# document

id: 3526a84bd382230185dd908b6cde2bdf
created_at: 2023-04-19 13:22:33
updated_at: 2023-04-19 13:22:33
content_type: text
index: climate_gpt_v2
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content:
"The Appendix to Chapter  1  (Appendix 1A) lists the key detection \r and attribution statements in the Summaries for Policymakers of \r WGI reports since 1990. The evolution of these statements over \r time reflects the improvement of scientific understanding and the \r corresponding decrease in uncertainties regarding human influence. \r The Second Assessment Report (SAR) stated that 'the balance of \r evidence suggests a discernible human influence on global climate' \r (IPCC, 1995b). Five years later, the Third Assessment Report (TAR) \r concluded that 'there is new and stronger evidence that most \r of the warming observed over the last 50  years is attributable to \r human activities' (IPCC, 2001b). The AR4 further strengthened \r previous statements, concluding that 'most of the observed increase \r in global average temperatures since the mid-20th century is very \r likely due to the observed increase in anthropogenic greenhouse \r [...]"


id: 3828daf763d3c5f4b6810892c557b8a6
created_at: 2023-04-19 13:22:33
updated_at: 2023-04-19 13:22:33
content_type: text
index: climate_gpt_v2
vector_id: 7084
content:
"[...] gas concentrations' (IPCC, 2007b). The AR5 assessed that a human \r contribution had been detected in: changes in warming of the \r atmosphere and ocean; changes in the global water cycle; reductions \r in snow and ice; global mean sea level rise; and changes in some \r climate extremes. The AR5 concluded that 'it is extremely likely that \r human influence has been the dominant cause of the observed \r warming since the mid-20th century' (IPCC, 2013b)."


id: 4d9b57300aa1e7ebac048663b6785d37
created_at: 2023-04-19 13:22:33
updated_at: 2023-04-19 13:22:33
content_type: text
index: climate_gpt_v2
vector_id: 10930
content:
"The FAR (IPCC, 1990a) concluded that while both theory and models \r suggested that anthropogenic warming was already well underway, \r its signal could not yet be detected in observational data against the \r 'noise' of natural variability (see also Section 1.4.2; and Barnett and \r Schlesinger, 1987). Since then, increased warming and progressively \r more conclusive attribution studies have identified human activities \r as the 'dominant cause of the observed warming since the mid\u000220th  century' (IPCC, 2013b). 'Fingerprint' studies seek to detect \r specific observed changes - expected from theoretical understanding \r and model results - that could not be explained by natural drivers \r alone, and to attribute statistically the proportion of such changes \r that is due to human influence. These include global-scale surface \r warming, nights warming faster than days, tropospheric warming \r and stratospheric cooling, a rising tropopause, increasing ocean heat \r [...]"


id: 29695f46a6961df38f5196e37559ce2
created_at: 2023-04-19 13:22:33
updated_at: 2023-04-19 13:22:33
content_type: text
index: climate_gpt_v2
vector_id: 4505
content:
"[...] content, changed global patterns of precipitation and sea level air \r pressure, increasing downward longwave radiation, and decreasing \r upward longwave radiation (Hasselmann, 1979; Karoly et al., 1994; \r Schneider, 1994; Santer et al., 1995, 2013; Hegerl et al., 1996, 1997; \r Gillett et al., 2003; Santer, 2003; Zhang et al., 2007; Stott et al., 2010; \r Davy et al., 2017; Mann et al., 2017). The Cross-Working Group Box \r on Attribution outlines attribution methods and uses from across \r AR6, now including event attribution (specifying the influence of \r climate change on individual extreme events such as floods, or on \r the frequency of classes of events such as tropical cyclones). Overall, \r the evidence for human influence has grown substantially over time \r and from each IPCC report to the next."


id: 4d30b68bdecf4f7e30bf736ce417c521
created_at: 2023-04-19 13:22:33
updated_at: 2023-04-19 13:22:33
content_type: text
index: climate_gpt_v2
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"Many different sets of climate projections have been produced over \r the past several decades, so it is valuable to assess how well those \r projections have compared against subsequent observations. Consistent \r findings build confidence in the process of making projections for the \r future. For example, Stouffer and Manabe (2017) compared projections \r made in the early 1990s with subsequent observations. They found that \r the projected surface pattern of warming, and the vertical structure of \r temperature change in both the atmosphere and ocean, were realistic. \r Rahmstorf et al. (2007, 2012) examined projections of global surface \r temperature and GMSL assessed by TAR and AR4 and found that \r the global surface temperature projections were in good agreement \r with the subsequent observations, but that sea level projections \r were underestimates compared to subsequent observations. The \r AR5 WGI also examined earlier IPCC assessment reports to evaluate \r [...]"


id: 435cf74bc34e3f4a6055437421fb496e
created_at: 2023-04-19 13:22:33
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content_type: text
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vector_id: 9068
content:
"[...] their projections of how global surface temperature and GMSL would \r change (Cubasch et al., 2013) with similar conclusions."


id: 34618ec5055b046fc989ec6d86bfe698
created_at: 2023-04-19 13:22:33
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content_type: text
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"The AR5 WGI assessed that there is a close relationship of cumulative \r total emissions of CO2 and GMST response that is approximately \r linear (IPCC, 2013b). This finding implies that continued emissions \r of CO2 will cause further warming and changes in all components of \r the climate system, independent of any specific scenario or pathway. \r Scenario-based climate projections using the Representative \r Concentration Pathways (RCPs) assessed in AR5 WGI result in \r continued warming over the 21st century in all scenarios except \r a  strong climate change mitigation scenario (RCP2.6). Similarly, \r under all RCP scenarios, AR5 assessed that the rate of sea level rise \r over the 21st  century will very likely exceed that observed during \r 1971-2010 due to increased ocean warming and increased loss of \r mass from glaciers and ice sheets. Further increases in atmospheric \r CO2 will also lead to further uptake of carbon by the ocean, which will \r [...]"


id: ae49da277ca8f0e6c3cf44a729de582d
created_at: 2023-04-19 13:22:33
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"[...] increase ocean acidification. By the mid-21st century the magnitudes \r of the projected changes are substantially affected by the choice of \r scenario. The set of scenarios used in climate change projections \r assessed as part of AR6 is discussed in Section 1.6."


id: 2a421813e865ad54e05b45671548fbb0
created_at: 2023-04-19 13:22:33
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"Although this approach has limitations when the modelled forcings \r differ greatly from the forcings subsequently experienced, they \r were generally able to project actual future global warming when \r the mismatches between forecast and observed radiative forcings \r are accounted for. For example, Scenario B  presented in Hansen \r et al. (1988) projected around 50% more warming than has been \r observed during the 1988-2017 period, but this is largely because \r it overestimated subsequent radiative forcings. Similarly, while FAR \r (IPCC, 1990a) projected a higher rate of global surface temperature \r warming than has been observed, this is largely because it \r overestimated future GHG concentrations: FAR's projected increase in \r total anthropogenic forcing between 1990 and 2017 was 1.6 W m-2, \r while the observational estimate of actual forcing during that \r period is 1.1 W m-2 (Dessler and Forster, 2018). Under these actual \r [...]"


id: d626cc427a15a4b4ab2a1ad5d334a3bd
created_at: 2023-04-19 13:22:33
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"[...] forcings, the change in temperature in FAR aligns with observations \r (Hausfather et al., 2020)."


id: 3f9cf65e20b0b5021f6c875ad3f65c86
created_at: 2023-04-19 13:22:33
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"The SR1.5 estimated with high confidence that human activities caused a  global warming of approximately 1degC between the \r 1850-1900 period and 2017. For the period 2006-2015, observed global mean surface temperature (GMST7) was 0.87degC +- 0.12degC \r higher than the average over the 1850-1900 period (very high confidence). Anthropogenic global warming was estimated to be \r increasing at 0.2 +- 0.1degC per decade (high confidence) and likely matches the level of observed warming to within +-20%. The SRCCL \r found with high confidence that over land, mean surface air temperature increased by 1.53degC +- 0.15degC between 1850-1900 and \r 2006-2015, or nearly twice as much as the global average. This observed warming has already led to increases in the frequency and \r intensity of climate and weather extremes in many regions and seasons, including heat waves in most land regions (high confidence), \r [...]"


id: 7ecf341b3257c595e57cf2211aeac904
created_at: 2023-04-19 13:22:33
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"[...] increased droughts in some regions (medium confidence), and increases in the intensity of heavy precipitation events at the global \r scale (medium confidence). These climate changes have contributed to desertification and land degradation in many regions (high \r confidence). Increased urbanization can enhance warming in cities and their surroundings (heat island effect), especially during heat \r waves (high confidence), and intensify extreme rainfall (medium confidence)."


id: 3ffaec2a4144522c8195a240e7a345bc
created_at: 2023-04-19 13:22:33
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"Concerning the cryosphere, SROCC reported widespread continued shrinking of nearly all components. Mass loss from the Antarctic \r Ice Sheet tripled over the period 2007-2016 relative to 1997-2006, while mass loss doubled for the Greenland Ice Sheet (likely,\r medium confidence). The Report concludes with very high confidence that due to the combined increased loss from the ice sheets, \r global mean sea level (GMSL) rise has accelerated (extremely likely). The rate of recent GMSL rise (3.6 +- 0.5 mm yr-1 for 2006-2015) is \r about 2.5 times larger than for 1901-1990. The report also found that Arctic sea ice extent has very likely decreased for all months of \r the year since 1979 and that September sea ice reductions of 12.8 +- 2.3% per decade are likely unprecedented for at least 1000 years. \r Feedbacks from the loss of summer sea ice and spring snow cover on land have contributed to amplified warming in the Arctic (high \r [...]"


id: 367128a6c9b5a8582d37cb014e83533a
created_at: 2023-04-19 13:22:33
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"[...] confidence), where surface air temperature likely increased by more than double the global average over the last two decades. By \r contrast, Antarctic sea ice extent overall saw no statistically significant trend for the period 1979-2018 (high confidence)."


id: a2ee5c924a835d974484e103054f583b
created_at: 2023-04-19 13:22:33
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"The SROCC projected that global-scale glacier mass loss, permafrost thaw, and decline in snow cover and Arctic sea ice extent will \r continue in the period 2031-2050 due to surface air temperature increases (high confidence). The Greenland and Antarctic ice sheets \r are projected to lose mass at an increasing rate throughout the 21st century and beyond (high confidence). Sea level rise will also \r continue at an increasing rate. For the period 2081-2100 with respect to 1986-2005, the likely ranges of GMSL rise are projected \r at 0.26-0.53 m for RCP2.6 and 0.51-0.92 m for RCP8.5. For the RCP8.5 scenario, projections of GMSL rise by 2100 are higher by \r 0.1 m than in AR5 due to a larger contribution from the Antarctic Ice Sheet (medium confidence). Extreme sea level events that \r occurred once per hundred years in the recent past are projected to occur at least once per year at many locations by 2050, especially \r [...]"


id: d772f668d01449e3d3720d0f315f26b
created_at: 2023-04-19 13:22:33
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"[...] in tropical regions, under all RCP scenarios (high confidence). According to SR1.5, by 2100 GMSL rise would be around 0.1 m lower \r with 1.5degC global warming compared to 2degC (medium confidence). If warming is held to 1.5degC, GMSL will still continue to rise well \r beyond 2100, but at a slower rate and a lower magnitude. However, instability and/or irreversible loss of the Greenland and Antarctic \r ice sheets, resulting in a multi-metre rise in sea level over hundreds to thousands of years, could be triggered at 1.5degC-2degC of global \r warming (medium confidence). According to SROCC, sea level rise in an extended RCP2.6 scenario would be limited to around 1 m in \r 2300 (low confidence) while under RCP8.5 multi-metre sea level rise is projected by then (medium confidence)."


id: aa7b4a827695097ff5ee0acf1e4e9c72
created_at: 2023-04-19 13:22:33
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"The AR6 WGI builds on previous assessments using well established \r foundations and concepts. This section highlights some of the cross\u0002cutting methods applied in the climate change literature and topics \r discussed repeatedly throughout this Report. First, the choices related \r to 'baselines', or 'reference periods', are highlighted (Section 1.4.1), \r including a specific discussion on the pre-industrial baseline used in \r AR6 WGI (Cross-Chapter Box 1.2). The relationships between long\u0002term trends, climate variability and the concept of 'emergence of \r changes' (Section  1.4.2) and the sources of uncertainty in climate \r simulations (Section  1.4.3) are discussed next. The topic of low\u0002likelihood outcomes, storylines, abrupt changes and surprises follows \r (Section  1.4.4), including a  description of AR6 WGI risk framing \r (Cross-Chapter Box 1.3). The Cross-Working Group Box on Attribution \r describes attribution methods, including those for extreme events. \r [...]"


id: ed899b9e86025366bb83fc2724959e30
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"[...] Various sets of geographical regions used in later chapters are also \r defined and introduced (Section 1.4.5)."


id: 205e8c9c8660866873c041727695bb0a
created_at: 2023-04-19 13:22:33
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"Third, different datasets can have different absolute values for the \r same climate variable that should be removed to allow effective \r comparisons of variations over time. This is often required when \r comparing climate simulations with each other, or when comparing \r simulations with observations, as simulated climate variables are \r also affected by model bias that can be removed when they are \r presented as anomalies. It can also be required when comparing \r observational datasets or reanalyses (Section 1.5.2) with each other, \r due to systematic differences in the underlying measurement system \r (Figure 1.11). Understanding the reasons for any absolute difference \r is important, but whether the simulated absolute value matters when \r projecting future change will depend on the variable of interest. For \r example, there is not a strong relationship between climate sensitivity \r of a model (which is an indicator of the degree of future warming) \r [...]"


id: 87a7e71bb98a25363053a33198cd4223
created_at: 2023-04-19 13:22:33
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"[...] and the simulated absolute global surface temperature (Mauritsen \r et al., 2012; Hawkins and Sutton, 2016)."


id: 68577e2c887b4d8329e33cc328f37dfc
created_at: 2023-04-19 13:22:33
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"The Paris Agreement aims to limit global temperatures to specific thresholds 'above pre-industrial levels'. In AR6 WGI, as in previous \r IPCC reports, observations and projections of changes in global temperature are generally expressed relative to 1850-1900 as an \r approximate pre-industrial state (SR1.5, IPCC, 2018). This is a pragmatic choice based upon data availability considerations, though \r both anthropogenic and natural changes to the climate occurred before 1850. The remaining carbon budgets, the chance of crossing \r global temperature thresholds, and projections of extremes and sea level rise at a  particular level of global warming can all be \r sensitive to the chosen definition of the approximate pre-industrial baseline (Millar et al., 2017b; Schurer et al., 2017; Pfleiderer et al., \r 2018; Rogelj et al., 2019; Tokarska et al., 2019). This Cross-Chapter Box assesses the evidence on change in radiative forcing and global \r [...]"


id: 1abd8a1b5c8a4abac7da7a576b211306
created_at: 2023-04-19 13:22:33
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"[...] temperature from the period around 1750 to 1850-1900; variations in the climate before 1750 are discussed in Chapter 2."


id: 8964c27009b87152d10138d4f48b1e84
created_at: 2023-04-19 13:22:33
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"Although there is some evidence for human influence on climate before 1750 (e.g., Ruddiman and Thomson, 2001; Koch et al., 2019), \r the magnitude of the effect is still disputed (Section 5.1.2.3; e.g., Joos et al., 2004; J. Beck et al., 2018), and most studies analyse \r the human influence on climate over the industrial period. Historically, the widespread use of coal-powered machinery started the \r Industrial Revolution in Britain in the late 18th century (Ashton, 1997), but the global effects were small for several decades. In line \r with this, previous IPCC assessment reports considered changes in radiative forcing relative to 1750, and temperature changes were \r often reported relative to the 'late 19th century'. The AR5 and SR1.5 made the specific pragmatic choice to approximate pre-industrial \r global temperatures by using the average of the 1850-1900 period, when permanent surface observing networks emerged that \r [...]"


id: 9d673c4c1e97ae38db189c02faf187d2
created_at: 2023-04-19 13:22:33
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"[...] provide sufficiently accurate and continuous measurements on a near-global scale (Sections 1.3.1 and 2.3.1.1), and because model \r simulations of the historical period used 1850 as their start date. For the same reasons, to ensure continuity with previous assessments, \r and because of larger uncertainties and lower confidence in climatic changes before 1850 than after, AR6 makes the same choice to \r approximate pre-industrial global temperatures by using the the average of the 1850-1900 period."


id: f62e1cc25bfde3f02bfe8da70c2208a7
created_at: 2023-04-19 13:22:33
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content:
"Here we assess improvements in our understanding of climatic changes in the period 1750-1850. Anthropogenic influences on \r climate between 1750 and 1900 were primarily increased anthropogenic GHG and aerosol emissions, and changes in land use. \r Between 1750 and 1850 atmospheric CO2 levels increased from about 278 ppm to about 285 ppm (equivalent to around 3 years \r of current rates of increase; Chapter 2, Section 2.2.3), corresponding to about 55 GtCO2 in the atmosphere. Estimates of emissions \r from fossil fuel burning (about 4 GtCO2, Boden et al., 2017) cannot explain the pre-1850 increase, so CO2 emissions from land-use \r changes are implicated as the dominant source. The atmospheric concentration of other GHGs also increased over the same period, \r and there was a cooling influence from other anthropogenic radiative forcings (such as aerosols and land-use changes), but with \r [...]"


id: bb024e47e33790ff07bffbc3294ed97f
created_at: 2023-04-19 13:22:33
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content:
"[...] a  larger uncertainty than for GHGs (Sections  2.2.6 and 7.3.5.2, and Cross-Chapter Box  1.2, Figure  1; e.g.,  Carslaw et al., 2017; "


id: bb95a67adf71a2d1f51770d315875287
created_at: 2023-04-19 13:22:33
updated_at: 2023-04-19 13:22:33
content_type: text
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vector_id: 30225
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"Several studies since AR5 have estimated changes in global temperatures following industrialisation and before 1850. Hawkins \r et al. (2017) used observations, radiative forcing estimates and model simulations to estimate the warming from 1720-1800 until \r 1986-2005 and assessed a  likely range of 0.55degC-0.80degC, slightly broader than the equivalent range starting from 1850-1900 \r (0.6degC-0.7degC). From proxy evidence, PAGES 2k Consortium (2019) found that GMST for 1850-1900 was 0.02 [-0.22 to +0.16] degC \r warmer than the 30-year period centred on 1750. Schurer et al. (2017) used climate model simulations of the last millennium to \r estimate that the increase in GHG concentrations before 1850 caused an additional likely range of 0.0degC -0.2degC global warming \r when considering multiple reference periods. Haustein et al. (2017) implies an additional warming of around 0.05degC attributable \r [...]"


id: 63df8ceb829ab6b73fe4c8f9d270b55
created_at: 2023-04-19 13:22:33
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vector_id: 14858
content:
"[...] to human activity from 1750 to 1850-1900, and the AR6 emulator (Section 7.3.5.3) estimates the likely range of this warming to \r be 0.04degC-0.14degC."


id: 187def625a5f266ae0f3721d23bc3e93
created_at: 2023-04-19 13:22:33
updated_at: 2023-04-19 13:22:33
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index: climate_gpt_v2
vector_id: 1520
content:
"see Section  1.5.4.2). A  set of illustrative examples using one such \r large ensemble (Maher et al., 2019) demonstrates how variability \r can influence trends on decadal time scales (Figure 1.13). The long\u0002term anthropogenic trends in this set of climate indicators are clearly \r apparent when considering the ensemble as a whole (grey shading), \r and all the individual ensemble members have very similar trends \r for ocean heat content (OHC), which is a robust estimate of the total \r energy stored in the climate system (e.g., Palmer and McNeall, 2014). \r However, the individual ensemble members can exhibit very different \r decadal trends in global surface air temperature (GSAT), UK summer \r temperatures, and Arctic sea ice variations. More specifically, for \r a representative 11-year period, both positive and negative trends \r can be found in all these surface indicators, even though the long\u0002term trend is for increasing temperatures and decreasing sea ice. \r [...]"


id: b38038d09f82b3473539c8c47953fd1c
created_at: 2023-04-19 13:22:33
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"[...] Periods in which the long-term trend is substantially masked or \r enhanced for more than 20 years are also visible in these regional \r examples. This highlights the fact that observations are expected \r to exhibit short-term trends which are larger or smaller than the \r long-term trend or that differ from the average projected trend from \r climate models, especially on continental spatial scales or smaller \r (Cross-Chapter Box  3.1). The actual observed trajectory can be \r considered as one realization of many possible alternative worlds \r that experienced different weather; this is also demonstrated by "


id: 5457e016e3a5219ecf3ad762bd5826b
created_at: 2023-04-19 13:22:33
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content:
"of a  climate change signal or trend refers to when a  change in \r climate (the 'signal') becomes larger than the amplitude of natural \r or internal variations (defining the 'noise'). This concept is often \r expressed as a 'signal-to-noise' ratio (S/N) and emergence occurs at \r a defined threshold of this ratio (e.g., S/N >1 or 2). Emergence can be \r estimated using observations and/or model simulations and can refer \r to changes relative to a historical or modern baseline (Section 12.5.2 \r and Glossary). The concept can also be expressed in terms of time \r (the 'time of emergence'; Glossary) or in terms of a global warming \r level (Section  11.2.5; Kirchmeier-Young et al., 2019) and is also \r used to refer to a time when we can expect to see a response of \r mitigation activities that reduce emissions of GHGs or enhance \r their sinks (emergence with respect to mitigation; Section 4.6.3.1). \r Whenever possible, emergence should be discussed in the context \r [...]"


id: 2013dfa00d3973d1727238d8bb89666a
created_at: 2023-04-19 13:22:33
updated_at: 2023-04-19 13:22:33
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index: climate_gpt_v2
vector_id: 2862
content:
"[...] of a clearly defined level of S/N or other quantification, such as 'the \r signal has emerged at the level of S/N >2', rather than as a simple \r binary statement. For an extended discussion, see Chapter  10 \r (Section 10.4.3)."


id: 46be15c8e24f9ab4b817cc83ad2243dd
created_at: 2023-04-19 13:22:33
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index: climate_gpt_v2
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"Although the magnitude of any change is important, regions which \r have a larger signal of change relative to the background variations \r will potentially face greater risks than other regions, as they will \r see unusual or novel climate conditions more quickly (Frame et al., \r 2017). As in Figure 1.14, the signal of temperature change is often \r smaller in tropical countries, but their lower amplitude of variability \r means they may experience the effects of climate change earlier \r than the mid-latitudes. In addition, these tropical countries are \r often among the most exposed, due to large populations (Lehner \r and Stocker, 2015), and often more vulnerable (Harrington et al., \r 2016; Harrington and Otto, 2018; Russo et al., 2019). Higher levels \r of exposure and vulnerability increase the risk from climate\u0002related impacts (Cross-Chapter Box 1.3). The rate of change is also \r important for many hazards (e.g., Loarie et al., 2009). Providing more \r [...]"


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created_at: 2023-04-19 13:22:33
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vector_id: 31374
content:
"[...] information about changes and variations on regional scales, and \r the associated attribution to particular causes (Cross-Working Group \r Box: Attribution), is therefore important for adaptation planning."


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created_at: 2023-04-19 13:22:33
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vector_id: 12466
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"Since AR5, the emergence of projected future changes has also \r been extensively examined, in variables including surface air \r temperature (Hawkins and Sutton, 2012; Kirtman et al., 2013; \r Tebaldi and Friedlingstein, 2013), ocean temperatures and salinity \r (Banks and Wood, 2002), mean precipitation (Giorgi and Bi, 2009; \r Maraun, 2013), drought (Orlowsky and Seneviratne, 2013), extremes \r (Diffenbaugh and Scherer, 2011; Fischer et al., 2014; King et al., 2015; \r Schleussner and Fyson, 2020), and regional sea level change (Lyu \r et al., 2014). The concept has also been applied to climate change \r impacts such as effects on crop growing regions (Rojas et al., 2019). \r In AR6, the emergence of oceanic signals such as regional sea \r level change and changes in water mass properties is assessed in \r Chapter 9 (Section 9.6.1.4); emergence of future regional changes is \r assessed in Chapter 10 (Section 10.4.3); the emergence of extremes \r [...]"


id: 85da1ac332ce3a8ee35ec35439f4d0ab
created_at: 2023-04-19 13:22:33
updated_at: 2023-04-19 13:22:33
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index: climate_gpt_v2
vector_id: 20693
content:
"Uncertainties also exist regarding past emissions and radiative \r forcings. These are especially important for simulations of paleoclimate \r time periods, such as the Pliocene, Last Glacial Maximum or the last \r millennium, but are also relevant for the CMIP historical simulations \r of the instrumental period since 1850. In particular, historical \r radiative forcings due to anthropogenic and natural aerosols are less \r well constrained by observations than the GHG radiative forcings. \r There is also uncertainty in the size of large volcanic eruptions (and \r in the location for some that occurred before around 1850), and the \r amplitude of changes in solar activity, before satellite observations. \r The role of historical radiative forcing uncertainty was considered \r previously (Knutti et al., 2002; Forster et al., 2013) but, since AR5, \r specific simulations have been performed to examine this issue, \r particularly for the effects of uncertainty in anthropogenic aerosol \r [...]"


id: 343e10d640bfbda17e1708be3dae3926
created_at: 2023-04-19 13:22:33
updated_at: 2023-04-19 13:22:33
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index: climate_gpt_v2
vector_id: 6427
content:
"[...] radiative forcing (e.g.,  Jimenez-de-la-Cuesta and Mauritsen, 2019; \r Dittus et al., 2020)."


id: f89d884b4a28f5d4cccae1d477e3170a
created_at: 2023-04-19 13:22:33
updated_at: 2023-04-19 13:22:33
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index: climate_gpt_v2
vector_id: 41106
content:
"Under any particular scenario (Section 1.6.1), there is uncertainty in \r how the climate will respond to the specified emissions or radiative \r forcing combinations. A  range of climate models is often used to \r estimate the range of uncertainty in our understanding of the key \r physical processes and to define the 'model response uncertainty' \r (Sections 1.5.4 and 4.2.5). However, this range does not necessarily \r represent the full 'climate response uncertainty' in how the climate \r may respond to a particular radiative forcing or emissions scenario. \r This is because, for example, the climate models used in CMIP \r experiments have structural uncertainties not explored in a typical \r multi-model exercise (e.g., Murphy et al., 2004) and are not entirely \r independent of each other (Section 1.5.4.8; Masson and Knutti, 2011; \r Abramowitz et al., 2019); there are small spatial-scale features which \r cannot be resolved; and long time-scale processes or tipping points \r [...]"


id: 495106f798f6eab8ed616688d5133db
created_at: 2023-04-19 13:22:33
updated_at: 2023-04-19 13:22:33
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index: climate_gpt_v2
vector_id: 10122
content:
"[...] are not fully represented. Section 1.4.4 discusses how some of these \r issues can still be considered in a risk assessment context. For some \r metrics, such as equilibrium climate sensitivity (ECS), the CMIP6 \r model range is found to be broader than the very likely range assessed \r by combining multiple lines of evidence (Sections 4.3.4 and 7.5.6)."


id: e2471f4c31becf2e5f5cc0112f036c1b
created_at: 2023-04-19 13:22:33
updated_at: 2023-04-19 13:22:33
content_type: text
index: climate_gpt_v2
vector_id: 37095
content:
"The relative uncertainty due to internal variability and model \r uncertainty increases for smaller spatial scales. In the regional \r example shown in Figure 1.15 for changes in temperature, the same \r scenario and model combination has produced two simulations \r which differ by 1degC in their projected 2081-2100 averages due solely \r to internal climate variability. For regional precipitation changes, \r emissions scenario uncertainty is often small relative to model \r response uncertainty. In the example shown in Figure 1.15, the SSPs \r overlap considerably, but SSP1-1.9 shows the largest precipitation \r change in the near term, even though global mean temperature \r warms the least; this is due to differences between regional aerosol \r emissions projected in this and other scenarios (Wilcox et al., 2020). \r These cascades of uncertainty would branch out further if applying the \r projections to derive estimates of changes in hazard (e.g., Wilby and \r [...]"


id: 5b90635d24fbaf16ef17c6dccf98b127
created_at: 2023-04-19 13:22:33
updated_at: 2023-04-19 13:22:33
content_type: text
index: climate_gpt_v2
vector_id: 13355
content:
"In the broader IPCC context, the term 'scenario storyline' refers to \r a narrative description of one or more scenarios, highlighting their \r main characteristics, relationships between key driving forces and \r the dynamics of their evolution (e.g., emissions of short-lived climate \r forcers assessed in Chapter  6  are driven by 'scenario storylines'; \r see Section  1.6). The AR6 WGI is mainly concerned with 'physical \r climate storylines'. A  physical climate storyline is a  self-consistent \r and plausible physical trajectory of the climate system, or a weather \r or climate event, on time scales from hours to multiple decades \r (T.G. Shepherd et al., 2018). This approach can be used to constrain \r projected changes or specific events on specified explanatory \r elements such as projected changes of large-scale indicators \r (Box  10.2). For example, Hazeleger et al. (2015) suggested using \r 'tales of future weather', blending numerical weather prediction with \r [...]"


id: 6e7469b1bbe30e7a2215ca4ab6d987e8
created_at: 2023-04-19 13:22:33
updated_at: 2023-04-19 13:22:33
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index: climate_gpt_v2
vector_id: 16697
content:
"[...] a  climate projection to illustrate the potential behaviour of future \r high-impact events (also see Hegdahl et al., 2020). Several studies \r describe how possible large changes in atmospheric circulation "


id: 8fbcca72bceb1950bd432e3bc9ccefd8
created_at: 2023-04-19 13:22:33
updated_at: 2023-04-19 13:22:33
content_type: text
index: climate_gpt_v2
vector_id: 22496
content:
"In the AR6 WGI Assessment Report, these different storyline \r approaches are used in several places (see Table 1.1). Chapter 4 uses \r a  storyline approach to assess the upper tail of the distribution \r of global warming levels (the storylines of high global warming \r levels) and their manifestation in global patterns of temperature \r and precipitation changes. Chapter  9  uses a  storyline approach to \r examine the potential for, and early warning signals of, a high-end \r sea level scenario, in the context of deep uncertainty related to our \r current understanding of the physical processes that contribute to \r long-term sea level rise. Chapter  10 assesses the use of physical \r climate storylines and narratives as a way to explore uncertainties \r in regional climate projections, and to link to the specific risk and \r decision context relevant to a  user, for developing integrated and \r context-relevant regional climate change information. Chapter  11 \r [...]"


id: 53179a2e7fae366bbf92fc1a6866f360
created_at: 2023-04-19 13:22:33
updated_at: 2023-04-19 13:22:33
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index: climate_gpt_v2
vector_id: 11888
content:
"[...] uses the term storyline in the framework of extreme event attribution. \r Chapter 12 assesses the use of a storylines approach with narrative \r elements for communicating climate (change) information in the \r context of climate services (Cross-Chapter Box 12.2)."


id: 35acfd3873ef61c53633889ed2151f51
created_at: 2023-04-19 13:22:33
updated_at: 2023-04-19 13:22:33
content_type: text
index: climate_gpt_v2
vector_id: 6695
content:
"As examples of the use of this terminology, the term 'flood risk' should not be used if it only describes changes in the frequency and \r intensity of flood events (a hazard); the risk from flooding to human and ecological systems is caused by the flood hazard, the exposure \r of the system affected (e.g., topography, human settlements or infrastructure in the area potentially affected by flooding) and the \r vulnerability of the system (e.g., design and maintenance of infrastructure, existence of early warning systems). As another example, \r climate-related risk to food security can arise from both potential climate change impacts and responses to climate change and can \r be exacerbated by other stressors. Drivers for risks related to climate change impacts include climatic impact- drivers (e.g., drought, \r temperature extremes, humidity) mediated by other climatic impact-drivers (e.g., increased CO2 fertilization of certain types of crops \r [...]"


id: 3eedcfa9f68d308bc6800547fd358419
created_at: 2023-04-19 13:22:33
updated_at: 2023-04-19 13:22:33
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index: climate_gpt_v2
vector_id: 8256
content:
"[...] may help increase yields), the potential for indirect climate-related impacts (e.g., pest outbreaks triggered by ecosystem responses to \r weather patterns), exposure of people (e.g., how many people depend on a particular crop) and vulnerability or adaptability (how able \r are affected people to substitute other sources of food, which may be related to financial access and markets). "


id: 6db255c026b62c7be220399b06715822
created_at: 2023-04-19 13:22:33
updated_at: 2023-04-19 13:22:33
content_type: text
index: climate_gpt_v2
vector_id: 16557
content:
"Figure 1.17 | Illustration of two types of tipping points: noise-induced (a, b) and bifurcation (c, d). (a) and (c) are example time-series (coloured lines) through \r the tipping point, with solid-black lines indicating stable climate states (e.g., low or high rainfall) and dashed lines representing the boundary between stable states. (b) and \r (d) are stability landscapes, which provide an intuitive understanding of the different types of tipping point. The 'valleys' represent different climate states the system can occupy, \r with 'hilltops' separating the stable states. The resilience of a climate state is implied by the depth of the valley. The current state of the system is represented by a ball. Both \r scenarios assume that the ball starts in the left-hand valley (dashed-black lines) and then through different mechanisms dependent on the type of tipping transitions to the \r [...]"


id: ccc42b0ca448fbb014ba6abe367e6078
created_at: 2023-04-19 13:22:33
updated_at: 2023-04-19 13:22:33
content_type: text
index: climate_gpt_v2
vector_id: 33358
content:
"[...] right-hand valley (coloured lines). Noise-induced tipping events (a, b), for instance drought events causing sudden dieback of the Amazon rainforest, develop from fluctuations \r within the system. The stability landscape in this scenario remains fixed and stationary. A series of perturbations in the same direction, or one large perturbation, are required to \r force the system over the hilltop and into the alternative stable state. Bifurcation tipping events (c, d), such as a collapse of the thermohaline circulation in the Atlantic Ocean \r under climate change, occur when a critical level in the forcing is reached. Here the stability landscape is subjected to a change in shape. Under gradual anthropogenic forcing \r the left-hand valley begins to shallow and eventually vanishes at the tipping point, forcing the system to transition to the right-hand valley."


id: 92eda5141b2aea73dd6803507744bfce
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
vector_id: 23060
content:
"The unambiguous framing of what changes are being attributed to what causes is a crucial first step for an assessment (Easterling \r et al., 2016; Hansen et al., 2016; Stone et al., 2021), followed by the identification of the possible and plausible drivers of change and \r the development of a hypothesis or theory for the linkage (Cross-Working Group Box: Attribution, Figure 1). The next step is to clearly \r define the indicators of the observed change or event and note the quality of the observations. There has been significant progress in \r the compilation of fragmented and distributed observational data, broadening and deepening the data basis for attribution research \r (WGI Section 1.5; e.g., Poloczanska et al., 2013; Ray et al., 2015; Cohen et al., 2018). The quality of the observational record of drivers \r should also be considered (e.g., volcanic eruptions: WGI Section 2.2.2). Impacted systems also change in the absence of climate \r [...]"


id: 8a3face0471e4e9bcaa93d0085e773ef
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
vector_id: 21502
content:
"[...] change; this baseline and its associated modifiers - such as agricultural developments or population growth - need to be considered, \r alongside the exposure and vulnerability of people depending on these systems."


id: 6dc79354fb23aa2f3f3a287e1e551716
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
vector_id: 16573
content:
"Changes in large-scale climate variables (e.g., global mean temperature) have been reliably attributed to anthropogenic and natural \r forcings (WGI Section 1.3.4; e.g., Hegerl et al., 2010; Bindoff et al., 2013). The most established method is to identify the 'fingerprint' \r of the expected space-time response to a particular climate forcing agent such as the concentration of anthropogenically induced \r GHGs or aerosols, or natural variation of solar radiation. This technique disentangles the contribution of individual forcing agents to \r an observed change (e.g., Gillett et al., 2021). New statistical approaches have been applied to better account for internal climate \r variability and the uncertainties in models and observations (WGI Section 3.2; e.g., Naveau et al., 2018; Santer et al., 2019). There \r are many other approaches, for example, global mean sea level change has been attributed to anthropogenic climate forcing by \r [...]"


id: 951fbff7631b7b3b6b87e5db2ac10bd
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
vector_id: 23441
content:
"[...] attributing the individual contributions from, for example, glacier melt or thermal expansion, while also examining which aspects of \r the observed change are inconsistent with internal variability (WGI Sections 3.5.2 and 9.6.1.4)."


id: 1ba4f697291d01a50d9d6ea260bd6d15
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 2050
content:
"Attribution of weather and climate events to anthropogenic forcing\r New methods have emerged since AR5 to attribute the change \r in likelihood or characteristics of weather or climate events or \r classes of events to underlying drivers (WGI Sections 10.4.1 and \r 11.2.2; NA SEM, 2016; Stott et al., 2016; Jezequel et al., 2018; \r Wehner et al., 2018; Wang et al., 2021). Typically, historical \r changes, simulated under observed forcings, are compared \r to a  counterfactual climate simulated in the absence of \r anthropogenic forcing. Another approach examines facets of the \r weather and thermodynamic status of an event through process\u0002based attribution (WGI Chapter  11 and Section  10.4.1; Hauser \r et al., 2016; Shepherd et al., 2018; Grose et al., 2019). Events \r where attributable human influence have been found include hot \r and cold temperature extremes (including some with widespread \r impacts), heavy precipitation, and certain types of droughts \r [...]"


id: 4b8c0a19eba8f56c343bd0dca0084db9
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
vector_id: 10553
content:
"[...] and tropical cyclones (AR6 WGI Section 11.9; e.g., Vogel et al., \r 2019; Herring et al., 2021). Event attribution techniques have \r sometimes been extended to 'end-to-end' assessments from \r climate forcing to the impacts of events on natural or human \r systems (Otto, 2017)."


id: 5d371fbf12cc64d7813d1a239dc65847
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 13675
content:
"Impact attribution covers a diverse set of qualitative and quantitative approaches, building on experimental approaches, observations \r from remote sensing, long-term in situ observations, and monitoring efforts, teamed with local knowledge, process understanding \r and empirical or dynamical modelling (WGII Section  16.2; Stone et al., 2013; Cramer et al., 2014). The attribution of a  change in \r a natural or human system (e.g., wild species, natural ecosystems, crop yields, economic development, infrastructure or human health) \r to changes in climate-related systems (i.e., climate, ocean acidification, permafrost thawing or sea level rise) requires accounting for \r other potential drivers of change, such as technological and economic changes in agriculture affecting crop production (Hochman \r et al., 2017; Butler et al., 2018), changes in human population patterns and vulnerability affecting flood- or wildfire-induced damages \r [...]"


id: dac0b84e553f57c494cc5eaa56989ebe
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
vector_id: 35780
content:
"[...] (Huggel et al., 2015; Sauer et al., 2021), or habitat loss driving declines in wild species (IPBES, 2019). These drivers are accounted \r for by estimating a baseline condition that would exist in the absence of climate change. The baseline might be stationary and be \r approximated by observations from the past, or it may change over time and be simulated by statistical or process-based impact models \r (WGII Section 16.2; Cramer et al., 2014). Assessment of multiple independent lines of evidence, taken together, can provide rigorous \r attribution when more quantitative approaches are not available (Parmesan et al., 2013). These include paleodata, physiological \r and ecological experiments, natural 'experiments' from very long-term datasets indicating consistent responses to the same climate \r trend/event, and 'fingerprints' in species' responses that are uniquely expected from climate change (e.g. poleward range boundaries \r [...]"


id: 3ed6ccfb018f313e2222c835d8f98f78
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
vector_id: 8243
content:
"[...] expanding and equatorial range boundaries contracting in a coherent pattern worldwide; Parmesan and Yohe, 2003). Meta-analyses \r of species/ecosystem responses, when conducted with wide geographic coverage, also provide a globally coherent signal of climate \r change at an appropriate scale for attribution to anthropogenic climate change (Parmesan and Yohe, 2003; Parmesan et al., 2013)."


id: f81f0cffdcfe8a0436a597ea432e33cc
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 41028
content:
"aggregation of climate variables was implicitly achieved through the \r consideration of biomes, giving rise to the traditional vegetation\u0002based classification of Koppen (1936). In the last decades, the \r substantial increases in climate observations, climate modelling, and \r data processing capabilities have allowed new approaches to climate \r classification, for example through interpolation of aggregated \r global data from thousands of stations (Peel et al., 2007; Belda \r et al., 2014; Beck et al., 2018) or through data-driven approaches \r applied to delineate ecoregions that behave in a coherent manner \r in response to  climate variability (Papagiannopoulou et al., 2018). \r Experience shows that each method has strengths and weaknesses \r through trade-offs between detail and convenience. For instance, \r a  very detailed classification, with numerous complexly shaped \r regions derived from a  large set of variables, may be most useful \r [...]"


id: 874d9eb9511c0816ea2f27b3972d6832
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 20964
content:
"[...] for the evaluation of climate models (Rubel and Kottek, 2010; Belda \r et al., 2015; Beck et al., 2018) and climate projections (Feng et al., \r 2014; Belda et al., 2016). In contrast, geometrically simple regions \r are often best suited for regional climate modelling and downscaling \r (e.g.,  the Coordinated Regional Climate Downscaling Experiment \r (CORDEX) domains; Section 1.5.3; Giorgi and Gutowski, 2015)."


id: f198823dfb69669044b2190b8e19c0bc
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 39888
content:
"The Reference Sets of Land and Ocean Regions are polygonal, \r sub-continental domains, defined through a  combination of \r environmental, climatic and non-climatic (e.g., pragmatic, technical, \r historical) factors, in accordance with the literature and climatological \r reasoning based on observed and projected future climate. Merging \r the diverse functions and purposes of the regions assessed in the \r literature into a common reference set implies a certain degree of \r compromise between simplicity, practicality and climate consistency. \r For instance, Spain is fully included in the Mediterranean (MED) \r Reference Region, but is one of the most climatically diverse \r countries in the world. Likewise, a  careful comparison of panels \r a and b of Figure 1.18 reveals that the simplified southern boundary \r of the Sahara (SAH) Reference Region slightly overlaps the northern \r boundary of the West African Monsoon Typological Region. As \r [...]"


id: 9da6ee930dcd1cf476d9089ceb736359
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updated_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
vector_id: 24969
content:
"[...] such, the resulting Reference Regions are not intended to precisely \r represent climates, but rather to provide simple domains suitable for \r regional synthesis of observed and modelled climate and climate \r change information (Iturbide et al., 2020). In particular, CMIP6 model \r results averaged over Reference Regions are presented in the Atlas."


id: 67f8c001f4e7a885675148970de49831
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 15562
content:
"Observed large-scale climatic changes assessed in Chapter  2, \r attribution of these changes in Chapter 3, and regional observations \r of specific physical or biogeochemical processes presented in other \r Chapters, are supported by improvements in observational capacity \r since AR5. Attribution assessments can be made at a higher likelihood \r level than in AR5, due in part to the availability of longer observational \r datasets (Chapter  3). Updated assessments are made based on \r new and improved datasets, for example of global temperature \r change (Cross-Chapter Box  2.3) or regional climate information \r (Section 10.2). Of particular relevance to the AR6 assessment are the \r Essential Climate Variables (ECVs; Hollmann et al., 2013; Bojinski et al., \r 2014), and Essential Ocean Variables (EOVs; Lindstrom et al., 2012), \r compiled by the Global Climate Observing System (GCOS; WMO, \r 2016), and the Global Ocean Observing System (GOOS), respectively. \r [...]"


id: 422e9fe9cd8267bad560501ff3c526d0
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updated_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
vector_id: 8847
content:
"[...] These variables include physical, chemical and biological variables \r or groups of linked variables, and underpin 'headline indicators' \r (a selected set of essential parameters representing the state of the \r climate system) for climate monitoring (Trewin et al., 2021)."


id: 84bdc1a7d50bb9e50437c3e16f9097dc
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 20499
content:
"Assessments of the hydrological cycle in Chapters  2  and 8  are \r supported by longer time series and new developments. Examples \r are new satellites (McCabe et al., 2017) and measurements of water \r vapour using commercial laser absorption spectrometers and water \r vapour isotopic composition (Steen-Larsen et al., 2015; Zannoni \r et al., 2019). Data products of higher quality have been developed \r since AR5, such as the multi-source weighted ensemble precipitation \r (Beck et al., 2017) and multi-satellite terrestrial evaporation \r products(Fisher et al., 2017). Longer series are available for satellite\u0002derived global inundation data (Prigent et al., 2020). Observations \r of soil moisture are now available via the Soil Moisture and Ocean \r Salinity (SMOS) and the Soil Moisture Active Passive (SMAP) satellite \r retrievals, filling critical gaps in the observation of hydrological \r trends and variability over land (Dorigo et al., 2017). Similarly, the \r [...]"


id: 8fa220de4cd472d5ee1929dd4276b43b
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
vector_id: 22480
content:
"[...] Gravity Recovery and Climate Experiment GRACE and GRACE\u0002FO satellites (Tapley et al., 2019) have provided key constraints \r on groundwater variability and trends around the world (Frappart \r and Ramillien, 2018). The combination of new observations with \r other sources of information has led to updated estimates of heat \r storage in inland waters (Vanderkelen et al., 2020), contributing to \r revised estimates of heat storage on the continents (Section 7.2.2.3; \r von Schuckmann et al., 2020)."


id: 228bffe59700683006db42e897673cc3
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 3295
content:
"Satellites provide observations of a large number of key atmospheric \r and land-surface variables, ensuring sustained observations over \r wide areas. Since AR5, such observations have expanded to include \r satellite retrievals of atmospheric CO2 via the NASA Orbiting Carbon \r Observatory satellites (OCO-2 and OCO-3; Eldering et al., 2017), \r following on from similar efforts employing the Greenhouse Gases \r Observing Satellite (GOSat; Yokota et al., 2009; Inoue et al., 2016). \r By combining remote sensing and in situ measurements, knowledge \r of fluxes between the atmosphere and land surface has improved \r (Rebmann et al., 2018). FLUXNET (https://fluxnet.org/) has been \r providing eddy covariance measurements of carbon, water, and \r energy fluxes between the land and the atmosphere, with some of \r the stations operating for over 20 years (Pastorello et al., 2017), while \r the Baseline Surface Radiation Network (BSRN) has been maintaining \r [...]"


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vector_id: 19304
content:
"[...] high-quality radiation observations since the 1990s (Ohmura et al., \r 1998; Driemel et al., 2018)."


id: 3f330bf4b41256b1624cafdfa577db13
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"The ongoing collection of information about the atmosphere as it \r evolves is supplemented by the reconstruction and digitization \r of data about past conditions. Programmes aimed at recovering \r information from sources such as handwritten weather journals and \r ships' logs continue to make progress, and are steadily improving \r spatial coverage and extending our knowledge backward in time. \r For example, Bronnimann et al. (2019a) has recently identified \r several thousand sources of climate data for land areas in the pre\u00021890 period, with many from the 18th  century. The vast majority \r of these data are not yet contained in international digital data \r archives, and substantial quantities of undigitized ships' weather \r log data exist for the same period (Kaspar et al., 2015). Since AR5 \r there has been a growth of 'citizen science' activities, making use \r of volunteers to rapidly transcribe substantial quantities of weather \r [...]"


id: c657dd37b36ce06f512b1fd93c595d86
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
vector_id: 32209
content:
"[...] observations. Examples of projects include: oldWeather.org and \r SouthernWeatherDiscovery.org (both of which used ship-based \r logbook sources); the DRAW project (Data Rescue: Archival and \r Weather, which recovered land-based station data from Canada); \r WeatherRescue.org (land-based data from Europe); JungleWeather.org\r (data from the Congo); and the Climate History Australia project \r (data from Australia; e.g.,  Park et al., 2018; Hawkins et al., 2019). \r Undergraduate students have also been recruited to successfully \r digitize rainfall data in Ireland (Ryan et al., 2018). Such observations \r are an invaluable source of weather and climate information for the \r early historical period that continues to expand the digital archives \r (e.g., Freeman et al., 2017) which underpin observational datasets \r used across several Chapters."


id: c2813517ea03cc79a864fbe407f8a922
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 31507
content:
"Observations of the composition of the atmosphere have been \r further improved through expansions of existing surface observation \r networks (Bodeker et al., 2016; De Maziere et al., 2018) and through \r in situ measurements such as aircraft campaigns (Sections  2.2, \r 5.2 and Section  6.2). Examples of expanded networks include the \r Aerosols, Clouds and Trace Gases Research Infrastructure (ACTRIS; \r Pandolfi et al., 2018), which focuses on short-lived climate forcers, \r and the Integrated Carbon Observation System (ICOS), which \r allows scientists to study and monitor the global carbon cycle \r and GHG emissions (Colomb et al., 2018). Examples of recent \r aircraft observations include the Atmospheric Tomography Mission \r (ATom), which has flown repeatedly along the north-south axis of \r both the Pacific and Atlantic oceans, and the continuation of the \r In-service Aircraft for a  Global Observing System (IAGOS) effort, \r which measures atmospheric composition from commercial aircraft \r [...]"


id: bc961a3aa0400eebdae1065ec9fbf093
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 30425
content:
"The value of gravity-based estimates of changes in ice-sheet mass \r has increased, as the time series from the GRACE and GRACE-FO \r satellites  - homogenized and absolutely calibrated  - is close to \r 20 years in length. The European Space Agency's (ESA's) Cryosat-2 \r radar altimetry satellite mission has continued to provide \r measurements of the changes in the thickness of sea ice and the \r elevation of the Greenland and Antarctic ice sheets (Tilling et al., \r 2018). Other missions include NASA's Operation IceBridge, collecting \r airborne remote-sensing measurements to bridge the gap between \r ICESat (Ice, Cloud and land Elevation Satellite) and the upcoming \r ICESat-2 laser altimetry missions. Longer time series from multiple \r missions have led to considerable advances in understanding the \r origin of inconsistencies between the mass balances of different \r glaciers and reducing uncertainties in estimates of changes in \r [...]"


id: b40cc52e45210a3764ec28dece552f73
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
vector_id: 28921
content:
"[...] the Greenland and Antarctic ice sheets (Bamber et al., 2018; A. \r Shepherd et al., 2018; Shepherd et al., 2020). Last, the first observed \r climatology of snowfall over Antarctica was obtained using the cloud/\r precipitation radar onboard NASA's CloudSat (Palerme et al., 2014)."


id: 6896db630182ef0577da29e50a90e12b
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 15670
content:
"Satellite observations have recently expanded to include data on the \r fluorescence of land plants as a measure of photosynthetic activity \r via the Global Ozone Monitoring Experiment (GOME; Guanter et al., \r 2014; Yang et al., 2015) and OCO-2 satellites (Sun et al., 2017). \r Climate data records of leaf area index (LAI), characterizing the area \r of green leaves per unit of ground area, and the fraction of absorbed \r photosynthetically active radiation (FAPAR) - an important indicator \r of photosynthetic activity and plant health (Gobron et al., 2009) - are \r now available for over 30 years (Claverie et al., 2016). In addition, \r key indicators such as fire disturbances/burned areas are now \r retrieved via satellite (Chuvieco et al., 2019). In the US, the National \r Ecological Observational Network (NEON) provides continental-scale \r observations relevant to the assessment of changes in aquatic and \r terrestrial ecosystems via a wide variety of ground-based, airborne, \r [...]"


id: 6896377af0061468995f2701719a277a
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 15669
content:
"[...] and satellite platforms (Keller et al., 2008). All these long-term \r records reveal range shifts in ecosystems (Section 2.3.4)."


id: d8a9132b890ec2489b3f6b63bc2f092a
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 35410
content:
"Threats to observational capacity also include the loss of natural \r climate archives that are disappearing as a  direct consequence of \r warming temperatures. Ice-core records from vulnerable alpine \r glaciers in the tropics (Permana et al., 2019) and the mid-latitudes \r (Gabrielli et al., 2016; Winski et al., 2018; Moreno et al., 2021) \r document more frequent melt layers in recent decades, with glacial \r retreat occurring at a rate and geographic scale that is unusual in \r the Holocene (Solomina et al., 2015). The scope and severity of coral \r bleaching and mortality events have increased in recent decades \r (Hughes et al., 2018), with profound implications for the recovery \r of coral climate archives from new and existing sites. An observed \r increase in the mortality of larger, long-lived trees over the last century \r is attributed to a  combination of warming, land-use change, and \r disturbance (e.g., McDowell et al., 2020). The ongoing loss of these \r [...]"


id: 38c65297fdb3a3e2a6597bdb0310e039
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 7192
content:
"[...] natural, high-resolution climate archives endanger an end in their \r coverage over recent decades, given that many of the longest \r monthly- to annually-resolved paleoclimate records were collected \r in the 1960s to 1990s (e.g., the PAGES2K database as represented \r in PAGES 2k Consortium, 2017). This gap presents a barrier to the \r calibration of existing decades-to-centuries-long records needed to \r constrain past temperature and hydrology trends and extremes."


id: bea94a330aab8818547e8b2589ec04ec
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 30806
content:
"The lockdowns and societal outcomes arising from the COVID-19 \r pandemic pose a  new threat to observing systems. For example, \r WMO and UNESCO-IOC (Intergovernmental Oceanographic \r Commission) published a summary of the changes to Earth system \r observations during COVID-19 (WMO, 2020b). Fewer aircraft flights \r (down 75-90% in May 2020, depending on region) and ship transits \r (down 20% in May 2020) mean that onboard observations from \r those networks have reduced in number and frequency (James \r et al., 2020; Ingleby et al., 2021). Europe has deployed more \r radiosonde soundings to account for the reduction in data from air \r traffic. Fewer ocean observing buoys were deployed during 2020, \r and reductions have been particularly prevalent in the tropics and \r Southern Hemisphere. The full consequences of the pandemic, and \r responses to it, will come to light over time. Estimates of the effect \r of the reduction in aircraft data assimilation on weather forecasting \r [...]"


id: 37b9dc8ae13ebce23789525ccfb25ea9
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 7013
content:
"[...] skill are small (James et al., 2020; Ingleby et al., 2021), potentially \r alleviating concerns about veracity of future atmospheric reanalyses \r of the COVID-19 pandemic period."


id: 209e57e516aded3d258bf7503ff50477
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 2962
content:
"Historical archives of weather and climate observations contained \r in ships' logs, weather diaries, observatory logbooks and other \r sources of documentary data also risk being lost, for example \r to natural disasters or accidental destruction. These archives \r include measurements of temperature (air and sea surface), \r rainfall, surface pressure, wind strength and direction, sunshine \r amount, and many other variables back into the 19th  century. \r While internationally coordinated data-rescue efforts are focused \r on recovering documentary sources of past weather and climate \r data (e.g.,  Allan et al., 2011), no such coordinated efforts exist \r for vulnerable paleoclimate archives. Furthermore, oral traditions \r about local and regional weather and climate from indigenous \r peoples represent valuable sources of information, especially when \r used in combination with instrumental climate data (Makondo \r and Thomas, 2018), but are in danger of being lost as indigenous \r [...]"


id: 32ba19f67a0335091af1dd0cd3a08571
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 6190
content:
"The intercomparison of reanalyses with each other, or with earlier \r versions, is often done for particular variables or aspects of the \r simulation. ERA5 is assessed as the most reliable reanalysis for \r climate trend assessment (Section  2.3). Compared to ERA-Interim, \r the ERA5 forecast model and assimilation system, as well as the \r availability of improved reprocessing of observations, resulted in \r relatively smaller errors when compared to observations, including \r a better representation of global energy budgets, radiative forcing \r from volcanic eruptions (e.g., Mt. Pinatubo: Allan et al., 2020), the \r partitioning of surface energy (Martens et al., 2020), and wind (Kaiser\u0002Weiss et al., 2015, 2019; Borsche et al., 2016; Scherrer, 2020). In \r ERA5, higher resolution means a better representation of Lagrangian \r motion convective updrafts, gravity waves, tropical cyclones, and \r other meso- to synoptic-scale features of the atmosphere (Hoffmann \r [...]"


id: 6405162c2fd014ffe960cf1ab01a02ca
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 14883
content:
"[...] et al., 2019; Martens et al., 2020). Low-frequency variability is found \r to be generally well represented and, from 10 hPa downwards, \r patterns of anomalies in temperature match those from the ERA\u0002Interim, MERRA-2 and JRA-55 reanalyses. Inhomogeneities in the \r water cycle have also been reduced (Hersbach et al., 2020)."


id: b24406fba16a3b042324acfcb4003c96
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 28600
content:
"The first Ocean Reanalyses Intercomparison project (ORA-IP; \r Balmaseda et al., 2015) focussed on the uncertainty in key climate \r indicators, such as ocean heat content (Palmer et al., 2017), \r thermosteric sea level (Storto et al., 2017, 2019), salinity (Shi \r et al., 2017), sea ice extent (Chevallier et al., 2017), and the AMOC \r (Karspeck et al., 2017). Reanalysis uncertainties occur in areas of \r inhomogeneous or sparse observational data sampling, such as for \r the deep ocean, the Southern Ocean, and western boundary currents \r (Lellouche et al., 2018; Storto et al., 2019). Intercomparisons have \r also been dedicated to specific variables such as mixed-layer depths \r (Toyoda et al., 2017), eddy kinetic energy, globally (Masina et al., \r 2017) and in the polar regions (Uotila et al., 2019). Karspeck et al. \r (2017) found disagreement in the AMOC variability and strength in \r reanalyses over observation-sparse periods, whereas Jackson et al. \r [...]"


id: 8957a9812132c7c02b25a0ea74c91c0
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
vector_id: 21336
content:
"[...] (2019) reported a lower spread in AMOC strength across an ensemble \r of ocean reanalyses of the recent period (1993-2010), linked to \r improved observation availability for assimilation. Reanalyses also \r have a larger spread of ocean heat uptake than data-only products \r and can produce spurious overestimates of heat uptake (Palmer \r et al., 2017), which is important in the context of estimating climate \r sensitivity (Storto et al., 2019). The ensemble approach for ocean \r reanalyses provides another avenue for estimating uncertainties \r across ocean reanalyses (Storto et al., 2019)."


id: 159edb29840d4c58d2598e1f65fd0327
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 1021
content:
"Although reanalyses such as ERA5 take advantage of new observational \r datasets and present a great improvement in atmospheric reanalyses, \r the issues introduced by the evolving observational network remain. \r Sparse input reanalyses, where only a  limited set of reliable and \r long-observed records are assimilated, address these issues, with \r the limitation of fewer observational constraints. These efforts are \r sometimes called centennial-scale reanalyses. One example is the \r atmospheric 20th century Reanalysis (Compo et al., 2011; Slivinski \r et al., 2021) which assimilates only surface and sea-level pressure \r observations, and is constrained by time-varying observed changes \r in atmospheric constituents, prescribed sea surface temperatures and \r sea ice concentration, creating a reconstruction of the weather over \r the whole globe every three hours for the period 1806-2015. The \r ERA-20C atmospheric reanalysis (covering 1900-2010; Poli et al., \r [...]"


id: 8aba9e2d689e7d41770b98daf746bc06
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
vector_id: 21579
content:
"[...] 2016) also assimilates marine wind observations, and CERA-20C is \r a centennial-scale reanalysis that assimilates both atmospheric and \r oceanic observations for the 1901-2010 period (Laloyaux et al., \r 2018). These centennial-scale reanalyses are often run as ensembles \r that provide an estimate of the uncertainty in the simulated \r variables over space and time. Slivinski et al. (2021) conclude that \r the uncertainties in surface circulation fields in version 3  of the \r 20th  century Reanalysis are reliable and that there is also skill in \r its tropospheric reconstruction over the 20th  century. Long-term \r changes in other variables, such as precipitation, also agree well with \r direct observation-based datasets (Sections 2.3.1.3 and 8.3.2.8)."


id: 3f7296d1558e71abb8b28be43582b2c4
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 8350
content:
"Longer reanalyses that extend further back in time than the \r beginning of the instrumental record are being developed. They \r include the complete integration of paleoclimate archives and newly \r available early instrumental data into extended reanalysis datasets. \r Such integration leverages ongoing development of climate models \r that can simulate paleoclimate records in their units of analysis \r (i.e., oxygen isotope composition, tree ring width, etc.), in many cases \r using physical climate variables as input for so-called proxy system \r models (Evans et al., 2013; Dee et al., 2015). Ensemble Kalman filter \r data assimilation approaches allow for combining paleoclimate \r data and climate model data to generate annually resolved fields \r (Last Millenium Reanalysis, Hakim et al., 2016; Tardif et al., 2019) \r or even monthly fields (Franke et al., 2017). This allows for a greater \r understanding of decadal variability (Parsons and Hakim, 2019) and \r [...]"


id: 49b79bc495cef7e8874642770505a3d5
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 10202
content:
"[...] greater certainty around the full range of the frequency and severity \r of climate extremes. This, in turn, allows for better-defined detection \r of change. It also helps to identify the links between biogeochemical "


id: 20bc83fff35bc78748eb5a8b512a78e7
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 2977
content:
"The developments in reanalyses described above mean that they \r are now used across a  range of applications. In AR6, reanalyses \r provide information for fields and in regions where observations \r are limited. There is growing confidence that modern reanalyses can \r provide another line of evidence in describing recent temperature \r trends (Tables 2.4 and 2.5). As their spatial resolution increases, the \r exploration of fine-scale extremes in both space and time becomes \r possible (e.g., wind; Kaiser-Weiss et al., 2015). Longer reanalyses can \r be used to describe the change in the climate over the last 100 to \r 1000 years. Reanalyses have been used to help post-process climate \r model output, and drive impact models; however, they are often bias \r adjusted first (Cross-Chapter Box  10.2; e.g., Weedon et al., 2014). \r Copernicus Climate Change Service (C3S) provides a bias-adjusted \r dataset for global land areas based on ERA5 called WFDE5 (Cucchi \r [...]"


id: 5fe4ff18904cfdd04dfb1827ce50ba31
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 14181
content:
"[...] et al., 2020) which, combined with ERA5 information over the ocean \r (W5E5; Lange, 2019), is used as the AR6 Interactive Atlas reference \r for the bias adjustment of model output."


id: f92133275affd9fd2cf67b0f2313934
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 41191
content:
"Earth system models are mathematical formulations of the natural \r laws that govern the evolution of climate-relevant systems: \r atmosphere, ocean, cryosphere, land, and biosphere, as well as the \r carbon cycle (Flato, 2011). They build on the fundamental laws of \r physics (e.g.,  Navier-Stokes or Clausius-Clapeyron equations) or \r empirical relationships established from observations and, when \r possible, they are constrained by fundamental conservation laws \r (e.g., mass and energy). The evolution of climate-relevant variables \r is computed numerically using high-performance computers (Andre \r et al., 2014; Balaji et al., 2017), on three-dimensional discrete grids \r (Staniforth and Thuburn, 2012). The spatial (and temporal) resolution \r of these grids in both the horizontal and vertical directions determines \r which processes need to be parameterized or whether they can be \r explicitly resolved. Developments since AR5 in model resolution, \r [...]"


id: 8de6b41171404ba3f7c3dde4789ea1c4
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 22157
content:
"[...] parameterizations and modelling of the land and ocean biosphere \r and of biogeochemical cycles are discussed below."


id: 3d62cb542ef48cd9886745ea430e7e6f
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 7973
content:
"The horizontal resolution and the number of vertical levels in ESMs \r is generally higher in CMIP6 than in CMIP5 (Figure  1.19). Global \r models with finer horizontal grids better represent many aspects of \r the circulation of the atmosphere (Gao et al., 2020; Schiemann et al., \r 2020) and ocean (Bishop et al., 2016; Storkey et al., 2018), bringing \r improvements in the simulation of the global hydrological cycle \r (Roberts et al., 2018). CMIP6 includes a dedicated effort (HighResMIP, \r Haarsma et al., 2016) to explore the effect of higher horizontal \r resolution, such as ~50 km, ~25 km and even ~10 km (Section 1.5.4.2 \r and Annex II, Table AII.6). Improvements are documented in the \r highest-resolution coupled models used for HighResMip (Hewitt \r et al., 2017; Roberts et al., 2019). Flexible grids allowing spatially \r variable resolution in the atmosphere (McGregor, 2015; Giorgetta \r et al., 2018) and in the ocean (Wang et al., 2014; Petersen et al., \r [...]"


id: 995dc1dbdba664b4ec528c1f6e503591
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 24204
content:
"Since AR5, more sophisticated land-use and land-cover change \r representations in ESMs have been developed to simulate the effects \r of land management on surface fluxes of carbon, water and energy \r (Lawrence et al., 2016), although the integration of many processes \r (e.g.,  wetland drainage, fire as a  management tool) remains \r a  challenge (Pongratz et al., 2018). The importance of nitrogen \r availability to limit the terrestrial carbon sequestration has been \r recognized (Section  5.4; Zaehle et al., 2014) and so an increasing \r number of models now include a prognostic representation of the \r terrestrial nitrogen cycle and its coupling to the land carbon cycle \r (Jones et al., 2016; Arora et al., 2020), leading to a  reduction in \r uncertainty for carbon budgets (Section 5.1; Jones and Friedlingstein, \r 2020). As was the case in CMIP5 (Ciais et al., 2013), the land surface \r processes represented vary across CMIP6 models, with at least \r [...]"


id: b001689297fcdeeb1ade820a53b000dc
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 28208
content:
"[...] some key processes (fire, permafrost carbon, microbes, nutrients, \r vegetation dynamics, plant demography) absent from any particular \r ESM land model (Table  5.4). Ocean biogeochemical models have \r evolved to enhance the consistency of the exchanges between ocean, \r atmosphere and land, through riverine input and dust deposition \r (Stock et al., 2014; Aumont et al., 2015). Other developments include \r flexible plankton stoichiometric ratios (Galbraith and Martiny, 2015), \r improvements in the representation of nitrogen fixation (Paulsen \r et al., 2017), and the limitation of plankton growth by iron (Aumont \r et al., 2015). Due to the long time scale of biogeochemical processes, \r how the models are initialized (spun up) strategies has been shown \r to affect their performance in AR5 (Seferian et al., 2016)."


id: d5192a60601665e8392bedcdebe893bf
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 34814
content:
"The representation of ocean and cryosphere processes has also \r evolved significantly since CMIP5. The explicit representation of \r ocean eddies, due to increased grid resolution (typically, from 1deg \r to  1/4deg), is a  major advance in a  number of CMIP6 ocean model \r components (Hewitt et al., 2017). Advances in sea ice models have \r been made, for example through correcting known shortcomings in \r CMIP5 simulations, in particular the persistent underestimation of \r the rapid decline in summer Arctic sea ice extent (Rosenblum and \r Eisenman, 2016, 2017; Turner and Comiso, 2017; Notz and Stroeve, \r 2018). The development of glacier and ice-sheet models has been \r motivated and guided by an improved understanding of key physical \r processes, including grounding line dynamics, stratigraphy and \r microstructure evolution, sub-shelf melting, and glacier and ice\u0002shelf calving, among others (Faria et al., 2014, 2018; Hanna et al., \r [...]"


id: 8f9a66655699b812daf0f71ad6e17df0
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 22476
content:
"[...] 2020). The resolution of ice-sheet models has continuously increased, \r including the use of nested grids, sub-grid interpolation schemes, \r and adaptive mesh approaches (Cornford et al., 2016), mainly for \r a  more accurate representation of grounding-line migration and \r data assimilation (Pattyn, 2018). Ice-sheet models are increasingly \r interactively coupled with global and regional climate models, \r accounting for the height-mass-balance feedback (Vizcaino et al., \r 2015; Le clec'h et al., 2019), and enabling a better representation of \r ice-ocean processes, in particular for the Antarctic Ice Sheet (Asay\u0002Davis et al., 2017)."


id: a6bad7798580a9093192151ed8c6ba58
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 26551
content:
"An initial set of such choices is usually made by (often extensive) \r groups of modellers working on individual components of the Earth \r system (e.g.,  ocean, atmosphere, land or sea ice). As components \r are assembled to build an ESM, the choices are refined so that the \r simulated climate best represents a number of pre-defined climate \r variables, or 'tuning targets'. When these are met the model is \r released for use in intercomparisons such as CMIP. Tuning targets \r can be one of three types: mean climate; regional phenomena and \r features; or historical trends (Hourdin et al., 2017). One example of \r such a goal is that when the simulated climate system receives energy \r from the sun in accordance with what we observe today, the resulting \r mean equilibrium temperature should also be close to observations. \r Whether tuning should be performed to facilitate accurate simulation \r of long-term trends such as changes in global mean temperature \r [...]"


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vector_id: 31024
content:
"Each modelling group has its own strategy and, after AR5, a survey \r was conducted to understand the tuning approach used in 23 CMIP5 \r modelling centres. The results are discussed in Hourdin et al. (2017), \r which stresses that the behaviour of ESMs depends on the tuning \r strategy. An important recommendation is that the calibration steps \r that lead to particular model tuning should be carefully documented. \r In CMIP6 each modelling group now describes the three levels of \r tuning, both for the complete ESM and for the individual components \r (available at https://explore.es-doc.org and in the published model \r descriptions, Annex II: Models). The most important global tuning \r target for CMIP6 models is the net top-of-the-atmosphere (TOA) heat \r flux and its radiative components. Other global targets include: the \r decomposition of the energy fluxes at TOA into a clear sky component \r and a component due to the radiative effect of clouds, global mean air \r [...]"


id: c36965239befba48efa3a784f4366788
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
vector_id: 31652
content:
"[...] and ocean temperature, sea ice extent, sea ice volume, glacial mass \r balance, and the global root mean square error of precipitation. The TOA \r heat flux balance is achieved using a diversity of approaches, usually \r unique to each modelling group. Adjustments are made for parameters \r associated with uncertain or poorly constrained processes (Schmidt \r et al., 2017), for example the aerosol indirect effects, adjustments to \r ocean albedo, marine dimethyl sulfide (DMS) parameterization, or \r cloud properties (Mauritsen and Roeckner, 2020)."


id: 2847a723200bd864fe5f33c6fb2b15ed
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 4284
content:
"More recently, a number of studies have pointed to the possibility \r of systematically different climate responses to external forcings in \r EMICs and complex ESMs (Frolicher and Paynter, 2015; Pfister and \r Stocker, 2017, 2018) that need to be considered in the context of \r this report. For example, Frolicher and Paynter (2015) showed that \r EMICs have a higher simulated realized warming fraction (i.e., the \r TCR/ECS ratio) than CMIP5 ESMs and speculated that this may bias \r the temperature response to zero carbon emissions. But, in a recent \r comprehensive multi-model analysis of the zero CO2 emissions \r commitment, MacDougall et al. (2020) did not find any significant \r differences between EMICs and ESMs in committed temperatures \r 90  years after halting emissions. While some EMICs contribute to \r parts of the CMIP6-endorsed MIPs, a coordinated EMICs modelling \r effort similar to those carried out for AR4 (Plattner et al., 2008) and \r [...]"


id: 11a3b4b97e39fa6a9658f1a3fc4fc226
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
vector_id: 294
content:
"[...] AR5 (Eby et al., 2013; Zickfeld et al., 2013) is not in place for IPCC AR6; \r however, EMICs are assessed in a number of chapters. For example, \r Chapters 4 and 5 use EMICs in the assessment of long-term climate \r change beyond 2100 (Section  5.5); zero-emissions commitments, \r overshoot and recovery (Section 4.7); consequences of CO2 removal \r (CDR) on the climate system and the carbon cycle (Sections 4.6 and \r 5.6); and long-term carbon cycle-climate feedbacks (Section 5.4)."


id: e12323529c27d5b628e75ffb3112b3f2
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
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content:
"information at the regional scale (Roberts et al., 2018). However, \r due to the large computational resources required by these models, \r only a  limited number of simulations per model are available. \r In  addition to CMIP global models, regional information can be \r derived using regional climate models (RCMs) and downscaling \r techniques, presented in Chapter  10 and the Atlas. RCMs are \r dynamical models, similar to GCMs, that simulate a limited region \r and are forced with boudary conditions from a  global simulation, \r often correcting for biases (Section 10.3, Cross-Chapter Box 10.2 and \r Annex II). This approach allows the use of a higher resolution within \r the chosen domain, and thus better represents important drivers of \r regional climate such as mountain ranges, land management and \r urban effects. RCMs resolving atmospheric convection explicitly are \r now included in intercomparisons (Coppola et al., 2020) and are \r [...]"


id: 4bec0963c12a401f55f851eda4c9602e
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 10616
content:
"[...] used in Chapters 10, 11 and 12. Other approaches, such as statistical \r downscaling, are also used to generate regional climate projections \r (Section 10.3; Maraun and Widmann, 2018)."


id: 4a742e0c2f63aae2ceddce834310a3aa
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 10338
content:
"Physical emulators and simple climate models make up a broad \r class of heavily parametrized models designed to reproduce the \r responses of the more complex, process-based models, and provide \r rapid translations of emissions, via concentrations and radiative \r forcing, into probabilistic estimates of changes to the physical climate \r system. The main application of emulators is to extrapolate insights \r from ESMs and observational constraints to a larger set of emissions \r scenarios (Cross-Chapter Box  7.1). The computational efficiency of \r various emulating approaches opens new analytical possibilities, \r given that ESMs take a  lot of computational resources for each \r simulation. The applicability and usefulness of emulating approaches \r are however constrained by their skill in capturing the global mean \r climate responses simulated by the ESMs (mainly limited to global \r mean or hemispheric land/ocean temperatures) and by their ability \r [...]"


id: 8fe4d360d6bb8d02596cc75c1cfb7fa
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 22525
content:
"Earth system models of intermediate complexity (EMICs) \r complement the model hierarchy and fill the gap between conceptual, \r simple climate models and complex GCMs or ESMs (Claussen \r et al., 2002). EMICs are simplified; they include processes in a more \r parameterized, rather than explicitly calculated, form and generally \r have lower spatial resolution compared to the complex ESMs. \r As a result, EMICs require much less computational resource and can \r be integrated for many thousands of years without supercomputers \r (Hajima et al., 2014). The range of EMICs used in climate change \r research is highly heterogeneous, ranging from zonally averaged or \r mixed-layer ocean models coupled to statistical-dynamical models \r of the atmosphere, to low-resolution three-dimensional ocean \r models coupled to simplified dynamical models of the atmosphere. \r An increasing number of EMICs include interactive representations \r of the global carbon cycle, with varying levels of complexity and \r [...]"


id: d9466ef814fa90c045f6578fe67c771a
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 35524
content:
"[...] numbers of processes considered (Plattner et al., 2008; Zickfeld \r et al., 2013; MacDougall et al., 2020). Given the heterogeneity of \r the EMIC community, modellers tend to focus on specific research \r questions and develop individual models accordingly. As for any \r type of models assessed in this Report, the set of EMICs undergoes \r thorough evaluation and fit-for-purpose testing before being applied \r to address specific climate aspects."


id: 1cea903356041be0d6e92568c74d73f1
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 2268
content:
"The terms 'emulator' and 'simple climate model' (SCM) are different, \r although they are sometimes used interchangeably. SCM refers to \r a  broad class of lower-dimensional models of the energy balance, \r radiative transfer, carbon cycle, or a  combination of such physical \r components. SCMs can also be tuned to reproduce the calculations \r of climate-mean variables of a  given ESM, assuming that their \r structural flexibility can capture both the parametric and structural \r uncertainties across process-oriented ESM responses. When run in \r this setup, they are termed emulators. Simple climate models do not \r have to be run in 'emulation' mode, though, as they can also be used \r to test consistency across multiple lines of evidence with regard to \r ranges in ECS, TCR, TCRE and carbon cycle feedbacks (Chapters 5 and \r 7). Physical emulation can also be performed with very simple \r parameterizations ('one-or-few-line climate models'), statistical \r [...]"


id: d5d70f3a71e133c4cdcc7d5a72dec38
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updated_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
vector_id: 34934
content:
"[...] methods like neural networks, genetic algorithms, or other artificial \r intelligence approaches, where the emulator behaviour is explicitly \r tuned to reproduce the response of a given ESM or model ensemble \r (Chapters 4, 5 and 7)."


id: 5f5a5b8d11b5d22db2145f64cc996ec9
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
vector_id: 14086
content:
"Typical strategies for enhancing the fitness-for-purpose of a model \r include increasing resolution in order to explicitly simulate key \r processes, improving relevant parameterizations, and careful tuning. \r Changes to a  model that enhance its fitness for one purpose can \r sometimes decrease its fitness for others, by upsetting a pre-existing \r balance of approximations. When it is unclear whether a model is \r fit for a purpose of interest, there is often a closely related purpose \r for which the evidence of fitness is clearer. For example, it might \r be unclear whether a  model is fit for providing highly accurate \r projections of precipitation changes in a region, but reasonable to \r think that the model is fit for providing projections of precipitation \r changes that cannot yet be ruled out (Parker, 2009). Such information \r about plausible or credible changes can be useful to inform \r adaptation. Note that challenges associated with assessing models' \r [...]"


id: da332175536f070770c907b8b84684bb
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
vector_id: 35684
content:
"[...] fitness-for-purpose need not prevent reaching conclusions with high \r confidence if there are multiple other lines of evidence supporting \r those same conclusions."


id: 9f61701a9c7cc288b09e8ebd17553dbf
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 25264
content:
"A key issue addressed in this Report is whether climate models \r are adequate or 'fit' for purposes of interest, that is, whether they \r can be used to successfully answer particular research questions, \r especially about the causes of recent climate change and the future \r evolution of climate (e.g.,  Parker, 2009; Notz, 2015; Knutti, 2018; \r Winsberg, 2018). Assessment of a  model's fitness-for-purpose can \r be informed both by how the model represents relevant physical \r processes and by relevant performance metrics  (Baumberger \r et al., 2017; Parker, 2020). The processes and metrics that are \r most relevant can vary with the question of interest. For example, \r a  question about changes in deep-ocean circulation compared \r with a  question  about  changes  in  regional precipitation (Notz, \r 2015; Gramelsberger et al., 2020). New model-evaluation \r tools (Section  1.5.4.5) and  emergent constraint methodologies \r [...]"


id: c2b4766dae07720e41d1df6fcab5ef78
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 31537
content:
"[...] (Section 1.5.4.7) can also aid the assessment of fitness-for-purpose, \r especially in conjunction with process understanding (Klein and \r Hall, 2015; Knutti, 2018). The broader availability of large model \r ensembles may allow for novel tests of fitness that better account \r for natural climate variability (Section 1.5.4.2). Fitness-for-purpose of \r models used in this Report is discussed in Chapter 3 (Section 3.8.4) \r for the global scale, in Chapter 10 (Section 10.3) for regional climate, \r and in the other chapters for the process level."


id: 103c6abb01ad155790f732fd6b72611e
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 43
content:
"A key approach in climate science is the comparison of results from \r multiple model simulations with each other and against observations. \r These simulations have typically been performed by separate models \r with consistent boundary conditions and prescribed emissions or \r radiative forcings, as in the Coupled Model Intercomparison Project \r phases (CMIP, Meehl et al., 2000, 2007a; Taylor et al., 2012; Eyring \r et al., 2016). Such multi-model ensembles (MMEs) have proven \r highly useful in sampling and quantifying model uncertainty, within \r and between generations of climate models. They also reduce the \r influence on projections of the particular sets of parametrizations \r and physical components simulated by individual models. The primary \r usage of MMEs is to provide a well-quantified model range, but when \r used carefully they can also increase confidence in projections (Knutti \r et al., 2010). Presently, however, many models also share provenance \r [...]"


id: 587286f30884aa996b3a0af0ff7d14ee
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updated_at: 2023-04-19 13:22:34
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vector_id: 12809
content:
"[...] (Masson and Knutti, 2011) and may have common biases that should \r be acknowledged when presenting and building on MME-derived \r conclusions (Section 1.5.4.6; Boe, 2018; Abramowitz et al., 2019)."


id: 86c2054a1ae1810faf320509ce021b5c
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 20874
content:
"Since AR5, an increase in computing power has made it possible \r to investigate simulated internal variability and to provide \r robust estimates of forced model responses, using large initial \r condition  ensembles (ICEs), also referred to as single model \r initial  condition large ensembles (SMILEs). Examples using GCMs \r or ESMs that support assessments in AR6 include the CESM \r Large Ensemble (Kay et al., 2015), the MPI Grand Ensemble \r (Maher et al., 2019), and the CanESM2 large ensembles (Kirchmeier\u0002Young et al., 2017). Such ensembles employ a single GCM or ESM \r in a  fixed configuration, but starting from a  variety of different \r initial states. In some experiments, these initial states only differ \r slightly. As the climate system is chaotic, such tiny changes in initial \r conditions lead to different evolutions for the individual realizations \r of the system as a  whole. Other experiments start from a  set of \r [...]"


id: 7c20684216c41f3daae1896f88c88ec
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updated_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
vector_id: 19047
content:
"[...] well-separated ocean initial conditions to sample the uncertainty in \r the circulation state of the ocean and its role in longer-time scale \r variations. These two types of ICEs have been referred to as 'micro' \r and 'macro' perturbation ensembles respectively (Hawkins et al., \r 2016). In support of this Report, most models contributing to CMIP6 \r have produced ensembles of multiple realizations of their historical \r and scenario simulations (Chapters 3 and 4)."


id: 95c9f3f9fc92fd0a5643575f1c5716c6
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
vector_id: 23545
content:
"The CMIP DECK simulations form the basis for a range of assessments \r and projections in the following chapters. As in CMIP5, they consist of: \r a 'pre-industrial' control simulation (piControl, where 'pre-industrial' \r is taken as fixed 1850 conditions in these experiments); an idealized, \r abrupt quadrupling of CO2 concentrations relative to piControl \r (to estimate equilibrium climate sensitivity); a 1% per year increase \r in CO2 concentrations relative to piControl (to estimate the transient \r climate response); and a  transient simulation with prescribed sea\u0002surface temperatures for the period 1979-2014 (termed 'AMIP' for \r historical reasons). In addition, all participating models perform \r a  historical simulation for the period 1850-2014. For the latter, \r common CMIP6 forcings are prescribed (Cross-Chapter Box  1.4, \r Table  2). Depending on the model setup, these include emissions \r and concentrations of short-lived species (Hoesly et al., 2018; Gidden \r [...]"


id: 69551db80731077876822d14577e4b06
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index: climate_gpt_v2
vector_id: 15798
content:
"[...] et al., 2019), long-lived GHGs (Meinshausen et al., 2017), biomass \r burning emissions (van Marle et al., 2017), global gridded land-use \r forcing data (Ma et al., 2020), solar forcing (Matthes et al., 2017), \r and stratospheric aerosol data from volcanoes (Zanchettin et al., \r 2016). The methods for generating gridded datasets are described \r in Feng et al. (2020). For AMIP simulations, common sea surface \r temperatures (SSTs) and sea ice concentrations (SICs) are prescribed. \r For simulations with prescribed aerosol abundances (i.e.,  not \r calculated from emissions), optical properties and fractional changes \r in cloud droplet effective radius are generally prescribed in order to \r provide a more consistent representation of aerosol forcing relative \r to earlier CMIP phases (Fiedler et al., 2017; Stevens et al., 2017). \r For models without ozone chemistry, time-varying gridded ozone \r concentrations and nitrogen deposition are also provided (Checa\u0002Garcia et al., 2018)."


id: e65ba33282fe8c323014219ba9e5e26a
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
vector_id: 37834
content:
"In addition, process- or regime-oriented evaluation of models has \r been expanded since AR5. By focusing on processes, causes of \r systematic errors in the models can be identified and insights can \r be gained as to whether a mean state or trend is correctly simulated \r and for the right reasons. This approach is commonly used for the \r evaluation of clouds (e.g., Williams and Webb, 2009; Konsta et al., \r 2012; Bony et al., 2015; Dal Gesso et al., 2015; Jin et al., 2017), \r dust emissions (e.g.,  Parajuli et al., 2016; Wu et al., 2016) as well \r as aerosol-cloud (e.g., Gryspeerdt and Stier, 2012) and chemistry-\r climate (SPARC, 2010) interactions. Process-oriented diagnostics \r have also been used to evaluate specific phenomena such as the \r El Nino-Southern Oscillation (ENSO; Guilyardi et al., 2016), the \r Madden-Julian Oscillation (MJO; Ahn et al., 2017; Jiang et al., 2018), \r Southern Ocean clouds (Hyder et al., 2018), monsoons (Boo et al., \r [...]"


id: 42b6cd1d972d5560924c47c280f1f7fa
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updated_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
vector_id: 8932
content:
"Emergent constraints use the spread in model projections to estimate the \r sensitivities of the climate system to anthropogenic forcing, providing \r another type of ensemble-wide information that is not readily available \r from simulations with one ESM alone. As emergent constraints depend on \r identifying those observable aspects of the climate system that are most \r related to climate projections, they also help to focus model evaluation on \r the most relevant observations (Hall et al., 2019). However, there is a chance \r that indiscriminate data-mining of the multi-dimensional outputs from \r ESMs could lead to spurious correlations (Caldwell et al., 2014; Wagman \r and Jackson, 2018) and less-than-robust emergent constraints on future \r changes (Bracegirdle and Stephenson, 2013). To avoid this, emergent \r constraints need to be tested 'out of sample' on parts of the dataset \r that were not included in its construction (Caldwell et al., 2018) and\r [...]"


id: 492be944bc0355bdb9e143ac7013ee28
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content:
"[...] should also always be based on sound physical understanding \r and mathematical theory (Hall et al., 2019). Their conclusions should also \r be reassessed when a new generation of MMEs becomes available, such \r as CMIP6. As an example, Chapter 7 (Section 7.5.4) discusses and assesses \r recent studies where equilibrium climate sensitivities (ECS) diagnosed in \r a multi-model ensemble are compared with the same models' estimates \r of an observable quantity, such as post-1970s global warming or \r tropical sea surface temperatures of past climates like the Last Glacial \r Maximum or the Pliocene. Assessments of other emergent constraints \r appear throughout later chapters, such as Chapter  4  (Section  4.2.5), \r Chapter 5 (Section 5.4.6) and Chapter 7 (Section 7.5.4)."


id: 86ebdeeb9b4032c1dbc67c2f566234e6
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updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 20901
content:
"Concern has been raised about the large extent to which code is \r shared within the CMIP5 multi-model ensemble (Sanderson et al., \r 2015a). Boe (2018) showed that a clear relationship exists between \r the number of components shared by climate models and how similar \r the simulations are. The resulting similarities in behaviour need to be \r accounted for in the generation of best-estimate multi-model climate \r projections. This has led to calls to move beyond equally-weighted \r multi-model means towards weighted means that take into account \r both model performance and model independence (Sanderson et al., \r 2015b, 2017; Knutti et al., 2017). Model independence has been \r defined in terms of performance differences within an ensemble \r (Masson and Knutti, 2011; Knutti et al., 2013, 2017, Sanderson \r et al., 2015a, b, 2017; Lorenz et al., 2018). However, this definition \r is sensitive to the choice of variable, observational dataset, metric, \r [...]"


id: a0c4d36cab41dfce60271c1e2ef0546d
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index: climate_gpt_v2
vector_id: 25492
content:
"[...] time period, and region, and a  performance-ranked ensemble has \r been shown to sometimes perform worse than a random selection \r (Herger et al., 2018a). The adequacy of the constraint provided by the \r data and experimental methods can be tested using a 'calibration\u0002validation' style partitioning of observations into two sets (Bishop and \r Abramowitz, 2013), or a 'perfect model approach' where one of the \r ensemble members is treated as the reference dataset and all model \r weights are calibrated against it (Bishop and Abramowitz, 2013; \r Wenzel et al., 2016; Knutti et al., 2017; Sanderson et al., 2017; Herger \r et al., 2018a, b). Sunyer et al. (2014) use a Bayesian framework to \r account for model dependencies and changes in model biases. Annan \r and Hargreaves (2017) provides a statistical, quantifiable definition of \r independence that is independent of performance-based measures."


id: a58e5271cd4943be09c0a316cb7d08e
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 26365
content:
"Assessments of climate model ensembles have commonly assumed \r that each individual model is of equal value ('model democracy') \r and when combining simulations to estimate the mean and variance \r of quantities of interest, they are typically unweighted (Haughton \r et al., 2015). This practice has been noted to diminish the influence \r of models exhibiting a  good match with observations (Tapiador \r et al., 2020). However, exceptions to this approach exist, notably \r AR5 projections of sea ice, which only selected a few models which \r passed a model performance assessment (Collins et al., 2013), and \r more studies on this topic have appeared since AR5 (e.g., Eyring et al., \r 2019). Ensembles are typically sub-selected by removing either poorly \r performing model simulations (McSweeney et al., 2015) or model \r simulations that are perceived to add little additional information, \r typically where multiple simulations have come from the same \r [...]"


id: 7487074c3a9e62b9a4dd49fcf181593a
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 17737
content:
"The AR5 quantified uncertainty in CMIP5 climate projections by \r selecting one realization per model per scenario, and calculating \r the 5-95% range of the resulting ensemble (Box 4.1) and the same \r strategy is generally still used in AR6. Broadly, the following chapters \r take the CMIP6 5-95% ensemble range as the likely uncertainty \r range for projections,8 with no further weighting or consideration \r of model ancestry and as long as no universal, robust method for \r weighting a multi-model projection ensemble is available (Box 4.1). \r A  notable exception to this approach is the assessment of future \r changes in global surface air temperature (GSAT), which also draws \r on the updated best estimate and range of equilibrium climate \r sensitivity assessed in Chapter 7. For a thorough description of the \r model-weighting choices made in this Report, and the assessment \r of GSAT, see Chapter  4  (Box  4.1). Model selection and weighting \r [...]"


id: 34f66d38e16997fcf6ae0d4f631c6b92
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updated_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
vector_id: 6559
content:
"[...] in downscaling approaches for regional assessment is discussed in \r Chapter 10 (Section 10.3.4)."


id: 13fbe024dda22ffbaa7c99d6ffcf1de8
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 731
content:
"This section introduces three ways to synthesize climate \r change  knowledge across topics and chapters. These 'dimensions \r of integration' include (i) emissions and concentration scenarios \r underlying the climate change projections assessed in this \r Report,  (ii)  levels of global mean surface warming relative to the \r 1850-1900 baseline ('global warming levels'), and (iii) cumulative \r carbon emissions (Figure 1.24). All three dimensions can, in principle, \r be used to synthesize physical science knowledge across WGI, and \r also across climate change impacts, adaptation, and mitigation \r research. Scenarios, in particular, have a  long history of serving \r as a  common reference point within and across IPCC Working \r Groups and research communities. Similarly, cumulative carbon \r emissions and global warming levels provide key links between WGI \r assessments and those of the other WGs; these two dimensions \r frame the cause-effect chain investigated by WGI. The closest links \r [...]"


id: 4fa58ee6f2e99e83ab1f1f13c2dcfe3c
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updated_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
vector_id: 11301
content:
"[...] to WGIII are the emissions scenarios, as WGIII considers drivers of \r emissions and climate change mitigation options. The links to WGII \r are the geophysical climate projections from the Earth system \r models, which are often used as the starting point in the literature on \r climate impacts and adaptation."


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"Figure 1.25 | Global mean surface air temperature (GSAT) illustrated as warming stripes from blue (cold) to red (warm) over three different time periods.\r From 1750-1850 based on PAGES 2K reconstructions (PAGES 2k Consortium, 2017, 2019); from 1850-2018 showing the composite GSAT time series assessed in Chapter 2; \r and from 2020 onwards using the assessed GSAT projections for each Shared Socio-economic Pathway (SSP) (from Chapter 4). For the projections, the upper end of each arrow \r aligns with the colour corresponding to the 95th percentile of the projected temperatures and the lower end aligns with the colour corresponding to the 5th percentile of the \r projected temperature range. Projected temperatures are shown for five scenarios from 'very low' SSP1-1.9 to 'very high' SSP5-8.5 (see Cross-Chapter Box 1.4 for more details \r [...]"


id: 50a45a8f6b467c93ff8de6d02594a16c
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"[...] on the scenarios). For illustrative purposes, natural variability has been added from a single CMIP6 Earth system model (MRI ESM2). The points in time when total CO2 emissions \r peak; reach halved levels of the peak; and reach net zero emissions are indicated with arrows, ' 1/2' and '0' marks, respectively. Further details on data sources and processing \r are available in the chapter data table (Table 1.SM.1)."


id: 9782b9577738847bd72d9785d36d2ac6
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"Figure 1.26 | Historical and projected future concentrations of carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) and global mean temperatures \r (GMST). GMST temperature reconstructions over the last 2000 years were compiled by the PAGES 2k Consortium (2017, 2019) (grey line, with 95% uncertainty range), joined \r by historical GMST time series assessed in Chapter 2 (black line) - both referenced against the 1850-1900 period. Future GSAT temperature projections are from CMIP6 ESM \r models across all concentration-driven SSP scenario projections (Chapter 4). The discontinuity around year 2100 for CMIP6 temperature projections results from the fact that not \r all ESM models ran each scenario past 2100. The grey vertical band indicates the future 2015-2300 period. The concentrations used to drive CMIP6 Earth system models are \r [...]"


id: ca473923e5515dad695fda83f985d5ac
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"[...] derived from ice core, firn and instrumental datasets (Meinshausen et al., 2017) and projected using an emulator (Cross-Chapter Box 7.1; Meinshausen et al., 2020). The colours \r of the lines indicate the SSP scenarios used in this Report (Cross-Chapter Box 1.4, Figure 1). Further details on data sources and processing are available in the chapter data \r table (Table 1.SM.1)."


id: 69f94fb4b5c31bb5c21a8f12384d7c75
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"The SSP scenarios can be used for either emissions- or concentration\u0002driven model experiments (Cross-Chapter Box 1.4). ESMs can be run \r with emissions and concentrations data for GHGs and aerosols and \r land-use or landcover maps and calculate levels of radiative forcing \r internally. The radiative forcing labels of the RCP and SSP scenarios, \r such as '2.6' in RCP2.6 or SSP1-2.6, are thus approximate labels \r for the year 2100 only. The actual global mean effective radiative \r forcing varies across ESMs due to different radiative transfer \r schemes, uncertainties in aerosol-cloud interactions, and different \r feedback mechanisms, among other reasons. Nonetheless, using \r approximate radiative forcing labels is advantageous because it \r establishes a clear categorization of scenarios, with multiple climate \r forcings and different combinations in those scenarios summarized in \r a single number. The classifications according to cumulative carbon \r [...]"


id: 37cea27fbf2eea0ed10477ccf28933c1
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"The core set of five illustrative SSP scenarios - SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 - was selected in this Report to \r align with the objective that the new generation of SSP scenarios should fill certain gaps identified in the RCPs. For example, a scenario \r assuming reduced air-pollution control and thus higher aerosol emissions was missing from the RCPs. Likewise, nominally the only 'no\u0002additional-climate-policy' scenario in the set of RCPs was RCP8.5. The new SSP3-7.0 'no-additional-climate-policy' scenario fills both \r these gaps. A very strong mitigation scenario in line with the 1.5degC goal of the Paris Agreement was also missing from the RCPs, and the \r SSP1-1.9 scenario now fills this gap, complementing the other strong mitigation scenario SSP1-2.6. The five core SSPs were also chosen \r to ensure some overlap with the RCP levels for radiative forcing at the year 2100 (specifically 2.6, 4.5, and 8.5; O'Neill et al., 2016; Tebaldi \r [...]"


id: 1bd7ba0f25b3c0f7effc491388c32e04
created_at: 2023-04-19 13:22:34
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content:
"[...] et al., 2021), although effective radiative forcings are generally higher in the SSP scenarios compared to the equivalently named RCP \r pathways (Section 4.6.2 and Cross-Chapter Box 1.4, Figure 1). In theory, running scenarios with similar radiative forcings would permit \r analysis of the CMIP5 and CMIP6 outcomes for pairs of scenarios (e.g., RCP8.5 and SSP5-8.5) in terms of varying model characteristics \r rather than differences in the underlying scenarios. In practice, however, there are limitations to this approach (Sections 1.6.1.1 and 4.6.2)."


id: 424754311506865459435c9810e18e2
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"Cross-Chapter Box 1.4, Figure 1 | The SSP scenarios used in this Report, their indicative temperature evolution and radiative forcing categorization, \r and the five socio-economic storylines upon which they are built. The core set of scenarios used in this report - i.e., SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0 \r and SSP5-8.5 - is shown together with an additional four SSPs that are part of ScenarioMIP, as well as previous RCP scenarios. In the left-hand panel, the indicative \r temperature evolution is shown (adapted from Meinshausen et al., 2020). The black stripes on the respective scenario family panels on the left-hand side indicate \r a larger set of IAM-based SSP scenarios that span the scenario range more fully, but are not used in this report. The SSP-radiative forcing matrix is shown on the \r right-hand panel, with the SSP socio-economic narratives shown as columns and the indicative radiative forcing categorization by 2100 shown as rows. Note that \r [...]"


id: 64d28e25906978fdba137722dd68e310
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"[...] the descriptive labels for the five SSP narratives refer mainly to the reference scenario futures without additional climate policies. For example, SSP5 can accommodate \r strong mitigation scenarios leading to net zero emissions; these do not match a 'fossil-fuelled development' label. Further details on data sources and processing are \r available in the chapter data table (Table 1.SM.1)."


id: 781042d159895679e29f4a2142dd2b7a
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"Cross-Chapter Box 1.4, Table 1 | Overview of SSP scenarios used in this report. The middle column briefly describes the SSP scenarios and the right-hand \r column indicates the previous RCP scenarios that most closely match that SSP's assessed global surface air temperature (GSAT) trajectory. RCP scenarios are generally \r found to result in larger modelled warming for the same nominal radiative forcing label (Section 4.6.2.2). The five core SSP scenarios used most commonly in this report \r are highlighted in bold . Further SSP scenarios are used where they allow assessment of specific aspects, e.g., air pollution policies in Chapter 6 (SSP3-7.0-lowNTCF). \r RCPs are used in this report wherever the relevant scientific literature makes substantial use of regional or domain-specific model output that is based on these previous \r [...]"


id: d41bca5bc96710ee604b40c5d9d4579
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"[...] RCP pathways, such as sea level rise projections in Chapter 9 (Section 9.6.3.1) or regional climate aspects in Chapters 10 and 12. See Chapter 4 (Section 4.3.4) for the \r GSAT assessment for the SSP scenarios and Section 4.6.2.2 for a comparison between SSPs and RCPs in terms of both radiative forcing and global surface temperature."


id: 2d6a946a71c0328d2ed2a2b5d07de33a
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index: climate_gpt_v2
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"ESMs are driven by either emissions or concentrations scenarios. Inferring concentration changes from emissions time series requires \r using carbon cycle and other gas cycle models. To aid comparability across ESMs, and in order to allow participation of ESMs that do \r not have coupled carbon and other gas cycle models in CMIP6, most of the CMIP6 ESM experiments are so-called 'concentration\u0002driven' runs, with concentrations of CO2, CH4, N2O and other well-mixed GHGs prescribed in conjunction with aerosol emissions, \r ozone changes and effects from human-induced land-cover changes that may be radiatively active via albedo changes (Cross-Chapter \r Box 1.4, Figure 2). In these concentration-driven climate projections, the uncertainty in projected future climate change resulting from \r our limited understanding of how the carbon cycle and other gas cycles will evolve in the future is not captured. For example, when \r [...]"


id: 8db6d062972479d073e5d4c2c82a8175
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"[...] deriving the default concentrations for these scenarios, permafrost and other carbon cycle feedbacks are considered using default \r settings, with a single time series prescribed for all ESMs (Meinshausen et al., 2020). Thus, associated uncertainties (Joos et al., 2013; \r Schuur et al., 2015) are not considered."


id: bd6c847739379f8fde5c1069ab33f9b1
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"The RCP scenarios (van Vuuren et al., 2011) then broke new ground \r by providing low-emissions pathways that implied strong climate \r change mitigation, including an example with negative CO2 emissions \r on a large scale, namely RCP2.6. As shown in Figure 1.28, the upper \r end of the scenario range has not substantially shifted. Building \r on the SRES multi-gas scenarios, the RCPs include time series of \r emissions and concentrations of the full suite of GHGs, aerosols and \r chemically active gases, as well as land use and land cover (Moss \r et al., 2010). The word 'representative' signifies that each RCP is \r only one of many possible scenarios that would lead to the specific \r radiative forcing characteristics. The term 'pathway' emphasizes that \r not only the long-term concentration levels are of interest, but also \r the trajectory taken over time to reach that outcome (Moss et al., \r 2010). RCPs usually refer to the concentration pathway extending to \r [...]"


id: 1506281a2798a454b267d7d64dca9178
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"[...] 2100, for which IAMs produced corresponding emissions scenarios. \r Four RCPs produced from IAMs were selected from the published \r literature and are used in AR5 as well as in this report, spanning \r approximately the range from below 2degC warming to high (above \r 4degC) warming best-estimates by the end of the 21st century: RCP2.6, \r RCP4.5 and RCP6.0 and RCP8.5 (Cross-Chapter Box  1.4, Table  1). \r Extended Concentration Pathways (ECPs) describe extensions of \r the RCPs from 2100 to 2300 that were calculated using simple rules \r generated by stakeholder consultations; these do not represent fully \r consistent scenarios (Meinshausen et al., 2011b)."


id: 68bb8f02e352ce039a375a60450eea77
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"In general, no likelihood is attached to the scenarios assessed in this \r Report. The use of different scenarios for climate change projections \r allows the exploration of 'scenario uncertainty' (Section 1.4.4; SR1.5; \r Collins et al., 2013). Scenario uncertainty is fundamentally different \r from geophysical uncertainties, which result from limitations in the \r understanding and predictability of the climate system (Smith and \r Stern, 2011). In scenarios, by contrast, future emissions depend to \r a large extent on the collective outcome of choices and processes \r related to population dynamics and economic activity, or on choices \r that affect a given activity's energy and emissions intensity (Jones, \r 2000; Knutti et al., 2008; Kriegler et al., 2012; van Vuuren et al., \r 2014). Even if identical socio-economic futures are assumed, the \r associated future emissions still face uncertainties, since different \r experts and model frameworks diverge in their estimates of future \r [...]"


id: adf829bd2b71b8fef45701b8a8f8ef7
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"When exploring various climate futures, scenarios with no, or no \r additional, climate policies are often referred to as 'baseline' or \r 'reference scenarios' (Section 1.6.1.1 and Glossary). Among the five \r core scenarios used most in this report, SSP3-7.0 and SSP5-8.5 are \r explicit 'no-climate-policy' scenarios (Cross-Chapter Box 1.4, Table 1; \r Gidden et al., 2019), assuming a carbon price of zero. These future \r 'baseline' scenarios are hence counterfactuals that include fewer \r climate policies compared to 'business-as-usual' scenarios  - given \r that 'business-as-usual' scenarios could be understood to imply \r a continuation of existing climate policies. Generally, future scenarios \r are meant to cover a broad range of plausible futures, due, for example \r to unforeseen discontinuities in development pathways  (Raskin \r and Swart, 2020), or to large uncertainties in underlying long\u0002term projections of economic drivers (Christensen et al., 2018). \r [...]"


id: f0a49b1e06349f32673dd775feeb396d
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"[...] However, the likelihood of high-emissions scenarios such as RCP8.5 \r or SSP5-8.5 is considered low in light of recent developments in "


id: 24e527187993d1b050c12cb80ac36fe7
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"the  energy sector (Hausfather and Peters, 2020a, b). Studies that \r consider possible future emissions trends in the absence of additional \r climate policies, such as the recent IEA 2020 World Energy Outlook \r 'stated policy' scenario (IEA, 2020), project approximately constant \r fossil fuel and industrial CO2 emissions out to 2070, approximately \r in line with the intermediate RCP4.5, RCP6.0 and SSP2-4.5 scenarios \r (Hausfather and Peters, 2020b) and the 2030 global emissions levels \r that are pledged as part of the Nationally Determined Contributions \r (NDCs) under the Paris Agreement (Section  1.2.2; Fawcett et al., \r 2015; Rogelj et al., 2016; UNFCCC, 2016; IPCC, 2018). On the other \r hand, the default concentrations aligned with RCP8.5 or SSP5-8.5 \r and resulting climate futures derived by ESMs could be reached by \r lower emissions trajectories than RCP8.5 or SSP5-8.5. That is because \r the uncertainty range on carbon cycle feedbacks includes stronger \r [...]"


id: cf0c519d7bf44db706bbe601a1cde6e2
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"[...] feedbacks than assumed in the default derivation of RCP8.5 and \r SSP5-8.5 concentrations (Section 5.4; Ciais et al., 2013; Friedlingstein \r et al., 2014; Booth et al., 2017)."


id: 102e41091f9414bca580ff2c13b86577
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content:
"The global mean surface temperature change, or 'global warming \r level' (GWL), is a 'dimension of integration' that is highly relevant \r across scientific disciplines and socio-economic actors. First, global \r warming levels relative to pre-industrial conditions are the quantity \r in which the 1.5degC and 'well below 2degC' Paris Agreement goals were \r formulated. Second, global mean temperature change has been \r found to be almost-linearly related to a number of regional climate \r effects (Mitchell et al., 2000; Mitchell, 2003; Tebaldi and Arblaster, \r 2014; Seneviratne et al., 2016; Li et al., 2020; Seneviratne and Hauser, \r 2020). Even where non-linearities are found, some regional climate \r effects can be considered to be almost scenario-independent for \r a given level of warming (Sections 4.2.4, 4.6.1, 8.5.3 and 10.4.3.1, \r and Cross-Chapter Box  11.1). Finally, the evolution of aggregated \r impacts with warming levels has been widely used and embedded \r [...]"


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"[...] in the assessment of the 'Reasons for Concern' (RFC) in IPCC WGII \r (Smith et al., 2009; IPCC, 2014a). The RFC framework was further \r expanded in SR1.5 (2018), SROCC (2019) and SRCCL (2019) by \r explicitly describing the differential impacts of half-degree warming \r steps (Section 1.4.4 and Cross-Chapter Box 12.1; cf. King et al., 2017)."


id: 90d8017705d6897c202b9dab4b5cbd66
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"To address long-term scenario uncertainties, scenario storylines \r (or  'narratives') are often used (see Section  1.4.4 for a  more \r general discussion on 'storylines', also covering 'physical climate \r storylines'; Rounsevell and Metzger, 2010; O'Neill et al., 2014). \r Scenario storylines are descriptions of a  future world, and the \r related large-scale socio-economic development pathways towards \r that world that are deemed plausible within the current state of \r knowledge and historical experience (Section 1.2.3; WGIII). Scenario \r storylines attempt to 'stimulate, provoke, and communicate visions of \r what the future could hold for us' (Rounsevell and Metzger, 2010) in \r settings where either limited knowledge or inherent unpredictability \r in social systems prevent a  forecast or numerical prediction. \r Scenario storylines have been used in previous climate research, \r and they are the explicit or implicit starting point of any scenario \r [...]"


id: d10556f93c0d2cbbae5f5f898e59149e
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"[...] exercise, including for the SRES scenarios (IPCC, 2000) and the SSPs \r (e.g., O'Neill et al., 2017a)."


id: d5e8efb9358d3f687bb2a3e1ef0fd9be
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"Recent technological or socio-economic trends might be informative \r for bounding near-term future trends, for example, if technological \r progress renders a  mitigation technology cheaper than previously \r assumed. However, short-term emissions trends alone do not \r generally rule out an opposite trend in the future (van Vuuren et al., \r 2010). The ranking of individual RCP emissions scenarios from the \r IAMs with regard to emissions levels is different for different time \r horizons, for example, 2020 compared with longer-term emissions \r levels. For example, the strongest climate change mitigation scenario, \r RCP2.6, was in fact the second highest CO2 emissions scenario (jointly \r with RCP4.5) before 2020 in the set of RCPs and the strong global \r emissions decline in RCP2.6 only followed after 2020. Implicitly, \r this scenario feature was cautioning against the assumption that \r short-term trends predicate particular long-term trajectories. This is \r [...]"


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"[...] also the case in relation to the COVID-19 related drop in 2020 \r emissions. Potential changes in underlying drivers of emissions, such \r as those potentially incentivized by COVID-19 recovery stimulus \r packages, are more significant for longer-term emissions than the \r short-term deviation from recent emissions trends (Cross-Chapter \r Box 6.1 on COVID-19)."


id: ee52a0c9d4df06c2ddf2140d4aa162fe
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"In order to fully derive climate impacts, warming levels will need to be \r complemented by additional information, such as their associated CO2\r concentrations (e.g., fertilization or ocean acidification), composition \r of the total radiative forcing (aerosols compared with GHGs, with \r varying regional distributions) or socio-economic conditions (e.g., to \r estimate societal impacts). More fundamentally, while a  global \r warming level is a good proxy for the state of the climate (Cross\u0002Chapter Box 11.1), it does not uniquely define a change in global or \r regional climate state. For example, regional precipitation responses \r depend on the details of the individual forcing mechanisms that \r caused the change (Samset et al., 2016); on whether the temperature \r level is stabilized or transient (King et al., 2020; Zappa et al., 2020); \r on the vertical structure of the troposphere (Andrews et al., 2010); \r and, in particular, on the global distribution of atmospheric aerosols \r [...]"


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"[...] (Frieler et al., 2012). Another aspect is how Earth system components \r with  century-to-millennial response time scales, such as long-term \r sea level rise or permafrost thaw, are affected by global mean \r warming. For example, sea level rise 50 years after a 1degC warming \r will be lower than sea level rise 150  years after that same 1degC \r warming (Chapter 9)."


id: 5bb06f960a770c18c52901631cad8551
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"There is also a  close relationship between cumulative total GHG \r emissions and cumulative CO2 emissions for scenarios in the \r SR1.5 scenario database (Figure 1.29; IPCC, 2018). The dominance \r of CO2 compared to other well-mixed GHGs (Figure  1.29 and \r Section 5.2.4) allows policymakers to make use of the carbon budget \r concept (Section  5.5) in a  policy context, in which GWP-weighted \r combinations of multiple GHGs are used to define emissions targets. \r A caveat is that cumulative GWP-weighted CO2 equivalent emissions \r over the next decades do not yield exactly the same temperature \r outcomes as the same amount of cumulative CO2 emissions, because \r atmospheric perturbation lifetimes of the various GHGs differ. While \r carbon budgets are not derived using GWP-weighted emissions \r baskets but rather by explicit modelling of non-CO2-induced warming \r (Section  5.5 and Cross-Chapter Box  7.1), the policy frameworks \r [...]"


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content:
"[...] based on GWP-weighted emissions baskets can still make use of the \r insights from remaining cumulative carbon emissions for different \r warming levels."


id: 85224acc0fa077d942ea3daeea4c6200
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content:
"The same cumulative CO2 emissions could lead to a slightly different \r level of warming over time (Box  1.4). Rapid emissions followed \r by steep cuts and potentially net negative emissions would be \r characterized by a  higher maximum warming and faster warming \r rate, compared with the same cumulative CO2 emissions spread \r over a longer period. As further explored in the WGIII assessment, \r one potential limitation when presenting emissions pathway \r characteristics in cumulative emissions budget categories is that path \r dependencies and lock-in effects (e.g.  today's decisions regarding \r fossil fuel-related infrastructure) play an important role in long-term \r mitigation strategies (Davis et al., 2010; Luderer et al., 2018). Similarly, \r high emissions early on might imply strongly net negative emissions \r (Minx et al., 2018) later on to reach the same target envelope for \r cumulative emissions and temperature by the end of the century \r [...]"


id: c154bd24579f4043852e457f3f824d2a
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content:
"[...] (Box  1.4). This report explores options to address some of those \r potential issues from a WGI perspective (Sections 5.5.2 and 5.6.2)."


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"Figure 1.29 | The role of CO2 in driving future climate change in comparison to other greenhouse gases (GHGs). The GHGs included here are CH4, N2O, and \r 40 other long-lived, well-mixed GHGs. The blue shaded area indicates the approximate forcing exerted by CO2 in Shared Socio-economic Pathways (SSP) scenarios, ranging from \r very low SSP1-1.9 to very high SSP5-8.5 (Chapter 7). The CO2 concentrations under the SSP1-1.9 scenarios reach approximately 350 ppm after 2150, while those of SSP5-8.5 \r exceed 2000 ppm CO2 in the longer term (up to year 2300). Similar to the dominant radiative forcing share at each point in time (lower area plots), cumulative GWP-100-\r weighted GHG emissions happen to be closely correlated with cumulative CO2 emissions, allowing policymakers to make use of the carbon budget concept in a policy context \r [...]"


id: 253477c3092ce77188196851bedfda9
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content:
"[...] with multi-gas GHG baskets as it exhibits relatively low variation across scenarios with similar cumulative emissions until 2050 (inset panel). Further details on data sources \r and processing are available in the chapter data table (Table 1.SM.1)."


id: f7456c51e1c254e1382c2aac900b6770
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vector_id: 40880
content:
"CDR can be achieved through a  number of measures (Section  5.6; SRCCL, IPCC, 2019a). These include additional afforestation, \r reforestation, soil carbon management, biochar, direct air capture and carbon capture and storage (DACCS), and bioenergy with \r carbon capture and storage (BECCS; de Coninck et al., 2018, SR1.5 Ch4; Minx et al., 2018; see also WGIII Chapters  7  and 12). \r Differences between land use, land-use change and forestry (LULUCF) accounting rules, and scientific bookkeeping approaches for CO2\r emissions and removals from the terrestrial biosphere, can result in significant differences between the amount of CDR that is reported \r in different studies (Grassi et al., 2017). Different measures to achieve CDR come with different risks, negative side effects and \r potential co-benefits - also in conjunction with sustainable development goals - that can inform choices around their implementation \r [...]"


id: e633e9ed69c4d9e1de63398dde50a4ad
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vector_id: 37805
content:
"[...] (Section 5.6; Fuss et al., 2018; Roe et al., 2019). Technologies to achieve direct large-scale anthropogenic removals of non-CO2 GHGs \r are speculative at present (Yoon et al., 2009; Ming et al., 2016; Kroeger et al., 2017; Jackson et al., 2019)."


id: 2a4fffdc817a04775162d3b5739481b9
created_at: 2023-04-19 13:22:34
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content_type: text
index: climate_gpt_v2
vector_id: 4667
content:
"As scientists seek to refine our understanding of Earth's climate system and how it may evolve in coming decades \r to centuries, past climate states provide a wealth of insights. Data about these past states help to establish the \r relationship between natural climate drivers and the history of changes in global temperature, global sea levels, \r the carbon cycle, ocean circulation, and regional climate patterns, including climate extremes. Guided by such \r data, scientists use Earth system models to identify the chain of events underlying the transitions between past \r climatic states (FAQ 3.3). This is important because during present-day climate change, just as in past climate \r changes, some aspects of the Earth system (e.g., surface temperature) respond to changes in greenhouse gases \r on a time scale of decades to centuries, while others (e.g., sea level and the carbon cycle) respond over centuries \r [...]"


id: fd9756d57d18061c773a47bb9ec7c086
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vector_id: 41966
content:
"[...] to millennia (FAQ 5.3). In this way, past climate states serve as critical benchmarks for climate model simulations, \r improving our understanding of the sequences, rates, and magnitude of future climate change over the next \r decades to millennia."


id: bba202652a0dbc21d74747e9e4137741
created_at: 2023-04-19 13:22:34
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content_type: text
index: climate_gpt_v2
vector_id: 30235
content:
"Analyzing previous warm periods caused by natural factors can help us understand how key aspects of the \r climate system evolve in response to warming. For example, one previous warm-climate state occurred roughly \r 125,000  years ago, during the Last Interglacial period, when slight variations in the Earth's orbit triggered \r a sequence of changes that caused about 1degC-2degC of global warming and about 2-8 m of sea level rise relative \r to the 1850-1900, even though atmospheric carbon dioxide concentrations were similar to 1850-1900 values \r (FAQ 1.3, Figure 1). Modelling studies highlight that increased summer heating in the higher latitudes of the \r Northern Hemisphere during this time caused widespread melting of snow and ice, reducing the reflectivity of \r the planet and increasing the absorption of solar energy by the Earth's surface. This gave rise to global-scale \r [...]"


id: ac87d7dfa0053df7eeb8ee639458000a
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
vector_id: 27593
content:
"[...] warming, which led in turn to further ice loss and sea level rise. These self-reinforcing positive feedback cycles are \r a pervasive feature of Earth's climate system, with clear implications for future climate change under continued \r greenhouse gas emissions. In the case of sea level rise, these cycles evolved over several centuries to millennia, \r reminding us that the rates and magnitude of sea level rise in the 21st century are just a fraction of the sea level \r rise that will ultimately occur after the Earth system fully adjusts to current levels of global warming."


id: 74911a678757a751076040f04ddbe514
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 17744
content:
"Roughly 3 million years ago, during the Pliocene Epoch, the Earth witnessed a prolonged period of elevated \r temperatures (2.5degC-4degC higher than 1850-1900) and higher sea levels (5-25 m  higher than 1850-1900), in \r combination with atmospheric carbon dioxide concentrations similar to those of the present day. The fact that \r Pliocene atmospheric carbon dioxide concentrations were similar to the present, while global temperatures and \r sea levels were significantly higher, reflects the difference between an Earth system that has fully adjusted to \r changes in natural drivers (the Pliocene) and one where greenhouse gases concentrations, temperature, and sea \r level rise are still increasing (present day). Much about the transition into the Pliocene climate state - in terms of \r key causes, the role of cycles that hastened or slowed the transition, and the rate of change in climate indicators \r [...]"


id: d25bdef9643539713dd1c33afe34c2ba
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vector_id: 34363
content:
"[...] such as sea level - remain topics of intense study by climate researchers, using a combination of paleoclimate \r observations and Earth system models. Insights from such studies may help to reduce the large uncertainties \r around estimates of global sea level rise by 2300, which range from 0.3 m to 3 m above 1850-1900 (in a low\u0002emissions scenario) to as much as 16 m higher than 1850-1900 (in a very high-emissions scenario that includes \r accelerating structural disintegration of the polar ice sheets)."


id: 13230807207fe91ba43556af9f76e592
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updated_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
vector_id: 579
content:
"Gudfinna Adalgeirsdottir (Iceland), Samuel Albani (Italy), Richard P. Allan (United Kingdom), \r Richard A. Betts (United Kingdom), Lea Beusch (Switzerland), Kinfe Beyene (Ethiopia), Jason E. Box \r (Denmark/United States of America), Denise Breitburg (United States of America), Kevin D. Burke \r (United States of America), Michael P. Byrne (United Kingdom/Ireland), John A. Church (Australia), \r Sloane Coats (United States of America), Naftali Cohen (United States of America), William Collins \r (United Kingdom), Owen R. Cooper (United States of America), Pedro Di Nezio (United States \r of America), Fabio Boeira Dias (Finland/Brazil), Ed J. Dlugokencky (United States of America), \r Timothy Dunkerton (United States of America), Paul J. Durack (United States of America/Australia), \r Tamsin L. Edwards (United Kingdom), Veronika Eyring (Germany), Chris  Fairall (United States \r of America), Vitali Fioletov (Canada), Piers Forster (United Kingdom), Gavin  L.  Foster (United \r [...]"


id: 81e460ef224f1f68bdceb4e57808a3e2
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updated_at: 2023-04-19 13:22:34
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vector_id: 19976
content:
"[...] Kingdom), Baylor Fox-Kemper (United States of America), Qiang Fu (United States of America), \r Jan S. Fuglestvedt (Norway), John C. Fyfe (Canada), Marie-Jose Gaillard (Sweden/Switzerland, \r Sweden), Joelle Gergis (Australia), Nathan P. Gillett (Canada), Hans Gleisner (Denmark/Sweden), \r Nadine Gobron (EU/France), Nicholas R. Golledge (New Zealand/United Kingdom), Bradley Hall \r (United States of America), Ed Hawkins (United Kingdom), Alan M. Haywood (United Kingdom), \r Armand Hernandez (Spain), Forrest M. Hoffman (United States of America), Yongyun Hu (China), \r Dale F. Hurst (United States of America), Masao Ishii (Japan), Samuel Jaccard (Switzerland), Dabang \r Jiang (China), Christopher Jones (United Kingdom), Bror  Jonsson (United Kingdom/Sweden), \r Andreas Kaab (Norway/Germany), Ralph Keeling (United States of America), Noel S. Keenlyside \r (Norway/Australia, United Kingdom), John Kennedy (United Kingdom), Elizabeth Kent (United \r [...]"


id: 3fc0a516179e9ad9b05b4b697cd59cf
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vector_id: 8412
content:
"[...] Kingdom), Nichol S. Khan (Hong Kong, China/United States of America), Wolfgang Kiessling \r (Germany), Stefan Kinne (Germany), Robert E. Kopp (United States of America), Svitlana Krakovska "


id: 456225dcdaec6c9a9cb8da88cb4be8c
created_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
vector_id: 9427
content:
"(Ukraine), Elmar Kriegler (Germany), Gerhard Krinner (France/Germany, France), Natalie Krivova \r (Germany), Paul B. Krummel (Australia), Werner L. Kutsch (EU/Germany), Ron  Kwok (United \r States of America), Florian Ladstadter (Austria), Peter Landschutzer (Germany/Austria), \r June-Yi Lee (Republic of Korea), Andrew Lenton (Australia), Lisa A. Levin (United States of America), \r Daniel J. Lunt (United Kingdom), Jochem Marotzke (Germany), Gareth J. Marshall (United Kingdom), \r Robert A. Massom (Australia), Katja Matthes (Germany), H. Damon Matthews (Canada), Thorsten \r Mauritsen (Sweden/Denmark), Gerard D. McCarthy (Ireland), Erin L. McClymont (United Kingdom), \r Shayne  McGregor (Australia), Jerry F. McManus (United States of America), Walter  N.  Meier \r (United States of America), Alan Mix (United States of America), Olaf Morgenstern (New Zealand/\r Germany), Lawrence R. Mudryk (Canada), Jens Muhle (United States of America/Germany), \r [...]"


id: e054f2fc2affad78a085746dfabcd8fa
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
vector_id: 36717
content:
"[...] Dirk Notz (Germany), Lisa C. Orme (Ireland/United Kingdom), Scott M. Osprey (United Kingdom), \r Matthew D. Palmer (United Kingdom), Camille Parmesan (France, United Kingdom/United States \r of America), Anna Pirani (Italy), Chris Polashenski (United States of America), Elvira Poloczsanka \r (Australia/United Kingdom), Marie-Fanny  Racault (United  Kingdom), Anthony  Richardson \r (Australia), Belen Rodriguez-Fonseca (Spain), Joeri  Rogelj (United Kingdom/Belgium), \r Steven K. Rose (United States of America), Yair Rosenthal (United States of America/Israel, United \r States of America), Alessio Rovere (Germany/Italy), Lucas  Ruiz (Argentina), Ulrich  Salzmann \r (United Kingdom/Germany, United Kingdom), Bjorn  H.  Samset (Norway), Abhishek  Savita \r (Australia/India), Margit  Schwikowski (Switzerland), Sonia  I.  Seneviratne (Switzerland), \r David Schoeman (Australia), Isobel J. Simpson (Canada), Aimee B.A. Slangen (The Netherlands), \r [...]"


id: 3c0107346eabdb06d73b56db23727241
created_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
vector_id: 7750
content:
"[...] Chris Smith (United Kingdom), Olga N. Solomina (Russian Federation), Joshua H.P. Studholme \r (United States of America/United Kingdom, New Zealand), Alessandro Tagliabue (United \r Kingdom), Claudia Tebaldi (United States of America), Jessica Tierney (United States of America), \r Matthew Toohey (Canada, Germany/Canada), Andrew Turner (United Kingdom), Osvaldo Ulloa \r (Chile), Caroline C. Ummenhofer (United States of America/Germany, United States of America), \r Axel von Engeln (Germany), Rachel Warren (United Kingdom), Kate Willett (United Kingdom), \r John W. Williams (United States of America)"


id: 13e12ac81e6ce8a62556b677764c02b7
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content_type: text
index: climate_gpt_v2
vector_id: 714
content:
"Changes across the terrestrial cryosphere are widespread, with \r several indicators now in states unprecedented in centuries to \r millennia (high confidence). Reductions in spring snow cover extent \r have occurred across the Northern Hemisphere since at least 1978 \r (very high confidence). With few exceptions, glaciers have retreated \r since the second half of the 19th century and have continued to \r retreat at increased rates since the 1990s (very high confidence); this \r behaviour is unprecedented in at least the last 2000 years (medium \r confidence). Greenland Ice Sheet (GrIS) mass loss has increased \r substantially since 2000 (high confidence). The Greenland Ice Sheet \r was smaller than at present during the Last Interglacial period (high \r confidence) and the mid-Holocene (high confidence). The Antarctic Ice \r Sheet (AIS) lost mass between 1992 and 2020 (very high confidence), \r with an increasing rate of mass loss over this period (medium \r [...]"


id: 57079e87b0731a0ea4cafe6c99d728ea
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vector_id: 12563
content:
"[...] confidence). Although permafrost persists in areas of the Northern \r Hemisphere where it was absent prior to 3000 years ago, increases in \r temperatures in the upper 30 m over the past three to four decades \r have been widespread (high confidence). {2.3.2}"


id: 7197c2e51c8ac29f13d19a0737beabe3
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vector_id: 17231
content:
"Recent ocean changes are widespread, and key ocean \r indicators are in states unprecedented for centuries to \r millennia (high confidence). Since 1971, it is virtually certain that \r global ocean heat content has increased for the upper (0-700 m) \r layer, very likely for the intermediate (700-2000 m) layer and likely\r below 2000 m, and is currently increasing faster than at any point \r since at least the last deglacial transition (18 to 11 thousand years \r ago) (medium confidence). It is virtually certain that large-scale near\u0002surface salinity contrasts have intensified since at least 1950. The \r Atlantic Meridional Overturning Circulation (AMOC) was relatively \r stable during the past 8000 years (medium confidence) but declined \r during the 20th century (low confidence). Ocean pH has declined \r globally at the surface over the past four decades (virtually certain) \r and in all ocean basins in the ocean interior (high confidence) over \r [...]"


id: a1d5063355d735893c03e6ad7abc73dc
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vector_id: 25691
content:
"[...] the past 2-3 decades. A long-term increase in surface open ocean \r pH occurred over the past 50 million years (high confidence), and \r surface ocean pH as low as recent times is uncommon in the last \r 2 million years (medium confidence). Deoxygenation has occurred in "


id: 21ba83f34c70d1fb6f0dfb85a7de9477
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
vector_id: 3148
content:
"Changes in the marine biosphere are consistent with large-scale \r warming and changes in ocean geochemistry (high confidence). \r The ranges of many marine organisms are shifting towards the \r poles and towards greater depths (high confidence), but a minority \r of organisms are shifting in the opposite directions. This mismatch \r in responses across species means that the species composition of \r ecosystems is changing (medium confidence). At multiple locations, \r various phenological metrics for marine organisms have changed \r in the last 50 years, with the nature of the changes varying with \r location and with species (high confidence). In the last two decades, \r the concentration of phytoplankton at the base of the marine food \r web, as indexed by chlorophyll concentration, has shown weak and \r variable trends in low and mid-latitudes and an increase in high \r latitudes (medium confidence). Global marine primary production \r [...]"


id: ab7229931f7f0ebb021024dd63be40f9
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updated_at: 2023-04-19 13:22:34
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vector_id: 27392
content:
"[...] decreased slightly from 1998-2018, with increasing production in \r the Arctic (medium confidence). {2.3.4.2}"


id: 833c899d21ebaccc0582ae512334ccdc
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updated_at: 2023-04-19 13:22:34
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vector_id: 20215
content:
"Since the late 19th century, major modes of climate variability \r show no sustained trends but do exhibit fluctuations in \r frequency and magnitude at inter-decadal time scales, with \r the notable exception of the Southern Annular Mode, which \r has become systematically more positive (high confidence). \r There is high confidence that these modes of variability have \r existed for millennia or longer, but low confidence in detailed \r reconstructions of most modes prior to direct instrumental \r records. Both polar annular modes have exhibited strong positive \r trends toward increased zonality of mid-latitude circulation over \r multi-decadal periods, but these trends have not been sustained for \r the Northern Annular Mode since the early 1990s (high confidence). \r For tropical ocean modes, a sustained shift beyond multi-centennial \r variability has not been observed for El Nino-Southern Oscillation \r (medium confidence), but there is limited evidence and low agreement \r [...]"


id: 432ae059d12ba70ad317fd39e9be29fe
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index: climate_gpt_v2
vector_id: 9026
content:
"[...] about the long-term behaviour of other tropical ocean modes. Modes \r of decadal and multi-decadal variability over the Pacific and Atlantic \r oceans exhibit no significant trends over the period of observational \r records (high confidence). {2.4}"


id: 71883db6f5009ad21cbdb3ed8aea6ff1
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vector_id: 17223
content:
"This chapter assesses the evidence basis for large-scale past changes \r in selected components of the climate system. As such, it combines \r much of the assessment performed in Chapters 2 through  5 of \r the Fifth Assessment Report (AR5) WGI contribution (IPCC, 2013) \r that, taken together, supported a finding of unequivocal recent \r warming of the climate system. The Sixth Assessment Report \r (AR6) WGI Report structure differs substantially from that in AR5 \r (Section  1.1.2). This  chapter focuses upon observed changes in \r climate system drivers and changes in key selected large-scale \r indicators of climate change and in important modes of variability \r (Cross-Chapter Box 2.2), which allow for an assessment of changes \r in the global climate system in an integrated manner. This chapter \r is complemented by Chapters 3 and 4, which respectively consider \r model assessment/detection and attribution, and future climate \r [...]"


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vector_id: 30459
content:
"[...] projections for subsets of these same indicators and modes. It does \r not consider changes in observed extremes, which are assessed in \r Chapter 11. The chapter structure is outlined in the visual abstract \r (Figure 2.1). "


id: 1d19d1204fb26c5dd1897eabb5b95103
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vector_id: 2298
content:
"Global mean surface temperature (GMST) is a key indicator of the changing state of the climate system. Earth's mean temperature \r history during the current geological era (Cenozoic, beginning 66 Ma (66 million years ago)) can be broadly characterized as follows \r (Cross-Chapter Box 2.1, Figure 1): (i) transient warming during the first 15 Myr (15 million years) of the Cenozoic, punctuated by \r the Paleocene-Eocene Thermal Maximum; (ii) a long-term cooling over tens of millions of years beginning around 50 Ma, driven by \r (among other factors) the slow drift of tectonic plates, which drove mountain building, erosion and volcanism, and reconfigured ocean \r passages, all of which ultimately moved carbon from the atmosphere to other reservoirs and led to the development of the Antarctic \r Ice Sheet (AIS) about 35-30 Ma; (iii) the intensification of cooling by climate feedbacks involving interactions among tectonics, ice \r [...]"


id: 277680c53aacddf5dc28a154e9192082
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updated_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
vector_id: 4122
content:
"[...] albedo, ocean circulation, land cover and greenhouse gases, causing ice sheets to develop in the Northern Hemisphere (NH) by about \r 3 Ma; (iv) glacial-interglacial fluctuations paced by slow changes in Earth's astronomical configuration (orbital forcing) and modulated \r by changes in the global carbon cycle and ice sheets on time scales of tens to hundreds of thousands of years, with particular \r prominence during the last 1 Myr; (v) a transition with both gradual and abrupt shifts from the Last Glacial Maximum to the present \r interglacial epoch (Holocene), with sporadic ice-sheet breakup disrupting ocean circulation; (vi) continued warming followed by minor \r cooling following the mid-Holocene, with superposed centennial- to decadal-scale fluctuations caused by volcanic activity, among \r other factors; (vii) recent warming related to the build-up of anthropogenic greenhouse gases (Sections 2.2.3 and 3.3.1)."


id: 20bf09f47d1fb68b17dbfa77ca649c53
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vector_id: 2980
content:
"GMST estimated for each of the reference periods based on proxy evidence (Section 2.3.1.1) can be compared with climate projections \r over coming centuries to place the range of possible futures into a longer-term context (Cross-Chapter Box 2.1, Figure 1). Here, the \r very likely range of GMST for the warmer world reference periods are compared with the very likely range of GSAT projected for \r the end the 21st century (2080-2100; Table 4.5) and the likely range for the end of the 23rd century (2300; Table 4.9) under multiple \r Shared Socio-economic Pathway (SSP) scenarios. From this comparison, there is medium confidence in the following: GMST estimated \r for the warmest long-term period of the Last Interglacial about 125 ka (125,000 years ago; 0.5degC-1.5degC relative to 1850-1900) \r overlaps with the low end of the range of temperatures projected under SSP1-2.6 including its negative emissions extension to the end \r [...]"


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vector_id: 31395
content:
"[...] of the 23rd century (1.0degC to 2.2degC). GMST estimated for a period of prolonged warmth during the mid-Pliocene Warm Period about \r 3 Ma [2.5degC to 4.0degC] is similar to temperatures projected under SSP2-4.5 for the end of the 23rd century (2.3degC to 4.6degC). GMST \r estimated for the Miocene Climatic Optimum [5degC to 10degC] and Early Eocene Climatic Optimum [10degC to 18degC], about 15 and 50 Ma, \r respectively, overlap with the range projected for the end of the 23rd century under SSP5-8.5 (6.6degC to 14.1degC)."


id: ea80153291a861ee6fd2b2a8705777a3
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index: climate_gpt_v2
vector_id: 38620
content:
"This section assesses the magnitude and rates of changes in both \r natural and anthropogenically mediated climate drivers over \r a range of time scales. First, changes in insolation (orbital and solar; \r Section 2.2.1), and volcanic stratospheric aerosol (Section 2.2.2) are \r assessed. Next, well-mixed greenhouse gases (GHGs; CO2, N2O and \r CH4) are covered in Section 2.2.3, with climate feedbacks and other \r processes involved in the carbon cycle assessed in Chapter 5. The \r section continues with the assessment of changes in halogenated \r GHGs (Section 2.2.4), stratospheric water vapour, stratospheric \r and tropospheric ozone (Section 2.2.5), and tropospheric aerosols \r (Section  2.2.6). Short-lived climate forcers (SLCFs), their precursor \r emissions and key processes are assessed in more detail in Chapter 6. \r Section 2.2.7 assesses the effect of historical land cover change \r on climate, including biophysical and biogeochemical processes. \r [...]"


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index: climate_gpt_v2
vector_id: 39661
content:
"[...] Section 2.2.8 summarizes the changes in the Earth's energy balance \r since 1750 using the comprehensive assessment of effective "


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content:
"Direct observations of volcanic gas-phase sulphur emissions (mostly \r SO2), sulphate aerosols, and their radiative effects are available from \r a variety of sources (Kremser et  al., 2016). New estimates of SO2\r emissions from explosive eruptions have been derived from satellite \r (beginning in 1979) and in situ measurements (Hopfner et  al., \r 2015; Carn et al., 2016; Neely III and Schmidt, 2016; Bruhl, 2018). \r Satellite observations of aerosol extinction after recent eruptions \r have uncertainties of about 15-25% (Vernier et al., 2011; Bourassa \r et al., 2012). Additional uncertainties occur when gaps in the satellite \r records are filled by complementary observations or using statistical \r methods (Thomason et al., 2018). Merged datasets (Thomason et al., \r 2018) and sparse ground-based measurements (Stothers, 1997) allow \r for volcanic forcing estimates back to 1850. In contrast to the CMIP5 \r historical volcanic forcing datasets (Ammann et al., 2003), updated \r [...]"


id: 4d822f609c8bf21b2478a7f41ee53a0d
created_at: 2023-04-19 13:22:34
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content:
"[...] time series (Figure 2.2d; Luo, 2018) feature a more comprehensive \r set of optical properties including latitude-, height- and wavelength\u0002dependent aerosol extinction, single scattering albedo and asymmetry \r parameters. A series of small-to-moderate eruptions since 2000 \r resulted in perturbations in SAOD of 0.004-0.006 (Andersson et al., \r 2015; Schmidt et al., 2018)."


id: 80e3719c58309e78cc0d3b324d8b3dc0
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 19813
content:
"The AR5 concluded that, on interannual time scales, the radiative \r effects of volcanic aerosols are a dominant natural driver of climate \r variability, with the greatest effects occurring within the first \r 2-5 years following a strong eruption. Reconstructions of radiative \r forcing by volcanic aerosols used in the Paleoclimate Modelling \r Intercomparison Project Phase III (PMIP3) simulations and in AR5 \r featured short-lived perturbations of a range of magnitudes, with \r events of greater magnitude than -1 W m-2 (annual mean) occurring \r on average every 35-40 years, although no associated assessment \r of confidence was given. This section focuses on advances in \r reconstructions of stratospheric aerosol optical depth (SAOD), \r whereas Chapter 7 focuses on the ERF of volcanic aerosols, and \r Chapter 5 assesses volcanic emissions of CO2 and CH4; tropospheric \r aerosols are discussed in Section 2.2.6. Cross-Chapter Box 4.1 \r [...]"


id: 11e6033a5ca703bafe7c0c796e2b5333
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vector_id: 340
content:
"[...] undertakes an integrative assessment of volcanic effects including \r potential for 21st century effects."


id: 1ef0735e4bebd880287269210eec96f2
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 2644
content:
"Advances in analysis of sulphate records from the Greenland Ice Sheet \r (GrIS) and AIS have resulted in improved dating and completeness of \r SAOD reconstructions over the past 2.5 kyr (Sigl et al., 2015), a more \r uncertain extension back to 10 ka (Kobashi et al., 2017; Toohey and \r Sigl, 2017), and a better differentiation of sulphates that reach high \r latitudes via stratospheric (strong eruptions) versus tropospheric \r pathways (A. Burke et  al., 2019; Gautier et  al., 2019). The PMIP4 \r volcanic reconstruction extends the period analysed in AR5 by 1 kyr \r (Figure 2.2c; Jungclaus et  al., 2017) and features multiple strong \r events that were previously misdated, underestimated or not detected, \r particularly before about 1500 CE. The period between successive \r large volcanic eruptions (Negative ERF greater than -1 W m-2), ranges \r from 3-130 years, with an average of 43 +- 7.5 years between such \r eruptions over the past 2.5 kyr (data from Toohey and Sigl, 2017). The \r [...]"


id: 634f9278bb7099f64135141ed7ffcc29
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
vector_id: 14771
content:
"[...] most recent such eruption was that of Mt Pinatubo in 1991. Century\u0002long periods that lack such large eruptions occurred once every \r 400 years on average. Systematic uncertainties related to the scaling \r of sulphate abundance in glacier ice to radiative forcing have been \r estimated to be about 60% (Hegerl et al., 2006). Uncertainty in the \r timing of eruptions in the proxy record is +- 2 years (95% confidence \r interval) back to 1.5 ka and +- 4 years before (Toohey and Sigl, 2017)."


id: d8b7f342ef5e4ed6e2c1d8ac89a6c13e
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
vector_id: 35424
content:
"Well-mixed greenhouse gases generally have lifetimes of more than \r several years. The AR5 assigned medium confidence to the values \r of atmospheric CO2 concentrations (mixing ratios) during the warm \r geological periods of the early Eocene and Pliocene. It concluded \r with very high confidence that, by 2011, the mixing ratios of CO2, \r CH4, and N2O in the atmosphere exceeded the range derived from \r ice cores for the previous 800 kyr, and that the observed rates of \r increase of the greenhouse gases were unprecedented on centennial \r timescales over at least the past 22 kyr. It reported that over \r 2005-2011 atmospheric burdens of CO2, CH4, and N2O increased, \r with 2011 levels of 390.5 parts per million (ppm), 1803.2 parts per \r billion (ppb) and 324.2 ppb, respectively. Increases of CO2 and N2O \r over 2005-2011 were comparable to those over 1996-2005, while \r CH4 resumed increasing in 2007, after remaining nearly constant over \r [...]"


id: d74b4a53dc1226ecbb69e72a17c381ba
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
vector_id: 35179
content:
"[...] 1999-2006. A comprehensive process-based assessment of changes \r in CO2, CH4, and N2O is undertaken in Chapter 5."


id: 113e5f924cd8c12c280681533402b4b2
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
vector_id: 228
content:
"in ocean sediments has improved (Penman et al., 2014; Anagnostou \r et al., 2016, 2020; Chalk et al., 2017; Gutjahr et al., 2017; Babila et al., \r 2018; Dyez et al., 2018; Raitzsch et al., 2018; Sosdian et al., 2018; \r Henehan et  al., 2019, 2020; de la Vega et  al., 2020; Harper et  al., \r 2020), the understanding of the alkenone CO2 proxy has increased \r (e.g.,  Badger et  al., 2019; Stoll et  al., 2019; Y. Zhang et  al., 2019; \r Zhang et al., 2020; Rae et al., 2021) and new phytoplankton proxies \r have been developed and applied (e.g.,  Witkowski et  al., 2018). \r Understanding of the boron isotope CO2 proxy has improved since \r AR5 with studies showing very good agreement between boron\u0002CO2 estimates and co-existing ice core CO2 (Honisch and Hemming, \r 2005; Foster, 2008; Henehan et al., 2013; Chalk et al., 2017; Raitzsch \r et al., 2018; see Figure 2.3c). Such independent validation has proven \r difficult to achieve with the other available CO2 proxies (e.g., Badger \r [...]"


id: bd11cc45ffc9e4329720fa08b9ee1014
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
vector_id: 30513
content:
"[...] et al., 2019; Da et al., 2019; Stoll et al., 2019; Y. Zhang et al., 2019). \r Remaining uncertainties in these ocean sediment based proxies \r (Hollis et al., 2019) partly limit the applicability of the alkenone d13C \r and boron d11B proxies beyond the Cenozoic, although new records \r are emerging, for example, Jurikova et al. (2020). CO2 estimates from \r the terrestrial CO2 proxies, such as stomatal density in fossil plants \r and d13C of palaeosol carbonates, are available for much of the \r last 420 Myr. Given the low sampling density, relatively large CO2 \r uncertainty, and high age uncertainty (relative to marine sediments) "


id: e314de4c2a6e428c0fc1980edfda5a5a
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 37247
content:
"Levels were close to 1750 values during at least one prolonged \r interval during the Carboniferous and Permian (350-252 Ma). \r During the Triassic (251.9-201.3 Ma), atmospheric CO2 mixing ratios \r reached a maximum of between 2000-5000 ppm (200-220  Ma). \r During the PETM (56 Ma) CO2 rapidly rose from about 900 ppm \r to about 2000 ppm (Table 2.1; Schubert and Jahren, 2013; Gutjahr \r et al., 2017; Anagnostou et al., 2020) in 3-20 kyr (Zeebe et al., 2016; \r Gutjahr et al., 2017; Turner, 2018). Estimated multi-millennial rates \r of CO2 accumulation during this event range from 0.3-1.5 PgC yr -1\r (Gingerich, 2019), at least 4-5 times lower than current centennial \r rates (Section 5.3.1.1). Based on boron and carbon isotope data, \r supported by other proxies (Hollis et  al., 2019), atmospheric CO2\r during the EECO (50 Ma) was between 1150 and 2500 ppm (medium \r confidence), and then gradually declined over the last 50 Myr at a \r [...]"


id: e7e73ed1e20e0fe2fe14fa431a9e495d
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 38112
content:
"[...] long-term rate of about 16 ppm Myr -1 (Figure 2.3). The last time the \r CO2 mixing ratio was as high as 1000 ppm (the level reached by some \r high emissions scenarios by 2100; Annex III) was prior to the Eocene\u0002Oligocene transition (33.5 Ma; Figure 2.3) that was associated with \r the first major advance of the AIS (Pearson et  al., 2009; Pagani \r et al., 2011; Anagnostou et al., 2016; Witkowski et al., 2018; Hollis "


id: 20caf7baf9b2a513f55d2833bb43fa3f
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 2990
content:
"Table 2.1 | Concentration (mixing ratios) and, where applicable, century time-scale rate of change of atmospheric CO2 based on multiple datasets for \r target paleoclimate reference (Cross-Chapter Box 2.1, and Figure 2.34) and selected other periods. Modern data are from Section 2.2.3.3 and Annex III. 'AR6' \r denotes best estimates assessed in this report and propagated to Figure 2.34. Units for the rate of change are given only for centennial periods characterized by rapid changes. \r Confidence levels are very high for instrumentally derived concentrations, high for values derived from air in glacier ice (back to LIG), medium for values supported by multiple \r proxy types (MPWP, EECO), and low for values from a single sedimentary proxy type (PETM). '-' indicates transition from the beginning to the end of the time interval. \r [...]"


id: 17354b868cefe7c54aedb231eda4f726
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 1298
content:
"[...] Uncertainties for Modern are based on 2019 estimates. Last Millennium rate of range shows lowest and highest values attained during this period; LDT shows highest rate of \r change. N/A indicates that values are not available. See chapter data table for bibliographic citation and auxiliary information for each dataset (Table 2.SM.1)."


id: 28e87900849d7f759875700273ec45ba
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 4417
content:
"Records of CO2 from the AIS formed during the last glacial period \r and the LDT show century-scale fluctuations of up to 9.6 ppm \r (Ahn et al., 2012; Ahn and Brook, 2014; Marcott et al., 2014; Bauska \r et al., 2015; Rubino et al., 2019). Although these rates are an order \r of magnitude lower than those directly observed over 1919-2019 CE \r (Section 2.2.3.3.1), they provide information on non-linear responses \r of climate-biogeochemical feedbacks (Section 5.1.2). Multiple \r records for 0-1850 CE show CO2 mixing ratios of 274-285  ppm. \r Offsets among ice core records are about 1%, but the long-term \r trends agree well and show coherent multi-centennial variations of \r about 10 ppm (Ahn et al., 2012; Bauska et al., 2015; Rubino et al., \r 2019). Multiple records show CO2 concentrations of 278.3 +- 2.9 ppm \r in 1750 and 285.5 +- 2.1 ppm in 1850 (Siegenthaler et  al., 2005; \r MacFarling Meure et al., 2006; Ahn et al., 2012; Bauska et al., 2015). \r [...]"


id: 135b717878b5746f1fe6dec4432260b3
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 617
content:
"[...] CO2 concentration increased by 5.0 +- 0.8 ppm during 970-1130 CE, \r followed by a decrease of 4.6 +- 1.7 ppm during 1580-1700 CE. \r The greatest rate of change over the CE prior to 1750 is observed \r at about 1600 CE, and ranges from -6.9 to +4.7 ppm per century in \r multiple high-resolution ice core records (Siegenthaler et al., 2005; \r MacFarling Meure et al., 2006; Ahn et al., 2012; Bauska et al., 2015; \r Rubino et  al., 2019). Although ice core records present low-pass \r filtered time series due to gas diffusion and gradual bubble close\u0002off in the snow layer over the ice sheet (Fourteau et al., 2020), the \r rate of increase since 1850 CE (about 125 ppm increase over about \r 170 years) is far greater than implied for any 170-year period by ice \r core records that cover the last 800 ka (very high confidence)."


id: f0cf420c6320c5b0c2c8f976489f0ba6
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 39728
content:
"et  al., 2019). The compilation of Foster et  al. (2017) constrained \r CO2 concentration to between 290 and 450 ppm during the MPWP, \r based primarily on the boron-isotope data reported by Martinez\u0002Boti et al. (2015b), consistent with the AR5 range of 300-450 ppm. \r A more recent high-resolution boron isotope-based study revealed \r that CO2 cycled during the MPWP from about 330 to about 390 ppm \r on orbital timescales, with a mean of about 370 ppm (de la Vega \r et al., 2020). Although data from other proxy types (e.g., stomatal \r density or d13C of alkenones) have too low resolution to resolve the \r orbital-related variability of CO2 during this interval (e.g., Kurschner \r et al., 1996; Stoll et al., 2019) there is general agreement among \r the different proxy types with the boron-derived mean (e.g., Stoll \r et al., 2019). High-resolution sampling (about 1 sample per 3 kyr) \r with the boron-isotope proxy indicates mean CO2 mixing ratios for \r [...]"


id: 8ba6c2c792f5a0e4c9be25bb4e4528d3
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index: climate_gpt_v2
vector_id: 21745
content:
"[...] the Marine Isotope Stage KM5c interglacial were 360-420 ppm \r (medium confidence) (de la Vega et al., 2020). "


id: 5ae23946168b5db8e8e4fe6e938657c1
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 13236
content:
"CH4 concentrations over the past 110 kyr are higher in the Northern \r Hemisphere (NH) than in the Southern Hemisphere (SH), but closely \r correlated on centennial and millennial timescales (Buizert et  al., \r 2015). On glacial to interglacial cycles, approximately 450 ppb \r oscillations in CH4 concentrations have occurred (Loulergue et  al., \r 2008). On millennial timescales, most rapid climate changes observed \r in Greenland and other regions are coincident with rapid CH4 changes \r (Buizert et al., 2015; Rhodes et al., 2015, 2017). The variability of CH4 on \r centennial timescales during the early Holocene does not significantly \r differ from that of the late Holocene prior to about 1850 (Rhodes et al., \r 2013; Yang et al., 2017). The LGM concentration was 390.5 +- 6.0 ppb \r (Kageyama et  al., 2017). The global mean concentrations during \r 0-1850 CE varied between 625 and 807 ppb. High-resolution ice core \r records from Antarctica and Greenland exhibit the same trends with \r [...]"


id: 430c5b23ded5d13f9dd8dbbcd1ea706a
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
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vector_id: 9000
content:
"[...] an inter-polar difference of 36-47 ppb (Sapart et al., 2012; L. Mitchell \r et al., 2013). There is a long-term positive trend of about 0.5 ppb per \r decade during the CE until 1750 CE. The most rapid CH4 changes \r prior to industrialization were as large as 30-50  ppb on multi\u0002decadal timescales. Global mean CH4 concentrations estimated from \r Antarctic and Greenland ice cores are 729.2 +- 9.4 ppb in 1750 and \r 807.6 +- 13.8 ppb in 1850 (L. Mitchell et al., 2013)."


id: 7b7c790f9db145be7487818b1a67ede9
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
vector_id: 18930
content:
"New records show that N2O concentration changes are associated \r with glacial-interglacial transitions (Schilt et  al., 2014). The most \r rapid change during the last glacial termination is a 30 ppb increase \r in a 200-year period, which is an order of magnitude smaller \r than the modern rate (Section 2.2.3.3). During the LGM, N2O was \r 208.5 +- 7.7 ppb (Kageyama et al., 2017). Over the Holocene the lowest \r value was 257 +- 6.6 ppb during 6-8 ka, but millennial variation is \r not clearly detectable due to analytical uncertainty and insufficient \r ice core quality (Fluckiger et al., 2002; Schilt et al., 2010). Recently \r acquired high-resolution records from Greenland and Antarctica for \r the last 2 kyr consistently show multi-centennial variations of about \r 5-10 ppb (Figure 2.4), although the magnitudes vary over time \r (Ryu  et  al., 2020). Three high temporal resolution records exhibit \r a short-term minimum at about 600 CE of 261 +- 4 ppb (MacFarling \r [...]"


id: ce19368ee8d5c10acc170d69ef54d548
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updated_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
vector_id: 33588
content:
"[...] Meure et al., 2006; Ryu et al., 2020). It is very likely that industrial \r N2O increase started before 1900 CE (Machida et al., 1995; Sowers, \r 2001; MacFarling Meure et al., 2006; Ryu et al., 2020). Multiple ice \r cores show N2O concentrations of 270.1 +- 6.0 ppb in 1750 and \r 272.1 +- 5.7 ppb in 1850 (Machida et al., 1995; Fluckiger et al., 1999; \r Sowers, 2001; Rubino et al., 2019; Ryu et al., 2020)."


id: 2f420ac75ce28117cbac8e497cc0ba7
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 5580
content:
"There has been a positive trend in globally averaged surface CO2 \r mixing ratios since 1958 (Figure 2.5a), that reflects the imbalance \r of sources and sinks (Section 5.2). The growth rate has increased \r overall since the 1960s (Figure 2.5a inset), while annual growth rates \r have varied substantially, for example, reaching a peak during the \r strong El Nino events of 1997-1998 and 2015-2016 (Bastos et al., \r 2013; Betts et al., 2016). The average annual CO2 increase from 2000 \r through 2011 was 2.0 ppm yr-1 (standard deviation 0.3 ppm yr-1), \r similar to what was reported in AR5. From 2011 through 2019 it was \r 2.4 ppm yr -1 (standard deviation 0.5 ppm yr -1), which is higher than \r that of any comparable time period since global  measurements \r began. Global networks consistently show that the globally averaged \r annual mean CO2 has increased by 5.0% since 2011, reaching \r 409.9 +- 0.4 ppm in 2019 (NOAA measurements). Further assessment \r [...]"


id: f0691b3c2148d5e1d8e4be5c19b75b05
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content_type: text
index: climate_gpt_v2
vector_id: 39679
content:
"Other radiatively important gases with predominantly anthropogenic \r sources also continue to increase in abundance. SF6, used in electrical \r distribution systems, magnesium production, and semi-conductor \r manufacturing, increased from 7.3 ppt in 2011 to 10.0 ppt in 2019 \r (+36%). Alternatives to SF6 or SF6-free equipment for electrical systems \r have become available in recent years, but SF6 is still widely in use in \r electrical switch gear (Simmonds et al., 2020). The global lifetime of \r SF6 has been revised from 3200 years to about 1000 years (Kovacs \r et al., 2017; Ray et al., 2017) with implications for climate emissions \r metrics (Section 7.6.2). NF3, which is used in the semi-conductor \r industry, increased 147% over the same period to 2.05 ppt in 2019. \r Its contribution to ERF remains small, however, at 0.0004 W m-2. The \r atmospheric abundance of SO2F2, which is used as a fumigant in place \r of ozone-depleting methyl bromide, reached 2.5 ppt in 2019, a 46% \r [...]"


id: e938ef225a7d905b4c9a9ec53979909c
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updated_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
vector_id: 38387
content:
"Recent studies of dynamical influences on SWV (Eguchi et  al., \r 2015; Evan et  al., 2015; Tao et  al., 2015; Konopka et  al., 2016; \r Diallo et  al., 2018; Garfinkel et  al., 2018) have demonstrated that \r the quasi-biennial oscillation (QBO), El Nino-Southern Oscillation \r (ENSO), Sudden Stratospheric Warming (SSW) events and possibly \r also Pacific Decadal Variability (PDV; W. Wang et  al., 2016), can \r significantly influence SWV abundance and the tropical cold point \r tropopause temperatures that largely control water vapour entering \r the stratosphere. It has also been shown that the convective lofting \r of ice can moisten the lower stratosphere over large regions (Dessler \r et al., 2016; Anderson et al., 2017; Avery et al., 2017). Near-global \r observations of SWV have revealed unusually strong and abrupt \r interannual changes, especially in the tropical lower stratosphere. \r Between December 2015 and November 2016, the tropical mean SWV \r [...]"


id: c3853814261bcb49cc481e79d06cc162
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vector_id: 31676
content:
"[...] anomaly at 82 hPa dropped from 0.9 +- 0.1 ppm to -1.0 +- 0.1 ppm, \r accompanied by highly anomalous QBO-related dynamics in the \r tropical stratosphere (P.A. Newman et al., 2016; Tweedy et al., 2017) \r and the transition of ENSO from strong El Nino to La Nina conditions \r (Davis et al., 2017). The tropical mean SWV anomaly then rose sharply \r to 0.7 +- 0.1 ppm in June 2017 as warm westerlies returned to the \r tropical lower stratosphere and ENSO neutral conditions prevailed \r (Davis et al., 2017). "


id: cd51909fd4266b0647557d2e4d174426
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updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 33463
content:
"The earliest observations of free tropospheric ozone (1934-1955) \r are available from northern mid-latitudes where limited data \r indicate a tropospheric column ozone increase of 48 +- 30% up to \r 1990-2012 (Tarasick et al., 2019). Starting in the 1960s, records from \r ozonesondes show no significant changes in the free troposphere \r over the Arctic and mid-latitude regions of Canada, but trends are \r mainly positive elsewhere in the northern mid-latitudes (Oltmans \r et  al., 2013; Cooper et  al., 2020). Tropospheric column and free \r tropospheric trends since the mid-1990s based on commercial aircraft, \r ozonesonde observations and satellite retrievals (Figure 2.8b,c), are \r overwhelmingly positive across the northern mid-latitudes (2-7%; \r 1-4 ppbv per decade) and tropics (2-14%; 1-5 ppbv per decade), with \r the largest increases (8-14%; 3-6 ppbv per decade) in the northern \r tropics in the vicinity of southern Asia and Indonesia. Observations \r [...]"


id: b9db9705b15ed94371f1a1f46fb5811
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 29916
content:
"[...] in the SH are limited, but indicate average tropospheric column \r ozone increases of 2-12% (1-5 ppbv) per decade in the tropics \r (Figure 2.8c), and weak tropospheric column ozone increases (<5%, \r <1 ppbv per decade) at mid-latitudes (Cooper et al., 2020). Above \r Antarctica, mid-tropospheric ozone has increased since the late 20th \r century (Oltmans et al., 2013). The total ozone ERF from 1750 to 2019 \r best estimate is assessed as 0.47 W m-2 (Section 7.3.2.5) and this is \r dominated by increases in the troposphere. The underlying modelled \r global tropospheric ozone column increase (Skeie et al., 2020) from \r 1850 to 2010 of 40-60%, is somewhat higher than the isotope \r based upper-limit of Yeung et al. (2019). At mid-latitudes (30deg-60degN) \r model increases of 30-40% since the mid-20th century are broadly \r consistent with observations."


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vector_id: 35818
content:
"Prior to 1850 ozone observations do not exist, but a recent analysis \r using clumped-isotope composition of molecular oxygen (18O18O in O2) \r trapped in polar firn and ice, combined with atmospheric chemistry \r model simulations, constrains the global tropospheric ozone increase \r to less than 40% between 1850 and 2005, with most of this increase \r occurring between 1950 and 1980 (Yeung et  al., 2019). Recently, \r the Tropospheric Ozone Assessment Report identified and evaluated \r 60 records of surface ozone observations collected at rural locations \r worldwide between 1896 and 1975, which were based on a range of \r measurement techniques with potentially large uncertainties (Tarasick \r et al., 2019). They found that from the mid-20th century (1930s to \r the early 1970s) to 1990-2014, rural surface ozone increased by \r 30-70% across the northern extra-tropics. This is smaller than the \r 100% 20th-century increase reported in AR5, which relied on far \r [...]"


id: b672c3da043a57bcfb795fb48bb5553c
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 29313
content:
"[...] fewer measurement sites, all in Europe. In the northern tropics limited \r low-elevation historical data (1954-1975) provide no clear indication \r of surface ozone increases (Tarasick et al., 2019). However, similar to \r the northern mid-latitude increases, lower-free tropospheric ozone \r at Mauna Loa, Hawaii increased by approximately 50% from the \r late 1950s to present (Cooper et  al., 2020). Historical observations \r are too limited to draw conclusions on surface ozone trends in "


id: cd7e997e25aa5b3e34084d2f05bc4236
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 33483
content:
"Spatially resolved trends of AOD derived from Aqua/Terra MISR and \r MODIS instruments over 2000-2019 range between -2% and +2% \r per year (Figure 2.9c). Ground-based solar attenuation networks \r help to constrain and improve the satellite-derived retrievals of \r AOD, and trends derived from the AERONET network (Figure 2.9c,d) \r corroborate satellite results (Georgoulias et  al., 2016; Wei et  al., \r 2019; Bauer et al., 2020; H. Yu et al., 2020) in particular for declines \r over Europe (Stjern et al., 2011; Cherian et al., 2014; Li et al., 2014) \r and the USA (Li et al., 2014; Jongeward et al., 2016). The tendency in \r AOD over East Asia reversed from positive (2000-2010) to negative \r (since 2010) (Sogacheva et al., 2018; Filonchyk et al., 2019; Ma et al., \r 2019; Samset et al., 2019). Over southern Asia, however, AOD from \r satellite (MODIS/MISR) and AERONET retrievals show continuing \r increases (Li et al., 2014; Zhao et al., 2017), with similar trends from \r [...]"


id: 686b9a5cffb52322fcaddc1a33c898a
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 15636
content:
"[...] UV-based aerosol retrievals from the Ozone Monitoring Instrument \r (OMI) on the Aura satellite (Dahutia et  al., 2018; Hammer et  al., \r 2018). A comparison of MODIS and MISR radiometric observations \r with the broadband CERES satellite instrument (Corbett and \r Loeb, 2015) showed that drifts in calibration are unlikely to affect \r the satellite derived trends. CERES shows patterns for clear-sky \r broadband radiation consistent with the aerosol spatio-temporal \r changes (Loeb et al., 2018; Paulot et al., 2018). "


id: de3233ec6e6a0f9bc956b587a76d1e8b
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 36355
content:
"with a slightly slower rise thereafter (Ghimire et  al., 2014). Recent \r radiative forcing estimates arising from biophysical processes generally \r fall at the lower end of the AR5 assessed range. For instance, based on \r historical simulations from 13 CMIP6 models, C.J. Smith et al. (2020) \r estimated that the ERF from surface albedo changes (including snow \r cover and leaf area) was -0.08 [-0.22 to +0.06] W m-2 since 1850. \r Similarly, based on simulations from 13 CMIP5 models, Lejeune et al. \r (2020) estimated the radiative forcing from transitions between trees, \r crops, and grasslands was -0.11 [-0.16 to +0.04] W m-2 since 1860. \r Andrews et  al. (2017) identified an ERF of -0.40 W  m-2 since \r 1860, ascribing much of the effect to increases in albedo (including \r the unmasking of underlying snow cover); notably, however, the \r analysis was based on a single model with a known tendency \r to overestimate the ERF (Collins et  al., 2011). Ward et  al. (2014) \r [...]"


id: 77a9b78d0d1e4e5f85f661ea9e4a616f
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 18258
content:
"[...] examined the combined effects of biophysical and biogeochemical \r processes, obtaining an RF of 0.9 +- 0.5 W m-2 since 1850 that was \r driven primarily by increases in land-use related GHG emissions from \r deforestation and agriculture (Ward and Mahowald, 2015). According \r to a large suite of historical simulations, the biophysical effects of \r changes in land cover (i.e., increased surface albedo and decreased \r turbulent heat fluxes) led to a net global cooling of 0.10degC +- 0.14degC \r at the surface (SRCCL). Available model simulations suggest that \r biophysical and biogeochemical effects jointly may have contributed \r to a small global warming of 0.078degC +- 0.093degC at the surface over \r about the past two centuries (SRCCL), with a potentially even larger \r warming contribution over the Holocene as a whole (He et al., 2014)."


id: 8a870c2117cc4a799940bd446ed564ab
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 21543
content:
"Much of the global land surface has been modified or managed to \r some extent by human activities during the Holocene. Reconstructions \r based on pollen data indicate that natural vegetation probably \r covered most of the Earth's ice-free terrestrial surface until roughly \r the mid-Holocene (Marquer et  al., 2017; Harrison et  al., 2020; \r F. Li et al., 2020). Reconstructions based on pollen, archaeological, \r and historical data indicate deforestation at the regional scale \r since at least 6 ka (Marquer et  al., 2017; Stephens et  al., 2019; \r Harrison et al., 2020; F. Li et al., 2020). From a global perspective, \r land-use forcing datasets (Lawrence et  al., 2016) estimate that \r changes in land use (and related deforestation) were small on the \r global scale until the mid-19th century and accelerated markedly \r thereafter, with larger uncertainties prior to industrialization (Kaplan \r et  al., 2017). Since the early 1980s, about 60% of all land cover \r [...]"


id: 91aa78349cab182b75a0c42e754a7ced
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 22857
content:
"[...] changes have been associated with direct human activities, with \r spatial patterns emphasizing the regional character of land use \r and land management, including tropical deforestation, temperate \r afforestation, cropland intensification, and increased urbanization \r (Song et al., 2018; Zeng et al., 2018). At present, nearly three-quarters \r of the ice-free terrestrial surface is under some form of human use \r (Venter et al., 2016; Erb et al., 2017), particularly in agriculture and \r forest management. "


id: b5f06a5c5b05146c97849f40f8751cfe
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 29233
content:
"Monitoring surface temperatures is integral to the negotiations of UNFCCC and the global mean temperature goals of the Paris \r Agreement. Upper-air temperatures are a key indicator of different causal mechanisms underlying climate change and underpinned \r the very first conclusion in the Second Assessment Report of a 'discernible human influence' (Santer et al., 1996). To ascertain large\u0002scale changes of and human influence on the global hydrological cycle (which includes terrestrial and oceanic components), a small \r subset of indicators across this cycle are chosen: ocean and land precipitation-evaporation (P-E), global precipitation, total column \r water vapour, surface humidity (specific and relative), and global river runoff. Chapter 8 performs a substantive and holistic assessment \r of a much broader range of components. Finally, a warming world may be accompanied by a change in large-scale circulation patterns \r [...]"


id: 35d2bd6b22e06db2f980d5275a79dcca
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 6718
content:
"[...] linked through energy/mass/momentum constraints such as the extent and strength of the Hadley circulation (HC), monsoon systems, \r and/or the position and strength of the sub-tropical and polar jets. "


id: 4fb7aa02d33789d4eb998b4089443ecd
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 11318
content:
"For the PETM, new reconstructions agree with those assessed by AR5. \r A major new compilation of proxy temperature data (Hollis et  al., \r 2019) analysed using multiple statistical approaches (Inglis et  al., \r 2020) indicates that GMST was 10degC-25degC (90% range) warmer \r than 1850-1900, or about 5degC warmer relative to the pre-PETM \r state. A related synthesis study also estimates that PETM warmed \r by 5degC (no uncertainty assigned; Zhu et al., 2019). A recent benthic \r isotope compilation (Westerhold et al., 2020) transformed to GMST \r based on the formulation by J. Hansen et al. (2013; Cross-Chapter \r Box 2.1, Figure 1), and adjusted to 1850-1900 by adding 0.36degC, \r shows an increase of GMST by about 10degC during the PETM. This \r reflects the expected higher variability at single sites that were used \r to splice together the composite time series, compared to the globally \r averaged composite time series of Zachos et al. (2008). The latter was \r [...]"


id: 2e8faf872b4ac20c043cfe6b749e5436
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 5470
content:
"[...] originally used by J. Hansen et al. (2013) to reconstruct GMST, and \r is the preferred representation of the global average bottom water \r conditions, despite its less well-refined chronology."


id: 14aaf300bd97842a2120dcf031cb1db0
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 863
content:
"This assessment of the paleo reference periods (Cross-Chapter Box 2.1) \r draws from studies based mostly or entirely on indirect observational \r evidence from geological archives (i.e.,  proxy records) rather than \r reconstructions that rely more heavily on modelled parameters and \r those based on deep-ocean temperatures (e.g.,  Kohler et  al., 2015; \r Friedrich et  al., 2016). In contrast to AR5, temperature estimates \r from climate models are not included in the assessed values for \r paleo reference periods in this chapter. The AR5 concluded that the \r reconstructed GMST during the PETM was 4degC-7degC warmer than \r pre-PETM mean climate (low confidence), and that the EECO and the \r MPWP were 9degC-14degC and 1.9degC-3.6degC warmer than pre-industrial, \r respectively (medium confidence). The GMST during the LIG was \r assessed at 1degC-2degC warmer than pre-industrial (medium confidence), \r whereas SROCC narrowed the range to 0.5degC-1.0degC warmer, but did \r [...]"


id: a3b610a0a4295ddf31b6aa76bd7aed89
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 26005
content:
"[...] not state a confidence level. The AR5 further concluded that it was \r very likely that the LGM was 3degC-8degC colder than pre-industrial, and \r likely that the maximum rate of global warming during the subsequent \r deglacial period was 1degC-1.5degC kyr-1."


id: a7f14f9ac65c81a5909a2f3023b41762
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 26757
content:
"6.1degC +- 0.4degC (2 SD) cooler than the late Holocene. Assuming that the \r 1850-1900 reference period was 0.2degC and 0.4degC cooler than the late \r and first part of the Holocene, respectively (Kaufman et al., 2020a), \r the midpoints of these three new GMST reconstructions average \r -5.8degC relative to 1850-1900. The coldest multi-century period of the \r LGM in the J. Hansen et al. (2013) reconstruction is 4.3degC colder than \r 1850-1900. This compares to land- and SST-only estimates of about \r -6.1degC +- 2degC and -2.2degC +- 1degC, respectively (2 SD), which are based \r on AR5-generation studies that imply a warmer GMST than more \r recent reconstructions (Figure 1c in Harrison et al., 2015; Figure 7 in \r Harrison et  al., 2016). A major new pollen-based data-assimilation \r reconstruction averages 6.9degC cooler over northern extratropical \r land (Cleator et al., 2020). LGM temperature variability on centennial \r [...]"


id: c3a9d412d7e40b674b820ba742cf328f
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 31701
content:
"[...] scales was about four times higher globally than during the Holocene, \r and even greater at high latitudes (Rehfeld et al., 2018). In summary, \r GMST is estimated to have been 5degC-7degC lower during the LGM \r (around 23-19 ka) compared with 1850-1900 (medium confidence). "


id: 15df5baef3caffd20ae912efe1ab685e
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 1065
content:
"For the LIG (Cross-Chapter Box 2.1, Figure 1, and Figure 2.11), \r a major new compilation of marine proxy data (Turney et al., 2020) \r from 203 sites indicates that the average SST from 129-125 ka was \r 1.0degC +- 0.2degC (2 SD) warmer than 1850-1900 (reported relative to \r 1981-2010 and adjusted here by 0.8degC). These temperatures represent \r the time of peak warmth, which may not have been synchronous \r among these sites. This compares with two other SST estimates \r for 125 ka of 0.5degC +- 0.3degC (+- 2 SD) warmer at 125 ka relative to \r 1870-1889 (Hoffman et al., 2017), and about 1.4degC (no uncertainty \r stated) warmer at 125 ka relative to 1850-1900 (Friedrich and \r Timmermann, 2020; reported relative to 10-5 ka and adjusted here \r by 0.4degC; Kaufman et  al., 2020a). The average of these post-AR5 \r global SST anomalies is 1degC. Commensurately (Figure 3.2b), GMST \r is estimated to have been roughly 1.1degC above 1850-1900 values, \r [...]"


id: e0b019e37f0ce35610562c462b0b4e19
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 36788
content:
"[...] although this value could be too high if peak warmth was not globally \r synchronous (Capron et al., 2017). A further estimate of peak GMST \r anomalies of 1.0degC-3.5degC (90% range; adjusted here to 1850-1900 \r by adding 0.2degC) based on 59 marine sediment cores (Snyder, 2016) is \r considerably warmer than remaining estimates and is therefore given \r less weight in the final assessment. The warmest millennium of the \r LIG GMST reconstruction in J. Hansen et  al. (2013) is 1.5degC above \r 1850-1900. In summary, GMST during the warmest millennia of the \r LIG (within the interval of around 129-125 ka) is estimated to have \r reached 0.5degC-1.5degC higher values than the 1850-1990 reference \r period (medium confidence). "


id: 8169b5544a3ab3d9131ed752ddfb8c6f
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
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vector_id: 19898
content:
"This section concerns the Holocene period prior to industrialization \r when GMST was overall highest. Whereas SR1.5 focussed upon the \r 'Holocene thermal maximum' when regional temperatures were \r up to 1degC higher than 1850-1900, though peak warming occurred \r regionally at different times between around 10 and 5 ka greatly \r complicating interpretation. A multi-method reconstruction (Kaufman \r et al., 2020a) based on a quality-controlled, multi-proxy synthesis of \r paleo-temperature records from 470 terrestrial and 209 marine sites \r globally (Kaufman et al., 2020b) indicates that the median GMST of \r the warmest two-century-long interval was 0.7 [0.3 to 1.8] degC warmer \r than 1800-1900 (which averaged 0.03degC colder than 1850-1900; \r PAGES 2k Consortium, 2019), and was centred around 6.5 ka. This is \r similar to Marcott et al. (2013), which is based on a smaller dataset \r (73 sites) and different procedures to estimate a maximum warmth \r [...]"


id: 9dbe6382561c6f20042f27e0c071dd5c
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
vector_id: 24985
content:
"[...] of 0.8degC +- 0.3degC (2 SD) at around 7.0 ka, adjusted here by adding \r 0.3degC to account for differences in reference periods. These may be "


id: bcd7d88d9d7a8c69c65fea59ce2ddc14
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 30465
content:
"proxy data syntheses (Luning et al., 2019a, b) have been published, \r extending back 1-2 kyr. In addition, a major new global compilation \r of multiproxy, annually resolved paleo-temperature records for the \r CE (PAGES 2k Consortium, 2017) has been analysed using a variety \r of statistical methods for reconstructing temperature (PAGES \r 2k Consortium, 2019). The median of the multi-method GMST \r reconstruction from this synthesis (Figure 2.11a) generally agrees \r with the AR5 assessment, while affording more robust estimates of \r the following major features of GMST during the CE: (i) an overall \r millennial-scale cooling trend of -0.18 [-0.28 to 0.00] degC kyr-1 prior \r to 1850; (ii) a multi-centennial period of relatively low temperature \r beginning around the 15th century, with GMST averaging -0.03 \r [-0.30 to 0.06] degC between 1450 and 1850 relative to 1850-1900; \r (iii) the warmest multi-decadal period occurring most recently; and \r [...]"


id: 2f9d825117f5c89df9e7ccd721eb92ec
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
vector_id: 5640
content:
"[...] (iv) the rate of warming during the second half of the 20th century \r (from instrumental data) exceeding the 99th percentile of all 51-year \r trends over the past 2 kyr. Moreover, the new proxy data compilation \r shows that the warming of the 20th century was more spatially \r uniform than any other century-scale temperature change of the \r CE (medium confidence) (Neukom et al., 2019). A new independent \r temperature reconstruction extending back to 1580 is based on an \r expanded database of subsurface borehole temperature profiles, \r along with refined methods for inverse modelling (Cuesta-Valero \r et al., 2021). The borehole data, converted to GMST based on the \r modelled relation between changes in land versus sea surface \r temperature outlined previously, indicate that average GMST for \r 1600-1650 was 0.12degC colder than 1850-1900, which is similar to \r the PAGES 2k reconstruction (0.09degC colder), although both estimates \r [...]"


id: d3a20e589f57e6da6359edb50f626950
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 34568
content:
"[...] are associated with relatively large uncertainties (0.8degC (95% range) \r and 0.5degC (90% range), respectively). "


id: a9121370544ccff871106cd1873c0976
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 26957
content:
"underestimates because averaging inherently smoothed proxy records \r with uncertain chronologies reduces the variability in the temperature \r reconstruction (e.g.,  Dolman and Laepple, (2018) for sedimentary \r archives). However, the general coincidence between peak warmth \r and astronomically driven boreal summer insolation might reflect \r a bias toward summer conditions (Liu et al., 2014; Hou et al., 2019; \r Bova et  al., 2021), suggesting that the estimate is too high. This \r possibility is supported by AR5-generation proxy data focusing on 6 ka \r (Harrison et al., 2014), the long-standing MH modelling target (Cross\u0002Chapter  Box 2.1), that indicate surface temperatures for land and \r ocean were indistinguishable from 'pre-industrial' climate (Figure 1c \r in Harrison et al., 2015; Figure 7 in Harrison et al., 2016). In contrast, \r the GMST estimate from the multi-method global reconstruction \r (Kaufman et al., 2020a) for the millennium centred on 6 ka is only \r [...]"


id: 3b276c07d16a0fc452deba940323d37d
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 7594
content:
"Improvements in global temperature datasets since AR5 have addressed two major systematic issues. First, new SST datasets (Huang \r et al., 2017; Kennedy et al., 2019) address deficiencies previously identified in AR5 relating to the shift from predominantly ship-based \r to buoy-based measurements; these improvements result in larger warming trends, particularly in recent decades. Second, all datasets \r now employ interpolation to improve spatial coverage. This is particularly important in the Arctic, which has warmed faster than the \r rest of the globe in recent decades (Atlas 5.9.2.2); under-sampling of the Arctic leads to a cool bias in recent decades (Simmons et al., \r 2017; Benestad et al., 2019). Some datasets are now spatially complete (Cowtan and Way, 2014; Kadow et al., 2020) while others \r have expanded spatial coverage (Lenssen et al., 2019; Rohde and Hausfather, 2020; Morice et al., 2021; Vose et al., 2021). Several \r [...]"


id: c9ce696e9c39571f21768e8150f984b1
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
vector_id: 32834
content:
"[...] interpolation methods have been benchmarked against test cases (e.g., Lenssen et al., 2019), and comparisons with reanalyses further \r confirm the value of such interpolation (Simmons et al., 2017). It is extremely likely that interpolation produces an improved estimate \r of the changes in GMST compared to ignoring data-void regions."


id: 637347f4fba535fac524af10fbc31062
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 14791
content:
"Effects of warming since AR5 and choice of metrics of global mean temperature change\r Each of the six years from 2015 to 2020 has likely been warmer than any prior year in the instrumental record. GMST for the decade \r 2011-2020 has been 0.19 [0.16 to 0.22] degC warmer than 2003-2012, the most recent decade used in AR5 (Cross-Chapter Box 2.3, \r Figure 1). A linear trend has become a poorer representation of observed change over time since most of the sustained warming has \r occurred after the 1970s (Cross-Chapter Box 2.3, Figure 1) and all values since 2012 are at least 0.2degC above a linear trendline for \r 1850-2020. For this reason, the primary method used to assess observed warming in this report is the change in temperature from \r 1850-1900 to the most recent decade (2011-2020) or the recent past (1995-2014), replacing the trend-based methods used in AR5 \r [...]"


id: a75ec26a542de68e0cde25f9f8bb3184
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 26664
content:
"[...] and earlier assessments. The effect of this change from trend-based to change-based metrics is currently relatively minor at -0.03degC \r (<5%) for the most recent decade, but this may not remain the case in future (high confidence)."


id: 624ddb0978a5f153b33c0b0a191f9949
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 14570
content:
"Addressing the non-equivalence of GMST and GSAT \r GMST is a combination of land surface air temperatures (LSAT) and SSTs, whereas GSAT is a combination of LSAT and marine air \r temperatures (MATs). Although GMST and GSAT are closely related, the two measures are physically distinct. The implications have \r become more apparent since AR5 (Merchant et al., 2013; Cowtan et al., 2015; Simmons et al., 2017; IPCC, 2018 (SR1.5); Richardson \r et  al., 2018), and it has been shown (Rubino et  al., 2020) that MAT and SST can show distinct multi-decadal-scale trends and \r patterns of interannual variability. Although SR1.5 used GMST for observational-based and GSAT for model-based headline warming \r statements, they noted the importance of the difference for their assessment (SR1.5 Section 1.2.1.1). The SR1.5 used information \r from CMIP5 models to estimate a GSAT equivalent from observation-based GMST for certain applications such as remaining carbon \r [...]"


id: f388effb9f65524cd4d4b145fb65c7cb
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 40222
content:
"[...] budgets. The following subsections assess available lines of evidence related to the equivalence between GMST and GSAT. "


id: 8337471407348b3edc3f300303b9e991
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 20212
content:
"heat flux effect cannot be considered in isolation. Attempts to further constrain the combination of fluxes (e.g., Lorenz et al., 2010; \r Siler et al., 2019) rely on parameterizations or output from Earth system models (ESMs) or reanalyses and so are not considered \r independent. Apart from the above global considerations, regional and seasonal effects such as changes to the frequency and intensity \r of storms, sea state, cloudiness, sea ice cover, vegetation and land use may all affect the GSAT to GMST difference, either directly or \r by altering the relationships between gradients and energy fluxes. These changing energy flux relationships are monitored through \r observing the stratification of the upper ocean (Section 9.2.1.3) and the response of upper ocean processes (Cross-Chapter Box 5.3) in \r ESMs and reanalyses, but such monitoring tasks rival the observational challenge of directly observing SSTs and 2 m air temperature \r [...]"


id: 7fe1c9b0edfe1de9a3ada04b59d1b634
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
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vector_id: 19639
content:
"[...] under a wide range of conditions. In summary, because of the lack of physical constraints and the complexity of processes driving \r changes in the GSAT to GMST temperature differences, there is no simple explanation based on physical grounds alone for how this \r difference responds to climate change."


id: be920ad31cf285958ff2f9c9b5030894
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 30789
content:
"There is currently no regularly updated, entirely observation-based dataset for GSAT. The best available observations of near-surface \r air temperature over ocean are datasets of night-time marine air temperature (NMAT; e.g., Cornes et al., 2020; Junod and Christy, \r 2020), though spatial coverage is less extensive than for SST. Night-time measurements are used to avoid potential biases from \r daytime heating of ship superstructures. Kennedy et al. (2019) show little difference between HadNMAT2 and HadSST4 between \r 1920 and 1990, but a warming of SST relative to NMAT manifesting as a step change of 0.05degC-0.10degC in the early 1990s, which \r may reflect an actual change, the impact of increasingly divergent spatial coverage between SST and MAT measurements, or \r unresolved structural uncertainties in one or both datasets. This leads to NMAT warming around 10% more slowly than SST over \r [...]"


id: aec199c29bd3c0a977fe75e3e90705f1
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
vector_id: 27983
content:
"[...] the last century. In contrast, Junod and Christy (2020) find NMAT trends which are 8-17% larger than those for SST in the ERSSTv4 \r and HadISST datasets for the period 1900 to 2010, but 11-15% smaller than the SST trends for the same datasets from 1979 to \r 2010. However, ERSSTv4 uses NMAT data as a basis for homogeneity adjustment so is not fully independent. Kent and Kennedy \r (2021) note sensitivity to methodological choices in comparisons but find that NMAT is warming more slowly than SST products \r over most periods considered. Rubino et al. (2020) exploit tropical Pacific moored buoy arrays, available since the early 1980s, and \r find differences in NMAT and SST anomalies, which are sensitive to the choice of period and show spatio-temporal ENSO-related \r (Annex IV) signals in the differences. "


id: bfaab3890c750337faff0cf07e529b80
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 30999
content:
"CMIP historical simulations and projections agree that GSAT increases faster than GMST, the reverse of what is indicated by many \r marine observations. Several studies approximate the approach used to derive GMST from observations by blending SST over open \r ocean and SAT over land and sea ice from model output (Cowtan et al., 2015; Richardson et al., 2018; Beusch et al., 2020; Gillett \r et al., 2021). Cowtan et al. found that trends in GSAT are of the order of 9% larger than for GMST in CMIP5, based on data from \r 1850-2100 (historical + RCP8.5), if anomalies are blended and sea ice is allowed to vary over time (Cowtan et al., 2015). Broadly \r consistent numbers are found for both CMIP5 and CMIP6, across a range of SSP and RCP scenarios and time periods (Richardson \r et al., 2018; Beusch et al., 2020; Gillett et al., 2021). Blending monthly anomalies and allowing sea ice to vary, the change in GSAT \r [...]"


id: 8066997174fd8c0eb2f0e21e8cc59fdd
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 19719
content:
"[...] for 2010-2019 relative to 1850-1900 is 2-8% larger than spatially-complete GMST in CMIP6 historical and SSP2-4.5 simulations \r (Gillett et al., 2021), and 6-12% larger in CMIP5 historical and RCP2.6 and 8.5 simulations for 2007-2016 relative to 1861-1880 \r (Richardson et al., 2018). However, a true like-for-like comparison to observational products is challenging because methodological \r choices have a large impact on the relationship between modelled GMST and GSAT and none of these studies fully reproduces the \r methods used to derive estimates of GMST in recent observational datasets, which use various ways to infill areas lacking in situ \r observations (Jones, 2020). "


id: ad7899ad9f93e20e8600b582b5ecd4a9
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 27761
content:
"There is a significant issue in areas where sea ice melts or grows, where the quantity used in observational-based GMST estimates \r switches between air temperature and sea surface temperature. This primarily affects analyses combining SAT anomalies over \r land and ice with SST anomalies over ocean. In areas where sea ice has recently melted, the climatological value changes from an \r air-temperature based estimate to an SST estimate based upon the freezing point of seawater (-1.8degC). This switch in climatology to, \r in general, a warmer climatology, leads to a bias towards reduced warming in anomalies compared with analyses based on absolute \r temperatures. Richardson et al. (2018) found this underestimation to amount to approximately 3% of observed warming in historical \r model simulations. Given the projected future sea ice losses, the effect will grow in future (low confidence), with potential effects of \r [...]"


id: 881ee811ccdd17d102fbe3a11a59d47
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 21098
content:
"[...] the order of 0.1degC in the second half of the 21st century under high warming scenarios, although with some uncertainty arising from \r the large spread of sea ice loss in model projections (Tokarska et al., 2019). "


id: ed1fbab9596c7e682d79fc4e91b90216
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 39093
content:
"The AR5 assessed estimate for historical warming between 1850-1900 and 1986-2005 is 0.61 [0.55 to 0.67] degC. The equivalent in \r AR6 is 0.69 [0.54 to 0.79] degC, and the 0.08 [-0.01 to 0.12] degC difference is an estimate of the contribution of changes in observational \r understanding alone (Cross-Chapter Box 2.3, Table 1). The exact value of this contribution depends upon the metric being compared \r (GMST/GSAT, the method used to calculate a trend or change between two periods, the exact reference period used), with the \r best estimates (with the exception of the SR1.5 GSAT estimate) falling between 0.07degC and 0.12degC. The choice of 1850-1900 to \r 1986-2005 as the basis is due to the widespread use of this period across AR5 and SR1.5 in several contexts. The AR6-assessed \r GMST warming between 1850-1900 and 2011-2020 is 1.09 [0.95 to 1.20] degC. An AR5-equivalent assessment using this estimated \r [...]"


id: c4144cc37505c46e0c8c94c31fea3157
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 31782
content:
"[...] difference in observational understanding is thus 1.01 [0.94 to 1.08] degC. These updates and improvements in observational datasets \r affect other quantities that derive from the assessment of GSAT warming, including estimates of remaining carbon budgets and \r estimates of crossing times of 1.5degC and 2degC of global warming (see Cross Chapter Box 2.3, Table 1)."


id: cc53b199f0344be6ad36c9e0acc23811
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 33265
content:
"The updated estimate of historical warming is one contribution to the revised time of projected crossing of the threshold of 1.5degC \r global warming in comparison with SR1.5, but is not the only reason for this update. The AR6 assessment of future change in GSAT \r (Table 4.5) results in the following threshold-crossing times, based on 20-year moving averages. The threshold-crossing time is defined \r as the midpoint of the first 20-year period during which the average GSAT exceeds the threshold. During the near term (2021-2040), \r a 1.5degC GSAT increase relative to the average over the period 1850-1900 is very likely to occur in scenario SSP5-8.5, likely to occur in \r scenarios SSP2-4.5 and SSP3-7.0, and more likely than not to occur in scenarios SSP1-1.9 and SSP1-2.6. In all scenarios assessed here \r except SSP5-8.5, the central estimate of crossing the 1.5degC global warming level lies in the early 2030s. This is in the early part of the \r [...]"


id: 1f858994d1a56a1d5b6fa06b746dca69
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
vector_id: 2754
content:
"[...] likely range (2030-2052) assessed in SR1.5, which assumed continuation of the then-reported warming rate; this estimated rate has \r been confirmed in AR6 (Section 3.3.1). Roughly half of this difference arises from the higher diagnosed historical warming in AR6. The \r other half arises because, for central estimates of climate sensitivity, most scenarios show stronger warming over the near term than \r was assessed as 'current' in SR1.5 (medium confidence). When considering scenarios similar to SSP1-1.9 instead of linear extrapolation, \r the SR1.5 estimate of when 1.5degC global warming is crossed is close to the central estimate reported here (SR1.5, Table 2.SM.12). "


id: 11a78516eb65fb9bb94ac111af1552a8
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 295
content:
"Implications for assessment of emissions scenarios and remaining carbon budgets\r To estimate the global warming implications of emissions scenarios, AR5 and SR1.5 combined estimates of observed GMST changes \r from 1850-1900 to 1986-2005 (Cross-Chapter Box 2.3, Table 1) with GSAT projections of subsequent warming. AR6 undertakes three \r changes to this approach. First, the AR6 assessment of improved observational records is used. Second, the recent past baseline period \r is updated from 1986-2005 to 1995-2014, and, third, historical estimates are expressed in GSAT instead of GMST for consistency \r of historical estimates with future projections. The updated estimates of warming to date in AR6 lead to higher estimates of future \r warming, all else being equal. The temperature classification of emissions scenarios in the WGIII report adopts the definition of \r temperature classes as introduced in SR1.5, and assigns emissions scenarios to these classes based on their AR6 assessed GSAT \r [...]"


id: 7afe210ff5fc16769ab2c535ba3c0e14
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 18840
content:
"In both AR5 and SR1.5, remaining carbon budgets were expressed as a function of GSAT warming, while also highlighting the \r implications of using historical warming estimates expressed in GMST. The AR5 reported total carbon budgets for GSAT warming \r relative to 1861-1880. The AR5 Synthesis Report (SYR) also includes remaining carbon budget estimates based on AR5 WGIII \r scenario projections that use the method for AR5 scenario projections described above. The SR1.5 integrated several methodological \r advancements to estimate remaining carbon budgets and reported budgets for additional GSAT warming since the 2006-2015 period, \r estimating, following the application of an adjustment (Richardson et al., 2016, Table 1.1, SR1.5) to GMST, that 0.97degC (+- 0.12degC) of \r GSAT warming occurred historically between 1850-1900 and 2006-2015. The AR6 assessment, above, leads to an estimate of 0.94degC \r [...]"


id: 84f17ebb6e1cf8778c24325e09de9648
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 20529
content:
"[...] of warming between 1850-1900 and 2006-2015. All other factors considered equal, the AR6 estimate thus implies that 0.03degC \r more warming is considered for remaining carbon budgets compared to SR1.5. Combining this 0.03degC value with the SR1.5 transient "


id: 4e3e1680c6c2c6e40e98e224d1627b9e
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 11035
content:
"The assessment of global warming to date now being larger than previously assessed has no consequence on the assessment of past \r climate impacts, nor does it generally imply that projected climate impacts are now expected to occur earlier. The implications are \r mainly that the level of warming associated with a particular impact has been revised. This has very limited practical implications \r for the assessment of the benefits of limiting global warming to specific levels, as well as for the urgency of adaptation action. For \r example, impacts that occurred in the period 1986-2005 were previously associated with a GMST increase of 0.61degC relative to \r 1850-1900, relative to AR5 estimates. These impacts are now instead associated with a GMST increase of 0.69degC, relative to the \r assessment in this Report. The impacts themselves have not changed. Similarly, the impacts previously associated with a GMST or \r [...]"


id: d95f613584c893aa2ee3081e19bbec17
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 35544
content:
"[...] GSAT increase of 1.5degC will now generally be associated with a slightly different global warming level. This is because projections of \r future warming and its impacts relative to 1850-1900 are normally made by adding projected warming from a recent past baseline \r to an estimate of the observed warming from 1850-1900, as in AR5 and SR1.5. "


id: 6a0b22d41b22b97b00bd6c072c7af3cf
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 15929
content:
"Most of the previously projected impacts and risks associated with global warming of 1.5degC have therefore not changed and are \r still associated with the same level of future warming (0.89degC) relative to 1986-2005. With this warming now estimated as 0.08degC \r larger than in AR5, the future impacts previously associated with 1.5degC warming are now associated with 1.58degC warming. Similarly, \r the impacts now associated with 1.5degC warming would have previously been associated with 1.42degC warming. There are exceptions \r where impacts studies have used a baseline earlier than 1986-2005 (e.g., King et al., 2017), for which the new estimate of the \r historical warming would mean an earlier occurrence of the projected impacts. However, even in these cases, the ostensible difference \r in impacts associated with a 0.08degC difference in global mean temperature will be small in comparison with the uncertainties. There \r [...]"


id: ab89cbdd09468409a3929d0ac2d309ad
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 27412
content:
"[...] are also substantial uncertainties in regional climate changes and the magnitude of climate impact-drivers projected to occur with \r global warming of 1.5degC (Betts et al., 2018; Seneviratne et al., 2018). Furthermore, the time of reaching global warming of 1.5degC is \r subject to uncertainties of approximately +-10 years associated with uncertainties in climate sensitivity, and +-3 to 4 years associated \r with the different SSP forcing scenarios (Section 4.3.4, Table 4.5, and see discussion above). "


id: 7e73a44c71afdb2a001d6a778b0a75fe
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
vector_id: 19408
content:
"For SST analyses, three products - HadSST4 (1850-present, Kennedy \r et  al., 2019), ERSSTv5 (1850-present, Huang et  al., 2017) and \r COBE SST2 (1880-present, (Hirahara et al., 2014) - now have bias \r adjustments applied throughout the record. The new SST datasets \r account for two major issues previously identified in AR5: that \r globally averaged buoy SSTs are about 0.12degC cooler than ship\u0002based SSTs (Kennedy et al., 2011; Huang et al., 2015), and that SSTs \r from ship engine room intakes may have biases for individual ships \r depending upon the sensor set-up (Kent and Kaplan, 2006) but have \r an overall warm bias when globally aggregated (Kennedy et al., 2019). \r The first issue primarily affects data since 1990, when buoys began to \r increasingly contribute to the observation network (Woodruff et al., \r 2011), and the second issue has its largest effect from the 1940s \r to the 1970s. From the standpoint of uncertainty, ERSSTv4 (W. Liu \r [...]"


id: 37d19782e70a6908f15ee6b932beb545
created_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
vector_id: 7029
content:
"[...] et  al., 2015; Huang et  al., 2016) and subsequent versions (Huang \r et al., 2017), and HadSST4 have estimates presented as ensembles \r that sample parametric uncertainty. Comparisons  between these "


id: 769c55c1a303cf86164b51279905d85
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 18088
content:
"A number of recent studies also corroborate important components of \r the SST record (Hausfather et al., 2017; Kent et al., 2017; Cowtan et al., \r 2018; Kennedy et al., 2019). In particular, ATSR SST satellite retrievals \r (Merchant et al., 2012; Berry et al., 2018), the near-surface records \r from hydrographical profiles (Gouretski et  al., 2012; Huang et  al., \r 2018), and coastal observations (Cowtan et al., 2018) have all been \r shown to be broadly consistent with the homogenized SST analyses. \r Hausfather et al. (2017) also confirmed the new estimate of the rate \r of warming seen in ERSSTv4 since the late 1990s through comparison \r with independent SST data sources such as Argo floats and satellite \r retrievals. Nevertheless, dataset differences remain in the mid-20th \r century when there were major, poorly-documented, changes in \r instrumentation and observational practices (Kent et  al., 2017), \r particularly during World War II, when ship observations were limited \r [...]"


id: da59f403c5383a6a7097ce5b65ed9ce9
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
vector_id: 35713
content:
"[...] and disproportionately originated from US naval sources (Thompson \r et al., 2008). Kennedy et al. (2019) also identify differences between \r the new HadSST4 dataset and other SST datasets in the 1980s and \r 1990s, indicating that some level of structural uncertainty remains \r during this period, whilst Chan et al. (2019) and Davis et al. (2019) \r document residual uncertainties in the early and later 20th century \r records respectively. "


id: 388d6eadfafb2a0b5db9d56dd860c061
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 7158
content:
"Historically, SST has been used as a basis for global temperature \r assessment on the premise that the less variable SST data provides \r a better estimate of marine temperature changes than marine \r air temperature (MAT) (Kent and Kennedy, 2021). However, MAT \r products are used to adjust SST biases in the NOAA SST product \r because they are assessed to be more homogeneous (Huang \r et  al., 2017). Observational datasets exist for night-marine air \r temperature (NMAT) (e.g., Cornes et al., 2020; Junod and Christy, \r 2020; Rayner et al., 2020) and there are methods to adjust daytime \r MATs (Berry et al., 2004), but there is to date no regularly updated \r dataset which combines MAT with temperatures over land. MAT \r datasets are more sparse in recent decades than SST datasets as \r marine datasets have become increasingly dependent on drifting \r buoys (Centurioni et al., 2019) which generally measure SST but not \r MAT, and there are almost no recent winter MAT data south of 40degS \r [...]"


id: c0ee029e371170f8c961098d5ee0ab2b
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
vector_id: 31246
content:
"[...] (Swart et  al., 2019). However, the situation reverses in the 19th \r century with a greater prevalence of MAT than SST measurements \r available in the ICOADS data repository (Freeman et  al., 2017, \r 2019; Kent and Kennedy, 2021)."


id: b388b54763bccea171c39385a5e4b4e6
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 28829
content:
"At the time of AR5 a limitation of conventional datasets was the lack \r of coverage, especially in high latitudes, which although recognized \r as an issue (Simmons et al., 2010) had not been addressed in most \r products. Interpolation involves the statistical imputation of values \r across regions with limited data and can add both systematic and \r random uncertainties (Lenssen et al., 2019). Cowtan and Way (2014) \r applied a kriging-based method to extend existing datasets to polar \r regions, while Kadow et al. (2020) used an artificial intelligence-based \r method, and Vaccaro et  al. (2021) used gaussian random Markov \r fields, for the same purpose, although only Kadow et al. (2020) uses \r the most recent generation of datasets as its base. The Berkeley Earth \r merged product (Rohde and Hausfather, 2020), HadCRUT5 (Morice \r et al., 2021) and NOAA GlobalTemp-Interim (Vose et al., 2021) all \r include interpolation over reasonable distances across data sparse \r [...]"


id: 91948bea93c8d472ab050a5b290a8e88
created_at: 2023-04-19 13:22:34
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vector_id: 22838
content:
"[...] regions which results in quasi-global estimates from the late 1950s \r when continuous Antarctic observations commenced. Interpolated \r datasets with substantial coverage of high latitudes show generally \r stronger warming of GMST than those with limited data in polar \r regions (Vose et al., 2021), and their strong warming at high northern \r latitudes is consistent with independent estimates from reanalyses \r (Simmons et al., 2017; Lenssen et al., 2019) and satellites (Cowtan \r and Way, 2014). Given the spatial scales of surface temperature "


id: dd24c53488a86d602314aab03786e388
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 36170
content:
"Temperature trends in RO products are most consistent with each other \r and with other observations between 8 km and 25 km (Ho et al., 2012; \r Steiner et al., 2013, 2020a). The uncertainty increases above 25 km for \r the early RO period, for which data are based on the single-satellite \r CHAMP mission, but data at higher altitudes become more reliable \r for later missions based on advanced receivers (Steiner et al., 2020a), \r along with the application of corrections for ionospheric effects \r (Danzer et al., 2020). The uncertainty due to the changing number of \r observations is reduced by correcting for the sampling uncertainty in \r RO climatological fields (e.g., Scherllin-Pirscher et al., 2011). For AIRS, \r thus far, stability of the instrument has been constrained to less than \r 0.03degC per decade for selected window channels in a comparison to \r SSTs measured by ocean buoys (Aumann et al., 2019). Trends were \r inter-compared with trends in RO data and reanalysis data to assess \r [...]"


id: 8650ac717c6c470ecd28fea41f4053e3
created_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
vector_id: 20787
content:
"There have been updated radiosonde estimates from the University \r of Vienna (RAOBCORE and RICH; Haimberger et al., 2012) and a new \r dataset from the State University of New York (UAHRD, Zhou et al., \r 2020). There are new versions of AMSU products from the University \r of Alabama in Huntsville (UAHv6.0; Spencer et al., 2017) and Remote \r Sensing Systems (RSSv4.0; Mears and Wentz, 2017). These updates \r have led to convergence in the lower stratosphere layer (Maycock \r et  al., 2018); in particular, the move to UAHv6.0 has addressed \r homogeneity issues identified by Seidel et al. (2016), although residual \r differences remain (Christy et  al., 2018). Reanalyses products had \r identified limitations near the 300 hPa level where the contribution \r of aircraft observations has increased rapidly in recent years (Dee \r et al., 2011; Gelaro et al., 2017), leading to identified biases (Dee and \r Uppala, 2009), that have been addressed in ERA5 (Hersbach et al., \r [...]"


id: cabafd09b9c32b3150ddaa816ec0f468
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updated_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
vector_id: 33007
content:
"[...] 2020). Modern reanalyses are generally well aligned with radiosonde \r and satellite observations in the middle and lower troposphere and \r lower stratosphere. A new operational mid- and upper-stratospheric \r dataset (STAR) has been developed by Zou and Qian (2016), merging \r the previous 1979-2006 SSU dataset (Zou et  al., 2014) with a \r dataset from 1998 onwards drawn from relevant AMSU channels \r (Wang and Zou, 2014). Further stratospheric satellite-based datasets \r from various combinations of satellites have been developed by \r McLandress et al. (2015) and Randel et al. (2016)."


id: b4d4c449f7a5b7f88930cd0fe572d5e3
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 29067
content:
"Warming has continued in the lower troposphere according to all \r radiosonde, reanalyses and satellite datasets, with a rate over \r 1980-2019 similar to surface warming rates (Table 2.5; c.f. Table 2.4). \r Radiosonde-based products generally show greater warming rates \r for 1980-2019 than satellite-based products and reanalyses. They \r also extend further back to the 1950s and trends since quasi-global \r coverage around 1960 also show warming (Table 2.5). Trends in RO \r and AIRS data, supported by radiosonde datasets, exhibit a warming \r trend in most of the mid- to upper- troposphere at all non-polar \r latitudes over 2002-2019. These also exhibit faster warming rates \r in the tropics in the upper troposphere than those observed at or \r near the surface (Figure 2.12); with the lowermost stratosphere also \r warming while above it is cooling. There is some spread between \r different data types in the tropics near the 15km level, although \r [...]"


id: 4650dd5370c73bdab6e0a42316a70ff2
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 9593
content:
"[...] these differences are reduced to near zero if a subset of radiosonde \r data, using only high-quality instruments, is used (Steiner et  al., \r 2020b). AMSU tropical middle troposphere data also show that \r warming rates are near or above those in the lower troposphere, but \r they are measuring much broader layers which greatly complicates \r interpretation (Steiner et al., 2020b). "


id: 9a9813e93e098963df74f75ad8efa7b6
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
vector_id: 24440
content:
"New assessments of free-atmosphere temperature are available \r through radio occultation (RO) and Atmospheric Infrared Sounder \r (AIRS) products which begin in the early 2000s (Section 1.5.1.1). Global \r Navigation Satellite System (GNSS)-RO datasets have been compared \r against AMSU data records, finding almost identical trends (Khaykin \r et al., 2017). Comparison of RO with collocated radiosondes, Vaisala \r RS90/92 and GCOS Reference Upper Air Network data (RS92-GDP; \r Dirksen et  al., 2014), show very good correspondence with global \r annual mean differences of less than 0.2degC in the upper troposphere \r and lower stratosphere. Radiosonde daytime radiation biases were \r identified at higher altitudes (Ladstadter et al., 2015; Ho et al., 2017). \r The stability of RO makes this data a useful comparator for AMSU \r (Chen and Zou, 2014) and radiosondes (Ho et al., 2017; Tradowsky \r et al., 2017), as well as anchoring post-2006 reanalyses datasets and \r [...]"


id: 32a78fe6d4c835a2be5f25d571d61c31
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
vector_id: 6174
content:
"[...] improving their consistency in the lower and middle stratosphere (Long \r et al., 2017; Ho et al., 2020). The effective vertical resolution of RO \r measurements in the upper troposphere and lower stratosphere was \r found to be up to 100 m at the tropical tropopause (Zeng et al., 2019a), \r which is favourable for resolving atmospheric variability (Scherllin\u0002Pirscher et  al., 2012; Wilhelmsen et  al., 2018; Stocker  et  al.,  2019). "


id: 336699fd4fdba5fa1eab1e105f64c774
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
vector_id: 6294
content:
"Substantial limitations exist in reconstructing the global hydrological \r cycle prior to the Quaternary, particularly during the Eocene, due to \r the lack of high-resolution proxy records and their sparsity. Spatial \r heterogeneity complicates identification of wetting and drying \r signals during the PETM and the EECO, with paleo data and model \r simulations suggesting an intensified global hydrological cycle \r (Carmichael et al., 2016, 2017; Hyland et al., 2017; West et al., 2020), \r in particular an increased specific humidity (Winnick et  al., 2015; \r van Dijk et al., 2020). Conditions wetter than present were inferred for \r the MPWP (Cross Chapter Box 2.4), with intensified Asian monsoons \r (An et al., 2015) but with nevertheless drier conditions over tropical \r and subtropical SH locations (Pontes et  al., 2020). A new global \r reconstruction of hydroclimate proxies for the LIG points to stronger \r boreal precipitation compared to 1850-1900 over high latitudes \r [...]"


id: a4708832513b6f69df8c3be799ff18ce
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
vector_id: 26135
content:
"[...] and especially over monsoon areas, with a  more heterogeneous \r signal for the SH (Scussolini et al., 2019). This heterogeneity is also \r present in the tropics, characterized by large zonal differences in \r precipitation change due to the variations in the intensity of Walker \r circulation (Section 2.3.1.4.1). Available records indicate reduced \r global vegetation cover and abundant atmospheric dust deposition \r during the LGM (increased aridity), particularly over the tropics and \r high latitudes (Lamy et  al., 2014; Ujvari et  al., 2017). This agrees \r with models and moisture-sensitive proxies, suggesting an overall \r decrease in global precipitation during the LGM relative to recent \r decades, albeit with regional-scale heterogeneity (Cao et al., 2019). \r Despite lower global precipitation amounts, research since AR5 has \r identified a wetting of mid-latitudes during the LGM (Putnam and \r Broecker, 2017; Lowry and Morrill, 2018; Morrill et al., 2018), thereby \r [...]"


id: 5bec84d49552856fc73490cc6f839c4d
created_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
vector_id: 13430
content:
"[...] complicating the characterization of the LGM as a  relatively 'dry' \r period. Low evaporation rates and increased top-soil moisture during \r the LGM may have contributed to elevated levels of large closed\u0002basin lakes located in the 30deg-45deg latitudinal belts (Putnam and \r Broecker, 2017; Scheff et al., 2017), such as the south-west United \r States (e.g., Ibarra et al., 2018), southern Australia (Petherick et al., \r 2013; Fitzsimmons et al., 2015; Sniderman et al., 2019) and Patagonia \r (e.g., Quade and Kaplan, 2017). "


id: 7088cf306b360318ce7c70e67991d815
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 17056
content:
"For the CE, new proxy records have led to the creation of continental \r drought atlases (Cook et al., 2015; Palmer et al., 2015; Stahle et al., 2016; \r Morales et al., 2020) and millennial reanalyses (Steiger et al., 2018; \r Tardif et al., 2019). These reconstructions highlighted the occurrence \r of multi-decadal regional mega-droughts in the NH before 1600 CE, \r particularly during 800-1200 CE, with a predominance of wet periods \r after 1700 CE (Cook et al., 2015; Rodysill et al., 2018; Shuman et al., \r 2018). In the SH, much of South America and the African tropics \r experienced a reduction of precipitation during 900-1200 CE and \r a wetting peak during 1500-1800 CE (Tierney et al., 2015; Nash et al., \r 2016; Fletcher et al., 2018; Luning et al., 2018; Campos et al., 2019), \r with an opposite pattern in southern subtropical Africa (Woodborne \r et al., 2015; Luning et al., 2018). Large multi-decadal variability was \r documented over Australia and New Zealand during the 800-1300 CE \r [...]"


id: 7b7ce00bf7c180d64114ff063125ba41
created_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
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content:
"[...] period, followed by a well-defined wet period during 1500-1800 CE \r (Barr et al., 2014; Evans et al., 2019). "


id: 168bd604359ac995dd48db5825820a60
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
vector_id: 1179
content:
"Since 1973, increases in specific humidity have been widespread \r and significant across the majority of the land and ocean regions \r where observations are available (Figure 2.13a). In contrast, trends \r in relative humidity show distinct spatial patterns with generally \r increasing trends over the higher latitudes and the tropics and \r generally decreasing trends over the sub-tropics and mid-latitudes, \r particularly over land areas (Figure 2.13c). Near-surface specific \r humidity over the oceans has increased since the 1970s according to \r several in-situ, satellite and reanalysis data records (Kent et al., 2014; \r Robertson et al., 2020; Willett et al., 2020). According to the HadISDH \r product, increases in specific humidity and decreases in relative \r humidity are significant particularly over the NH mid-latitudes \r (Figure 2.13a,c). Poor data coverage over the SH south of 20degS does \r not allow for the robust assessment of trends. Sources of uncertainty \r [...]"


id: 9bb9f4d77ef34c8dfc3eb9391eeb10f5
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 24658
content:
"[...] include the initial measurement accuracy, homogenization over land, \r observational height at ships and instrument bias adjustment over \r ocean, and sparse spatio-temporal sampling (Prytherch et al., 2015; \r Roberts et al., 2019; Willett et al., 2020)."


id: 536529bcbfca64cf81c50a2ffb979c2
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 11945
content:
"Since the late 1970s a range of satellite missions permit a quasi-global \r assessment of TCWV. Several satellite products provide water vapour \r retrievals based upon distinct spectral domains, in addition to products \r from radiosondes, reanalyses and GNSS radio occultation. The GEWEX \r Water Vapour Assessment (G-VAP) provided an intercomparison of \r several TCWV data records, with global coverage but limited timespan \r (Schroder et al., 2018). The various global products generally exhibit \r a positive trend since 1979 (Figure 2.14; Allan et al., 2014; Mieruch \r et al., 2014; Schroder et al., 2016; J. Wang et al., 2016), most evident \r over the tropics (Gu and Adler, 2013; Chen and Liu, 2016; Mears et al., \r 2018; Wang and Liu, 2020; Salamalikis et  al., 2021). The existence \r of apparent breakpoints in several products, which are generally \r coincident with changes in the observing system, lead to trend \r estimates that are not in line with theoretical expectations imposed by \r [...]"


id: e85bebbeb7db605ec4a48fc1c6f266c9
created_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
vector_id: 38213
content:
"[...] the Clausius-Clapeyron relationship (Schroder et al., 2019), although \r other factors such as regional moisture divergence/convergence could "


id: 5ef22278230a021a5e83da8bedf948a4
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 14001
content:
"Recent trends (1980-2019) for GPCC V2020, CRU TS 4.04 and GPCP \r V2.3 show significant increases in land precipitation over tropical \r Africa, the eastern portions of Europe and North America, central Asia \r and the Maritime Continent (Figure 2.14d-f). Significant decreases \r are observed over central South America, western North America, \r northern Africa and the Middle East. A detailed assessment of the \r recent regional precipitation trends using the same datasets can \r be found in the Atlas. Global trends for 1980-2019 show a general \r increase in annual precipitation over land, which is particularly marked \r for CRU TS 4.04 and GHCNv4 (Table 2.6). These changes have been \r accompanied by a strengthening of precipitation seasonality over \r tropical land areas, although with broad spread between different \r satellite-based (GPCP, MSWEP_V1.2, PERSIANN-CDR) and in situ \r gridded datasets (GPCC, CRU TS; Chou et al., 2013; Li et al., 2016; \r [...]"


id: 750e609a2085eb77d8071bfe08bab628
created_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
vector_id: 17830
content:
"[...] Tan et al., 2020). Increasing trends since 1980, in contrast to longer\u0002term declining trends since 1901, are particularly evident over much \r of Africa, while more widespread negative trends were observed over \r much of southern South America in the more recent period (Atlas 7.2; \r Knutson and Zeng, 2018). A faster recent increase in precipitation \r over global land is inferred comparing the precipitation trends over \r 1960-2019 with 1980-2019 (Table 2.6). Over the global ocean, the \r comparison between precipitation datasets is compromised by the \r different measurement periods, as well as the spatial coverage of the \r available products (Adler et al., 2017; Nguyen et al., 2018; Jaber and \r Abu-Allaban, 2020; Nogueira, 2020), limiting the ability to assess the \r sign and magnitude of precipitation trends. The GPCPv2.3 database \r (Adler et al., 2017, 2018) exhibits an increase of 2.94 mm yr-1 per \r decade over 1980-2019, principally due to the trends over the Indian \r [...]"


id: 88ebe3b206ed9d1e8e0d97ffc44c790f
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
vector_id: 21254
content:
"[...] ocean and in the tropical western Pacific (Figure 2.15f). The regional \r patterns of recent trends are consistent with the documented increase \r in precipitation over tropical wet regions and the decrease over \r dry areas, estimated through GPCP v2.2 data (Liu and Allan, 2013; \r Trammell et al., 2015; Kao et al., 2017; Polson and Hegerl, 2017)."


id: 1907fcdc87df53a61c75b0ce59471a8a
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 1619
content:
"In situ precipitation records over land extend back for centuries in \r a few locations, and to the early to mid-20th century quasi-globally. \r Datasets differ in their input data, completeness of records, period \r covered, and the gridding procedures applied, which, given spatial \r clustering and the small spatial scales of precipitation, results in \r differences in global and regional estimates of precipitation changes \r (Q. Sun et al., 2018; Nogueira, 2020). The spatial variability of observed \r long-term trends (1901-2019) based on GPCC V2020 and CRU \r TS 4.04 (Figure 2.15a,b) indicates significant increases in precipitation \r mainly over eastern North America, northern Eurasia, southern South \r America and north-western Australia. Decreases are strongest across \r tropical western and equatorial Africa and southern Asia. The temporal \r evolution of global annual land precipitation anomalies exhibits \r little consistency between GPCC V2020, CRU TS 4.04 and GHCNv4 \r [...]"


id: 70c4439546fe29a2f97580a23e37489b
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
vector_id: 17101
content:
"[...] datasets, especially prior to 1950, that is associated with limitations \r in data coverage (Figure 2.15c; Wu et al., 2013; Shen et al., 2014; Gu \r and Adler, 2015). These disagreements between datasets prior to the \r 1950s result in differences in trend estimates over global land (Table \r 2.6). A qualitative consistency in decadal and interdecadal variations \r between the products is only observed since the 1950s, with primarily \r positive land precipitation anomalies during the 1950s, 1970s and \r during 2000 to 2019 (Figure 2.15c). "


id: be31deddcced316c0d722f263f13ed6c
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 30731
content:
"Several satellite-based precipitation datasets improve the \r representation of the spatio-temporal changes since the late 20th \r century. Some of these are based exclusively on satellite data \r (e.g., CMORPH, Joyce et al., 2004; GSMaP, Okamoto et al., 2005), with \r others being combinations of in situ observations, reanalyses and \r satellite retrievals (e.g., CMAP, Xie and Arkin, 1997; TRMM 3B43 V7, \r Huffman et al., 2007 ; PERSIANN-CDR, Ashouri et al., 2015; CHIRPS, \r Funk et al., 2015; GPCP V2.3, Adler et al., 2018). These can be affected \r by systematic and random uncertainties due to inhomogeneities \r in the satellite-derived precipitation and station data and the \r uncertainties of blending algorithms (Hegerl et al., 2015; Q. Sun et al., \r 2018). The spatial coverage of these products is near-global, with \r available estimations formally covering 60degS-60degN with decreasing \r quality from low to high latitudes, depending on the sensors and \r [...]"


id: 534198cd95b52358a0706b29c19d1497
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
vector_id: 11918
content:
"[...] algorithms used (Hu et al., 2019). A detailed description of the most \r relevant satellite products is provided in section 10.2.1.1."


id: 9f5416eca196ca8aa8e3607056b21648
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 25255
content:
"precipitation exceeds evaporation (P-E >0) for all the reanalysis \r products (Figure 2.16c), with decreasing trends in P-E for ERA5 and \r JRA-55 and increasing trends for MERRA-2 and CFSR. The P-E over \r the global ocean is negative (evaporation exceeding precipitation) \r for most reanalyses (Figure 2.16d), with declining trends in ERA5 \r and MERRA-2 dominated by trends in evaporation (Bosilovich et al., \r 2017; Hersbach et al., 2020) (Figure 2.16d). The recent increase in \r ocean evaporation was also documented for several reanalyses (Craig \r et al., 2017) and in satellite data (Andersson et al., 2011; Robertson \r et  al., 2014), although with considerable differences between \r available estimates (Chandanpurkar et al., 2017; L. Yu et al., 2020). \r An alternative indirect approach to estimate P-E changes is based on \r near-surface ocean salinity (Section 2.3.3.2), which is partially driven \r by the freshwater flux at the ocean surface. The near-surface salinity \r [...]"


id: 5d28c9887aab13a4a39d238edb729682
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
vector_id: 13666
content:
"[...] trends are more spatially coherent compared to those revealed by \r P-E estimates from reanalyses, with an intensification of the water \r cycle over oceans, especially in subtropical regions (Durack et  al., \r 2012; Skliris et al., 2014; L. Yu et al., 2020). However, the precise rate \r of water cycle intensification implied by salinity trends is sensitive to \r methodological choices (e.g., Skliris et al., 2016; Zika et al., 2018)."


id: a294484edf4206621196099609c6e860
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 25818
content:
"Paleo reconstructions of rainfall and trade winds extending over the \r last 100 kyr show an intensification of the NH HC concurrently with \r a weakening of the SH HC and a southward shift of the inter tropical \r convergence zone (ITCZ) during Heinrich stadials (Deplazes et  al., \r 2013; McGee et al., 2018; Strikis et al., 2018; Wendt et al., 2019). An \r intensification of the HC associated with conditions similar to La Nina \r (northward migrations of both the ITCZ and the SH westerlies) was \r found in reconstructions for the MH (McGee et al., 2014; Mollier-Vogel \r et  al., 2019). Changes in insolation from the mid to late Holocene \r favoured a southward migration in the position of the ITCZ and \r the descending branch of the HC in the NH, approaching its current \r width and position (Wirth et al., 2013; Thatcher et al., 2020). Tree ring \r chronologies from the NH mid-latitudes over the last 800 years show \r that the northern edge of the HC tended to migrate southward during \r [...]"


id: abae9844aac257495efac50e63e47c42
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
vector_id: 27450
content:
"[...] positive phases of ENSO and PDV, with northward shifts during negative \r phases (Alfaro-Sanchez et al., 2018). Between 1400 and 1850 CE the \r HC over both hemispheres and the ITCZ were displaced southward, \r consistent with occurrence of drought conditions in several NH regions \r (Wirth et  al., 2013; Burn and Palmer, 2014; Lechleitner et  al., 2017; \r Alfaro-Sanchez et al., 2018; Flores-Aqueveque et al., 2020). Moreover, \r several proxy records showed not only inter-hemispheric shifts in the \r ITCZ but a contraction of the tropical belt during 1400-1850 CE, which \r followed an expansion during 950-1250 CE (Denniston et al., 2016; \r Griffiths et al., 2016)."


id: 76cc571987bc64902be48bb837c53ad
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 18113
content:
"Human intervention on river discharge linked to increases in \r evapotranspiration and some reduction of intra-annual streamflow \r variability (Jaramillo and Destouni, 2015; Chai et  al., 2020) might \r affect the detection of trends in extreme daily streamflow events \r (Do et al., 2017; Gudmundsson et al., 2019). However, these activities \r have a minor impact on annual streamflow compared to climate \r variations (Dai et  al., 2009; Alkama et  al., 2013). Available global \r studies post-1950 generally concur that there have been more rivers \r experiencing decreases than increases in runoff (Do et  al., 2017; \r Su et al., 2018; Gudmundsson et al., 2019; X. Shi et al., 2019). Most \r of the rivers have not experienced statistically significant changes \r in streamflow, and when globally aggregated there is no significant \r change (Dai and Zhao, 2017). Global streamflow variability is strongly \r modulated by ENSO and PDV, with below-normal global streamflow \r [...]"


id: f42698b68a71bf9c80a36d8ca40f3e03
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 40328
content:
"[...] as a response to El Nino events and vice-versa during La  Nina \r episodes (Dai, 2016; Liang et al., 2016; Kim, 2019). The response of \r streamflow to changes in precipitation associated with ENSO and \r PDV has heterogeneous regional patterns at subcontinental scales \r (Section  8.3.2.9.1). No significant trends are found for reanalysis\u0002based discharge estimates over 1993 to 2015 (Chandanpurkar \r et  al., 2017). Uncertainties in global streamflow trends arise \r predominantly from changes in instrumentation, gauge restoration, \r recalibration of rating curves, flow regulation or channel engineering \r (Alkama et al., 2011; Gudmundsson et al., 2018; Ghiggi et al., 2019). "


id: 73aec9b70f8fc099ee7e7a8b2d20901d
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 17600
content:
"Since AR5 several studies based upon a range of metrics and \r different reanalyses products have suggested that the annual \r mean HC extent has shifted poleward at an approximate rate \r of 0.1deg-0.5deg latitude per decade over the last about 40 years \r (Allen  and Kovilakam, 2017; Davis and Birner, 2017; Grise et  al., \r 2018; Staten et al., 2018, 2020; Studholme and Gulev, 2018; Grise \r and Davis, 2020). The observed widening of the annual mean HC, \r revealed by a variety of metrics, is primarily due to poleward shift \r of the Northern Hemisphere HC. There have been stronger upward \r trends in the NH extent of HC after 1992 (Figure 2.17a). The \r estimated magnitude of the recent changes based on modern-era \r reanalyses is not as large as that in AR5, due to apparent biases \r in older-generation reanalyses (Grise et al., 2019). Moreover, large \r interannual variability leads to uncertainties in estimates of long\u0002term changes (Nguyen et al., 2013; Garfinkel et al., 2015b; Seviour \r [...]"


id: 695be9a7b8cc5cc447ef53af514b5ae5
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 15804
content:
"[...] et al., 2018; Staten et al., 2018), particularly for the NH given its \r zonal asymmetries (Staten et al., 2020; Wang et al., 2020). These \r large-scale features of the HC based on reanalyses agree with \r estimates revealed from the Integrated Global Radiosonde Archive \r (IGRA) during 1979-2012 (Lucas and Nguyen, 2015; Mathew \r et al., 2016). Recent trends based on reanalyses indicate a larger \r seasonal widening in the HC for summer and autumn in each \r hemisphere, although the magnitude of changes in HC extent is \r strongly dependent on dataset and metrics used (Grise et al., 2018; \r Y. Hu et al., 2018; Staten et al., 2018). The shifts in the HC position \r were accompanied by a narrowing ITCZ over the Atlantic and Pacific \r basins, with no significant change in its location and increases in \r the precipitation intensity (Byrne et al., 2018)."


id: f061eae3529baf077319b72afea6e6f6
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 39670
content:
"over the last 30-40 years (e.g., Hu et al., 2013; L'Heureux et al., 2013; \r Yim et al., 2017). Based on estimation of changes in mid-tropospheric \r velocity from changes in observed cloud cover, Bellomo and Clement \r (2015) suggest a weakening and eastward shift of the WC over \r 1920-2010, however the robustness of this signal is questionable due \r to high uncertainty in the ship-reported cloud data used before 1954. \r Using centennial-scale 20CR reanalysis Tseng et  al. (2019) showed \r that the vertical westerly wind shear over the western Pacific does \r not indicate any long-term change during 1900-1980, but shows \r a marked increase since the 1980s that is not present in ERA-Interim \r and JRA-55, again calling into question the ability of centennial\u0002scale reanalyses to capture tropical circulation changes. Recent \r strengthening together with a westward shift of the WC (Bayr et al., \r 2014; Ma and Zhou, 2016) was identified across several reanalysis \r [...]"


id: b7c47d2af8a886c3bfa458731e7c8064
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 29548
content:
"[...] products and observational datasets, and using different metrics for \r quantifying WC. Nevertheless, satellite observations of precipitation \r and analyses of upper tropospheric humidity suggest substantially \r weaker strengthening of the WC than implied by reanalyses \r (Chung et al., 2019). This recent strengthening in the WC is associated \r with enhanced precipitation in the tropical western Pacific, anomalous \r westerlies in the upper troposphere, strengthened downwelling in the \r central and eastern tropical Pacific, and anomalous surface easterlies \r in the western and central tropical Pacific (Dong and Lu, 2013; \r McGregor et al., 2014; Choi et al., 2016). Positive trends in sea level \r pressure over the eastern Pacific and concurrent negative trends over \r the Indonesian region result in a pattern implying a shift towards a La \r Nina-like WC regime, with strengthening of the Pacific Trade Winds \r mainly over 1979-2012 (L'Heureux et al., 2013; England et al., 2014; \r [...]"


id: 6241a39afca1c817f13aecad92c952bf
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 14564
content:
"[...] Sohn et al., 2016; Zhao and Allen, 2019). Seasonal assessment of the \r WC showed significant changes in the vertical westerly wind shear \r over the Pacific during the austral summer and autumn implying \r a strengthening (Clem et al., 2017)."


id: 27394cc287441150d192d1574319c330
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 4067
content:
"An intensification of the NH monsoons in the early to mid-Holocene \r with increased precipitation and regional expansions of rainfall areas \r identified through a variety of proxy records is shown by Biasutti \r et  al. (2018) and P.X. Wang et  al. (2017). The response for the SH \r monsoons during this period indicates a weakening in both summer \r and winter precipitation (P.X. Wang et al., 2014, 2017; Sachs et al., \r 2018). A decline in GM precipitation and a retraction of the northern \r fringes of monsoon areas was inferred from the mid-Holocene \r onwards, with some regions experiencing wetter conditions during \r the mid to late Holocene compared with present and a strengthening \r of the SH monsoons (P.X. Wang et al., 2014, 2017; Sachs et al., 2018). \r For the CE, GM reconstructions exhibit inter-hemispheric contrast \r during the period 950-1250 CE, with intensified NH monsoons and \r weakened SH monsoons, and the opposite pattern during 1400-1850 \r [...]"


id: baa7872491e43edc97759e1298fdfd2b
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 30053
content:
"Direct observations highlight that the GM land precipitation, \r particularly over the NH, experienced a slight increase from 1900 \r through the early 1950s, followed by an overall decrease from the \r 1950s to the 1980s, and then an increase to present (Kitoh et al., \r 2013; B. Wang et  al., 2018, 2021; X. Huang et  al., 2019b). This \r highlights the existence of multi-decadal variations in the NH \r monsoon circulation patterns and precipitation intensity (Wang et al., \r 2013; P.X. Wang et al., 2014, 2017; Monerie et al., 2019). An overall \r increase in monsoon precipitation during extended boreal summer \r (JJAS) over the NH since 1979 is revealed by GPCP (Deng et  al., \r 2018; Han et al., 2019) and CMAP for 1980-2010 (Jiang et al., 2016). \r SH summer monsoon behaviour is dominated by strong interannual \r variability and large regional differences (Kitoh et al., 2013; Lin et al., \r 2014; Jiang et  al., 2016; Kamae et  al., 2017; Deng et  al., 2018; \r [...]"


id: acbd2c0c95d9d7db432782dcf2c8fc72
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 27634
content:
"[...] Han et al., 2019), with no significant trends reported by GPCP and \r CMAP (Deng et al., 2018). Uncertainty predominantly arises from the \r observed increase in tropical precipitation seasonality (Feng et  al., \r 2013) and the estimation of GM precipitation over the ocean areas, \r leading to a large apparent spread across datasets (Kitoh et al., 2013; \r Kamae et al., 2017). "


id: d7dc4d95599bd830536cd02c42bbcd3b
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 35284
content:
"Multiple reanalyses and radiosonde observations show an increasing \r number of extratropical cyclones over the NH since the 1950s \r (Chang  and Yau, 2016; X.L. Wang et  al., 2016). The positive trends \r are generally consistent among reanalyses since 1979, though with \r considerable spread (Tilinina et  al., 2013; X.L. Wang et  al., 2016). \r In  recent decades the number of deep extratropical cyclones has \r increased over the SH (Section 8.3.2.8.1 and Figure 8.12; Reboita et al., \r 2015; X.L. Wang et  al., 2016), while the number of deep cyclones \r has decreased in the NH in both winter and summer (Neu et  al., \r 2013; Coumou et al., 2015; Chang et al., 2016; J. Wang et al., 2017a; \r Gertler and O'Gorman, 2019). The regional changes for different \r intensity extratropical cyclones are assessed in Section 8.3.2.8.1. \r The  assessment of trends is complicated by strong interannual \r to decadal variability, sensitivity to dataset choice and resolution \r [...]"


id: 98ef485ce64fb42f93ac24ee9e471868
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 24120
content:
"[...] (Tilinina et al., 2013; Lucas et al., 2014; X.L. Wang et al., 2016; Pepler \r et al., 2018; Rohrer et al., 2018) and cyclone identification/tracking \r methods (Neu et al., 2013; Grieger et al., 2018). Thus there is overall \r low confidence for recent changes in global extratropical storm tracks. "


id: 61be42d44aae615cfb70c457d9520b0a
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 14487
content:
"Proxy records consistent with modelling results imply a southward \r shift of the storm tracks over the North Atlantic during the LGM \r (Raible et al., 2021). A variety of proxies are available for the changes \r in the position of the extratropical jets/westerlies during the Holocene. \r Recent syntheses of moisture-sensitive proxy records indicate \r drier-than-present conditions over mid-latitudes of western North \r America (Hermann et  al., 2018; Liefert and Shuman, 2020) during \r the MH, which together with a weakened Aleutian Low (Bailey et al., \r 2018) implies that the winter North Pacific jetstream was shifted \r northward. A synthesis of lines of evidence from the SH indicates \r that the westerly winds were stronger over 14-5 ka, followed by \r regional asymmetry after 5 ka (Fletcher and Moreno, 2012). There \r is no consensus on the shifts of the SH westerlies with some studies \r implying poleward migrations (Lamy et al., 2010; Voigt et al., 2015; \r [...]"


id: 24f4dd65d39d97e61ae5cdc6c6abbfa
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 3694
content:
"[...] Turney et al., 2017; Anderson et al., 2018) and others suggesting an \r equatorward shift (Kaplan et al., 2016) in the MH."


id: 2ba98fa23aa02a2a4f0d914f408ae278
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 4942
content:
"A consistent poleward shift of the tropospheric extratropical jets \r since 1979 is reported by multiple reanalyses (Figure 2.18; Davis and \r Rosenlof, 2012; Davis and Birner, 2013; Pena-Ortiz et al., 2013; Manney \r and Hegglin, 2018), and radiosonde winds (Allen et al., 2012). This \r is generally consistent with the previously reported shifts retrieved \r from satellite temperature observations (Fu and Lin, 2011; Davis and \r Rosenlof, 2012). After the 1960s the magnitude of meridional shifts \r in extratropical jets over both the North Atlantic and North Pacific \r in August is enhanced compared to multi-century variability (Trouet \r et  al., 2018). Despite some regional differences (Woollings et  al., \r 2014; Norris et al., 2016; J. Wang et al., 2017a; Xue and Zhang, 2017; \r Ma and Zhang, 2018; Melamed-Turkish et al., 2018), overall poleward \r deflection of storm tracks in boreal winter over both the North Atlantic \r [...]"


id: e1e62266773501a2922d945463108e09
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 37027
content:
"[...] and the North Pacific was identified during 1979-2010 (Tilinina et al., \r 2013). Over the SH extra-tropics there is a similarly robust poleward \r shift in the polar jet since 1979 (Pena-Ortiz et al., 2013; Manney and \r Hegglin, 2018; WMO, 2018), although after 2000 the December-\r January-February (DJF) tendency to poleward shift of the SH jet \r stream position ceased (Banerjee et al., 2020). The general poleward \r movement in midlatitude jet streams (Lucas et al., 2014) is consistent \r with the expansion of the tropical circulation (Section  2.3.1.4.1). \r The changes of extratropical jets and westerlies are also related to the \r annular modes of variability (Section 2.4 and Annex IV)."


id: b5914cc95e1e158f65e827c636b184d1
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 29171
content:
"increasing global marine wind speeds over 1979-2015, though \r flattening since 2000, whereas MERRA-2 agrees until 1998, but then \r exhibits increased variability and an overall decrease in the last two \r decades (Azorin-Molina et al., 2019). This agrees with estimates by \r Sharmar et al. (2021) showing upward ocean wind trends from 1979 \r to 2000 which are consistent in ERA-Interim, ERA5 and MERRA-2, \r but disagree with CFSR trends for the same period. Over 2000-2019 \r all reanalyses show diverging tendencies. An updated multiplatform \r satellite database (comprising data from altimeters, radiometers, and \r scatterometers) from 1985-2018 shows small increases in mean wind \r speed over the global ocean, with the largest increase observed in the \r Southern Ocean (Young and Ribal, 2019), consistent with signals in \r ERA-Interim, ERA5 and MERRA-2 (Sharmar et al., 2021). Overall, most \r products suggest positive trends over the Southern Ocean, western \r [...]"


id: 6cf705550c51349ed7a20cd38428ad2b
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 16451
content:
"Sudden stratospheric warming (SSW), a phenomenon of rapid \r stratospheric air temperature increases (sometimes by more than \r 50degC in 1-2 days), is tightly associated with the reversal of upper \r stratospheric zonal winds, and a resulting collapse or substantial \r weakening of the stratospheric polar vortex (Butler et  al., 2015; \r Butler and Gerber, 2018) and on average occurs approximately \r 6 times per decade in the NH winter (Charlton et al., 2007; Butler \r et  al., 2015). The SSW record from all modern reanalyses is very \r consistent. There is a higher occurrence of major midwinter SSWs \r in the 1980s and 2000s with no SSW events during 1990-1997 \r (Reichler et al., 2012; Butler et al., 2015). An assessment of multi\u0002decadal variability and change in SSW events is sensitive to both \r chosen metric and methods (Palmeiro et al., 2015). Due to the lack of \r assimilation of upper air data, the centennial-scale reanalyses do not \r [...]"


id: 72438344b1ef8c06b7d68349ed32e39c
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 17345
content:
"[...] capture SSW events, even for the most recent decades (Butler et al., \r 2015, 2017) and hence cannot inform on earlier behaviour. There \r has been considerably less study of trends in the SH stratosphere \r polar vortex strength despite the interest in the ozone hole and the \r potential impact of the SH stratosphere polar vortex strength on it. \r The occurrence of SSW events in the SH is not as frequent as in the \r NH, with only 3 documented events in the last 40 years (Shen et al., \r 2020)."


id: 3e35b4833c8634c2866bffcb7de4c949
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 8125
content:
"The northern stratospheric polar vortex has varied intra-seasonally \r and with altitude during recent decades. Multiple reanalysis and \r radiosonde datasets show that the midwinter lower stratospheric \r geopotential height (150 hPa) over the polar region north of 60degN \r has increased significantly since the early 1980s (Bohlinger et al., \r 2014; Garfinkel et al., 2017). This signal extends to the middle and \r upper stratosphere. In January-February zonal winds north of 60degN \r at 10 hPa have been weakening (Kim et al., 2014; Kretschmer et al., \r 2018). Daily atmospheric circulation patterns over the northern \r polar stratosphere exhibit a decreasing frequency of strong vortex \r events and commensurate increase in more-persistent weak events, \r which largely explains the observed significant weakening of the \r vortex during 1979-2015 (Kretschmer et  al., 2018). The northern \r polar vortex has weakened in early winter but strengthened during \r [...]"


id: a467c9243c50c64dcaa62b8ef1a0ecd8
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 26131
content:
"[...] late winter (Bohlinger et al., 2014; Garfinkel et al., 2015a, 2017; Ivy \r et al., 2016; Seviour, 2017; Kretschmer et al., 2018). In the middle \r and upper stratosphere, a strengthening trend of the northern \r polar vortex during DJF has occurred since 1998, contrasting the \r weakening trend beforehand (D. Hu et al., 2018). The position of the \r polar vortex also has long-term variations, exhibiting a persistent \r shift toward Northern Siberia and away from North America in \r February over the period 1979-2015 (Zhang et al., 2016; J. Zhang \r et  al., 2018). Multiple measures show similar location changes \r (Seviour, 2017). "


id: db25b89868a3a714c8243ff307840e43
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 35832
content:
"There has been a continuing decline in SIE and Arctic sea ice area \r (SIA) in recent years (Figure 2.20a). To reduce grid-geometry \r associated biases and uncertainties (Notz, 2014; Ivanova et al., 2016; \r Meier and Stewart, 2019) SIA is used in addition to, or instead of SIE \r herein (see also section 9.3.1). A record-low Arctic SIA since the start \r of the satellite era (1979) occurred in September 2012 (Figure 2.20a). \r Decadal SIA means based on the average of three different satellite \r products decreased from 6.23 to 3.76 million km2 for September and \r 14.52 to 13.42 million km2 for March SIA (Figure 2.20a). Initial SIA \r data for 2020 (OSISAF) are within the range of these recent decadal \r means or slightly below (Figure 2.20a). SIA has declined since 1979 \r across the seasonal cycle (Figure 9.13). Most of this decline in SIA has \r occurred after 2000, and is superimposed by substantial interannual \r variability. The sharp decline in Arctic summer SIA coincides with \r [...]"


id: 499cc889225cc323045fb72d0b68cc87
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 10179
content:
"[...] earlier surface melt onset (Mortin et  al., 2016; Bliss et  al., 2017), \r later freeze-up, and thus a longer ice retreat and open water period \r (Stammerjohn et al., 2012; Parkinson, 2014; Peng et al., 2018)."


id: 674e09d0450d6ed4f8fb6e8b43a8a99f
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 15446
content:
"of different platforms show for the central and western Arctic \r Ocean (Arctic Ocean north of Canada and Alaska) negative trends \r since the mid-1970s (Lindsay and Schweiger, 2015; Kwok, 2018), \r with a particularly rapid decline during the 2000s, which coincided \r with a large loss of multiyear sea ice. Direct observations from 2004 \r and 2017 indicate a decrease of modal ice thickness in the Arctic \r Ocean north of Greenland by 0.75 m, but with little thinning between \r 2014 and 2017 (Haas et al., 2017). This agrees with data based on \r satellite altimetry and airborne observations, showing no discernible \r thickness trend since 2010 (Kwok and Cunningham, 2015; Kwok, \r 2018; Kwok and Kacimi, 2018; see Figure 2.21). However, sea-ice \r thickness derived from airborne and spaceborne data is still subject \r to uncertainties imposed by snow loading. For radar altimeters, \r insufficient penetration of radar signal into the snowpack results in \r [...]"


id: 435ed807682fdadac584ecc3f103e3fa
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 9070
content:
"[...] overestimation of ice thickness (e.g., Ricker et al., 2015; King et al., \r 2018; Nandan et al., 2020). Negative trends in ice thickness since the \r 1990s are also reported from the Fram Strait in the Greenland Sea, \r and north of Svalbard (E. Hansen et al., 2013; Renner et al., 2014; \r King et al., 2018; Rosel et al., 2018; Spreen et al., 2020). Thickness \r data collected in the Fram Strait originate from ice exported from \r the interior of the Arctic Basin and are representative of a larger \r geographical area upstream in the transpolar drift. A reduction of \r survival rates of sea ice exported from the Siberian shelves by 15% \r per decade has interrupted the transpolar drift and affected the long\u0002range transport of sea ice (Krumpen et al., 2019). The thinner and on \r average younger ice has less resistance to dynamic forcing, resulting \r in a more dynamic ice cover (Hakkinen et  al., 2008; Spreen et  al., \r 2011; Vihma et al., 2012; Kwok et al., 2013)."


id: 7fa5a7c627732a1214bd2400c682458d
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 19594
content:
"More recent sea ice reconstructions are based on diverse sources \r including whaling records (de La Mare, 1997, 2009; Cotte and Guinet, \r 2007), old ship logbooks (Ackley et  al., 2003; Edinburgh and Day, \r 2016), and ice core records (Curran et al., 2003; Abram et al., 2010; \r Sinclair et  al., 2014), amongst other methods (e.g.,  Murphy et  al., \r 2014). These reconstructions, in combination with recent satellite\u0002based observations indicate: (i) a decrease in summer SIE across all \r Antarctic sectors since the early- to mid-20th century; (ii) a decrease \r in winter SIE in the East Antarctic and Amundsen-Bellingshausen \r Seas sectors starting in the 1960s; and (iii) small fluctuations in \r winter SIE in the Weddell Sea over the 20th century (Hobbs et al., \r 2016a, b). There are also ice-core indications that the pronounced \r Ross Sea increase dates back to the mid-1960s (Sinclair et al., 2014; \r Thomas and Abram, 2016). While there is reasonable broad-scale \r [...]"


id: f90b434ed53c19084e606d3b6dff96aa
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 41179
content:
"[...] concurrence across these estimates, the uncertainties are large, there \r is considerable interannual variability, and reconstructions require \r further validation (Hobbs et  al., 2016a, b). New reconstructions \r (Thomas et  al., 2019) from Antarctic land ice cores show that SIE \r in the Ross Sea had increased between 1900 and 1990, while the \r Bellingshausen Sea had experienced a decline in SIE; this dipole \r pattern is consistent with satellite-based observations from 1979 \r to 2019 (Parkinson, 2019), but the recent rate of change then has \r been larger. Records of Antarctic SIE for the late 19th and early \r 20th centuries (Edinburgh and Day, 2016), show SIE comparable \r with the satellite era, although with marked spatial heterogeneity \r (e.g., Thomas et al., 2019)."


id: f1b3147ca6865bc6dc35ae1ac7dc2796
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 39906
content:
"Analysis of the combined in situ observations (Brown, 2002) and the \r multi-observation product (Mudryk et al. 2020) indicates that since \r 1922, April SCE in the NH has declined by 0.29 million km2 per decade, \r with significant interannual variability (Figure 2.22) and regional \r differences (Section 9.5.3.1). The limited pre-satellite era data does \r not allow for a similar assessment for the entire spring-summer \r period. Assessment of SCE trends in the NH since 1978 indicates that \r for the October to February period there is substantial uncertainty \r in trends with the sign dependent on the observational product. \r Analysis using the NOAA Climate Data Record shows an increase \r in October to February SCE (Hernandez-Henriquez et  al., 2015; \r Kunkel et al., 2016) while analyses based on satellite borne optical \r sensors (Hori et  al., 2017) or multi-observation products (Mudryk \r et al., 2020) show a negative trend for all seasons (Section 9.5.3.1 \r [...]"


id: 4601d8fcb4dcc53b1cf66664ee4643fc
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 9538
content:
"[...] and Figure 9.23). The greatest declines in SCE have occurred during \r boreal spring and summer, although the estimated magnitude is \r dataset dependent (Rupp et al., 2013; Estilow et al., 2015; Bokhorst \r et al., 2016; Thackeray et al., 2016; Connolly et al., 2019). "


id: cc3b6c7643903b37bd4e5a805031a79f
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 33255
content:
"Early Nimbus satellite visible and infrared imagery from the 1960s \r (Meier et  al., 2013; Gallaher et  al., 2014) indicate higher overall \r SIE compared to 1979-2013 (Hobbs et  al., 2016a, b), but with \r large uncertainties and poorly quantified biases (NA SEM, 2017). \r The continuous satellite passive-microwave record shows that there \r was a modest increase in overall Antarctic SIA of 2.5% +- 0.2% per \r decade (1 standard error over 1979-2015; Comiso et  al., 2017). \r For overall ice coverage and for this period, positive long-term trends \r were most pronounced during austral autumn advance (Maksym, \r 2019), being moderate in summer and winter, and lowest in spring \r (Holland, 2014; Turner et al., 2015; Hobbs et al., 2016a, b; Comiso \r et al., 2017). Since 2014, overall Antarctic SIE (and SIA) has exhibited \r major fluctuations from record-high to record-low satellite era \r extents (Massonnet et al., 2015; Reid and Massom, 2015; Reid et al., \r [...]"


id: dfcd3f6531e1d51953f36b0936c88830
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 36619
content:
"[...] 2015; Comiso et  al., 2017; Parkinson, 2019). After setting record\u0002high extents each September from 2012 through 2014, Antarctic SIE \r (and SIA) dipped rapidly in mid-2016 and remained predominantly \r below average through 2019 (Reid et al., 2020). For the most recent \r decade of observations (2010-2019), the decadal means of three \r SIA products (Figure 2.20b) were 2.17 million km2 for February and \r 15.75  million km2 for September, respectively. The corresponding \r levels for the means for the first decade of recordings (1979-1988) \r were 2.04 million km2 for February and 15.39 million km2 for \r September indicating little overall change. Initial SIA data for 2020 \r (OSISAF) show SIA for September above, and for February slightly \r below the recent decadal means (Figure 2.20b). The 2020 September \r level (OSISAF) remains below the levels observed over 2012-2014."


id: 81e329a9f8d2a0bbd6ba594245d6bbb5
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 19973
content:
"Two recent global reviews on glaciers over the Holocene (Solomina \r et al., 2015) and the past 2 kyr (Solomina et al., 2016) summarize \r the chronologies of respectively 189 and 275 glaciers. The former \r shows that glaciers retreated during the LDT and retracted to their \r minimum extent between 8 ka and 6 ka. Except for some glaciers \r in the SH and tropics, glaciers expanded thereafter, reaching their \r maximum extent beyond their present-day margins during the mid\u000215th to late 19th centuries CE. With few exceptions, glacier margins \r worldwide have retreated since the 19th century, with the rate of \r retreat and its global character since the late 20th century being \r unusual in the context of the Holocene (Solomina et  al.,  2016, \r Figure 2.23a). However, the areal extents of modern glaciers \r in most places in the NH are still larger than those of the early \r and/or middle Holocene (Solomina et al., 2015). When considering \r [...]"


id: 525e85a44fc7edbd4caea1ca2de49931
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 11769
content:
"[...] Holocene and present glaciers extents, it is important to account \r for the relatively long adjustment time of glaciers (often referred to \r as response time; Section 9.5.1.3); the majority of modern glaciers \r are currently out of equilibrium with current climate, even without \r further global warming (Mernild et al., 2013; Christian et al., 2018; \r Marzeion et  al., 2018; Zekollari et  al., 2020). The size of glaciers \r during other periods warmer than the Early to Mid-Holocene, such \r as the MPWP and LIG, is largely unknown because the deposits \r marking previous extents were in almost all cases over-ridden \r by later glaciations. For Arctic glaciers, different regional studies \r consistently indicate that in many places glaciers are now smaller \r than they have been in millennia (Lowell et al., 2013; Miller et al., \r 2013, 2017; Harning et al., 2016, 2018; Schweinsberg et al., 2017, \r 2018; Pendleton et al., 2019)."


id: 214fc81fd1a19568e00e8dd150775233
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 3078
content:
"New glacier outline (RGI Consortium, 2017) and glacier mass \r compilations (Zemp et al., 2019, 2020; Ciraci et al., 2020; Hugonnet \r et  al., 2021) improve, refine and update the quantification of \r glacier areal and mass changes based on observations from in situ \r and remote sensing data. Observations between the 1960s and \r 2019 indicate that mass loss has increased over recent decades \r (Figure 2.23b). The overall global glacier mass loss rate has increased \r from 240  +- 9 Gt yr -1 over 2000-2009 to 290 +- 10 Gt yr -1 over \r 2010-2019 (Hugonnet et al., 2021), confirming that the last decade \r exhibits the most negative glacier mass balance since the beginning \r of the observational record. Observations are in general consistent \r with trends revealed by global glacier mass change modelling for \r almost the entire 20th century (1901-1990) implying an estimated \r mass loss (without uncharted glaciers (Parkes and Marzeion, 2018) \r [...]"


id: 43df2c15de61ccd668cd03e19977570f
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 9171
content:
"[...] and excluding peripheral glaciers of Greenland and Antarctica) of very \r likely 210 +- 90 Gt yr -1 and very likely 170 +- 80 Gt yr -1 for the period \r 1971-2019 (Marzeion et al., 2015; Section 9.5.1 and Table 9.5)."


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vector_id: 30057
content:
"time with extended periods when it was predominantly deglaciated \r (Bierman et al., 2016; Schaefer et al., 2016). Geological evidence and \r modelling studies suggest periods of glacial intensification during the \r Pliocene at 4.9 Myr, 4.0 Myr, 3.6 Myr and 3.3 Myr (De Schepper et al., \r 2014; Bierman et al., 2016; Bachem et al., 2017). Retreat of the GrIS \r occurred during the MPWP and GrIS extent was reduced compared \r to today with some studies suggesting that the ice sheet was limited \r to the highest elevations (De Schepper et al., 2014; Koenig et al., 2015; \r Haywood et al., 2016; Blake-Mizen et al., 2019). There is apparent \r glacial intensification following the MPWP, 2.75-2.72 Myr (Nielsen \r and Kuijpers, 2013; De Schepper et  al., 2014; Blake-Mizen et  al., \r 2019; Knutz et al., 2019). Several studies agree that during the LIG \r the total GrIS extent was likely less than present day (Section 9.4.1, \r Figure 9.17) with the total mass loss ranging from 0.3-6.2 m sea \r [...]"


id: fb6c1321cea9594f673d6121239bc8db
created_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
vector_id: 41610
content:
"[...] level equivalent (SLE), although timing and magnitude of this mass \r loss are not well constrained (Helsen et al., 2013; Stone et al., 2013; \r Vasskog et al., 2015; Goelzer et al., 2016; Sinclair et al., 2016; Yau \r et al., 2016; Clark et al., 2020). During the LGM, the GrIS reached \r a peak ice volume greater than present (2-5 m SLE), as revealed by \r the limited number of available geological records (Simpson et al., \r 2009; Lecavalier et al., 2014; Batchelor et al., 2019)."


id: 5a32077ec897f216ce11c671d363792c
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 13118
content:
"Geological evidence has been used to reconstruct Holocene glacial \r fluctuations of the ice sheet margin and lowerings of its surface, \r which occurred at different times in different places, as recently \r reviewed by Noble et al. (2020). In West Antarctica, marine sediments \r below the ice sheet (Kingslake et al., 2018) corroborate a previous \r glacial isostatic adjustment modelling study (Bradley et al., 2015), \r which suggests that ice had retreated behind the present grounding \r line prior to about 10 ka, and then readvanced. Geophysical imaging \r indicates a readvance in this area around 6 +- 2 ka (Wearing and \r Kingslake, 2019). Other studies from the region conclude that ice\u0002sheet retreat and thinning was fastest from 9 to 8 ka (Johnson \r et al., 2014; McKay et al., 2016; Spector et al., 2017), or millennia \r later, during the MH (Hein et al., 2016; Johnson et al., 2019), with \r indications of a subsequent readvance (Venturelli et al., 2020). In \r [...]"


id: 2fd59d5e730f2fbc287f12a1cf405625
created_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
vector_id: 5670
content:
"[...] East Antarctica, rapid ice-sheet thinning occurred between around \r 9 and 5 ka (Jones et  al., 2015), consistent with previous work \r indicating that the ice sheet in many regions was at or close to \r its current position by 5 ka (Bentley et  al., 2014). Overall, during \r the MH, the AIS was retreating, but remained more extensive than \r present, while some parts of the ice sheet might have been smaller \r than now (low confidence). "


id: 662e2371687e1291a4d0adfb3d1aba12
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 15240
content:
"Improved estimates of surface mass balance (SMB) in Antarctica \r from 67  ice core records do not show any substantial changes in \r accumulation rates over most of Antarctica since 1200 CE (Frezzotti \r et al., 2013). The SMB growth rate in Antarctica is estimated to be \r 7.0 +- 0.1 Gt per decade between 1800 and 2010 and 14.0 +- 1.8 Gt \r per decade since 1900 (Thomas et  al., 2017). For the period \r 1979-2000, an insignificant Antarctic-wide negative SMB trend has \r been estimated (Medley and Thomas, 2019). The Antarctic Ice Sheet \r lost 2670 [1800 to 3540] Gt (SLE 7.4 [5.0 to 9.8] mm) of ice between \r 1992 and 2020. The rate of ice-sheet (including peripheral glaciers) \r mass loss rose from 0 [-36 to +40] Gt yr-1 (SLE  0.0 [-0.10 to \r 0.11]  mm  yr-1) in 1901-1990 to 192 [145 to 239]  Gt yr-1 (SLE \r 0.54 [0.47 to 0.61] mm yr -1) for 2006-2018 (Section 9.4.2, Figure 2.24, \r and Table 9.5). Within quantified uncertainties, this estimate agrees \r [...]"


id: 3226f75130b218696cb94d6bb9ed8de0
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 6080
content:
"[...] with other recent estimates (Rignot et al., 2019; B. Smith et al., 2020; \r Velicogna et al., 2020). There is therefore very high confidence that \r the AIS has been losing mass over 1992-2020 (Section 9.4.2.1 and \r Figure 2.24). Major contributions to recent AIS changes arise from \r West Antarctica and Wilkes Land in East Antarctica (Rignot et  al., \r 2019). For the East Antarctic most studies suggest that the mass "


id: d8f4e81dc3038e62c7fbb210619572d6
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 35463
content:
"Permafrost occurrence during the Pliocene has been inferred from \r pollen in lake sediments in NE Arctic Russia and permafrost-vegetation \r relationships which indicate that permafrost was absent during the \r MPWP in this region (Brigham-Grette et al., 2013; Herzschuh et al., \r 2016). Analysis of speleothem records in Siberian caves, indicates \r that permafrost was absent in the current continuous permafrost \r zone at 60degN at the start of the 1.5 Ma record, with aggradation \r occurring around 0.4 Ma (Vaks et al., 2020). There are indications of \r extensive permafrost thaw during subsequent interglacials especially \r further south in the current permafrost zone (Vaks et  al., 2013). \r Reconstruction of permafrost distribution during the LGM indicates \r that permafrost was more extensive in exposed areas (Vandenberghe \r et al., 2014). In non-glaciated areas of the North American Arctic there \r is permafrost that survived the LIG (French and Millar, 2014). Trends \r [...]"


id: a7644a1b7ae8d4a556dcf27afccbe138
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
vector_id: 26666
content:
"[...] and timing of permafrost aggradation and thaw over the last 6 kyr in \r peatlands of the NH were recently summarized (Hiemstra, 2018; Treat \r and Jones, 2018). Three multi-century periods (ending 1000 Before the \r Common Era (BCE), 500 CE and 1850 CE) of permafrost aggradation, \r associated with neoglaciation periods are inferred resulting in more \r extensive permafrost in peatlands of the present-day discontinuous \r permafrost zone, which reached a peak approximately 250 years ago, \r with thawing occurring concurrently with post 1850 warming (Treat \r and Jones, 2018). Although permafrost persists in peatlands at the \r southern extent of the permafrost zone where it was absent prior \r to 3 ka, there has been thawing since the 1960s (James et al., 2013; \r B.M. Jones et al., 2016; Holloway and Lewkowicz, 2020)."


id: 2717bdba5a95442fcfb99651a59c96af
created_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
vector_id: 4042
content:
"Assessment of trends in ALT is complicated by considerable ALT \r interannual variability. For example, in north-western North America \r during the extreme warm year of 1998, ALT was greater than in \r prior years. Although ALT decreased over the following few years, \r it has generally increased again since the late 2000s (Duchesne \r et  al., 2015; Romanovsky et  al., 2017b, 2020). However, at some \r sites there has been little change in ALT due to ground subsidence \r that accompanies thaw of ice-rich permafrost (Streletskiy et  al., \r 2017; O'Neill et al., 2019). In the European and Russian Arctic there \r has been a broad-scale increase in ALT during the 21st century \r (Streletskiy et al., 2015; Romanovsky et al., 2020). In high elevation \r areas in Europe and Asia, increases in ALT have occurred since the \r mid-1990s (Y. Liu et al., 2017; Cao et al., 2018; Noetzli et al., 2019, \r 2020; Zhao et al., 2020). Limited and shorter records for Antarctica \r [...]"


id: 41278775477c00f778bdcf8754a163a8
created_at: 2023-04-19 13:22:34
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content_type: text
index: climate_gpt_v2
vector_id: 8644
content:
"[...] show marked interannual variability and no apparent trend with \r ALT being relatively stable or decreasing at some sites since 2006 \r (Hrbacek et al., 2018)."


id: 4883a6160911582d965c8ac25c537ea2
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
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vector_id: 9971
content:
"a short period of rapid warming at the end of the Younger Dryas \r (12.75-11.55 ka) were rates comparable to those observed since \r the 1970s (Bereiter et al., 2018; Shackleton et al., 2019). Ice cores \r imply a small decrease in the global mean ocean temperature during \r the early Holocene (<0.4degC) (Bereiter et al., 2018; Baggenstos et al., \r 2019). Sediment cores from the equatorial Pacific and Atlantic Ocean \r (0-1000 m) indicate a stronger regional cooling (compared to mean \r ocean temperature) of 1.0degC +- 0.7degC to 1.8degC +- 0.4degC from the early/\r mid-Holocene to ca.1750 CE (Rosenthal et al., 2013, 2017; Morley \r et al., 2014; Kalansky et al., 2015). Sediment cores from the western \r equatorial Pacific suggest 0.8degC +- 0.1degC higher temperatures in \r the upper 700 m of the ocean during 950-1100 CE compared to \r 1400-1750 CE. These changes are consistent with a global estimate \r derived from combined surface and subsurface ocean temperature \r [...]"


id: 82360a33d22772984f36398529b9f284
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
vector_id: 20039
content:
"[...] proxy records (PAGES 2k Consortium, 2013; McGregor et al., 2015). \r A combined study of model and observational data further confirmed \r these results, treating temperature as a passive tracer (Gebbie and \r Huybers, 2019) and addressing the role of circulation dynamics \r (Scheen and Stocker, 2020). Collectively, the proxy records indicate \r a global OHC decrease of about 400 +- 70 ZJ (about 170 +- 100 ZJ \r in the Pacific) in the upper 700 m between 950-1100 CE and \r 1400-1750 CE, and also suggest that the deep Pacific is still adjusting \r to this cooling (Rosenthal et al., 2013), partially offsetting the global \r increase since 1750 CE (Gebbie and Huybers, 2019; Gebbie, 2021)."


id: 5d9e9ab9a64715ff1de1cade93d4bf94
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 13766
content:
"For the instrumental era, since AR5 and SROCC, new and updated \r OHC and ThSL observation-based analyses (Johnson et  al., 2020; \r von Schuckmann et al., 2020) enhance an existing large ensemble \r of direct and indirect OHC estimates (Figure 2.26), although some \r rely to varying degrees upon information from ocean-climate models. \r Direct estimates benefit from improved: bias adjustments (e.g., Cheng \r et  al., 2018; Leahy et  al., 2018; Palmer et  al., 2018; Ribeiro et  al., \r 2018; B. Wang et  al., 2018; Bagnell and DeVries, 2020; Gouretski \r and Cheng, 2020); interpolation methods (Kuusela and Stein, 2018; \r Su et  al., 2020); and characterization of sources of uncertainty \r (e.g.,  Good, 2017; Wunsch, 2018; Allison et  al., 2019; Garry et  al., \r 2019; Meyssignac et al., 2019; Palmer et al., 2021), including those \r originating from forced and intrinsic ocean variability (Penduff et al., \r 2018). After 2006 direct OHC estimates for the upper 2000 m layer \r [...]"


id: 24286c28236f501313b722b05ec4fece
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
vector_id: 3545
content:
"[...] benefit from the near-global ARGO array with its superior coverage \r over 60degS-60degN (Roemmich et al., 2019). Indirect estimates include \r OHC and ThSL series inferred from satellite altimetry and gravimetry \r since 2003 (Meyssignac et al., 2019), the passive uptake of OHC (ThSL) \r at centennial timescales inferred from observed SST anomalies, and \r time-invariant circulation processes from an ocean state estimation \r (e.g., Zanna et al., 2019). Resplandy et al. (2019) estimate the rate \r of global OHC uptake over 1991-2016 from changes in atmospheric \r composition and physical relationships based on CMIP5 model \r simulations. The uncertainties are broader than from direct estimates \r but the estimate is qualitatively consistent."


id: a8836b79b4f037ae3cd0571c2b577a3f
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 26856
content:
"Table 2.7 | Rates of global ocean heat content (OHC) and global mean thermosteric sea level (ThSL) change for four depth integrations over different \r periods. For the period up to 1971, the assessment for all depth layers is based on Zanna et al. (2019). From 1971 onwards, consistent with AR5, Domingues et al. (2008, updated) \r is the central estimate for 0-700 m along with uncertainty from a five-member ensemble (Domingues et al., 2008, updated; Levitus et al., 2012; Good et al., 2013; Cheng et al., \r 2017; Ishii et al., 2017), following the approach of Palmer et al. (2021). Similarly, Ishii et al. (2017) is the central estimate for 700-2000 m with uncertainty based on a 3-member \r ensemble (Levitus et al., 2012; Cheng et al., 2017; Ishii et al., 2017). For depths below 2000 m, both central estimate and uncertainty are from Purkey and Johnson (2010, \r [...]"


id: 801837fa36bf9877bcd7ed4554c90f0a
created_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
vector_id: 19668
content:
"[...] updated). In cases when OHC estimates do not have a ThSL counterpart (e.g., Good et al., 2013; Cheng et al., 2017), OHC was converted into ThSL using the average linear \r regression coefficients for 0-700 m and 700-2000 m from all available ensemble members. For consistency with the energy and sea-level budgets presented in Chapters 7 and \r 9, reported rates are based on the difference between the first and last annual mean value in each period (Palmer et al., 2021, Box 7.2, Cross-Chapter Box 9.1). N/A indicates \r not applicable. Further details on data sources and processing are available in the chapter data table (Table 2.SM.1)."


id: 8af8709905f594f881f79c58fd2f9029
created_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
vector_id: 21619
content:
"Prior to the instrumental record, reconstructions of near-surface \r salinity change are accomplished by combining isotopic and \r elemental proxy data from microfossil plankton shells and skeletons \r preserved in deep-sea sediments. These data highlight changes in \r the salinity contrast between the Pacific and Atlantic oceans during \r past glacials (Broecker, 1989; Keigwin and Cook, 2007; Costa et al., \r 2018) and for repeated episodes of increased subtropical salinity \r (Schmidt et al., 2004, 2006) and subpolar freshening (Cortijo et al., \r 1997; Thornalley et  al., 2011) in the North Atlantic ocean. These \r episodes were associated with disruptions to the large-scale deep \r ocean circulation (Buizert et  al., 2015; Henry et  al., 2016; Lynch\u0002Stieglitz, 2017). Further quantification of paleo salinity changes \r is complicated by incomplete understanding of proxy-salinity \r relationships and the relative influence of atmospheric and ocean \r [...]"


id: f17721f232406c64c20fdcd6bd26721a
created_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
vector_id: 39867
content:
"[...] processes across regions and paleo periods (Rohling, 2007; LeGrande \r and Schmidt, 2011; Holloway et al., 2016; Conroy et al., 2017)."


id: fde8e60c8e4d0e4aeb4718bb7dfa052a
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 42037
content:
"The 1950-2019 trends reveal near-surface freshening of the northern \r and western Warm (and fresh) Pool of the Pacific and increased \r salinity maxima in the subtropical Atlantic, strengthening the inter\u0002basin contrast (Figure 2.27a). There are indications that the subpolar \r freshening and subtropical salinification of the Atlantic ocean may \r extend back to at least 1896 (Friedman et  al., 2017). Over recent \r decades, new observations from Argo floats and ocean reanalyses \r provide general support that changes in the global patterns of near\u0002surface salinity contrast are broadly associated with an intensification \r of the hydrological cycle (Sections 2.3.1.3.5 and 8.3.1.1). However, \r this assessment is complicated by changing observational techniques \r (Section 1.5.1), temporally and spatially inhomogeneous sampling \r and uncertainties in interpolation algorithms and the substantial \r influence of modes of natural variabiltity and ocean circulation \r [...]"


id: 81e44232f663f736c2202acf8745eb63
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index: climate_gpt_v2
vector_id: 19975
content:
"[...] processes over interannual timescales (Skliris et  al., 2014; Durack, \r 2015; Grist et al., 2016; Aretxabaleta et al., 2017; Vinogradova and \r Ponte, 2017; Liu et al., 2020). Following AR5, based on the updated \r analysis from Durack and Wijffels (2010) which infills in situ gaps \r to recover large-scale patterns the mean salinity contrast between \r high- and low- near-surface salinity regions increased by 0.14 \r [0.07 to 0.20] from 1950 to 2019."


id: 93cb4d52a4ea309e8af0b20f67f73a49
created_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
vector_id: 23211
content:
"The AR5 concluded based on proxy and instrumental data that the \r rate of global mean sea level (GMSL) rise since the mid-19th century \r was larger than the mean rate during the previous two millennia \r (high confidence). The SROCC reported with high confidence that \r GMSL increases were 1.5 [1.1 to 1.9] mm yr-1 for 1902-2010 \r (with an acceleration rate between -0.002 and +0.019 mm yr -2), \r 2.1  [1.8  to  2.3] mm yr-1 for 1970-2015, 3.2 [2.8 to 3.5] mm yr-1\r for 1993-2015 and 3.6 [3.1 to 4.1] mm yr -1 for 2006-2015. AR5 \r reported that GMSL during the LIG was, over several thousand years, \r between 5 and 10 m higher than 1985-2004 (medium confidence) \r whereas SROCC concluded it was virtually certain that GMSL \r exceeded current levels (high confidence), and reached a peak that \r was likely 6-9 m higher than today, but did not exceed 10 m (medium \r confidence). The AR5 concluded with high confidence that there were \r [...]"


id: c14eaea056ee2981b7e93a393547aac5
created_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
vector_id: 31311
content:
"[...] two intra-LIG GMSL peaks and that the millennial-scale rate during \r these periods exceeded 2 mm yr -1. The AR5 had high confidence that \r GMSL during the MPWP did not exceed 20 m above present. Based \r on new understanding, SROCC placed the upper bound at 25 m but \r with low confidence."


id: 518195d875ed109b07d8ac6a6bd8ed32
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index: climate_gpt_v2
vector_id: 11616
content:
"Recent studies have highlighted uncertainties in estimates of GMSL \r during the LIG, including the extent of GMSL variability (Capron et al., \r 2019). Vertical land motions (Austermann et al., 2017) are starting to \r be considered quantitatively (e.g., Stephenson et al., 2019), but are \r still bounded by large uncertainties. The distribution and thickness \r of pre-LIG ice sheets (Dendy et al., 2017; Rohling et al., 2017) and \r isostasy driven by sediment loading since the LIG (Pico, 2020) \r add further uncertainty. In light of these recent studies and previous \r assessments, there is medium confidence that peak GMSL during the \r LIG was likely between 5 and 10 m higher than modern. Relative \r sea-level estimates from some sites (e.g., Bahamas and Seychelles) \r report ephemeral, metre-scale fluctuations (Vyverberg et al., 2018). \r Different generations of LIG reef growth at other sites (e.g., Yucatan \r Peninsula, Western Australia) suggest the occurrence of sudden \r [...]"


id: b5297ef492e60dfeaeeea93e8ebfad9c
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 29110
content:
"[...] accelerations in GMSL change (Blanchon et al., 2009; O'Leary et al., \r 2013). However, other sites (e.g.,  South Australia, Mediterranean), \r indicate that LIG sea level was substantially stable (T.-Y. Pan et al., \r 2018; Polyak et  al., 2018). In addition, there are uncertainties \r in the interpretation of local relative sea level from some GMSL \r reconstructions (Barlow et  al., 2018). Therefore, low confidence is \r assigned to any GMSL rate of change estimated within the LIG."


id: 5dd012d7a7c97f4ba3fc12a9e808a183
created_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
vector_id: 13800
content:
"Since SROCC, two new tide gauge reconstructions of 20th century \r GMSL change have been published, although both rely upon CMIP \r models to varying degrees (Figure 2.28). Frederikse et al. (2020) used \r a 'virtual station' method and a probabilistic framework to estimate \r GMSL change and its uncertainties since 1900. Dangendorf et al. (2019) \r combined a Kalman Smoother (Hay et al., 2015) with Reduced Space \r Optimal Interpolation (Church and White, 2011; Ray and Douglas, \r 2011) in an effort to better represent both the long-term GMSL change \r while preserving information on sea-level variability. In  addition, \r new ensemble-based methods for quantifying GMSL change have \r been presented that account for both structural and parametric \r uncertainty (Palmer et al., 2021). Altimeter time series of GMSL change \r (Figure 2.28) have been extended to 2019/2020 but bias adjustments \r (Watson et al., 2015; Beckley et al., 2017; Dieng et al., 2017; Ablain \r [...]"


id: 93495b55a1ffdd466830f2bcf74f416b
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
vector_id: 23112
content:
"On multi-millennial timescales, proxy evidence indicates that the \r AMOC varied repeatedly in strength and vertical structure. During \r the last glacial period, particularly around the LGM, AMOC was \r estimated to be shallower than present, although there is continued \r debate about the magnitude of the shoaling (Lynch-Stieglitz et al., \r 2007; Gebbie, 2014), and whether this change was associated with \r a weaker overturning (Ritz et al., 2013; Menviel et al., 2017; Muglia \r et al., 2018). There are indications that substantial variations in AMOC \r were associated with abrupt climate changes during the glacial \r intervals, including Dansgaard-Oeschger and Heinrich events (14-\r 70 ka) (McManus et al., 2004; Bohm et al., 2015; Henry et al., 2016; \r Lynch-Stieglitz, 2017). During these millennial-scale oscillations, \r weakened AMOC was associated with dramatic cooling in the NH \r and warming in the SH (Buizert et al., 2015; Henry et al., 2016), while \r [...]"


id: 855c3f7b8c54db4d2ee667d7c7179f9c
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 20601
content:
"[...] hemispheric changes of opposite sign accompanied strengthened \r AMOC. After the final demise of the Laurentide ice sheet about 8 \r ka, the mean overall strength of AMOC has been relatively stable \r throughout the rest of the Holocene compared to the preceding 100 \r kyr (Hoffmann et al., 2018; Lippold et al., 2019). There are however \r indications of episodic variations in AMOC during the Holocene \r (Bianchi and McCave, 1999; Oppo et  al., 2003; Thornalley et  al., \r 2013; Ayache et al., 2018), and past interglacial intervals (Galaasen \r et  al., 2014, 2020; Hayes et  al., 2014; Mokeddem et  al., 2014; H. \r Huang et al., 2020). Over the last 3 kyr, there are indications that \r AMOC variability was potentially linked to decreasing production of \r Labrador Sea Water (LSW), one of the water masses contributing to \r AMOC (Alonso-Garcia et  al., 2017; Moffa-Sanchez and Hall, 2017; \r Moffa-Sanchez et al., 2019)."


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vector_id: 13154
content:
"Based on the ensemble approach of Palmer et  al. (2021) and an \r updated WCRP Global Sea Level Budget Group (2018) assessment \r (Figure 2.28) GMSL rose at a rate of 1.32 [0.58 to 2.06] mm yr -1 for the \r period 1901-1971, increasing to 1.87 [0.82 to 2.92] mm yr -1 between \r 1971 and 2006, and further increasing to 3.69 [3.21 to 4.17] mm yr -1\r for 2006-2018 (high confidence). The average rate for 1901-2018 \r was 1.73 [1.28 to 2.17] mm yr-1 with a total rise of 0.20 [0.15 to \r 0.25] m (Table 9.5). The acceleration rate very likely is 0.094 [0.082 to \r 0.115] mm yr-2 for 1993-2018 (WCRP Global Sea Level Budget Group, \r 2018, updated), consistent with other estimates (Watson et al., 2015; \r X. Chen et al., 2017; Nerem et al., 2018; WCRP Global Sea Level Budget \r Group, 2018; Ablain et al., 2019; Legeais et al., 2020). For the period \r 1902-2010 the updated tide gauge reconstructions published since \r SROCC also show a robust acceleration over the 20th century and \r [...]"


id: 5cb2acd48a481d39377ce50d0659d136
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 13573
content:
"[...] the ensemble estimate of Palmer et al. (2021) gives a value of 0.0053 \r [0.0042 to 0.0073] mm yr-2, based on an unweighted quadratic fit."


id: 962c2b964bf5ac5f9b46f80b25763a9b
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 23617
content:
"Since the 1980s, multiple lines of observational evidence for AMOC \r change exist. Ship-based hydrographic estimates of AMOC as \r far back as the 1980s show no overall decline in AMOC strength \r (Fu  et  al., 2020; Worthington et  al., 2021). Direct indications from \r in-situ observations report a -2.5 +- 1.4 Sv change between 1993 and \r 2010 across the OVIDE section, superimposed on large interannual to \r decadal variability (Mercier et al., 2015). At 41degN and 26degN, a decline \r of -3.1 +- 3.2 Sv per decade and -2.5 +- 2.1 Sv per decade respectively \r has been reported over 2004-2016 (Baringer et  al., 2018; Smeed \r et al., 2018). However, Moat et al. (2020) report an increase in AMOC \r strength at 26degN over 2009-2018. Recent time series of moored \r observations at 11oS (Hummels et  al., 2015), 34degS (Meinen et  al., \r 2018; Kersale et al., 2020), and between 57 and 60oN (Lozier et al., \r 2019) are currently too short to permit robust conclusions about \r [...]"


id: 379c0d2e06aa01e03ef77de5b113253b
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 6992
content:
"[...] changes. The directly observed AMOC weakening since 2004, while \r significant, is over too short a period to assess whether it is part of \r a longer term trend or dominated by decadal-scale internal variability \r (Smeed et al., 2014; Collins et al., 2019; Moat et al., 2020). Notably an \r increase and subsequent decline in the 1990s is present in estimates \r of AMOC and associated heat transport constructed from reanalyses \r or auxiliary data (Section 9.2.3.1; Frajka-Williams, 2015; Jackson \r et al., 2016; Trenberth and Fasullo, 2017; Jackson and Wood, 2020)."


id: 956a3da756d6cc155692fdab2b44519b
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 23489
content:
"The intensity of the Kuroshio current system in the north-west Pacific \r varied in conjunction with the glaciation cycles over the last 1 Myr, \r with some limited glacial-interglacial variability in position (Jian et al., \r 2000; Gallagher et al., 2015). The Agulhas current has strengthened \r substantially during the warming associated with deglaciations \r of the past 1 Myr (Peeters et  al., 2004; Bard and Rickaby, 2009; \r Martinez-Mendez et  al., 2010; Marino et  al., 2013; Ballalai et  al., \r 2019). According to sediment core analyses, the Agulhas leakage \r varied by about 10 Sv during major climatic transitions over the past \r 640 kyr (Caley et al., 2014). Available data suggests that there was \r relatively little change in the net flow of the ACC during the LGM, \r with no consensus on the sign of changes (McCave et al., 2013; Lamy \r et al., 2015; Lynch-Stieglitz et al., 2016), except at one location at \r the northern edge of the Drake Passage where a 40% decrease of \r [...]"


id: 293fdd1e39180ab057a9ec5353c3556b
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 4471
content:
"[...] transport had been reported (Lamy et al., 2015). Longer time series \r from the northern entrance to Drake Passage suggest a consistent \r transport variability of 6-16% through glacial climate cycles, with \r higher current speeds during interglacial times and reduced current \r speeds during glacial intervals (Toyos et al., 2020). Inferred variability \r in the size and strength of the North Atlantic subpolar gyre was \r substantial, and included rapid changes on millennial time scales \r during both interglacial and glacial intervals over the last 150 kyr \r (Born and Levermann, 2010; Mokeddem et al., 2014; Irvali et al., 2016; \r Mokeddem and McManus, 2016). North Atlantic - Arctic exchange \r has also varied in the past, with indications of an increasing inflow of \r Atlantic waters into the Arctic during the late Holocene (Slubowska \r et al., 2005) with an acceleration to the recent inflow that is now the \r largest of the past 2 kyr (Spielhagen et al., 2011)."


id: 917d882b107cf96c1f8b183515fa9dde
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 22806
content:
"Understanding of changes in surface pH at paleo time-scales has \r increased since AR5 (Clarkson et  al., 2015; Foster and Rae, 2016; \r Zeebe et al., 2016; Gutjahr et al., 2017; Sosdian et al., 2018; Henehan \r et al., 2019; Anagnostou et al., 2020; Harper et al., 2020; Muller et al., \r 2020). Over the last 65 million years there have been several intervals \r when the pH of surface waters varied concurrently with climate \r change such as during the PETM, EECO, and MCO (Figure  2.29a \r and Section 5.3.1.1). However, only during the PETM is the change \r sufficiently well-constrained to allow for a direct comparison with \r recent and current trends (Kirtland Turner, 2018). This event was \r associated with profound perturbations of the global carbon cycle, \r ocean warming, deoxygenation and a surface ocean pH decrease \r likely ranging from 0.15 to 0.30 units (Penman et al., 2014; Gutjahr \r et al., 2017; Babila et al., 2018) - a rate that was likely at least an \r [...]"


id: 9416121b4a585d74f737f722b2b5875c
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 23259
content:
"[...] order of magnitude slower than today (Cui et al., 2011; Bowen et al., \r 2015; Frieling et al., 2016; Zeebe et al., 2016; Gutjahr et al., 2017; \r Kirtland Turner, 2018; Gingerich, 2019)."


id: 8d8f3fe721b0f80110808ba90579d396
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 22097
content:
"Paleo evidence suggests that surface ocean pH has gradually \r increased over the last 50 Myr (Anagnostou et  al., 2016, 2020; \r Sosdian et al., 2018) (Figure 2.29a). Global mean surface pH values \r as low as observed during recent decades are uncommon in the \r last 2 Myr (Figure 2.29b) (Martinez-Boti et al., 2015a; Chalk et al., \r 2017; Dyez et  al., 2018; Sosdian et  al., 2018), and have not been \r experienced in at least the last 25 kyr (Figure 2.29c; Palmer and \r Pearson, 2003; Foster, 2008; Palmer et al., 2010; Henehan et al., 2013; \r Kirschke et al., 2013; Martinez-Boti et al., 2015a; Naik et al., 2015; \r Ezat et al., 2017; Gray et al., 2018; Shao et al., 2019). The magnitude \r of pH change during the Pleistocene glacial-interglacial cycles was \r 0.1-0.15 pH units  - similar to recent changes in the modern era \r (Figure 2.29c and Section 5.3.1.2; Honisch et al., 2009; Chalk et al., \r 2017; Shao et al., 2019). Maximum rates of pH change during the \r [...]"


id: cc72d4d70cd353c34c98baa57edab5a7
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 33287
content:
"[...] LDT, inferred from changes in atmospheric CO2 recorded in ice cores \r (Marcott et al., 2014) and the established relationships between pH \r and CO2 changes and the boron isotope proxy (Hain et al., 2018), \r reached -0.02 pH units per century at about 11.7 ka, about 14.8 ka \r and about 16.3 ka, as previously sequestered CO2 was transferred \r from the ocean interior to the subsurface ocean (Martinez-Boti et al., \r 2015a; Jaccard et al., 2016; Rae et al., 2018)."


id: ddfc6a5d54b9a5b294ce011db9920a3c
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 36319
content:
"Since the 1980s, the global ocean has experienced a decline in \r surface pH of 0.016 +- 0.006 pH units per decade based on indirect \r pH products (Figure 2.29d; Lauvset et  al., 2015; Hurd et  al., 2018; \r IPCC,  2019; Gehlen et  al., 2020) that agrees with the decline of \r 0.017-0.025  pH units per decade assessed in SROCC from direct \r time-series measurements of pH. Section 5.3.2.2 assesses a decline \r that ranges from 0.01 to 0.026 pH units per decade for the tropical and \r subtropical open ocean areas, and 0.003-0.026 pH units per decade \r for the polar and subpolar open ocean regions by using time series \r and ship-based datasets from the surface ocean CO2 measurement \r network (Bakker et al., 2016; Gehlen et al., 2020; Gregor and Gruber, \r 2021). There is general consensus that global surface ocean pH trends \r over the past two decades have exceeded the natural background \r variability (Lauvset et  al., 2015; Bindoff et  al., 2019; Gehlen et  al., \r [...]"


id: 3de203d96a37b197ff1be6ebff1e153e
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 8054
content:
"[...] 2020). However, for some areas sparse data coverage, and large \r year-to-year variations hinders the detection of long-term surface \r ocean pH trends; for example in the Southern Ocean (Lauvset et al., \r 2015; Bindoff et al., 2019) and in the Arctic Ocean (Lauvset et al., 2015; \r Bindoff et al., 2019; Meredith et al., 2019)."


id: cece01fdcc1b30b5e07cc2542527c7c4
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 33725
content:
"Since AR5 evidence for changes in oxygen content based on new \r proxy reconstructions has increased (Hoogakker et  al., 2015, 2018; \r Gottschalk et al., 2016, 2020; Anderson et al., 2019). Paleo records \r point to past periods of reduced oceanic oxygen levels during the late \r Permian (about 250 Ma), the Jurassic and the Cretaceous (180-100 \r Ma), alongside associated global scale disturbances of the global \r carbon cycle. Emerging studies for the Cenozoic Era suggest more \r stable ocean oxygenation conditions throughout the interval on \r million-year time scales (X. Wang et al., 2016). Sedimentary proxy data \r indicate, however, that the seemingly stable Cenozoic was punctuated \r by transient, widespread deoxygenation during the PETM (Dickson \r et al., 2012; Winguth et al., 2012; Remmelzwaal et al., 2019), with \r parts of the ocean reaching anoxic levels (Section 5.3.1.1; Yao et al., \r 2018). Since the LGM, there was an overall emergence and expansion \r [...]"


id: 71efcbeb33488f2843844de5238e5844
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 17294
content:
"[...] of low-oxygen waters into the ocean intermediate depths as a result \r of rapid warming and a reorganization of the global overturning \r circulation (Galbraith and Jaccard, 2015). The maximum expansion \r of oxygen-depleted waters during the LDT occurred coincidently with \r rapid warming in the NH at 14.7-12.9 ka (Jaccard and Galbraith, \r 2012; Moffitt et al., 2015; Hoogakker et al., 2018). Deep (>1500 m) \r ocean oxygen levels increased by 100-150 umol kg-1 since the LGM, \r reaching modern oxygen levels at about 10 ka (Section 5.3.1.2; \r Hoogakker et al., 2015; Gottschalk et al., 2016; Anderson et al., 2019). "


id: c4ada347c0a0a8d880f6f1d3b867b7bb
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 31910
content:
"Subsurface (100-400 m) oxygen in the California Current system is \r estimated to have declined by 24 +- 2 mmol kg-1 (1.0 mmol kg-1 per \r decade) between 1993 and 2018, a rate similar to the global upper \r 1000 m average (Bindoff et  al., 2019). In some locations, however, \r the magnitude of oxygen loss substantially exceeds global averages \r (Queste et al., 2018; Bronselaer et al., 2020; Cummins and Ross, 2020; \r Stramma et  al., 2020). For example, a decline in oxygen content of \r 11.7 +- 3.5% in the upper 4000 m, including a decline of 20.4 +- 7.2% \r in the upper 1550 m, is reported in the North Pacific over the period \r 1958-2018 (Cummins and Ross, 2020) (equivalent to 2.3 mmol kg-1 per \r decade in the upper 1550 m and 2.0 mmol kg-1 decade- 1 throughout the \r 4000 m water column). In some regions of the Southern Ocean south \r of 65degS oxygen in the upper 2000 m has declined by 60 mmol kg-1\r (~52  mmol kg-1 per decade) based on comparisons of 2014-2019 \r [...]"


id: bfda7fc69ca88abbe67cdae4b59991d3
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 31040
content:
"[...] and 1985-2005 observations (Bronselaer et  al., 2020). Within some \r OMZs regions of the Indian ocean, oxygen has declined from 6-12 to \r <2 mmol kg-1 between the 1960s and 2015-2016 (Bristow et al., 2017; \r Al-Said et al., 2018; Queste et al., 2018)."


id: 821e62c57e2bb32e3197a88eb566a3d
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 20014
content:
"Since AR5 WGII, analysis of a longer time series of ocean-colour data \r (1998-2012) has shown (Aiken et al., 2017) that the expansion of \r the low nutrient part of the North Atlantic oligotrophic gyre was \r significant, at 0.27 x 106 km2 per decade, but that the rate was \r much lower than that reported earlier by Polovina et  al. (2008). \r Furthermore, Aiken et al. (2017) reported no significant trend in the \r oligotrophic area of the South Atlantic Gyre. With the time series \r extended to 2016, von Schuckmann et al. (2018) reported that since \r 2007, there was a general decreasing trend in the areas of the North \r and South Pacific oligotrophic Gyres, while the North and South \r Atlantic oligotrophic Gyres remained stable, with little change in \r area, consistent with Aiken et al. (2017). The changing sign of trends \r in the areal extent of the oligotrophic gyres with increase in the \r length of the time series raises the possibility that these changes \r [...]"


id: ecc3f9cef471b1246c7f8c366b0621cd
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 39019
content:
"[...] arise from interannual to multi-decadal variability. The time series \r of ocean-colour data is too short to discern any trend that might be \r superimposed on such variability. "


id: b32fc65f10f6b25fedf3d76b7cb3e0cd
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 28766
content:
"Similarly, there is limited consistent and long-term information on \r large-scale distributions of marine organisms at higher trophic levels. \r But there are increased indications since AR5 and SROCC that the \r distributions of various higher trophic-level organisms are shifting \r both polewards and to deeper levels (Edwards et al., 2016; Haug et al., \r 2017; Atkinson et al., 2019; Lenoir et al., 2020; Pinsky et al., 2020), \r mostly consistent with changes in temperature. However observations \r also show a smaller set of counter-intuitive migrations towards \r warmer and shallower waters, which could be related to changes \r in phenology and in larval transport by currents (Fuchs et al., 2020). \r There are also strengthening indications of greater representation by \r species with warm-water affinity in marine communities, consistent \r with expectations under observed warming (Burrows et  al., 2019). \r There are indications that pre-1850 CE plankton communities are \r [...]"


id: ce7f33b1b1a09b258b48cbeb7c5e5635
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 33675
content:
"[...] different from their modern counterparts globally (Jonkers et al., 2019). \r Indicators of geographical distributions of species (mostly from coastal \r waters) suggest that the rates at which some species are leaving or \r arriving at an ecosystem are variable, leading to changes in community \r composition (Blowes et al., 2019), with likely greater representation of \r warm-water species in some locations (Burrows et al., 2019). "


id: d9b3ef03272400e7004b5929f308045f
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 35596
content:
"Recent model-based results with assimilation of satellite data (Gregg \r and Rousseaux, 2019), show global annual mean marine primary \r production of around 38 (+-1.13) PgC yr -1 over 1998-2015. This new \r result lies towards the low end of values reported in earlier, satellite\u0002based, studies (range 36.5-67 PgC yr -1, reported in Sathyendranath \r et  al. (2020)). Reconciling the results of Gregg and Rousseaux \r (2019) with earlier satellite-based studies leads to a mean of \r 47 (+-7.8) PgC yr-1. There is a strong correlation between interannual \r regional variability in marine primary production and climate \r variability (Racault et al., 2017b; Gregg and Rousseaux, 2019). The \r increase in primary production in the Arctic has been associated \r with retreating sea ice and with increases in nutrient supply and \r chlorophyll concentration (Lewis et al., 2020). Gregg and Rousseaux \r (2019) reported a decreasing trend in marine primary production, of \r [...]"


id: 48573b9530e6c93212ac8bd042a151e8
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 9939
content:
"[...] -0.8 PgC (-2.1%) per decade globally. There is low confidence in this \r trend because of the small number of studies and the short length of \r the time series (<20 years)."


id: 848f496527919ce18dec059b5fb91780
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 20466
content:
"Phenological links between multiple components of an ecosystem \r have to be maintained intact, to retain system integrity. Since all \r higher pelagic organisms depend on phytoplankton for their food, \r either directly or indirectly, a match favours survival, and a mismatch \r is antagonistic to survival. Match represents synchronicity in the \r phenological events of both prey and predator. There are indications \r from ocean-colour data used in conjunction with fisheries data that \r the survival rate of various larger marine organisms depends on \r phenological metrics related to the seasonality of phytoplankton \r growth. Such links have been demonstrated, for example, for \r haddock (Melanogrammus aeglefinus) in the North-west Atlantic \r (Platt et  al., 2003); northern shrimp in the North Atlantic (Koeller \r et al., 2009; Ouellet et al., 2011); sardine (Sardinella aurita) off the \r Ivory coast (Kassi et  al., 2018); cod (Gadus morhua) and haddock \r [...]"


id: d724aaeee6de8bbdc60d92985888928b
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 35160
content:
"[...] (Melanogrammus aeglefinus) larvae in the North-West Atlantic \r (Trzcinski et al., 2013); and oil sardine (Sardinella longiceps) off the \r south-west coast of India. Borstad et al. (2011) showed that fledgling \r production rate of rhinoceros auklets (Cerorhinca monocerata) \r on a  remote island in coastal north-eastern Pacific was related to \r seasonal values of chlorophyll-a biomass in the vicinity of the island. "


id: b04ffa10636fbd5f7e2137dfb4f66d7a
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 28256
content:
"Changes in the length of the photosynthetically active growing \r season (derived from the Normalized Difference Vegetation Index \r (NDVI)) are also evident over many land areas since the early 1980s. \r Increases of about 2.0 days per decade are apparent north of 45degN \r since the early 1980s (centred over mid-latitude Eurasia and north\u0002eastern North America), with indications of a reversal to a decline \r in season length starting in the early 2000s (Barichivich et al., 2013; \r Zhao et al., 2015; Garonna et al., 2016; Q. Liu et al., 2016; Park et al., \r 2016). Satellite-based records suggest that most NH regions have \r experienced both an earlier start and a later end to the growing \r season, a finding supported by ground-based data (Piao et al., 2020). \r A number of studies also capture increases in growing season length \r over the Canadian Arctic (W. Chen et al., 2016), Fennoscandia (Hogda \r et  al., 2013), most of Europe (Garonna et  al., 2014), and parts of \r [...]"


id: 3a4346d4e23dec57de9aac15b2f0f7fb
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 7456
content:
"Binney et al., 2017), the northern treeline being about 17deg to 23deg \r latitude south of its present-day location in most areas. During the \r LDT, pervasive ecosystem transformations occurred in response to \r warming and other climatic changes (Nolan et  al., 2018; Fordham \r et al., 2020). By the MH, North Africa had experienced a widespread \r conversion from grasslands to desert (Hoelzmann et al., 1998; Prentice \r et  al., 2000; Sha et  al., 2019), and the northernmost treeline had \r shifted poleward again to about 1deg to 3deg latitude north of its current \r location (MacDonald et al., 2000; Binney et al., 2009; Williams et al., \r 2011). Over the past half century, there has been an increase in the \r spatial synchrony of annual tree growth across all continents that is \r unprecedented during the past millennium (Manzanedo et al., 2020). \r Elevated rates of vegetation change in the Holocene are consistent \r with climate variability (Shuman et al., 2019), intensified human land \r [...]"


id: 5775ebc9bd191711e492796d7aca468a
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 12653
content:
"[...] use (Fyfe et al., 2015; Marquer et al., 2017), and resulting increased \r ecosystem novelty (Finsinger et al., 2017; K.D. Burke et al., 2019)."


id: a36dee989ee645c3edd9142fe02b5a45
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 25962
content:
"Long-term ecological records capture extensive range shifts during \r the 20th and early 21st centuries (Lenoir and Svenning, 2015; \r Pecl  et  al., 2017). Research has been most extensive for North \r America and western Eurasia, with fewer studies for central Africa, \r eastern Asia, South America, Greenland, and Antarctica (Lenoir and \r Svenning, 2015). Most documented changes are toward cooler \r conditions  - that is, poleward and upslope (Lenoir et  al., 2008; \r Harsch et  al., 2009; Elmendorf et  al., 2015; Parmesan and Hanley, \r 2015; Evans and Brown, 2017). Notably, a large, quasi-global \r analysis (Chen et  al., 2011) estimated that many insect, bird, and \r plant species had shifted by 17 (+-3) km per decade toward higher \r latitudes and 11 (+-2) m per decade toward higher elevations since \r the mid-20th century, with changes in both the leading and trailing \r edges of species ranges (Rumpf et al., 2018). Over the past century, \r [...]"


id: f8161d40535f1e225b3598e385921dd5
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 41021
content:
"[...] long-term ecological surveys also show that species turnover (i.e., the \r total number of gains and losses of species within an area) has \r significantly increased across a broad array of ecosystems (Dornelas \r et al., 2014, 2019), including undisturbed montane areas worldwide \r (Gibson-Reinemer et al., 2015). Despite global losses to biodiversity, \r however, most local assemblages have experienced a change in \r biodiversity rather than a systematic loss (Pimm et al., 2014). With \r increased species turnover, the novelty of contemporary communities \r relative to historical baselines has risen (Hobbs et al., 2009; Radeloff \r et  al., 2015) due to greater spatial homogenization, mixtures of \r exotic and native species, altered disturbance regimes, and legacies \r of current or historic land use (Olden and Rooney, 2006; Schulte \r et al., 2007; Thompson et al., 2013; Goring et al., 2016). In general, \r terrestrial species have had lower rates of turnover than marine \r [...]"


id: daa5c7fd3016c3bc1b6c69a660836830
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
vector_id: 35760
content:
"changes in specific vegetation types, such as a  7% rise in global \r tree cover for 1982-2016 (Song et al., 2018) and an expansion of \r shrub extent in the Arctic tundra over 1982-2017 (Myers-Smith \r et al., 2020). The increased greening is largely consistent with CO2\r fertilization at the global scale, with other changes being noteworthy \r at the regional level (Piao et al., 2020); examples include agricultural \r intensification in China and India (X. Chen et al., 2019; Gao et al., \r 2019) and temperature increases in the northern high latitudes \r (Kong et al., 2017; Keenan and Riley, 2018) and in other areas such \r as the Loess Plateau in central China (Y. Wang et al., 2018). Notably, \r some areas (such as parts of Amazonia, central Asia, and the Congo \r basin) have experienced browning (i.e.,  decreases in green leaf \r area and/or mass) (Hoogakker et al., 2015; Gottschalk et al., 2016; \r Anderson et  al., 2019). Because rates of browning have exceeded \r [...]"


id: 2256904060ae124b173931216cd122ec
created_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
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content:
"[...] rates of greening in some regions since the late 1990s, the increase in \r global greening has been somewhat slower in the last two decades \r (T.-Y. Pan et al., 2018). "


id: 1f2fd97ca879de0642b244ccd3fd732b
created_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
vector_id: 2696
content:
"Consistent with species range shifts, SRCCL noted that there have \r been changes in the geographical distribution of climate zones. \r Poleward shifts in temperate and continental climates are evident \r across the globe over 1950-2010, with decreases in the area \r (and increases in the average elevation) of polar climates (Chan and \r Wu, 2015). Zonal changes towards higher latitudes in winter plant \r hardiness regions are apparent since the 1970s over the central \r and eastern USA, with elevational changes also being important \r in the western USA (Daly et  al., 2012). A clear northward shift in \r winter plant hardiness zones is detectable across western Canada \r since 1930, with somewhat lesser changes in the south-eastern part \r of the country (McKenney et  al., 2014). A northward migration of \r agro-climate zones is also evident over Europe since the mid-1970s \r (Ceglar et al., 2019). In addition, a shift toward more arid climate \r [...]"


id: b2152fa5adb3c7a45273eba5f3fe8bc8
created_at: 2023-04-19 13:22:34
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content:
"[...] zones is apparent in some areas, such as the Asian monsoon region \r (Son and Bae, 2015) as well as parts of South America and Africa \r (Spinoni et al., 2015)."


id: fe15065d00417bdbde02bb6b90e33a0f
created_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
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content:
"Global-scale linear trends differ substantially across products for the \r same periods and trend metrics used (Jiang et al., 2017). Several factors \r contribute to this large span in estimated changes. Remotely sensed \r vegetation products vary in their spatial and temporal completeness as \r well as resolution and are sensitive to contamination from atmospheric \r composition, clouds, snow cover, and anisotropy, as well as orbital \r changes and sensor degradations (de Jong et  al., 2012; Zhu et  al., \r 2016; Jiang et al., 2017; Xiao et al., 2017; N. Pan et al., 2018). Ground\u0002based measurements suitable for calibration and validation are scarce \r before 2000 (Xiao et al., 2017), and the recalibration of satellite records \r (e.g., as in from MODIS Collection 5 to 6) can affect trends (Piao et al., \r 2020). It is possible that the increase in greenness over 2000-2015 \r is larger than the increase in gross primary production (based on flux \r [...]"


id: a0f8176547dc8202919c57489b0de192
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index: climate_gpt_v2
vector_id: 25543
content:
"[...] tower measurements and MODIS Collection 6 data) (L. Zhang et al., \r 2018). Land use changes and altered disturbance regimes (e.g., floods, \r fires, diseases) may mask large-scale signals (Franklin et  al., 2016). \r In addition, there is a plethora of models for the identification of "


id: 893cb7065ffa6f3a3526c50474aabdb
created_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
vector_id: 21314
content:
"Vegetation index data derived from AVHRR and MODIS depicts \r increases in aspects of vegetation greenness (i.e.,  green leaf area \r and/or mass) over the past four decades (Piao et  al., 2020). NDVI \r increased globally from the early 1980s through the early 2010s \r (Y.  Liu et  al., 2015a). N. Pan et  al. (2018) found NDVI increases \r over about 70% of the Earth's vegetated surface through 2013, \r and Osborne et  al. (2018) noted strong upward changes in NDVI \r in the circumpolar Arctic through 2016. Globally integrated Leaf \r Area Index (LAI) also rose from the early 1980s through at least \r the early 2010s (Zhu et al., 2016; Forzieri et al., 2017; Jiang et al., \r 2017; Xiao et  al., 2017) and probably through near-present; for \r example, C. Chen et  al. (2019) documented an LAI increase over \r one-third of the global vegetated area from 2000-2017. Although \r less frequently analysed for temporal trends, Fraction of Absorbed \r [...]"


id: c24acd68745650e871c5cb6417664d1a
created_at: 2023-04-19 13:22:34
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content:
"[...] Photosynthetically Active Radiation (FAPAR) likewise increased \r over many global land areas (particularly China, India, and eastern \r Europe) in the past two decades (Figure 2.33; Forkel et  al., 2014; \r Gobron, 2018; Keenan and Riley, 2018). There are also documented "


id: 53cd0ffd24204893b89926e15374e6e4
created_at: 2023-04-19 13:22:34
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content_type: text
index: climate_gpt_v2
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content:
"The Pliocene Epoch is one of the best-documented examples of a warmer world during which the slow responding components of \r the climate system were approximately in balance with concentrations of atmospheric CO2, similar to present (e.g., Haywood et al., \r 2016). It provides a means to constrain Earth's equilibrium climate sensitivity (Section 7.5.3) and to assess climate model simulations \r (Section 7.4.4.1.2). During the Pliocene, continental configurations were similar to present (Cross-Chapter Box 2.4 Figure 1a), and \r many plant and animal species living then also exist today. These similarities increase reliability of paleo-environmental reconstructions \r compared with those for older geological periods. Within the well-studied mid-Pliocene Warm Period (MPWP, also called the \r mid-Piacenzian Warm Period, 3.3-3.0 Ma), the interglacial period KM5c (3.212-3.187 Ma) has become a focus of research because \r [...]"


id: 20dc599a0d0409b2ecd9f97763b4ef1a
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index: climate_gpt_v2
vector_id: 3000
content:
"[...] its orbital configuration, and therefore insolation forcing, was similar to present (global mean insolation = -0.022 W m-2 relative to \r modern; Haywood et al., 2013), allowing for the climatic state associated with relatively high atmospheric CO2 to be assessed with \r fewer confounding variables."


id: dac08383fe20543eb05e916ba2c0b91b
created_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
vector_id: 35778
content:
"During the KM5c interglacial, atmospheric CO2 concentration was typically between 360 and 420 ppm (Section 2.2.3.1). New climate \r simulations of this interval from the Pliocene Model Intercomparison Project Phase 2 (PlioMIP2) show a multi-model mean global \r surface air temperature of 3.2 [2.1 to 4.8] degC warmer than control simulations (Cross-Chapter Box 2.4, Figure 1a; Haywood et al., 2020). \r This is consistent with proxy evidence for the broader MPWP, which indicates that global mean surface temperature was 2.5degC-4.0degC \r higher than 1850-1900 (Section 2.3.1.1.1). Global mean sea level was between 5 and 25 m higher than present (Section 2.3.3.3). \r Geological evidence (Section 2.3.2.4) and ice-sheet modelling (Section 9.6.2) indicate that both the Antarctic and Greenland Ice \r Sheets were substantially smaller than present (Cross-Chapter Box 2.4, Figure 1c). Attribution of sea level highstands to particular \r [...]"


id: 7f2106f0fa52af2a62f2cdf51c11560
created_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
vector_id: 19518
content:
"[...] ice-sheet sources (Section 9.6.2) is challenging (DeConto and Pollard, 2016; Golledge, 2020), but improving (Berends et al., 2019; \r Grant et al., 2019). "


id: 71898f5995a970710d8415159cd678a0
created_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
vector_id: 17224
content:
"The latitudinal temperature gradient during the MPWP was reduced relative to present-day and the consistency between proxy and \r modelled temperatures has improved since AR5 (Section 7.4.4.1.2). Northern high latitude (>60degN) SSTs were up to 7degC higher than \r 1850-1900 (Bachem et al., 2016; McClymont et al., 2020; Sanchez-Montes et al., 2020), and terrestrial biomes were displaced poleward \r (e.g., Dowsett et al., 2019) (Cross-Chapter Box 2.4, Figure 1b). Arctic tundra regions currently underlain by permafrost were warm \r enough to support boreal forests, which shifted northward by approximately 250 km in Siberia, and up to 2000 km in the Canadian \r Arctic Archipelago (Salzmann et al., 2013; Fletcher et al., 2017). The shift caused high-latitude surface albedo changes, which further \r amplified the Pliocene global warming (Zhang and Jiang, 2014). Vegetation changes in north-east Siberia indicate that MPWP summer \r [...]"


id: 1fd76e3a8b2d607aa89cbd1fe64ad27b
created_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
vector_id: 2808
content:
"[...] temperatures were up to 6degC higher than present day (Brigham-Grette et al., 2013). Farther south, modern boreal forest regions in \r Russia and eastern North America were covered with temperate forests and grasslands, whereas highly diverse, warm-temperate \r forests with subtropical taxa were widespread in central and eastern Europe (Cross-Chapter Box 2.4, Figure 1). While seasonal sea ice \r was present in the North Atlantic and Arctic oceans, its winter extent was reduced relative to present (Knies et al., 2014; Clotten et al., \r 2018), and some models suggest that the Arctic was sea ice free during the summer (Howell et al., 2016; Feng et al., 2020). "


id: 324f25e928cb9976c04ca3828e86d611
created_at: 2023-04-19 13:22:34
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vector_id: 6109
content:
"The average longitudinal temperature gradient in the tropical Pacific was weaker during the Pliocene than during 1850-1900 \r (Section 7.4.4.2.2). Changes in Pacific SSTs and SST gradients had far-reaching impacts on regional climates through atmospheric \r teleconnections, affecting rainfall patterns in western North America (Burls and Fedorov, 2017; Ibarra et al., 2018). The reduced zonal \r SST gradient has led to suggestions that the Pliocene Pacific experienced a 'permanent El Nino' state (Molnar and Cane, 2002; Fedorov, \r 2006). However, there is no direct geological evidence, nor support from climate models, that ENSO variability collapsed during the \r Pliocene. Although not located in the centre-of-action region for ENSO, Pliocene corals show temperature variability over 3-7 year \r timescales (Watanabe et  al., 2011). In addition, a multi-model intercomparison indicates that ENSO existed, albeit with reduced \r [...]"


id: d5e60c42f04279d629db40c3e3352ed2
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index: climate_gpt_v2
vector_id: 34946
content:
"[...] variability (Brierley, 2015). Thus, there is high confidence that ENSO variability existed during the Pliocene."


id: 812a3bd473a60a2c0ef8f64dd307a7dc
created_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
vector_id: 19856
content:
"Vegetation reconstructions for the late Pliocene indicate regionally wetter conditions resulting in an expansion of tropical savannas \r and woodlands in Africa and Australia at the expense of deserts (Cross-Chapter Box 2.4, Figure 1b). PlioMIP2 climate models \r generally simulate higher rates of mean annual precipitation in the tropics and high latitudes, and a decrease in the subtropics, with \r a multi-model mean global increase of 0.19 [0.13-0.32] mm day-1 relative to control simulations (Cross-Chapter Box 2.4, Figure 1a; \r Haywood et al., 2020). Both simulations and limited proxy evidence indicate stronger monsoons in northern Africa, Asia, and northern \r Australia relative to present, but trends are uncertain in other monsoon regions (X. Li et al., 2018; Yang et al., 2018; X. Huang et al., \r 2019a; R. Zhang et al., 2019). There is thus medium confidence that monsoon systems were stronger during the Pliocene. Simulations \r [...]"


id: d2f46b477f05427ed73c199cc2c68d28
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
vector_id: 34460
content:
"[...] of MPWP climate show that global tropical cyclone intensity and duration increased during the MPWP (Yan et al., 2016); however, \r there is low confidence in this result because inter-model variability is high."


id: efc4408743ff698fc3d292a70f69a269
created_at: 2023-04-19 13:22:34
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content:
"Cross-Chapter Box 2.4, Figure 1 | Climate indicators of the mid-Pliocene Warm Period (MPWP; 3.3-3.0 million years ago, Ma) from models \r and proxy data. (a) Simulated surface air temperature (left) and precipitation rate anomaly (right) anomaly (relative to 1850-1900) from the Pliocene Model \r Intercomparison Project Phase 2 multi-model mean, including CMIP6 (n = 4) and non-CMIP6 (n = 12) models. Symbols represent site-level proxy-based estimates of \r sea-surface temperature for KM5c (n = 32), and terrestrial temperature (n = 8) and precipitation rate for the MPWP (n = 8). (b) Distribution of terrestrial biomes was \r considerably different during the Piacenzian Stage (3.6-2.6 Ma) (upper) compared with present-day (lower). Biome distributions simulated with a model (BIOME4) \r in which Pliocene biome classifications are based on 208 locations, with model-predicted biomes filling spatial gaps, and the present day, with the model adjusted \r [...]"


id: 9ed9691682631859e7fd68d1b9c67e8f
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vector_id: 25171
content:
"[...] for CO2 concentration of 324 parts per million (ppm). (c) Ice-sheet extent predicted using modelled climate forcing and showing where multiple models consistently \r predict the former presence or absence of ice on Greenland (n = 8 total) and Antarctica (n = 10 total). Further details on data sources and processing are available \r in the chapter data table (Table 2.SM.1)."


id: 4a852af17cba4394d12c5d265503e4ab
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index: climate_gpt_v2
vector_id: 10351
content:
"winter NAM and NAO since the late 1990s (Pinto and Raible, 2012; \r Hanna et  al., 2018). This is particularly evident in December NAO \r (Hanna et al., 2015) and NAM (Overland and Wang, 2015) indices, and \r is not unusual on multi-decadal time scales (Woollings et al., 2018a). \r Since the 1990s, a statistically significant summer NAO decline was \r reported, which is, to a lesser extent, also evident in the winter NAO, \r linked to an enhanced blocking activity over Greenland (Hanna et al., \r 2015, 2016; Wachowicz et al., 2021). However, this was moderated by \r persistent positive NAO values since 2015 (Annex IV.2.1). Based on \r observations and reanalysis datasets, multi-decadal variations were \r found for the NAM patterns: the Atlantic centre remained unchanged \r throughout 1920-2010, whereas the Pacific centre was stronger \r during 1920-1959 and 1986-2010 and weaker during 1960-1985 \r (Gong et  al., 2018). Multidecadal changes were also observed in \r [...]"


id: 86a86c11f63ced1f7c154c35b368bc40
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content:
"[...] the position of the centres of action of the NAO (Moore et al., 2013; \r Zuo et al., 2016). "


id: 4d88a873b47390e5464b5af39b0e8d55
created_at: 2023-04-19 13:22:34
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content:
"New multi-millennial-timescale NAO reconstructions are derived \r from marine and lake sediments, speleothems, tree rings and ice \r cores (Ortega et  al., 2015; Faust et  al., 2016; Sjolte et  al., 2018). \r NAO variability over the past 8 kyr suggests the presence of \r a  significant 1.5  kyr periodicity (Darby et  al., 2012). Positive NAO \r conditions dominated during the MH, while the prevailing NAO sign \r during the early and late Holocene was negative according to most \r reconstructions (Olsen  et  al., 2012; Ait Brahim et  al., 2019; Rothe \r et  al., 2019; Hernandez et  al., 2020). For the CE, reconstructions \r developed since AR5 indicate no dominant NAO phase during \r 1000-1300 CE (Jones et al., 2014; Baker et al., 2015; Ortega et al., \r 2015; Lasher and Axford, 2019; Hernandez et al., 2020), with either \r negative (Baker et al., 2015; Faust et al., 2016; Mellado-Cano et al., \r 2019) or a more variable phase of the NAO (Jones et al., 2014; Ortega \r [...]"


id: 7f89ae7ea73d481eccbe22ae0d46d8db
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index: climate_gpt_v2
vector_id: 19582
content:
"[...] et al., 2015; Sjolte et al., 2018; Cook et al., 2019) between 1400 and \r 1850 CE. Several instrument-based NAO reconstructions extending \r back to the 17th and 18th centuries highlight the presence of multi\u0002decadal variations in the NAO phases (Cornes et al., 2013; Cropper \r et al., 2015), although these studies have limitations considering the \r seasonality of the centres of action and the locations of the stations \r used. Recent reconstructions of the large scale sea level pressure field \r yield more robust NAO analysis, showing a persistently negative NAO \r phase from the 1820s to the 1870s, with positive values dominating \r during the beginning of the 20th century followed by a declining \r trend over 1920-1970, with a recovery thereafter to a period of \r consistently high values between 1970 and the early 1990s (Delaygue \r et al., 2019; Mellado-Cano et al., 2019). Based on the evaluation of \r several NAO reconstructions for recent centuries, Hernandez et  al. \r [...]"


id: 60e0017bfebc7b2360eb29869dcb42b2
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content:
"[...] (2020) highlighted that the strong positive NAO phases of the 1990s \r and early 21st century were not unusual. "


id: 7ac8b0ba38bae18b0f8ca44fc369ace4
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
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content:
"Several studies have attempted to reconstruct the evolution of the \r SAM during the Holocene using proxies of the position and strength \r of the SH zonal winds, although with no clear consensus regarding \r the timing and phase of the SAM (Hernandez et al., 2020). The early \r Holocene was dominated by SAM positive phases (Moreno et  al., \r 2018; Reynhout et  al., 2019), consistent with increasing westerly \r wind strength (Lamy et al., 2010), with some reconstructions showing \r significant centennial and millennial variability but no consistent trend \r after 5 ka (Hernandez et  al., 2020). For the CE, enhanced westerly \r winds occurred over 0-1000 CE, as reflected in increased burning \r activity in Patagonia (Turney et al., 2016a) and tree ring records from \r southern New Zealand (Turney et al., 2016b) imply a predominantly \r positive SAM phase. Pollen records and lake sediments from Tasmania, \r southern mainland Australia, New Zealand and southern South \r [...]"


id: 4d9b47fb3fdfc95bb35dafdc0e1314d3
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index: climate_gpt_v2
vector_id: 10929
content:
"[...] America, inferred the period of 1000 to 1400 CE to be characterized \r by anomalously dry conditions south of 40degS, implying a positive \r SAM (Moreno et al., 2014; Fletcher et al., 2018; Evans et al., 2019; \r Matley et al., 2020). Nevertheless, proxy reconstructions of the SAM \r based on temperature-sensitive records from tree rings, ice cores, \r lake sediments and corals spanning the mid-to-polar latitudes show \r alternating positive and negative phases (Figure 2.35)."


id: a8276da50dcc73d15c4da1911efed752
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
vector_id: 26802
content:
"Before the mid-1950s, SAM indices derived from station-based \r datasets, and centennial reanalyses show pronounced interannual \r and decadal variability but no significant trends, with low correlation \r between SAM indices due to the diversity across different datasets \r and sensitivity to the definition used for the index calculation \r (Barrucand et  al., 2018; Schneider and Fogt, 2018; J. Lee et  al., \r 2019). Various SAM indices exhibit significant positive trends \r since the 1950s, particularly during austral summer and autumn \r (Barrucand et al., 2018; Schneider and Fogt, 2018; J. Lee et al., 2019), \r unprecedented for austral summer over the last 150 years (J.M. Jones \r et al., 2016; Fogt and Marshall, 2020). This indicates a strengthening \r of the surface westerly winds around Antarctica, related to both the \r position and intensity of the subpolar jet in the SH (Section 2.3.1.4.3; \r Ivy et al., 2017; IPCC, 2019). The SAM trends have slightly weakened \r [...]"


id: 7ab71019a5d34ad9f2d976ffea6fef51
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index: climate_gpt_v2
vector_id: 18788
content:
"Manucharyan and Fedorov (2014) found that ENSO-like variability \r has been present, at least sporadically, during epochs of millions \r of years (including the MPWP; Cross-Chapter Box 2.4), with proxy \r records indicating that this was the case even when cross-Pacific \r SST gradients were much weaker than present. There is substantial \r disagreement between proxy records for ENSO activity during \r the early Holocene (Zhang et  al., 2014; White et  al., 2018), and \r for ENSO activity and mean state at the LGM (Leduc et  al., 2009; \r Koutavas and Joanides, 2012; Sadekov et al., 2013; Ford et al., 2015, \r 2018; Zhu  et  al., 2017; Tierney et  al., 2020). A number of studies \r (Cobb  et  al., 2013; H.  McGregor et  al., 2013; Carre et  al., 2014; \r Emile-Geay et  al., 2016; Thompson et  al., 2017; Tian et  al., 2017; \r White et al., 2018; Grothe et al., 2019) have found that ENSO was \r [...]"


id: 8271c3fdfe7c79c89f6dc377f770f2fb
created_at: 2023-04-19 13:22:34
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index: climate_gpt_v2
vector_id: 20071
content:
"[...] substantially weaker than at present at various times in the mid\u0002Holocene within the period from 6 to 3 ka, with stronger decreases \r in variability revealed by remote proxies than by those close to the \r core region of ENSO activity. However, Karamperidou et al. (2015) \r find that weakening in ENSO-related variability in eastern Pacific \r proxies does not necessarily correspond to weakening in central \r Pacific proxies. Barrett et  al. (2018) concluded that multi-proxy \r reconstructions are more efficient at identifying eastern Pacific than \r central Pacific events. This suggests that a weakening of proxy-based \r signals may indicate an along-equatorial shift in ENSO activity rather \r than a weakening of ENSO during some periods. Following the period \r of weak ENSO variability in the mid-Holocene, a number of studies \r find an increase in ENSO activity which, depending upon the study, \r commences between 4.4 and 3 ka (Koutavas and Joanides, 2012; \r [...]"


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content:
"[...] Cobb et al., 2013; Zhang et al., 2014; S. Chen et al., 2016; Emile-Geay \r et al., 2016; Thompson et al., 2017; Du et al., 2021). "


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vector_id: 5115
content:
"ERSSTv5, addressed known SST biases in ERSSTv3 in the equatorial \r Pacific which affected the derived mean state and amplitude of indices \r based on that dataset (Huang et al., 2015). During the instrumental \r period, there is no robust indication of any significant century-scale \r trend in the east-west SST gradient across the equatorial Pacific \r Ocean, with periods when gradients have been stronger and weaker \r than the long-term average on decadal timescales, associated \r with a predominance of La Nina or El Nino events respectively. The \r frequency of El Nino and La Nina events is also subject to considerable \r decadal variability (e.g.,  Hu et  al., 2013) but with no indication of \r a long-term signal in the frequency of events. The ENSO amplitude \r since 1950 has increased relative to the 1910-1950 period, as \r confirmed by independent proxy records (e.g.,  Gergis and Fowler, \r 2009), the Southern Oscillation Index (SOI) (Braganza et al., 2009) and \r [...]"


id: 290043211dc50232d4cbcaf27d211fac
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 4426
content:
"[...] SSTs (e.g., Ohba, 2013; Yu and Kim, 2013), although there is a spread \r between different proxy and instrumental sources as to the magnitude \r of that increase (Figure 2.36). The El Nino events of 1982-1983, \r 1997-1998 and 2015-2016 had the strongest anomalies in the Nino \r 3.4 SST index since 1950. Their predominance was less evident from \r indices based on de-trended data such as the Oceanic Nino Index (ONI) \r (which still ranked them as the three strongest events since 1950, but \r only by a small margin), and in the SOI. B. Huang et al. (2019a) also \r note that analyses based upon buoy and Argo data, which are only \r available since the 1990s, are more capable of resolving strong events \r than analyses which do not include such data."


id: 712041f3589d45b6883dc6b357f43dc1
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 17164
content:
"Prior to the 1950s, SST observations in the tropical Pacific were much \r sparser and hence uncertainties in Nino indices are much larger \r (B. Huang et al., 2020). SOI data and some newer SST-based studies \r show high ENSO amplitude, comparable to the post-1950 period, in \r the period from the mid-late 19th century to about 1910, but proxy \r indicators generally indicate that the late 19th and early 20th century \r were less active than the late 20th century (Figure 2.36). Yu and Kim's \r (2013) implementation of the ONI found a number of events with the \r ONI above 1.5degC between 1888 and 1905, then no such events until \r 1972, whilst the SOI indicates comparable or stronger events to the \r three strongest post-1950 events in 1896 and 1905. Giese and Ray \r (2011) also found a number of such events between 1890 and 1920 \r in the SODA ocean reanalysis, corroborated further by B. Huang et al. \r (2020) and Vaccaro et  al. (2021), who found that the strength of \r [...]"


id: 8c981ec58adeb300c9f9d3b286f8ece8
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 21923
content:
"[...] the 1877-1878 event was comparable with that of the 1982-1983, \r 1997-1998 and 2015-2016 events. There have also been a number of \r strong La Nina events (e.g., 1973-1974, 1975-1976 and 2010-2011), \r with few clear analogues in the 1920-1970 period; the proxy-based \r analysis of McGregor et  al. (2010) indicates that the mid-1970s \r La Nina period was also extreme in a multi-centennial context. There \r is no indication that the frequency of high-amplitude events since the \r 1970s reflects a long-term trend which can be separated from multi\u0002decadal variability, given apparent presence of several high-amplitude \r events in the late 19th and early 20th centuries, and the relatively \r large uncertainty in pre-1950 SST data in the tropical Pacific region. "


id: 1b04b9e2ff5ea77c53e89ced7e086dd9
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 1951
content:
"There is no robust indication of any changes in ENSO teleconnections \r over multi-centennial timescales (Hernandez et al., 2020) despite multi\u0002decadal variability. Shi and Wang (2018) found that teleconnections \r with the broader Asian summer monsoon, including the Indian and \r the East Asian monsoon, were generally stable since the 17th century \r during the developing phase of the monsoon, and showed substantial \r decadal variability, but no clear trend, during the decaying phase. \r They also found that the weakening of teleconnections between the \r Indian summer monsoon and ENSO in recent decades had numerous \r precedents over the last few centuries. Rasanen et  al. (2016) also \r found substantial decadal variability, but little trend, in the strength \r of the relationship between ENSO and monsoon precipitation in \r South East Asia between 1650 and 2000. Datwyler et  al. (2019) \r identified a number of multi-decadal periods with apparently changed \r [...]"


id: 2c2379e2c70d27bf1627ef6db6f5783e
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 5022
content:
"For the LGM, enhanced equatorial Indian Ocean productivity \r in marine sediment records was associated with strengthened \r westerly jets, in line with a shallower central-western Indian Ocean \r thermocline and stronger negative IOD events (Punyu et al., 2014). \r The LGM Indian Ocean basin was substantially modified by the \r exposure of the tropical shelves (DiNezio et  al., 2018) and this \r has been associated with an Indian Ocean 'El Nino' (Thirumalai \r et  al., 2019). Wurtzel et  al. (2018) contend that during the LDT, \r including the Younger Dryas event, Indian Ocean precipitation did \r not mirror the zonal asymmetry observed in Indian Ocean SSTs \r in the Holocene based upon a speleothem record from Sumatra, \r but instead reflected shifts in moisture transport pathways and \r sources. Using Seychelles corals, representing the western pole \r of the IOD, spanning from the MH to present, Zinke et al. (2014) \r identified changes in seasonality, with the lowest seasonal SST \r [...]"


id: 49aa0dde0ce74b9d2782612528682c58
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 10191
content:
"[...] range in the MH and then again around 2 ka, while the largest \r seasonal range occurred around 4.6 ka and then again during \r the near-present (1990-2003). Reconstructions from fossil corals \r for the eastern Indian Ocean point to stronger negative IOD SST \r anomalies due to the enhanced upwelling and cooling driven by \r a stronger monsoon with enhanced anomalous easterly winds in \r the eastern Indian Ocean during the MH (Abram et  al., 2020b). \r Niedermeyer et al. (2014) from the analysis of stable isotopes in \r terrestrial plant waxes, suggest that the period 6.5 ka to 4.5 ka was \r characterized by an anomalously positive IOD mean state. During \r various parts of the Holocene, periods of a mean positive IOD-like \r state were associated with increased IOD variability, including \r events that exceed the magnitude of the strongest events during \r the instrumental period (Abram et al., 2020a)."


id: 6a13b60217dd5f0e39f31490a5cd3430
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 15936
content:
"In the instrumental period, teleconnections associated with ENSO \r are well known to vary on decadal to multi-decadal timescales \r (e.g., He et al., 2013; Lee and Ha, 2015; Ashcroft et al., 2016; Jin et al., \r 2016; Wang et al., 2019). Yun and Timmermann (2018) found that \r decadal variations in teleconnections between ENSO and the \r Indian monsoon did not extend beyond what would be expected \r from a  stochastic process. Many observed decadal changes in \r teleconnections in the instrumental period are consistent with a shift \r to more central Pacific El Nino events (Evtushevsky et  al., 2018; \r Yeh et al., 2018; Yu and Sun, 2018; Zhao and Wang, 2019). Effects \r of the PDV (Kwon et  al., 2013; S. Wang et  al., 2014; Dong  et  al., \r 2018) and the AMV (Kayano et  al., 2019) can also modulate \r ENSO teleconnections, and affect the frequency of CP versus EP \r events (Ashok et al., 2007). Chiodi and Harrison (2015) found that \r [...]"


id: 4ade0eb39c885fa863689b42756e9e75
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 10412
content:
"[...] teleconnections over the most recent decades are broadly consistent \r with those over the last 100 years. Variability in teleconnections can \r also occur on timescales longer than characteristic PDV timescales \r (e.g., Gallant et al., 2013). "


id: 774e8549df792a1cfe549c7e3a1acda4
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 18195
content:
"The existence of the PDV in the centuries prior to the instrumental \r period is evidenced by a variety of proxy records based on tree rings \r (Biondi et al., 2001; D'Arrigo and Ummenhofer, 2015), corals (Felis \r et al., 2010; Deng et al., 2013; Linsley et al., 2015) and sediments \r (Lapointe et al., 2017; O'Mara et al., 2019). There is little coherence \r between the various paleo-proxy indices prior to the instrumental \r record, and neither these nor the instrumental records provide \r indications of a clearly defined spectral peak (Chen and Wallace, 2015; \r M. Newman et al., 2016; Henley, 2017; L. Zhang et al., 2018; Buckley \r et al., 2019). For instance, spectral analysis from millennia length PDV \r reconstructions shows spectral peaks at multi-decadal, centennial \r and bi-centennial time scales (Beaufort and Grelaud, 2017), while \r only multi-decadal oscillations can be detected in the shorter (less \r than 400 years into the past) paleoclimate reconstructions. A variety \r [...]"


id: 5c582313ee7bc3e8db75698edb81f554
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 13500
content:
"[...] of proxies suggest a shift in the PDV from the early-mid Holocene, \r which was characterized by a persistently negative phase of the PDO \r (i.e., weak Aleutian Low), to the late Holocene, and more variable and \r more positive PDO (i.e., strong Aleutian Low) conditions. This shift at \r around 4.5 ka is also evident in the PDO periodicities, changing from \r bidecadal and pentadecadal variability in the early Holocene to only \r pentadecadal periodicities in the late Holocene (Hernandez et  al., \r 2020). Several proxy records indicate that the strengthening in the \r Aleutian Low inferred since the late 17th century is unprecedented \r over the last millennium (Z. Liu et al., 2017; Osterberg et al., 2017; \r Winski et al., 2017), in line with an increase in PDV low-frequency \r variability (Williams et al., 2017; Hernandez et al., 2020)."


id: b2e5c78b70202547acd6fb93005e3b11
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 28709
content:
"During the observational period the AZM and AMM (Figure 2.37) are \r related to the AMV largely controlling the interhemispheric gradient \r of the SST at decadal to multi-decadal timescales (Tokinaga and \r Xie, 2011; Polo et  al., 2013; Svendsen et  al., 2014a; Li et  al., 2015; \r Lubbecke et  al., 2018). The AZM interannual variability is enhanced \r (Lubbecke et  al., 2018; Foltz et  al., 2019), and is more strongly \r related to ENSO, during the negative phase of the AMV (Martin-Rey \r et al., 2014, 2018; Polo et al., 2015; Nnamchi et al., 2020). The AZM \r displayed a persistent weakened variability over the 1960-2009 period \r associated with declined cold tongue upwelling (Tokinaga and Xie, \r 2011), which became pronounced since 2000 (Prigent et al., 2020a, \r b). Despite these multi-decadal fluctuations, there is limited evidence\r for any sustained change in the AMM (Chang et  al., 2011; Martin\u0002Rey et al., 2018) and AZM (Martin-Rey et al., 2018; Nnamchi et al., \r [...]"


id: b52497234ff460545ef48720be309c2d
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 29106
content:
"[...] 2020) during the instrumental period. The AZM and AMM interact \r on interannual timescales (Servain et al., 1999; Foltz and McPhaden, \r 2010; Pottapinjara et al., 2019) leading in 2009 to extremes of both \r modes in which the negative phase of the AMM (Foltz et al., 2012; \r Burmeister et al., 2016) preceded an equatorial cold tongue cold event \r that was unprecedented in the prior 30 years (Foltz and McPhaden, \r 2010; Burmeister et al., 2016)."


id: b6ebb3cb9e82a89dffaf81f3e6ffce43
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 29387
content:
"It's warming rapidly. Over the past 2 million years, Earth's climate has fluctuated between relatively warm \r interglacial periods and cooler glacial periods, when ice sheets grew over vast areas of the northern continents. \r Intervals of rapid warming coincided with the collapse of major ice sheets, heralding interglacial periods such as \r the present Holocene Epoch, which began about 12,000 years ago. During the shift from the last glacial period \r to the current interglacial, the total temperature increase was about 5degC. That change took about 5000 years, \r with a maximum warming rate of about 1.5degC per thousand years, although the transition was not smooth. In \r contrast, Earth's surface has warmed approximately 1.1degC since 1850-1900. However, even the best reconstruction \r of global surface temperature during the last deglacial period is too coarsely resolved for direct comparison with \r [...]"


id: a612b281929b71d5ded36b172995dea7
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 26437
content:
"[...] a period as short as the past 150 years. But for the past 2000 years, we have higher-resolution records that show \r that the rate of global warming during the last 50 years has exceeded the rate of any other 50-year period."


id: b746901a5598c97e1f75a03cf84a8ebc
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 29459
content:
"Lisa Bock (Germany), Elizaveta Malinina (Canada/Russian Federation), Gudfinna Adalgeirsdottir \r (Iceland), Jonathan L. Bamber (United Kingdom), Chris Brierley (United Kingdom), Lee de Mora \r (United Kingdom), John P. Dunne (United States of America), John C. Fyfe (Canada), Peter J. Gleckler \r (United States of America), Peter Greve (Austria/Germany), Lukas Gudmundsson (Switzerland/\r Germany, Iceland), Karsten Haustein (United Kingdom, Germany/Germany), Ed  Hawkins \r (United Kingdom), Benjamin J. Henley (Australia), Marika M. Holland (United States of America), \r Chris Huntingford (United Kingdom), Colin Jones (United Kingdom), Masa Kageyama (France), \r Remi Kazeroni (Germany/France), Yeon-Hee Kim (Republic of Korea), Charles Koven (United States \r of America), Gerhard Krinner (France/Germany, France), Eunice Lo (United  Kingdom/China), \r Daniel J. Lunt (United Kingdom), Sophie Nowicki (United States of America/France, United States \r [...]"


id: 2e7e2db166d4012d32fe517a82a89c72
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 5460
content:
"[...] of America), Adam S. Phillips (United States of America), Valeriu Predoi (United Kingdom), \r Krishnan Raghavan (India), Malcolm J. Roberts (United Kingdom), Jon Robson (United Kingdom), \r Lucas Ruiz (Argentina), Jean-Baptiste Sallee (France), Benjamin D. Santer (United States \r of America), Andrew P. Schurer (United Kingdom), Jessica Tierney (United States of America), \r Blair  Trewin (Australia), Katja Weigel (Germany), Xuebin Zhang (Canada), Anni Zhao \r (United Kingdom/China), Tianjun Zhou (China)"


id: 9c11481bd4f4a6160bffd1f90e7fedaa
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 24709
content:
"The likely range of human-induced warming in global-mean \r surface air temperature (GSAT) in 2010-2019 relative to \r 1850-1900 is 0.8degC-1.3degC, encompassing the observed warming \r of 0.9degC-1.2degC, while the change attributable to natural forcings \r is only -0.1degC to +0.1degC. The best estimate of human-induced \r warming is 1.07degC. Warming can now be attributed since 1850-1900, \r instead of since 1951 as done in AR5, thanks to a better understanding \r of uncertainties and because observed warming is larger. The likely\r ranges for human-induced GSAT and global mean surface temperature \r (GMST) warming are equal (medium confidence). Attributing \r observed warming to specific anthropogenic forcings remains more \r uncertain. Over the same period, forcing from greenhouse gases1\r likely increased GSAT by 1.0degC-2.0degC, while other anthropogenic \r forcings including aerosols likely decreased GSAT by 0.0degC-0.8degC. It is \r [...]"


id: 9f777737089490843cdd375ca588aee5
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 25273
content:
"[...] very likely that human-induced greenhouse gas increases were the \r main driver2 of tropospheric warming since comprehensive satellite \r observations started in 1979, and extremely likely that human-induced \r stratospheric ozone depletion was the main driver of cooling in the \r lower stratosphere between 1979 and the mid-1990s. {3.3.1}"


id: 12328bea97ca62e34a83d834b854723
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 394
content:
"The evidence for human influence on recent climate change \r strengthened from the IPCC Second Assessment Report to the IPCC Fifth \r Assessment Report, and is now even stronger in this assessment. The \r IPCC Second Assessment Report (SAR, 1995) concluded 'the balance \r of evidence suggests that there is a discernible human influence on \r global climate'. In subsequent assessments (TAR, 2001; AR4, 2007; and \r AR5, 2013), the evidence for human influence on the climate system \r was found to have progressively strengthened. The AR5 concluded \r that human influence on the climate system is  clear, evident from \r increasing greenhouse gas concentrations in the atmosphere, positive \r radiative forcing, observed warming, and physical understanding of \r the climate system. This chapter updates the assessment of human \r influence on the climate system for large-scale indicators of climate \r change, synthesizing information from paleo records, observations and \r [...]"


id: 7dcace340144dfea9773e7c383f0b302
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 19307
content:
"[...] climate models. It also provides the primary evaluation of large-scale \r indicators of climate change in this Report, complemented by fitness\u0002for-purpose evaluation in subsequent chapters."


id: f7e82f2aafbad27b5f88b7908115d0e7
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 40993
content:
"The CMIP6 model ensemble reproduces the observed \r historical global surface temperature trend and variability \r with biases small enough to support detection and attribution \r of human-induced warming (very high confidence). The CMIP6 \r historical simulations assessed in this report have an ensemble mean \r global surface temperature change within 0.2degC of the observations \r over most of the historical period, and observed warming is within the \r 5-95% range of the CMIP6 ensemble. However, some CMIP6 models \r simulate a warming that is either above or below the assessed 5-95% \r range of observed warming. CMIP6 models broadly reproduce surface \r temperature variations over the past millennium, including the cooling \r that follows periods of intense volcanism (medium confidence). For \r upper air temperature, there is medium confidence that most CMIP5 \r and CMIP6 models overestimate observed warming in the upper \r tropical troposphere by at least 0.1degC per decade over the period \r [...]"


id: ed1a9b55c9c68f1e84886a569033d8e7
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 39091
content:
"[...] 1979 to 2014. The latest updates to satellite-derived estimates of \r stratospheric temperature have resulted in decreased differences \r between simulated and observed changes of global mean temperature \r through the depth of the stratosphere (medium confidence). {3.3.1}"


id: 52dafa166478ae646ab2259f2eda26a5
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 11844
content:
"For most large-scale indicators of climate change, the \r simulated recent mean climate from the latest generation \r Coupled Model Intercomparison Project Phase 6 (CMIP6) \r climate models underpinning this assessment has improved \r compared to the Coupled Model Intercomparison Project \r Phase 5 (CMIP5) models assessed in AR5 (high confidence).\r Some differences from observations remain, for example in regional \r precipitation patterns. High-resolution models exhibit reduced \r biases in some but not all aspects of surface and ocean climate \r (medium confidence), and most Earth system models, which include \r biogeochemical feedbacks, perform as well as their lower-complexity \r counterparts (medium confidence). The multi-model mean captures \r most aspects of observed climate change well (high confidence). The \r multi-model mean captures the proxy-reconstructed global-mean \r surface air temperature (GSAT) change during past high- and low-CO2\r [...]"


id: a748b8f86416502a91dd8df93f7cc2ee
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 26647
content:
"[...] climates (high confidence) and the correct sign of temperature and \r precipitation change in most assessed regions in the mid-Holocene \r (medium confidence). The simulation of paleoclimates on continental \r scales has improved compared to AR5 (medium confidence), but \r models often underestimate large temperature and precipitation \r differences relative to the present day (high confidence). {3.8.2}"


id: 33599f2cf177aad3dd89069a856dc583
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 6285
content:
"It is likely that human influence has contributed to observed \r large-scale precipitation changes since the mid-20th century.\r New attribution studies strengthen previous findings of a detectable \r increase in Northern Hemisphere mid- to high-latitude land \r precipitation (high confidence). Human influence has contributed to \r strengthening the zonal mean precipitation contrast between the wet \r tropics and dry subtropics (medium confidence). Yet, anthropogenic \r aerosols contributed to decreasing global land summer monsoon \r precipitation from the 1950s to 1980s (medium confidence). There \r is also medium confidence that human influence has contributed \r to high-latitude increases and mid-latitude decreases in Southern \r Hemisphere summertime precipitation since 1979 associated with \r the trend of the Southern Annular Mode toward its positive phase. \r Despite improvements, models still have deficiencies in simulating \r [...]"


id: 13ea7ba40157815c96a7e7ce8687385a
created_at: 2023-04-19 13:22:34
updated_at: 2023-04-19 13:22:34
content_type: text
index: climate_gpt_v2
vector_id: 722
content:
"[...] precipitation patterns, particularly over the tropical ocean (high \r confidence). {3.3.2, 3.3.3, 3.5.2}"


id: 5a60db746faa2169d232ea150bf0a44b
created_at: 2023-04-19 13:22:35
updated_at: 2023-04-19 13:22:35
content_type: text
index: climate_gpt_v2
vector_id: 13153
content:
"This chapter assesses the evidence for human influence on \r observed large-scale indicators of climate change that are described \r in Cross-Chapter Box  2.2 and assessed in Chapter  2. It takes \r advantage of the longer period of record now available in many \r observational datasets. The assessment of the human-induced \r contribution to observed climate change requires an estimate of the \r expected response to human influence, as well as an estimate of the \r expected climate evolution due to natural forcings and an estimate of \r variability internal to the climate system (internal climate variability). \r For this we need high quality models, primarily climate and Earth \r system models. Since AR5, a new set of coordinated model results \r from the World Climate Research Programme (WCRP) Coupled Model \r Intercomparison Project Phase 6 (CMIP6; Eyring et al., 2016a) has \r become available. Together with updated observations of large-scale \r [...]"


