


id: 7d07abeb3892e632eb4f44d2544f8b38
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vector_id: 19181
content:
"[...] (Singarayer et al., 2011) thus disputing the early anthropogenic \r influence on the global CH4 budget. Atmospheric N2O concentrations \r increased slightly (20 ppb) across the Holocene, associated with \r a gradual decline in its nitrogen stable isotope composition (H. Fischer \r et al., 2019). The combined signal is consistent with a small increase \r in terrestrial emissions, offset by a  reduction in marine emissions \r (Schilt et al., 2010b; H. Fischer et al., 2019)."


id: 7abe859ad87f57a9a1342e6de61a35bd
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 18792
content:
"There are two anthropogenic sources of carbon dioxide (CO2): fossil \r emissions and net emissions (including removals) resulting from \r land-use change and land management (also shown in this chapter \r as LULUCF: land use, land-use change, and forestry; in previous \r IPCC reports it has been termed forestry and other land use, FOLU). \r Fossil CO2 emissions include the combustion of the fossil fuels \r coal, oil and gas, covering all sectors of the economy (electricity, \r transport, industrial, and buildings), fossil carbonates such as in \r cement manufacturing, and other industrial processes such as the \r production of chemicals and fertilizers (Figure  5.5a). Fossil CO2\r emissions are estimated by combining economic activity data and \r emissions factors, with different levels of methodological complexity \r (tiers) or approaches (e.g., IPCC Guidelines for National Greenhouse \r Gas Inventories). Several organizations or groups provide estimates \r [...]"


id: ed215073c433cf0074178b7a47296fdb
created_at: 2023-04-19 13:22:36
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content:
"[...] of fossil CO2 emissions, with each dataset having slightly different \r system boundaries, methods, activity data, and emissions factors \r (Andrew, 2020). Datasets cover different time periods, which can \r dictate the datasets and methods that are used for a  particular \r application. The data reported here is from an annually updated \r data source that combines multiple sources to maximise temporal \r coverage (Friedlingstein et al., 2020). The uncertainty in global fossil \r CO2 emissions is estimated to be +-5% (1 standard deviation)."


id: beeef0937ad9da5ab0f836b33a54489a
created_at: 2023-04-19 13:22:36
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index: climate_gpt_v2
vector_id: 30858
content:
"The early Holocene decrease in CO2 concentration by about 5 ppm \r (Schmitt et al., 2012) has been attributed to post-glacial regrowth \r in terrestrial biomass and a gradual increase in peat reservoirs over \r the Holocene, resulting in the sequestration of several hundred PgC \r (Yu et al., 2010; Nichols and Peteet, 2019). Peat accumulation \r rates in boreal and temperate regions were higher under warmer \r summer conditions in the early to mid-Holocene (Loisel et al., 2014; \r Stocker et al., 2017). The 20 ppm gradual increase of atmospheric \r CO2 starting 7  ka has been attributed to a  decrease in natural \r terrestrial biomass due to climate change, carbonate compensation \r and enhanced shallow water carbonate deposition (Menviel and \r Joos, 2012; Brovkin et al., 2016), consistent with stable carbon \r isotope measurements on CO2 extracted from Antarctic ice (Elsig \r et al., 2009; Schmitt et al., 2012). These isotopic measurements do \r [...]"


id: dad87640918035460a784fc5a0ad056d
created_at: 2023-04-19 13:22:36
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content:
"[...] not support an early anthropogenic influence on atmospheric CO2\r due to land-use change and forest clearing (Ruddiman et al., 2016). \r Recent paleoceanographic evidence suggests that remineralized \r carbon outgassing associated with increased Southern Ocean \r circulation and upwelling (Studer et al., 2018), possibly promoted \r by stronger Southern Hemisphere westerly winds (Saunders et al., \r 2018), could have additionally contributed to the late Holocene \r increase in atmospheric CO2 concentrations. However, the role of \r these mechanisms remained insignificant in transient Holocene \r ESM simulations (Brovkin et al., 2019). Overall, as in AR5 (WGI, \r Chapter  5), there is medium confidence in the key drivers of \r the  CO2 increase between the early Holocene and the  beginning \r of the industrial era, yet there is low confidence in the relative \r contributions of these drivers due to insufficient quantitative \r constraints on particular processes."


id: d2b8bafe655c40f8d87b7966463911b5
created_at: 2023-04-19 13:22:36
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index: climate_gpt_v2
vector_id: 34420
content:
"CO2 emissions from coal use grew at 4.8% yr-1 in the 2000s, but \r slowed to 0.4% yr-1 in the 2010s. CO2 emissions from oil use grew \r steadily at 1.1% yr-1 in both the 2000s and 2010s. CO2 emissions \r from gas use grew at 2.5% yr-1 in the 2000s and 2.4% yr-1 in 2010s, \r but has shown signs of accelerated growth of 3% yr-1 since 2015 \r (G.P. Peters et al., 2020). Direct CO2 emissions from carbonates in \r cement production are around 4% of total fossil CO2 emissions, \r and grew at 5.8% yr-1 in the 2000s but a slower 2.4% yr-1 in the \r 2010s. The uptake of CO2 in cement infrastructure (carbonation) \r offsets about one half of the carbonate emissions from current \r cement production (Friedlingstein et al., 2020). These results are \r robust across the different fossil CO2 emissions datasets, despite \r minor differences in levels and rates, as expected given the \r reported uncertainties (Andrew, 2020). During 2020, the COVID-19 \r [...]"


id: bc432e888efc6be57b36e6acb72cf381
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vector_id: 30366
content:
"[...] pandemic led to a rapid, temporary decline in fossil CO2 emissions, \r estimated to be around 7% based on a synthesis of four estimates. \r (Cross-Chapter Box  6.1; Forster et al., 2020; Friedlingstein et al., \r 2020; Le Quere et al., 2020; Z. Liu et al., 2020)."


id: d57d40ac1650a2cd483add474e56d84
created_at: 2023-04-19 13:22:36
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content_type: text
index: climate_gpt_v2
vector_id: 34878
content:
"Progress since AR5 and SRCCL (IPCC, 2019a) allows more accurate \r estimates of gross and net fluxes due to the availability of more \r models, model advancement in terms of inclusiveness of land-use \r practices, and advanced land-use forcings (Ciais et al., 2013; Klein \r Goldewijk et al., 2017; Hurtt et al., 2020). In addition, important \r terminological discrepancies were resolved. First, synergistic effects \r of land-use change and environmental changes have been identified \r as a key reason for the large discrepancies between model estimates \r of the LULUCF flux, explaining up to 50% of differences (Pongratz \r et al., 2014; Stocker and Joos, 2015; Gasser et al., 2020). Another \r reason for discrepancies relates to natural fluxes being considered \r as part of the LULUCF flux when occurring on managed land in the \r United Nations Framework Convention on Climate Change (UNFCCC) \r national GHG inventories; these fluxes are considered part of the \r [...]"


id: 83e8e488ee19ace265f02ea50ed6601e
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index: climate_gpt_v2
vector_id: 20355
content:
"[...] natural terrestrial sink in global vegetation models and excluded in \r bookkeeping models (Grassi et al., 2018). LULUCF fluxes following \r national GHG inventories or Food and Agriculture Organization \r of the United Nations (FAO) datasets, including recent estimates \r (Tubiello et al., 2021), are thus excluded from our global assessment, \r but their comparison against the academic approach is available \r elsewhere - at the global scale (Jia et al., 2019) and European level \r (Petrescu et al., 2020)."


id: 484059fac865fd2d967ffdcc95a9e142
created_at: 2023-04-19 13:22:36
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index: climate_gpt_v2
vector_id: 9924
content:
"For the decade 2010-2019, average emissions were estimated at \r 1.6 +- 0.7 PgC yr-1 (mean +- standard deviation, 1 sigma; Friedlingstein \r et al., 2020). A likely general upward trend since 1850 is reversed \r during the second part of the 20th century (Figure  5.5b). Trends \r since the 1980s have low confidence because they differ between \r estimates, which is related, among other things, to Houghton and \r Nassikas (2017) using a different land-use forcing than Hansis et al. \r (2015) and the DGVMs. Higher emissions estimates are expected \r from DGVMs run under transient environmental conditions compared \r to bookkeeping estimates, because the DGVM estimate includes \r the loss of additional sink capacity. Because the transient setup \r requires a reference simulation without land-use change to separate \r anthropogenic fluxes from natural land fluxes, LULUCF estimates \r by DGVMs include the sink forests that would have developed in \r [...]"


id: 6538e04f7834d5400b2019ecc28bbb38
created_at: 2023-04-19 13:22:36
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content:
"[...] response to environmental changes on areas that in reality have been \r cleared (Pongratz et al., 2014). The agricultural areas that replaced \r these forests have a reduced residence time of carbon, lacking woody \r material, and thus provide a substantially smaller additional sink over \r time (Gitz and Ciais, 2003). The loss of additional sink capacity is \r growing in particular with atmospheric CO2 and increases DGVM\u0002based LULUCF flux estimates relative to bookkeeping estimates over \r time (Figure 5.5)."


id: 6ac4e398392f2afb56de265320d190b
created_at: 2023-04-19 13:22:36
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index: climate_gpt_v2
vector_id: 16064
content:
"Atmospheric CO2 concentration measurements in remote locations \r began in 1957 at the South Pole Observatory (SPO) and in 1958 \r at Mauna Loa Observatory (MLO), Hawaii, USA (Keeling, 1960) \r (Figure  5.6a). Since then, measurements have been extended to \r multiple locations around the world (Bacastow et al., 1980; Conway \r et al., 1994; Nakazawa et al., 1997). In addition, high-density global \r observations of total column CO2 measurements by dedicated GHG\u0002observing satellites began in 2009 (Yoshida et al., 2013; O'Dell \r et al., 2018). Annual mean CO2 growth rates are observed to be \r 1.56 +- 0.18 ppm yr-1 (average and range from 1 standard deviation \r of annual values) over the 61 years of atmospheric measurements \r (1959-2019), with the rate of CO2 accumulation almost tripling \r from an average of 0.82 +-  0.29  ppm yr-1 during the decade of \r 1960-1969 to 2.39 +- 0.37 ppm yr-1 during the decade of 2010-2019 \r (Chapter 2). The latter agrees well with that derived for total column \r [...]"


id: 629c77665761db05503f4ce02387e1e6
created_at: 2023-04-19 13:22:36
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content_type: text
index: climate_gpt_v2
vector_id: 14637
content:
"[...] (XCO2) measurements by the Greenhouse Gases Observing Satellite \r (GOSAT; Figure 5.6b). The interannual oscillations in monthly mean \r CO2 growth rates (Figure 5.6b) show a close relationship with the \r El Nino-Southern Oscillation (ENSO) cycle (Figure 5.6b) due to the \r ENSO-driven changes in terrestrial and ocean CO2 sources and sinks \r on the Earth's surface (Section 5.2.1.4)."


id: 2905e9c787ae03749208d2ef586ca1ed
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 4428
content:
"Multiple lines of evidence unequivocally establish the dominant \r role of human activities in the growth of atmospheric CO2. First, the \r systematic increase in the difference between the MLO and SPO \r records (Figure 5.6a) is caused primarily by the increase in emissions \r from fossil fuel combustion in industrialized regions that are situated \r predominantly in the Northern Hemisphere (Ciais et al., 2019). Second, \r measurements of the stable carbon isotope in the atmosphere (d13C-\r CO2) are more negative over time because CO2 from fossil fuels \r extracted from geological storage is depleted in 13C (Figure  5.6c; \r Rubino et al., 2013; Keeling et al., 2017). Third, measurements of the \r d(O2/N2) ratio show a declining trend because for every molecule of \r carbon burned, 1.17 to 1.98 molecules of oxygen (O2) is consumed \r (Figure 5.6d; Ishidoya et al., 2012; Keeling and Manning, 2014). These \r three lines of evidence confirm unambiguously that the atmospheric \r [...]"


id: 4a011a44c9a94c94d1a7f8d4a9c629a5
created_at: 2023-04-19 13:22:36
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index: climate_gpt_v2
vector_id: 10263
content:
"[...] increase of CO2 is due to an oxidative process (i.e.,  combustion). \r Fourth, measurements of radiocarbon (14C-CO2) at sites around the \r world (Levin et al., 2010; Graven et al., 2017; Turnbull et al., 2017) \r show a continued long-term decrease in the 14C/12C ratio. Fossil fuels \r are devoid of 14C and therefore fossil fuel-derived CO2 additions \r decrease the atmospheric 14C/12C ratio (Suess, 1955)."


id: ba41f35f5d1b0394ed53b889a4ad219b
created_at: 2023-04-19 13:22:36
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content_type: text
index: climate_gpt_v2
vector_id: 29988
content:
"More evidence on the pre-industrial LULUCF flux has emerged \r since AR5 in the form of new estimates of cumulative carbon losses \r until today, and of a better understanding of natural carbon cycle \r processes over the Holocene (Ciais et al., 2013). Cumulative carbon \r losses by land-use activities since the start of agriculture and forestry \r (pre-industrial and industrial era) have been estimated at 116 PgC \r based on global compilations of carbon stocks for soils (Sanderman \r et al., 2017) with about 70  PgC of this occurring prior to 1750, \r and for vegetation as 447  PgC (inner quartiles of 42 calculations: \r 375-525  PgC) (Erb et al., 2018). Emissions prior to 1750 can be \r estimated by subtracting the post-1750 LULUCF flux from Table 5.1 \r from the combined soil and vegetation losses until today; they would \r then amount to 328 (161-501) PgC assuming error ranges are \r independent. A share of 353 (310-395) PgC from prior to 1800 has \r [...]"


id: d17d63a4d7e501f89a388d522e3106ce
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index: climate_gpt_v2
vector_id: 34209
content:
"[...] indirectly been suggested as the difference between net biosphere \r flux and terrestrial sink estimates, which is compatible with ice-core \r records due to a low airborne fraction of anthropogenic emissions in "


id: 836eca519cc9104d116d8dea43120e75
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 20258
content:
"Figure 5.6 | Time series of CO2 concentrations and related measurements \r in ambient air. (a) Concentration time series and MLO-SPO difference, (b) growth \r rates, (c) 14C and 13C isotopes, and (d) O2/N2 ratio. The data for Mauna Loa \r Observatory (MLO) and South Pole Observatory (SPO) are taken from the Scripps \r Institution of Oceanography (SIO)/University of California, San Diego (Keeling et al., \r 2001). The global mean CO2 are taken from National Oceanic and Atmospheric \r Administration (NOAA) cooperative network (as in Chapter 2), and Greenhouse Gases \r Observing Satellite (GOSAT) monthly mean XCO2 (mixing ratio) time series are taken \r from National Institute for Environmental Studies (Yoshida et al., 2013). CO2 growth \r rates are calculated as the time derivative of deseasonalized time series (Nakazawa \r et al., 1997). The D(O2/N2) are expressed in per meg units (= (FF/M) x 106, where \r FF = moles of O2 consumed by fossil-fuel burning, M = 3.706 x 1019, total number \r [...]"


id: b9507ba7ba75d2942c8504de44848368
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 29814
content:
"[...] of O2 molecules in the atmosphere (Keeling and Manning, 2014). The 14CO2 time \r series at Barring Head, Wellington, New Zealand (BHD) is taken from GNS Science \r and NIWA (Turnbull et al., 2017). The multivariate ENSO index (MEI) is shown as the \r shaded background in panel (b); (warmer shade indicates El Nino). Further details on \r data sources and processing are available in the chapter data table (Table 5.SM.6)."


id: 8bc686ae6956d304991ca5bcedddc0ee
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 21761
content:
"2014). The sink rate has declined over the past six decades, which \r indicates that the combined ocean and land sinks are not growing \r as fast as the growth in atmospheric CO2 (Raupach et al., 2014; \r Bennedsen et al., 2019). Possible explanations for the sink rate decline \r are that the land and/or ocean CO2 sinks are no longer responding \r linearly with CO2 concentrations or that anthropogenic emissions \r are slower than exponential (Figure  5.7 and Sections  5.2.1.3 and \r 5.2.1.4; Gloor et al., 2010; Raupach et al., 2014; Bennedsen et al., \r 2019). In addition, both diagnostics are influenced by major climate \r modes (e.g., ENSO) and volcanic eruptions that contribute to high \r interannual variability (Gloor et al., 2010; Frolicher et al., 2013; \r Raupach et al., 2014), suggesting high sensitivity to future climate \r change. Uncertain land-use change fluxes (Section 5.2.1.2) influence \r the robustness of the trends. Based on the airborne fraction (AF), \r [...]"


id: 1555315fb982bd8091838b8caa6109f1
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 966
content:
"[...] it is concluded with medium confidence that both ocean and land \r CO2 sinks have grown consistent with the rising of anthropogenic \r emissions. Further research is needed to understand the drivers of \r changes in the CO2 sink rate."


id: 7e36646e9b6dd4f27479fcc0f70ab69f
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 19374
content:
"In the first assessment approach, the mean global multi-decadal \r (1960-2019) trends in the ocean sink (Socean) for CO2 show a high \r degree of coherence across the nine GOBMs and six pCO2-based \r observational product reconstructions (1987-2018) which, despite \r a  temporary slowdown (or 'hiatus') in the 1990s, is also quasi\u0002linear over that period (Figure 5.8a; Gregor et al., 2019; Hauck et al., \r 2020). This coherence between the GOBMs and observations-based \r reconstructions (1987-2018; r2=0.85) provides high confidence\r that the ocean sink (Socean in Section  5.2.1.5) evaluated from \r GOBMs (1960-2019) grew quasi-linearly from 1.0 +-  0.3  PgC yr-1\r to 2.5 +- 0.6 PgC yr-1 between the decades 1960-1969 and 2010-\r 2019 in response to global CO2 emissions (Figure  5.8a; Table  5.1; \r Friedlingstein et al., 2020; Hauck et al., 2020). The cumulative ocean \r CO2 uptake (105  +-  20  PgC) is 23% of total anthropogenic CO2\r [...]"


id: 3e7feaa42ba88ef00b8161e3f10d8301
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
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index: climate_gpt_v2
vector_id: 8180
content:
"[...] emissions (450 +- 50 PgC) for the same period (Friedlingstein et al., \r 2020). Notwithstanding the high confidence in the magnitude of \r the annual to decadal trends for Socean, this assessment is moderated \r to medium confidence by the low confidence in the currently \r inadequately constrained uncertainties in the pre-industrial land-to\u0002ocean carbon flux, the uncertain magnitude of winter outgassing from \r the Southern Ocean, and the uncertain effect of the ocean surface \r cool-skin, the effect of data sparsity, differences between wind \r products and the uncertain contribution from the changing land-\r ocean continuum on global and regional fluxes (Jacobson et al., 2007; \r Resplandy et al., 2018; Roobaert et al., 2018; Bushinsky et al., 2019; \r Hauck et al., 2020; Watson et al., 2020; Gloege et al., 2021). However, \r both GOBMs and pCO2-based observational products independently \r reveal a slowdown or 'hiatus' of the ocean sink in the 1990s, which \r [...]"


id: be3cea8ffa51d153072228df9dc96aed
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 30737
content:
"[...] provides a  valuable constraint for model verification and leads to \r greater confidence in the model outputs (Figure 5.8a; Landschutzer \r et al., 2016; Gregor et al., 2018; DeVries et al., 2019; Hauck et al., \r 2020). A number of studies point to the role of the Southern Ocean in \r the global '1990s hiatus' in air-sea CO2 fluxes, but provide different \r process-based explanations linking ocean temperature, mixing and \r meridional overturning circulation (MOC) responses to variability \r in large-scale climate systems, wind stress and volcanic activity, as \r well as the sensitivity of the air-sea CO2 flux to small changes in \r the atmospheric forcing from anthropogenic CO2 (Landschutzer et al., \r 2016; DeVries et al., 2017; Bronselaer et al., 2018; Gregor et al., 2018; \r Gruber et al., 2019a; Keppler and Landschutzer, 2019; McKinley et al., \r 2020; Nevison et al., 2020). Data sparsity in the Southern Ocean could "


id: 9347e7676415183fcfad79e76cc7efba
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
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index: climate_gpt_v2
vector_id: 23110
content:
"Since AR5 and SROCC, major advances in globally coordinated \r ocean CO2 observations (Surface Ocean CO2 Atlas, SOCAT; and \r Global Ocean Data Analysis Project, GLODAP), the harmonization \r of ocean and coastal-observation-based products, atmospheric and \r oceanic inversion models and forced global ocean biogeochemical \r models (GOBMs) have increased the level of confidence in the \r assessment of trends and variability of air-sea fluxes and storage of \r CO2 in the ocean during the historical period (1960-2018; see also \r Supplementary Materials 5.SM.1; Ciais et al., 2013; Bakker et al., \r 2016; Landschutzer et al., 2016, 2020; Bindoff et al., 2019; DeVries \r et al., 2019; Gregor et al., 2019; Gruber et al., 2019a, b; Tohjima et al., \r 2019; Friedlingstein et al., 2020; Hauck et al., 2020; Olsen et al., \r 2020). A major advance since SROCC is that, for the first time, all six \r published observational product fluxes used in this assessment, are \r [...]"


id: ca9d868cf4031a1115cc84f90582b1f5
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
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index: climate_gpt_v2
vector_id: 32988
content:
"[...] made more comparable using a common ocean and sea ice cover \r area, integration of climatological coastal fluxes scaled to increasing \r atmospheric CO2 and an ensemble mean of ocean fluxes calculated \r from three re-analysis wind products (Supplementary Materials \r 5.SM.2; Landschutzer et al., 2014, 2020; Rodenbeck et al., 2014; \r Zeng et al., 2014; Denvil-Sommer et al., 2019; Gregor et al., 2019; \r Iida et al., 2021). From a process point of view, the ocean uptake \r of anthropogenic carbon is a two-step set of abiotic processes that \r involves the exchange of CO2, first across the air-sea boundary into \r the surface mixed layer, followed by its transport into the ocean \r interior where it is stored for decades to millennia, depending on \r the depth of storage (Gruber et al., 2019b). Two definitions of air-\r sea fluxes of CO2 are used in this assessment for both observational \r products and models: Socean is the global mean ocean CO2 sink and \r [...]"


id: fd63888d3158313280ed89306167016e
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 41934
content:
"[...] Fnet denotes the net spatially varying CO2 fluxes (Hauck et al., 2020). \r Adjustment of the mean global Fnet for the pre-industrial sea-to-air \r CO2 flux associated with land-to-ocean carbon flux term makes Fnet\r comparable to Socean (Jacobson et al., 2007; Resplandy et al., 2018; \r Hauck et al., 2020).\r There are multiple lines of observational and modelling evidence that \r support with high confidence the finding that, in the historical period "


id: 8640309330e4a8e25ded24015f3d4ed5
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 20775
content:
"Here we provide a third comparative assessment approach depicting \r the spatial coherence of ocean air-sea fluxes and storage rates \r of CO2 as well as a quantitative assessment of both fluxes for the \r same period (1994-2007; Figure  5.9). Observation-based pCO2\r flux products show that emissions of natural CO2 occur mostly in \r the tropics and high-latitude Southern Ocean, and that the uptake \r and storage of anthropogenic CO2 occurs predominantly in the mid\u0002latitudes (Chapter 9, Figure 5.9 and Cross-Chapter Box 5.3). Strong \r ocean CO2 sink regions are those in the mid-latitudes associated \r with the cooling of poleward flowing subtropical surface waters \r as well as equatorward flowing sub-polar surface waters, both of \r which contribute to the formation of Mode, Intermediate and Deep \r water masses that transport anthropogenic CO2 into the ocean \r interior on time scales of decades to centuries in both hemispheres \r [...]"


id: b8c22d53ff2dd37aa1444a5eab2b6462
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
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index: climate_gpt_v2
vector_id: 29712
content:
"[...] (Section 9.2.2.3 and Figure 5.9; DeVries, 2014; Gruber et al., 2019b; \r Wu et al., 2019). The mean decadal scale magnitude and uncertainties \r of Socean from net air sea fluxes (Fnet) were calculated from an ensemble \r of six observational-based product reconstructions (Figure  5.9a) \r and the storage rates in the ocean interior derived from multiple \r ocean interior CO2 datasets (Gruber et al., 2019b; Figure 5.9b). The \r cumulative CO2 stored in the ocean interior from 1800 to 2007 has \r been estimated at 140 +-18 PgC (Gruber et al., 2019b). As reported in \r SROCC (Section 5.2.2.3.1; IPCC, 2019b), the net ocean CO2 storage \r between 1994-2007 was 29 +- 4 PgC, which corresponds to a mean \r storage of 26 +- 5% of anthropogenic CO2 emissions for that period \r (Gruber et al., 2019b). The resulting net annual storage rate of \r anthropogenic CO2, equivalent to Socean for the period mid-1994 to \r mid-2007 is 2.2 +- 0.3 PgC yr-1, which is in very close agreement with \r [...]"


id: 80f1813c74c3420282da605acb37c525
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 19823
content:
"[...] the top-down air-sea flux estimate of Socean of 2.1 +-  0.5  PgC yr-1\r from GOBMs and 1.9 +- 0.3PgC yr-1 from pCO2-based observational \r products with the steady river carbon flux correction of 0.62 PgC yr-1\r for the same time period (Gruber et al., 2019b; Hauck et al., 2020). \r This close agreement between these independent ocean CO2 sink \r estimates derived from air-sea fluxes and storage rates in the ocean "


id: 112f00f095e5f724e8767ce80332ce1e
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 215
content:
"The intent of this assessment is to show how global variability can \r be regionally forced (Gregor et al., 2019; Landschutzer et al., 2019; \r Hauck et al., 2020). Since AR5 and SROCC, advances in global \r ocean CO2 flux products, GOBMs and atmospheric inversion models \r have strengthened confidence in the assessment of how ocean \r regions influence mean global variability and trends of ocean CO2\r air-sea fluxes (Fnet; see Supplementary Materials Figure  5.SM.1; \r Ciais et al., 2013; Landschutzer et al., 2014, 2015; Rodenbeck et al., \r 2014; McKinley et al., 2017; Bindoff et al., 2019; Gregor et al., 2019; \r Friedlingstein et al., 2020; Hauck et al., 2020). The coherence in the \r regional variability of the anomalies in Fnet from three independent \r lines of evidence support with high confidence that the non-steady \r state global interannual-decadal variability of Fnet has clear regional \r influences (Gregor et al., 2019; Landschutzer et al., 2019). The tropical \r [...]"


id: 9ca15c6ea2d93662aa2248f5c0f1b470
created_at: 2023-04-19 13:22:36
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index: climate_gpt_v2
vector_id: 24801
content:
"[...] oceans contribute the most to the global mean interannual variability \r (Supplementary Materials Figure 5.SM.1d). The high latitude oceans, \r particularly the Southern Ocean, contribute the most to the global\u0002scale decadal variability (Supplementary Materials Figure 5.SM5.1b,c; \r (Landschutzer et al., 2016, 2019; Gregor et al., 2019; Gruber et al., \r 2019a; Hauck et al., 2020). The influence of the Southern Ocean \r on the global mean decadal variability and the 1990s hiatus is \r supported by the highest regional-global correlation coefficients \r (Supplementary Materials Figures  5.SM.1a,c). In contrast, the "


id: 82a7320a3fa6f43c93f549746b5e71ac
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 20117
content:
"Regional decadal-scale anomalies in the variability of ocean CO2\r storage have also emerged, probably associated with changes in the \r MOC, which may influence the global variability in Fnet (Chapter 9; \r DeVries et al., 2017). In the interior of the Indian and Pacific sectors \r of the Southern Ocean, and the North Atlantic, the increase in the \r CO2 inventory from 1994 to 2007 was about 20% smaller than \r expected from the atmospheric CO2 increase during the same period \r and the anthropogenic CO2 inventory in 1994 (Sabine eta al., 2004; \r Gruber et al., 2019a). There is medium confidence that the ocean \r CO2 inventory strengthened again in the decade 2005-2015 (DeVries \r et al., 2017). In the North Atlantic, a low rate of anthropogenic CO2\r storage at 1.9 +- 0.4 PgC per decade during the time period of 1989-\r 2003 increased to 4.4 +-  0.9  PgC per decade during 2003-2014. \r This is associated with changing ventilation patterns driven by the \r [...]"


id: 69bac41c1a18565d232728749a72dde9
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 15868
content:
"[...] North Atlantic Oscillation (Woosley et al., 2016). In the Pacific sector \r of the Southern Ocean, the rate of anthropogenic CO2 storage also \r increased from 8.8 +- 1.1 (1s) PgC per decade during 1995-2005 to \r 11.7 +- 1.1 PgC per decade during 2005-2015 (Carter et al., 2019). \r However, in the Subantarctic Mode Water of the Atlantic sector of "


id: a3b88e095c2b2b1b2379212801e4635c
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 26006
content:
"Lower fire emissions of CO2 and enhanced vegetation carbon \r uptake due to reduced global burned area have contributed to the \r increasing global net land CO2 sink in the recent decade (Arora \r and Melton, 2018; Yin et al., 2020) (low to medium confidence). \r Satellite observations reveal a declining trend in global burned area \r by about 20% over past two decades (Andela et al., 2017; Earl \r and Simmonds, 2018; Forkel et al., 2019), a trend most pronounced \r in regions like northern Africa (Forkel et al., 2019; Zubkova et al., \r 2019; Bowman et al., 2020) and Mediterranean Europe (Turco et al., \r 2016). However, burned area trends are highly heterogeneous \r regionally with increasing trends reported in regions like western \r United States (Holden et al., 2018; Abatzoglou et al., 2019). Some \r regions (e.g.,  Amazon basin and Australia) experienced record\u0002breaking fire events in 2019 and 2020 (e.g.,  Boer et al., 2020), \r whose effects on burned area trends remain to be explored. The \r [...]"


id: 16021908bef5bd291de5739c1957cde2
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 1089
content:
"[...] burned area trends were primarily attributed to both human\u0002induced climate change and human activities (Jolly et al., 2015; \r Andela et al., 2017; Holden et al., 2018; Turco et al., 2018; Teckentrup \r et al., 2019; Bowman et al., 2020), as well as changing frequency of \r lightning in the boreal region (Veraverbeke et al., 2017). In addition \r to changes in the burned area, fire dynamics could affect the trend \r in land-atmosphere CO2 exchange indirectly through increasing \r concentration of air pollutants (see Section  6.3.4 for impacts \r of ozone and aerosol on the carbon cycle; Yue and Unger, 2018; \r Lasslop et al., 2019)."


id: ec2396e99485cf604297f790b9125355
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 38914
content:
"The global net land CO2 sink is assessed to have grown over the \r past six decades (Sarmiento et al., 2010; Ballantyne et al., 2017; Le \r Quere et al., 2018b; Ciais et al., 2019; Friedlingstein et al., 2020) \r (high confidence). Estimated as residual from the mass balance \r budget of fossil fuel CO2 emissions minus atmospheric CO2 growth \r and the ocean CO2 sink, the global net land CO2 sink (including both \r land CO2 sink and net land-use change emissions) increased from \r 0.3 +- 0.6 PgC yr-1 during the 1960s to 1.8 +- 0.8 PgC yr-1 during the \r 2010s (Friedlingstein et al., 2020). An increasing global net land CO2\r sink since the 1980s (Figure 5.10) was consistently suggested both \r by atmospheric inversions (e.g., Peylin et al., 2013) and by DGVMs \r (e.g.,  Sitch et al., 2015; Friedlingstein et al., 2019). The Northern \r Hemisphere contributes more to the net increase in the land CO2 sink \r compared to the Southern Hemisphere (Ciais et al., 2019), and boreal \r [...]"


id: 79da91dadcf6682123616d3f7dda87f8
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 18643
content:
"[...] and temperate forests probably contribute the most (Tagesson et al., \r 2020). Attributing an increased net land CO2 sink to finer regional \r scales remains challenging, but inversions of satellite-based column \r CO2 products that have emerged since AR5 are a promising tool to \r further constrain regional land-atmosphere CO2 exchange (Ciais \r et al., 2013; Houweling et al., 2015; Reuter et al., 2017; O'Dell et al., \r 2018; Palmer et al., 2019)."


id: 2efac274568d74c63f4b63396e4143df
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 5540
content:
"carbon-nitrogen interactions vary greatly among models, large \r uncertainties remain on how nitrogen cycling regulates the \r response of ecosystem carbon uptake to higher atmospheric CO2\r (Walker et al., 2015; Wieder et al., 2019; Davies-Barnard et al., 2020; \r Meyerholt et al., 2020; see Section 5.4.1). Fire modules have been \r incorporated into 10 of 16 DGVMs contributing to the global carbon \r budget (Le Quere et al., 2018a), and a growing number of models \r have representations of human ignitions and fire suppression \r processes (Rabin et al., 2017; Teckentrup et al., 2019). There are also \r growing DGVM developments to include management practices \r (Pongratz et al., 2018) and the effects of secondary forest regrowth \r (Pugh et al., 2019), though models still under-represent intensively \r managed ecosystems, such as croplands and managed forests \r (Guanter et al., 2014; Thurner et al., 2017). Processes that have \r [...]"


id: a7aab2b40773a038ece1f10b39fd4483
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 26713
content:
"[...] not yet played a significant role in the land CO2 sink of the past \r decades but can grow in importance, include permafrost (Box 5.1) \r and peatlands dynamics (Dargie et al., 2017; Gibson et al., 2019), \r have also been incorporated in some DGVMs (Koven et al., 2015b; \r Burke et al., 2017a; Guimberteau et al., 2018). Growing numbers \r and varieties of Earth observations are being jointly used to drive \r and benchmark models, helping to further identify missing key \r processes or mechanisms that are poorly represented in the current \r generation of DGVMs (e.g., Collier et al., 2018)."


id: 8a9324617a94c3a53c0d6a7b3d281dfa
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 21554
content:
"The AR5 stated that the interannual variability of the atmospheric \r CO2 growth rate is dominated by tropical land ecosystems. A set of \r new satellite measurements applied to assess the variability of the \r tropical land carbon balance since AR5 (Ciais et al., 2013) confirm \r this statement, including satellite column CO2 measurements, \r estimating the recent anomalous land-atmosphere CO2 exchange \r induced by El Nino at continental scale (e.g.,  J.  Liu et al., 2017; \r Palmer et al., 2019), and L-band vegetation optical depth, \r estimating tropical above-ground biomass carbon stock changes \r (Fan et al., 2019). In addition, based on medium evidence and \r medium agreement between studies with DGVMs and atmospheric \r inversions, semi-arid ecosystems over the tropical zones have \r a  larger contribution to interannual variability in global land-\r atmosphere CO2 exchange than moist tropical forest ecosystems \r (low to medium confidence) (Poulter et al., 2014; Ahlstrom et al., \r [...]"


id: 27bfdcdb126bf36fbba882dbfa731e73
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 4183
content:
"Figure 5.11 | Interannual variation in detrended anomalies of the net land CO2 sink and land surface air temperature during 1980-2019. Correlation \r coefficients between the net land CO2 sink anomalies and temperature anomalies are show on the right bar plots. The net land CO2 sink is estimated by four atmospheric \r inversions (blue) and 15 Dynamic Global Vegetation Models (DGVMs) (green), respectively (Friedlingstein et al., 2020). Solid blue and green lines show model mean detrended \r anomalies of the net land CO2 sink. The ensemble mean of DGVMs is bounded by the 1-s inter-model spread in each large latitude band (North 30degN-90degN, Tropics \r 30degS-30degN, South 90degS-30degS) and the globe. The ensemble mean of atmospheric inversions is bounded by model spread. For each latitudinal band, the anomalies of the net \r land CO2 sink and temperature (orange) were obtained by removing the long-term trend and seasonal cycle. A 12-month running mean was taken to reduce high-frequency \r [...]"


id: db6fadff9305b54b5165ff8fbe6df86c
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 35882
content:
"[...] noise. The bars in the right panels show correlation coefficients between the net land CO2 sink anomalies and temperature anomalies for each region. ** indicates P<0.01; \r * indicates P<0.05. The grey shaded area shows the intensity of El Nino-Southern Oscillation (ENSO) as defined by the Nino 3.4 index. Two volcanic eruptions (El Chichon and \r Mount Pinatubo) are indicated with light blue dashed lines. Temperature data are from the Climatic Research Unit (CRU), University of East Anglia (Harris et al., 2014). Anomalies \r were calculated following Patra et al. (2005), but using a 12-month low-pass filter and detrended to obtain interannual variations. Further details on data sources and processing \r are available in the chapter data table (Table 5.SM.6)."


id: 78ce79ddc729745d42dc6cb95d854489
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 18441
content:
"Multiple lines of evidence suggest that WUE has increased in near proportionality to atmospheric CO2 (high confidence) at a rate \r generally consistent with Earth system models (ESMs), despite variation in the WUE response to CO2 (De Kauwe et al., 2013; Frank \r et al., 2015; Keeling et al., 2017; Lavergne et al., 2019; Walker et al., 2021). Both field-scale CO2 enrichment experiments and process \r models show the effect of physiologically induced water savings, particularly under water-limiting conditions (De Kauwe et al., 2013; \r Farrior et al., 2015; Lu et al., 2016; Roy et al., 2016). Plants can also benefit from reduced drought stress due to enhanced CO2\r without ecosystem-scale water savings (Jiang et al., 2021). To some extent, this increased WUE offsets the effects of enhanced vapour \r pressure deficit (VPD) on plant transpiration (Bobich et al., 2010; Creese et al., 2014; Jiao et al., 2019), but will have limited effect on \r [...]"


id: 4e72471e3b29565fbd8927c7a50d1d0d
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 11079
content:
"[...] ameliorating plant water stress during extreme drought events (Xu et al., 2016; Menezes-Silva et al., 2019; L. Liu et al., 2020), when \r leaf stomata are governed primarily by soil moisture (Roy et al., 2016)."


id: be15c3ad842cfd9ca2338945272f4ea7
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 30712
content:
"Uncertainties remain on the magnitude of sensitivity of the land carbon fluxes to droughts. Global studies indicate stronger control \r of soil moisture to variations in satellite proxies of GPP than VPD (Stocker et al., 2019; L. Liu et al., 2020). However, given that VPD \r increases exponentially with atmospheric warming, some studies suggest that VPD in stomatal regulation will become increasingly \r more important under a warmer climate (Novick et al., 2016; Grossiord et al., 2020). It is difficult to isolate the relative contributions of \r warmer temperature, higher VPD and lower soil moisture. This is because land-atmosphere feedbacks cause a simultaneous increase \r of plant evaporative demand and of root zone water deficit impairing plant root uptake (Berg et al., 2016). These physiological \r responses can be further compounded by drought legacies (Anderegg et al., 2015), changes in structure and population dynamics due \r [...]"


id: 59e48e4983ac2ce1bb333cb6db1befd2
created_at: 2023-04-19 13:22:36
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index: climate_gpt_v2
vector_id: 13061
content:
"[...] to forest mortality (McDowell et al., 2020), disturbances associated with drought (fire, insects damage; Anderegg et al., 2020) and \r possible trade-offs between resistance and resilience (X. Li et al., 2020). Nonetheless, ESMs suggest that increased drought effects \r under very high levels of global warming (about 4degC at the end of the 21st century) contribute to the reduced efficiency of the land \r sink (Green et al., 2019)."


id: ddbcdf5e14f72cf31c11874abe10ca58
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 36273
content:
"What are the limits of carbon dioxide removal from a water cycle perspective?\r Carbon dioxide removal (CDR) options based on terrestrial carbon sinks will require the appropriation of significant amounts of \r water at the landscape level. Most mitigation pathways that seek to limit global warming to 1.5degC or less than 2degC require the \r removal of about 30 to 300 GtC from the atmosphere by 2100 (Rogelj et al., 2018b). Bioenergy with carbon capture and storage \r (BECCS), and afforestation/reforestation are the dominant CDR options used in climate stabilization scenarios, implying large \r requirements for land and water (Section 5.6; Beringer et al., 2011; Boysen et al., 2017b; Fajardy and Mac Dowell, 2017; Jans et al., \r 2018; Seferian et al., 2018b; Yamagata et al., 2018; Stenzel et al., 2019). A review of freshwater requirements for irrigating biomass \r plantations shows a range between 15 and 1250 km3 per GtC of biomass harvest. This is equivalent to a water requirement of \r [...]"


id: 5e991e530f87ecf993a79e47b5d829bc
created_at: 2023-04-19 13:22:36
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index: climate_gpt_v2
vector_id: 13934
content:
"[...] 99-8250 km3 for the median BECCS deployment of around 3.3 GtC yr-1 (Smith et al., 2016) in <2degC-scenarios (Stenzel et al., \r 2021), assuming that biomass is converted to electricity, which is substantially less efficient than converting biomass to heat. \r These large ranges are the result of different assumptions about the type of biomass and yield improvements, management, and \r land availability. The use of alternative feedstocks, such as wastes, residues and algae, would lead to smaller water requirements \r (Smith et al., 2019)."


id: 2590daec21e069dfeb185698d89e2479
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 3796
content:
"Over the past decade (2010-2019), 10.9 +-  0.9  PgC yr-1 were \r emitted from human activities, which were distributed between \r three Earth system components: 46% accumulated in the \r atmosphere (5.1 +- 0.02 PgC yr-1), 23% was taken up by the ocean \r (2.5 +- 0.6 PgC yr-1) and 31% was stored by vegetation in terrestrial \r ecosystems (3.4 +-  0.9  PgC yr-1) (Table  5.1). There is a  budget \r imbalance of 0.1  PgCyr-1 which is within the uncertainties of the \r other terms. Over the industrial era (1750-2019), the total cumulative \r CO2 fossil fuel and industry emissions were 445 +- 20 PgC, and the \r LULUCF flux (= net land-use change in Figure 5.12) was 240 +- 70 PgC \r (medium confidence). The equivalent total emissions (685 +- 75 PgC) \r was distributed between the atmosphere (285  +-  5  PgC), oceans \r (170 +- 20 PgC) and land (230 +- 60 PgC; Table 5.1), with a budget \r imbalance of 20  PgC.  This budget (Table  5.1) does not explicitly \r [...]"


id: 91995276f41222d97c3c8a372da0926e
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 22843
content:
"The  CH4 variability in the atmosphere is mainly the result of the \r net balance between the sources and sinks on the Earth's surface \r and chemical losses in the atmosphere. Atmospheric transport \r evens out the regional CH4 differences between different parts of \r the Earth's atmosphere. The steady-state lifetime is estimated to \r be 9.1 +- 0.9 years (Section 6.3.1 and Table 6.2). About 90% of the \r loss of atmospheric CH4 occurs in the troposphere by reaction with \r hydroxyl radical (OH), 5% by bacterial soil oxidation, and the rest \r 5% by chemical reactions with OH, excited state oxygen (O1D), \r and atomic chlorine (Cl) in the stratosphere (Saunois et al., 2020). \r Methane has large emissions from natural and anthropogenic origins, \r but a clear demarcation of their nature is difficult because of the use \r and conversions of the natural ecosystem for human activities. The \r largest natural sources are from wetlands, freshwater and geological \r [...]"


id: db29bacb17cb37d25d1f6d528032f58f
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 35834
content:
"[...] process, while the largest anthropogenic emissions are from enteric \r fermentation and manure treatment, landfills and waste treatment, \r rice cultivation and fossil fuel exploitation (Table 5.2). In the past two \r centuries, CH4 emissions have nearly doubled, predominantly human \r driven since 1900, and persistently exceeded the losses (virtually \r certain), thereby increasing the atmospheric abundance as evidenced \r from the ice core and firn air measurements (Ferretti et al., 2005; \r Ghosh et al., 2015)."


id: c467aec3054de0712afd40f96949be4c
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 31854
content:
"Since the start of direct measurements of CH4 in the atmosphere in \r the 1970s (Figure 5.13), the highest growth rate was observed from \r 1977 to 1986 at 18 +- 4 ppb yr-1 (multi-year mean and 1 standard \r deviation) (Rice et al., 2016). This rapid CH4 growth followed the \r green revolution with increased crop production and a fast rate of \r industrialization that caused rapid increases in CH4 emissions from \r ruminant animals, rice cultivation, landfills, oil and gas industry and \r coal mining (Ferretti et al., 2005; Ghosh et al., 2015; Crippa et al., \r 2020). Due to increases in oil prices in the early 1980s, emissions \r from gas flaring declined significantly (Stern and Kaufmann, 1996). \r This explains the first reduction in CH4 growth rates from 1985 to \r 1990 (Steele et al., 1992; Chandra et al., 2021). Further emissions \r reductions occurred following the Mt Pinatubo eruption in 1991 \r that triggered a reduction in CH4 growth rate through a decrease in \r [...]"


id: ab1fd6fc66af3a619fc79d10814df41c
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
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index: climate_gpt_v2
vector_id: 27333
content:
"[...] wetland emissions driven by lower surface temperatures due to the \r light scattering by aerosols (Banda et al., 2016; Chandra et al., 2021). \r In the late 1990s through to 2006 there was a temporary pause in the \r CH4 growth rate, with higher confidence on its causes than in AR5: "


id: dd6a531635e72a16411b0fd9ce4a72ed
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
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index: climate_gpt_v2
vector_id: 36223
content:
"2016; Patra et al., 2016; Schaefer et al., 2016; Schwietzke et al., 2016; \r Turner et al., 2017; Worden et al., 2017; He et al., 2020); studies \r disagree on the relative contribution of thermogenic, pyrogenic \r and biogenic emission processes and variability in tropospheric \r OH concentration. The renewed CH4 increase is accompanied by \r a reversal of d13C trend to more negative values post 2007; opposite \r to what occurred in the 200 years prior (Ferretti et al., 2005; Ghosh \r et al., 2015; Schaefer et al., 2016; Schwietzke et al., 2016; Nisbet et al., \r 2019), suggesting an increasing contribution from animal farming, \r landfills and waste, and a slower increase in emissions from fossil \r fuel exploitation since the early 2000s (Patra et al., 2016; Jackson \r et al., 2020; Chandra et al., 2021). Atmospheric concentrations of \r CH4 reached 1866.3 ppb in 2019 (Figure  5.14). A  comprehensive \r assessment of the CH4 growth rates over the past four decades is \r [...]"


id: 95d05358385a33ad9a6e782d59941518
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
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index: climate_gpt_v2
vector_id: 23555
content:
"In the agriculture and waste sectors (Table 5.2), livestock production \r has the largest emissions source (109 Tg yr-1 in 2008-2017) \r dominated by enteric fermentation by about 90%. Methane is \r formed during the storage of manure, when anoxic conditions are \r developed (Hristov et al., 2013). Emissions from enteric fermentation \r and manure have increased gradually from about 87 Tg yr-1 in \r 1990-1999 to 109 Tg yr-1 in 2008-2017 mainly due to the increase \r in global total animal numbers. Methane production in livestock \r rumens (cattle, goats, sheep, water buffalo) are affected by the type, \r amount and quality of feeds, energy consumption, animal size, health \r and growth rate, meat and milk production rate, and temperature \r (Broucek, 2014; S.R.O. Williams et al., 2020; SRCCL Section  5.4.3). \r Waste management and landfills produced 64 Tg yr-1 in 2008-2017, \r with global emissions increasing steadily since the 1970s and, \r [...]"


id: a3db2500e7ab6a4d80d76257754a84cb
created_at: 2023-04-19 13:22:36
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index: climate_gpt_v2
vector_id: 26023
content:
"[...] despite significant declines in the USA, western Europe and Japan \r (Crippa et al., 2020; Hoglund-Isaksson et al., 2020)."


id: 4523c208561cd5b1f5f3f51aa89a5fc1
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 9388
content:
"The positive gradient between CH4 at Cape Grim, Australia (41degS) and \r Trinidad Head, USA (41degN), and the bigger difference between Trinidad \r Head and global mean CH4 compared to that between global mean \r CH4 and Cape Grim, strongly suggest that the Northern Hemisphere \r is the dominant origin of anthropogenic CH4 emissions (Figure 5.13). \r The loss rate of CH4 in troposphere does not produce a large positive \r north-south hemispheric gradient in CH4 due to parity in hemispheric \r mean OH concentration (Patra et al., 2014), or in the case of greater OH \r concentrations in the northern rather than the Southern Hemisphere \r as simulated by the chemistry-climate models (Naik et al., 2013). Coal \r mining contributed about 35% of the total CH4 emissions from all \r fossil fuel-related sources. Top-down estimates of fossil fuel emissions \r (106 Tg yr-1) are smaller than bottom-up estimates (115  Tg yr-1) \r during 2008-2017 (Table  5.2). Inventory-based estimates suggest \r [...]"


id: 68d9f935e80caa3feac267d6cf5b3acd
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
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index: climate_gpt_v2
vector_id: 15715
content:
"[...] that CH4 emissions from coal mining increased by 17 Tg yr-1 between \r the periods 2002-2006 and 2008-2012, with a dominant contribution \r from China (Peng et al., 2016; Crippa et al., 2020; Hoglund-Isaksson \r et al., 2020). Inventory-based estimates suggest that CH4 emissions \r from coal mining increased by 17 Tg yr-1 between the periods 2002-\r 2006 and 2008-2012, with a dominant contribution from China (Peng \r et al., 2016; Crippa et al., 2020; Hoglund-Isaksson et al., 2020). Recent \r country statistics and detailed inventory-based estimates show that \r CH4 emissions from coal mining in China declined between 2012 \r and 2016 (Sheng et al., 2019; Gao et al., 2020), while atmospheric\u0002based estimates suggest a  continuation of CH4 emissions growth \r but at a slower rate to the year 2015 (Miller et al., 2019) and 2016 \r (Chandra et al., 2021). Emissions from oil and gas extraction and \r use decreased in the 1980s and 1990s, but increased in the 2000s \r [...]"


id: 335d9d602049d0bf5c4d2334ac695f1b
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
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index: climate_gpt_v2
vector_id: 6288
content:
"[...] and 2010s (Dlugokencky et al., 1994; Stern and Kaufmann, 1996; \r Howarth, 2019; Crippa et al., 2020). The attribution to multiple CH4\r sources using spatially aggregated atmospheric d13C data remained \r underdetermined to infer the global total emissions from the fossil \r fuel industry, biomass burning and agriculture (Rice et al., 2016; \r Schaefer et al., 2016; Schwietzke et al., 2016; Worden et al., 2017; \r Thompson et al., 2018)."


id: ee626c811886d4b2529ba38c9056b5b2
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 39314
content:
"Freshwater wetlands are the single largest global natural source \r of CH4 in the atmosphere, accounting for about 26% of the total \r CH4 source (robust evidence, medium agreement). Progress has \r been made since AR5 (Ciais et al., 2013) in better constraining \r freshwater lake and river emissions and reducing double counting \r with wetland emissions. Bottom-up and top-down estimates for \r 2008-2017 are 149 and 180 Tg yr-1, respectively, with a  top\u0002down uncertainty range of 159-199 Tg yr-1 (Table  5.2). The large \r uncertainties stem from challenges in mapping wetland area and \r temporal dynamics to landscape estimates, and in scaling methane \r production, transport and consumption processes that are measured \r with small chambers or flux towers (Pham-Duc et al., 2017). Both the \r top-down and bottom-up estimates presented in Table 5.2 indicate \r little increase in wetland CH4 emissions during the last three decades, \r with the new estimates being slightly smaller than in AR5 due to \r [...]"


id: fb0caa0d7eff92d82cd3646e324fe5c2
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
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index: climate_gpt_v2
vector_id: 41546
content:
"[...] updated wetland maps and ecosystem model simulations (Melton \r et al., 2013; Poulter et al., 2017). Wetland emissions show strong \r interannual variability due to the changes in inundated land area, air \r temperature and microbial activity (Bridgham et al., 2013). Present \r terrestrial ecosystem model simulated CH4 emissions variability does \r not produce strong correlation with the El Nino-Southern Oscillation \r (ENSO) cycle (Cross-Chapter Box 5.2, Figure 2), although observation \r evidence is emerging for lower CH4 emissions during El Ninos and \r greater emissions during La Nina (Pandey et al., 2017)."


id: 7ea740bc058ad73b9924704e457ff7dd
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 19443
content:
"In AR5, the ocean CH4 emissions were reported together with \r geological emissions, summing up to 54 (33-75) Tg yr-1. Coastal \r oceans, fjords and mud volcanos are major sources of CH4 in the \r marine environment, but CH4 flux measurements are sparse. Saunois \r et al. (2020) estimate that the oceanic budget, including biogenic, \r geological and hydrate emissions from coastal and open ocean, is \r 6 (range 4-10) Tg yr-1 for the 2000s, which is in good agreement with \r an air-sea flux measurement-based estimate of 6-12 Tg yr-1 (Weber \r et al., 2019). When estuaries are included, the total oceanic budget \r is 9-22 Tg yr-1, with a mean value of 13 Tg yr-1. A recent synthesis \r suggests that CH4 emissions from shallow coastal ecosystems, \r particularly from mangroves, can be as high as 5-6 Tg yr-1 (Al-Haj \r and Fulweiler, 2020). The reservoir emissions, including coastal \r wetlands and tidal flats, contribute up to 13 Tg yr-1 (Borges and Abril, \r [...]"


id: 863afecb10d085b0202ebb72e098de0b
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
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index: climate_gpt_v2
vector_id: 20770
content:
"[...] 2011; Deemer et al., 2016). Methane seepage from the Arctic shelf, \r possibly triggered by the loss of geological storage due to warming \r and thawing of permafrost and hydrate decomposition, has a wide \r estimated range of 0.0-17 Tg yr-1 (Shakhova et al., 2010, 2014, 2017; \r Berchet et al., 2016); advanced eddy covariance measurements put \r the best estimate at about 3 Tg yr-1 from the East Siberian Arctic \r shelf (Thornton et al., 2020). The current flux is expected to be a mix \r of pre-industrial and climate change-driven fluxes, CH4 seepage is \r anticipated to increase in a warmer world (Dean et al., 2018)."


id: 89e885ee25b89ed089b7bc085f273899
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 21434
content:
"Inland water (lakes, rivers, streams, ponds, estuaries) emissions are \r proportionally the largest source of uncertainty in the CH4 budget. \r Since AR5 (Ciais et al., 2013), the inland water CH4 source has been \r revised from 8-73 Tg yr-1 (1980s) to 117-212 Tg yr-1 (2000s) with \r the availability of more observational data and improved areal \r estimates (Bastviken et al., 2011; Deemer et al., 2016; Stanley \r et al., 2016; DelSontro et al., 2018; Saunois et al., 2020). However, \r it is difficult to estimate bottom-up CH4 emissions, due to the large \r spatial and temporal variation in lake and river CH4 fluxes (Wik et al., \r 2016; Crawford et al., 2017; Natchimuthu et al., 2017), uncertainties \r in their global area (Allen and Pavelsky, 2018), a  relatively small \r number of observations, and varying measurement methods -  for \r example, those neglecting ebullition, varying upscaling methods, and \r lack of appropriate processes (Sanches et al., 2019; Engram et al., \r [...]"


id: 911111f3c7846bda1f1818a6dc74b220
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 22723
content:
"[...] 2020; L. Zhang et al., 2020). Accordingly, there is no clear accounting \r of inland waters in top-down budgets, which is the main reason \r for the large gap in bottom-up and top-down estimates of 'other \r sources' in the CH4 budget (Table  5.2). Despite recent progress in \r separating wetlands from inland waters, there is double-counting in "


id: 97e19f7daf385460bd32cefd37f65fe4
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 23936
content:
"The atmospheric methane (CH4) growth rate has varied widely over the past three decades, and the causes have been extensively \r studied since AR5. The mean growth rate decreased from 15 +- 5 ppb yr-1 in the 1980s to 0.48 +- 3.2 ppb yr-1 during 2000-2006 \r (the so-called quasi-equilibrium phase) and returned to an average rate of 7.6 +- 2.7 ppb yr-1 in the past decade (2010-2019) (based \r on data in Figure 5.14). Atmospheric CH4 grew faster (9.3 +- 2.4 ppb yr-1) over the last six years (2014-2019) - a period with prolonged \r El Nino conditions, which contributed to high CH4 growth rates consistent with behaviour during previous El Nino events (Figure 5.14b). \r Because of large uncertainties in both the emissions and sinks of CH4, it has been challenging to quantify accurately the methane \r budget and ascribe reasons for the growth over 1980-2019. In the context of CH4 emissions mitigation, it is critical to understand if \r [...]"


id: 37cb6ac3c1623c8e8f67fc2fc5a9baa6
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 7025
content:
"[...] the changes in growth rates are caused by emissions from human activities or by natural processes responding to changing climate. \r If CH4 continues to grow at rates similar to those observed over the past decade, it will contribute to decadal scale climate change and \r hinder the achievement of the long-term temperature goals of the Paris Agreement (Section 7.3.2.2; Nisbet et al., 2019)."


id: 3092d98e5859ebd10b527bd7c5524494
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
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index: climate_gpt_v2
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content:
"Cross-Chapter Box 5.2, Figure 1 | Methane sources and sinks for four decades from atmospheric inversions with the budget imbalance (source-\r sink; dark blue bars) (plotted on the left y-axis). Top-down analysis from Kirschke et al. (2013); Saunois et al. (2020). The global CH4 concentration seen in the black \r line (plotted on the right y-axis), representing National Oceanic and Atmospheric Administration (NOAA) observed global monthly mean atmospheric CH4 in dry-air \r mole fractions for 1983-2019 (Chapter 2, Annex V). Natural sources include emissions from natural wetlands, lakes and rivers, geological sources, wild animals, \r termites, wildfires, permafrost soils, and oceans. Anthropogenic sources include emissions from enteric fermentation and manure, landfills, waste and wastewater, rice \r cultivation, coal mining, oil and gas industry, biomass and biofuel burning. The top-down total sink is determined from global mass balance that includes chemical \r [...]"


id: a74d420f3be4e6cfdfaddadbe0eff198
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 26656
content:
"[...] losses due to reactions with hydroxyl (OH), atomic chlorine (Cl), and excited atomic oxygen (O1D), and oxidation by bacteria in aerobic soils (Table 5.2). Further details \r on data sources and processing are available in the chapter data table (Table 5.SM.6)."


id: 98f8331c83572ac93bd16c335973b10e
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 24128
content:
"A few studies emphasize the role of chemical destruction by hydroxyl (OH; the primary sink of methane), in driving changes in \r the growth of atmospheric methane abundance, in particular after 2006 (Rigby et al., 2017; Turner et al., 2017). Studies applying \r three-dimensional atmospheric inversion (McNorton et al., 2018), simple multi-species inversion (Thompson et al., 2018), as well as \r empirical methods using a variety of observational constraints based on OH chemistry (Nicely et al., 2018; Patra et al., 2021), do not \r find trends in OH large enough to explain the methane changes post-2006. On the contrary, global chemistry-climate models based \r on fundamental principles of atmospheric chemistry and known emissions trends of anthropogenic non-methane short-lived climate \r forcers simulate an increase in OH over this period (Zhao et al., 2019; Stevenson et al., 2020; see Section 6.2.3). These contrasting lines \r [...]"


id: 24901770ef052e1ccd41e19e4e715f8e
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updated_at: 2023-04-19 13:22:36
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vector_id: 3625
content:
"[...] of evidence suggest that OH changes may have had a small moderating influence on methane growth since 2007 (low confidence)."


id: 4fdf81f3a096f3a2c67373b84ca20d38
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
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index: climate_gpt_v2
vector_id: 11347
content:
"Cross-Chapter Box 5.2 Figure 2 shows that modelled wetland emissions anomalies for all regions did not exhibit statistically significant \r trends (high agreement between models, medium evidence). Thus, the inter-decadal difference of total CH4 emissions derived from \r inversion models and wetland emissions, arises mainly from anthropogenic activities. The time series of regional emissions suggest \r that progress towards atmospheric CH4 quasi-equilibrium was primarily driven by reductions in anthropogenic (fossil fuel exploitation) \r emissions in Europe, Russia and temperate North America over 1988-2000. In the global totals, emissions equalled loss in the early \r 2000s. The growth since 2007 is driven by increasing agricultural emissions from East Asia (1997-2017), West Asia (2005-2017), \r Brazil (1988-2017) and Northern Africa (2005-2017), and fossil fuel exploitations in temperate North America (2010-2017; Lan et al., \r [...]"


id: 96cc58f09c3904353616dd2217240dc0
created_at: 2023-04-19 13:22:36
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index: climate_gpt_v2
vector_id: 23728
content:
"[...] 2019; Crippa et al., 2020; Hoglund-Isaksson et al., 2020; Jackson et al., 2020; Chandra et al., 2021)."


id: 6a42fe00cec16dbe55d362fe80366c36
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 15975
content:
"There is evidence from emissions inventories at country level and regional scale inverse modelling that CH4 growth rate variability \r between 1988 and 2017 is closely linked to anthropogenic activities (medium agreement). Isotopic composition observations and \r inventory data suggest that concurrent emissions changes from both fossil fuels and agriculture are playing roles in the resumed CH4\r growth since 2007 (high confidence). Shorter-term decadal variability is predominantly driven by the influence of El Nino-Southern \r Oscillation on emissions from wetlands and biomass burning (Cross-Chapter Box 5.2, Figure 2), and loss due to OH variations (medium \r confidence), but lacking quantitative contribution from each of the sectors. By synthesizing all available information regionally from \r a priori (bottom-up) emissions, satellite and surface observations, including isotopic information, and inverse modelling (top-down), \r [...]"


id: ac830cc26901db73d64a62216518ab79
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 27591
content:
"[...] the capacity to track and explain changes in, and drivers of, natural and anthropogenic CH4 regional and global emissions has \r improved since AR5, but fundamental uncertainties related to OH variations remain unchanged."


id: 8996b7d125b6a94fe08ebd80c35ad427
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vector_id: 21382
content:
"The tropospheric abundance of N2O was 332.1 +- 0.4 ppb in 2019 \r (Figure  5.15), which is 23% higher than pre-industrial levels of \r 270.1 +- 6.0 ppb (robust evidence, high agreement). Current estimates \r are based on atmospheric measurements with high accuracy and \r density (Francey et al., 2003; Elkins et al., 2018; Prinn et al., 2018), \r and pre-industrial estimates are based on multiple ice-core records\r (Section 2.2.3.2.3). The average annual tropospheric growth rate was \r 0.85 +- 0.03 ppb yr-1 during the period 1995 to 2019 (Figure 5.15a). \r The atmospheric growth rate increased by about 20% between \r the decade 2000-2009 and the most recent decade of 2010-2019 \r (0.95 +- 0.04 ppb yr-1) (robust evidence, high agreement). The growth \r rate in 2010-2019 was also higher than during 1970-2000 (0.6-\r 0.8 ppb yr-1; Ishijima et al., 2007) and the 30-year period prior to 2011 \r (0.73 +- 0.03 ppb yr-1), as reported by AR5. New evidence since AR5 \r [...]"


id: a42e758d90afeaaeea06e05c20675f17
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
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index: climate_gpt_v2
vector_id: 26078
content:
"[...] (WGI, Section 6.4.3) confirms that, in the tropics and subtropics, large \r interannual variations in the atmospheric growth rate are negatively \r correlated with the multivariate ENSO index (MEI) and associated \r anomalies in land and ocean fluxes (Ji et al., 2019; Thompson et al., \r 2019; S. Yang et al., 2020) (Figure 5.15a)."


id: 447dd31b7e687178c6a69443ef4a2769
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 9280
content:
"The AR5 (WGI, Section 6.4.3) and SRCCL (Section 2.3.3) concluded \r that agriculture is the largest anthropogenic source of N2O \r emissions. Since SRCCL (2.3.3), a  new synthesis of inventory\u0002based and modelling studies shows that the widespread use of \r synthetic fertilizers and manure on cropland and pasture, manure \r management and aquaculture resulted in 3.8 (2.5-5.8) TgN yr-1\r (average 2007-2016) (robust evidence, high agreement) (Table 5.3; \r Winiwarter et al., 2018; FAO, 2019; Janssens-Maenhout et al., 2019; \r Tian et al., 2020). Observations from field-measurements (Song et al., \r 2018), inventories (Wang et al., 2020) and atmospheric inversions \r (Thompson et al., 2019) further corroborate the assessment of SRCCL \r that there is a non-linear relationship between N2O emissions and \r nitrogen input, implying an increasing fraction of fertilizer lost as \r N2O with larger fertilizer excess (medium evidence, high agreement). \r [...]"


id: 70475f6ad951cb4c5dd8748b5a113dd3
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
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index: climate_gpt_v2
vector_id: 17010
content:
"[...] Several studies using complementary methods indicate that \r agricultural N2O emissions have increased by more than 45% since \r the 1980s (high confidence) (Figure  5.16 and Table  5.3; Davidson, \r 2009; Winiwarter et al., 2018; Janssens-Maenhout et al., 2019; Tian \r et al., 2020), mainly due to the increased use of nitrogen fertilizer \r and manure. N2O emissions from aquaculture are among the fastest \r rising contributors of N2O emissions, but their overall magnitude is \r still small in the overall N2O budget (Tian et al., 2020)."


id: 591b86d236949a770c7457e44b9f4641
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 12919
content:
"The principal non-agricultural anthropogenic sources of N2O are \r industry, specifically chemical processing, wastewater, and the \r combustion of fossil fuels (Table  5.3). Industrial emissions of N2O \r mainly due to nitric and adipic acid production have decreased \r in North America and Europe since the widespread installation \r of abatement technologies in the 1990s (Perez-Ramirez et al., \r 2003; Lee et al., 2011; Janssens-Maenhout et al., 2019). There is \r still considerable uncertainty in industrial emissions from other \r regions of the world with contrasting trends between inventories \r (Thompson et al., 2019). Globally, industrial emissions and emissions \r from fossil fuel combustion by stationary sources, such as power \r plants, as well as smaller emissions from mobile sources (e.g., road \r transport and  aviation) have remained nearly constant between \r the 1980s and 2007-2016 (medium evidence, medium agreement) \r [...]"


id: e93c5a3d214b6a2487318bce9d05242e
created_at: 2023-04-19 13:22:36
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vector_id: 38391
content:
"[...] (Winiwarter et al., 2018; Janssens-Maenhout et al., 2019; Tian et al., \r 2020). Wastewater N2O emissions, including those from domestic \r and industrial sources, have increased from 0.2 (0.1-0.3) TgN yr-1\r to 0.35 (0.2-0.5) TgN yr-1 between the 1980s and 2007-2016 (Tian \r et al., 2020)."


id: da3ce30ead1259c513c6f77e2e4f5ecc
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 35695
content:
"Soils are the largest natural source of N2O, arising primarily from \r nitrogen processing associated with microbial nitrification and \r denitrification (Table 5.3; Butterbach-Bahl et al., 2013; Snider et al., \r 2015). Under some conditions, soils can also act as a net sink of N2O, \r but this effect is small compared to the overall source (Schlesinger, \r 2013). Since AR5 (WGI, Section  6.4.3), improved global process\u0002based models (Tian et al., 2019) suggest a present-day source of 6.7 \r (5.3-8.1) TgN yr-1 (2007-2016 average), which is consistent with \r the estimate in AR5. Process-based models and inventory-based \r methods show that increased N deposition has enhanced terrestrial \r N2O emissions by 0.8 (0.4-1.4 TgN  yr-1) relative to approximately \r pre-industrial times, and by 0.2 (0.1-0.2) TgN yr-1 between the 1980s \r and 2007-2016 (limited evidence, medium agreement) (Figure 5.16; \r Tian et al., 2019). This estimate is at the high end of the range \r [...]"


id: 4264f7ec2b3ffdee83766e41d696e3ba
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 8878
content:
"Atmospheric abundance of GHGs is proportional to their emissions\u0002loss budgets in the Earth's environment. There are multiple metrics \r to evaluate the relative importance of different GHGs for the global \r atmospheric radiation budget and the socio-economic impacts \r (Section 7.6). Metrics for weighting emissions are further developed \r in AR6 WGIII. Figure 5.19 shows the regional emissions of the three \r main GHGs. For North Asia, Europe, Temperate North America and \r West Asia, the most dominant GHG source is CO2 (high confidence) \r (Figure  5.19) while, for East Asia, South Asia, South East Asia, \r Tropical South America, Temperate North America and Central Africa, \r the source is CH4 (Figure  5.19). The N2O emissions are dominant \r in regions with intense use of nitrogen fertilizers in agriculture. \r Only boreal North America showed net sinks of CO2, while close \r to flux neutrality is observed for North Asia, Southern Africa, and \r [...]"


id: 20a93200d087a0b3faac79eb1954803d
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
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index: climate_gpt_v2
vector_id: 2973
content:
"[...] Australasia. Persistent emissions of CO2 are observed for Tropical \r and South America, northern Africa, and South East Asia (medium \r confidence). The medium confidence arises from large uncertainties \r in the estimated non-fossil fuel CO2 fluxes over these regions due to \r the lack of high-quality atmospheric measurements."


id: 247217d448de97f3b24fa34fbcf4ef2
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
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index: climate_gpt_v2
vector_id: 3606
content:
"The surface ocean has absorbed a quarter of all anthropogenic CO2\r emissions, mainly through physical-chemical processes (McKinley \r et al., 2016; Gruber et al., 2019b; Friedlingstein et al., 2020). Once \r dissolved in seawater, CO2 reacts with water and forms carbonic acid, \r which in turn dissociates, leading to a decrease in the concentration \r of carbonate (CO3\r -2) ions, and increasing both bicarbonate (HCO3-\r ) \r and hydrogen (H+) ion concentration. This process has caused a shift \r in the carbonate chemistry towards a  less basic state, commonly \r referred to as 'ocean acidification' (Caldeira and Wickett, 2003; \r Orr et al., 2005; Doney et al., 2009). Although the societal concern \r regarding ocean acidification is relatively recent (about the last \r 20  years), the physical-chemical basis for the ocean absorption \r (sink) of atmospheric CO2 has been discussed much earlier by Revelle \r and Suess (1957). The AR5 and SROCC assessments were of robust \r [...]"


id: 3c61b0956450096bd36989dbdacd16da
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
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index: climate_gpt_v2
vector_id: 7814
content:
"[...] evidence that the H+ ion concentration is increasing in the surface \r ocean, thereby reducing seawater pH (= -log [H+]) (Section 2.3.4.1; Orr \r et al., 2005; Feely et al., 2009; Ciais et al., 2013; Bindoff et al., 2019), \r and there is high confidence that ocean acidification is impacting \r marine organisms (Bindoff et al., 2019)."


id: e0c2ccac843b4041f5345e65b3a1432e
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
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index: climate_gpt_v2
vector_id: 36803
content:
"A limited number of independent proxy records indicate that the \r PETM was associated with a  surface ocean pH decline ranging \r from 0.15 to 0.30 units (Penman et al., 2014; Gutjahr et al., 2017; \r Babila et al., 2018). It was also accompanied by a rapid (<10 ka) \r shallowing of the carbonate saturation horizon, resulting in \r the widespread dissolution of sedimentary carbonate, followed \r by a  gradual (100  kyr) recovery (Zachos et al., 2005; Bralower \r et al., 2018). The remarkable similarity among sedimentary records \r spanning a  wide range of ecosystems suggests with medium \r confidence that the perturbation in the ocean carbonate saturation \r was global (Babila et al., 2018) and directly resulted from elevated \r atmospheric CO2 levels. The degree of acidification is similar to the \r 0.4 pH unit decrease projected for the end of the 21st century under \r RCP8.5 (Gattuso et al., 2015) and is estimated to have occurred at \r [...]"


id: a9ef8e453f39cdb2cee1ef53cdec16e8
created_at: 2023-04-19 13:22:36
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content_type: text
index: climate_gpt_v2
vector_id: 27120
content:
"[...] a rate about one order of magnitude slower than the current rate \r of ocean acidification (Zeebe et al., 2016). There is low confidence\r in the inferred rates of ocean acidification inherent to the range of \r uncertainties affecting rates estimates based on marine sediments \r (Section 5.1.2.1)."


id: eefdc3be707629a75e227adb98f74306
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 39415
content:
"Ocean oxygen decline, or deoxygenation, is driven by changes in ocean \r ventilation and solubility (Bindoff et al., 2019). It is virtually certain\r that anthropogenic forcing has made a  substantial contribution to \r the ocean heat content increase over the historical period (Bindoff \r et al., 2019; IPCC, 2019c; Chapter 9, Section 2.3.3.1), strengthening \r upper water column stratification. Ocean warming decreases the \r solubility of dissolved oxygen in seawater, and it contributes to about \r 15% of the  dissolved oxygen decrease in the oceans according to \r estimates based on solubility and the recent SROCC assessment \r (medium confidence), especially in sub-surface waters, between \r 100-600 m depth (Helm et al., 2011; Schmidtko et al., 2017; Breitburg \r et al., 2018; Oschlies et al., 2018; SROCC, Section 5.3.1). Stratification \r reduces the ventilation flux into the ocean interior, contributing to \r most of the remaining ocean deoxygenation (Schmidtko et al., 2017; \r [...]"


id: 90f2782e6c5b0363186eaea6271d5d8d
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
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vector_id: 22708
content:
"[...] Breitburg et al., 2018; Section  3.6.2). Deoxygenation may enhance \r emissions of nitrous oxide, especially from oxygen minimum zones \r (OMZs) or hypoxic coastal areas (Breitburg et al., 2018; Oschlies et al., \r 2018). Since SROCC (Bindoff et al., 2019), CMIP6 model simulation \r results agree with the reported 2% loss (4.8 +- 2.1 Pmoles O2) in total \r dissolved oxygen in the upper ocean layer (100-600 m) for the 1970-\r 2010 period (Helm et al., 2011; Ito et al., 2017; Schmidtko et al., 2017; \r Kwiatkowski et al., 2020; Section  2.3.4.2). The response of marine \r organisms to the coupled effects of ocean warming, acidification and \r deoxygenation occur at different metabolic levels on different groups, \r and include respiratory stress and reduction of thermal tolerance \r (Gruber, 2011; Bindoff et al., 2019; IPCC, 2019c; Kawahata et al., \r 2019). An assessment of these effects on marine biota is found in WGII \r AR6 Chapter 2."


id: 8f1b8d643f4565ef988938489486c092
created_at: 2023-04-19 13:22:36
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vector_id: 22376
content:
"Ocean pH time series are based on the reconstruction of coral \r boron isotope ratios (d11B). A majority of coral d11B data have been \r generated from the western Pacific region with a few records from \r the Atlantic Ocean. Biweekly resolution paleo-pH records show \r monsoonal variation of about 0.5 pH unit in the South China Sea \r (Liu et al., 2014). Interannual ocean pH variability in the range of \r 0.07-0.16 pH unit characterizes southwest Pacific corals that are \r attributed to El Nino-Southern Oscillation (ENSO) (H.C. Wu et al., \r 2018) and river runoff (D'Olivo et al., 2015). Decadal (10-, 22- and 48-\r year) ocean pH variations in the south-west Pacific have been linked \r to the Inter-decadal Pacific Oscillation, causing variations of up to \r 0.30 pH unit in the Great Barrier Reef (Pelejero et al., 2005; Wei et al., \r 2009) but weaker (about 0.08 pH unit) in the open ocean (H.C. Wu \r et al., 2018). Decadal variations in the South China Sea pH changes \r [...]"


id: 12500eab5d4081927e3606330a4c2684
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vector_id: 421
content:
"[...] of 0.10-0.20 have also been associated with the variation in the East \r Asian monsoon (Liu et al., 2014; Wei et al., 2015), as a weakening of \r the Asian winter monsoon leads to sluggish water circulation within \r the reefs, building up localised CO2 concentration in the water due to \r calcification and respiration."


id: a0fbaa5ad4850b44ca7f6231e5d21a98
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vector_id: 25546
content:
"Since the beginning of the industrial period in the mid-19th century, \r coral d11B-derived ocean pH has decreased by 0.06-0.24 pH unit in \r the South China Sea (Liu et al., 2014; Wei et al., 2015) and 0.12 pH \r unit in the south-west Pacific (H.C. Wu et al., 2018). Since the mid\u000220th century, a distinct feature of coral d11B records relates to ocean \r acidification trends, albeit having a wide range of values: 0.12-0.40 pH \r unit in the Great Barrier Reef (Wei et al., 2009; D'Olivo et al., 2015), \r 0.05-0.08 pH unit in the north-west Pacific (Shinjo et al., 2013) and \r 0.04-0.09 pH unit in the Atlantic Ocean (Goodkin et al., 2015; Fowell \r et al., 2018). Concurrent coral carbon isotopic (d13C) measurements \r infer ocean uptake of anthropogenic CO2 from the combustion of \r fossil fuel, based on the lower abundance of 13C in fossil fuel carbon. \r Western Pacific coral records show depleted d13C trends since the \r late 19th century that are more prominent since the mid-20th century \r [...]"


id: fd7c13c813363383a74b9a5ce5d68401
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vector_id: 41953
content:
"[...] (high confidence) (Pelejero et al., 2005; Wei et al., 2009; Shinjo et al., \r 2013; Liu et al., 2014; Kubota et al., 2017; H.C. Wu et al., 2018)."


id: e350c21e60c9a2c14052c51d0340a6a4
created_at: 2023-04-19 13:22:36
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index: climate_gpt_v2
vector_id: 37290
content:
"During the LDT, deep ocean ventilation increased as Antarctic Bottom \r Water (AABW) (Skinner et al., 2010; Gottschalk et al., 2016; Jaccard \r et al., 2016) and subsequently the Atlantic meridional overturning \r circulation (McManus et al., 2004; Lippold et al., 2016) resumed, \r transferring previously sequestered remineralized carbon from the \r ocean interior to the upper ocean, and eventually the atmosphere \r (Skinner et al., 2010; Galbraith and Jaccard, 2015; Gottschalk et al., \r 2016; Ronge et al., 2016, 2020; Sikes et al., 2016; Rae et al., 2018), \r contributing to the deglacial CO2 rise. Intermediate depths lost \r oxygen as a result of sluggish ventilation and increasing temperatures \r (decreasing saturation). As the world emerged from the last Glacial \r period, OMZs underwent a large volumetric increase at the beginning \r of the Bolling-Allerod (B/A), a  northern-hemisphere wide warming \r event, 14.7 ka (Jaccard and Galbraith, 2012; Praetorius et al., 2015) \r [...]"


id: ad11175e6609bbb6f8c99b2be1af2478
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vector_id: 27685
content:
"[...] with deleterious consequences for benthic ecosystems (e.g., Moffitt \r et al., 2015). These observations indicate with high confidence that \r the rate of warming, affecting the solubility of oxygen and upper \r water column stratification, coupled with changes in subsurface \r ocean ventilation, impose a  direct control on the degree of ocean \r deoxygenation, implying a  high sensitivity of ocean oxygen loss \r to warming. The expansion of OMZs contributed to a  widespread \r increase in water column (de)nitrification (Galbraith and Kienast, \r 2013), which contributed substantially to enhanced marine N2O "


id: 881609065ac242eb9dd04a81cc30453
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content:
"Ono et al., 2019; Bates and Johnson, 2020; Supplementary Material \r Table  5.SM.3). These rates are consistent with the rates expected \r from the transient equilibration with increasing atmospheric CO2\r concentrations, but the variability of rate in decadal time scale has \r also been detected with robust evidence (Ono et al., 2019; Bates \r and Johnson, 2020). In the tropical Pacific, its central and eastern \r upwelling zones exhibited a faster pH decline of -0.022 to -0.026 pH \r unit per decade due to increased upwelling of CO2-rich sub-surface \r waters in addition to anthropogenic CO2 uptake (Sutton et al., 2014; \r Lauvset et al., 2015). By contrast, warm pools in the western tropical \r Pacific exhibited slower pH decline of -0.010 to -0.013 pH unit per \r decade (Supplementary Material Table 5.SM.3; Lauvset et al., 2015; \r Ishii et al., 2020). Observational and modelling studies (Nakano et al., \r 2015; Ishii et al., 2020) consistently suggest that slower acidification \r [...]"


id: 9a87778f81b49a620e5d73591a57c414
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vector_id: 24429
content:
"[...] in this region is attributable to the anthropogenic CO2 taken up in the \r extratropics around a decade ago and transported to the tropics via \r shallow meridional overturning circulations."


id: ea900dfa6294ddb32165adf40fd5133f
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vector_id: 38628
content:
"Figure 5.20 | Multi-decadal trends of pH (Total Scale) in surface layer at various sites of the oceans and a global distribution of annual mean pH adjusted \r to the year 2000. Time-series data of pH are from Dore et al. (2009), Olafsson et al. (2009), Gonzalez-Davila et al. (2010), Bates et al. (2014), Takahashi et al. (2014), \r Wakita et al. (2017), Merlivat et al. (2018), Ono et al. (2019), and Bates and Johnson (2020). Global distribution of annual mean pH have been evaluated from data of surface \r ocean pCO2measurements (Bakker et al., 2016; Jiang et al., 2019). Acronyms in panels: KNOT and K2 - Western Pacific subarctic gyre time series; HOT - Hawaii Ocean Time\u0002series; BATS - Bermuda Atlantic Time-series Study; DYFAMED - Dynamics of Atmospheric Fluxes in the Mediterranean Sea; ESTOC - European Station for Time-series in the \r [...]"


id: 6fb6a7f313c12f5d5fe3c86ea23e3a2a
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vector_id: 16908
content:
"[...] Ocean Canary Islands; CARIACO - Carbon Retention in a Colored Ocean Time-series. Further details on data sources and processing are available in the chapter data table \r (Table 5.SM.6)."


id: b49656471a027f023d98f796a3f53130
created_at: 2023-04-19 13:22:36
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index: climate_gpt_v2
vector_id: 29019
content:
"Takahashi et al., 2014; Lauvset et al., 2015; Wakita et al., 2017; Merlivat \r et al., 2018). Nevertheless, the high agreement of pH decline among \r these available time-series studies leads to high confidence in the \r trend of acidification in these zones. In the Arctic Ocean, a temporally \r limited time series of carbonate chemistry measurements prevents \r drawing robust conclusions on ocean acidification trends. However, \r the carbonate saturation state (O) is generally low, and observational \r studies show with robust evidence that the recent extensive melting \r of sea ice leading to enhanced air-sea CO2 exchange, large freshwater \r inputs, together with river discharge and glacial drainage, as well \r as the degradation of terrestrial organic matter in seawater, result \r in the decline of  O  of aragonite to undersaturation (Bates et al., \r 2009; Chierici and  Fransson, 2009; Yamamoto-Kawai et al., 2009; \r Azetsu-Scott et al., 2010; Robbins et al., 2013; Fransson et al., 2015; \r [...]"


id: 3c0502f2c3a65d1db249c17eda62b66a
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content:
"[...] Semiletov et al., 2016; Anderson et al., 2017; Qi et al., 2017; Beaupre\u0002Laperriere et al., 2020; Y. Zhang et al., 2020; SROCC Section 3.2.1.2.4, \r IPCC, 2019b). The low saturation state of aragonite (O about 1) has \r also been observed in surface waters of the Antarctic coastal zone \r associated with freshwater input from glaciers (Mattsdotter Bjork \r et al., 2014) and upwelling of deep water (Hauri et al., 2015) as well \r as along eastern boundary upwelling systems (Feely et al., 2016)."


id: e0ace9d5797aebee65e45c217eb6799e
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vector_id: 36783
content:
"A significant increase in acidification resulting from net metabolic CO2\r release coupled with ocean circulation changes has been shown with \r high confidence in large swathes of intermediate waters in the Pacific \r and Atlantic oceans (Dore et al., 2009; Byrne et al., 2010; Rios et al., \r 2015; Chu et al., 2016; Carter et al., 2017; Lauvset et al., 2020). For \r example, ocean circulation contributes a pH change of -0.013 +- 0.013 \r to the overall observed change of -0.029 +- 0.014 for 1993-2013 at \r depths around 1000 m at 30degS-40degS in the South Atlantic ocean (Rios \r et al., 2015). Long-term repeated observations in the North Pacific show \r a decline in dissolved oxygen (-4.0 mmol kg-1 per decade at maximum) \r being sustained in the intermediate water since the 1980s (Takatani \r et al., 2012; Sasano et al., 2015). The amplification of acidification \r associated with the weakening ventilation is thought to have been \r [...]"


id: 76a3c29f204342616bc0104cb2527ed9
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vector_id: 18092
content:
"[...] occurring persistently. In contrast, for the North Pacific subtropical \r mode water, large decadal variability in pH and aragonite saturation \r state with amplitudes of about 0.02 and about 0.1, respectively, are \r superimposed on secular declining trends due to anthropogenic CO2\r invasion (Oka et al., 2019). This is associated with the variability in \r ventilation due to the approximately 50% variation in the formation \r volume of the mode water that is forced remotely by the Pacific \r Decadal Oscillation (Qiu et al., 2013; Oka et al., 2015)."


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content:
"In oxygen-depleted waters, microbial processes (denitrification and \r anammox, i.e., anaerobic ammonium oxidation; Kuypers et al., 2005; \r Codispoti, 2007; Gruber and Galloway, 2008) remove fixed nitrogen, \r and when upwelled waters reach the photic zone, primary production \r becomes nitrogen-limited (Tyrrell and Lucas, 2002). However, in \r other oceanic regions, increased water-column stratification due \r to warming may reduce the amount of N2O reaching the surface \r and thereby decrease N2O flux to the atmosphere. Landolfi et al. \r (2017) suggest that, by 2100, under the RCP8.5 scenario, total N2O \r production in the ocean may decline by 5% and N2O emissions be \r reduced by 24% relative to the pre-industrial era due to decreased \r organic matter export and anthropogenic-driven changes in ocean \r circulation and atmospheric N2O concentrations. Projected oxygen \r loss in the ocean is thought to result in an ocean-climate feedback \r [...]"


id: f6e601c341ac8d6b5eba4f865fbd1b9f
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
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index: climate_gpt_v2
vector_id: 40807
content:
"Projections with CMIP5 ESMs, reported in AR5 (Section  6.4.4) \r and SROCC (Section  5.2.2.3; IPCC, 2019b), showed changes in \r global mean surface ocean pH from 1870-1899 to 2080-2099 of \r -0.14 +- 0.001 (inter-model standard deviation) under RCP2.6 and \r -0.38 +-  0.005 under RCP8.5 with pronounced regional variability \r (Bopp et al., 2013; Hurd et al., 2018). They also projected faster pH \r declines in mode waters below seasonal mixed layers (Resplandy \r et al., 2013; Watanabe and Kawamiya, 2017) as has been observed in \r the Atlantic (Salt et al., 2015) and in the Pacific (Carter et al., 2019), \r because of the net CO2 release by respiration and lowering CO2\r buffering capacity of seawater. In these CO2 concentration-driven \r simulations, the level of acidification in the surface ocean is primarily \r determined by atmospheric CO2 concentration and regional seawater \r carbonate chemistry, thereby providing consistent projections across \r [...]"


id: 3f08aa228c3c03a5b65254ce4959916
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 8274
content:
"[...] models. New projections with CMIP6 ESMs show greater surface pH \r decline of -0.16 +- 0.002 under the SSP1-2.6 and -0.44 +- 0.005 under \r SSP5-8.5 from 1870-1899 to 2080-2099 (Section 4.3.2.5 and Cross\u0002Chapter Box 5.3; Kwiatkowski et al., 2020). The greater pH declines \r in CMIP6 are primarily a  consequence of higher atmospheric CO2\r concentrations in SSPs than their CMIP5-RCP analogues (Kwiatkowski \r et al., 2020). Ocean acidification is also projected to occur with high \r confidence in the Abyssal Bottom Waters in regions such as the \r northern North Atlantic and the Southern Ocean (Sulpis et al., 2019), \r with the rates of global mean pH decline of -0.018 +- 0.001 under \r SSP1-2.6 and -0.030 +-  0.002 under SSP5-8.5 from 1870-1899 to \r 2080-2099 in CMIP6 (Kwiatkowski et al., 2020)."


id: 5c77d18c89938b975051d0d57b20af17
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
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index: climate_gpt_v2
vector_id: 13525
content:
"The areas with relatively rapid oxygen decrease include OMZs in the \r tropical oceans, where oxygen content has been decreasing at \r a rate of 0.9-3.4 umol kg-1 per decade in the thermocline for the \r past five decades (Stramma et al., 2008). Low oxygen, low pH and \r shallow aragonite saturation horizons in the OMZs of the eastern \r boundary upwelling regions co-occur, affecting ecosystem structure \r (Chavez et al., 2008) and function in the water column, including the \r presently unbalanced nitrogen cycle (Paulmier and Ruiz-Pino, 2009). \r The coupling between upwelling, productivity, and oxygen depletion \r feeds back to biological productivity and the role of these regions as \r sinks or sources of climate active gases. When OMZ waters upwell and \r impinge on the euphotic zone, they release significant quantities of \r GHGs, including N2O (0.81-1.35 TgN yr-1), CH4 (0.27-0.38 TgCH4 yr-1), \r and CO2 (yet to be quantified) to the atmosphere, exacerbating \r [...]"


id: 6d5724d13600de9515b5dcc80f9cf8a2
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
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index: climate_gpt_v2
vector_id: 16514
content:
"[...] global warming (Paulmier et al., 2008; Naqvi et al., 2010; Kock \r et al., 2012; Arevalo-Martinez et al., 2015; Babbin et al., 2015; \r Farias et al., 2015). Modelling projections suggest a global decrease \r of 4-12% in oceanic N2O emissions (from 3.71-4.03  TgN  yr-1 to \r 3.54-3.56  TgN  yr-1) from 2005 to 2100 under RCP8.5, despite \r a  tendency to increased N2O production in the OMZs, associated \r primarily with denitrification (Martinez-Rey et al., 2015). It is difficult \r to single out the contribution of nitrification and denitrification, \r which can occur simultaneously. A rigorous separation of these two \r processes would require more mechanistic parametrization, which \r has been hindered by the still large conceptual and parametric "


id: f932b2c5a6f7440f85e0cc3c0090c87
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
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index: climate_gpt_v2
vector_id: 41208
content:
"[H+] and -16 +-  7% for pH from 1990-1999 to 2090-2099 under \r RCP8.5 in CMIP5 (Kwiatkowski and Orr, 2018).\r The signal of ocean acidification in surface ocean is large and is \r projected to emerge beyond the range of natural variability within \r the time scale of a decade in all ocean basins (Schlunegger et al., \r 2019). There is high agreement among modelling studies that the \r largest pH decline and large-scale undersaturation of aragonite \r in surface seawater start to occur first in polar oceans (Orr et al., \r 2005; Steinacher et al., 2009; Hurd et al., 2018; Jiang et al., 2019). \r Under SSP5-8.5, the largest surface pH decline, exceeding 0.45 \r between 1995-2014 and 2080-2099, occurs in the Arctic Ocean \r (Kwiatkowski et al., 2020). The freshwater input from sea ice melt \r is an additional factor leading to a  faster decline of aragonite \r saturation level than expected from the anthropogenic CO2 uptake \r (Yamamoto et al., 2012). The increase in riverine and glacial \r [...]"


id: 9c1ab04691b69d358c4bbecb7012f930
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 24719
content:
"[...] discharges that provide terrigenous carbon, nutrients and alkalinity \r as well as freshwater are the other factors modifying the rate of \r acidification in the Arctic Ocean. However, their impacts have been \r projected in a limited number of studies with extensive knowledge \r gaps and model simplifications leading to low confidence in \r their impacts (Terhaar et al., 2019; Hopwood et al., 2020). In the \r Southern Ocean, the aragonite undersaturation starts in the 2030s \r in RCP8.5, and the area that experiences aragonite undersaturation \r for at least one month per year by 2100 is projected to be more \r than 95%. Under RCP2.6, short periods (less than one month) of \r aragonite undersaturation are expected to be found in less than \r 2% of the area during this century (Sasse et al., 2015; Hauri et al., \r 2016; Negrete-Garcia et al., 2019). These long term projections are \r modified at interannual time scales by large-scale climate modes \r [...]"


id: eeb222ff0d38851d267c2236e23eeaf0
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 39375
content:
"[...] (Rios et al., 2015) such as the ENSO and the Southern Annular \r Mode (Conrad and Lovenduski, 2015). In other regions, acidification \r trends are influenced by a range of processes such as changes in \r ocean circulation, temperature, salinity, carbon cycling, and the \r structure of the marine ecosystem. As, at present, models do not \r resolve fine-scale variability of these processes, current projections \r do not fully capture the changes that the marine environment will \r experience in the future (Takeshita et al., 2015; Turi et al., 2016)."


id: 3c44aa5d615892ca0a0ef6e55f186c24
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 7796
content:
"Observations and data products including models (Astor et al., 2013; \r Bakker et al., 2016; Kosugi et al., 2016; Vargas et al., 2016; Laruelle \r et al., 2017, 2018; Orselli et al., 2018; Roobaert et al., 2019; Cai et al., \r 2020; H. Sun et al., 2020) confirm the strong spatial and temporal \r variability in the coastal ocean surface carbonate chemistry and sea\u0002air CO2 fluxes (high agreement, robust evidence). The anthropogenic \r CO2-induced acidification is either mitigated or enhanced through \r biological processes; primary production removes dissolved CO2\r from the surface, and respiration adds CO2 and consumes oxygen \r in the subsurface layers. The relative intensity of these processes is \r controlled by natural or anthropogenic eutrophication. Other drivers \r of variability include biological community composition, freshwater \r input from rivers or melting ice, sea ice cover and calcium carbonate \r precipitation/dissolution dynamics, coastal upwelling and regional \r [...]"


id: 53d8ad6412a3cf8b2138699319620f30
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 12023
content:
"[...] circulation, and seasonal surface cooling (Fransson et al., 2015, \r 2017; Feely et al., 2018; Roobaert et al., 2019; Cai et al., 2020; Hauri \r et al., 2020; Monteiro et al., 2020b; H. Sun et al., 2020). Near-shore \r surface waters are often supersaturated with CO2, regardless of the \r latitude, especially in highly populated areas receiving substantial \r amounts of domestic and industrial sewage (Chen and Borges, 2009). \r Nevertheless, thermal or haline-stratified eutrophic coastal areas \r may act as net atmospheric CO2 sinks (Chou et al., 2013; Cotovicz \r Jr. et al., 2015). Continental shelves, excluding near-shore areas, \r act as CO2 sinks at a  rate of 0.2 +-  0.02  PgC yr-1 (Laruelle et al., \r 2014; Roobaert et al., 2019), considering ice-free areas only. Under \r increasing atmospheric CO2 and eutrophication, such ecosystems \r would be more vulnerable to ecological and seawater chemistry \r changes, impacting the local economy."


id: 25350ca3a8ec60406533d952c0d213d7
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 3739
content:
"Observations and models predict an expansion and intensification \r of low-pH deep water intrusions for the north-east Pacific coastal \r upwelling area (high agreement, robust evidence) (Hauri et al., 2013; \r Feely et al., 2016; Cai et al., 2020). Areas such as the California Current \r System are naturally exposed to intrusions of low-pH, high pCO2sea\r deep waters from remineralization processes and anthropogenic CO2\r intrusion (Feely et al., 2008, 2010, 2018; Chan et al., 2019; Lilly et al., \r 2019; Cai et al., 2020).The eastern Pacific coastal upwelling displays \r seasonality in subsurface aragonite undersaturation as a consequence \r of the interplay between anthropogenic CO2, respiration and \r intrusion  of upwelling waters (Feely et al., 2008, 2010, 2016, 2018; \r Hauri et al., 2013; Vargas et al., 2016; Chan et al., 2019; Lilly et al., \r 2019). The  coastal south-east Pacific upwelling combined with low\u0002pH, low-alkalinity, organic matter-rich river inputs display extreme \r [...]"


id: c262cabbc2930f7edb8590ef03256cdf
created_at: 2023-04-19 13:22:36
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index: climate_gpt_v2
vector_id: 31478
content:
"[...] temporal variability in surface seawater pCO2 and low aragonite \r saturation (Vargas et al., 2016; Osma et al., 2020)."


id: 857dd2067d7e940ab5ac6cbfd1d0d748
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 20626
content:
"Since SROCC (Bindoff et al., 2019), there is further evidence that \r anthropogenic eutrophication via continental runoff and atmospheric \r nutrient deposition, and ocean warming are very likely the main \r drivers of deoxygenation in coastal areas (Levin and Breitburg, \r 2015; Levin et al., 2015; Royer et al., 2016; Breitburg et al., 2018; \r Cocquempot et al.,  2019; Fagundes et al., 2020; Limburg et al., \r 2020). Increasing intensity and frequency of wind-driven upwelling \r is responsible for longer and more intense coastal hypoxia, fuelled \r by organic matter degradation from primary production (medium \r to high agreement, medium evidence) (Rabalais et al., 2010; \r Bakun  et al.,  2015; Varela et al., 2015; Fennel and Testa, 2019; \r Limburg et al., 2020). Locally, submarine groundwater discharge may \r enhance the eutrophication state (low agreement, limited evidence, \r Luijendijk et al., 2020). Since AR5 (Ciais et al., 2013) and SROCC \r [...]"


id: 4a6d06c8d18d57d30c59272d3cd7d83
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
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index: climate_gpt_v2
vector_id: 10334
content:
"[...] (Bindoff et al., 2019) new observations and model studies confirm the \r trends in increasing coastal hypoxia caused by eutrophication, ocean \r warming and changes in circulation (Claret et al., 2018; Dussin et al., \r 2019; Limburg et al., 2020), as well as the ubiquitous impacts on \r marine organisms and fisheries (AR6 WGII Chapter 3; Carstensen and \r Conley, 2019; Fennel and Testa, 2019; Osma et al., 2020). Following \r open ocean deoxygenation trends since the 1950s, more than \r 700 coastal regions are being reported as hypoxic (dissolved oxygen \r concentration <2  mg O2 L-1) (Limburg et al., 2020). Additionally, \r deoxygenation or increasing severe hypoxic periods in coastal areas \r may enhance the sea-to-air fluxes of N2O and CH4 especially through \r microbial-mediated processes in the water column-sediment \r interface (medium agreement) (Middelburg and Levin, 2009; Naqvi \r et al., 2010; Farias et al., 2015; Limburg et al., 2020)."


id: 9078d168d98ae217470d3d9ca8f829ae
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 22622
content:
"Temperate, non-upwelling coastal areas along the north-west \r Atlantic display a trend of decreasing seawater pH, mainly attributed \r to the combined effects of eutrophication and decreasing seawater \r buffering capacity (high agreement, robust evidence). Observations \r show an increasing north to south gradient of aragonite saturation \r state (Sutton et al., 2016; Fennel et al., 2019; Cai et al., 2020). Low \r alkalinity and total inorganic carbon concentration, combined with \r an ocean signal of acidification, diminishes the buffering capacity \r along the decreasing salinity gradient from the ocean to the coast. \r Models suggest that, in this area, the aragonite saturation is \r seasonally controlled by nutrient availability and primary production, \r supporting the finding that eutrophication is the main driver for \r exacerbating acidification (Cai et al., 2017, 2020). The coastal Gulf of \r Mexico is facing a parallel increase in bottom water acidification and \r [...]"


id: 27a93479d8ab8b25d402a623a6b6cbaf
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 4160
content:
"[...] deoxygenation off the Mississippi Delta driven by eutrophication (Cai \r et al., 2011; Laurent et al., 2017; Fennel et al., 2019)."


id: cc7dccec0a1334d122b9f36d3a604d80
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 33298
content:
"The AR5 (WGI, Box  6.3) and SRCCL (IPCC, 2019a) concluded with \r high confidence that rising atmospheric CO2 increases leaf-level \r photosynthesis. This effect is represented in all ESMs. New studies since \r AR5 add evidence that the leaf-level CO2 fertilization is modulated \r by acclimation of photosynthesis to long-term CO2 exposure, \r growth temperature, seasonal drought, and nutrient availability, \r but these effects are not yet routinely represented in ESMs (Smith \r and Dukes, 2013; Baig et al., 2015; Kelly et al., 2016; Drake et al., \r 2017; Jiang et al., 2020a). Cross-Chapter Box 5.1 assesses multiple \r lines of evidence, which suggest that the ratio of plant CO2 uptake \r to water loss - plant water-use efficiency (WUE) - increases in near \r proportionality to atmospheric CO2. Despite advances in the regional \r coverage of field experiments, observations of the consequences of \r CO2 fertilization at ecosystem level are still scarce, in particular from \r [...]"


id: 53043d65e4ef5899e3af0ddc9ddbb80e
created_at: 2023-04-19 13:22:36
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index: climate_gpt_v2
vector_id: 11864
content:
"[...] outside the temperate zone (Song et al., 2019). New syntheses since \r AR5 corroborate that the effect of elevated CO2 on plant growth and \r ecosystem carbon storage is generally positive (high confidence), \r but is modulated by temperature, water and nutrient availability \r (Reich et al., 2014; Obermeier et al., 2017; Penuelas et al., 2017; \r Hovenden et al., 2019; Song et al., 2019). Plant carbon allocation, \r changes in plant community composition, disturbance, and natural \r plant mortality are important processes affecting the magnitude of the \r response, but are currently poorly represented in models (De Kauwe \r et al., 2014; Friend et al., 2014; Reich et al., 2018; A.P. Walker et al., \r 2019; K. Yu et al., 2019), and thus contribute strongly to uncertainty \r in ESM projections (Arora et al., 2020)."


id: c6ba304696968dfc1eff72e9abab42b
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index: climate_gpt_v2
vector_id: 32262
content:
"Field studies with elevated CO2 have demonstrated that the initial \r stimulation of above-ground growth may decline if insufficient \r nutrients such as nitrogen or phosphorus are available (Finzi et al., \r 2007; Norby et al., 2010; Hungate et al., 2013; Reich and Hobbie, \r 2013; Talhelm et al., 2014; Terrer et al., 2018). Model-data syntheses \r have demonstrated that capturing the observed long-term effect \r of elevated CO2 depends on the ability of models to predict the \r effect of vegetation on soil biogeochemistry (Zaehle et al., 2014; \r Koven et al., 2015b; Medlyn et al., 2015; Walker et al., 2015). Meta\u0002analyses of CO2 manipulation experiments point to increased soil \r microbial activity and accelerated turnover of soil organic matter \r (van Groenigen et al., 2017) as a result of increased below-ground \r carbon allocation by plants (Song et al., 2019), and increased root \r exudation or mycorrhizal activity due to enhanced plant nutrient \r [...]"


id: d7803ccd496650a31b517dfc4eb418b4
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
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index: climate_gpt_v2
vector_id: 35220
content:
"[...] requirements under elevated CO2 (Drake et al., 2011; Terrer et al., \r 2016; Meier et al., 2017). These effects are not considered in most \r ESMs. One global model that attempts to represent these processes \r suggests that elevated CO2-related carbon accumulation is reduced \r in soils but increased in vegetation relative to more conventional \r models (Sulman et al., 2019)."


id: b6ad2fd8a9516d6e775f463211cd72ad
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 29340
content:
"Our understanding of the effects of phosphorus limitation is less \r developed than for nitrogen, but a  growing body of literature \r suggests that it is just as important, particularly in regions with \r highly weathered soils (Wang et al., 2018; Terrer et al., 2019; Du \r et al., 2020). CO2 experiments collectively show that soil phosphorus \r is an important constraint on the CO2 fertilization effect on plant \r biomass (Terrer et al., 2019; Jiang et al., 2020a). For example, despite \r increases in photosynthesis after four years of CO2 exposure, a free\u0002air CO2 enrichment experiment in a phosphorus-limited mature forest \r ecosystem did not find an increase in biomass production (Jiang \r et al., 2020b). The lack of free-air CO2 enrichment experiments in \r phosphorus-limited tropical forests limits our understanding of the \r role of phosphorus availability in constraining the CO2 fertilization \r effect globally (Norby et al., 2016; Fleischer et al., 2019). Models \r [...]"


id: 5c1eb68db02047c26a850008a3e695df
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
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index: climate_gpt_v2
vector_id: 13461
content:
"[...] accounting for the effects of phosphorus availability, in addition to \r nitrogen, generally show an even stronger reduction of the response \r of ecosystem carbon storage to elevated CO2 (Goll et al., 2012; Zhang \r et al., 2014; X. Yang et al., 2019). Insufficient data and uncertainties \r in the process formulation cause large uncertainty in the magnitude \r of this effect (Medlyn et al., 2016; Fleischer et al., 2019)."


id: 42040b7fb8bf318687f3e6cd078131fb
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 8814
content:
"As a  major advance since AR5 (WGI, Section  6.4.2), six out of \r 11  models in the C4MIP-CMIP6 ensemble account for nitrogen \r cycle dynamics over land (Table  5.4). On average, these models \r exhibit a  25-30% lower CO2 fertilization effect on land carbon \r storage, compared to models that do not account for nitrogen cycle \r dynamics (Figure 5.29 and Table 5.5). The only model in the C4MIP\u0002CMIP6 ensemble that explicitly represents the effect of P availability \r on  plant growth suggests the lowest carbon storage response \r to increasing CO2 (Arora et al., 2020). The lower CO2 effect due to \r decreased nutrient availability is generally consistent with analyses \r of the implicit nutrient limitation in CMIP5 simulations (Wieder \r et al., 2015; Zaehle et al., 2015) and independent assessments by \r stand-alone land models (Zaehle et al., 2010; Warlind et al., 2014; \r Zhang et al., 2014; Goll et al., 2017; Meyerholt et al., 2020). The \r [...]"


id: 676727392d600a17340ac761cb261baf
created_at: 2023-04-19 13:22:36
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index: climate_gpt_v2
vector_id: 15461
content:
"[...] simulated effects are generally consistent with expectations based \r on independent observations (Walker et al., 2021). However, the \r magnitude of nutrient feedbacks in these models is poorly constrained \r by observations, owing to the limited geographic distribution of \r available observations and the uncertain scaling of results obtained \r from manipulation experiments to transient system dynamics (Song \r et al., 2019; Wieder et al., 2019; Meyerholt et al., 2020)."


id: 4d086c3fe6d39464f5fd225b051b604c
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 10823
content:
"The projected weakening of ocean carbon uptake is driven by \r a  combination of decreasing carbonate buffering capacity and \r warming, which are positive feedbacks under weak to no mitigation \r scenarios (SSP4 and 5). In high mitigation scenarios (SSP1-2.6), \r weakening ocean carbon uptake is driven by decreasing emissions \r (Cross-Chapter Box  5.3). The detailed understanding of carbonate \r chemistry in seawater that has accumulated over more than half \r a  century (e.g.,  Revelle and Suess, 1957; Egleston et al., 2010), \r provides high confidence that the excess CO2 dissolved in seawater \r leads to a non-linear reduction of the CO2 buffering capacity, that \r is smaller dissolved inorganic carbon (DIC) increase with respect \r to pCO2 increase along with the increase in cumulative ocean CO2\r uptake. Recent studies (Katavouta et al., 2018; Jiang et al., 2019; Arora \r et al., 2020; Rodgers et al., 2020) suggest with medium confidence\r [...]"


id: 6e780edb916af7eb487968070eaf4fe4
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content_type: text
index: climate_gpt_v2
vector_id: 16700
content:
"[...] that the decrease in the ocean CO2 uptake ratio to anthropogenic \r CO2 emissions, under low to no mitigation scenarios over the \r 21st century, is predominantly attributable to the ocean carbon\u0002concentration feedback through the reduction of the seawater CO2\r buffering capacity, but with contributions from physical drivers such \r as warming and wind stress (medium confidence) and biological \r drivers (low confidence) (Sections 5.2.1.3.3 and 5.4.4)."


id: 2aecc982b82da9e3b7c59d9c92a8039e
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 4788
content:
"Climate change also drives changes to vegetation composition \r and ecosystem carbon storage through other disturbances such \r as forest dieback that lead to biome shifts in tropical forests (Cox \r et al., 2004; Jones et al., 2009; Brando et al., 2014; Le Page et al., \r 2017; Zemp et al., 2017), and temperate and boreal regions (Joos \r et al., 2001; Lucht et al., 2006; Scheffer et al., 2012; Lasslop et al., \r 2016). The AR5 assessed that large-scale loss of tropical forests due \r to climate change is unlikely (WGI, Section 6.4.9). Newer ecosystem \r modelling approaches that include a  greater degree of ecosystem \r heterogeneity and diversity show a reduced sensitivity of such forest \r dieback-type changes (Levine et al., 2016; Sakschewski et al., 2016), \r supporting the AR5 assessment (Section 5.4.9). Beyond such biome \r shifts, observations of tropical forests also show that increasing tree \r mortality rates within tropical forests may reduce carbon turnover \r [...]"


id: 7ab01eebdf9ed288226f5204319f9b0f
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updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 18781
content:
"[...] times and storage (Brienen et al., 2015), that increased tree mortality \r rates in tropical forests and elsewhere are expected with increased \r temperatures and vapour pressure deficit (Cross-Chapter Box  5.1; "


id: 60ce430220301bed2f06a84db007adb
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vector_id: 14330
content:
"In nutrient-limited ecosystems, prolonged soil warming can induce \r a  fertilization effect through increased decomposition, which \r increases nutrient availability and thereby vegetation productivity \r (Melillo  et al., 2011). Models that include this process tend to \r show  a  weaker carbon-climate feedback than those that do not \r (Thornton et al., 2009; Zaehle et al., 2010; Warlind et al., 2014; \r Meyerholt et al., 2020). In CMIP6, six out of 11 ESMs include \r a representation of the nitrogen cycle, and the mean of those models \r predicts a weaker carbon-climate feedback than the overall ensemble \r mean (Arora et al., 2020; Section  5.4.8). These models only partly \r account for the interactions of nutrient effects with other processes, \r such as shifts of vegetation zones under climate changes (Sakaguchi \r et al., 2016) leading to either changes in species composition or \r changes in plant tissue nutrient to carbon ratios (Thomas et al., 2015; \r [...]"


id: 9977572c91727eadfa79d513d1d45b62
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 24226
content:
"CMIP6 ESMs predict losses of soil carbon with warming, which are \r larger than climate-driven vegetation carbon losses (Arora et al., \r 2020). As in CMIP5 (Todd-Brown et al., 2013), there is also a large \r CMIP6 ensemble spread in climate-driven soil carbon changes, \r partially driven by a large spread in the current soil carbon stocks \r predicted by the models. In CMIP5 ESMs, much of the soil carbon \r losses with warming can be traced to decreased carbon inputs, \r with a  weaker contribution from changing soil carbon lifetimes \r due to faster decomposition rates (Koven et al., 2015b), which may \r be an artefact of the lack of permafrost carbon (Box 5.1). Isotopic \r constraints suggest that CMIP5 ESMs systematically overestimated \r the transient sensitivity of soil 14C responses to atmospheric 14C changes, implying that the models respond too quickly to \r changes in either inputs or turnover times, and that therefore the \r [...]"


id: eb49cbfd869e2fce8672d442967f9624
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 38753
content:
"[...] soil contribution to all feedbacks may be weaker than currently \r projected (He et al., 2016). Using natural gradients of soil carbon \r turnover as a constraint on long-term responses to warming suggests \r that both CMIP5 and CMIP6 ESMs may systematically underestimate \r the temperature sensitivity at high latitudes, and may overestimate \r the temperature sensitivity in the tropics (Koven et al., 2017; Wieder "


id: d9f4f4a75f91f036bf824ce1fc5e62a4
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 35645
content:
"Despite accumulating evidence of increased carbon losses, it is difficult to scale up site- and ecosystem-level measurements to assess \r the net carbon balance over the entire permafrost region, due to the high spatial heterogeneity, the strong seasonal cycles, and the \r difficulty in monitoring these regions consistently across the year. The Special Report on Ocean and the Cryosphere in a Changing \r Climte (SROCC) assessed with high confidence that ecosystems in the permafrost region act as carbon sinks during the summer \r growing season, and that wintertime carbon losses are significant, consistent with a multi-decadal small increase in CO2 emissions \r during early winter at Barrow, Alaska (Sweeney et al., 2016; Webb et al., 2016; Meredith et al., 2019). These findings have been further \r [...]"


id: 6161de7033744cf80f173d181b83e1f4
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 14431
content:
"[...] strengthened by recent comprehensive synthesis of in-situ wintertime flux observations that show large carbon losses during the non\u0002growing season (Natali et al., 2019). Increased autumn and winter respiration are a key large-scale fingerprint of top-down permafrost \r thaw predicted by ecosystem models (Parazoo et al., 2018). However, the length of these wintertime observational records is too short \r to unequivocally determine whether winter carbon losses are higher now than they used to be. One study inferred a multi-year net CO2\r source for the tundra in Alaska (Commane et al., 2017), which is equivalent to 0.3 PgC yr-1 when scaled up to the northern permafrost \r region (low confidence) (Meredith et al., 2019)."


id: 8c444caa4db32249f6df4e810150c4c
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 21851
content:
"In addition to CO2, CH4 emissions from the northern permafrost region contribute to the global methane budget, but evidence as to \r whether these emissions have increased from thawing permafrost is mixed. The SROCC assigned low confidence to the degree of \r recent additional CH4 emissions from diverse sources throughout the permafrost region. These include observed regional lake area \r change, which suggest a 1.6-5 Tg CH4 yr-1 increase over the last 50 years (Walter Anthony et al., 2016), ice-capped geological sources \r (Walter Anthony et al., 2012; Kohnert et al., 2017), and shallow Arctic Ocean shelves. The shallow subsea emissions are particularly \r uncertain due to the wide range of estimates (3 Tg CH4 yr-1 (Thornton et al., 2016b) to 17 Tg CH4 yr-1 (Shakhova et al., 2014)), and \r the lack of a baseline with which to infer any changes; however, the upper half of this range in flux estimates is inconsistent with the \r [...]"


id: be0e1347006344587d1f2534636f1770
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 30703
content:
"[...] atmospheric inversions constrained by the pan-Arctic CH4 concentration measurements (Berchet et al., 2016)."


id: 1063ba2ea17d0bc236238132647776b1
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 72
content:
"Radiogenic and stable isotopic measurements on CH4 trapped in Antarctic ice support the view that CH4 emissions from fossil carbon \r reservoirs, including permafrost and methane hydrates, remained small in response to the deglacial warming. Mass-balance calculations \r reveal that geological CH4 emissions have not exceeded 19 Tg yr-1, highlighting that the deglacial increase in CH4 emissions was \r predominantly related to contemporary CH4 emissions from tropical wetlands and seasonally inundated floodplains (Bock et al., 2017; \r Petrenko et al., 2017; Dyonisius et al., 2020). Isotopic constraints on CO2 losses from permafrost with warming after the Last Glacial \r Maximum (LGM) are weaker than for CH4. While the biosphere as a whole held less carbon during the LGM than the pre-industrial, \r that change in stocks was smaller than the change in plant productivity, and so carbon losses at high latitudes may have been offset \r [...]"


id: 2315aa94b95d60767543885b5ae7b42a
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 3367
content:
"[...] by increased tropical productivity in response to warming during the Last Deglacial Transition (LDT; Ciais et al., 2012). There is also \r paleoclimate evidence for processes that mitigate carbon losses with warming on longer time scales, such as longer-term carbon \r accumulation in lake deposits following thermokarst thaw (Walter Anthony et al., 2014), and long-term accumulation of carbon in \r permafrost soils following LDT carbon loss (Lindgren et al., 2018), particularly in peatlands which accumulated carbon at a slow but \r persistent rate in warm paleoclimates (Treat et al., 2019)."


id: ac7193b244a06986fb9cde565d903a76
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 27579
content:
"The processes that govern permafrost carbon loss are grouped into gradual and abrupt mechanisms. Gradual processes include the \r deepening of the seasonally thawed active layer into perennially frozen permafrost layers and lengthening of the thawed season \r within the active layer, which increases the amount of organic carbon that is thawed and the duration of thaw. Abrupt thaw processes \r include ice-wedge polygon degradation, hillslope collapse, thermokarst lake expansion and draining, all of which are processes largely \r occurring in regions with very high soil carbon content (Olefeldt et al., 2016a, b). Abrupt thaw processes can contribute up to half of \r the total net greenhouse gas release from permafrost loss, the rest attributed to gradual thaw (Schneider von Deimling et al., 2015; \r Turetsky et al., 2020). Increased fire frequency and severity (Hu et al., 2010) also contributes to abrupt emissions and the removal of \r [...]"


id: a08fb3d2859b2c6c884d2f6e38a6050
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 25459
content:
"[...] the insulating cover which leads to an acceleration of permafrost thaw (Genet et al., 2013). Ecological feedbacks can both mitigate \r and amplify carbon losses: nutrient release from increased organic matter decomposition can drive vegetation growth that partially \r offsets soil carbon losses (Salmon et al., 2016), but also lead to biophysical feedbacks that further amplify warming (Myers-Smith \r et al., 2011)."


id: ab9757cc556f618edd9be2f19c0f5784
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 27424
content:
"Ocean warming reduces the vertical supply of nutrients to the \r upper ocean due to increasing stratification (Section 9.2.1.4) but may \r also act to alleviate seasonal light limitation. The projected effect is \r to decrease PP at low latitudes and increase PP at high latitudes \r (Kwiatkowski et al., 2020). Future changes to dust deposition due to \r desertification (Mahowald et al., 2017), alterations to the nitrogen \r cycle (Section 5.3.3.2; SROCC, Section 5.2.3.1.2), and reducing sea \r ice cover (Ardyna and Arrigo, 2020) all have the potential to alter \r PP regionally. Higher ocean temperatures tend to result in higher \r metabolic rates, although respiration may increase more rapidly \r than PP (Boscolo-Galazzo et al., 2018; Brewer, 2019; Cavan et al., \r 2019). Ocean warming and reduced PP are expected to result in \r lower zooplankton abundance, and the expansion of oxygen \r minimum zones (OMZs) may reduce the ability of zooplankton to \r [...]"


id: 5462985e8844f8798f5548ffac28832c
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 12125
content:
"[...] remineralize POC, thus increasing the efficiency of the BCP and \r forming a negative climate feedback (Cavan et al., 2017). Increased \r microbial respiration due to warming may result in greater quantities \r of organic carbon transferred into the dissolved organic carbon \r pool (Jiao et al., 2014; Legendre et al., 2015; Roshan and DeVries, \r 2017) which, while increasing the residence time of carbon in the \r ocean, would ultimately reduce the sedimentary burial, and hence \r sequestration on geologic time scales (Olivarez Lyle and Lyle, 2006)."


id: 1713e9ec815bdeb235d55737340ae17d
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 1280
content:
"Most models project that smaller phytoplankton are favoured in \r future ocean conditions (medium confidence; Cabre et al., 2015; Fu \r et al., 2016; Flombaum et al., 2020) driven by warming water and/\r or changing nutrient availability, which would alter the magnitude \r and efficiency of the BCP by altering the sinking speed, respiration \r rate and aggregation/fragmentation of sinking particles. There is low \r confidence in the sign of the resulting feedback: regions in which \r small phytoplankton dominate may have a  more efficient pump, \r although the total amount of organic carbon reaching the sea floor \r is lower (Herndl and Reinthaler, 2013; Bach et al., 2016; Richardson, \r 2019). Alternatively, an increase in small phytoplankton could result \r in a less efficient pump, due either to a greater fraction of PP being \r processed through the upper ocean microbial loop (Jiao et al., 2014) \r or generation of slower sinking particles (Guidi et al., 2009; Leung \r [...]"


id: 9768ec4196ab197c87b8b7c4e53a91f7
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 23850
content:
"[...] et al., 2021). Variable phytoplankton stoichiometry is predicted to \r increase the amount of carbon stored via the BCP relative to the \r amount of PP, so that fixed stoichiometry models (as in CMIP5) may \r underestimate cumulative ocean carbon uptake to 2100 by 0.5-3.5% \r (2-15 PgC; RCP8.5 scenario; Kwiatkowski et al., 2020). Other climate \r effects such as deoxygenation or ocean acidification could also result \r in alterations to the magnitude and efficiency of the BCP (Krumhardt \r et al., 2019; Raven et al., 2021; Taucher et al., 2021)."


id: 47cb79cb960b1d90df690741e54c2fd1
created_at: 2023-04-19 13:22:36
updated_at: 2023-04-19 13:22:36
content_type: text
index: climate_gpt_v2
vector_id: 9846
content:
"Since AR5 (Section  6.3.2.5.6), progress has been made in \r understanding the biological drivers of ocean carbon uptake in both \r coupled climate models and observations (SROCC, Section 5.2.2.6). \r Here we focus on potential feedbacks between biological processes \r and climate. In CMIP5 models, the direction of modelled PP in response \r to increased atmospheric CO2 concentration and climate warming \r was unclear (Taucher and Oschlies, 2011; Laufkotter et al., 2015). \r This remains the case in the CMIP6 models; inter-model uncertainty \r has increased in CMIP6 models, compared to CMIP5. The projected \r global multi-model mean change in PP in 13 models run under the \r SSP5-8.5 scenario is -3 +- 9% (2080-2099 mean values relative to \r 1870-1899 +- the inter-model standard deviation; Kwiatkowski et al., \r 2020). Under the low-emissions, high-mitigation scenario SSP1-2.6, \r the global change in PP is -0.56  +-  4%. Observations in the \r [...]"


id: 97dbdd877fe055367afe8ed847bc8727
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 23931
content:
"[...] contemporary period provide little direct constraint on the modelled \r responses of PP to climate change, partly due to insufficiently long \r records (Henson et al., 2016). However, there is some indication of an \r emergent constraint on changes in tropical PP based on interannual \r variability derived from remote sensing (Section 5.4.6; Kwiatkowski \r et al., 2017)."


id: f4927d419f6777f40d0e3fc741010096
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 40404
content:
"This distinction between the relatively high fidelity with which \r the ocean carbon sink is simulated, and the much wider range of \r simulations of the land carbon sink, is also evident in the zonal \r distribution of the sinks (Figure  5.24). We compare the ESM \r simulations to estimates from three atmospheric inversion models: \r Copernicus Atmosphere Monitoring Service (CAMS; Chevallier \r et al., 2005), Carbon Tracker 2017 (Peters et al., 2007) and Model for \r Interdisciplinary Research on Climate Atmospheric Transport Model \r (MIROC-ATM4; Saeki and Patra, 2017). The ocean carbon cycle \r components of CMIP6 ESMs are able to simulate the tropical CO2 \r source and mid-latitude CO2 sink, with relatively small model spread \r (Figure 5.24a). The CMIP6 ensemble (red wedge) simulates a larger \r ocean carbon sink at 50degN and a weaker sink in the Southern Ocean, \r than the inversion estimate, but with some evidence of a reduction in \r [...]"


id: 3816e01f5b47321bc1e3638cb4387256
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 7070
content:
"[...] these residual errors compared to CMIP5 (blue wedge). The spread in \r inversion fluxes arises primarily from differences in the atmospheric \r CO2 measurement networks and from transport model uncertainties."


id: 8a26406cdb77f77aed1a9a766c006ce1
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 21480
content:
"Figure 5.27 | Maps of carbon-concentration and carbon-climate feedback \r terms, as well as net carbon changes under the idealized 1% per year \r carbon dioxide (CO2) scenario, as evaluated from CMIP6 Earth system \r models (ESMs). Shown are the model means from nine CMIP6 ESMs. Uncertainty \r is represented using the simple approach (see Cross-Chapter Box Atlas.1 for more \r information): No overlay indicates regions with high model agreement, where >=80% \r of models agree with the ensemble mean on the sign of change; diagonal lines \r indicate regions with low model agreement, where <80% of models agree with the \r ensemble mean on the sign of change. Also shown are zonal-mean latitude profiles \r of land (green) and ocean (blue) feedbacks. On the land, the zonal mean feedback for \r the mean of the ensemble of models that include nitrogen is shown as dashed lines, \r and for carbon-only models as dash-dotted lines, and the carbon-climate feedback \r [...]"


id: 2c7ca466a9530de01c82eb9e9149ffc2
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 5094
content:
"[...] from one permafrost-carbon enabled ESM is shown as a dotted line. Carbon changes \r are calculated as the difference between carbon stocks at different times on land and \r for the ocean as the time integral of atmosphere-ocean CO2 flux anomalies relative \r to the pre-industrial. The denominator for gamma here is the global mean surface \r air temperature. Further details on data sources and processing are available in the \r chapter data table (Table 5.SM.6)."


id: 9f3ec53f1737cb71caa1fcea22b56ce6
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 25240
content:
"Land biosphere models show high agreement that long-term \r warming will increase N2O release from terrestrial ecosystems (Xu\u0002Ri et al., 2012; B.D.  Stocker et al., 2013; Zaehle, 2013; Tian et al., \r 2019). A positive land N2O climate feedback is consistent with paleo\u0002evidence based on reconstructed and modelled emissions during \r the last deglacial period (Schilt et al., 2014; H. Fischer et al., 2019; \r Joos et al., 2020). The response of terrestrial N2O emissions to \r atmospheric CO2 increase and associated warming is dependent on \r nitrogen availability (van Groenigen et al., 2011; Butterbach-Bahl \r et al., 2013; Tian et al., 2019). Model-based estimates do not account \r for the potentially strong emissions increases in boreal and  arctic \r ecosystems associated with future warming and permafrost thaw \r (Elberling et al., 2010; Voigt et al., 2017). There is medium confidence \r that the land N2O climate feedback is positive, but low confidence in \r [...]"


id: 821eda5a6ec40c1fcc3261794ab37c7c
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 20015
content:
"The CH4 feedbacks may arise from changing wetland emissions \r (including rice farming) and from sources that are expected to \r grow under climate change (e.g., related to permafrost thaw, fires, \r and freshwater bodies). CH4 emissions from wetlands and landfills \r generally increase with warming due to enhanced decomposition \r with higher temperatures, thereby potentially providing a  positive \r CH4 feedback on climate (Dean et al., 2018). The contribution of \r wetlands to interannual variability of atmospheric CH4 is shaped by \r the different impacts of temperature and precipitation anomalies \r on wetland emissions (e.g., during El Nino episodes) and therefore \r the relationship between climate anomalies and the wetland \r contribution to the CH4 growth rate is complex (Pison et al., 2013; \r Nisbet et al., 2016; X.  Zhang et al., 2020). As assessed by SROCC \r (IPCC, 2019b), there is high agreement across model simulations \r [...]"


id: ecd2ec4cdfbc4ea4d0a9b86356ea9399
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 39032
content:
"[...] that wetlands CH4 emissions will increase in the 21st century, but \r low agreement in the magnitude of the change (Denisov et al., 2013; \r Shindell et al., 2013; B.D. Stocker et al., 2013; Zhang et al., 2017; Koffi \r et al., 2020). Climate change increases wetland emissions (Gedney \r et al., 2004, 2019; Volodin, 2008; Ringeval et al., 2011; Denisov et al., \r 2013; Shindell et al., 2013) and gives rise to an estimated wetland \r CH4-climate feedback of 0.03 +- 0.01 W m-2 degC-1 (mean +- 1 standard \r deviation; limited evidence, high agreement) (Arneth et al., 2010; \r Shindell et al., 2013; B.D. Stocker et al., 2013; Zhang et al., 2017). The \r effect of rising CO2 on productivity, and therefore on the substrate \r for methanogenesis, can further increase the projected increase in \r wetland CH4 emissions (Ringeval et al., 2011; Melton et al., 2013). \r Model projections accounting for the combined effects of CO2 and \r climate change suggest a potentially larger climate feedback (0.01-\r [...]"


id: e9646141268699b52c9e5294c38886ce
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 38420
content:
"[...] 0.16 W m-2 degC-1) (limited evidence, low agreement) (Gedney et al., \r 2019; Thornhill et al., 2021). Methane release from wetlands depends \r on the nutrient availability for methanogenic and methanotrophic \r microorganisms that can further modify this feedback (Stepanenko \r et al., 2016; Donis et al., 2017; Beaulieu et al., 2019). Methane \r emissions from thermokarst ponds and wetlands resulting from "


id: 9e28a7c061436e3198a14fef6027e187
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 25058
content:
"Figure 5.29 | Estimates of the biogeochemical climate feedback parameter (a). The parameter a (W m-2 degC-1) for a feedback variable x is defined as ax = dN\r dx\r dx\r dT where dN\r dx is the change in top-of-atmosphere energy balance in response to a change in x induced by a change in surface temperature (T), as in Section 7.4.1.1. (a) Synthesis of \r biogeochemical feedbacks from panels (b) and (c). Orange (blue) bars correspond to positive (negative) feedbacks increasing (decreasing) radiative forcing at the top of the \r atmosphere. Bars denote the mean and the error bar represents the 5-95% range of the estimates; (b) carbon-cycle feedbacks as estimated by coupled carbon-cycle climate \r models in the CMIP5 (Arora et al., 2013) and CMIP6 (Arora et al., 2020) ensembles, where dots represent single model estimates, and filled (open) circles are those estimates \r [...]"


id: 4be631f9ebe641f562e76b980178596f
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 10612
content:
"[...] which do (not) include the representation of a terrestrial nitrogen cycle; (c) Estimates of other biogeochemical feedback mechanisms based on various modelling studies. Dots \r represent single estimates, and coloured bars denote the mean of these estimates with no weighting being made regarding the likelihood of any single estimate, and error bars \r the 5-95% range derived from these estimates. Results in panel (c) have been compiled from (a) Section 5.4.3.2 (Eliseev et al., 2014a; Harrison et al., 2018); (b) Section 5.4.3.3 \r (Schneider von Deimling et al., 2012; Burke et al., 2013, 2017b; Koven et al., 2015a, c; MacDougall and Knutti, 2016b; Gasser et al., 2018; Kleinen and Brovkin, 2018), where \r the estimates from Burke et al., 2013 have been constrained as assessed in their study (c) Section 5.4.7 (Schneider von Deimling et al., 2012, 2015; Koven et al., 2015c; Turetsky \r [...]"


id: 48b318e589f44579da1907d3b6538f11
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 10011
content:
"[...] et al., 2020); (d) Section 5.4.7 (Arneth et al., 2010; Denisov et al., 2013; Shindell et al., 2013; B.D. Stocker et al., 2013; Zhang et al., 2017); (f) Section 5.4.7 (Xu-Ri et al., 2012; \r B.D. Stocker et al., 2013; Zaehle, 2013; Tian et al., 2019); (g) Section 5.4.7 (Martinez-Rey et al., 2015; Landolfi et al., 2017; Battaglia and Joos, 2018b). (h) Section 6.3, Table 6.9 \r mean and the 5-95% range the assessed feedback parameter. Further details on data sources and processing are available in the chapter data table (Table 5.SM.6)."


id: 5893c7a607c4dd8bb54aaff0ffe612c6
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 12828
content:
"Other feedback contributions, such as the non-CO2 biogeochemical \r feedback, can be converted into a  carbon-equivalent feedback \r term (g; Section  5.4.5.5, 7.6) by reverse application of the linear \r feedback approximation (Gregory et al., 2009). The contributions \r of non-CO2 biogeochemical feedbacks combine to a  linear \r feedback term of 30 +- 27 PgCeq degC-1 (1 standard deviation range, \r 111 +- 98 Gt CO2-eq degC-1), including a feedback term of -11 [-18 to \r -5]  PgCeq degC-1 (5-95% range, -40 [-62 to -18] Gt  CO2-eq degC-1) \r from natural CH4 and N2O sources.  The biogeochemical feedback \r from permafrost thaw leads to a combined linear feedback term of \r -21 +- 12 PgCeq degC-1 (1 standard deviation range -77 +- 44 Gt CO2-\r eq  degC-1). For the integration of these feedbacks in the assessment \r of the remaining carbon budget (Section  5.5.2), two individual \r non-CO2 feedbacks (tropospheric ozone, and methane lifetime) are \r [...]"


id: 6748d32e65e24bc6bc99edbb9e95c156
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 15443
content:
"[...] captured in the AR6-calibrated emulators (Box 7.1). Excluding those \r two contributions, the resulting combined linear feedback term for \r application in Section 5.5.2 is assessed at a reduction of 7 +- 27 PgCeq \r degC-1 (1 standard deviation range, -26 +- 97 PgCeq degC-1). For the same \r reasons as for the feedback terms expressed in W m-2 degC-1 (see above), \r there is overall low confidence in the magnitude of these feedbacks."


id: bdb0a8c165e4c913cc2318174a32cc89
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 30634
content:
"The climate feedback parameter for CO2 (-1.13 +- 0.45 W m-2 degC-1, mean \r and 1  standard-deviation range) is dominated by the contribution \r of the CO2-induced increase of ocean and land carbon \r storage (-1.46  +-  0.41  W m-2 degC-1, corresponding to a  bL+O of \r 1.66 +- 0.31 PgC ppm-1), with smaller contributions from the carbon \r cycle's response to climate (0.24 +- 0.17 W m-2 degC-1, corresponding \r to gL+O of -50 +-  34  PgC degC-1), and emissions from permafrost \r thaw (0.09  [0.02  to 0.20] W  m-2 degC-1, corresponding to g  of \r -18 [3 to 41] PgC degC-1, mean and 5-95% range) (Figure 5.29a). This \r estimate does not include an estimate of the fire-related CO2 feedback \r (range: 0.01-0.06 W m-2 degC-1), as only limited evidence was available \r to inform its assessment. The sum (mean and 5-95th  percentile \r range) of feedbacks from natural emissions of CH4 including \r permafrost thaw, and N2O (0.05 [0.02 to 0.09]  W  m-2  degC-1), and \r [...]"


id: 1a7791ac16dbf9271c75b8cd15f1edc3
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 1861
content:
"[...] feedbacks from aerosol and atmospheric chemistry (-0.20 [-0.41 to \r 0.01] W m-2 degC-1) leads to an estimate of the non-CO2 biogeochemical \r feedback parameter of -0.15 [-0.36 to +0.06] W m-2 degC-1. There is \r low confidence in the estimate of the non-CO2 biogeochemical \r feedbacks, due to the large range in the estimates of a  for some \r individual feedbacks (Figure 5.29c), which can be attributed to the \r diversity in how models account for these feedbacks, limited process\u0002level understanding, and the existence of known feedbacks where \r there is insufficient evidence to assess the feedback strength."


id: 2ddddb7c8ca1900e24aa6eeb60d0ab3f
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 5348
content:
"Published examples of abrupt biogeochemical changes in models \r include tropical rain forest dieback (Cox et al., 2004; Jones et al., \r 2009; Brando et al., 2014; Le Page et al., 2017; Zemp et al., 2017), \r and temperate and boreal forest dieback (Joos et al., 2001; Lucht \r et al., 2006; Scheffer et al., 2012; Lasslop et al., 2016; Section 5.4.3). \r Such transitions may be related to: (i) large-scale changes in mean \r climate conditions crossing particular climate thresholds (Joos et al., \r 2001; Cox et al., 2004; Lucht et al., 2006; Hirota et al., 2011; Scheffer \r et al., 2012; Le Page et al., 2017; Zemp et al., 2017); (ii) temperature \r and precipitation extremes (Staver et al., 2011; Higgins and Scheiter, \r 2012; Scheffer et al., 2012; Pavlov, 2015; Zemp et al., 2017); or \r (iii) possible enhancement and intermittency in fire activity (Staver \r et al., 2011; Higgins and Scheiter, 2012; Lasslop et al., 2016; Brando \r [...]"


id: 2d582ee99ba25b85b663a747ce52b9ba
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 5264
content:
"[...] et al., 2020). Simulated changes in forest cover are a combination \r of the effects of CO2 on photosynthesis and water-use efficiency \r (Section 5.4.1), and the effects of climate change on photosynthesis, \r respiration and disturbance (Section  5.4.3). In ESMs, direct CO2 \r effects tend to enhance forest growth, but the impacts of climate \r change vary between being predominantly negative in the tropics \r and predominantly positive in the boreal zone (Figure 5.27)."


id: f01cd5fe3ea757bca86df18686061fa9
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 39627
content:
"There is large uncertainty in release of GHGs from permafrost in the \r 21st century. The largest of these estimates implies tens to hundreds \r of gigatons of carbon released in the form of CO2 (Box 5.1) and CH4 \r emissions up to 100 TgCH4 yr-1 (Box 5.1). A carbon dioxide release of \r such magnitude would lead to an increase in the CO2 accumulation \r rate in the atmosphere of <=1 ppm yr-1. These emissions develop at \r a multi-decadal time scale. Assuming a CH4 lifetime in the atmosphere \r of the order of 10 years and the associated feedback parameter of \r 1.34 +- 0.04 (Section 6.2.2.1), this would increase the atmospheric CH4\r content by about 500 ppb over the century, corresponding to a rate of \r <=10 ppb yr-1. Irrespective of its origin, additional CH4 accumulation of \r such a magnitude is not expected to modify the temperature response \r to anthropogenic emissions by more than a few tenths of a degC (Gedney \r et al., 2004; Eliseev et al., 2008; Denisov et al., 2013). Emissions from \r [...]"


id: 3297b5fbac98759983ff8b0ab821285
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 6161
content:
"Most ESMs project continuing carbon accumulation in tropical forests \r as a result of direct CO2 effects overwhelming the negative effects of \r climate change (Huntingford et al., 2013; Drijfhout et al., 2015; Boulton \r et al., 2017). In the real world, forests may be less vulnerable to climate \r changes than those modelled in ESMs because of the greater plant \r trait diversity, which confers additional resilience (Reyer et al., 2015; \r Levine et al., 2016; Sakschewski et al., 2016), and because of possible \r acclimation of vegetation to warming (Good et al., 2011, 2013; Lloret \r et al., 2012; Mercado et al., 2018). On the contrary, forests may be more \r vulnerable in the real world due to indirect climate change effects such \r as insect outbreaks and diseases not considered here (Section 5.4.3.2) \r or model limitations in representing the effects disturbances such as \r wildfire and droughts. In general, forests are most vulnerable when \r [...]"


id: 1583cd79ba2005fa01f6bce3fd64b0fe
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 998
content:
"[...] climate change is combined with increased rates of direct deforestation \r (Nobre et al., 2016; Le Page et al., 2017)."


id: 4cd51d054dbbfb8dc47e013230229c3e
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 10787
content:
"The total global clathrate reservoir is estimated to contain 1500-\r 2000  PgC (Archer et al., 2009; Ruppel and Kessler, 2017), held \r predominantly in ocean sediments, with only an estimated 20 PgC \r in and under permafrost (Ruppel, 2015). The present-day CH4\r release from shelf clathrates is <10 TgCH4 yr-1 (Kretschmer et al., \r 2015; Saunois et al., 2020). Despite polar amplification (Chapter 7), \r substantial releases from the permafrost-embedded subsea \r clathrates is very unlikely (Minshull et al., 2016; Malakhova and \r Eliseev, 2017, 2020). This is consistent with an overall small release \r of CH4 from the shelf clathrates during the last deglacial transition, \r despite large reorganizations in climate state (Bock et al., 2017; \r Petrenko et al., 2017; Dyonisius et al., 2020). The long time scales \r associated with clathrate destabilization makes it unlikely that CH4\r release from the ocean to the atmosphere will deviate markedly from \r [...]"


id: f7082485c9b9d509bd821306f26762d6
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 40830
content:
"[...] the present-day value through the 21st century (Hunter et al., 2013), \r corresponding to no more than additional 20 ppb of atmospheric CH4\r (i.e., <0.2 ppb  yr-1). Another possible source of CH4 is gas clathrates \r in deeper terrestrial permafrost and below it (Buldovicz et al., 2018; \r Chuvilin et al., 2018), which may have caused recent craters in the \r north of Russia (Arzhanov et al., 2016, 2020; Arzhanov and Mokhov, \r 2017; Kizyakov et al., 2017, 2018). Land clathrates are formed at \r depths greater than 200  m  (Ruppel and Kessler, 2017; Malakhova "


id: 2945b5d2b1d2a4f26675a3b623d48c01
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 4481
content:
"feedbacks weaken after emissions peak at 2100, which together drives \r the land to become a net carbon source after 2100 (Tokarska et al., \r 2016). The difference in both timing and magnitude of this transition \r across the ensemble, leads to an assessment of medium confidence in \r the likelihood and low confidence in the timing and strength, of the \r land transitioning from a net sink to a net source under such a scenario. \r Based on high agreement across all available models, we assess with \r high confidence that the ocean sink strength would weaken but not \r reverse under a long-term high emissions scenario. In the SSP5-3.4-\r overshoot scenario, both the terrestrial and ocean reservoirs act \r as transient carbon sources during the overshoot period, when net \r anthropogenic CO2 emissions are negative and CO2 concentrations \r are falling, and then revert to near-zero (land) or weak sink (ocean) \r fluxes after stabilization of atmospheric CO2. The SSP1-2.6 scenario, \r [...]"


id: e3231440f64d5731fa765022b688d989
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 37259
content:
"[...] characterized by lower peak CO2 concentrations, a smaller overshoot, \r and much less carbon loss from land-use change, shows instead \r a  relaxation towards a  neutral biosphere on land, and a  sustained \r weak sink in the ocean (see also Section 5.6.2.2.1.2)."


id: a51fa90c488b1bd25bcd81de8f36671d
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 26279
content:
"The AR5 assessed with very high confidence that the carbon cycle in \r the ocean and on land will continue to respond to climate change and \r rising atmospheric CO2 concentrations created during the 21st century \r (WGI, Chapter  6, Executive Summary). Since AR5, experiments with \r the Community Earth System Model version 1  (CESM1) under the \r RCP8.5 extension scenario to 2300, suggest that both land and ocean \r carbon-climate feedbacks strengthen in time, land and ocean carbon\u0002concentration feedbacks weaken, and the relative importance of ocean \r sinks versus land sinks increases (Randerson et al., 2015). Under high \r emissions scenarios, this relative strengthening of land carbon-climate \r feedbacks leads the terrestrial biosphere to shift from sink to source at \r some point after 2100 in all of the CMIP5 ESMs and CMIP5-era Earth \r system models of intermediate complexity (EMICs) (Tokarska et al., \r 2016). The strengthening of land and ocean carbon-climate feedbacks \r [...]"


id: 866ae316560be0d1aac66bdeb756937e
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 20813
content:
"[...] projected beyond 2100 under high emissions scenarios offsets the \r declining climate sensitivity to incremental increases of CO2, leading \r to a net strengthening of carbon cycle feedbacks, as measured by the \r gain parameter, from one century to the next (Randerson et al., 2015)."


id: 748036a66cc2fab859adcaeca9c7d82e
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 17731
content:
"Longer-term memory residing in the ocean enables predictability \r of the ocean carbon sink (McKinley et al., 2017; Li and Ilyina, 2018). \r The predictive horizon of the globally integrated air-sea CO2 fluxes \r has been assessed in perfect-model frameworks that are based on an \r idealized ensemble of simulations in which each ensemble member \r serves as a verification, while no observations are assessed. Perfect\u0002model studies provide an estimate of the upper range of potential \r predictability for the integrated air-sea CO2 fluxes of about two years \r globally and up to a decade in some regions (Seferian et al., 2018a; \r Spring and Ilyina, 2020). Evidence is also emerging for predictive skill \r of the global air-sea CO2 fluxes of up to six years based on prediction \r systems initialized with observed physical climate states (Ilyina et al., \r 2021), with a potential for even longer-term regional predictability \r in some regions, including the North Atlantic and subpolar Southern \r [...]"


id: 8dc66db80b581a54ee66573b205b12a0
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 22135
content:
"Science at the time of AR5 established a  near-linear relationship \r between cumulative emissions of CO2 and the resulting global \r warming (Allen et al., 2009; Matthews et al., 2009; Meinshausen \r et al., 2009; Zickfeld et al., 2009; T.F. Stocker et al., 2013). The amount \r of global warming per unit of cumulated carbon dioxide emissions \r is called the transient climate response to cumulative CO2 emissions \r (TCRE). This TCRE relationship is now used to estimate the \r amount of CO2 emissions that would be consistent with limiting \r global warming to specific levels (Allen et al., 2009; Matthews \r et al., 2009, 2012;  Meinshausen et al., 2009; Zickfeld et al., 2009; \r M.  Collins  et al.,  2013; T.F.  Stocker et al., 2013; Knutti and Rogelj, \r 2015; Rogelj et al., 2016, 2019; Goodwin et al., 2018). The remainder \r of CO2 emissions that would be in line with limiting global warming \r to a specific temperature level (while accounting for all other factors \r [...]"


id: ee879658a94e3c94ad9f28c529f46fb1
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 39343
content:
"[...] affecting global warming) can be estimated with the help of the TCRE \r and is referred to as the remaining carbon budget (Rogelj et al., 2019; \r Matthews et al., 2020). Section 5.5.1 first assesses the TCRE as one \r of the core concepts underlying the notion of a  remaining carbon \r budget, and Section 5.5.2 then integrates this with other contributing \r factors from across this assessment to provide a  consolidated \r assessment following the approach of the Special Report on Global \r Warming of 1.5degC (SR1.5) (Rogelj et al., 2018b). The historical carbon \r budget of CO2 already emitted is assessed in Section 5.2.1.5."


id: 420cf9a3db39bdb9bd0109a1a480c3f2
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 8819
content:
"Models suggest that predictability of the air-sea CO2 flux is related \r to predictability of ocean biogeochemical state variables such as \r dissolved inorganic carbon (DIC) and total alkalinity (TA; Lovenduski \r et al., 2019a), as well as the mixed layer depth (H.  Li et al., 2016). \r Temperature variations largely control shorter-term predictability of \r the ocean carbon sink, while longer-term predictability is related to \r non-thermal drivers such as ocean circulation and biology (Li et al., \r 2019). Although there is a substantial spatial heterogeneity, initialized \r predictions suggest stronger multi-year variations of the air-sea CO2\r flux and generally tend to outperform uninitialized simulations on the \r global scale (Li et al., 2019). The predictive skill of air-sea CO2 flux \r shows a  consistent spatial pattern in different models, despite the \r wide range of techniques used to assimilate observational information \r [...]"


id: d7d8658192f66ea781d140158dd54b3f
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 35282
content:
"[...] (Regnier et al., 2013). ESM-based prediction systems also demonstrate \r predictability of other marine biogeochemical properties such as net \r primary production (Seferian et al., 2014; Yeager et al., 2018; Park et al., \r 2019) and seawater pH (Brady et al., 2020)."


id: c47e63ac8742aa366ee7f2ee00aab918
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 31872
content:
"that the cumulative fraction of carbon taken up by the terrestrial \r biosphere is constant, and that the climate feedback parameter and \r ocean heat uptake efficacy do not change in time, both studies agree \r that pathway independence is sensitive to the rate of CO2 emissions, \r such that pathway independence is expected to break down at both \r very high and very low absolute CO2 emissions rates (MacDougall, \r 2017; Seshadri, 2017). Note that, in pathways with strongly \r declining emissions, the cumulative sink fraction by the combined \r terrestrial biosphere and ocean is expected to increase (Figure 5.25). \r The studies also agree that no similar relationship analogous to TCRE \r can be expected for short-lived non-CO2 forcers, for which the annual \r emissions are a  closer proxy for the implied warming (M.  Collins \r et al., 2013; Sections 6.4, 7.6). MacDougall (2017) suggests that two \r additional constraints are required to create pathway independence: \r [...]"


id: fdeaa409567e5c4e805264afb3dbe6e2
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 42041
content:
"[...] first, the transport of heat and carbon into the deep ocean should be \r governed by processes with similar time scales; and second, the ratio \r of the net change in the atmospheric carbon pool to the net change \r in the ocean carbon pool should be close to the ratio of the enhanced \r longwave radiation to space (i.e.,  the radiative response of the \r surface) to ocean heat uptake. If these ratios are identical, then TCRE \r would be completely path independent (MacDougall, 2017). If the \r ratios are close but not identical, TCRE would be only approximately \r path independent over a wide range of cumulative emissions (Cross\u0002Chapter Box 5.3; MacDougall, 2017)."


id: f6d28fd7dfae2033be188d28de743f30
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 40794
content:
"There is increased confidence in the near-constancy of TCRE because of \r the variety of methods that have been used to examine this relationship: \r sensitivity studies with Earth system models of intermediate complexity \r (EMICs; Herrington and Zickfeld, 2014; Ehlert et al., 2017); theory\u0002based equations used to examine ESM and EMIC output (Goodwin \r et al., 2015; R.G. Williams et al., 2016, 2017b); and simple analytical \r models that capture aspects of the TCRE relationship (MacDougall and \r Friedlingstein, 2015). All studies agree that the near-constancy of the \r TCRE arises from compensation between the diminishing sensitivity \r of radiative forcing to CO2 at higher atmospheric concentration and \r the diminishing ability of the ocean to take up heat and carbon at \r higher cumulative emissions (Allen et al., 2009; Matthews et al., 2009; \r Frolicher and Paynter, 2015; Goodwin et al., 2015; Gregory et al., 2015; \r MacDougall and Friedlingstein, 2015; MacDougall, 2016; Tokarska \r [...]"


id: a1bd7c254af3f351370414e61e9b0310
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 25677
content:
"The role of the ocean in setting the coupled climate-carbon cycle response is threefold. First, the ocean and land carbon sinks together \r set the airborne fraction (AF) of CO2 in the atmosphere, which sets the radiative forcing that drives the additional heat in the atmosphere, \r most of which is taken up by the ocean (Sections 7.2 and 9.2; Katavouta et al., 2019; Williams et al., 2019). the land carbon sink does \r not appear to play an important role in determining the linearity and path-independence of TCRE (Section 5.5.1.1; Goodwin et al., 2015; \r MacDougall and Friedlingstein, 2015; Ehlert et al., 2017). Second, the ocean sets the thermal response through ocean heat uptake \r (Section 9.2; Frolicher et al., 2015; Bronselaer and Zanna, 2020). Third, there is a feedback within the ocean carbon-heat nexus as ocean \r warming, particularly under low or no mitigation scenarios, weakens the ocean sink of CO2, which influences the AF, and hence the \r [...]"


id: 904c477625b2a84dab715003d603927a
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 22589
content:
"[...] radiative forcing (Box 7.1; Williams et al., 2019). The near-linear relationship between cumulative CO2 emissions and global warming \r (TCRE) is thought to arise, to a large extent, from the compensation between the decreasing ability of the ocean to take up heat and \r CO2 at higher cumulative CO2 emissions, pointing to similar processes that determine ocean uptake of heat and carbon (Section 5.5.1.1; \r Goodwin et al., 2015; MacDougall and Friedlingstein, 2015; Williams et al., 2016; Zickfeld et al., 2016; Ehlert et al., 2017)."


id: d89cf5e0cb7317b646baee95714fe54d
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 35402
content:
"Processes that drive the ocean carbon-heat nexus and its change\r The air-sea flux of heat and all gases across the ocean interface is driven by a common set of complex and turbulent diffusion and \r mixing processes that are difficult to observe (Sections 5.2.1.3 and 9.2.1.2; Wanninkhof et al., 2009; Wanninkhof, 2014; Cronin et al., \r 2019; Watson et al., 2020). These processes are typically simplified into widely verified expressions that link the flux to wind stress, the \r solubility and the gradient across the air-sea interface (medium confidence). Because the ocean has a higher heat capacity than the \r atmosphere (the heat capacity of the upper 100 m of the ocean is about 30 times larger than the heat capacity of the atmosphere), \r the partitioning of heat between the atmosphere and the ocean is primarily influenced by the temperature differences between air\r [...]"


id: 6db713b31c12f3ac82e5e116915d4ed4
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 16564
content:
"[...] and seawater. Similarly, the unique seawater carbonate buffering capacity enables CO2 to be stored in the ocean as dissolved salts, \r rather than just as dissolved gas; this increases the capacity of seawater to store CO2 by two orders of magnitude beyond the solubility \r of CO2 gas and approximates the partitioning ratio of heat between the atmosphere and the ocean (Section 9.2.2.1; Zeebe and Wolf\u0002Gladrow, 2009; Bronselaer and Zanna, 2020). The role of the biological carbon pump in influencing the ocean sink of anthropogenic \r carbon into the ocean interior is assessed to be minimal during the historical period, but this may change, particularly in regional \r contexts, by 2100 (medium confidence) (Laufkotter et al., 2015; Kwiatkowski et al., 2020). Its role is important in the natural or pre\u0002industrial carbon cycle (medium confidence) (Henson et al., 2016)."


id: 13142d051ba87e62ccdb8a5bf2a954be
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 569
content:
"Under climate change, the buffering capacity of the ocean decreases (increasing Revelle Factor), which reflects a decreasing capacity \r for the ocean to take up additional anthropogenic CO2 and store it in the dissolved inorganic carbon reservoir (Egleston et al., 2010). \r In contrast to CO2, there is no physical limitation that would reduce the ability of surface ocean temperature to equilibrate with the \r atmospheric temperature. However, both carbon and heat fluxes depend on air-sea heat fluxes that in turn depend on gradients of \r characteristics at the air-sea interface. These gradients at the air-sea interface respond to ocean dynamics, such as the volume of \r the surface mixed-layer that equilibrates with the atmosphere, and ocean circulation that can flush the surface layer with water \r masses that have not equilibrated with the atmosphere for a long time. Limited recent evidence suggests that the effect of small-scale \r [...]"


id: c74aa1a68c3a04a21b403feb012b8478
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 32380
content:
"[...] dynamics absent in climate and Earth system models might be locally important (Bachman and Klocker, 2020). In summary, changes \r in heat and carbon uptake by the ocean rely on a combination of unique chemical and shared physical processes, any of which have \r the potential to disrupt the coherence of heat and CO2 change in the ocean."


id: 43a1e1ec3c0e28c893bd05cf9d4a328f
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 9120
content:
"The role of the large-scale circulation in shaping these fluxes is to: (i) flush the ocean surface layer with deep waters that are relatively \r cold and with weak or no anthropogenic CO2 and heat content because they have been isolated from the atmosphere for centuries; \r and (ii) transport the anthropogenic CO2 and heat at depth, away from the atmosphere (Frolicher et al., 2015; Marshall et al., 2015; \r Armour et al., 2016). For instance, in the Southern Ocean, upwelled water masses take up a large amount of anthropogenic CO2\r and heat (Cross-Chapter Box  5.3, Figure  1), which are then exported northward by the circulation to be stored at depth in the \r Southern Hemisphere subtropical gyres (Cross-Chapter Box 5.3, Figure 1; Figure 9.7). In the North Atlantic, the signature of the Atlantic \r meridional overturning circulation (AMOC) is also clearly visible, with large amounts of heat and carbon being stored beneath the \r [...]"


id: b770199f5bf924ff1d7c51405dabaf01
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 29485
content:
"[...] North Atlantic subtropical gyre at 1 km depth (Cross-Chapter Box 5.3, Figure 1). In summary, the net air-sea fluxes of anthropogenic \r CO2 and heat depend on large-scale circulation, which is associated with upper ocean stratification, mixed-layer depth, and water\u0002mass formation, transport and mixing (Sections 9.1-9.3)."


id: 8196682199575e664d10bcff2b358a57
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
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index: climate_gpt_v2
vector_id: 19927
content:
"gpp\r Future projections of the ocean carbon-heat nexus in the second half of the 21st century, particularly those under weak or no \r mitigation scenarios, are characterized by the strengthening of the two largest positive feedbacks: weakening surface ocean CO2 \r buffering capacity (increasing Revelle Factor) and warming that further reduces CO2 solubility and strengthens ocean stratification, \r which reduces exchange between the ocean surface and interior (Jiang et al., 2019; Bronselaer and Zanna, 2020). These are offset \r by a growing but scenario-dependent negative feedback from increasing carbon and heat air-sea fluxes towards the ocean, due to \r increased atmospheric temperature and CO2 concentrations (Talley et al., 2016; Jiang et al., 2019; McKinley et al., 2020). The Southern \r Ocean in particular is one of the regions where the projected feedback can be largest and where inter-model differences are strongest \r [...]"


id: caede1f801ac6fe16311af93db123c53
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 33040
content:
"[...] (Roy et al., 2011; Frolicher et al., 2015; Hewitt et al., 2016; Mongwe et al., 2018). These projected trends in ocean carbonate chemistry \r (Section 5.4.2), together with surface ocean warming (Section 9.2.1.1), explain the slow down and long-term reduction of the ocean \r sink for anthropogenic CO2 even as emissions continue to rise beyond 2050 under weak-to-no-mitigation scenarios (Figures 2.7.1 \r and 5.25, and Technical Summary TS Box 7). Projected change in the North Atlantic and Southern Ocean overturning circulation \r also impact air-sea fluxes of heat and carbon. The very likely decline in AMOC in the 21st century for all shared socio-economic \r pathways (SSP) scenarios (Section 9.2.3.1) tends to reduce heat and carbon uptake, resulting in a positive feedback. In contrast, in \r the Southern Ocean, the future 21st century projected increase in upper ocean overturning circulation (low confidence) - due to \r [...]"


id: 6cc20923ee5228640efacac6c3456157
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 16413
content:
"[...] increasing wind forcing projected for all scenarios, except those with large mitigation (SSP1-2.6) - produces a negative feedback, with \r increasing heat and carbon uptake and storage despite the increasing stratification and outgassing of natural CO2 in the upwelling \r zone (Sections 9.2.3.2 and 5.2.1.3)."


id: 74b20b4c4734f3b6b674d45b65681a97
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 17768
content:
"In summary, a combination of unique chemical properties of seawater carbonate combined with shared physical ocean processes \r explain the coherence and scaling in the uptake and storage of both CO2 and heat in the ocean, which is the basis for the carbon-\r heat nexus (high confidence). In this way, the processes of the ocean carbon-heat nexus help understand the quasi-linear and path \r independence of properties of TCRE, which forms the basis for the zero emissions commitment (ZEC; Section 5.5) (medium confidence). \r Future projections under low or no mitigation indicate with high confidence that carbon chemistry and warming will strengthen the \r positive feedback to climate change by reducing ocean carbon uptake, and medium confidence that ocean circulation may partially \r compensate that positive feedback by slightly increasing anthropogenic carbon storage. Increasing ocean warming and stratification \r [...]"


id: c96f5a09d065302cbf64e18a98fca3f7
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 32764
content:
"[...] may decrease exchanges between the surface and subsurface ocean, which could reduce the path independence of TCRE, though this \r effect can be partially counterbalanced regionally by increasing circulation associated with increasing winds (low confidence)."


id: f1ec11ae5c57688cd5e2fb0e588bdb12
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 39951
content:
"The AR5 indicated that the concept of a constant ratio of cumulative \r emissions of CO2 to temperature was applicable to scenarios with \r increasing cumulative CO2 emissions up to 2000  PgC (M.  Collins \r et al., 2013). Recent analyses added confidence to this insight \r (Herrington and Zickfeld, 2014; Steinacher and Joos, 2016) and \r showed some evidence of a potentially larger window of constant \r TCRE (Leduc et al., 2015; Tokarska et al., 2016). Using an analytical \r approach, MacDougall and Friedlingstein (2015) quantified \r a  window of constant TCRE -  defined as the range in cumulative \r emissions over which the TCRE remains within 95% of its maximum \r value - as between 360 to 1560 PgC. However, models with a more \r sophisticated ocean representation suggest that TCRE could also \r remain constant for considerably larger quantities of cumulative \r emissions, up to at least 3000 PgC (Leduc et al., 2015; Tokarska et al., \r [...]"


id: fdf3fc5f533ac208e1b35b12199ad086
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
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index: climate_gpt_v2
vector_id: 42047
content:
"[...] 2016). Beyond this upper limit, studies are inconclusive, with some \r suggesting that TCRE will decrease (Leduc et al., 2015) and others \r indicating that the linearity would hold up to as much as 5000 PgC \r (Tokarska et al., 2016)."


id: a12b29a262202cf4c530de884ad97a6b
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 25578
content:
"Global average temperature increase responds over a time scale of \r about 10 years following the emission of a 100 PgC pulse of CO2\r (Joos et al., 2013; Ricke and Caldeira, 2014), with larger emission \r pulses associated with longer time scales and smaller pulses with \r shorter ones (Joos et al., 2013; Matthews and Solomon, 2013; \r Zickfeld and Herrington, 2015). This behaviour is confirmed in other \r studies, including those that calculate the temperature response \r to an instantaneous doubling or quadrupling of atmospheric CO2\r (Matthews et al., 2009; Gillett et al., 2013; Herrington and Zickfeld, \r 2014; Leduc et al., 2015; Hajima et al., 2020b). These findings \r suggest that the TCRE is sensitive to the rate of emissions, but \r studies assessing this sensitivity have found diverging results. For \r example, an increase in TCRE and its surrounding uncertainty was \r reported for experiments that imply a  gradual decline in annual \r [...]"


id: d4e28f0d444e7684498dc6c0b263be6b
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
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index: climate_gpt_v2
vector_id: 34767
content:
"[...] CO2 emissions (Tachiiri et al., 2019). These studies suggest that, in \r most cases, TCRE would be expected to increase in scenarios with \r decreasing annual emissions rates. This increase in TCRE for annual \r CO2 emissions declining towards zero can be the result of the zero \r emissions commitment (ZEC) which is the amount of warming \r projected to occur following a complete cessation of emissions (see \r Section 4.7.1.1 for its assessment), as well as Earth system processes \r that are unrepresented in current TCRE estimates (Section 5.5.2.2.4) \r and other factors. When using TCRE to estimate CO2 emissions \r consistent with a  specific maximum warming level, these factors \r have to be taken into account (see Figure  5.31). Combined with \r recent literature on the ZEC (MacDougall et al., 2020) and emissions "


id: 6a5ad5e36a81301069f851e4ca0d0ca3
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 15991
content:
"As cumulative emissions increase, weakening land and ocean carbon \r sinks increase the airborne fraction of CO2 emissions (see Figure 5.25), \r but each unit increase in atmospheric CO2 has a smaller effect on \r global temperature owing to the logarithmic relationship between \r CO2 and its radiative forcing (Matthews et al., 2009; Etminan et al., \r 2016). At high values of cumulative emissions, some models simulate \r less warming per unit CO2 emitted, suggesting that the saturation \r of CO2 radiative forcing becomes more important than the effect of \r weakened carbon sinks (Herrington and Zickfeld, 2014; Leduc et al., \r 2015). The behaviour of carbon sinks at high emissions levels remains \r uncertain, as models used to assess the limits of the TCRE show \r a large spread in net land carbon balance (Section 5.4.5), and most \r estimates did not include the effect of permafrost carbon feedbacks \r (Sections 5.5.1.2.3 and 5.4). The latter would tend to further increase \r [...]"


id: 601f4ad541fe816a0e3b17d280872f5
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 14214
content:
"[...] the airborne fraction at high cumulative emissions levels, and could \r therefore extend the window of linearity to higher total amounts of \r emissions (MacDougall et al., 2015). Leduc et al. (2016) suggested \r further that a declining strength of snow and sea ice feedbacks in \r a  warmer world would also contribute to a  smaller TCRE at high "


id: 5bd0651031fd64a7b1fc8f175c2960de
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 13412
content:
"Whether TCRE remains an accurate predictor of CO2-induced \r warming when annual CO2 emissions reach zero and are followed \r by net carbon dioxide removal (also referred to as TCRE reversibility) \r therefore hinges on contributions of slow components of the climate \r system that cause unrealized warming from past CO2 emissions. Such \r slow components can arise from either physical climate (i.e., ocean \r heat uptake) or carbon cycle (i.e.,  ocean carbon uptake and \r permafrost carbon release) processes. The combined effect of these \r processes determines the magnitude and sign of the ZEC (MacDougall \r et al., 2020), which in turn impacts TCRE reversibility. As discussed in \r Section 4.7.1.1, recent model estimates of the ZEC suggest a range \r of +-0.19degC centred on zero (MacDougall et al., 2020). This suggests \r low agreement among models as to the reversibility of the TCRE in \r response to net-negative CO2 emissions. Furthermore, most models \r [...]"


id: 697ff9a3b33662db186e935bb3e55e85
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
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index: climate_gpt_v2
vector_id: 15827
content:
"[...] used for ZEC assessments to date do not represent permafrost carbon \r processes, although understanding their contribution is essential to \r quantify the TCRE contribution. There is therefore limited evidence\r that quantifies the impact of permafrost carbon feedbacks on the \r reversibility of TCRE, leading to low confidence that the TCRE remains \r an accurate predictor of temperature changes in scenarios of net\u0002negative CO2 emissions on time scales of more than a half a century."


id: d03714134e967782d2749e2a46f82f5e
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 33995
content:
"There are relatively few studies that have assessed how the TCRE \r is expected to change in scenarios of declining emissions followed \r by net negative annual CO2 emissions. Conceptually, the literature \r suggests that the small lag of about a decade between CO2 emissions \r and temperature change (Ricke and Caldeira, 2014; Zickfeld and \r Herrington, 2015) would result in more warming at a given amount \r of cumulative emissions in a scenario where that emissions level is \r first exceeded and then returned to by deploying negative emissions \r (referred to as an 'overshoot', as is often the case in scenarios that \r aim to limit radiative forcing in 2100 to 2.6 or 1.9 W m-2 (Riahi et al., \r 2017; Rogelj et al., 2018a). Zickfeld et al. (2016) showed this to \r hold across a range of scenarios, with positive emissions followed \r by negative emissions, whereby the TCRE increased by about 10% \r across the transition from positive to negative emissions as a result \r [...]"


id: fc1596a897915ac1c24348303d52c8f5
created_at: 2023-04-19 13:22:37
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vector_id: 41712
content:
"[...] of the thermal and carbon inertia of the deep ocean. However, CMIP6 \r results for the SSP5-3.4-overshoot scenario show diverging trends \r across various ESMs (Figure 5.30). In an idealized CO2-concentration\u0002driven setting, Tachiiri et al. (2019) also reported an increase in \r TCRE. Exploring pathways with emissions rates and overshoots closer \r to mitigation pathways considered over the 21st century (in this case \r up to about 300 PgC), a recent emissions-driven EMIC experiment \r showed pathway independence of TCRE (Tokarska et al., 2019a). \r Furthermore, also in absence of net negative emissions, warming \r would not necessarily remain perfectly constant on time scales of \r centuries and millennia, but could decrease or increase (Frolicher and \r Paynter, 2015; R.G. Williams et al., 2017a; Hajima et al., 2020b). These \r additional changes in global mean temperature increase at various \r time scales are known as the ZEC (C.D. Jones et al., 2019; MacDougall \r [...]"


id: 94822d260a1313373d86f26a24331a6f
created_at: 2023-04-19 13:22:37
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index: climate_gpt_v2
vector_id: 23335
content:
"[...] et al., 2020), assessed in Section 4.7.1.1, and have to be integrated \r when using TCRE to estimate warming or remaining carbon budgets \r in overshoot scenarios."


id: 89ffe675926c20e9453ab32985c1f417
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 21448
content:
"Since AR5, new studies have further expanded the evidence base for \r estimating the value of TCRE. These studies rely on ESMs or EMICs, \r observational constraints and concepts of attributable warming, \r or theoretically derived equations (see Table  5.7 for an overview). \r Several studies have endeavoured to partition the uncertainty in \r the value of TCRE into constituent sources. For example, TCRE can \r be decomposed into terms of TCR and the airborne fraction of \r anthropogenic CO2 emissions over time (Allen et al., 2009; Matthews \r et al., 2009). These two terms are assessed individually (see Section 5.4 \r and Chapter 7, respectively) and allow the integration of evidence \r assessed elsewhere in the report into the assessment of TCRE \r (Section 5.5.1.4). Further studies use a variety of methods, including \r analysing the outputs from CMIP5 (R.G. Williams et al., 2017b) or \r CMIP6 (Arora et al., 2020; Jones and Friedlingstein, 2020), conducting \r [...]"


id: 5b040f0703d400e3939db27c7db20bf5
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
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index: climate_gpt_v2
vector_id: 13264
content:
"[...] perturbed parameter experiments with a single model (MacDougall \r et al., 2017), Monte-Carlo methods applied to a simple climate model \r (Spafford and Macdougall, 2020), or observations and estimates of \r the contribution of CO2 and non-CO2 forcers (Matthews et al., 2021). \r All of the studies agree that uncertainty in climate sensitivity - either "


id: 6a845d918b0fa281774697f81488cc45
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 16024
content:
"The AR5-assessed (W.J. Collins et al., 2013) TCRE range was based \r in part on the ESMs available at the time, which did not include \r some potentially important Earth system feedbacks. Since then, \r a  number of studies have assessed the importance of permafrost \r carbon feedbacks, in particular on remaining carbon budgets \r (MacDougall and Friedlingstein, 2015; MacDougall et al., 2015; \r Burke et al., 2017b; Gasser et al., 2018; Lowe and Bernie, 2018), \r a development highlighted and assessed in the IPCC Special Report \r on Global Warming of 1.5degC (Rogelj et al., 2018b). MacDougall and \r Friedlingstein (2015) reported a TCRE increase of about 15% when \r including permafrost carbon feedbacks. The overall linearity of the \r TCRE during the 21st century was not affected, but they also found \r that permafrost carbon feedbacks caused an increase in TCRE on \r multi-century time scales under declining CO2 emissions rates. In \r [...]"


id: 64f1206a8c7ff3efa81fcbb7e924175b
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 15033
content:
"Studies differ in how they define TCRE, in the methods they use, and \r their assumptions, such as the assumed climate sensitivity distribution \r or the choice of metrics of global temperature change (e.g., GMST \r or GSAT, see Table 5.7). This makes TCRE estimates from individual \r studies difficult to compare. The combined assessment of TCRE \r therefore takes advantage of the well-established decomposition \r of TCRE in two factors: the TCR and the AF (Section  5.5.1.3). This \r provides a  TCRE assessment range for CO2-induced warming \r at the time of doubling CO2 concentrations that builds on the \r broader Working Group 1 assessment. Expert judgement based on \r the airborne fraction range found in CMIP6 models (Arora et al., \r 2020; Jones and Friedlingstein, 2020) suggest a value of 53% with \r a 1-sigma range of +-6%, which is double the sigma range based on \r the spread of CMIP6 models only. Combining this range with the \r [...]"


id: c6561bd0c51ea39eda6041e86f3fcce1
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
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index: climate_gpt_v2
vector_id: 32207
content:
"[...] AR6 TCR assessment (Section 7.5, best estimate 1.8degC, 1.4degC-2.2degC \r likely and 1.2degC-2.4degC very likely range) results in a 5-95% range of \r 1.0-2.3degC per 1000 PgC (0.27degC-0.63degC per 1000 GtCO2). Based on \r expert judgement that accounts for the incomplete coverage of all \r Earth system components, this results in a consolidated assessment \r that TCRE would fall likely in the range of 1.0-2.3degC per 1000 PgC, \r with a best estimate of 1.65degC per 1000 PgC (0.45degC per 1000 GtCO2). \r Warming here reflects the human-induced GSAT increase and assumes \r a normal distribution. Some studies using observational constraints \r support a lognormal shape for the TCRE distribution (Spafford and \r Macdougall, 2020), but such a distribution is currently not supported \r by the combined assessment of TCR and airborne fraction. Finally, \r this assessed TCRE range needs to be considered in combination with \r the ZEC (Section 4.7.1.1) when estimating the CO2-induced warming \r [...]"


id: 1ab6dc930a2fe93e9466be6729238c2f
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 1905
content:
"Two approaches were used in AR5 to determine carbon budgets \r (M. Collins et al., 2013; T.F. Stocker et al., 2013; Clarke et al., 2014; \r IPCC, 2014; Rogelj et al., 2016). Working Group I (WGI) reported \r threshold exceedance budgets (TEB) that correspond to the amount \r of cumulative CO2 emissions at the time a  specific temperature \r threshold is exceeded, with a  given probability in a  particular \r greenhouse-gas and aerosol (pre-cursor) emissions scenario \r (M. Collins et al., 2013; IPCC, 2013b; T.F. Stocker et al., 2013). WGI \r also reported TEBs for the hypothetical case that only CO2 would be \r emitted by human activities (M. Collins et al., 2013; IPCC, 2013b; \r T.F. Stocker et al., 2013). The AR5 Working Group III used threshold \r avoidance budgets (TAB) that correspond to the cumulative CO2\r [...]"


id: b2dc5f6796c4d2b7448c41dabe569b60
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
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index: climate_gpt_v2
vector_id: 28704
content:
"[...] emissions over a  given time period of a  subset of greenhouse\u0002gas and aerosol (precursor) emissions scenarios in which global\u0002mean temperature increase stays below a  specific temperature \r threshold with at least a  given probability (Clarke et al., 2014). \r The AR5 synthesis report used TABs defined until the time of peak \r warming over the 21st century (IPCC, 2014). Drawbacks have been \r identified for TEBs and TABs (Rogelj et al., 2016). TABs provide \r an estimate of the cumulative CO2 emissions under pathways "


id: 86e2521981c1857f556da665748bbe14
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 20895
content:
"Since the publication of AR5 (M.  Collins et al., 2013), several \r new approaches have been proposed that provide a  solution \r to the identified limitations of TABs and TEBs. Most of these \r approaches indirectly rely on the concept of TCRE (Section 5.5.1), \r for example, because they estimate modelled cumulative CO2\r emissions until a  temperature threshold is crossed and use this \r budget to infer insights for pathways that attempt to limit warming \r to below this threshold and thus need to follow a different path \r (Friedlingstein et al., 2014a; Matthews et al., 2017; Millar et al., \r 2017b; Goodwin  et al., 2018; Tokarska and Gillett, 2018). In this \r report, the assessment framework of SR1.5 for remaining carbon \r budgets is applied (Rogelj et al., 2018b, 2019). This framework allows \r integration of multiple lines of evidence to assess the contributions \r of five components that together result in a consolidated assessment \r [...]"


id: 1e3e9efbfa7f388665b9ccec8a6564a7
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
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index: climate_gpt_v2
vector_id: 2521
content:
"[...] of the remaining carbon budget (TCRE, historical human-induced \r warming, non-CO2 warming, the ZEC, and adjustments due to \r additional Earth system feedbacks, see Section  5.5.2.2). It builds \r on the advances in estimating remaining carbon budgets or \r related quantities that have been published since AR5 (Rogelj \r et al., 2015a; Haustein et al., 2017; Matthews et al., 2017, 2021; "


id: 1ab42e5b74f4224a97253172d1ea4ae4
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 1901
content:
"that have as a  common characteristic the fact that they do not \r exceed a  specific global warming threshold. However, the actual \r level of maximum warming can vary between pathways, leading \r to an unnecessary and poorly constrained spread in TAB estimates \r (Rogelj et al., 2016). Therefore, the TAB approach typically does \r not result in accurate projections of the remaining carbon budget. \r One drawback of TEBs is that they provide an estimate of the \r cumulative CO2 emissions at the time global warming crosses \r a given threshold of interest in a specific emissions scenario - for \r example, most of the standard scenarios used in climate change \r research, such as the Representative Concentration Pathways \r (RCPs) or Shared Socio-economic Pathways (SSPs), exceed global \r warming of 1.5degC or 2degC (see Cross-Chapter Box 1.5) (M. Collins \r et al., 2013; T.F.  Stocker et al., 2013; Friedlingstein et al., 2014a; \r Millar et al., 2017b). Because of potential variations in non-CO2\r [...]"


id: aabb2fd842adca43b49935fc5e36e856
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 27263
content:
"[...] warming at that point in time, or potential lags of about a decade \r in CO2 warming (Joos et al., 2013; Ricke and Caldeira, 2014; Rogelj \r et al., 2015a, 2016, 2018b; Zickfeld and Herrington, 2015) TEBs also \r do not provide a precise estimate of the remaining carbon budget \r for limiting warming to a specific level."


id: f14048eba667a388a6222c52e6a2e66c
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 39831
content:
"Non-CO2 emissions contribute either cumulatively (N2O, and other \r long-lived climate forcers) or in proportion to their annual emissions \r (CH4 and other short-lived climate forcers) to global warming, and \r thus also affect estimates of remaining carbon budgets by reducing \r the amount of warming that could still result from CO2 emissions \r (Meinshausen et al., 2009; Friedlingstein et al., 2014a; Knutti and \r Rogelj, 2015; Rogelj et al., 2015a, 2016; R.G. Williams et al., 2016, \r 2017b; Matthews et al., 2017; Collins et al., 2018; Mengis et al., 2018; \r Tokarska et al., 2018; Zickfeld et al., 2021). The size of this contribution \r has been estimated both implicitly (Meinshausen et al., 2009; \r Friedlingstein et al., 2014a; Rogelj et al., 2016; Matthews et al., 2017; \r Mengis et al., 2018; Tokarska et al., 2018) and explicitly (Rogelj et al., \r 2015a, 2018b; Collins et al., 2018; Matthews et al., 2021) by varying \r the assumptions of non-CO2 emissions and associated warming. \r [...]"


id: a1777d1e7c97207cd92fbfa69bf01cb2
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 25622
content:
"[...] Internally consistent evolutions of future CO2 and non-CO2 emissions \r allow for derivation of non-CO2 warming contributions consistent \r with global CO2 emissions reaching net zero levels, and therewith \r capping maximum future CO2 emissions (Smith and Mizrahi, 2013; \r Clarke et al., 2014; Huppmann et al., 2018; Rogelj et al., 2018b; \r Matthews et al., 2021). Pathways that reflect such development \r typically show a stabilization or decline in non-CO2 radiative forcing \r and warming at, and after the time of, global CO2 emissions reaching \r net zero levels, as illustrated in the scenario database underlying \r SR1.5 (Huppmann et al., 2018; Rogelj et al., 2018b)."


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vector_id: 25013
content:
"The impact of non-CO2 emissions on remaining carbon budgets \r is  assessed with emulators (Meinshausen et al., 2009; Millar \r et al., 2017b; Gasser et al., 2018; Goodwin et al., 2018; Rogelj et al., \r 2018b; C.J. Smith et al., 2018; Matthews et al., 2021) that incorporate \r synthesized climate and carbon-cycle knowledge (Cross-Chapter \r Box 7.1). The estimated implied non-CO2 warming can subsequently \r be applied to reduce the remaining allowable warming for estimating \r the remaining carbon budget (Figure  5.31; Rogelj et al., 2018b, \r 2019). Alternative methods estimate the non-CO2 fraction of total \r anthropogenic forcing (Matthews et al., 2021), or do not correct for \r non-CO2 warming directly. The latter methods instead consider CO2\r and non-CO2 warming together to define a CO2-forcing equivalent \r carbon budget from which eventual non-CO2 contributions expressed \r in CO2-forcing-equivalent emissions have to be subtracted to obtain \r [...]"


id: ff8e34b530a5e8c3f0a7546c863d6f29
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 42362
content:
"[...] a  remaining carbon budget (Jenkins et al., 2018; Matthews et al., \r 2020). These studies also use emulators to invert a specified evolution \r of non-CO2 forcing to a  corresponding amount of equivalent CO2\r emissions (Matthews et al., 2020), or alternatively use empirical \r relationships linking changes in non-CO2 greenhouse gas emissions \r to warming (Cain et al., 2019). Methods to express non-CO2 emissions \r in CO2 equivalence are assessed in Section 7.6, yet their applicability \r and related uncertainties for remaining carbon budgets have not yet \r been covered in-depth in the literature."


id: 9077fcdedfc410107363c22a636cc510
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 22620
content:
"Advances in methods to estimate remaining carbon budgets have \r shown the importance of applying an estimate of historical warming \r to date that is as accurate as possible (Millar et al., 2017b; Tokarska \r and Gillett, 2018). This becomes particularly important when \r assessing remaining carbon budgets for global warming levels \r that are relatively close to present-day warming, such as a 1.5degC \r or 2degC levels (Rogelj et al., 2018b). Also shown to be important is \r the definition of global average temperature by which historical \r warming is estimated (Cross-Chapter Box  2.3; Cowtan and Way, \r 2014; Allen et al., 2018; Pfleiderer et al., 2018; Richardson et al., \r 2018; Tokarska et al., 2019b), as is the correct isolation of human\u0002induced global warming (Haustein et al., 2017; Allen et al., 2018) to \r remove the effect of internal variability. Based on the assessment \r in Section 3.3 (Table 3.1), here we apply an assessed best-estimate \r [...]"


id: b1f8e18e0813cfbb7081cc32495ec80e
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 28532
content:
"[...] of historical warming expressed as an increase in GSAT of 1.07degC \r (0.8-1.3degC, likely range) between 1850-1900 and 2010-2019. \r This choice implies global coverage and is consistent with AR5 \r where carbon budgets were reported in GSAT (M.  Collins et al., \r 2013; T.F. Stocker et al., 2013), SR1.5 where GSAT was the central \r metric for remaining carbon budgets (Rogelj et al., 2018b), and \r recent studies that highlight how GSAT enables an easy translation \r with AR5 (Tokarska et al., 2019b). The  use of other historical "


id: b65a2382dd0a694c39e3aeb5f78ad844
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 29300
content:
"The combination of the five components assessed in \r Sections  5.5.2.2.1-5.5.2.2.5 allows for an overall assessment of \r the remaining carbon budget in line with different levels of global \r average warming, as documented in SR1.5 (Rogelj et al., 2018b). The \r overall assessment of remaining carbon budgets (Table 5.8) reflects \r the uncertainty in TCRE quantification and provides estimates of the \r uncertainties surrounding the contributions of each of the respective \r further components. A  formal combination of all uncertainties is \r not possible because they are not all independent, or because they \r represent choices rather than probabilistic uncertainties (Matthews \r et al., 2021). In light of all uncertainties related to TCRE, non-CO2\r forcing and response, the level of non-CO2 mitigation, and historical \r warming, there is a  small probability that the remaining carbon \r budget for limiting warming to 1.5degC since the 1850-1900 period \r [...]"


id: 43c2f87463ea93235745949cfd943443
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 9146
content:
"[...] is effectively zero. However, applying best estimate values for all but \r uncertainties in Earth system feedbacks and TCRE, the remaining \r carbon budgets in line with the Paris Agreement are generally small \r yet not zero (see Table 5.8)."


id: 1bf69be5865b835049251ae430897183
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 2105
content:
"Use of TCRE for estimating remaining carbon budgets needs to \r consider the zero emissions commitment (ZEC), the potential \r additional warming after a complete cessation of net CO2 emissions. \r Based on the ZEC assessment presented in Section 4.7.1.1, the ZEC's \r central value is taken to be zero with a  likely range of +-0.19degC, \r noting that it might either increase or decrease after half a century. \r ZEC uncertainty is assessed for a time frame of half a century, as this \r most appropriately reflects the time between stringent mitigation \r pathways reaching net zero CO2 emissions and the end of the \r century. For shorter time horizons, a similar central zero value applies, \r but with a smaller range (MacDougall et al., 2020). Experiments that \r ramped up and down emissions following a bell-shaped trajectory \r (MacDougall and Knutti, 2016a) show that when annual CO2\r emissions decline to zero at a pace consistent with those currently \r [...]"


id: b4d354c3394c6fc94ddef20491e3a0ac
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 29064
content:
"[...] assumed in mitigation scenarios (Huppmann et al., 2018; Rogelj \r et al., 2018b), the ZEC will already be realized to a large degree at the \r time of reaching net zero CO2 emissions (MacDougall et al., 2020)."


id: 26e876102f049806b7576bc5d6ac47ac
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 4004
content:
"There is robust evidence supporting the concept of TCRE as well \r as high confidence in the range of historical human-induced \r warming. Combined with the assessed uncertainties in the Earth \r system's response to non-CO2 emissions and less well-established \r quantification of some of the effect of non-linear Earth system \r feedbacks, this leads to medium confidence being assigned \r to  the assessed remaining carbon budget estimates while noting \r the  identified and assessed uncertainties and potential variations. \r The reported values are applicable to warming and cumulative \r emissions over the 21st century. For climate stabilization beyond the \r 21st century, this confidence would decline to very low confidence\r due to uncertainties in Earth system feedbacks and the ZEC.  For \r estimates of total carbon budgets in line with limiting global \r warming to a specific level, an estimate of historical CO2 emissions \r should be added to the remaining carbon budget values reported \r [...]"


id: 29621f8e2e95d2a0a10efdf1452bfda1
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 4498
content:
"[...] in Table  5.8. Historical CO2 emissions between 1850 and 2019 \r have been estimated at about 655 +-  65  PgC (1-sigma range, or \r 2390 +- 240 GtCO2, see Table 5.1), while since 1 January 2015, an \r additional 57  PgC (210  GtCO2) has been emitted until the end of \r 2019 (Friedlingstein et al., 2020)."


id: 6850d7c289074694bea6cd79ffe5075b
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 15621
content:
"b\r  Historical CO2 emissions between 1850 and 2019 have been estimated at about 655 +- 65 PgC (likely range, or 2390 +- 240 GtCO2, see Table 5.1). Note that 57 PgC \r (210 GtCO2) have been emitted from the middle of the 2010-2019 reference period (2015) until the end of 2019 (Friedlingstein et al., 2020).\r c\r  TCRE: transient climate response to cumulative CO2 emissions, assessed to fall likely between 1.0-2.3degC EgC-1 with a normal distribution. PgC values are rounded to the \r nearest 10; GtCO2 values to the nearest 50. For comparison, assuming a lognormal distribution with a 1.0-2.3degC EgC-1 central 66% range instead of a normal distribution \r would increase remaining carbon budgets at the 17th, 33rd, 50th, 67th, and 83rd percentile with 3%, 10%, 12%, 9%, 2%, respectively. Future non-CO2 contributions in \r these remaining carbon budget estimates are based on the scenarios assessed in the SR1.5 report and estimated as the median quantile regression of non-CO2 warming since \r [...]"


id: 1ec234d7a611049b8b59b71f86f7384
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 2614
content:
"[...] 2010-2019 relative to total additional warming since 2010-2019 at the time scenarios reach net-zero CO2 emissions (Forster et al., 2018; Huppmann et al., 2018; Rogelj et al., \r 2018b)."


id: 1564dbe944d25400466173382baaffa5
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 977
content:
"d\r  Additional Earth system feedbacks are included in the remaining carbon budget estimates as discussed in Section 5.5.2.2.5. The tropospheric ozone and methane lifetime \r contributions are included through the non-CO2 warming projections by the AR6-calibrated Model for the Assessment of Greenhouse Gas Induced Climate Change (MAGICC) \r emulator, while the remaining feedbacks are assessed totalling a combined feedback of magnitude 7 +- 27 PgC K-1 (1-sigma range, or 26 +- 97 GtCO2 degC-1).\r e\r  Variations due to different scenario assumptions related to the future evolution of non-CO2 emissions in mitigation scenarios reaching net zero CO2 emissions (Huppmann \r et al., 2018; Rogelj et al., 2018b) of at least +-0.1degC (spread across scenarios). Combined with a central estimate of TCRE (1.65degC EgC-1) this results in at least +-60 PgC or \r [...]"


id: 901be0baae88853b12d20c27bb85da78
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 22563
content:
"[...] +-220 GtCO2. This spread reflects the variation in the underlying scenario ensemble but is not a formal likelihood. WGIII will re-assess the potential for non-CO2 mitigation based \r on literature since SR1.5."


id: b96af1ad44873f0409fbc9f95d81f13e
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 29835
content:
"The reliance on an ensemble of opportunity (i.e., a serendipitous collection of scenario data from a variety of sources and studies) is \r avoided by methodologically separating the assessment of future warming contributions of non-CO2 emissions from the spread in \r transient climate response to cumulative CO2 emissions (TCRE; Section 5.5.2; Rogelj et al., 2018b, 2019). This facilitates the explicit \r representation of TCRE uncertainty by a formal distribution, in this case a normal distribution with a 1.0-2.3degC 1000 PgC-1 1-sigma \r range (Section  5.5.1.4). The effect of this methodological advance can be estimated from a  direct comparison of the frequency \r distribution of TCRE in CMIP5 models that were used in AR5, and the formal TCRE distribution used in AR6, but is limited in precision. \r For estimates of the remaining carbon budget in line with limiting warming to 1.5degC or 2degC relative to pre-industrial levels, this \r [...]"


id: 50077e78ae714170a632e969e439651c
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 11372
content:
"[...] improvement is estimated to lead to a reduction of budgets of the order of about 100 GtCO2 between AR5 and AR6."


id: e346a05f10e109f6258119f50d551697
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 37283
content:
"The fourth and fifth methodological advancements are to explicitly account for the zero emissions commitment (ZEC; Section 5.5.2.2.4) \r and adjust estimates for Earth system feedbacks that are typically not represented in Earth system models (ESMs; Section 5.5.2.2.5). \r The central estimate of the assessed ZEC used in SR1.5 and AR6 is zero (Section 4.7.1.1). ZEC uncertainties are reported separately \r (Table 5.8), and the additional consideration of ZEC therefore does result in a better understanding but not in a net shift of central \r estimates of the remaining carbon budget compared to AR5. Furthermore, AR5 did not explicitly account for Earth system feedbacks \r not represented in ESMs. The SR1.5 assessed that they could reduce the remaining carbon budgets by about 100 GtCO2 over centennial \r time scales. This assessment has been updated in AR6, including a wider range of biogeochemical feedbacks and new evidence \r [...]"


id: ce70606e63a8a137adca495787526ed8
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 33661
content:
"[...] (Section 5.5.2.2.5). Some of these feedbacks are captured in the estimation of non-CO2 warming (see below), while the combined \r effect of remaining positive and negative feedbacks is assessed to reduce the remaining carbon budget estimates by 7 +- 27 PgC K-1\r (1-sigma range, or 26 +- 97 GtCO2 degC-1) compared to AR5."


id: 74a5abedf0a35a3ba04e42af6b5174a0
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 17758
content:
"For historical warming, SR1.5 used GSAT increase between 1850-1900 and 2006-2015 of 0.97degC as its main starting point, while \r also providing values for other temperature metrics. Remaining carbon budgets were expressed starting from 1 January 2018 by \r accounting for historical emissions emitted from 1 January 2011 until the end of 2017. AR6 uses anthropogenic (human-induced) \r warming until the 2010-2019 period, which is assessed at the 0.8-1.3degC range, with a best estimate of 1.07degC (Table 3.1), and \r subsequently accounts for historical emissions from 1  January 2015 until the end of 2019 to express remaining carbon budget \r estimates from 1 January 2020 onwards. The human-induced warming between the 1850-1900 and 2006-2015 periods used in SR1.5 \r was assessed by AR6 at 0.97degC (Table 3.1). In a like-with-like comparison, the combined effect of data and methodological updates \r [...]"


id: 5b9c2db5c104cf7a5120976115e316e4
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 13368
content:
"[...] in historical warming estimates thus results in no shift in estimated remaining carbon budgets between SR1.5 and AR6. However, the \r emissions of the years passed since SR1.5 reduce the remaining carbon budget by about 85 GtCO2. Note that AR6 also updated its \r GSAT assessment for total warming between the 1850-1900 and 2006-2015 periods, reporting 0.94degC of warming. On a like-with\u0002like basis, this would have resulted in slightly larger remaining carbon budgets compared to SR1.5 (Cross-Chapter Box 2.3)."


id: cabcf24eda0438ecdf8c4c3f78377c77
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 33009
content:
"The non-CO2 contribution to future warming in emissions scenarios (Huppmann et al., 2018) is re-assessed with AR6-calibrated \r emulators, in this case MAGICC7 (Cross-Chapter Box 7.1; Meinshausen et al., 2009, 2011a, 2020). The re-assessment of non-CO2\r warming with MAGICC7 results in a relationship that closely matches the average relationship applied in SR1.5 (shown in Section 2.\r SM.1.1.2 in Forster et al., 2018), and therefore does not change estimates of the remaining carbon budget relative to SR1.5. The \r median ZEC assessment remained the same between SR1.5 and AR6, and therefore does not change the median remaining carbon \r budget estimates. Finally, as indicated above, AR6 expanded the assessment of Earth system feedbacks compared to SR1.5 and \r included it in its central remaining carbon budget estimates. Some feedbacks are accounted for through the non-CO2 warming \r [...]"


id: 216bdb4a394e621595efe273e6f6a017
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
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index: climate_gpt_v2
vector_id: 3089
content:
"[...] estimate (Section 5.5.2.2.5), while the remainder combines to reduce the median remaining carbon budget estimates for 1.5degC and \r 2degC of warming by about 10 to 20 GtCO2, respectively, compared to SR1.5."


id: 770f23045e163830890026b11ff66e87
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 18146
content:
"This subsection assesses evidence about the response of the global \r carbon cycle to CDR from idealized model simulations which \r assume that CO2 is removed from the atmosphere directly and \r stored permanently in the geologic reservoir, which is analogous to \r direct air carbon capture with carbon storage (DACCS) (Table 5.9). \r The carbon cycle response to specific land and ocean-based CDR \r methods is assessed in Section  5.6.2.2.2. At the time of AR5 \r there were very few studies about the global carbon cycle response \r to CDR. Based on these studies and general understanding of the \r carbon cycle, AR5 WGI Chapter 6 assessed that it is virtually certain\r that deliberate removal of CO2 from the atmosphere will be partially \r offset by outgassing of CO2 from the ocean and land carbon sinks. \r Low confidence was placed on any quantification of effects. Since \r AR5 WGI Chapter 6, several studies have investigated the carbon \r [...]"


id: 7d4e292a1747897b33013309078186
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 19220
content:
"[...] cycle response to CDR in idealized 'pulse' removal simulations, \r whereby a specified amount of CO2 is removed instantly from the \r atmosphere, and scenario simulations with CO2 emissions and \r removals following a plausible trajectory. In addition, a dedicated \r carbon dioxide removal model intercomparison project was \r initiated (CDRMIP; Keller et al., 2018b) which includes a  range \r of CDR experiments from idealized simulations to simulations of "


id: 97da075c06df5ef61e811e0c5d81d919
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 23928
content:
"then rebounds (Figure  5.32a). This rebound is due to CO2 release \r by the terrestrial biosphere and the ocean in response to declining \r atmospheric CO2 levels (Figure 5.32b,c; M. Collins et al., 2013). For \r the model simulations shown in Figure 5.32, 23 +- 6% (mean +- 1 \r standard deviation) of the 100  PgC removed remains out of the \r atmosphere 80-100  years after the instantaneous removal. The \r remainder is offset by CO2 outgassing from the land (49 +-  12%) \r and ocean (29 +- 7%). While the direction of the CO2 flux is robust \r across models, the relative contribution of the outgassing from land \r and ocean reservoirs to the atmospheric CO2 rebound after removal \r varies. These results corroborate the high confidence placed by AR5 \r WGI (Chapter 6) on the partial compensation of CO2 removal from \r the atmosphere by CO2 outgassing from the land and ocean. Due \r to disagreement between models, the magnitude of this outgassing \r [...]"


id: c001efc009217f2e49231929d9362cd9
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 31070
content:
"Since AR5 WGI (Chapter  6), studies with ESMs have explored \r the land and ocean carbon sink response to scenarios with CO2\r emissions gradually declining during the 21st century. As CDR \r and other mitigation activities are ramped up, CO2 emissions in \r these scenarios reach net zero and, as removals exceed emissions, \r become net negative. Studies exploring the carbon sink response \r to such scenarios (e.g., RCP2.6, SSP1-2.6) show that, when net CO2\r emissions are positive but start to decline, uptake of CO2 by the land \r and ocean begins to weaken (compare land and ocean CO2 fluxes \r in panels a and b of Figure 5.33; Tokarska and Zickfeld, 2015; Jones \r et al., 2016b). During the first decades after CO2 emissions become \r net negative, both the ocean and land carbon sinks continue to take \r up CO2, albeit at a lower rate. For the land carbon sink, the sink-to\u0002source transition occurs decades to a  century after CO2 emissions \r [...]"


id: 3996fc42616e0a9dd1acdb2714c87aad
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 7343
content:
"[...] become net negative (Figure 5.33c). The ocean remains a sink of CO2\r for centuries after emissions become net negative (Figure 5.33c-e; \r Section 5.4.9; Figure 5.30). Whether the transition to source occurs \r at all, the timing of the transition and the magnitude of the CO2\r source are determined by the magnitude of the removal and the rate \r and amount of net CO2 emissions prior to emissions becoming net \r negative (medium confidence) (Tokarska and Zickfeld, 2015; Jones \r et al., 2016b). For scenarios with large amounts of CO2 removal, such \r as SSP5-3.4-overshoot, the land source is larger than for SSP1-2.6 \r and the ocean also turns into a source (Section 5.4.10, Figure 5.30). \r While the qualitative response to scenarios with net-negative \r emissions is largely robust across models, the timing of the sink-to\u0002source transition and the magnitude of the CO2 source vary between \r models, particularly for the land sink. Due to low agreement between \r [...]"


id: ff0e91421407e269d4d3ee04c4ef8633
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 42278
content:
"[...] models, there is low confidence in the timing of the sink-to-source \r transition and the magnitude of the CO2 source in scenarios with \r net-negative CO2 emissions."


id: cdfe41464c0298c85f3cb8030069c959
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 33574
content:
"It is well understood that land and ocean carbon fluxes are sensitive \r to the level of atmospheric CO2 and climate change and differ under \r varied future scenarios (Section  5.4). It is therefore important to \r establish to what extent the removal effectiveness of CDR -  here \r defined as the fraction of total CO2 removed that remains out of \r the atmosphere -  is dependent on the scenario in which CDR is \r applied. Different metrics have been proposed to quantify the \r removal effectiveness of CDR (Tokarska and Zickfeld, 2015; Jones \r et al., 2016b; Zickfeld et al., 2016). One is the airborne fraction (AF) \r of cumulative CO2 emissions, defined in the same way as for positive \r emissions (i.e., as the fraction of total CO2 emissions remaining in the \r atmosphere), with its use extended to periods of declining and net \r negative CO2 emissions. This metric, however, has not proven to be \r useful to quantify the removal effectiveness of CDR in simulations \r [...]"


id: 16c70cff811bce024c7325c999aa4deb
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 1220
content:
"[...] where CDR is applied from a trajectory of increasing atmospheric CO2\r concentration. This is because it measures the carbon cycle response \r to CDR as well as to the prior atmospheric CO2 trajectory (Tokarska \r and Zickfeld, 2015; Jones et al., 2016b). A  more useful metric is \r the perturbation airborne fraction (PAF; Jones et al., 2016b), which \r measures the AF of the perturbation (in this case the CO2 removal) \r relative to a reference scenario (Tokarska and Zickfeld, 2015; Jones \r et al., 2016b). The advantage of this metric is that it isolates the \r response to a CO2 removal from the response to atmospheric CO2\r prior to when the removal is applied. A disadvantage is that the PAF \r cannot be calculated from a  single model simulation, but instead "


id: b323bb28dfee7d97cdbca5b91e24a5a7
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 28760
content:
"In scenario simulations and idealized simulations with instantaneous \r CO2 removals applied from an equilibrium state, the removal \r effectiveness of CDR is found to be slightly dependent on the rate \r and amount of CDR (Tokarska and Zickfeld, 2015; Jones et al., \r 2016b; Zickfeld et al., 2021), and to be strongly dependent on the \r emissions scenario from which CDR is applied (Jones et al., 2016b; \r Zickfeld et al., 2021). The fraction of CO2 removed remaining out of \r the atmosphere decreases slightly for larger removals and decreases \r strongly when CDR is applied from a lower background atmospheric \r CO2 concentration (Figure  5.34), due to state dependencies and \r climate-carbon cycle feedbacks that lead to a  stronger overall \r response to CO2 removal (Zickfeld et al., 2021). Based on the high \r agreement between studies, we assess with medium confidence that \r the removal effectiveness of CDR is only slightly dependent on the \r [...]"


id: 336b023f2c2946c3029449c2f83e5770
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 6299
content:
"[...] rate and magnitude of removal and is smaller at lower background \r atmospheric CO2 concentrations. Simulations with Earth system \r models of intermediate complexity (EMIC) with instantaneous CO2\r removal from different equilibrium initial states suggest that the \r smaller removal effectiveness of CDR at lower background CO2\r levels results in greater cooling per unit CO2 removed (Zickfeld et al., \r 2021). However, there is low confidence in the robustness of this \r result as climate sensitivity has been shown to exhibit opposite state \r dependence in EMICs and ESMs (Section 7.4.3.1)."


id: ea446c82fecea3f2239a4bb660633297
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 38573
content:
"in model simulations with positive and negative CO2 emission pulses \r applied from a pre-industrial climate state. Simulations with seven \r CMIP6 ESMs and the UVic Earth System Climate Model (ESCM) of \r intermediate complexity suggest that the carbon cycle response is \r asymmetric for pulse emissions/removals of +-100 PgC (Figure 5.35). \r For all models, the fraction of CO2 remaining in the atmosphere \r after an emission is larger than the fraction of CO2 remaining out \r of the atmosphere after a  removal (by 4  +- 3%; mean +-  standard \r deviation). In other words, an emission of CO2 into the atmosphere \r is more effective at raising atmospheric CO2 than an equivalent \r CO2 removal is at lowering it. Sensitivity experiments with the UVic \r ESCM suggest that the asymmetry increases for larger amounts of \r emissions/removals and is insensitive to the background atmospheric \r CO2 concentration from which the emissions/removals are applied \r [...]"


id: 52c8e23f14fc000d740ef5b33bad76bc
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 11835
content:
"[...] (Figure  5.35). This asymmetry in the atmospheric CO2 response \r originates from asymmetries in the land and ocean carbon fluxes \r due to non-linearities in the carbon cycle response to CO2 and \r temperature (Section  5.4) (Zickfeld et al., 2021). Given medium \r evidence and high agreement, there is medium confidence in the \r sign of the asymmetry of the carbon cycle response to positive and \r negative CO2 emissions. The sign of the symmetry of the temperature \r response differs between models, with three out of seven examined \r ESMs showing a smaller temperature response to a 100 PgC emission \r than to an equivalent CO2 removal. Therefore, there is low confidence\r in the sign of the asymmetry of the temperature response to positive \r and negative CO2 emissions."


id: 763b393dea73bdb39b47030c2e31bca
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 18022
content:
"y\r The AR5 WGI (Chapter 6) discussed the CDR methods, their implications \r and unintended side effects on carbon cycle and climate, including \r their time scales and potentials. Since then, three IPCC special \r reports (SR) have been published. First, SR1.5 Chapter  4  (IPCC, \r 2018) assessed the potentials and current understanding, including \r the side effects, of bioenergy with carbon capture and storage \r (BECCS), afforestation/reforestation, soil carbon sequestration, \r biochar, enhanced weathering, ocean alkalinization, direct air carbon \r capture with storage (DACCS) and ocean fertilization. Second, SRCCL \r Chapter  6  (IPCC, 2019a) assessed the potentials, co-benefits and \r trade-offs of land-based mitigation options. It assessed with high \r confidence that land-based CDR options do not sequester carbon \r indefinitely, except for peatland restoration. Multiple co-benefits \r were identified in the deployment of CDR options, many of them \r [...]"


id: 61ab7fe2c34e5733cbf9c53885bd1820
created_at: 2023-04-19 13:22:37
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index: climate_gpt_v2
vector_id: 14471
content:
"[...] with a  potential to make positive contributions to sustainable \r development, enhancement of ecosystem functions and services \r and other societal goals. However, their potential was concluded to \r be context specific, and limits were identified in their contribution \r to global mitigation, such as competition for land. The third report, \r the Special Report on Ocean and the Cryosphere in a  Changing \r Climate (SROCC) Chapter 5 (IPCC, 2019b), assessed the potential of \r marine options for climate change mitigation. It concluded that the \r feasibility of open ocean fertilization and alkalinization approaches \r were negligible, due to their inconclusive influence on ocean carbon \r storage on long time scales, due to the unintended side effects on \r marine ecosystems, and the associated governance challenges. The \r assessment of blue carbon ecosystems concluded that they could \r contribute only minimally to atmospheric CO2 reduction globally, \r [...]"


id: de6366a3db346a607b1fe5bc07ce94e6
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 36390
content:
"[...] but emphasized that the benefits of protecting and restoring coastal \r blue carbon extend beyond climate change mitigation (SROCC \r Section 5.5.12)."


id: 1b20ef7765f029bf5215ca42d3a3e25c
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 1967
content:
"A range of mechanisms could enhance CO2 sequestration of forest\u0002based methods under future scenarios, including CO2 fertilization, \r soil carbon enrichment due to enhanced litter input, or the \r northward shift of the tree line in future climate projection (Bathiany \r et al., 2010; Sonntag et al., 2016; Boysen et al., 2017b; Harper et al., \r 2018). There is low confidence in the net direction of feedbacks of \r afforestation on global mean temperature. The feedbacks are highly \r region dependent. For instance, afforestation at high latitudes \r would decrease albedo and increase local warming, while at low \r latitudes,  the cooling effect of enhanced evapotranspiration could \r exceed the warming effect due to albedo decrease (Pearson et al., \r 2013; W. Zhang et al., 2013; Jia et al., 2019, SRCCL, Section 2.6.1). \r Both afforestation and reforestation affect the hydrological cycle \r through increased volatile organic compound (VOC) emissions and \r [...]"


id: 79793540f7841fc5662b861dc63a25a9
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 18573
content:
"[...] cloud albedo (Teuling et al., 2017; Kalliokoski et al., 2020), enhanced \r precipitation (Ellison et al., 2017) and increased transpiration, with \r potential effects on runoff and, especially in dry regions, on water \r supply (Figure 5.36 and Cross-Chapter Box 5.1; Farley et al., 2005; \r Smith et al., 2016; Krause et al., 2017; Teuling et al., 2019). Forest\u0002based methods can either raise or lower N2O emissions, depending \r on tree species, previous land use, soil type and climatic factors \r (low confidence) (Figure 5.36 and Supplementary Materials, Table 5.\r SM.4; Benanti et al., 2014; Chen et al., 2019; McDaniel et al., 2019). \r Afforestation will decrease biodiversity if native species are replaced \r by monocultures (high confidence), while there is medium confidence\r that biodiversity is improved when forests are introduced into land \r areas with degraded soils or intensive monocultures, or where \r native species are re-introduced into managed land (Figure  5.36, \r [...]"


id: 592a008df49c3a8d3c43d559b21d3259
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 12930
content:
"[...] Supplementary Materials Table 5.SM.4; Hua et al., 2016; Williamson \r and Bodle, 2016; P. Smith et al., 2018; Holl and Brancalion, 2020)."


id: 2233670ced37b1b20700b50335ff43bd
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 3236
content:
"Peatlands are less extensive than forests, croplands and grazing \r lands, yet per unit area, they hold high carbon stocks (Griscom et al., \r 2017). Peatland restoration relies on back-conversion or building \r of high-carbon-density soils through flooding -  that is, rewetting \r (Leifeld et al., 2019). High water level and anoxic conditions are \r prerequisites for restoring by returning drained and/or degraded \r peatlands back to their natural state as CO2 sinks, but restoration \r also results in enhanced CH4 emissions which are similar to or higher \r than the pre-drainage fluxes (high confidence) (Koskinen et al., 2016; \r Wilson et al., 2016a; Hemes et al., 2019; Renou-Wilson et al., 2019; \r Holl et al., 2020). In a  multi-decadal time frame, the reduction in \r CO2 emissions from rewetting more than compensates for the \r initial increase in radiative forcing due to enhanced CH4 emissions \r (Gunther et al., 2020). Rewetting drained peatlands will decrease \r [...]"


id: 81af89834ba14401f4a0431943f06972
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
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index: climate_gpt_v2
vector_id: 19946
content:
"[...] N2O emissions (medium confidence) (Wilson et al., 2016b; H.  Liu \r et al., 2020; Tiemeyer et al., 2020). Restored wetlands and peatlands \r act as buffer zones that provide infiltration and nutrient retention \r and offer protection to water quality (Daneshvar et al., 2017; Lundin \r et al., 2017), particularly in nutrient-loaded agricultural catchments. \r Peatland restoration can also recover much of the original biodiversity \r (medium confidence) (Meli et al., 2014; P. Smith et al., 2018)."


id: c670e959bf8f3b75f4280a5708af73b8
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
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index: climate_gpt_v2
vector_id: 32223
content:
"Soil carbon losses from human agriculture accounted for about \r 116 PgC in the last 12,000 years (Section 5.2.1.2; Sanderman et al., \r 2017). With best management practices, two-thirds of these losses \r may be recoverable, setting a  theoretical maximum of 77  PgC \r that can be sequestered in soils. Methods to increase soil carbon \r content may be applied to the restoration of marginal or degraded \r land (Paustian et al., 2016; Smith, 2016), but may also be used in \r traditional agricultural lands. A  simple practice is to increase the \r input of carbon to the soil by selecting appropriate varieties or \r species with greater root mass (Kell, 2011) or higher yields and net \r primary production (NPP) (Burney et al., 2010). In addition, improved \r agricultural practices also increase soil carbon content. These \r include  using crop  rotation cycles, increasing the amount of crop \r residues, using crop cover to prevent periods of bare soil (Poeplau \r [...]"


id: a6a5f0712dda1a4eef84d0bc3de787ac
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
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index: climate_gpt_v2
vector_id: 26534
content:
"[...] and Don, 2015; Griscom et al., 2017), optimizing grazing (Henderson \r et al., 2015) and residue management (Wilhelm et al., 2004), using \r irrigation (Campos et al., 2020), employment of low-tillage or no\u0002tillage (W. Sun et al., 2020), agroforestry, cropland nutrient recycling, \r and avoiding grassland conversion (Paustian et al., 2016; Fargione \r et al., 2018). With medium confidence, methods that seek soil \r carbon sequestration will diminish nitrous oxide (N2O) emissions and \r nutrient leaching, and improve soil fertility and biological activity \r (Figure  5.36; Tonitto et al., 2006; Fornara et al., 2011; Paustian \r et al., 2016; Smith et al., 2016; SRCCL, Section 2.6.1.3, IPCC, 2019a). \r However, if improved soil carbon sequestration practices involve \r higher fertilization rates, N2O emissions would increase (Gu et al., \r 2017). Some soil carbon sequestration methods, such as cover crops \r and crop diversity, can increase biodiversity (medium confidence) \r [...]"


id: 941dea0dd44492f50b1abe18b9cbd366
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
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index: climate_gpt_v2
vector_id: 23265
content:
"The concept of BECCS rests on the premise that bioenergy production \r is carbon neutral - that is, as much CO2 is sequestered when growing \r biomass as feedstock as is released by its combustion. If these \r emissions are also captured and stored, the net effect is removal of \r CO2 from the atmosphere (Fuss et al., 2018). Sequestration potentials \r from BECCS depend strongly on the feedstock, climate, and \r management practices (Beringer et al., 2011; Kato and Yamagata, \r 2014; Heck et al., 2016; Smith et al., 2016; Krause et al., 2017). \r If woody bioenergy plants replace marginal land, net carbon uptake \r increases, enriching soil carbon (Don et al., 2012; Heck et al., 2016; \r Boysen et al., 2017a, b). However, replacing carbon-rich ecosystems \r with herbaceous bioenergy plants could deplete soil-carbon stocks \r and reduce the additional sink capacity of standing forests (Don et al., \r 2012; Harper et al., 2018). Furthermore, wood-based BECCS may not \r [...]"


id: ee9f840b51385bced3b806d4d1d067d2
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
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index: climate_gpt_v2
vector_id: 39360
content:
"[...] be carbon negative in the first decades, initially emitting more CO2\r than sequestering (Sterman et al., 2018). BECCS has several trade\u0002offs to deal with, including possible threats to water supply and soil \r nutrient deficiencies (medium confidence) (SRCCL Chapters 2 and 6, \r and Cross-Chapter Box 5.1; Smith et al., 2016; Krause et al., 2017; de \r Coninck et al., 2018; Heck et al., 2018; Roy et al., 2018). Deployment \r of BECCS at the scales envisioned by many 1.5degC-2.0degC mitigation \r scenarios could threaten biodiversity and require large land areas, \r competing with afforestation, reforestation and food security \r (Anderson and Peters, 2016; P. Smith et al., 2018). Additional risks \r and side effects are related to geologic carbon storage (Fuss et al., \r 2018; see also Section 5.6.2.2.4)."


id: f278ac1b91d86e87245693e8f3c848fb
created_at: 2023-04-19 13:22:37
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index: climate_gpt_v2
vector_id: 40050
content:
"Biochar is produced by burning biomass at high temperatures under \r anoxic conditions (pyrolysis) and, when added to soils, can increase \r soil carbon stocks and fertility for decades to centuries (Woolf et al., \r 2010; Lehmann et al., 2015). Biochar application improves many soil \r qualities and increases crop yield (medium confidence) (Ye et al., 2020; \r SRCCL, Chapter 4.9.5), particularly in already degraded or weathered \r soils (Woolf et al., 2010; Lorenz and Lal, 2014; Jeffery et al., 2016), \r increases soil water holding capacity (medium confidence) (Karhu \r et al., 2011; Liu et al., 2016; B.M.C. Fischer et al., 2019; Verheijen et al., \r 2019) and evapotranspiration (low confidence) (B.M.C. Fischer et al., \r 2019). The use of biochar reduces nutrient losses (low confidence) \r (Woolf et al., 2010), enhances fertilizer nitrogen use efficiency and \r improves the bioavailability of phosphorus (Figure  5.36; Clough \r [...]"


id: 1d9e23eac0eeb69a974ce10ad518bf69
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
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vector_id: 2395
content:
"[...] et al., 2013; Shen et al., 2016; Z. Liu et al., 2017). Biochar addition \r may decrease methane (CH4) emissions in inundated and acid soils \r such as rice fields (low confidence) (Jeffery et al., 2016; Huang et al., \r 2019; S. Wang et al., 2019; S. Yang et al., 2019). In non-inundated, \r neutral soils, CH4 uptake from the atmosphere is suppressed after \r biochar application (low confidence) (Jeffery et al., 2016), and soil \r N2O emissions decline (medium confidence) (Cayuela et al., 2014; \r Kammann et al., 2017). The potential risks of introducing harmful \r contaminants into the soil environment are not well understood \r (Lorenz and Lal, 2014). With low confidence, application of biochar \r can have co-benefits for soil microbial biodiversity (P. Smith et al., \r 2018), while the potential trade-offs for biodiversity are due to land \r requirements (Tisserant and Cherubini, 2019)."


id: 388cf6973d28656b9ea4b64d1d51b59e
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 7157
content:
"Both ocean biological and physical processes drive the CO2 exchange \r between the ocean and atmosphere. However, the ocean physical \r processes that remove CO2 from the atmosphere, such as large\u0002scale circulation, cannot be feasibly altered, so ocean CDR methods \r focus on increasing the productivity of ocean ecosystems, and \r subsequent sequestration of carbon (GESAMP, 2019). There has \r been no change to the assessment of SROCC (SROCC Section 5.5.1): \r there is low confidence that nutrient addition to the open ocean, \r either through artificial ocean upwelling or iron fertilization, could \r contribute to climate change mitigation, due to its inconclusive \r effect on carbon sequestration and risks of adverse side effects on \r marine ecosystems (Figure 5.36, Table 5.9; Supplementary Materials \r Text 5.SM.3 and Table 5.SM.4; AR6 WGIII Chapter 12; Gattuso et al., \r 2018; Boyd and Vivian, 2019; Feng et al., 2020). In addition, ocean \r [...]"


id: f6186af7beb25e22d6b161f1e210b69e
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index: climate_gpt_v2
vector_id: 40655
content:
"[...] fertilization is currently prohibited by the London Protocol (Dixon \r et al., 2014; GESAMP, 2019)."


id: 5c5427d8cdc39c9d1619ae7b42b6f385
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 13496
content:
"Enhanced weathering (EW) is based on naturally occurring \r weathering processes of silicate and carbonate rocks, removing CO2\r from the atmosphere. Weathering is accelerated by spreading ground \r rocks on soils, coasts or oceans. EW increases the alkalinity and pH \r of natural waters, helps dampen ocean acidification and increases \r ocean carbon uptake (Beerling et al., 2018). The dissolution of \r minerals stimulates biological productivity of croplands (Hartmann \r et al., 2013; Beerling et al., 2018), but can also liberate toxic trace \r metals (such as nickel, chromium, copper) into soil or water bodies \r (Keller et al., 2018a; Strefler et al., 2018). EW can also contribute \r to freshwater salinization as a  result of increased salt inputs and \r cation exchange in watersheds, and so adversely affecting drinking \r water quality (low confidence) (Kaushal et al., 2018). With a medium \r confidence, amendment of soils with minerals will have lower N2O \r [...]"


id: e8a4560642cc26d5a79a0cc0e1e627b1
created_at: 2023-04-19 13:22:37
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vector_id: 38272
content:
"[...] emissions (Kantola et al., 2017; Blanc-Betes et al., 2020) but will not \r have a marked effect on evapotranspiration or albedo (Fuss et al., \r 2018; de Oliveira Garcia et al., 2020). The mining of minerals can \r cause adverse impacts on biodiversity, however, the use of waste \r materials such as concrete demolition or steel slags for EW can reduce \r the need for mining (Renforth, 2019). The spreading of minerals on \r land has a neutral impact on biodiversity (P. Smith et al., 2018)."


id: 4e4a6c7d6e6f695207db909b8556ee3d
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 11045
content:
"Coastal wetlands and seagrass meadows store significant amounts \r of carbon and are among the most productive ecosystems per unit \r area (Griscom et al., 2017, 2020; Ortega et al., 2019; Serrano et al., \r 2019). These rates could be reduced in the future, since these habitats \r are vulnerable to changing conditions, such as temperature, salinity, \r sediment supply, storm severity and continued coastal development \r (Bindoff et al., 2019; NASEM, 2019). These ecosystems are under \r threat from anthropogenic conversion and degradation and are being \r lost at rates between 0.7% and 7% per annum with consequent CO2\r emissions (e.g., Atwood et al., 2017; Howard et al., 2017; Hamilton \r and Friess, 2018; Sasmito et al., 2019). Although sea level rise might \r lead to greater carbon sequestration in coastal wetlands (Rogers \r et al., 2019), there is high confidence that the frequency and intensity \r of marine heatwaves will increase (Cross-Chapter Box 9.1; Frolicher \r [...]"


id: 87a3a92cac33fd4af9002d2a42313407
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 21020
content:
"Direct air carbon capture with carbon storage (DACCS) is a combination \r of two techniques, direct capture of CO\r 2 from ambient air (DAC) and \r carbon storage. DAC entails contacting the air, capturing the CO\r 2\r on a liquid solvent or solid sorbent, and regenerating the solvent or \r sorbent. Different DAC methods have been proposed, which differ \r by the chemical process used to capture the CO\r 2 and to recover it \r from the sorbent or solvent (NASEM, 2019). The captured CO\r 2 may \r be either stored geologically as a high-pressure gas or sequestered \r by a mineral carbonation process. Storage is potentially permanent \r in both pressurised gas and mineral form (Fuss et al., 2018). DACCS \r has significant requirements of energy and, (depending on the type \r of technology), water and materials (Smith et al., 2016; NASEM, \r 2019). Compared to other CDR methods, it has a small land footprint \r (Smith et al., 2016; NASEM, 2019). Side effects of DACCS include \r CO\r [...]"


id: 3f430c35a9220ffd6a307940bc347547
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
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index: climate_gpt_v2
vector_id: 8321
content:
"[...] 2-depleted air leaving the air contactor, which could have adverse \r effects on crop and ecosystem productivity, and VOC emissions \r (NASEM, 2019). Additional risks and side effects are related to the \r high pressure at which CO\r 2 is stored in geologic formations (Fuss \r et al., 2018). DACCS is assessed in detail in WGIII Chapter 12."


id: bba78b440bef73eedf83610f3e90e180
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
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index: climate_gpt_v2
vector_id: 30239
content:
"SRM-mediated sunlight changes directly affect the carbon cycle. In \r particular, SAI would reduce the sunlight reaching the Earth's surface, \r but also increase the fraction of sunlight that is diffuse. These changes \r in the quantity and quality of the sunlight have opposing effects \r on the photosynthesis of land plants. On their own, reductions in \r photosynthetically active radiation (PAR) will reduce photosynthesis. \r However, diffuse light is more effective than direct light in accessing \r the light-limited leaves within plant canopies, leading to the so-called \r 'diffuse-radiation' fertilization effect (Mercado et al., 2009). The \r estimated balance between the negative impacts of reducing PAR \r and the positive impacts of increasing diffuse fraction differ between \r models (Kalidindi et al., 2015; Xia et al., 2016; C.-E. Yang et al., 2020) \r and across different ecosystems. The change in the absolute amount \r of direct and diffuse radiation could also depend on the height of \r [...]"


id: 4bf0e7a914ad2d8eb00141e087789610
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index: climate_gpt_v2
vector_id: 10626
content:
"[...] the additional sulphate aerosol layer in the stratosphere and the \r hygroscopic growth of aerosols (Krishnamohan et al., 2019, 2020). "


id: 6ea860c4e094eb0e13a1586a69d4731d
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 16736
content:
"SRM-mediated cooling also affects the terrestrial carbon cycle. \r Relative to a high-GHG world without SRM, the simulated responses \r of net primary production (NPP) to SRM differ widely between \r models, such that even the sign of global mean change is uncertain \r (Glienke et al., 2015). SRM-induced cooling would decrease NPP at \r high latitudes by reducing the length of the growing season (Glienke \r et al., 2015). At low latitudes, the NPP response to SRM-induced \r cooling is sensitive to the effect of nitrogen limitation (Glienke et al., \r 2015; Duan et al., 2020). SRM-induced cooling tends to increase NPP \r in models without the nitrogen cycle because of reduced heat stress. \r However, in models including the nitrogen cycle, this is counteracted \r by reductions in NPP because of reductions in nitrogen mineralization \r and nitrogen availability (Glienke et al., 2015). SRM-induced \r changes in the hydrological cycle (Section 8.6.3), including changes \r [...]"


id: b95a475a9bff39cf79615d7a6860c147
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 29823
content:
"[...] in evapotranspiration, precipitation, and soil moisture, also pose \r strong constraints on the vegetation response (Dagon and Schrag, \r 2019). For the same amount of global mean cooling, different SRM \r options, such as SAI, MCB, and CCT, would have different effects on \r gross primary production (GPP) and NPP because of different spatial \r patterns of temperature, available sunlight and hydrological cycle \r changes (Section 4.6.3.3) (Duan et al., 2020). Modelling studies show \r that SRM-induced cooling would reduce plant and soil respiration \r (Tjiputra et al., 2016; Cao and Jiang, 2017; Muri et al., 2018; \r C.-E. Yang et al., 2020). Despite the large uncertainty in modelled \r NPP response, existing modelling studies consistently show that SRM "


id: feea9b1c94c4c2c68a5d05b9c44de1e2
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 42245
content:
"Modelling studies consistently show that, relative to a  high-CO2 \r world without SRM, SRM-induced cooling (Section  4.6.3.3) would \r reduce plant and soil respiration, and also reduce the negative \r effects of warming on ocean carbon uptake (Tjiputra et al., 2016; Xia \r et al., 2016; Cao and Jiang, 2017; Jiang et al., 2018; Muri et al., 2018; \r Sonntag et al., 2018; Plazzotta et al., 2019; C.-E. Yang et al., 2020). \r A multi-model study (Plazzotta et al., 2019) indicates that, relative \r to a  high-CO2 concentration world without SRM, stratospheric \r sulphur dioxide (SO2) injection increases the allowable CO2 emissions \r by enhancing CO2 uptake by both land and ocean (Figure  5.37). \r As a result of enhanced global carbon uptake, SRM would reduce the \r burden of atmospheric CO2 (high confidence). However, the amount \r of SRM-induced reduction in atmospheric CO2 depends on the future \r emissions scenario and modelled oceanic and terrestrial carbon \r [...]"


id: 80380e29e7af68c85f96c117d5748979
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 19688
content:
"[...] sinks, which differ widely between models (Tjiputra et al., 2016; Xia \r et al., 2016; Cao and Jiang, 2017; Muri et al., 2018). Models that \r include the terrestrial nitrogen cycle usually report a much smaller \r reduction of atmospheric CO2 in response to SRM than models \r without the nitrogen cycle, mainly because nitrogen limitation leads \r to a weaker terrestrial carbon sink (Tjiputra et al., 2016; Muri et al., \r 2018; C.-E. Yang et al., 2020). Large-scale application of SAI is found "


id: bcb7634429341301cf2e7be1468da4f8
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 30448
content:
"On land, it is mainly the vegetation that captures CO2\r from the atmosphere through plant photosynthesis, \r which ultimately accumulates both in vegetation and \r soils. As more CO2 accumulates in the atmosphere, \r plant carbon capture increases through the CO2 \r fertilization effect in regions where plant growth is not \r limited by, for instance, nutrient availability. Climate \r change affects the processes responsible for the uptake \r and release of CO2 on land in multiple ways. Land CO2\r uptake is generally increased by longer growing seasons \r due to global warming in cold regions and by nitrogen \r deposition in nitrogen-limited regions. Respiration by \r plants and soil organisms, natural disturbances such \r as fires, and human activities such as deforestation all \r release CO2 back into the atmosphere. The combined \r effect of climate change on these processes is to weaken \r the future land sink. In particular, extreme temperatures \r [...]"


id: a7f636a70b8ce452a57d01e7ab451f0
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 26760
content:
"[...] and droughts as well as permafrost thaw (see FAQ 5.2) \r tend to reduce the land sink regionally. In the ocean, "


id: ea777d106d721ec198651cca170a0ad2
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 38615
content:
"While permafrost processes are complex, they are beginning to be included in models that represent the \r interactions between the climate and the carbon cycle. The projections from these permafrost carbon models show \r a wide range in the estimated strength of a carbon-climate vicious circle, from both carbon dioxide and methane, \r equivalent to 14-175 billion tonnes of carbon dioxide released per 1degC of global warming. By comparison, in \r 2019, human activities have released about 40 billion tonnes of carbon dioxide into the atmosphere. This has two \r implications. First, the extra warming caused by permafrost thawing is strong enough that it must be considered \r when estimating the total amount of remaining emissions permitted to stabilize the climate at a given level of \r global warming (i.e., the remaining carbon budget, see FAQ 5.4). Second, the models do not identify any one \r [...]"


id: 90ce8c40bd40a6781f66ccb5c8f72b75
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 22684
content:
"[...] amount of warming at which permafrost thaw becomes a 'tipping point' or threshold in the climate system \r that would lead to a runaway global warming. However, models do project that emissions would continuously \r increase with warming, and that this trend could last for hundreds of years."


id: 7dfc073b33173ff10f6545573bb96f2b
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 19345
content:
"Permafrost can also be found in other cold places (e.g., mountain ranges), but those places contain much less \r carbon than in the Arctic. For instance, the Tibetan plateau contains about 3% as much carbon as is stored in \r the Arctic. There is also concern about carbon frozen in shallow ocean sediments. These deposits are known as \r methane hydrates or clathrates, which are methane molecules locked within a cage of ice molecules. They formed \r as frozen soils that were flooded when sea levels rose after the last ice age. If these hydrates thaw, they may \r release methane that can bubble up to the surface. The total amount of carbon in permafrost-associated \r methane hydrates is much less than the carbon in permafrost soils. Global warming takes millennia to penetrate \r into the sediments beneath the ocean, which is why these hydrates are still responding to the last deglaciation. \r [...]"


id: fcbfb29560f70d29d67c692385dc68ea
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 41831
content:
"[...] As a result, only a small fraction of the existing hydrates could be destabilised during the coming century. Even \r when methane is released from hydrates, most of it is expected to be consumed and oxidised into carbon dioxide \r in the ocean before reaching the atmosphere. The most complete modelling of these processes to date suggests \r a release to the atmosphere at a rate of less than 2% of current human-induced methane emissions."


id: c6be05b2e68b3fa6895ec3d293240bc8
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 32269
content:
"The term negative carbon dioxide (CO2) emissions\r refers to the removal of CO2 from the atmosphere by \r deliberate human activities, in addition to removals \r that occur naturally, and is often used as synonymous \r with carbon dioxide removal. Negative CO2 emissions \r can compensate for the release of CO2 into the \r atmosphere by human activities. They could be \r achieved by strengthening natural CO2 sequestration \r processes on land (e.g., by planting trees or through \r agricultural practices that increase the carbon content \r of soils) and/or in the ocean (e.g.,  by restoration of \r coastal ecosystems) or by removing CO2 directly from \r the atmosphere. If CO2 removals are greater than \r human-caused CO2 emissions globally, emissions are \r said to be net negative. It should be noted that CO2\r removal technologies are unable, or not yet ready, to \r achieve the scale of removal that would be required to \r compensate for current levels of emissions, and most \r [...]"


id: 979fe0b082b48965f05187d358ff199e
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 23893
content:
"If the CO2 concentration in the atmosphere starts to go down, the Earth's climate would respond to this change \r (FAQ 5.3, Figure 1). Some parts of the climate system take time to react to a change in CO2 concentration, so \r a  decline in atmospheric CO2 as a  result of net negative emissions would not lead to immediate reversal of \r all climate change trends. Recent studies have shown that global surface temperature starts to decline within \r a few years following a decline in atmospheric CO2, although the decline would not be detectable for decades \r due to natural climate variability (see FAQ 4.2). Other consequences of human-induced climate change, such as \r reduction in permafrost area, would take decades; yet others, such as warming, acidification and oxygen loss \r of the deep ocean, would take centuries to reverse following a decline in the atmospheric CO2 concentration. \r [...]"


id: 4b99a19e476027fc10ce738fa6e7f613
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 10562
content:
"[...] Sea level would continue to rise for many centuries to millennia, even if large deliberate CO2 removals were \r successfully implemented."


id: fe0269e2e83db3f41ffbb422e68ce396
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 42056
content:
"The concept of a  remaining carbon budget implies that, to stabilize global warming at any particular level, \r global emissions of CO2 need to be reduced to net zero levels at some point. 'Net zero CO2 emissions' describes \r a  situation where all the anthropogenic emissions of CO2 are counterbalanced by deliberate anthropogenic \r removals so that, on average, no CO2 is added or removed from the atmosphere by human activities. Atmospheric \r CO2 concentrations in such a situation would gradually decline to a long-term stable level as excess CO2 in the \r atmosphere is taken up by ocean and land sinks (see FAQ 5.1). The concept of a remaining carbon budget also \r means that, if CO2 emissions reductions are delayed, deeper and faster reductions are needed later to stay within \r the same budget. If the remaining carbon budget is exceeded, this will result in either higher global warming \r [...]"


id: 1de641f582521feda7f1d2cebd68fcdf
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 2452
content:
"[...] or a need to actively remove CO2 from the atmosphere to reduce global temperatures back down to the desired \r level (see FAQ 5.3)."


id: bd7a8ed9fd0534acf6480fcafc5a4360
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 30595
content:
"Estimating the size of remaining carbon budgets depends on a set of choices. These choices include: (1) the global \r warming level that is chosen as a limit (for example, 1.5degC or 2degC relative to pre-industrial levels); (2) the probability \r with which we want to ensure that warming is held below that limit (for example, a one-in-two, two-in-three, \r or higher chance), and (3) how successful we are in limiting emissions of other greenhouse gases that affect the \r climate, such as methane or nitrous oxide. These choices can be informed by science, but ultimately represent \r subjective choices. Once these choices have been made, to estimate the remaining carbon budget for a given \r temperature goal, we can combine knowledge about: how much our planet has warmed already; the amount \r of warming per cumulative tonne of CO2; and the amount of warming that is still expected once global net \r [...]"


id: 1431784cdbcd3d47590953d082bd08a3
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 769
content:
"[...] CO2 emissions are brought down to zero. For example, to limit global warming to 1.5degC above pre-industrial \r levels with either a  one-in-two (50%) or two-in-three (67%) chance, the remaining carbon budgets amount \r to 500 and 400 billion tonnes of CO2, respectively, from 1 January 2020 onward (FAQ 5.4, Figure 1). Currently, \r human activities are emitting around 40 billion tonnes of CO2 into the atmosphere in a single year."


id: 258ccb6fbe56362bbc877ae3b7d90c90
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 3792
content:
"The remaining carbon budget depends on how much the world has already warmed to date. This past warming \r is caused by historical emissions, which are estimated by looking at the historical carbon budget - a scientific way \r to describe all past and present sources and sinks of CO2. It describes how the CO2 emissions from human activities \r have redistributed across the various CO2 reservoirs of the Earth system. These reservoirs are the ocean, the land \r vegetation, and the atmosphere (into which CO2 was emitted). The share of CO2 that is not taken up by the \r ocean or the land, and that thus increases the concentration of CO2 in the atmosphere, causes global warming. \r The historical carbon budget tells us that, of the about 2560 billion tonnes of CO2 that were released into the \r atmosphere by human activities between the years 1750 and 2019, about a quarter were absorbed by the ocean \r [...]"


id: 1252233d901473eb50abc922acc612c2
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 426
content:
"[...] (causing ocean acidification) and about a third by the land vegetation. About 45% of these emissions remain in \r the atmosphere (see FAQ 5.1). Adding these historical CO2 emissions to estimates of remaining carbon budgets \r allows an estimate of the total carbon budget consistent with a specific global warming level."


id: 39210f737f212294ac83504c1c625035
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 7260
content:
"Wenche Aas (Norway), Dimitris Akritidis (Greece), Robert J. Allen (United States of America), \r Nicolas Bellouin (United Kingdom/France), Sophie Berger (France/Belgium), Sara M. Blichner (Norway), \r Josep G. Canadell (Australia), William Collins (United Kingdom), Owen R. Cooper (United States \r of America), Frank J. Dentener (EU/The Netherlands), Sarah Doherty (United States of America), \r Jean-Louis Dufresne (France), Sergio Henrique Faria (Spain/Brazil), Piers Forster (United Kingdom), \r Tzung-May Fu (China), Jan S. Fuglestvedt (Norway), John C. Fyfe (Canada), Aristeidis K. Georgoulias \r (Greece), Matthew J. Gidden (Austria/United States of America), Nathan P. Gillett (Canada), \r Paul Ginoux (United States of America), Paul T. Griffiths (United Kingdom), Jian He (United States \r of America/China), Christopher Jones (United Kingdom), Svitlana Krakovska (Ukraine), \r Chaincy Kuo (United States of America), David S. Lee (United Kingdom), Maurice Levasseur \r [...]"


id: 4859c5a9239e5bf2b2439deaf3999b3
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 9940
content:
"[...] (Canada), Martine Lizotte (Canada), Thomas K. Maycock (United States of America), \r Jean-Francois Muller (Belgium), Helene Muri (Norway), Lee T. Murray (United States of America), \r Zebedee R. J. Nicholls (Australia), Jurgita Ovadnevaite (Ireland/Lithuania), Prabir K.  Patra \r (Japan/India), Fabien Paulot (United States of America/France, United States of America), \r Pallav Purohit (Austria/India), Johannes Quaas (Germany), Joeri Rogelj (United Kingdom/Belgium), \r Bjorn H.  Samset (Norway), Chris Smith (United Kingdom), Izuru Takayabu (Japan), \r Marianne  Tronstad Lund (Norway), Alexandra P. Tsimpidi (Germany/Greece), Steven Turnock \r (United Kingdom), Rita Van Dingenen (Italy/Belgium), Hua Zhang (China), Alcide Zhao \r (United Kingdom/China) "


id: d432db2276b0b01f62de77821f5ee575
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 34665
content:
"Short-lived climate forcers (SLCFs) affect climate and are, in most \r cases, also air pollutants. They include aerosols (sulphate, nitrate, \r ammonium, carbonaceous aerosols, mineral dust and sea spray), \r which are also called particulate matter (PM), and chemically \r reactive gases (methane, ozone, some halogenated compounds, \r nitrogen oxides, carbon monoxide, non-methane volatile organic \r compounds, sulphur dioxide and ammonia). Except for methane and \r some halogenated compounds whose lifetimes are about a decade \r or more, SLCF abundances are spatially highly heterogeneous since \r they only persist in the atmosphere from a few hours to a couple \r of months.  SLCFs are either radiatively active or influence the \r abundances of radiatively active compounds through chemistry \r (chemical adjustments), and their climate effect occurs predominantly \r in the first two decades after their emission or formation. They can \r [...]"


id: 65a681ffc92d1a34e05eabc28b6602b5
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 15147
content:
"[...] have either a  cooling or warming effect on climate, and they also \r affect precipitation and other climate variables. Methane and some \r halogenated compounds are included in climate treaties, unlike \r the other SLCFs that are nevertheless indirectly affected by climate \r change mitigation since many of them are often co-emitted with \r CO2 in combustion processes. This chapter assesses the changes, in \r the past and in a selection of possible futures, of the emissions and \r abundances of individual SLCFs primarily on global to continental \r scales, and how these changes affect the Earth's energy balance \r through radiative forcing and feedback in the climate system. \r The  attribution of climate and air-quality changes to emissions \r sectors and regions, and the effects of SLCF mitigations defined for \r various environmental purposes, are also assessed. "


id: 26a4a6768900d685d218b5b2f0cdba3b
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 3960
content:
"Over the last decade (2010-2019), strong shifts in the \r geographical distribution of emissions have led to changes \r in atmospheric abundances of highly variable SLCFs (high \r confidence). Evidence from satellite and surface observations \r shows strong regional variations in trends of ozone (O3), \r aerosols and their precursors (high confidence). In particular, \r tropospheric columns of nitrogen dioxide (NO2) and sulphur dioxide \r (SO2) continued to decline over North America and Europe (high \r confidence), and to increase over Southern Asia (medium confidence), \r but have declined over Eastern Asia (high confidence). Global \r carbon monoxide (CO) abundance has continued to decline (high \r confidence). The concentrations of hydrofluorocarbons (HFCs) are \r increasing (high confidence). Global carbonaceous aerosol budgets \r and trends remain poorly characterized due to limited observations, \r but sites representative of background conditions have reported \r [...]"


id: 4043c85b69b171558664bbb34651631a
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 8494
content:
"[...] multi-year declines in black carbon (BC) over several regions of the \r Northern Hemisphere. {6.2, 6.3, 2.2.4, 2.2.5, 2.2.6}"


id: ba49000911361fa1a597c6e3021f5cfd
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 29993
content:
"In the next two decades, it is very likely that the SLCF \r emissions changes in the WGI core set of SSPs will cause \r a  warming relative to 2019, whatever the SSPs, in addition \r to the warming from long-lived greenhouse gases. The net \r effect of SLCF and hydrofluorocarbon (HFC) changes on \r GSAT across the SSPs is a likely warming of 0.06degC-0.35degC in \r 2040 relative to 2019. Warming over the next two decades \r is quite similar across the SSPs due to competing effects of \r warming (methane, ozone) and cooling (aerosols) SLCFs. For \r the scenarios with the most stringent climate and air pollution \r mitigations (SSP1-1.9 and SSP1-2.6), the likely near-term warming \r from the SLCFs is predominantly due to sulphate aerosol reduction, \r but this effect levels off after 2040. In the absence of climate change \r policies and with weak air pollution control (SSP3-7.0), the likely\r near-term warming due to changes in SLCFs is predominantly due \r [...]"


id: ef1ef099e4a70b155df2e0a421b94234
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 39441
content:
"[...] to increases in methane, ozone and HFCs, with smaller contributions \r from changes in aerosols. SSP5-8.5 has the highest SLCF-induced \r warming rates due to warming from methane and ozone increases \r and reduced aerosols due to stronger air pollution control compared \r to the SSP3-7.0 scenario. {6.7.2}"


id: c0e59f0e061516e1b0bf09758ca3fd7d
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 31240
content:
"Future air quality (in term of surface ozone and PM \r concentrations) on global to local scales will be primarily \r driven by changes in precursor emissions as opposed to \r climate change (high confidence) and climate change is \r projected to have mixed effects. A warmer climate is expected \r to reduce surface ozone in regions remote from pollution sources \r (high confidence) but is expected to increase it by a few parts per \r billion over polluted regions, depending on ozone precursor levels \r (medium to high confidence). Future climate change is expected to \r have mixed effects, positive or negative, with an overall low effect, \r on global surface PM and more generally on the aerosol global \r burden (medium confidence), but stronger effects are not excluded in \r regions prone to specific meteorological conditions (low confidence). \r Overall, there is low confidence in the response of surface ozone and \r PM to future climate change due to the uncertainty in the response \r [...]"


id: 29a63d6b78e2b0b69fbae2981ca30fe4
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 4554
content:
"[...] of the natural processes (e.g., stratosphere-troposphere exchange, \r natural precursor emissions, particularly including biogenic volatile "


id: 2fed0c4bf99cd026b34d113b54830a64
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 5685
content:
"Over time scales of 10 to 20 years, the global temperature \r response to a year's worth of current emissions of SLCFs is at \r least as large as that due to a year's worth of CO2 emissions \r (high confidence). Sectors producing the largest SLCF-induced \r warming are those dominated by methane emissions: fossil \r fuel production and distribution, agriculture and waste \r management (high confidence). On these time scales, SLCFs \r with cooling effects can significantly mask the CO2 warming in the \r case of fossil fuel combustion for energy and land transportation, \r or completely offset the CO2 warming and lead to an overall net \r cooling in the case of industry and maritime shipping (prior to the \r implementation of the revised fuel-sulphur limit policy for shipping \r in 2020) (medium confidence). Ten years after a one-year pulse of \r present-day aviation emissions, SLCFs induce strong but short-lived \r warming contributions to the GSAT response (medium confidence), \r [...]"


id: 62e9beb54fdda64448e57819a14b6ed1
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 14702
content:
"[...] while CO2 both gives a warming effect in the near term and dominates \r the long-term warming impact (high-confidence). {6.6.1, 6.6.2}"


id: 97a6a5f63ab68896fa92d667a814443d
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 23899
content:
"Future changes in SLCFs are expected to cause additional \r warming. This warming is stable after 2040 in scenarios leading \r to lower global air pollution as long as methane emissions \r are also mitigated, but the overall warming induced by SLCF \r changes is higher in scenarios in which air quality continues to \r deteriorate (induced by growing fossil fuel use and limited air \r pollution control) (high confidence). If a strong air pollution control \r resulting in reductions in anthropogenic aerosols and non-methane \r ozone precursors was considered in SSP3-7.0, it would lead to a likely\r additional near-term global warming of 0.08 [0.00  to  0.10]  degC in \r 2040. An additional concomitant methane mitigation (consistent \r with SSP1's stringent climate change mitigation policy implemented \r in the SSP3 world) would not only alleviate this warming but would \r turn this into a cooling of 0.07degC with a likely range of [-0.02 to \r [...]"


id: 3dc594390dbe55e5ea37ed9e3885725
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 8031
content:
"[...] +0.14] degC (compared with SSP3-7.0 in 2040). Across the SSPs, the \r collective reduction of methane, ozone precursors and HFCs can \r make a difference of 0.2degC with a very likely range of [0.1 to 0.4] degC \r in 2040 and 0.8degC with a very likely range of [0.5 to 1.3] degC at the \r end of the 21st century (comparing SSP3-7.0 and SSP1-1.9), which is \r substantial in the context of the Paris Agreement. Sustained methane \r mitigation, wherever it occurs, stands out as an option that combines \r near- and long-term gains on surface temperature (high confidence)\r and leads to air-quality benefits by reducing surface ozone levels \r globally (high confidence). {6.6.3, 6.7.3, 4.4.4} "


id: 6baef22ed1fb3e5beac51439cb7a63fc
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 16253
content:
"The effects of SLCFs decay rapidly over the first few decades \r after pulse emission. Consequently, on time scales longer \r than about 30 years, the net long-term global temperature \r effects of sectors and regions are dominated by CO2 (high \r confidence). The global mean temperature response following \r a climate change mitigation measure that affects emissions of both \r short- and long-lived climate forcers depends on their atmospheric \r decay times, how fast and for how long the emissions are reduced, \r and the inertia in the climate system. For SLCFs including methane, \r the rate of emissions drives the long-term global temperature effect, \r as opposed to CO2 for which the long-term global temperature effect \r is controlled by the cumulative emissions. About 30 years or more \r after a one-year emission pulse occurs, the sectors contributing the \r most to global warming are industry, fossil fuel combustion for energy \r [...]"


id: ef0c36f6a8f40365e011bf2f5a0975c9
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 39424
content:
"[...] and land transportation, essentially through CO2 (high confidence). \r Current emissions of SLCFs, CO2 and N2O from Eastern Asia and North \r America are the largest regional contributors to additional net future \r warming on both short (medium confidence) and long time scales \r (high confidence). {6.6.1, 6.6.2}"


id: 967adbd5c4234b0b4ed1219c22d0b2ca
created_at: 2023-04-19 13:22:37
updated_at: 2023-04-19 13:22:37
content_type: text
index: climate_gpt_v2
vector_id: 23677
content:
"Rapid decarbonization strategies lead to air-quality \r improvements but are not sufficient to achieve, in the near \r term, air-quality guidelines set for fine PM by the World Health \r Organization (WHO), especially in parts of Asia and in some \r other highly polluted regions (high confidence). Additional \r methane and BC mitigation would contribute to offsetting the \r additional warming associated with SO2 reductions that would \r accompany decarbonization (high confidence). Strong air pollution \r control as well as strong climate change mitigation, implemented \r separately, lead to large reductions in exposure to air pollution by the \r end of the century (high confidence). Implementation of air pollution \r controls, relying on the deployment of existing technologies, leads \r more rapidly to air quality benefits than climate change mitigation \r (high confidence), which requires systemic changes. However, \r in both cases, significant parts of the population are projected to \r [...]"


