# document

id: e7d1eca1aed35f0ffc54c2f96753ffbc
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 38091
content:
"distribution. It is virtually certain that the net climate feedback is \r negative, primarily due to the Planck temperature response, indicating \r that climate acts to stabilize in response to radiative forcing imposed \r to the system. Supported by the level of confidence associated with \r the individual feedbacks, it is also virtually certain that the sum of \r the non-Planck feedbacks is positive. Based on Table  7.10 these \r climate feedbacks amplify the Planck temperature response by about \r 2.8 [1.9 to 5.9] times. Cloud feedback remains the largest contributor \r to uncertainty of the net feedback, but the uncertainty is reduced \r compared to AR5. A  secondary contribution to the net feedback \r uncertainty is the biogeophysical and non-CO2 biogeochemical \r feedbacks, which together are assessed to have a central value near \r zero and thus do not affect the central estimate of ECS. The net climate \r feedback is assessed to be -1.16 W m-2 degC-1, likely from -1.54  to \r [...]"


id: c8818b9f24142dbfbcdbe10c071fdec6
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 32580
content:
"Feedback parameters in climate models are calculated assuming \r that they are independent of each other, except for a  well-known \r co-dependency between the water vapour (WV) and lapse rate (LR) \r feedbacks. When the inter-model spread of the net climate feedback is \r computed by adding in quadrature the inter-model spread of individual \r feedbacks, it is 17% wider than the spread of the net climate feedback \r directly derived from the ensemble. This indicates that the feedbacks in \r climate models are partly co-dependent. Two possible co-dependencies \r have been suggested (Huybers, 2010; Caldwell et  al., 2016). One is \r a negative covariance between the LR and longwave cloud feedbacks, \r which may be accompanied by a  deepening of the troposphere \r (O'Gorman and Singh, 2013; Yoshimori et al., 2020) leading both to \r greater rising of high-clouds and a larger upper-tropospheric warming. \r The other is a  negative covariance between albedo and shortwave \r [...]"


id: 494fa5bbaa4e55eaf657f83d9c7abf2
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 10120
content:
"[...] cloud feedbacks, which may originate from the Arctic regions: \r a reduction in sea ice enhances the shortwave cloud radiative effect \r because the ocean surface is darker than sea ice (Gilgen et al., 2018). \r This covariance is reinforced as the decrease of sea ice leads to an \r increase in low-level clouds (Mauritsen et  al., 2013). However, the \r mechanism causing these co-dependences between feedbacks is not \r well understood yet and a quantitative assessment based on multiple \r lines of evidence is difficult. Therefore, this synthesis assessment does \r not consider any co-dependency across individual feedbacks."


id: 26a95d65c134e90546d8367f7356389e
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 3961
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"A remarkable improvement of cloud representation in some CMIP6 \r models is the reduced error of the too-weak negative shortwave CRE \r over the Southern Ocean (Bodas-Salcedo et al., 2019; Gettelman et al., \r 2019) due to a more realistic simulation of supercooled liquid droplets \r and associated cloud optical depths that were biased low commonly \r in CMIP5 models (McCoy et  al., 2014a, b). Because the negative \r cloud optical depth feedback occurs due to 'brightening' of clouds via \r phase change from ice to liquid cloud particles in response to surface \r warming (Cesana and Storelvmo, 2017), the extratropical cloud \r shortwave feedback tends to be less negative or even slightly positive \r in models with reduced errors (Bjordal et al., 2020; Zelinka et al., 2020). \r The assessment of cloud feedbacks in Section  7.4.2.4 incorporates \r estimates from these improved ESMs. Yet, there still remain other \r shared model errors, such as in the subtropical low-clouds (Calisto \r [...]"


id: 47ab540578ef18d0075209afacd2cce3
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
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content:
"[...] et al., 2014) and tropical anvil clouds (Mauritsen and Stevens, 2015), \r hampering an assessment of feedbacks associated with these cloud \r regimes based only on ESMs (Section 7.4.2.4)."


id: 4585dffd3ed2ccca6c38f2f0ff677ccf
created_at: 2023-04-19 13:22:38
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"There are several modelling studies since AR5 in which ESMs \r of varying complexity have been used to explore temperature \r dependence of feedbacks, either under modern (Hansen et al., 2013; \r Jonko et  al., 2013; Meraner et  al., 2013; Good et  al., 2015; Duan \r et al., 2019; Mauritsen et al., 2019; Rohrschneider et al., 2019; Stolpe \r et al., 2019; Bloch-Johnson et al., 2020; Rugenstein et al., 2020) or \r paleo (Caballero and Huber, 2013; Zhu et al., 2019a) climate conditions, \r typically by carrying out multiple simulations across successive CO2\r doublings. A  non-linear temperature response to these successive \r doublings may be partly due to forcing that increases more (or less) \r than expected from a purely logarithmic dependence (Section 7.3.2; \r Etminan et  al., 2016), and partly due to state-dependence in \r feedbacks; however, not all modelling studies have partitioned the \r non-linearities in temperature response between these two effects. \r [...]"


id: 745b1ff7c80a31bacca315a590365240
created_at: 2023-04-19 13:22:38
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"[...] Nonetheless, there is general agreement among ESMs that the net \r feedback parameter, a, increases (i.e., becomes less negative) as \r temperature increases from pre-industrial levels (i.e.,  sensitivity to \r forcing increases as temperature increases; e.g., Meraner et al., 2013; \r see Figure 7.11). The associated increase in sensitivity to forcing is, \r in most models, due to the water vapour (Section 7.4.2.2) and cloud \r (Section  7.4.2.4) feedback parameters increasing with warming \r (Caballero and Huber, 2013; Meraner et al., 2013; Zhu et al., 2019a; \r Rugenstein et al., 2020; Sherwood et al., 2020). These changes are \r offset partially by the surface-albedo feedback parameter decreasing \r (Jonko et  al., 2013; Meraner et  al., 2013; Rugenstein et  al., 2020), \r as a consequence of a reduced amount of snow and sea ice cover \r in a much warmer climate. At the same time, there is little change \r in the Planck response (Section 7.4.2.1), which has been shown in one \r [...]"


id: 1eeac462b74171d4def4be488a50003e
created_at: 2023-04-19 13:22:38
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"[...] model to be due to competing effects from increasing Planck emission \r at warmer temperatures and decreasing planetary emissivity due to \r increased CO2 and water vapour (Mauritsen et al., 2019). Analysis of \r the spatial patterns of the non-linearities in temperature response \r (Good et al., 2015) suggests that these patterns are linked to a reduced \r weakening of the AMOC, and changes to evapotranspiration. The \r temperature dependence of a is also found in model simulations of \r high-CO2 paleoclimates (Caballero and Huber, 2013; Zhu et al., 2019a). \r The temperature dependence is not only evident at very high CO2\r concentrations in excess of 4xCO2, but also apparent in the difference \r in temperature response to a  2xCO2 forcing compared with to \r a 4xCO2 forcing (Mauritsen et al., 2019; Rugenstein et al., 2020), and \r as such is relevant for interpreting century-scale climate projections.\r Despite the general agreement that a increases as temperature \r [...]"


id: 30532a86bf45e00ebf94dee4829622de
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
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"[...] increases from pre-industrial levels (Figure  7.11), other modelling \r studies have found the opposite (Duan et  al., 2019; Stolpe et  al., \r 2019). Modelling studies exploring state-dependence in climates \r colder than today, including in cold paleoclimates such as the "


id: 13bfe412a09bb75fd168ea9a1d7aebc8
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
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"(Kohler et  al., 2015; Stap et  al., 2019), which is not well known, \r due to a  relative lack of proxy indicators of ice-sheet extent and \r distribution prior to the LGM (Cross-Chapter Box 2.1). Furthermore, \r many of these glacial-interglacial studies estimate a  very strong \r temperature-dependence of a (Figure 7.11) that is hard to reconcile \r with the other lines of evidence, including proxy estimates from \r warmer paleoclimates. However, if the analysis excludes time periods \r when the temperature and CO2 data are not well correlated, which \r occurs in general at times when sea level is falling and obliquity \r is decreasing, the state-dependence reduces (Kohler et  al., 2018). \r Despite these uncertainties, due to the agreement in the sign of the \r temperature-dependence from all these studies, there is medium \r confidence from the paleoclimate proxy record that the net feedback \r parameter, a, was less negative in the warm periods than in the cold \r [...]"


id: bf9d4d8f008bf1f3b6b9b6f3d94ba705
created_at: 2023-04-19 13:22:38
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"Figure 7.11 | Feedback parameter, a (W m-2 degC-1), as a function of global \r mean surface air temperature anomaly relative to pre-industrial, for ESM \r simulations (red circles and lines) (Caballero and Huber, 2013; Jonko et al., 2013; \r Meraner et al., 2013; Good et al., 2015; Duan et al., 2019; Mauritsen et al., 2019; \r Stolpe et al., 2019; Zhu et al., 2019a), and derived from paleoclimate proxies \r (grey squares and lines) (von der Heydt et al., 2014; Anagnostou et al., 2016, \r 2020; Friedrich et al., 2016; Royer, 2016; Shaffer et al., 2016; Kohler et al., 2017; \r Snyder, 2019; Stap et al., 2019). For the ESM simulations, the value on the x-axis \r refers to the average of the temperature before and after the system has equilibrated \r to a forcing (in most cases a CO2 doubling), and is expressed as an anomaly relative \r to an associated pre-industrial global mean temperature from that model. The light \r [...]"


id: b2062c9002828a833f3d326cd5841629
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
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index: climate_gpt_v2
vector_id: 28542
content:
"[...] blue shaded square extends across the assessed range of a (Table 7.10) on the y-axis, \r and on the x-axis extends across the approximate temperature range over which the \r assessment of a is based (taken as from zero to the assessed central value of ECS; \r see Table 7.13). Further details on data sources and processing are available in the \r chapter data table (Table 7.SM.14)."


id: 51872839030b509997fa37565de896b9
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 11620
content:
"Overall, independent lines of evidence from models (Section 7.4.3.1) \r and from the paleoclimate proxy record (Section  7.4.3.2) lead to \r high confidence that the net feedback parameter, a, increases \r (i.e., becomes less negative) as temperature increases; that is, \r that sensitivity to forcing increases as temperature increases \r (Figure  7.11). This temperature-dependence should be considered \r when estimating ECS from ESM simulations in which CO2 is \r quadrupled (Section 7.5.5) or from paleoclimate observations from \r past time periods colder or warmer than today (Section  7.5.4). \r Although individual lines of evidence give only medium confidence, \r the overall high confidence comes from the multiple models that \r show the same sign of the temperature-dependence of a, the \r general agreement in evidence from the paleo proxy and modelling \r lines of evidence, and the agreement between proxy evidence from \r both cold and warm past climates. However, due to the large range \r [...]"


id: 5850fd3e1b87e43cd4ca46f84f3c5c6d
created_at: 2023-04-19 13:22:38
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"[...] in estimates of the magnitude of the temperature-dependence of \r a across studies (Figure  7.11), a  quantitative assessment cannot \r currently be given, which provides a  challenge for including this \r temperature-dependence in emulator-based future projections \r (Cross-Chapter Box 7.1). Greater confidence in the modelling lines of \r evidence could be obtained from simulations carried out for several \r hundreds of years (Rugenstein et al., 2020), substantially longer than \r in many studies, and from more models carrying out simulations at \r multiple CO2 concentrations. Greater confidence in the paleoclimate "


id: 9c7f0c465325c9eb3da2465431682836
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 24784
content:
"Polar amplification describes the phenomenon where surface \r temperature change at high latitudes exceeds the global average \r surface temperature change in response to radiative forcing of the \r climate system. Arctic amplification, often defined as the ratio of \r Arctic to global surface warming, is a ubiquitous emergent feature \r of climate model simulations (Section  4.5.1 and Figure  7.12a; \r Holland and Bitz, 2003; Pithan and Mauritsen, 2014) and is also \r seen in observations (Section 2.3.1). However, both climate models \r and observations show relatively less warming of the SH high \r latitudes compared to the Northern Hemisphere (NH) high latitudes \r over the historical record (Section  2.3.1), a  characteristic that is \r projected to continue over the 21st  century (Section  4.5.1). Since \r AR5 there is a  much-improved understanding of the processes \r that drive polar amplification in the NH and delay its emergence \r [...]"


id: b9b1e758cba289d76a714dc5e847ad99
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
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"[...] in the SH (Section 7.4.4.1.1). Furthermore, the paleoclimate record \r provides evidence for polar amplification from multiple time periods \r associated with changes in CO2 (Hollis et  al., 2019; Cleator et  al., \r 2020; McClymont et  al., 2020; Tierney et  al., 2020b), and allows \r an evaluation of polar amplification in model simulations of these \r periods (Section 7.4.4.1.2). Research since AR5 identifies changes in \r the degree of polar amplification over time, particularly in the SH, \r as a key factor affecting how radiative feedbacks may evolve in the \r future (Section 7.4.4.3)."


id: af7bad6405e0444c3667d9b4c0821c05
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 28106
content:
"Energy balance models that approximate atmospheric heat transport \r in terms of a  diffusive flux down the meridional gradient of \r near-surface moist static energy (sum of dry-static and latent energy) \r are able to reproduce the atmospheric heat transport changes seen \r within ESMs (Flannery, 1984; Hwang and Frierson, 2010; Hwang \r et al., 2011; Rose et al., 2014; Roe et al., 2015; Merlis and Henry, \r 2018), including the partitioning of latent and dry-static energy \r transports (Siler et  al., 2018b; Armour et  al., 2019). These models \r suggest that polar amplification is driven by enhanced poleward \r latent heat transport and that the magnitude of polar amplification \r can be enhanced or diminished by the latitudinal structure of radiative \r feedbacks. Amplifying polar feedbacks enhance polar warming and \r in turn cause a decrease in the dry-static energy transport to high \r latitudes (Alexeev and Jackson, 2013; Rose et al., 2014; Roe et al., \r [...]"


id: 5b9cd955f13efa9448e341539e4dcf32
created_at: 2023-04-19 13:22:38
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"[...] 2015; Bonan et  al., 2018; Merlis and Henry, 2018; Armour et  al., \r 2019; Russotto and Biasutti, 2020). Poleward latent heat transport \r changes act to favour polar amplification and inhibit tropical \r amplification (Armour et  al., 2019), resulting in a  strongly polar\u0002amplified warming response to polar forcing and a more latitudinally \r uniform warming response to tropical forcing within ESMs (Alexeev \r et al., 2005; Rose et al., 2014; Stuecker et al., 2018). The important \r role for poleward latent energy transport in polar amplification is \r supported by studies of atmospheric reanalyses and ESMs showing \r that episodic increases in latent heat transport into the Arctic can \r enhance surface downwelling radiation and drive sea ice loss on \r sub-seasonal time scales (Woods and Caballero, 2016; Gong et al., \r 2017; Lee et  al., 2017; B. Luo et  al., 2017), however this may be \r a smaller driver of sea ice variability than atmospheric temperature \r [...]"


id: 3defa24b0a59668fee7323d9809b742c
created_at: 2023-04-19 13:22:38
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"Figure 7.12 (continued): (a) Pattern of near-surface air temperature change. (b-d) Contributions to net Arctic (>60degN), tropical (30degS-30degN), and Antarctic (<60degS) warming \r calculated by dividing regional-average energy inputs by the magnitude of the regional-average Planck response. The contributions from radiative forcing, changes in moist, \r dry-static, and total atmospheric energy transport, ocean heat uptake, and radiative feedbacks (orange bars) all sum to the value of net warming (grey bar). Inset shows regional \r warming contributions associated with individual feedbacks, all summing to the total feedback contribution. Uncertainties (represented by black whiskers) show the interquartile \r range (25th and 75th percentiles) across models. The warming contributions (units of degC) for each process are diagnosed by calculating the energy flux (units of W m-2) that each \r [...]"


id: 9ecaa4627e71a16f4176f6714493355c
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
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content:
"[...] process contributes to the atmosphere over a given region, either at the top-of-atmosphere or surface, then dividing that energy flux by the magnitude of the regional Planck \r response (around 3.2 W m-2 degC-1 but varying with region). By construction, the individual warming contributions sum to the total warming in each region. Radiative kernel methods \r (Section 7.4.1) are used to decompose the net energy input from radiative feedbacks into contributions from changes in atmospheric water vapour, lapse rate, clouds, and surface \r albedo (Zelinka et al. (2020) using the Huang et al. (2017) radiative kernel). The CMIP6 models included are those analysed by Zelinka et al. (2020) and the warming contribution \r analysis is based on that of Goosse et al. (2018). Further details on data sources and processing are available in the chapter data table (Table 7.SM.14)."


id: 15bbe9898f559419a08b439ec1e2707d
created_at: 2023-04-19 13:22:38
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index: climate_gpt_v2
vector_id: 1039
content:
"waters that have not yet felt the effects of the radiative forcing; the \r heat taken up is predominantly transported away from Antarctica \r by northward-flowing surface waters (Section  9.2.1; Marshall \r et al., 2015; Armour et al., 2016). Strong surface heat uptake also \r occurs in the subpolar North Atlantic Ocean under global warming \r (Section 9.2.1). However, this heat is partially transported northward \r into the Arctic, which leads to increased heat fluxes into the Arctic \r atmosphere (Figure 7.12b; Rugenstein et al., 2013; Jungclaus et al., \r 2014; Koenigk and Brodeau, 2014; Marshall et al., 2015; Nummelin \r et al., 2017; Singh et al., 2017; Oldenburg et al., 2018). The second \r main process contributing to differences in Arctic and Antarctic \r warming is the asymmetry in radiative feedbacks between the poles \r (Yoshimori et al., 2017; Goosse et al., 2018). This primarily reflects the \r weaker lapse-rate and surface-albedo feedbacks and more-negative \r [...]"


id: eed054650f0b968b392a78a55b5ee53c
created_at: 2023-04-19 13:22:38
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"[...] cloud feedbacks in the SH high latitudes (Figure  7.12). However, \r note the SH cloud feedbacks are uncertain due to possible biases in \r the treatment of mixed phase clouds (Hyder et al., 2018). Idealized \r modelling suggests that the asymmetry in the polar lapse-rate \r feedback arises from the height of the Antarctic Ice Sheet precluding \r the formation of deep atmospheric inversions that are necessary \r to produce the stronger positive lapse-rate feedbacks seen in the \r Arctic (Salzmann, 2017; Hahn et al., 2020). ESM projections of the \r equilibrium response to CO2 forcing show polar amplification in both \r hemispheres, but generally with less warming in the Antarctic than \r the Arctic (C. Li et al., 2013; Yoshimori et al., 2017)."


id: 3b910f2c262b86543676a26055d6b995
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
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vector_id: 7663
content:
"the surface-albedo and lapse-rate feedbacks,  which preferentially \r warm the Arctic (Graversen et al., 2014; Pithan and Mauritsen, 2014; \r Goosse et al., 2018). Latitudinal structure in the lapse-rate feedback \r reflects weak radiative damping to space with surface warming in \r polar regions, where atmospheric warming is constrained to the \r lower troposphere owing to stably stratified conditions, and strong \r radiative damping in the tropics, where warming is enhanced in the \r upper troposphere owing to moist convective processes. This is only \r partially compensated by latitudinal structure in the water-vapour \r feedback (Taylor et al., 2013), which favours tropical warming (Pithan \r and Mauritsen, 2014). While cloud feedbacks have been found to \r play little role in Arctic amplification in CMIP5 models (Pithan and \r Mauritsen, 2014; Goosse et  al., 2018; Figure  7.12b), less-negative \r cloud feedbacks at high latitude, as seen within some CMIP6 models \r [...]"


id: 28ba2d4ab9fc8c4a48d07900d63ae7aa
created_at: 2023-04-19 13:22:38
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vector_id: 4380
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"[...] (Zelinka et  al., 2020), tend to favour stronger polar amplification \r (Dong et al., 2020). A weaker Planck response at high latitudes, owing \r to less efficient radiative damping where surface and atmospheric \r temperatures are lower, also contributes to polar amplification (Pithan \r and Mauritsen, 2014). The effective radiative forcing of CO2 is larger in \r the tropics than at high latitudes, suggesting that warming would be \r tropically amplified if not for radiative feedbacks and poleward latent \r heat transport changes (Figure 7.12b-d; Stuecker et al., 2018)."


id: 42eff2a38720d2c03ced609187887941
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 8972
content:
"While the contributions to regional warming can be diagnosed within \r ESM simulations (Figure 7.12), assessment of the underlying role of \r individual factors is limited by interactions inherent to the coupled \r climate system. For example, polar feedback processes are coupled and \r influenced by warming at lower latitudes (Screen et al., 2012; Alexeev \r and Jackson, 2013; Graversen et  al., 2014; Graversen and Burtu, \r 2016; Rose and Rencurrel, 2016; Feldl et al., 2017a, 2020; Yoshimori \r et al., 2017; Garuba et al., 2018; Po-Chedley et al., 2018b; Stuecker \r et al., 2018; Dai et al., 2019), while atmospheric heat transport changes \r are in turn influenced by the latitudinal structure of regional feedbacks, \r radiative forcing, and ocean heat uptake (Hwang et al., 2011; Zelinka \r and Hartmann, 2012; Feldl and Roe, 2013; Huang and Zhang, 2014; \r Merlis, 2014; Rose et al., 2014; Roe et al., 2015; Feldl et al., 2017b; \r [...]"


id: 19b67275ff90b57de48d7b973c17d14f
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 1725
content:
"[...] Stuecker et al., 2018; Armour et al., 2019). The use of different feedback \r definitions, such as a lapse-rate feedback partitioned into upper and \r lower tropospheric components (Feldl et  al., 2020) or including the \r influence of water vapour at constant relative humidity (Held and Shell, \r 2012; Section 7.4.2), would also change the interpretation of which \r feedbacks contribute most to polar amplification."


id: 2a5aeb15a4095c6b2c25ce71ced5125
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 4675
content:
"Because multiple processes contribute to polar amplification, it is \r a robust feature of the projected long-term response to greenhouse \r gas forcing in both hemispheres. At the same time, contributions \r from multiple processes make projections of the magnitude of polar \r warming inherently more uncertain than global mean warming \r (Holland and Bitz, 2003; Roe et  al., 2015; Bonan et  al., 2018; \r Stuecker et al., 2018). The magnitude of Arctic amplification ranges \r from a  factor of two to four in ESM projections of 21st-century \r warming (Section  4.5.1). While uncertainty in both global and \r tropical warming  under greenhouse gas forcing is dominated by \r cloud feedbacks (Section  7.5.7; Vial et  al., 2013), uncertainty in \r polar warming arises from polar surface-albedo, lapse-rate, and \r cloud feedbacks, changes in atmospheric and oceanic poleward heat \r transport, and ocean heat uptake (Hwang et al., 2011; Mahlstein and \r [...]"


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"the emergence of SH polar amplification remains uncertain due to \r insufficient knowledge of the time scales associated with Southern \r Ocean warming and the response to surface wind and freshwater \r forcing (Bintanja et al., 2013; Kostov et al., 2017, 2018; Pauling et al., \r 2017; Purich et  al., 2018). ESM simulations indicate that freshwater \r input from melting ice shelves could reduce Southern Ocean warming \r by up to several tenths of a  degC over the 21st  century by increasing \r stratification of the surface ocean around Antarctica (low confidence\r due to medium agreement but limited evidence) (Sections 7.4.2.6 \r and 9.2.1, and Box 9.3; Bronselaer et al., 2018; Golledge et al., 2019; \r Lago and England, 2019). However, even a large reduction in the Atlantic \r Meridional Overturning Circulation (AMOC) and associated northward \r heat transport due, for instance, to greatly increased freshwater runoff \r from Greenland would be insufficient to eliminate Arctic amplification \r [...]"


id: 3136127ff948e186958aed73269e8ff0
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
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index: climate_gpt_v2
vector_id: 5912
content:
"[...] (medium confidence based on medium agreement and medium \r evidence) (Liu et al., 2017; Y. Liu et al., 2018; Wen et al., 2018)."


id: b139b99695d57fb73ca012bba961293e
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 28413
content:
"Arctic amplification has a distinct seasonality with a peak in early \r winter (November to January) owing to sea ice loss and associated \r increases in heat fluxes from the ocean to the atmosphere resulting \r in strong near-surface warming (Pithan and Mauritsen, 2014; Dai \r et al., 2019). Surface warming may be further amplified by positive \r cloud and lapse-rate feedbacks in autumn and winter (Burt et  al., \r 2016; Morrison et al., 2019; Hahn et al., 2020). Arctic amplification \r is weak in summer owing to surface temperatures remaining stable \r as excess energy goes into thinning the summertime sea ice cover, \r which remains at the melting point, or into the ocean mixed layer. \r Arctic amplification can also be interpreted through changes in \r the surface energy budget (Burt et al., 2016; Woods and Caballero, \r 2016; Boeke and Taylor, 2018; Kim et  al., 2019), however such \r analyses are complicated by the finding that a  large portion of \r [...]"


id: 21545bc618750edd0e0a4b3905577e19
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 3079
content:
"[...] the changes in downward longwave radiation can be attributed to \r the lower troposphere warming along with the surface itself (Vargas \r Zeppetello et al., 2019)."


id: 18d143df36ba38a562d782383b9c36dd
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 1585
content:
"Paleoclimate proxy data provide observational evidence of large-scale \r patterns of surface warming in response to past forcings, and allow an \r evaluation of the modelled response to these forcings (Sections 3.3.1.1 \r and 3.8.2.1). In particular, paleoclimate data provide evidence for \r long-term changes in polar amplification during time periods in which \r the primary forcing was a change in atmospheric CO2, although data \r sparsity means that for some time periods this evidence may be limited \r to a  single hemisphere or ocean basin, or the evidence may come \r primarily from the mid-latitudes as opposed to the polar regions. In \r this context, there has been a modelling and data focus on the Last \r Glacial Maximum (LGM) in the context of PMIP4 (Cleator et al., 2020; \r Tierney et al., 2020b; Kageyama et al., 2021), the mid-Pliocene Warm \r Period (MPWP) in the context of PlioMIP2 (Cross-Chapter Box  2.4; \r Salzmann et  al., 2013; Haywood et  al., 2020; McClymont et  al., \r [...]"


id: 11cc7e718a6bc23d558979779f96ac15
created_at: 2023-04-19 13:22:38
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index: climate_gpt_v2
vector_id: 321
content:
"[...] 2020), the Early Eocene Climatic Optimum (EECO) in the context of \r DeepMIP (Hollis et al., 2019; Lunt et al., 2021), and there is growing \r interest in the Miocene (Goldner et al., 2014b; Steinthorsdottir et al., \r 2021; for definitions of time periods see Cross-Chapter Box  2.1). \r For all these time periods, in addition to the CO2 forcing there are "


id: 623d2fd0274ba12f3f273805db1bff22
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 14560
content:
"Since AR5, there has been progress in the simulation of polar \r amplification by paleoclimate models of the Early Eocene. Initial \r work indicated that changes to model parameters associated with \r aerosols and/or clouds could increase simulated polar amplification \r and improve agreement between models and paleoclimate data \r (Kiehl and Shields, 2013; Sagoo et  al., 2013), but such parameter \r changes were not physically based. In support of these initial findings, \r a more recent (CMIP5) climate model, that includes a process-based \r representation of cloud microphysics, exhibits polar amplification \r in better agreement with proxies when compared to the models \r assessed in AR5 (Zhu et al., 2019a). Since then, some other CMIP3 \r and CMIP5 models in the DeepMIP multi-model ensemble (Lunt \r et  al., 2021) have obtained polar amplification for the EECO that \r is consistent with proxy indications of both polar amplification and \r [...]"


id: 83fecc5a42ef162045e33ccb6913efcf
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 20376
content:
"Figure 7.13 (continued): Temperature anomalies compared with pre-industrial (equivalent to CMIP6 simulation 'piControl') are shown for the high-CO2 EECO and MPWP \r time periods, and for the low-CO2 LGM (expressed as pre-industrial minus LGM). (a), (b) and (c) Modelled near-surface air temperature anomalies for ensemble-mean \r simulations of the (a) EECO (Lunt et al., 2021); (b) MPWP (Haywood et al., 2020; Zhang et al., 2021); and (c) LGM (Kageyama et al., 2021; Zhu et al., 2021). Also shown are proxy \r near-surface air temperature anomalies (coloured circles). (d), (e) and (f) Proxy near-surface air temperature anomalies (grey circles), including published uncertainties (grey \r vertical bars), model ensemble mean zonal mean anomaly (solid red line) for the same model ensembles as in (a-c), light-red lines show the modelled temperature anomaly \r [...]"


id: 82d96d2fccc3368c55d3ae7d22746e8a
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 20147
content:
"[...] for the individual models that make up each ensemble (LGM, N=9; MPWP, N=17; EECO, N=5). Black dashed lines show the average of the proxy values in each latitude band: \r 90degS-30degS, 30degS-30degN, and 30degN-90degN. Red dashed lines show the same banded average in the model ensemble mean, calculated from the same locations as the proxies. \r Black and red dashed lines are only shown if there are five or more proxy points in that band. Mean differences between the 90degS/N to 30degS/N and 30degS to 30degN bands are \r quantified for the models and proxies in each plot. Panels (g), (h) and (i) are like panels (d-f) but for sea surface temperature (SST) instead of near-surface air temperature. \r Panels (j), (k) and (l) are like panels (a-c) but for SST instead of near-surface air temperature. For the EECO maps - (a) and (j) - the anomalies are relative to the zonal mean of \r [...]"


id: efb867ceef964268e1f856adbc088ba9
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 39554
content:
"[...] the pre-industrial, due to the different continental configuration. Proxy datasets are: (a) and (d) Hollis et al. (2019); (b) and (e) Salzmann et al. (2013); Vieira et al. (2018), (c) and \r (f) Cleator et al. (2020) at the sites defined in Bartlein et al. (2011); (g) and (j) Hollis et al. (2019); (h) and (k) McClymont et al. (2020); (i) and (l) Tierney et al. (2020b). Where \r there are multiple proxy estimations at a single site, a mean is taken. Model ensembles are (a), (d), (g) and (j) DeepMIP (only model simulations carried out with a mantle-frame \r paleogeography, and carried out under CO2 concentrations within the range assessed in Table 2.2, are shown); (b), (e), (h) and (k) PlioMIP; and (c), (f), (i) and (l) PMIP4. Further \r details on data sources and processing are available in the chapter data table (Table 7.SM.14)."


id: 34e6a1b6e5c30782db40780ff9df3d40
created_at: 2023-04-19 13:22:38
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content_type: text
index: climate_gpt_v2
vector_id: 6548
content:
"For the MPWP, model simulations are now in better agreement with \r proxies than at the time of AR5 (Haywood et al., 2020; McClymont \r et  al., 2020). In particular, in the tropics new proxy reconstructions \r of SSTs are warmer and in better agreement with the models, due in \r part to the narrower time window in the proxy reconstructions. There \r is also better agreement at higher latitudes (primarily in the North \r Atlantic), due in part to the absence of some very warm proxy SSTs \r due to the narrower time window (McClymont et al., 2020), and in \r part to a modified representation of Arctic gateways in the most recent \r Pliocene model simulations (Otto-Bliesner et  al., 2017), which have \r resulted in warmer modelled SSTs in the North Atlantic (Haywood \r et  al., 2020). Furthermore, as for the Eocene, improvements in the \r representation of aerosol-cloud interactions have also led to improved \r model-data consistency at high latitudes (Feng et al., 2019). Although \r [...]"


id: e389bf14230e7166cf34fb775ec21a88
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
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index: climate_gpt_v2
vector_id: 37325
content:
"[...] all PlioMIP2 models exhibit polar amplification of SAT, due to the \r relatively narrow time window there are insufficient terrestrial proxies \r to assess this (Figure 7.13b,e). However, polar SST amplification in the \r PlioMIP2 ensemble mean is in reasonably good agreement with that \r from SST proxies in the Northern Hemisphere (Figure 7.13h,k)."


id: 7c1521a477f0e7b8e2e7ba9fd3a9fe82
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 19039
content:
"Based on mature process understanding of the roles of poleward \r latent heat transport and radiative feedbacks in polar warming, \r a high degree of agreement across a hierarchy of climate models, \r observational evidence, paleoclimate proxy records of past climates \r associated with CO2 change, and ESM simulations of those past \r climates, there is high confidence that polar amplification is a robust \r feature of the long-term response to greenhouse gas forcing in both \r hemispheres. Stronger warming in the Arctic than the global average \r has already been observed (Section  2.3.1) and its causes are well \r understood. It is very likely that the warming in the Arctic will be \r more pronounced than the global average over the 21st century (high \r confidence) (Section 4.5.1.1). This is supported by models' improved \r ability to simulate polar amplification during past time periods, \r compared with at the time of AR5 (high confidence); although this is \r [...]"


id: 95f5e7b4dada92a6778409fa520cf88f
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
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index: climate_gpt_v2
vector_id: 23577
content:
"A weakening of the equatorial Pacific Ocean east-west SST gradient, \r with greater warming in the east than the west, is a common feature \r of the climate response to greenhouse gas forcing as projected by \r ESMs on centennial and longer time scales (e.g., Figure 7.14b; see \r Section 4.5.1). There are thought to be several factors contributing to \r this pattern. In the absence of any changes in atmospheric or oceanic \r circulations, the east-west surface temperature difference is theorized \r to decrease owing to weaker evaporative damping, and thus greater \r warming in response to forcing, where climatological temperatures \r are lower in the eastern Pacific cold tongue (Xie et  al., 2010; Luo \r et  al., 2015). Within atmospheric ESMs coupled to a  mixed-layer \r ocean, this gradient in damping has been linked to the rate of change \r with warming of the saturation specific humidity, which is set by the \r Clausius-Clapeyron relation (Merlis and Schneider, 2011). Gradients \r [...]"


id: bbd6654315edbe987bdd052fdcdc3955
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vector_id: 30279
content:
"[...] in low-cloud feedbacks may also favour eastern equatorial Pacific \r warming (DiNezio et al., 2009)."


id: f83f17699086094161a778b17a442c52
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 41050
content:
"Since 1870, observed SSTs in the tropical western Pacific Ocean have \r increased while those in the tropical eastern Pacific Ocean \r have  changed less (Figure  7.14a and Section  9.2.1). Much of the \r resultant strengthening of the equatorial Pacific temperature gradient \r has occurred since about 1980 due to strong warming in the west and \r cooling in the east (Figure 2.11b) concurrent with an intensification \r of the surface equatorial easterly trade winds and Walker circulation \r (Sections 3.3.3.1, 3.7.6, 8.3.2.3 and 9.2, and Figures 3.16f and 3.39f; \r England et  al., 2014). This temperature pattern is also reflected in \r regional ocean heat content trends and sea level changes observed \r from satellite altimetry since 1993 (Bilbao et al., 2015; Richter et al., \r 2020). The observed changes may have been influenced by one or \r a combination of temporary factors including sulphate aerosol forcing \r (Smith et al., 2016; Takahashi and Watanabe, 2016; Hua et al., 2018), \r [...]"


id: 598c0d1417d8f821a17251a9606660da
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 12989
content:
"[...] internal variability within the Indo-Pacific Ocean (Luo et al., 2012; Chung \r et al., 2019), teleconnections from multi-decadal tropical Atlantic SST \r trends (Kucharski et  al., 2011, 2014, 2015; McGregor et  al.,  2014; \r Chafik et al., 2016; X. Li et al., 2016; Kajtar et al., 2017; Sun et al., \r 2017), teleconnections from multi-decadal Southern Ocean SST trends \r (Hwang et al., 2017), and coupled ocean-atmosphere dynamics which \r slow warming in the equatorial eastern Pacific (Clement et al., 1996; \r Cane et al., 1997; Seager et al., 2019). CMIP3 and CMIP5 ESMs have \r difficulties replicating the observed trends in the Walker circulation \r and Pacific Ocean SSTs over the historical record (Sohn et al., 2013; \r Zhou et al., 2016; Coats and Karnauskas, 2017), possibly due to model \r deficiencies including insufficient multi-decadal Pacific Ocean SST \r variability (Laepple and Huybers, 2014; Bilbao et  al., 2015; Chung \r [...]"


id: 190275338865a2eec69e0d9762e17f8e
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 1617
content:
"[...] et al., 2019), mean state biases affecting the forced response or the \r connection between Atlantic and Pacific basins (Kucharski et al., 2014; \r Kajtar et al., 2018; Luo et al., 2018; McGregor et al., 2018; Seager et al., \r 2019), and/or a misrepresentation of radiative forcing (Sections 9.2.1 \r and 3.7.6). However, the observed trends in the Pacific Ocean SSTs \r are still within the range of internal variability as simulated by large \r initial condition ensembles of CMIP5 and CMIP6 models (Olonscheck \r et al., 2020; Watanabe et al., 2021). Because the causes of observed \r equatorial Pacific temperature gradient and Walker circulation trends \r are not well understood (Section 3.3.3.1), there is low confidence in \r their attribution to anthropogenic influences (Section 8.3.2.3), while \r there is medium confidence that the observed changes have resulted \r from internal variability (Sections 3.7.6 and 8.2.2.2)."


id: 605d8bd791c7bb227e3b777966bcc688
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 14262
content:
"Since AR5, the generation of new SST records has led to a variety \r of revised gradient estimates, specifically the generation of a new \r record for the warm pool (Zhang et al., 2014), the inclusion of SST \r reconstructions from sites in the South China Sea as warm pool \r estimates (O'Brien et al., 2014; Zhang et al., 2014), and the inclusion \r of several new sites from the eastern Pacific as cold tongue estimates \r (Zhang et al., 2014; Fedorov et al., 2015). Published estimates of the \r reduction in the longitudinal SST difference for the Late Pliocene, \r relative to either Late Quaternary (0-0.5 million years ago) or \r pre-industrial values, include 1degC to 1.5degC (Zhang et al., 2014), 0.1degC \r to 1.9degC (Tierney et al., 2019), and about 3degC (Ravelo et al., 2014; \r Fedorov et al., 2015; Wycech et al., 2020). All of these studies report \r a further weakening of the longitudinal gradient based on records \r extending into the Early Pliocene. While these revised estimates differ \r [...]"


id: 3479fc8f025e1453c1b2576bbf198b5b
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
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index: climate_gpt_v2
vector_id: 6467
content:
"[...] in magnitude due to differences in the sites and SST proxies used, \r they all agree that the longitudinal gradient was weaker, and this \r is supported by the probabilistic approach of Tierney et al. (2019). \r However, given that there are currently relatively few western \r equatorial Pacific records from independent site locations, and due \r to uncertainties associated with the proxy calibrations (Haywood \r et  al., 2016a), there is only medium confidence that the average \r longitudinal gradient in the tropical Pacific was weaker during the \r Pliocene than during the Late Quaternary."


id: 2ee056214ef4dcbc30f9c658649fbe40
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
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index: climate_gpt_v2
vector_id: 5524
content:
"To avoid the influence of local biases, changes in the longitudinal \r temperature difference within Pliocene model simulations are typically \r evaluated using domain-averaged SSTs within chosen east and west \r Pacific regions and as such there is sensitivity to methodology. Unlike \r the reconstructed estimates, longitudinal gradient changes simulated \r by the Pliocene Model Intercomparison Project Phase 1 (PlioMIP1) \r models do not agree on the change in sign and are reported as \r spanning approximately -0.5degC to +0.5degC by Brierley et  al. (2015) \r and approximately -1degC to +1degC by Tierney et  al. (2019). Initial \r PlioMIP  Phase 2 (PlioMIP2) analysis suggests responses similar to \r PlioMIP1 (Feng et al., 2019; Haywood et al., 2020). Models that include \r hypothetical modifications to cloud albedo or ocean mixing are \r required to simulate the substantially weaker longitudinal differences \r seen in reconstructions of the Early Pliocene (Fedorov et  al., 2013; \r [...]"


id: c81c813092b1c518f0cd2ac072e495d5
created_at: 2023-04-19 13:22:38
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index: climate_gpt_v2
vector_id: 32521
content:
"The expected time-evolution of the spatial pattern of surface warming \r in the future has important implications for values of ECS inferred \r from the historical record of observed warming. In particular, changes \r in the global top-of-atmosphere (TOA) radiative energy budget can be \r induced by changes in the regional variations of surface temperature, \r even without a change in the global mean temperature (Zhou et al., \r 2016; Ceppi and Gregory, 2019). Consequently, the global radiative \r feedback, characterizing the net TOA radiative response to global \r surface warming, depends on the spatial pattern of that warming. \r Therefore, if the equilibrium warming pattern under CO2 forcing \r (similar to CMIP6 projections in Figure 7.12a) is distinct from that \r observed over the historical record or indicated by paleoclimate \r proxies (Sections 7.4.4.1 and 7.4.4.2), then ECS will be different \r from the effective ECS (Box 7.1) that is inferred from those periods. \r [...]"


id: cbb1e8ddab3ad1b5ea1fe4cf3d7682f8
created_at: 2023-04-19 13:22:38
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index: climate_gpt_v2
vector_id: 33161
content:
"[...] Accounting for the dependence of radiative feedbacks on the spatial \r pattern of warming has helped to reconcile values of ECS inferred \r from the historical record with values of ECS based on other lines of \r evidence and simulated by climate models (Section 7.5.2.1; Armour, \r 2017; Proistosescu and Huybers, 2017; Andrews et al., 2018) but has \r not yet been examined in the paleoclimate context."


id: 76fb71189d4c30e9725a68f90ab831c5
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
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index: climate_gpt_v2
vector_id: 18137
content:
"This temperature 'pattern effect' (Stevens et  al., 2016) can result \r from both internal variability and radiative forcing of the climate \r system. Importantly, it is distinct from potential radiative feedback \r dependencies on the global surface temperature, which are assessed \r in Section 7.4.3. While changes in global radiative feedbacks under \r transient warming have been documented in multiple generations \r of climate models (Williams et al., 2008; Andrews et al., 2015; Ceppi \r and Gregory, 2017; Dong et  al., 2020), research published since \r AR5 has developed a much-improved understanding of the role of \r evolving SST patterns in driving feedback changes (Armour et  al., \r 2013; Andrews et  al., 2015, 2018; Gregory and Andrews, 2016; \r Zhou et al., 2016, 2017b; Ceppi and Gregory, 2017; Haugstad et al., \r 2017; Proistosescu and Huybers, 2017; Andrews and Webb, 2018; \r Marvel et al., 2018; Silvers et al., 2018; Dong et al., 2019, 2020). This \r [...]"


id: 3877640c8000fa68b971b008cfd67687
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
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index: climate_gpt_v2
vector_id: 7145
content:
"[...] section assesses process understanding of the pattern effect, which \r is dominated by the evolution of SSTs. Section 7.5.2.1 describes how \r potential feedback changes associated with the pattern effect are \r important to interpreting ECS estimates based on historical warming."


id: 72799234feabed7735ee2ca9f35bb392
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 17378
content:
"The radiation changes most sensitive to warming patterns are those \r associated with low-cloud cover (affecting global albedo) and the \r tropospheric temperature profile (affecting thermal emission to \r space) (Ceppi and Gregory, 2017; Zhou et al., 2017b; Andrews et al., \r 2018; Dong et  al., 2019). The mechanisms and radiative effects \r of these changes are illustrated in Figure  7.14a,b. SSTs in regions \r of deep convective ascent (e.g., in the western Pacific warm pool) \r govern the temperature of the tropical free troposphere and, in turn, \r affect low-clouds through the strength of the inversion that caps the \r boundary layer (i.e., the lower-tropospheric stability) in subsidence \r regions (Wood and Bretherton, 2006; Klein et  al., 2017). Surface \r warming within ascent regions thus warms the free troposphere \r and increases low-cloud cover, causing an increase in emission \r of thermal radiation to space and a  reduction in absorbed solar \r [...]"


id: b373b8da0006d013ee7ef5d270f1364e
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updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 28813
content:
"[...] radiation. In contrast, surface warming in regions of overall descent \r preferentially warms the boundary layer and enhances convective \r mixing with the dry free troposphere, decreasing low-cloud cover \r (Bretherton et al., 2013; Qu et al., 2014; Zhou et al., 2015). This leads \r to an increase in absorption of solar radiation but little change in \r thermal emission to space. Consequently, warming in tropical ascent \r regions results in negative lapse-rate and cloud feedbacks while \r warming in tropical descent regions results in positive lapse-rate \r and cloud feedbacks (Figure  7.14; Rose and Rayborn, 2016; Zhou \r et al., 2017b; Andrews and Webb, 2018; Dong et al., 2019). Surface \r warming in mid-to-high latitudes causes a weak radiative response \r owing to compensating changes in thermal emission (Planck and \r lapse-rate feedbacks) and absorbed solar radiation (shortwave cloud \r and surface-albedo feedbacks; Rose and Rayborn, 2016; Dong et al., \r [...]"


id: 20f05ad44e0b47a1535bea053b279dbf
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 3016
content:
"[...] 2019), however this compensation may weaken due to less-negative \r shortwave cloud feedbacks at high warming (Frey and Kay, 2018; \r Bjordal et al., 2020; Dong et al., 2020)."


id: bebfd3f59f00c0892cd1b49e3bd06aff
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 30826
content:
"Figure  7.14 | Illustration of tropospheric temperature and low-cloud \r response to observed and projected Pacific Ocean sea surface temperature \r trends. (a) Atmospheric response to linear sea surface temperature trend observed \r over 1870-2019 (HadISST1 dataset; Rayner et al., 2003). (b) Atmospheric response \r to linear sea-surface temperature trend over 150 years following abrupt4xCO2 \r forcing as projected by CMIP6 ESMs (Dong et al., 2020). Relatively large historical \r warming in the western tropical Pacific has been communicated aloft (a shift from \r grey to red atmospheric temperature profile), remotely warming the tropical free \r troposphere and increasing the strength of the inversion in regions of the tropics \r where warming has been slower, such as the eastern equatorial Pacific. In turn, an \r increased inversion strength has increased the low-cloud cover (Zhou et al., 2016) \r causing an anomalously negative cloud and lapse-rate feedbacks over the historical \r [...]"


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content:
"[...] record (Andrews et al., 2018; Marvel et al., 2018). Relatively large projected warming \r in the eastern tropical Pacific is trapped near the surface (shift from grey to red \r atmospheric temperature profile), decreasing the strength of the inversion locally. \r In turn, a decreased inversion strength combined with surface warming is projected to \r decrease the low-cloud cover, causing the cloud and lapse-rate feedbacks to become \r less negative in the future. Figure adapted from Mauritsen (2016). Further details on \r data sources and processing are available in the chapter data table (Table 7.SM.14)."


id: ecc5a34ff6b133009e97ba6047f7ced1
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
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index: climate_gpt_v2
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content:
"that found when prescribing observed warming patterns within \r atmosphere-only ESMs (Figure  7.15; Andrews et  al., 2018). This \r arises from the fact that the forced spatial pattern of warming within \r transient simulations of most coupled ESMs are distinct from observed \r warming patterns over the historical record in key regions such as \r the equatorial Pacific Ocean and Southern Ocean (Sections 7.4.4.1 \r and 7.4.4.2), while being more similar to the equilibrium pattern \r simulated under abrupt4xCO2. However, historical simulations \r with HadGEM3-GC3.1-LL (Andrews et  al., 2019) and GFDL CM4.0 \r (Winton et  al., 2020) show substantial spread in the value of \r a'  across ensemble members, indicating a  potentially important \r role for internal variability in setting the magnitude of the pattern \r effect over the historical period. Using the  100-member historical \r simulation ensemble of MPI-ESM1.1, Dessler et al. (2018) find that \r [...]"


id: 977e26c551c7373499e1a33ccc600af4
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
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index: climate_gpt_v2
vector_id: 23864
content:
"[...] internal climate variability alone results in a 0.5 W m-2 degC-1 spread in \r the historical effective feedback parameter, and thus also in the value \r of a'. Estimates of a' using prescribed historical warming patterns \r provide a more realistic representation of the historical pattern effect \r because they account for the net effect of the transient response \r to historical forcing and internal variability in the observed record \r (Andrews et al., 2018)."


id: b6a84dfd196190db3344a84628c2e10b
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 29337
content:
"Andrews et al. (2018) analysed available CMIP5/6 ESM simulations \r (six in total) comparing effective feedback parameters diagnosed \r within atmosphere-only ESMs using prescribed historical SST and sea \r ice concentration patterns with the equilibrium feedback parameters \r as estimated within coupled ESMs (using identical atmospheres) \r driven by abrupt 4xCO2 forcing. The atmosphere-only ESMs show \r pronounced multi-decadal variations in their effective feedback \r parameters over the last  century, with a  trend towards strongly \r negative values since about 1980 owing primarily to negative \r shortwave cloud feedbacks driven by warming in the western \r equatorial Pacific Ocean and cooling in the eastern equatorial \r Pacific Ocean (Zhou et al., 2016; Andrews et al., 2018; Marvel et al., \r 2018; Dong et  al., 2019). Yet, all six models show a  less-negative \r net feedback parameter under abrupt4xCO2 than for the historical \r [...]"


id: 737279cab02b0c9628b50364a00a162d
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 17565
content:
"[...] period (based on regression since 1870 following Andrews et  al., \r 2018). The average change in net feedback parameter between \r the historical period and the equilibrium response to CO2 forcing, \r denoted here as a', for these simulations is a' = +0.6 W m-2 degC-1\r (+0.3 to +1.0 W m-2 degC-1 range across models; Figure 7.15b). These \r feedback parameter changes imply that the value of ECS may be \r substantially larger than that inferred from the historical record \r (Section  7.5.2.1). These findings can be understood from the fact \r that, due to a  combination of internal variability and transient \r response to forcing (Section 7.4.4.2), historical sea surface warming \r has been relatively large in regions of tropical ascent (Figure 7.14a), \r leading to an anomalously large net negative radiative feedback; \r however, future warming is expected to be largest in tropical \r descent regions, such as the eastern equatorial Pacific, and at high \r [...]"


id: dc342e850fb99f62e4a3d947ec2475d2
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 36015
content:
"[...] latitudes (Sections 7.4.4.1 and 7.4.4.2 and Figure 7.14b), leading to \r a less-negative net radiative feedback and higher ECS."


id: aedd599935dcdcc86215215eb8775848
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 28009
content:
"The magnitude of a', as quantified by ESMs, depends on the accuracy \r of both the projected patterns of SST and sea ice concentration \r changes in response to CO2 forcing and the radiative response to \r those patterns (Andrews et al., 2018). Model biases that affect the \r long-term warming pattern (e.g.,  SST and relative humidity biases \r in the equatorial Pacific cold tongue as suggested by Seager et al., \r 2019) will affect the value of a'. The value of a' also depends on the \r accuracy of the historical SST and sea ice concentration conditions \r prescribed within atmosphere-only versions of ESMs to quantify \r the historical radiative feedback (Figure 7.15b). Historical SSTs are \r particularly uncertain for the early portion of the historical record \r (Section 2.3.1), and there are few constraints on sea ice concentration \r prior to the satellite era. Using alternative SST datasets, Andrews \r et al. (2018) found little change in the value of a' within two models \r [...]"


id: 9351eb61b835d13b6dd8d2756307d62c
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 23122
content:
"[...] (HadGEM3 and HadAM3), while Lewis and Mauritsen (2021) found \r a smaller value of a' within two other models (ECHAM6.3 and CAM5). \r The sensitivity of results to the choice of dataset represents a major \r source of uncertainty in the quantification of the historical pattern \r effect using atmosphere-only ESMs that has yet to be systematically \r explored, but the preliminary findings of Lewis and Mauritsen (2021) \r and Fueglistaler and Silvers (2021) suggest that a' could be smaller \r than the values reported in Andrews et al. (2018)."


id: 43214a6ead2874b8ee47e5aab4723c08
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 9020
content:
"A similar behaviour is seen within transient simulations of coupled \r ESMs, which project SST warming patterns that are initially \r characterized by relatively large warming rates in the western \r equatorial Pacific Ocean on decadal time scales and relatively large \r warming in the eastern equatorial Pacific and Southern Ocean \r on centennial time scales (Andrews et  al., 2015; Proistosescu \r and Huybers, 2017; Dong et  al., 2020). Recent studies based on \r simulations of 1%  yr -1 CO2 increase (1pctCO2) or abrupt4xCO2 as \r analogues for historical warming suggest characteristic values of \r a' = +0.05 W m-2 degC-1 (-0.2 to +0.3 W m-2 degC-1 range across models) \r based on CMIP5 and CMIP6 ESMs (Armour 2017, Lewis and Curry \r 2018, Dong et al. 2020). Using historical simulations of one CMIP6 ESM \r (HadGEM3-GC3.1-LL), Andrews et al. (2019) find an average feedback \r parameter change of a' = +0.2 W m-2 degC-1 (-0.2 to +0.6 W m-2 degC-1 \r [...]"


id: 3ae90363dc3988fae92a65636c8f4fd2
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 7562
content:
"[...] range across four ensemble members). Using historical simulations \r from another CMIP6 ESM (GFDL CM4.0), Winton et al. (2020) find \r an average feedback parameter change of a' = +1.5 W m-2 degC-1 \r (+1.2  to +1.7 W m-2 degC-1 range across three ensemble members). \r This value is larger than the a' = +0.7 W m-2 degC-1 within GFDL CM4.0 \r for historical CO2 forcing only, suggesting that the value of a' may \r depend on historical non-CO2 forcings such as those associated with \r tropospheric and stratospheric aerosols (Marvel et al., 2016; Gregory \r et al., 2020; Winton et al., 2020)."


id: cf61d23a6e150e03930299a9e5a74cb
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 33837
content:
"equilibrium - an improbable scenario given that Earth is in a relatively \r early phase of transient warming and that reaching equilibrium would \r take multiple millennia (C. Li et  al., 2013). Moreover, paleoclimate \r proxies, ESM simulations, and process understanding indicate that \r strong warming in the eastern equatorial Pacific Ocean (with medium \r confidence) and Southern Ocean (with high confidence) will emerge \r on centennial time scales as the response to CO2 forcing dominates \r temperature changes in these regions (Sections 7.4.4.1, 7.4.4.2 \r and 9.2.1). However, there is low confidence that these features, which \r have been largely absent over the historical record, will emerge this \r century (Sections 7.4.4.1, 7.4.4.2 and Section 9.2.1). This leads to high \r confidence that radiative feedbacks will become less negative as the \r CO2-forced pattern of surface warming emerges (a' > 0 W m-2 degC-1), \r but low confidence that these feedback changes will be realized this \r [...]"


id: f8e0099ce3844ba60984f07ea27e0d3f
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 41149
content:
"[...] century. There is also substantial uncertainty in the magnitude of the \r net radiative feedback change between the present warming pattern \r and the projected equilibrium warming pattern in response to CO2\r forcing owing to the fact that its quantification currently relies solely \r on ESM results and is subject to uncertainties in historical SST patterns. \r Thus, based on the pattern of warming since 1870, a' is estimated to \r be in the range 0.0 to 1.0 W m-2 degC-1 but with a low confidence in \r the upper end of this range. A value of a' = +0.5 +- 0.5 W m-2 degC -1\r is used to represent this range in Box 7.2 and Section 7.5.2, which \r respectively assess the implications of changing radiative feedbacks \r for Earth's energy imbalance and estimates of ECS based on the \r instrumental record. The value of a' is larger if quantified based on \r the observed pattern of warming since 1980 (Figure 2.11b) which is "


id: 61f43d330ce9ef5b47ccf34a29da15e1
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 14508
content:
"Before AR6, the assessment of ECS relied on either CO2-doubling \r experiments using global atmospheric models coupled with \r mixed-layer ocean or standardized CO2-quadrupling (abrupt4xCO2) \r experiments using fully coupled ocean-atmosphere models or \r Earth system models (ESMs). The TCR has similarly been diagnosed \r from ESMs in which the CO2 concentration is increased at 1% yr -1\r (1pctCO2, an approximately linear increase in ERF over time) and is \r in practice estimated as the average over a 20-year period centred \r at the time of atmospheric CO2 doubling, that is, year 70. In AR6, \r the assessments of ECS and TCR are made based on multiple lines \r of evidence, with ESMs representing only one of several sources \r of information. The constraints on these climate metrics are based \r on radiative forcing and climate feedbacks assessed from process \r understanding (Section  7.5.1), climate change and variability seen \r [...]"


id: 4bade4ff37a87c891e2f029fd150a142
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 10572
content:
"[...] within the instrumental record (Section 7.5.2), paleoclimate evidence \r (Section 7.5.3), emergent constraints (Section 7.5.4), and a synthesis \r of all lines of evidence (Section 7.5.5). In AR5, these lines of evidence \r were not explicitly combined in the assessment of climate sensitivity, \r but as demonstrated by Sherwood et  al. (2020) their combination \r narrows the uncertainty ranges of ECS compared to that assessed \r in AR5. ECS values found in CMIP6 models, some of which exhibit \r values higher than 5degC (Meehl et al., 2020; Zelinka et al., 2020), are \r discussed in relation to the AR6 assessment in section 7.5.6."


id: 1ef4d64267c0af638979aaf8cedad7db
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 2647
content:
"The wide range of the process-based ECS estimate is not due \r solely to uncertainty in the estimates of DF2xCO2 and a, but is partly \r explained by the assumption that DF2xCO2 and a are independent in \r this approach. In CMIP5 and CMIP6 ensembles, DF2xCO2 and a are \r negatively correlated when they are calculated using linear regression \r in abrupt4xCO2 simulations (r 2 = 0.34; Andrews et al., 2012; Webb \r et al., 2013; Zelinka et al., 2020). The negative correlation leads to \r compensation between the inter-model spreads of these quantities, \r thereby reducing the ECS range estimated directly from the models. \r If the process-based ECS distribution is reconstructed from probability \r distributions of DF2xCO2 and a assuming that they are correlated as in \r CMIP model ensembles, the range of ECS will be narrower by 14% \r (pink curved bar in Figure 7.16). If, however, the covariance between \r DF2xCO2 and a is not adopted, there is no change in the mean, but the \r [...]"


id: 2bd9efb6b80878c31b50093cd1118309
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 4966
content:
"A significant correlation between DF2xCO2 and a also occurs when \r the two parameters are estimated separately from atmospheric ESM \r fixed-SST experiments (Section  7.3.1) or fixed CO2 concentration \r experiments (Section  7.4.1; Ringer et  al., 2014; Chung and Soden, \r 2018). Hence the relationship is not expected to be an artefact of \r calculating the parameters using linear regression in abrupt4xCO2\r simulations. A  possible physical cause of the correlation may be \r a  compensation between the cloud adjustment and the cloud \r feedback over the tropical ocean (Ringer et  al., 2014; Chung and \r Soden, 2018). It has been shown that the change in the hydrological \r cycle is a controlling factor for the low-cloud adjustment (Dinh and \r Fueglistaler, 2019) and for the low-cloud feedback (Watanabe et al., \r 2018), and therefore the responses of these clouds to the direct CO2\r radiative forcing and to the surface warming may not be independent. \r [...]"


id: 10e7985119ed53d7840c6185ce6e4a07
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 161
content:
"[...] However, robust physical mechanisms are not yet established, \r and furthermore, the process-based assessment of the tropical \r low-cloud feedback is only indirectly based on ESMs given that \r physical processes which control the low-clouds are not sufficiently \r well-simulated in models (Section  7.4.2.4). For these reasons, the \r co-dependency between DF2xCO2 and a is assessed to have low \r confidence and, therefore, the more conservative assumption that \r they are independent for the process-based assessment of ECS \r is retained."


id: 91843aee451f34e292d7b5150bcc17c7
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 22819
content:
"The analytical solution of the energy balance emulator reveals \r that the global surface temperature change to abrupt increase of \r the atmospheric CO2 concentration is expressed by a  combination \r of a  fast adjustment of the surface components of the climate \r system and a  slow response of the deep ocean, with time scales \r of several years and several centuries, respectively (grey curve in \r Figure 7.17b). The equilibrium response of upper ocean temperature, \r approximating SST and the surface air temperature response, \r depends, by definition, only on the radiative forcing and the net \r feedback parameter. Uncertainty in a dominates (80-90%) the \r corresponding uncertainty range for ECS in CMIP5 models (Vial et al., \r 2013), and also an increase of ECS in CMIP6 models (Section 7.5.5) \r is attributed by about 60-80% to a  change in a (Zelinka et  al., \r 2020). For the range of TCR, the contribution from uncertainty in a is \r [...]"


id: 6d15860bc3395f5df70729099c29f964
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
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index: climate_gpt_v2
vector_id: 16468
content:
"[...] reduced to 50-60% while uncertainty in DF2xCO2 becomes relatively \r more important (Geoffroy et al., 2012; Lutsko and Popp, 2019). TCR \r reflects the fast response occurring approximately during the first \r 20 years in the abrupt4xCO2 simulation (Held et al., 2010), but the \r fast response is not independent of the slow response because there \r is a non-linear co-dependence between them (Andrews et al., 2015). \r The non-linear  relationship between ECS and TCR indicates that \r the probability of high TCR is not very sensitive to changes in the \r probability of high ECS (Meehl et al., 2020)."


id: 8fef594abe03ff9d73b01508978e2950
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 22530
content:
"Considering an idealized time evolution of ERF (1% increase per year \r until CO2 doubling and held fixed afterwards, see Figure  7.17a), \r the TCR defined by the surface temperature response at year 70 is \r derived by substituting the process-based ECS into the analytical \r solution of the emulator (Figure  7.17b, see also Supplementary \r Material 7.SM.2.1). When additional parameters in the emulator \r are prescribed by using CMIP6 multi-model mean values of those \r estimates (Smith et al., 2020b), this calculation translates the range of \r ECS in Section 7.5.2.1 to the range of TCR. The transient temperature \r response, in reality, varies with different estimates of the ocean \r heat uptake efficiency (g) and efficacy (e). When the emulator was \r calibrated to the transient responses in CMIP5 models, it shows that \r uncertainty in heat capacities is negligible and differences in g and \r e explain 10-20% of the inter-model spread of TCR among GCMs \r [...]"


id: 4fc03b1552815ebd6810c8f56624ae7c
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 11324
content:
"[...] (Geoffroy et al., 2012). Specifically, their product, k = ge, appearing \r in a simplified form of the solution, that is, TCR  -DF2xCO2/(a - k), \r gives a  single parameter quantifying the damping effects of heat \r uptake (Jimenez-de-la-Cuesta and Mauritsen, 2019). This parameter \r is positive and acts to slow down the temperature response in \r a similar manner to the 'pattern effect' (Sections 7.4.4.3 and 7.5.2.1). \r The ocean heat uptake in nature is controlled by multiple processes \r associated with advection and mixing (Exarchou et al., 2014; Kostov \r et al., 2014; Kuhlbrodt et al., 2015) but is simplified to be represented \r by a  single term of heat exchange between the upper and deep \r ocean in the emulator. Therefore, it is challenging to constrain \r g and e from process-based understanding (Section 7.5.2). Because \r the estimated values are only weakly correlated across models, \r the mean value and one standard deviation of k are calculated as "


id: ee4fddb46f7e4445d5cff70c0265d55
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 39299
content:
"The GSAT change from 1850-1900 to 2006-2019 is estimated to be \r 1.03 [0.86 to 1.18] degC (Cross-chapter Box 2.3). Together with estimates \r of Earth's energy imbalance (Section 7.2.2) and the global ERF that \r has driven the observed warming (Section  7.3), the instrumental \r temperature record enables global energy budget estimates of ECS \r and TCR. While energy budget estimates use instrumental data, they \r are not based purely on observations. A conceptual model typically \r based on the global mean forcing and response energy budget \r framework (Box 7.1) is needed to relate ECS and TCR to the estimates \r of global warming, ERF and Earth's energy imbalance (Forster, 2016; \r Knutti et al., 2017). Moreover, ESM simulations partly inform estimates \r of the historical ERF (Section 7.3) as well as Earth's energy imbalance \r in the 1850-1900 climate (the period against which changes are \r measured; Forster, 2016; Lewis and Curry, 2018). ESMs are also used \r [...]"


id: 7f956480b17849531a3d045e69c05288
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
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index: climate_gpt_v2
vector_id: 19588
content:
"[...] to estimate uncertainty due the internal climate variability that may \r have contributed to observed changes in temperature and energy \r imbalance (e.g., Palmer and McNeall, 2014; Sherwood et al., 2020). \r Research since AR5 has shown that global energy budget estimates \r of ECS may be biased low when they do not take into account how \r radiative feedbacks depend on the spatial pattern of surface warming \r (Section 7.4.4.3) or when they do not incorporate improvements in \r the estimation of global surface temperature trends which take better \r account of data-sparse regions and are more consistent in their \r treatment of surface temperature data (Section 2.3.1). Together with \r updated estimates of global ERF and Earth's energy imbalance, these \r advances since AR5 have helped to reconcile energy budget estimates \r of ECS with estimates of ECS from other lines of evidence."


id: d3a53f0e2d0082e0e07d478794556384
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 34574
content:
"The traditional global mean forcing and response energy budget \r framework (Section 7.4.1 and Box 7.1; Gregory et al., 2002) relates \r the difference between the ERF (DF) and the radiative response to \r observed global warming (aDT) to the Earth's energy imbalance (DN): \r DN = aDT + DF. Given the relationship ECS = -DF2xCO2/a, where DF2xCO2\r is the ERF from CO2 doubling, ECS can be estimated from historical \r estimates of DT, DF, DN and DF2xCO2: ECS = DF2xCO2DT/(DF - DN). \r Since TCR is defined as the temperature change at the time of \r CO2 doubling under an idealized 1% yr-1 CO2 increase, it can be \r inferred from the historical record as: TCR  =  DF2xCO2 DT/DF, under \r the assumption that radiative forcing increases quickly compared to \r the adjustment time scales of the deep ocean, but slowly enough \r and over a sufficiently long time that the upper ocean is adjusted, \r so that DT and DN increases approximately in proportion to DF. \r [...]"


id: e5b3d3e9f78ee53033ab4553a6f0380
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 37716
content:
"[...] Because DN is positive, TCR is always smaller than ECS, reflecting \r weaker transient warming than equilibrium warming. TCR is better \r constrained than ECS owing to the fact that the denominator of TCR, \r without the quantity DN, is more certain and further from zero than \r is the denominator of ECS. The upper bounds of both TCR and ECS \r estimated from historical warming are inherently less certain than \r their lower bounds because DF is uncertain and in the denominator."


id: fc02f3697d5253ae0b562e7bd75ace66
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 41696
content:
"The traditional energy budget framework lacks a representation of \r how radiative feedbacks depend on the spatial pattern of warming. \r Thus, studies employing this framework (Otto et  al., 2013; Lewis \r and Curry, 2015, 2018; Forster, 2016) implicitly assume that the net \r radiative feedback has a constant magnitude, producing an estimate \r of the effective ECS (defined as the value of ECS that would occur \r if a does not change from its current value) rather than of the true \r ECS. As summarized in Section  7.4.4.3, there are now multiple \r lines of evidence providing high confidence that the net radiative \r feedback will become less negative as the warming pattern evolves \r in the future (the pattern effect). This arises because historical \r warming has been relatively larger in key negative feedback regions \r (e.g.,  western tropical Pacific Ocean) and relatively smaller in key \r positive feedback regions (e.g.,  eastern tropical Pacific Ocean and \r [...]"


id: 464cb9e93067453fd23531ef39ec7577
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 9590
content:
"[...] Southern Ocean) than is projected in the near-equilibrium response \r to CO2 forcing (Section 7.4.4.3; Held et al., 2010; Proistosescu and \r Huybers, 2017; Dong et  al., 2019), implying that the true ECS will \r be larger than the effective ECS inferred from historical warming. \r This section first assesses energy budget constraints on TCR and the \r effective ECS based on updated estimates of historical warming, ERF, \r and Earth's energy imbalance. It then assesses what these energy \r budget constraints imply for values of ECS once the pattern effect is \r accounted for."


id: e1552d10c15e46dce671b02ea2bac694
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 36907
content:
"balance modelling (Armour, 2017), inferred ocean warming given \r observed SSTs using ocean models (Gebbie and Huybers, 2019; \r Zanna et  al., 2019), and ocean warming reconstructed from noble \r gas thermometry (Baggenstos et  al., 2019) suggest a  1850-1900 \r Earth energy imbalance of 0.2 +- 0.2 W m-2. Combined with estimates \r of internal variability in Earth's energy imbalance, calculated using \r periods of equivalent lengths of years as used in unforced ESM \r simulations (Palmer and McNeall, 2014; Sherwood et al., 2020), the \r anomalous energy imbalance between 1850-1900 and 2006-2019 \r is estimated to be DN = 0.59 +- 0.35 W m-2. GSAT change between \r 1850-1900 and 2006-2019 is estimated to be DT = 1.03degC +- 0.20 degC \r (Cross-Chapter Box  2.3 and Box  7.2) after accounting for internal \r temperature variability derived from unforced ESM simulations \r (Sherwood et al., 2020). The ERF change between 1850-1900 and \r 2006-2019 is estimated to be DF  =  2.20 [1.53 to 2.91] W  m-2\r [...]"


id: 52c748a86479e83875873a0e684af125
created_at: 2023-04-19 13:22:38
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index: climate_gpt_v2
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"[...] (Section 7.3.5) and the ERF for a doubling of CO2 is estimated to be \r DF2xCO2 = 3.93 +- 0.47 W m-2 (Section 7.3.2). Employing these values \r within the traditional global energy balance framework described \r above (following the methods of Otto et al. (2013) and accounting \r for correlated uncertainties between DF and DF2xCO2) produces a TCR \r of 1.9 [1.3 to 2.7] degC and an effective ECS of 2.5 [1.6 to 4.8] degC. \r These TCR and effective ECS values are higher than those in the \r recent literature (Otto et al., 2013; Lewis and Curry, 2015, 2018) but \r are comparable to those of Sherwood et al. (2020) who also used \r updated estimates of observed warming, Earth's energy imbalance, \r and ERF."


id: 26390fe1cf95d5435c0562c1a82963f2
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
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index: climate_gpt_v2
vector_id: 3894
content:
"Energy budget estimates of TCR and ECS have evolved in the \r literature over recent decades. Prior to AR4, the global energy \r budget provided relatively weak constraints, primarily due to large \r uncertainty in the tropospheric aerosol forcing, giving ranges of the \r effective ECS that typically included values above 10degC (Forster, 2016; \r Knutti et  al., 2017). Revised estimates of aerosol forcing together \r with a larger greenhouse gas forcing by the time of AR5 led to an \r estimate of DF that was more positive and with reduced uncertainty \r relative to AR4. Using energy budget estimates and radiative forcing \r estimates updated to 2009, Otto et  al. (2013) estimated that TCR \r was 1.3 [0.9 to 2.0] degC, and that the effective ECS was 2.0 [1.2 to \r 3.9] degC. This AR5-based energy budget estimate of ECS was lower \r than estimates based on other lines of evidence, leading AR5 to \r expand the assessed likely range of ECS to include lower values \r [...]"


id: 3079d64fb108a0d88de2e62dca1954f3
created_at: 2023-04-19 13:22:38
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vector_id: 5783
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"[...] relative to AR4. Studies since AR5 using similar global energy budget \r methods have produced similar or slightly narrower ranges for TCR \r and effective ECS (Forster, 2016; Knutti et al., 2017)."


id: 662ae80a278d77c0efb5cccf953d1092
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
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index: climate_gpt_v2
vector_id: 15237
content:
"The trend estimation method applied to global surface temperature \r affects derived values of ECS and TCR from the historical record. \r In this Report, the effective ECS is inferred from estimates that \r use global coverage of GSAT to estimate the surface temperature \r trends. The GSAT trend is assessed to have the same best estimate \r as the observed global mean surface temperature (GMST), \r although the  GSAT trend is assessed to have larger uncertainty \r (see Cross-Chapter Box 2.3). Many previous studies have relied on \r HadCRUT4 GMST estimates that used the blended observations \r and did not interpolate over regions of incomplete observational \r coverage such as the Arctic. As a result, the ECS and TCR derived \r from these studies has smaller ECS and TCR values than those \r derived from model-inferred estimates (M. Richardson et al., 2016, \r 2018). The energy budget studies assessing ECS in AR5 employed \r HadCRUT4 or similar measures of GMST trends. As other lines of \r [...]"


id: 6526fa93b278b6c120dd73bd19f69e36
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
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index: climate_gpt_v2
vector_id: 15061
content:
"[...] evidence in that report used GSAT trends, this could partly explain \r why AR5-based energy budget estimates of ECS were lower than \r those estimated from other lines of evidence, adding to the overall \r disparity in M. Collins et al. (2013). In this report, GSAT is chosen \r as the standard measure of global surface temperature to aid \r comparison with previous model- and process-based estimates of \r ECS, TCR and climate feedbacks (see Cross-Chapter Box 2.3)."


id: 1130e8c968053bfce4dccc7047f012d2
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
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index: climate_gpt_v2
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content:
"(HadGEM3-GC3.1-LL) the effective ECS derived from historical energy \r budget constraints is 4.1degC (average of four ensemble members) while \r ECS is estimated to be 5.5degC (Andrews et al., 2019). These modelling \r results suggest that the effective ECS under historical forcing could be \r lower than the true ECS owing to differences in radiative feedbacks \r induced by the distinct patterns of historical and equilibrium warming \r (Section 7.4.4.3). Using GFDL-CM4, Winton et al. (2020) also find that \r the value of TCR estimated from energy budget constraints within \r a  historical simulation (1.3degC) is substantially lower than the true \r value of TCR (2.1degC) diagnosed within a 1pctCO2 simulation owing to \r a combination of the pattern effect and differences in the efficiency \r of ocean heat uptake between historical and 1pctCO2 forcing. This \r section next considers how the true ECS can be estimated from \r the historical energy budget by accounting for the pattern effect. \r [...]"


id: b94d948936e3e25b7814dfe3a0dc8da2
created_at: 2023-04-19 13:22:38
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index: climate_gpt_v2
vector_id: 29813
content:
"[...] However, owing to limited evidence this section does not attempt to \r account for these effects in estimates of TCR."


id: 49b7753d922ae622b2253da2ddcbc7d6
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 10201
content:
"Overall, there is high confidence that the true ECS is higher than the \r effective ECS as inferred from the historical global energy budget, \r but there is substantial uncertainty in how much higher because of \r limited evidence regarding how radiative feedbacks may change in \r the future. While several lines of evidence indicate that a' > 0, the \r quantitative accuracy of feedback changes is not known at this time \r (Section 7.4.4.3). Global energy budget constraints thus provide high \r confidence in the lower bound of ECS which is not sensitive to the \r value of a': ECS is extremely unlikely to be less than 1.6degC. Estimates \r of a' that are informed by idealized CO2 forcing simulations of \r coupled ESMs (Armour, 2017; Lewis and Curry, 2018; Andrews et al., \r 2019; Dong et al., 2020; Winton et al., 2020) indicate a median value \r of ECS of around 2.7degC while estimates of a' that are informed by \r observed historical sea surface temperature patterns (Andrews et al., \r [...]"


id: 6bde0295bb8e0c4d48e9276dc52067f4
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
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index: climate_gpt_v2
vector_id: 16279
content:
"[...] 2018) indicate a  median value of ECS of around 3.5degC. Owing to \r large uncertainties in future feedback changes, the historical energy \r budget currently provides little information about the upper end of \r the ECS range."


id: 8627532197ec1fef30935a8fdd541b29
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 20751
content:
"Research since AR5 has introduced extensions to the traditional \r energy budget framework that account for the feedback dependence \r on temperature patterns by allowing for multiple radiative \r feedbacks operating on different time scales (Armour et al., 2013; \r Geoffroy et  al., 2013a; Armour, 2017; Proistosescu and Huybers, \r 2017; Goodwin, 2018; Rohrschneider et  al., 2019), by allowing \r feedbacks to vary with the spatial pattern or magnitude of ocean \r heat uptake (Winton et al., 2010; Rose et al., 2014; Rugenstein et al., \r 2016a), or by allowing feedbacks to vary with the type of radiative \r forcing agent (Kummer and Dessler, 2014; Shindell, 2014; Marvel \r et al., 2016; Winton et al., 2020). A direct way to account for the \r pattern effect is to use the relationship ECS = -DF2xCO2/(a  + a'), \r where a = (DN  - DF)/DT is the effective feedback parameter \r (Box 7.1) estimated from historical global energy budget changes \r [...]"


id: 83e9e3066df3c4ce26dfbe39978bed62
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
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index: climate_gpt_v2
vector_id: 20357
content:
"[...] and a' represents the change in the feedback parameter between \r the historical period and the equilibrium response to CO2 forcing, \r which can be estimated using ESMs (Section 7.4.4.3; Armour, 2017; \r Andrews et  al., 2018, 2019; Lewis and Curry, 2018; Dong et  al., \r 2020; Winton et al., 2020)."


id: ee3b401d7af433962ea7c2422cfb64b
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 39285
content:
"Energy budget emulators are far less complex than comprehensive \r ESMs (Section  1.5.3 and Cross-Chapter Box  7.1). For example, an \r emulator could represent the atmosphere, ocean, and land using \r a small number of connected boxes (e.g., Goodwin, 2016), or it could \r represent the global mean climate using two connected ocean layers \r (e.g., Cross-Chapter Box 7.1 and Supplementary Material 7.SM.2). The \r numerical efficiency of emulators means that they can be empirically \r constrained by observations: a  large number of possible parameter \r values (e.g., feedback parameter, aerosol radiative forcing, and ocean \r diffusivity) are randomly drawn from prior distributions; forward \r integrations of the model are performed with these parameters and \r weighted against observations of surface or ocean warming, producing \r posterior estimates of quantities of interest such as TCR, ECS and \r aerosol forcing (Section  7.3). Owing to their reduced complexity, \r [...]"


id: 36d5be51b0c60380b2ad8a0ca05fdb03
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
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index: climate_gpt_v2
vector_id: 6874
content:
"[...] emulators lack full representations of the spatial patterns of sea surface \r temperature and radiative responses to changes in those patterns \r (discussed in Section  7.4.4.3) and many represent the net feedback \r parameter using a  constant value. The ranges of ECS reported by \r studies using emulators are thus interpreted here as representative of \r the effective ECS over the historical record rather than of the true ECS."


id: 7259f088afabe7704733594377552e57
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
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index: climate_gpt_v2
vector_id: 17362
content:
"The net radiative feedback change between the historical warming \r pattern and the projected equilibrium warming pattern in response to \r CO2 forcing (a') is estimated to be in the range 0.0 to 1.0 W m-2 degC-1 \r (Figure  7.15). Using the value a' = +0.5 +- 0.5 W  m-2 degC  -1 to \r represent this range illustrates the effect of changing radiative \r feedbacks on estimates of ECS. While the effective ECS inferred from \r historical warming is 2.5 [1.6 to 4.8] degC, ECS = -DF2xCO2/(a + a') \r is 3.5 [1.7  to 13.8] degC. For comparison, values of a' derived from \r the response to historical and idealized CO2 forcing within coupled \r climate models (Armour, 2017; Lewis and Curry, 2018; Andrews et al., \r 2019; Dong et al., 2020; Winton et al., 2020) can be approximated \r as a' = +0.1 +- 0.3 W m-2 degC-1 (Section 7.4.4.3), corresponding to \r a value of ECS of 2.7 [1.7 to 5.9] degC. In both cases, the low end of \r the ECS range is similar to that of the effective ECS inferred using the \r [...]"


id: c72ef19f6a1b3d900f82ea079a0bfd30
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
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index: climate_gpt_v2
vector_id: 32362
content:
"[...] traditional energy balance model framework that assumes a' = 0, \r reflecting a weak dependence on the value of a' when ECS is small \r (Armour, 2017; Andrews et al., 2018); the low end of the ECS range \r is robust even in the hypothetical case that a' is slightly negative. \r However, the high end of the ECS range is substantially larger than \r that of the effective ECS and strongly dependent on the value of a'."


id: 6abb690974d024e8eb28bc8ac4c20340
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 16059
content:
"Improved estimates of ocean heat uptake over the past two decades \r (Section 7.2) have diminished the role of ocean diffusivity in driving \r uncertainty in ECS estimates, leaving the main trade-off between \r posterior ranges in ECS and aerosol radiative forcing (Forest, 2002; \r Knutti et al., 2002; Frame et al., 2005). The AR5 (Bindoff et al., 2013) \r assessed a variety of estimates of ECS based on emulators and found \r that they were sensitive to the choice of prior parameter distributions \r and temperature datasets used, particularly for the upper end of the \r ECS range, though priors can be chosen to minimize the effect on \r results (e.g., Lewis, 2013). Emulators generally produced estimates \r of effective ECS between 1degC and 5degC and ranges of TCR between \r 0.9degC and 2.6degC. Padilla et al. (2011) use a simple global-average \r emulator with two time scales (Section  7.5.1.2; Supplementary \r Material 7.SM.2) to estimate a TCR of 1.6 [1.3 to 2.6] degC. Using the \r [...]"


id: 7fc37d8a2768140b49adea8c2b2eb88b
created_at: 2023-04-19 13:22:38
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content:
"[...] same model, Schwartz (2012) finds TCR in the range 0.9degC-1.9degC \r while Schwartz (2018) finds that an effective ECS of 1.7degC provides \r the best fit to the historical global surface temperature record \r while also finding a  median aerosol forcing that is smaller than \r that assessed in Section  7.3. Using an eight-box representation \r of the atmosphere-ocean-terrestrial system constrained by \r historical warming, Goodwin (2016) found an effective ECS of \r 2.4  [1.4 to 4.4] degC while Goodwin (2018) found effective ECS \r to be in the range 2degC-4.3degC when using a prior for ECS based on \r paleoclimate constraints."


id: d42ada459d55f5dbdebab046b73d6c92
created_at: 2023-04-19 13:22:38
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index: climate_gpt_v2
vector_id: 34660
content:
"While continuous satellite measurements of top-of-atmosphere \r (TOA) radiative fluxes (Figure  7.3) do not have sufficient accuracy \r to determine the absolute magnitude of Earth's energy imbalance \r (Section 7.2.1), they provide accurate estimates of its variations and \r trends since the year 2002 that agree well with estimates based on \r observed changes in global ocean heat content (Loeb et al., 2012; \r Johnson et  al., 2016; Palmer, 2017). When combined with global \r surface temperature observations and simple models of global energy \r balance, satellite measurements of TOA radiation afford estimates \r of the net feedback parameter associated with recent climate \r variability (Tsushima and Manabe, 2013; Donohoe et  al., 2014; \r Dessler and Forster, 2018). These feedback estimates, derived from \r the regression of TOA radiation on surface temperature variability, \r imply values of ECS that are broadly consistent with those from \r [...]"


id: 1bb9c1c11c8591e863e383db6fb9e728
created_at: 2023-04-19 13:22:38
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vector_id: 2062
content:
"[...] other lines of evidence (Forster, 2016; Knutti et al., 2017). A history of \r regression-based feedbacks and their uncertainties is summarized in \r Bindoff et al. (2013), Forster (2016), and Knutti et al. (2017)."


id: daa252898b7aac140cc20073e6a9525b
created_at: 2023-04-19 13:22:38
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vector_id: 35758
content:
"Using an emulator comprised of Northern and Southern hemispheres \r and an upwelling-diffusive ocean (Aldrin et al., 2012), with surface \r temperature and ocean heat content datasets updated to 2014, \r Skeie et al. (2018) estimate a TCR of 1.4 [0.9 to 2.0] degC and a median \r effective ECS of 1.9 [1.2 to 3.1] degC. Using a  similar emulator \r comprised of land and ocean regions and an upwelling-diffusive \r ocean, with global surface temperature and ocean heat content \r datasets up to 2011, Johansson et al. (2015) find an effective ECS of \r 2.5 [2.0 to 3.2] degC. The estimate is found to be sensitive to the choice \r of dataset endpoint and the representation of internal variability \r meant to capture the El  Nino-Southern Oscillation and Pacific \r Decadal Variability. Differences between these two studies arise, \r in part, from their different global surface temperature and ocean \r heat content datasets, different radiative forcing uncertainty ranges, \r [...]"


id: 99fd5c4cd2af8a531c33553d6f7d3f7
created_at: 2023-04-19 13:22:38
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index: climate_gpt_v2
vector_id: 24327
content:
"[...] different priors for model parameters, and different representations \r of internal variability. This leads to different estimates of effective \r ECS, with the median estimate of Skeie et  al. (2018) lying below \r the 5-95% range of effective ECS from Johansson et  al. (2015). \r Moreover, while the Skeie et al. (2018) emulator has a constant value \r of the net feedback parameter, the Johansson et al. (2015) emulator \r allows distinct radiative feedbacks for land and ocean, contributing \r to the different results."


id: 3f9557857fe253eb2686901556e04442
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 8376
content:
"Research since AR5 has noted that regression-based feedback \r estimates depend on whether annual- or monthly-mean data are used \r and on the choice of lag employed in the regression, complicating \r their interpretation (Forster, 2016). The observed lead-lag relationship \r between global TOA radiation and global surface temperature, and \r its dependence on sampling period, is well replicated within unforced \r simulations of ESMs (Dessler, 2011; Proistosescu et al., 2018). These \r features arise because the regression between global TOA radiation \r and global surface temperature reflects a blend of different radiative \r feedback processes associated with several distinct modes of \r variability acting on different time scales (Annex IV), such as monthly \r atmospheric variability and interannual El Nino-Southern Oscillation \r (ENSO) variability (Lutsko and Takahashi, 2018; Proistosescu et al., \r 2018). Regression-based feedbacks thus provide estimates of \r [...]"


id: 6b046c9aa174b9671dea68b8a1cd5f6a
created_at: 2023-04-19 13:22:38
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vector_id: 16124
content:
"[...] the radiative feedbacks that are associated with internal climate \r variability (e.g.,  Brown et  al., 2014), and do not provide a  direct \r estimate of ECS (high confidence). Moreover, variations in global \r surface temperature that do not directly affect TOA radiation may \r lead to a positive bias in regression-based feedback, although this \r bias appears to be small, particularly when annual-mean data are \r used (Murphy and Forster, 2010; Spencer and Braswell, 2010, 2011; \r Proistosescu et al., 2018). When tested within ESMs, regression-based \r feedbacks have been found to be only weakly correlated with values \r of ECS (Chung et al., 2010), although cloudy-sky TOA radiation fluxes \r have been found to be moderately correlated with ECS at ENSO time \r scales within CMIP5 models (Lutsko and Takahashi, 2018)."


id: 942d3d1ebf19ceb103e2a7ccba62dc3e
created_at: 2023-04-19 13:22:38
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index: climate_gpt_v2
vector_id: 23277
content:
"Estimates of ECS based on paleoclimate data are complementary \r to, and largely independent from, estimates based on process-based \r studies (Section  7.5.1) and the instrumental record (Section  7.5.2). \r The  strengths of using paleoclimate data to estimate ECS include: \r (i) the estimates are based on observations of a real-world Earth system \r response to a forcing, in contrast to using estimates from process\u0002based modelling studies or directly from models; (ii) the forcings \r are often relatively large (similar in magnitude to a CO2 doubling or \r more), in contrast to data from the instrumental record; (iii) the forcing \r often changes relatively slowly so the system is close to equilibrium; \r as such, all individual feedback parameters, ax, are included, and \r complications associated with accounting for ocean heat uptake are \r reduced or eliminated, in contrast to the instrumental record. However, \r there can be relatively large uncertainties on estimates of both the \r [...]"


id: ebfd0381b7db7e8f1606ca680d31fbd7
created_at: 2023-04-19 13:22:38
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index: climate_gpt_v2
vector_id: 38884
content:
"[...] paleo forcing and paleo global surface temperature response, and \r care must be taken to account for long-term feedbacks associated \r with ice sheets (Section 7.4.2.6), which often play an important role \r in the paleoclimate response to forcing, but which are not included in \r the definition of ECS. Furthermore, the state-dependence of feedbacks \r (Section 7.4.3) means that climate sensitivity during Earth's past may \r not be the same as it is today, which should be accounted for when \r interpreting paleoclimate estimates of ECS."


id: e882dd0aca254f9a1d1b65f21c89eb6b
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
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index: climate_gpt_v2
vector_id: 38244
content:
"A number of studies consider the observed climate response to volcanic \r eruptions over the 20th  century (Section  3.3.1 and Cross-Chapter \r Box 4.1; Knutti et al., 2017). However, the direct constraint on ECS \r is weak, particularly at the high end, because the temperature \r response to short-term forcing depends only weakly on radiative \r feedbacks and because it can take decades of a sustained forcing \r before the magnitude of temperature changes reflects differences in \r ECS across models (Geoffroy et al., 2013b; Merlis et al., 2014). It is \r also a challenge to separate the response to volcanic eruptions from \r internal climate variability in the years that follow them (Wigley et al., \r 2005). Based on ESM simulations, radiative feedbacks governing \r the global surface temperature response to volcanic eruptions can \r be substantially different than those governing long-term global \r warming (Merlis et al., 2014; Marvel et al., 2016; Ceppi and Gregory, \r [...]"


id: 1df22ec6a70f718672a798fa49e7eab9
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
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index: climate_gpt_v2
vector_id: 2458
content:
"[...] 2019). Estimates based on the response to volcanic eruptions agree \r with other lines of evidence (Knutti et  al., 2017), but they do not \r constitute a direct estimate of ECS (high confidence). The 'emergent \r constraints' on ECS based on climate variability, including volcanic \r eruptions, are summarized in Section 7.5.4.1."


id: 2988c8a697fa10b5039eb1f30f9537bb
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
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index: climate_gpt_v2
vector_id: 4526
content:
"Evidence from the instrumental temperature record, including \r estimates using global energy budget changes (Section  7.5.2.1), \r climate emulators (Section 7.5.2.2), variability in the TOA radiation \r budget (Section  7.5.2.3), and the climate response to volcanic \r eruptions (Section 7.5.2.4) produce median ECS estimates that range \r between 2.5degC and 3.5degC, but a best estimate value cannot be given \r owing to a strong dependence on assumptions about how radiative \r feedbacks will change in the future. However, there is robust \r evidence and high agreement across the lines of evidence that ECS is \r extremely likely greater than 1.6degC (high confidence). There is robust \r evidence and medium agreement across the lines of evidence that \r ECS is very likely greater than 1.8degC and likely greater than 2.2degC \r (high confidence). These ranges of ECS correspond to estimates \r based on historical global energy budget constraints (Section 7.5.2.1) \r [...]"


id: 16694639d5b0d9854e2ef1296cbd7989
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
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index: climate_gpt_v2
vector_id: 1160
content:
"[...] under the assumption of no feedback dependence on evolving SST \r patterns (i.e., a' = 0) and thus represent an underestimate of the \r true ECS ranges that can be inferred from this line of evidence (high \r confidence). Historical global energy budget changes do not provide "


id: 18ada90b7112e534d7ff41ff6f05f63d
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
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index: climate_gpt_v2
vector_id: 1556
content:
"(von  der  Heydt  et  al., 2014; Kohler et  al., 2015, 2017, 2018; \r Friedrich et al., 2016; Royer, 2016; Snyder, 2019; Stap et al., 2019; \r Friedrich and Timmermann, 2020; see Table  7.11). Compared to \r the LGM, uncertainties in the derived ECS from these periods are \r in general greater, due to greater uncertainty in global surface \r temperature (due to fewer individual sites with proxy temperature \r records), ice-sheet forcing (due to a  lack of detailed ice-sheet \r reconstructions), and CO2 forcing (for those studies that include \r the pre-ice-core period, where CO2 reconstructions are substantially \r more uncertain). Furthermore, accounting for varying orbital forcing \r in the traditional global mean forcing and response energy budget \r framework (Box 7.1) is challenging (Schmidt et al., 2017b), due to \r seasonal and latitudinal components of the forcing that, despite \r a close-to-zero orbital forcing in the global annual mean, can directly \r [...]"


id: 690675b187467eb46dbd1a4736aa1ad6
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vector_id: 15747
content:
"[...] result in responses in annual mean global surface temperature \r (Liu et al., 2014), ice volume (Abe-Ouchi et al., 2013), and feedback \r processes such as those associated with methane (Singarayer et al., \r 2011). In addition, for time periods in which the forcing relative to \r the modern era is small (interglacials), the inferred ECS has relatively \r large uncertainties because the forcing and temperature response \r (DF and DT in Equation 7.1, Box 7.1) are both close to zero."


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"The LGM (Cross-Chapter Box 2.1) has been used to provide estimates \r of ECS (see Table 7.11 for estimates since AR5; Sherwood et al., 2020; \r Tierney et al., 2020b). The major forcings and feedback processes that \r led to the cold climate at that time (e.g., CO2, non-CO2 greenhouse \r gases, and ice sheets) are relatively well-known (Section  5.1), \r orbital forcing relative to pre-industrial was negligible, and there \r are relatively high spatial resolution and well-dated paleoclimate \r temperature data available for this time period (Section  2.3.1). \r Uncertainties in deriving global surface temperature from the LGM \r proxy data arise partly from uncertainties in the calibration from the \r paleoclimate data to local annual mean surface temperature, and \r partly from uncertainties in the conversion of the local temperatures to \r an annual mean global surface temperature. Overall, the global mean \r LGM cooling relative to pre-industrial is assessed to be very likely\r [...]"


id: 1ffb1edfe989084b5b3255025329c2a4
created_at: 2023-04-19 13:22:38
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"[...] from 5 to 7 degC (Section 2.3.1). The LGM climate is often assumed to \r be in full equilibrium with the forcing, such that DN in Equation 7.1, \r Box 7.1, is zero. A calculation of sensitivity using solely CO2 forcing, \r and assuming that the LGM ice sheets were in equilibrium with that \r forcing, would give an Earth System Sensitivity (ESS) rather than an \r ECS (see Box 7.1). In order to calculate an ECS, which is defined here \r to include all feedback processes except ice sheets, the approach \r of Rohling et  al. (2012) can be used. This approach introduces an \r additional forcing term in Equation 7.1, Box 7.1, that quantifies the \r resulting forcing associated with the ice-sheet feedback (primarily \r an estimate of the radiative forcing associated with the change in \r surface albedo). However, differences between studies as to which \r processes are considered as forcings (for example, some studies \r also include vegetation and/or aerosols, such as dust, as forcings), \r [...]"


id: 97fb9546c45c384eb84e10c8f533b663
created_at: 2023-04-19 13:22:38
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"[...] means that published estimates are not always directly comparable. \r Additional uncertainty arises from the magnitude of the ice-sheet \r forcing itself (Stap et  al., 2019; Zhu and Poulsen, 2021), which is \r often estimated using ESMs. Furthermore, the ECS at the LGM may \r differ from that of today due to state-dependence (Section  7.4.3). \r Here, only studies that report values of ECS that have accounted for \r the long-term feedbacks associated with ice sheets, and therefore \r most closely estimate ECS as defined in this chapter, are assessed \r here (Table 7.11)."


id: 9ec8b48bd5497f603cc55e758418bc35
created_at: 2023-04-19 13:22:38
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index: climate_gpt_v2
vector_id: 25156
content:
"In the pre-Quaternary (prior to about 2.5 million years ago), \r the forcings and response are generally of the same sign and \r similar magnitude as future projections of climate change (Burke \r et  al., 2018; Tierney et  al., 2020a). Similar uncertainties as for \r the LGM apply, but in this case a major uncertainty relates to the \r forcing, because prior to the ice-core record there are only indirect \r estimates of CO2 concentration. However, advances in pre-ice-core \r CO2 reconstruction (e.g.,  Foster and Rae, 2016; Super et  al., 2018; \r Witkowski et al., 2018) mean that the estimates of pre-Quaternary \r CO2 have less uncertainty than at the time of AR5, and these time \r periods can now contribute to an assessment of climate sensitivity \r (Table 7.11). The mid-Pliocene Warm Period (MPWP; Cross-Chapter \r Box 2.1 and Cross-Chapter Box 2.4) has been targeted for constraints \r on ECS (Martinez-Boti et al., 2015; Sherwood et al., 2020), due to \r [...]"


id: b409ff710939cb338153fe192d4399f0
created_at: 2023-04-19 13:22:38
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content:
"[...] the fact that CO2 concentrations were relatively high at this time \r (350-425  ppm) and because the MPWP is sufficiently recent that \r topography and continental configuration are similar to modern-day. \r As such, a comparison of the MPWP with the pre-industrial climate \r provides probably the closest natural geological analogue for the \r modern day that is useful for assessing constraints on ECS, despite \r the effects of different geographies not being negligible (global \r surface temperature patterns; ocean circulation). Furthermore, \r the global surface temperature of the MPWP was such that non\u0002linearities in feedbacks (Section 7.4.3) were relatively modest. Within \r the MPWP, the KM5c interglacial has been identified as a particularly \r useful time period for assessing ECS (Haywood et al., 2013, 2016b) \r because Earth's orbit during that time was very similar to that of the \r modern day."


id: 54dd3e186004cb8148d32c5c6e8a883c
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
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index: climate_gpt_v2
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content:
"lines of paleoclimate evidence regarding ECS, from differing past \r time periods: LGM (Sherwood et  al., 2020; Tierney et  al., 2020b); \r glacial-interglacial (Royer, 2016; Kohler et  al., 2017; Snyder, \r 2019; Friedrich and Timmermann, 2020); Pliocene (Martinez-Boti \r et  al., 2015; Sherwood et  al., 2020); and the Eocene (Anagnostou \r et  al.,  2016, 2020; Shaffer et  al., 2016; Inglis et  al., 2020), with \r differing proxies for estimating forcing (e.g.,  CO2 from ice cores \r or boron isotopes) and response (e.g.,  global surface temperature \r from d18O, Mg/Ca or Antarctic dD). Furthermore, although different \r studies have uncertainty estimates that account for differing sources \r of uncertainty, some studies (Snyder, 2019; Inglis et  al., 2020; \r Sherwood et al., 2020; Tierney et al., 2020b) do consider many of the \r uncertainties discussed in Sections 7.5.3.1-7.5.3.3. All the studies \r based on glacial-interglacial cycles account for some aspects of the \r [...]"


id: 7bb83fb4f67d0076f5a66f3e08859714
created_at: 2023-04-19 13:22:38
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content:
"[...] state-dependence of climate sensitivity (Section 7.4.3) by considering \r only the warm phases of the Pleistocene, although what constitutes \r a warm phase is defined differently across the studies."


id: 69df7a153e344d5b385f00b2e70df8c0
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
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content:
"the sum of all feedbacks, and furthermore unlike constraints on the \r instrumental record they are based on climate states that are at, or \r close to, equilibrium. So far, these emergent constraints have been \r limited to the Last Glacial Maximum (LGM; Cross-Chapter Box 2.1) \r cooling (Hargreaves et al., 2012; Schmidt et al., 2014; Renoult et al., \r 2020) and warming in the mid-Pliocene Warm Period (MPWP; \r Cross-Chapter Box 2.1 and Cross-Chapter Box 2.4; Hargreaves and \r Annan, 2016; Renoult et al., 2020) due to the availability of sufficiently \r large multi-model ensembles for these two cases. The paleoclimate \r emergent constraints are limited by structural uncertainties in the \r proxy-based global surface temperature and forcing reconstructions \r (Section  7.5.3), possible differences in equilibrium sea surface \r temperature patterns between models and the real world, and \r a small number of model simulations participating, which has led to \r [...]"


id: 92562a227d9e9a5fbfcd7580b4e164dc
created_at: 2023-04-19 13:22:38
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content:
"[...] divergent results. For example, Hopcroft and Valdes (2015) repeated \r the study based on the LGM by Hargreaves et  al. (2012) using \r another model ensemble and found that the emergent constraint \r was not robust, whereas studies using multiple available ensembles \r retain useful constraints (Schmidt et al., 2014; Renoult et al., 2020). \r Also, the results are somewhat dependent on the applied statistical \r methods (Hargreaves and Annan, 2016). However, Renoult et  al. \r (2020) explored this and found 95th percentiles of ECS below 6degC for \r LGM and Pliocene individually, regardless of statistical approach, and \r by combining the two estimates the 95th percentile dropped to 4.0degC. \r The consistency between the cold LGM and warm MPWP emergent \r constraint estimates increases confidence in these estimates, and \r further suggests that the dependence of feedback on climate mean \r state (Section 7.4.3) as represented in PMIP models used in these \r studies is reasonable."


id: ac929eaea85d328e5169146cfcb577b0
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vector_id: 27604
content:
"A number of considerations must be taken into account when \r assessing the diverse literature on ECS and TCR emergent constraints. \r For instance, it is important to have physical and theoretical bases \r for the connection between the observable and modelled ECS or \r TCR since in model ensembles thousands of relationships that pass \r statistical significance can be found simply by chance (Caldwell \r et al., 2014). It is also important that the underlying model ensemble \r does not exhibit a shared bias that influences the simulation of the \r observable quantity on which the emergent constraint is based. \r Also, correctly accounting for uncertainties in both the observable \r (including measurement uncertainty and natural variability) and \r the emergent constraint statistical relationship can be challenging, \r in particular in cases where the latter is not expected to be linear \r (Annan et  al., 2020). A  number of proposed emergent constraints \r [...]"


id: aa2ec5fa34ec079f53163aece49d07a6
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
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index: climate_gpt_v2
vector_id: 27163
content:
"[...] leverage variations in modelled ECS arising from tropical low-clouds, \r which was the dominant source of inter-model spread in the CMIP5 \r ensemble used in most emergent constraint studies. Since ECS is \r dependent on the sum of individual feedbacks (Section 7.5.1) these \r studies implicitly assume that all other feedback processes in models \r are unbiased and should therefore rather be thought of as constraints \r on tropical low-cloud feedback (Klein and Hall, 2015; Qu et al., 2018; \r Schlund et al., 2020). The following sections go through a range of \r emergent constraints and assess their strengths and limitations."


id: c78efda7a3a3670660a7f23bd1c8dfad
created_at: 2023-04-19 13:22:38
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index: climate_gpt_v2
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content:
"Various emergent constraint approaches using global warming over \r the instrumental record have been proposed. These benefit from more \r accurate data compared with paleoclimates, but suffer from the fact \r that the climate is not in equilibrium, thereby assuming that ESMs \r on average accurately depict the ratio of short-term to long-term \r global warming. Global warming in climate models over 1850 to the \r present day exhibits no correlation with ECS, which is partly due to \r a  substantial number of models exhibiting compensation between \r a high climate sensitivity with strong historical aerosol cooling (Kiehl, \r 2007; Forster et al., 2013; Nijsse et al., 2020). However, the aerosol \r cooling increased up until the 1970s, when air quality regulations \r reduced the emissions from Europe and North America whereas \r other regions saw increases resulting in a subsequently reduced pace \r of global mean aerosol ERF increase (Section 2.2.8 and Figure 2.10). \r [...]"


id: 7b40d703d569f365a3723ea713b58f5e
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content:
"[...] Energy balance considerations over the 1970-2010 period gave \r a  best estimate ECS of 2.0degC (Bengtsson and Schwartz, 2013), \r however this estimate did not account for pattern effects. To address \r this limitation an emergent constraint on 1970-2005 global warming \r was demonstrated to yield a  best estimate ECS of 2.83 [1.72 to \r 4.12] degC (Jimenez-de-la-Cuesta and Mauritsen, 2019). The study \r was followed up using CMIP6 models yielding a best estimate ECS \r of 2.6 [1.5 to 4.0] degC based on 1975-2019 global warming (Nijsse \r et  al., 2020), thereby confirming the emergent constraint. Internal \r variability and forced or unforced pattern effects may influence the \r results (Jimenez-de-la-Cuesta and Mauritsen, 2019; Nijsse et  al., \r 2020). For instance the Atlantic Multi-decadal Oscillation changed \r from negative to positive anomaly, while the Indo-Pacific Oscillation "


id: 45db73d85e27d9e743685cd78e34e0a1
created_at: 2023-04-19 13:22:38
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content:
"A substantial number of emergent constraint studies focus on \r observables that are related to tropical low-cloud feedback processes \r (Volodin, 2008; Sherwood et al., 2014; Zhai et al., 2015; Brient and \r Schneider, 2016; Brient et al., 2016). These studies yield median ECS \r estimates of 3.5degC-4degC and in many cases indicate low likelihoods \r of values below 3degC. The approach has attracted attention since \r most of the spread in climate sensitivity seen in CMIP5, and earlier \r climate model ensembles, arises from uncertainty in low-cloud \r feedbacks (Bony and Dufresne, 2005; Wyant et  al., 2006; Randall \r et al., 2007; Vial et al., 2013). Nevertheless, this approach assumes \r that all other feedback processes are unbiased (Klein and Hall, 2015; \r Qu et al., 2018; Schlund et al., 2020), for instance the possibly missing \r negative anvil area feedback or the possibly exaggerated mixed\u0002phase cloud feedback (Section 7.4.2.4). Thus, the subset of emergent \r [...]"


id: 965b5512a019e747aec1235b95e759cd
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content:
"[...] constraints that focus on low-level tropical clouds are not necessarily \r inconsistent with other emergent constraints of ECS. Related \r emergent constraints that focus on aspects of the tropical circulation \r and ECS have led to conflicting results (Su et al., 2014; Tian, 2015; \r Lipat et  al., 2017; Webb and Lock, 2020), possibly because these \r processes are not the dominant factors in causing the inter-model \r spread (Caldwell et al., 2018)."


id: c3b0252598f156d708770920d716c7c2
created_at: 2023-04-19 13:22:38
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content:
"Lagged correlations present in short-term variations in the global surface \r temperature can be linked to ECS through the fluctuation-dissipation \r theorem, which is derived from a single heat-reservoir model (Einstein, \r 1905; Hasselmann, 1976; Schwartz, 2007; Cox et al., 2018a). From this \r it follows that the memory carried by the heat capacity of the ocean \r results in low-frequency global temperature variability (red noise) \r arising from high-frequency (white noise) fluctuations in the radiation \r balance, for example, caused by weather. Initial attempts to apply the \r theorem to observations yielded a fairly low median ECS estimate of \r 1.1degC (Schwartz, 2007), a result that was disputed (Foster et al., 2008; \r Knutti et al., 2008). Recently it was proposed by Cox et al. (2018a) to use \r variations in the historical experiments of the CMIP5 climate models as \r an emergent constraint giving a median ECS estimate of 2.8 [1.6 to \r [...]"


id: 24eedbcca17d33b111d4fedd8dcb2d36
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"[...] 4.0] degC. A  particular challenge associated with these approaches is \r to separate short-term from long-term variability, and slightly arbitrary \r choices regarding the methodology of separating these in the global \r surface temperature from long-term signals in the historical record, \r omission of the more strongly forced period after 1962, as well as input \r data choices, can lead to median ECS estimates ranging from 2.5degC to \r 3.5degC (Brown et al., 2018; Po-Chedley et al., 2018a; Rypdal et al., 2018). \r Calibrating the emergent constraint using CMIP5 modelled internal \r variability as measured in historical control simulations (Po-Chedley \r et  al., 2018a) will inevitably lead to an overestimated ECS due to \r externally forced short-term variability present in the historical record \r (Cox et  al., 2018b). Contrary to constraints based on paleoclimates \r or global warming since the 1970s, when based on CMIP6 models \r [...]"


id: fd33eb01c08a94278531c51fd97ceac5
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content:
"[...] a  higher, yet still well-bounded ECS estimate of 3.7 [2.6 to 4.8] degC \r is obtained (Schlund et al., 2020). A more problematic issue is raised \r by Annan et  al. (2020) who showed that the upper bound on ECS \r estimated this way is less certain when considering deep-ocean heat \r uptake. In conclusion, even if not inconsistent, these limitations prevent \r us from directly using this type of constraint in the assessment."


id: a075839f8a84e13c4fa2034aad81cec4
created_at: 2023-04-19 13:22:38
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"(Gillett et al., 2013), and updated by Schurer et al. (2018). A related \r data-assimilation-based approach that accounted also for uncertainty \r in response patterns gave 1.33degC-2.36degC (Ribes et al., 2021), but is \r dependent on the choice of prior ensemble distribution (CMIP5 or \r CMIP6). Another study used the response to the Pinatubo volcanic \r eruption to obtain a  range of 0.8degC-2.3degC (Bender et  al., 2010). \r A tighter range, notably at the lower end, was found in an emergent \r constraint focusing on the post-1970s warming exploiting the lower \r spread in aerosol forcing change over this period (Jimenez-de-la\u0002Cuesta and Mauritsen, 2019). Their estimate was 1.67  [1.17 to \r 2.16] degC. Two studies tested this idea: Tokarska et al. (2020) estimates \r TCR was 1.60 [0.90 to 2.27] degC based on CMIP6 models, whereas \r Nijsse et al. (2020) found 1.68 [1.0 to 2.3] degC. In both cases there was \r a small sensitivity to choice of ensemble, with CMIP6 models yielding \r [...]"


id: 23f46e22802d4faf989613b08b28820c
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vector_id: 3509
content:
"[...] slightly lower values and ranges. Combining these studies gives a best \r estimate of 1.7degC and a very likely range of TCR of 1.1 to 2.3 degC with \r high confidence."


id: 29bed76887ef48ad591311f8a8be2fe2
created_at: 2023-04-19 13:22:38
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content:
"In the first group of emergent constraints there is broad agreement \r on the best estimate of ECS ranging from 2.4degC-3.3degC. At the lower \r end, nearly all studies find lower bounds (5th percentiles) around \r 1.5degC, whereas several studies indicate 95th percentiles as low \r as 4degC. Considering both classes of studies, none of them yield \r upper very likely bounds above 5degC. Since several of the emergent \r constraints can be considered nearly independent one could assume \r that emergent constraints provide very strong evidence on ECS by \r combining them. Nevertheless, this is not done here because there \r are sufficient cross-dependencies, as for instance models are re-used \r in many of the derived emergent constraints, and furthermore the \r methodology has not yet reached a sufficient level of maturity since \r systematic biases may not have been accounted for. Uncertainty is \r therefore conservatively added to reflect these potential issues. This \r [...]"


id: 784571102acd7424f21289f1b4b0090d
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"[...] leads to the assessment that ECS inferred from emergent constraints \r is very likely 1.5 to 5 degC with medium confidence."


id: a4e827bf6c71f764982426b55fe99309
created_at: 2023-04-19 13:22:38
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vector_id: 26232
content:
"considered. As in earlier reports, a 0.5degC precision is used. Starting \r with the very likely lower bound, there is broad support for a value of \r 2.0degC, including process understanding and the instrumental record \r (Table 7.13). For the very likely upper bound, emergent constraints \r give a  value of 5.0degC whereas the three other lines of evidence \r are individually less tightly constrained. Nevertheless, emergent \r constraints are a relatively recent field of research, in part taken into \r account by adding uncertainty to the upper bound (Section 7.5.4.3), \r and the underlying studies use, to a varying extent, information that \r is also used in the other three lines of evidence, causing statistical \r dependencies. However, omitting emergent constraints and \r statistically combining the remaining lines of evidence likewise yields \r 95th percentiles close to 5.0degC (Sherwood et al., 2020). Information \r for the likely range is partly missing or one-sided, however it must \r [...]"


id: 1a8cdb43c65ef20257d33603f0805f74
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
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index: climate_gpt_v2
vector_id: 1874
content:
"[...] necessarily reside inside the very likely range and is therefore \r supported by evidence pertaining to both the likely and very likely\r ranges. Hence, the upper likely bound is assessed to be about halfway \r between the best estimate and the upper very likely bound while \r the lower likely bound is assessed to be about halfway between the \r best estimate and the lower very likely bound. In summary, based on \r multiple lines of evidence the best estimate of ECS is 3degC, it is likely\r within the range 2.5 to 4 degC and very likely within the range 2 to 5 degC. \r It is virtually certain that ECS is larger than 1.5degC. Whereas there is \r high confidence based on mounting evidence that supports the best \r estimate, likely range and very likely lower end, a higher ECS than \r 5degC cannot be ruled out, hence there is medium confidence in the \r upper end of the very likely range. Note that the best estimate of ECS \r made here corresponds to a feedback parameter of -1.3 W m-2 degC-1\r [...]"


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content:
"[...] which is slightly more negative than the feedback parameter from \r process-based evidence alone that is assessed in Section 7.4.2.7."


id: 747000be077b2263c26fbee393b4f2c2
created_at: 2023-04-19 13:22:38
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vector_id: 17719
content:
"The AR5 assessed ECS to have a  likely range from 1.5 to 4.5 degC \r (M. Collins et al., 2013) based on the majority of studies and evidence \r available at the time. The broader evidence base presented in this \r Report and the general agreement among different lines of evidence \r means that they can be combined to yield a narrower range of ECS \r values. This can be done formally using Bayesian statistics, though \r such a  process is complex and involves formulating likelihoods \r and priors (Annan and Hargreaves, 2006; Stevens et  al., 2016; \r Sherwood et al., 2020). However, it can be understood that if two \r lines of independent evidence each give a  low probability of an \r outcome being true, for example, that ECS is less than 2.0degC, then \r the combined probability that ECS is less than 2.0degC is lower than \r that of either line of evidence. On the contrary, if one line of evidence \r is unable to rule out an outcome, but another is able to assign a low \r [...]"


id: 1b3861b528ccb1939c2fc495acef9e54
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vector_id: 1977
content:
"[...] probability, then there is a low probability that the outcome is true \r (Stevens et al., 2016). This general principle applies even when there \r is some dependency between the lines of evidence (Sherwood et al., \r 2020), for instance between historical energy budget constraints \r (Section  7.5.2.1) and those emergent constraints that use the \r historically observed global warming (Section 7.5.4.1). Even in this \r case the combined constraint will be closer to the narrowest range \r associated with the individual lines of evidence."


id: cb1bc9b6dee9382fcc2ee916354f392a
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
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index: climate_gpt_v2
vector_id: 33072
content:
"arguing that the atmosphere was already saturated in infrared \r absorption such that adding more CO2 would not lead to warming. \r The assertion of Angstrom was understood half a century later to be \r incorrect. History has seen a multitude of studies (e.g., Svensmark, \r 1998; Lindzen et al., 2001; Schwartz, 2007) mostly implying lower \r ECS than the range assessed as very likely here. However, there are \r also examples of the opposite, such as very large ECS estimates based \r on the Pleistocene records (Snyder, 2016), which have been shown to \r be overestimated due to a lack of accounting for orbital forcing and \r long-term ice-sheet feedbacks (Schmidt et al., 2017b), or suggestions \r that global climate instabilities may occur in the future (Steffen et al., \r 2018; Schneider et  al., 2019). There is, however, no evidence for \r unforced instabilities of such magnitude occurring in the paleo-record \r temperatures of the past 65 million years (Westerhold et al., 2020), \r [...]"


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vector_id: 21281
content:
"[...] possibly short of the Paleocene-Eocene Thermal Maximum (PETM) \r excursion (Section 5.3.1.1) that occurred at more than 10degC above \r present-day levels (Anagnostou et  al., 2020). Looking back, the \r resulting debates have led to a deeper understanding, strengthened \r the consensus, and have been scientifically valuable."


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"less than ECS (see the introduction to Section  7.5). Furthermore, \r estimates of TCR from the historical record are not as strongly \r influenced by externally forced surface temperature pattern effects \r as estimates of ECS are since both historical transient warming and \r TCR are affected by this phenomenon (Section  7.4.4). A  slightly \r higher weight is given to instrumental record warming and emergent \r constraints since these are based on observed transient warming, \r whereas the process-understanding estimate relies on pattern \r effects and ocean heat uptake efficiency from ESMs to represent \r the transient dampening effects of the ocean. If these effects are \r underestimated by ESMs then the resulting TCR would be lower. \r Given the interdependencies of the other two lines of evidence, \r a  conservative approach to combining them as reflected in the \r assessment is adopted. Since uncertainty is substantially lower than \r [...]"


id: afeb263107ec19f6665d60bad9b67cc2
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 28189
content:
"[...] in AR5 a 0.1degC precision is therefore used here. Otherwise the same \r methodology for combining the lines of evidence as applied to ECS \r is used for TCR. Based on process understanding, warming over the \r instrumental record and emergent constraints, the best estimate TCR \r is 1.8degC, it is likely 1.4 to 2.2 degC and very likely 1.2 to 2.4 degC. The \r assessed ranges are all assigned high confidence due to the high \r level of agreement among the lines of evidence."


id: 780cf0d7f09bbea097a567212cc0f881
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 18330
content:
"In the climate sciences, there are often good reasons to consider \r representing deep uncertainty, or what are sometimes referred to as \r 'unknown unknowns'. This is natural in a field that considers a system \r that is both complex and at the same time challenging to observe. \r For instance, since emergent constraints represent a relatively new \r line of evidence, important feedback mechanisms may be biased \r in process-level understanding; pattern effects and aerosol cooling \r may be large; and paleo evidence inherently builds on indirect and \r incomplete evidence of past climate states, there certainly can be \r valid reasons to add uncertainty to the ranges assessed on individual \r lines of evidence. This has indeed been addressed throughout \r Sections  7.5.1-7.5.4. Since it is neither probable that all lines of \r evidence assessed here are collectively biased nor is the assessment \r sensitive to single lines of evidence, deep uncertainty is not considered \r [...]"


id: a4253eee9bdedeae3b7d18647e4bc6bf
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 26074
content:
"Coupled climate models, such as those participating in CMIP, have \r long played a central role in assessments of ECS and TCR. In reports \r up to and including the IPCC Third Assessment Report (TAR), climate \r sensitivities derived directly from ESMs were the primary line of \r evidence. However, since AR4, historical warming and paleoclimate \r information provided useful additional evidence and it was noted \r that assessments based on models alone were problematic (Knutti, \r 2010). As new lines of evidence have evolved, in AR6 various \r numerical models are used where they are considered accurate, or \r in some cases the only available source of information, and thereby \r support all four lines of evidence (Sections 7.5.1-7.5.4). However, \r AR6 differs from previous IPCC reports in excluding direct estimates \r of ECS and TCR from ESMs in the assessed ranges (Section 7.5.5), \r following several recent studies (Annan and Hargreaves, 2006; \r [...]"


id: 3cb4cc42fb4f382afef23a3ed7ad6bfc
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 7862
content:
"[...] Stevens et  al., 2016; Sherwood et  al., 2020). The purpose of this \r section is to explain why this approach has been taken and to \r provide a perspective on the interpretation of the climate sensitivities \r exhibited in CMIP6 models."


id: e739873194ec5441efac6596ed91b577
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 37977
content:
"cases entire sub-model systems are shared and slightly modified. \r Therefore, models cannot be considered independent developments, \r but rather families of models with interdependencies (Knutti et al., \r 2013). It is therefore difficult to interpret the collection of models \r (Knutti, 2010), and it cannot be ruled out that there are common \r limitations and therefore systematic biases to model ensembles \r that are reflected in the distribution of ECS as derived from them. \r Although ESMs are typically well-documented, in ways that \r increasingly include information on critical decisions regarding tuning \r (Mauritsen et al., 2012; Hourdin et al., 2017; Schmidt et al., 2017a; \r Mauritsen  and Roeckner, 2020), the full history of development \r decisions could involve both process-understanding and sometimes \r also other information such as historical warming. As outlying or \r poorly performing models emerge from the development process, \r [...]"


id: 75e0e788be1900f3903a1bd80d0a3855
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 17967
content:
"[...] they can become re-tuned, reconfigured or discarded and so might \r not see publication (Hourdin et al., 2017; Mauritsen and Roeckner, \r 2020). In the process of addressing such issues, modelling groups \r may, whether intentionally or not, modify the modelled ECS."


id: 28a6eb4b7356cd1cb878d5fcbfbac07a
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 4364
content:
"The ECS of a model is the net result of the model's effective radiative \r forcing from a doubling of CO2 and the sum of the individual feedbacks \r and their interactions. It is well known that most of the model spread \r in ECS arises from cloud feedbacks, and particularly the response of \r low-level clouds (Bony and Dufresne, 2005; Zelinka et al., 2020). Since \r these clouds are small-scale and shallow, their representation in climate \r models is mostly determined by sub-grid-scale parametrizations. \r It is sometimes assumed that parametrization improvements will \r eventually lead to convergence in model response and therefore \r a decrease in the model spread of ECS. However, despite decades of \r model development, increases in model resolution and advances in \r parametrization schemes, there has been no systematic convergence \r in model estimates of ECS. In fact, the overall inter-model spread \r in ECS for CMIP6 is larger than for CMIP5; ECS and TCR values are \r [...]"


id: b746fedd0c4e9055c46590b8ba1856eb
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 29461
content:
"[...] given for CMIP6 models in Supplementary Material 7.SM.4 based \r on Schlund et al. (2020) for ECS and Meehl et al. (2020) for TCR (see \r also Figure  7.18 and FAQ 7.3). The upward shift does not apply to \r all models traceable to specific modelling centres, but a substantial \r subset of models have seen an increase in ECS between the two model \r generations. The increased ECS values, as discussed in Section 7.4.2.8, \r are partly due to shortwave cloud feedbacks (Flynn and Mauritsen, \r 2020) and it appears that in some models extra-tropical clouds with \r mixed ice and liquid phases are central to the behaviour (Zelinka et al., \r 2020), probably borne out of a recent focus on biases in these types of \r clouds (McCoy et al., 2016; Tan et al., 2016). These biases have recently \r been reduced in many ESMs, guided by process understanding from \r laboratory experiments, field measurements and satellite observations \r (Lohmann and Neubauer, 2018; Bodas-Salcedo et al., 2019; Gettelman \r [...]"


id: 86c3cae85cab2c0b400884c2171330e6
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 20875
content:
"[...] et  al., 2019). However, this and other known model biases are \r already factored into the process-level assessment of cloud feedback \r (Section 7.4.2.4), and furthermore the emergent constraints used here \r focus on global surface temperature change and are therefore less \r susceptible to shared model biases in individual feedback parameters \r than emergent constraints that focus on specific physical processes \r (Section 7.5.4). The high values of ECS and TCR in some CMIP6 models \r lead to higher levels of surface warming than CMIP5 simulations and \r also the AR6 projections based on the assessed ranges of ECS, TCR \r and ERF (Box 4.1 and FAQ 7.3; Forster et al., 2020).\r It is generally difficult to determine which information enters the \r formulation and development of parametrizations used in ESMs. \r Climate models frequently share code components, and in some "


id: 2cc6e13539ffdf55f79e86d6f548bb55
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 5144
content:
"It is problematic and not obviously constructive to provide weights \r for, or rule out, individual CMIP6 model ensemble members based \r solely on their ECS and TCR values. Rather these models must be \r tested in a like-with-like way against observational evidence. Based \r on the currently published CMIP6 models we provide such an analysis, \r marking models with ECS above and below the assessed very likely\r range (Figure 7.19). In the long-term historical warming (Figure 7.19a) \r both low- and high-ECS models are able to match the observed \r warming, presumably in part as a  result of compensating aerosol \r cooling (Kiehl, 2007; Forster et al., 2013; Wang et al., 2021). In several \r cases of high ECS models that apply strong aerosol cooling it is found \r to result in surface warming and ocean heat uptake evolutions that \r are inconsistent with observations (Golaz et al., 2019; Andrews et al., \r 2020; Winton et  al., 2020). Modelled warming since the 1970s is \r [...]"


id: 17262c44f21d88cf5a5fc10e998f09e7
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 1287
content:
"[...] less influenced by compensation between climate sensitivity and \r aerosol cooling (Jimenez-de-la-Cuesta and Mauritsen, 2019; Nijsse \r et  al., 2020) resulting in the high-ECS models in general warming \r more than observed, whereas low-sensitivity models mostly perform \r better (Figure 7.19b); a result that may also have been influenced by \r temporary pattern effects (Sections 7.4.4 and 7.5.4). Paleoclimates \r are not influenced by such transient pattern effects, but are limited by \r structural uncertainties in the proxy-based temperature and forcing \r reconstructions as well as possible differences in equilibrium sea \r surface temperature patterns between models and the real world \r (Section 7.5.4). Across the LGM, MPWP and EECO (Figure 7.19c-e), \r the few high-ECS models that simulated these cases were outside the \r observed very likely ranges (see also Feng et al., 2020; Renoult et al., \r 2020; Zhu et al., 2020). Also the low-ECS model is either outside or \r [...]"


id: 4d8523d0c074548f1fc3295a0c0f1dc0
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 10920
content:
"Figure 7.20 | Contributions of effective radiative forcing, ocean heat uptake and radiative feedbacks to global atmospheric energy input and near-surface \r air temperature change at year 100 of abrupt4xCO2 simulations of CMIP6 models. (a) The energy flux to the global atmosphere associated with the effective CO2\r forcing, global ocean heat uptake, Planck response, and radiative feedbacks, which together sum to zero. The inset shows energy input from individual feedbacks, summing \r to the total feedback energy input. (b) Contributions to net global warming are calculated by dividing the energy inputs by the magnitude of the global Planck response \r (3.2 W m-2 degC-1), with the contributions from radiative forcing, ocean heat uptake, and radiative feedbacks (orange bars) summing to the value of net warming (grey bar). The \r [...]"


id: af7aa1f167e8b2aa92f218d60a1315d9
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 28105
content:
"[...] inset shows warming contributions associated with individual feedbacks, summing to the total feedback contribution. Uncertainties show the interquartile range (25th and \r 75th percentiles) across models. Radiative kernel methods (see Section 7.4.1) were used to decompose the net energy input from radiative feedbacks into contributions from \r changes in atmospheric water vapour, lapse rate, clouds, and surface albedo (Zelinka et al. (2020) using the Huang et al. (2017) radiative kernel). The CMIP6 models included \r are those analysed by Zelinka et al. (2020) and the warming contribution analysis is based on that of Goosse et al. (2018). Further details on data sources and processing are \r available in the chapter data table (Table 7.SM.14)."


id: c9f97afc22678f4d1f3aee9ca5214867
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 32862
content:
"evaluate the effect of each of these processes on surface changes \r within coupled ESM simulations by diagnosing so-called 'warming \r contributions' (Dufresne and Bony, 2008; Crook et  al., 2011; Feldl \r and Roe, 2013; Vial et al., 2013; Pithan and Mauritsen, 2014; Goosse \r et  al., 2018). By construction, the individual warming contributions \r sum to the total global surface warming (Figure 7.20b). For long-term \r warming in response to CO2 forcing in CMIP5 models, the energy \r added to the climate system by radiative feedbacks is larger than the \r ERF of CO2 (Figure 7.20a), implying that feedbacks more than double \r the magnitude of global warming (Figure  7.20b). Radiative kernel \r methods (see Section 7.4.1) can be used to decompose the net energy \r input from radiative feedbacks into its components. The water-vapour, \r cloud and surface-albedo feedbacks enhance global warming, while \r the lapse-rate feedback reduces global warming. Ocean heat uptake \r [...]"


id: ce0e89c3207346c007cb88b7ae21aa14
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 33582
content:
"Interactions between different feedbacks within the coupled \r climate system pose a challenge to our ability to understand global \r warming and its uncertainty based on energy budget diagnostics \r (Section 7.4.2). For example, water-vapour and lapse-rate feedbacks \r are correlated (Held and Soden, 2006) owing to their joint dependence \r on the spatial pattern of warming (Po-Chedley et  al., 2018b). \r Moreover, feedbacks are not independent of ocean heat uptake \r because the uptake and transport of heat by the ocean influences \r the SST pattern on which global feedbacks depend (Section 7.4.4.3). \r However, alternative decompositions of warming contributions that \r better account for correlations between feedbacks produce similar \r results (Caldwell et al., 2016). The key role of radiative feedbacks in \r governing the magnitude of global warming is also supported by the \r high correlation between radiative feedbacks (or ECS) and transient \r [...]"


id: b47131bfc85e175e824913ca94b3df24
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 28985
content:
"Another approach to evaluating the roles of forcing, feedbacks and \r ocean heat uptake in projected warming employs idealized energy \r balance models that emulate the response of ESMs, and which \r preserve the interactions between system components. One such \r emulator, used in Section  7.5.1.2, resolves the heat capacity of \r both the surface components of the climate system and the deep \r ocean (Held et  al., 2010; Geoffroy et  al., 2013a, b; Kostov et  al., \r 2014; Armour, 2017). Using this emulator, Geoffroy et al. (2012) find \r that: under an idealized 1% per year increase in atmospheric CO2, \r radiative feedbacks constitute the greatest source of uncertainty \r (about 60% of variance) in transient warming beyond several \r decades; ERF uncertainty plays a  secondary but important role in \r warming uncertainty (about 20% of variance) that diminishes beyond \r several decades; and ocean heat uptake processes play a minor role \r [...]"


id: c01aea466a29b468b69699a54d1c5092
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 31089
content:
"More computationally intensive approaches evaluate how the \r climate response depends on perturbations to key parameter  or \r structural choices within ESMs. Large 'perturbed parameter \r ensembles', wherein a  range of parameter settings associated with \r cloud physics are explored within atmospheric ESMs, produce a wide \r range of ECS due to changes in cloud feedbacks, but often produce \r unrealistic climate states (Joshi et al., 2010). Rowlands et al. (2012) \r generated an ESM perturbed-physics ensemble of several thousand \r members by perturbing model parameters associated with radiative \r forcing, cloud feedbacks and ocean vertical diffusivity (an important \r parameter for ocean heat uptake). After constraining the ensemble to \r have a reasonable climatology and to match the observed historical \r surface warming, they found a wide range of projected warming by \r the year 2050 under the SRES A1B scenario (1.4degC-3degC relative to \r [...]"


id: f0aeb73cffa8240d49a8f31ff5aa90b1
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 39715
content:
"[...] the 1961-1990 average) that is dominated by differences in cloud \r feedbacks. The finding that cloud feedbacks are the largest source \r of spread in the net radiative feedback has since been confirmed in \r perturbed parameter ensemble studies using several different ESMs \r (Gettelman et al., 2012; Tomassini et al., 2015; Kamae et al., 2016b; \r Rostron et al., 2020; Tsushima et al., 2020). By swapping out different \r versions of the atmospheric or oceanic components in a  coupled \r ESM, Winton et al. (2013) found that TCR and ECS depend on which \r atmospheric component was used (using two versions with different \r atmospheric physics), but that only TCR is sensitive to which oceanic \r component of the model was used (using two versions with different "


id: edeeb771d8f651d15926e28edfa2d2fa
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 39228
content:
"The physical emissions metrics described above are functions of time \r since typically the physical effects reach a peak and then decrease in \r the period after a pulse emission as the concentrations of the emitted \r compound decay. The value of the metrics can therefore be strongly \r dependent on the time horizon of interest. All relative metrics (GWP, \r GTP etc.) are also affected by the time dependence of the CO2 metrics \r in the denominator. Instantaneous or endpoint metrics quantify the \r change (e.g., in radiative forcing, global surface temperature, global \r mean sea level) at a particular time after the emission. These can be \r appropriate when the goal is to not exceed a  fixed target such as \r a temperature or global mean sea level rise at a specific time. Emissions \r metrics can also be integrated from the time of emission. The most \r common of these is the absolute global warming potential (AGWP), \r which is the integral of the AGFP. The physical effect is then in units \r [...]"


id: 54f40be7140e5f6abc06f3c5890c188
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 12217
content:
"[...] of forcing-years, degree-years or metre-years for forcing, temperature, \r or sea level rise, respectively. These can be appropriate for trying to \r reduce the overall damage potential when the effect depends on \r how long the change occurs for, not just how large the change is. \r The integrated metrics still depend on the time horizon, though for \r the shorter-lived compounds this dependence is somewhat smoothed \r by the integration. The integrated version of a metric is often denoted \r as iAGxx, although the integral of the forcing-based metric (iAGFP) \r is known as the AGWP. Both the endpoint and integrated absolute \r metrics for non-CO2 species can be divided by the equivalent for CO2\r to give relative emissions metrics (e.g., GWP (=iGFP), GTP, iGTP)."


id: 691553dcc80af6152a30df418cd29727
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 15755
content:
"Any agent that warms the surface perturbs the terrestrial and oceanic \r carbon fluxes (Sections 5.4.3 and 5.4.4), typically causing a net flux \r of CO2 into the atmosphere and hence further warming. This aspect is \r already included in the carbon cycle models that are used to generate \r the radiative effects of a  pulse of CO2 (Joos et  al., 2013), but was \r neglected for non-CO2 compounds in the conventional metrics so \r this introduces an inconsistency and bias in the metric values (Gillett \r and Matthews, 2010; MacDougall et al., 2015; Tokarska et al., 2018). \r A  simplistic account of the carbon cycle response was tentatively \r included in AR5 based on a single study (W.J. Collins et al., 2013). \r Since AR5 this understanding has been revised (Gasser et al., 2017b; \r Sterner and Johansson, 2017) using simple parametrized carbon cycle \r models to derive the change in CO2 surface flux for a unit temperature \r pulse as an impulse response function to temperature. In W.J. Collins \r [...]"


id: 14df1c44d1dc4d724a82e6f3721a264
created_at: 2023-04-19 13:22:38
updated_at: 2023-04-19 13:22:38
content_type: text
index: climate_gpt_v2
vector_id: 899
content:
"[...] et al. (2013) this response function was assumed to be simply a delta \r function, whereas the newer studies include a  more complete \r functional form accounting for subsequent re-uptake of CO2 after the \r removal of the temperature increase. Accounting for re-uptake has \r the effect of reducing the carbon-cycle responses associated with \r the metrics compared to AR5, particularly at large time horizons. The \r increase in any metric due to the carbon cycle response can be derived \r from the convolution of the global surface temperature response with \r the CO2 flux response to temperature and the equivalent metric for \r CO2 (Equation 7.SM.5.5 in the Supplementary Material). Including \r this response also increases the duration of the effect of short-lived \r GHGs on climate (Fu et al., 2020). An alternative way of accounting for \r the carbon cycle temperature response would be to incorporate it into \r the temperature response function (the response functions used here \r [...]"


id: a0e29c2e5193993b186abdc639399ecb
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 25530
content:
"[...] and given in Supplementary Material 7.SM.5.2 do not explicitly do \r this). If this were done, the correction could be excluded from both the \r CO2 and non-CO2 forcing responses as, in Hodnebrog et al. (2020a).\r Including the carbon cycle response for non-CO2 treats CO2 and \r non-CO2 compounds consistently and therefore we assess that its \r inclusion more accurately represents the climate effects of non-CO2"


id: 57f6da4be67b3f196a83cb2e548c5015
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 12736
content:
"species. There is high confidence in the methodology of using carbon \r cycle models for calculating the carbon cycle response. The magnitude \r of the carbon cycle response contributions to the emissions metrics \r varies by a factor of two between Sterner and Johansson (2017) and \r Gasser et al. (2017b). The central values are taken from Gasser et al. \r (2017b) as the OSCAR 2.2 model used is based on parameters derived \r from CMIP5 models, and the climate-carbon feedback magnitude is \r therefore similar to the CMIP5 multi-model mean (Arora et al., 2013; \r Lade et al., 2018). As values have only been calculated in two simple \r parametrized carbon cycle models the uncertainty is assessed to be \r +-100%. Due to there being few studies and a factor of two difference \r between them, there is low confidence that the magnitude of the \r carbon cycle response is within the higher end of this uncertainty \r range, but high confidence that the sign is positive. Carbon cycle \r [...]"


id: d236f2bd4af221efc0ee65b755ff1093
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 34337
content:
"[...] responses are included in all the metrics presented in Table 7.15 and \r Supplementary Table 7.SM.7. The carbon cycle contribution is lower \r than in AR5, but there is high confidence in the need for its inclusion \r and the method by which it is quantified."


id: 9dee70c7fd142a29622ad5bd335bc959
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 25019
content:
"Oxidation of methane leads ultimately to the net production of \r atmospheric CO2 (Boucher et al., 2009). This yield is less than 100% \r (on a molar basis) due to uptake by soils and some of the reaction \r products (mainly formaldehyde) being directly removed from the \r atmosphere before being completely oxidized. Estimates of the yield \r are 61% (Boucher et al., 2009) and 88% (Shindell et al., 2017), so \r the assessed range is 50-100% with a central value of 75% (low \r confidence). For methane and hydrocarbons from fossil sources, \r this will lead to additional fossil CO2 in the atmosphere whereas \r for biogenic sources of methane or hydrocarbons, this replaces CO2\r that has been recently removed from the atmosphere. Since the ratio \r of molar masses is 2.75, 1 kg of methane generates 2.1 +- 0.7 kgCO2\r for a 75% yield. For biogenic methane the soil uptake and removal of \r partially oxidized products is equivalent to a sink of atmospheric CO2 \r [...]"


id: 772fbf0a13ab410e49e050ea573794e6
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 18175
content:
"[...] of 0.7 +- 0.7 kg per kg methane. The contributions of this oxidation \r effect to the methane metric values allow for the time delay in the \r oxidation of methane. Methane from fossil fuel sources has therefore \r slightly higher emissions metric values than those from biogenic \r sources (high confidence). The CO2 can already be included in carbon \r emissions totals (Munoz and Schmidt, 2016) so care needs to be \r taken when applying the fossil correction to avoid double counting."


id: e0915874dba384eb02824ca2b7f6d33
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vector_id: 36762
content:
"Indirect contributions from chemical production or destruction of \r other GHGs are quantified in Chapter 6 (Section 6.4). For methane \r (CH4), AR5 (Myhre et al., 2013b) assessed that the contributions from \r effects on ozone and stratospheric water vapour add 50% +- 30% \r and 15% +- 11% to the emissions-based ERF, which were equivalent \r to 1.8 +- 0.7 x10-4 and 0.5 +- 0.4 x10-4 W m-2 ppb (CH4)\r -1. In AR6 \r the radiative efficiency formulation is preferred as it is independent \r of the assumed radiative efficiency for methane. The assessed \r contributions to the radiative efficiency for methane due to ozone \r are 1.4 +- 0.7 x10-4 W m-2 ppb (CH4)\r -1, based on 0.14 W  m-2\r forcing from a  1023 ppb (1850-2014) methane change (Thornhill \r et  al., 2021b). The contribution from stratospheric water vapour is \r 0.4 +- 0.4 x10-4 W m-2 ppb (CH4)\r -1, based on 0.05 W m-2 forcing from \r a 1137 ppb (1750-2019) methane change (Section 7.3.2.6). Nitrous \r [...]"


id: 2f3f881fcf24bf6939ef46cdfdf2471
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 5578
content:
"[...] oxide (N2O) depletes upper stratospheric ozone (a positive forcing) \r and reduces the methane lifetime. In AR5 the methane lifetime effect \r was assessed to reduce methane concentrations by 0.36 ppb per ppb \r increase in N2O, with no assessment of the effective radiative forcing \r from ozone. This is now increased to -1.7 ppb methane per ppb N2O \r (based on a methane lifetime decrease of 4% +- 4% for a 55 ppb \r increase in N2O (Thornhill et al., 2021b) and a radiative efficiency of \r 5.5 +- 0.4 x10-4 W m-2 ppb (N2O)-1 through ozone (Thornhill et al., \r 2021b)). In summary, GWPs and GTPs for methane and nitrous oxide \r are slightly lower than in AR5 (medium confidence) due to revisions \r in their lifetimes and updates to their indirect chemical effects."


id: 795acff9c5e6a00c0fc369924a1ec3d7
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
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index: climate_gpt_v2
vector_id: 18547
content:
"Since AR5 there have been developments in how to account for \r the different behaviours of short-lived and long-lived compounds. \r Pulse-based emissions metrics for short-lived GHGs with lifetimes \r less than 20 years are very sensitive to the choice of time horizon \r (e.g.,  Pierrehumbert, 2014). Global surface temperature changes \r following a  pulse of CO2 emission are roughly constant in time \r (the principle behind TCRE; Section 5.5.1 and Figure 7.21b) whereas \r the temperature change following a  pulse of short-lived GHG \r emission declines with time. In contrast to a  one-off pulse, a  step \r change in short-lived GHG emissions that is maintained indefinitely \r causes a  concentration increase that eventually equilibrates to \r a steady state in a way that is more comparable to a pulse of CO2. \r Similarly the resulting change in global surface temperature from \r a step change in short-lived GHGs (Figure 7.21a) after a few decades \r [...]"


id: 49dbc5c072ac846aa6d08e24504118d2
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 10233
content:
"[...] increases only slowly (due to accumulation of heat in the deep ocean) \r and hence its effects are more similar to a pulse of CO2 (Smith et al., \r 2012; Lauder et  al., 2013; Allen et  al., 2016, 2018b). The different \r time dependence of short-lived and long-lived compounds can be "


id: 46ed7a1dfb5c1e179350286f9394c43f
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 9682
content:
"The similarity in behaviour of step changes in short-lived GHG \r emissions and pulses of CO2 emissions has recently been used to \r formulate new emissions metric concepts (Collins et al., 2020). For \r short-lived GHGs, these new concepts use a step change in the rate \r of emissions, in contrast to an instantaneous pulse in a given year \r that is typically used (e.g.,  Myhre et  al., 2013b). Metrics for step \r emissions changes are denoted here by a superscript 'S' (e.g., AGTP S\r X\r is the absolute global surface temperature-change potential from \r a  unit step change in emissions of species \"X\"). These can be \r derived by integrating the more standard pulse emission changes \r up to the time horizon. The response to a step emissions change is \r therefore equivalent to the integrated response to a pulse emission \r (AGTP S\r X  =  iAGTPX); and the radiative forcing response to a  step \r emissions change AGFP S\r X is equivalent to the integrated forcing \r [...]"


id: 8bcb710ed52af7fada1cd0ce4048f7bf
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
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index: climate_gpt_v2
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content:
"[...] response iAGFPX which is the AGWP. The step metric for short-lived \r GHGs can then be compared with the pulse metric for CO2 in "


id: f6fc3d7332271e1ffd5894ede9f7a498
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 40823
content:
"actual temperature response computed with the two-layer emulator. \r Note that GWP and GTP metrics were not designed for use under \r a cumulative carbon dioxide equivalent emissions framework (Shine \r et al., 1990, 2005), even if they sometimes are (e.g., Cui et al., 2017; \r Howard et al., 2018) and analysing them in this way can give useful \r insights into their physical properties. Using these standard metrics \r under such frameworks, the cumulative CO2 equivalent emissions \r associated with methane emissions would continue to rise if methane \r emissions were substantially reduced but remained above zero. \r In reality, a decline in methane emissions to a smaller but still positive \r value could cause a declining warming. GSAT changes estimated with \r cumulative CO2 equivalent emissions computed with GWP-20 matches \r the warming trend for a few decades but quickly overestimates the \r response. Cumulative emissions using GWP-100 perform well when \r [...]"


id: 8d7c507cb45dbaae88010e41753aaac9
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
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index: climate_gpt_v2
vector_id: 22077
content:
"[...] emissions are increasing but not when they are stable or decreasing. \r Cumulative emissions using GTP-100 consistently underestimate \r the warming. Cumulative emissions using either CGTP or GWP* \r approaches can more closely match the GSAT evolution (Allen et al., \r 2018b; Cain et al., 2019; Collins et al., 2020; Lynch et al., 2020)."


id: a22642519887eefaa58acc5e66033a30
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 25763
content:
"When individual gases are treated separately in climate model emulators (Cross-Chapter Box  7.1), or weighted and aggregated \r using an emissions metric approach (such as CGTP or GWP*) which translate the distinct behaviour from cumulative emissions of \r short-lived gases, ambiguity in the future warming trajectory of a given emissions scenario can be substantially reduced (Cain et al., \r 2019; Denison et al., 2019; Collins et al., 2020; Lynch et al., 2021). The degree of ambiguity varies with the emissions scenario. For \r mitigation pathways that limit warming to 2degC with an even chance, the ambiguity arising from using GWP-100 as sole constraint \r on emissions of a mix of greenhouse gases (without considering their economic implications or feasibility) could be as much as \r 0.17degC, which represents about one-fifth of the remaining global warming in those pathways (Denison et al., 2019). If the evolution \r [...]"


id: ebc67acd7637a43ff1db671dadced77a
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 38847
content:
"[...] of the individual GHGs is not known, this can make it difficult to evaluate how a given global multi-gas emissions pathway specified \r only in CO2 equivalent emissions would achieve (or not) global surface temperature goals. This is potentially an issue as Nationally \r Determined Contributions frequently make commitments in terms of GWP-100-based CO2 equivalent emissions at 2030 without \r specifying individual gases (Denison et al., 2019). Clear and transparent representation of the global warming implications of future \r emissions pathways including Nationally Determined Contributions could be achieved either by their detailing pathways for multiple \r gases or by detailing a pathway of cumulative carbon dioxide equivalent emissions approach aggregated across GHGs evaluated \r by either GWP* or CGTP metric approaches (Cain et  al., 2019; Collins et  al., 2020; Lynch et  al., 2021). It should be noted that \r [...]"


id: 52fc1772b700bd5b16f355173ed89405
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 11859
content:
"[...] although the Paris Agreement Rulebook asks countries to report emissions of individual GHGs separately for the global stocktake \r (Decision 18/CMA.1, annex, paragraph 38), which can allow the current effects of their emissions on global surface temperature to \r be accurately estimated, estimates of future warming are potentially ambiguous where emissions are aggregated using GWP-100 or \r other pulse metrics."


id: 2271bd14ed56212c905de9ec0cdd6578
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 3280
content:
"is an active area of research (Schleussner et  al., 2016, 2019; \r Fuglestvedt et al., 2018; Collins et al., 2020). Several possible scientific \r interpretations of the Article 2 and 4 goals can be devised, and these, \r along with emissions metric choice, have implications both for when \r a balance in GHG emissions, net zero CO2 emissions or net zero GHG \r emissions are achieved, and for their meaning in terms of temperature \r outcome (Fuglestvedt et al., 2018; Rogelj et al., 2018; Wigley, 2018). \r In AR6 net zero GHG emissions is defined as the condition in which \r metric-weighted anthropogenic GHG emissions are balanced by \r metric-weighted anthropogenic GHG removals over a specified period \r (see Box  1.4 and Appendix VII: Glossary). The quantification of net \r zero GHG emissions depends on the GHG emissions metric chosen \r to compare emissions and removals of different gases, as well as the \r time horizon chosen for that metric. As the choice of emissions metric \r [...]"


id: 66ac946939cf23d3d71e3ed54a54f867
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
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index: climate_gpt_v2
vector_id: 15322
content:
"[...] affects the quantification of net zero GHG emissions, it therefore \r affects the resulting temperature outcome after net zero emissions \r are achieved (Lauder et  al., 2013; Rogelj et  al., 2015; Fuglestvedt \r et al., 2018; Schleussner et al., 2019). Schleussner et al. (2019) note \r that declining temperatures may be a desirable outcome of net zero. \r Rogelj and Schleussner (2019) also point out that the use of physical \r metrics raises questions of equity and fairness between developed and \r developing countries."


id: a38ef3b9c0507ffaefe94c82e9c70bd4
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 25983
content:
"One prominent use of emissions metrics is for comparison of efforts \r measured against climate change goals or targets. One of the most \r commonly discussed goals is in Article 2 of the Paris Agreement \r which aims to limit the risks and impacts of climate change by setting \r temperature goals. In addition, the Paris Agreement has important \r provisions which relate to how the goals are to be achieved, including \r making emissions reductions in a  manner that does not threaten \r food production (Article 2), an early emissions peaking target, and \r the aim to 'achieve a balance between anthropogenic emissions by \r sources and removals by sinks of greenhouse gases in the second half \r of this century' (Article 4). Article 4 also contains important context \r regarding international equity, sustainable development, and poverty \r reduction. Furthermore, the United Nations Framework Convention \r on Climate Change (UNFCCC) sets out as its ultimate objective, the \r [...]"


id: 3082166bab233c53c33b7677d8e92c4b
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 5789
content:
"[...] 'stabilization of greenhouse gas concentrations in the atmosphere \r at a level that would prevent dangerous anthropogenic interference \r with the climate system.'"


id: 452ef7c7d6941deeef1ef6b923be1524
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 9400
content:
"Based on SR1.5 (Allen et al., 2018a), there is high confidence that \r achieving net zero CO\r 2 emissions and declining non-CO2 radiative \r forcing would halt human-induced warming. Based on (Bowerman \r et  al., 2013; Pierrehumbert, 2014; Fuglestvedt et  al., 2018; Tanaka \r and O'Neill, 2018; Schleussner et  al., 2019) there is also high \r confidence that reaching net zero GHG emissions as quantified by \r GWP-100 typically leads to reductions from peak global surface \r temperature after net zero GHGs emissions are achieved, depending \r on the relative sequencing of mitigation of short-lived and long-lived \r species. If both short- and long-lived species are mitigated together, \r then temperatures peak and decline. If mitigation of short-lived \r species occurs much earlier than that of long-lived species, then \r temperatures stabilize very near peak values, rather than decline. \r Temperature targets can be met even with positive net GHG emissions \r [...]"


id: 61275a9104bb47c38c8c26baf43ac1bc
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 14389
content:
"[...] based on GWP-100 (Tanaka and O'Neill, 2018). As demonstrated \r by Allen et al. (2018b), Cain et al. (2019), Schleussner et al. (2019) \r and Collins et  al. (2020) reaching net zero GHG emissions when \r quantified using the new emissions metric approaches such as \r CGTP or GWP* would lead to an approximately similar temperature \r evolution as achieving net zero CO\r 2. Hence, net zero CO2 and net zero \r GHG, quantified using these new approaches, would both lead to \r approximately stable contributions to temperature change after net \r zero emissions are achieved (high confidence)."


id: 1fb9e59ba8333b29fb076966caff82c1
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 2789
content:
"Comparisons with emissions or global surface temperature \r stabilization goals are not the only role for emissions metrics. \r Other important roles include those in pricing approaches where \r policymakers choose to compare short-lived and long-lived climate \r forcers (e.g.,  Manne and Richels, 2001), and in life cycle analyses \r (e.g., Hellweg and Mila i Canals, 2014). Several papers have reviewed \r the issue of metric choice for life cycle analyses, noting that analysts \r should be aware of the challenges and value judgements inherent in \r attempting to aggregate the effects of forcing agents with different \r time scales onto a common scale (e.g., Mallapragada and Mignone, \r 2017) and recommend aligning metric choice with policy goals as \r well as testing sensitivities of results to metric choice (Cherubini et al., \r 2016). Furthermore, life cycle analyses approaches which are sensitive \r to choice of emissions metric benefit from careful communication of \r [...]"


id: 75bf1af27c721ce3aaab52723840bf13
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 17946
content:
"We measure the influence of various human and natural factors on the energy flows at the top of our atmosphere \r in terms of radiative forcings, where a positive radiative forcing has a warming effect and a negative radiative \r forcing has a cooling effect. In response to these forcings, the Earth system will either warm or cool, so as to \r restore balance through changes in the amount of outgoing thermal radiation (the warmer the Earth, the more \r radiation it emits). Changes in Earth's temperature in turn lead to additional changes in the climate system \r (known as climate feedbacks) that either amplify or dampen the original effect. For example, Arctic sea ice has \r been melting as the Earth warms, reducing the amount of reflected sunlight and adding to the initial warming \r (an amplifying feedback). The most uncertain of those climate feedbacks are clouds, as they respond to warming in \r [...]"


id: 71d10d42fe5d535893f2173c4e84bac7
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 17267
content:
"[...] complex ways that affect both the emission of thermal radiation and the reflection of sunlight. However, we are \r now more confident that cloud changes, taken together, will amplify climate warming (see FAQ 7.2)."


id: d2c7cae34962079c3df5c6705a47f83f
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 34432
content:
"Clouds cover roughly two-thirds of the Earth's surface. They consist of small droplets and/or ice crystals, which \r form when water vapour condenses or deposits around tiny particles called aerosols (such as salt, dust, or smoke). \r Clouds play a critical role in the Earth's energy budget at the top of our atmosphere and therefore influence \r Earth's surface temperature (see FAQ 7.1). The interactions between clouds and the climate are complex and \r varied. Clouds at low altitudes tend to reflect incoming solar energy back to space, creating a cooling effect by \r preventing this energy from reaching and warming the Earth. On the other hand, higher clouds tend to trap \r (i.e., absorb and then emit at a lower temperature) some of the energy leaving the Earth, leading to a warming \r effect. On average, clouds reflect back more incoming energy than the amount of outgoing energy they trap, \r [...]"


id: b808ed5e4aab80e4960ddc418c613912
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 29592
content:
"[...] resulting in an overall net cooling effect on the present climate. Human activities since the pre-industrial era \r have altered this climate effect of clouds in two different ways: by changing the abundance of the aerosol \r particles in the atmosphere and by warming the Earth's surface, primarily as a result of increases in greenhouse \r gas emissions."


id: 167ffde038ae40e646d7535acf3f88a2
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 1174
content:
"Since the last IPCC Report in 2013 (the Fifth Assessment Report, or AR5), understanding of cloud processes has \r advanced with better observations, new analysis approaches and explicit high-resolution numerical simulation of \r clouds. Also, current global climate models simulate cloud behaviour better than previous models, due both to \r advances in computational capabilities and process understanding. Altogether, this has helped to build a more \r complete picture of how clouds will change as the climate warms (FAQ 7.2, Figure 1). For example, the amount \r of low-clouds will reduce over the subtropical ocean, leading to less reflection of incoming solar energy, and \r the altitude of high-clouds will rise, making them more prone to trapping outgoing energy; both processes \r have a warming effect. In contrast, clouds in high latitudes will be increasingly made of water droplets rather \r [...]"


id: b7406ef8b7b180be6eef8bcea6b9b11b
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 29455
content:
"[...] than ice crystals. This shift from fewer, larger ice crystals to smaller but more numerous water droplets will \r result in more of the incoming solar energy being reflected back to space and produce a cooling effect. Better \r understanding of how clouds respond to warming has led to more confidence than before that future changes \r in clouds will, overall, cause additional warming (i.e., by weakening the current cooling effect of clouds). This is \r called a positive net cloud feedback."


id: 74d3a41f5921e870b4566710d305bf0a
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 17791
content:
"Gabriel Abramowitz (Australia), Gudfinna Adalgeirsdottir (Iceland), Andrea Alessandri \r (Italy), Robert  J.  Allen (United States of America), Kevin Anchukaitis (United States \r of America), Richard A. Betts (United Kingdom), Celine J. W. Bonfils (United States of America/\r France), Michael Bosilovich (United  States of America), Olivier  Boucher (France), \r Josephine Brown (Australia), Michael  P.  Byrne (United Kingdom/Ireland), Robin Chadwick \r (United  Kingdom), Sarah  Connors (France/United  Kingdom), Benjamin Cook (United  States \r of America), Erika Coppola (Italy), Alejandro  Di  Luca (Australia, Canada/Argentina), \r Aida Diongue Niang (Senegal), Petra Doll (Germany), Ellen Douglas (United States of America), \r Paul  J.  Durack (United States of America/Australia), Hayley J.  Fowler (United  Kingdom), \r Alexander Gershunov (United States of America), Nicholas R.  Golledge (New  Zealand/\r [...]"


id: 1237f81d5c71d5c238e2678151ccabcc
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
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index: climate_gpt_v2
vector_id: 400
content:
"[...] United Kingdom), James Kossin (United States of America), Won-Tae Kwon (Republic of Korea), \r Flavio Lehner (United States of America/Switzerland), Eric Maloney (United States of America), \r Vimal Mishra (India), Angeline Pendergrass (United States of America), Stefan Pfahl (Germany), \r Catherine Prigent (France), Catherine Rio (France), Alex C.  Ruane (United  States of America), \r Benjamin Sanderson (United Kingdom), Sonia I. Seneviratne (Switzerland), Shoichi Shige (Japan), \r Vijay Singh (United States of America), Abigail Swann (United States of America), Richard G. Taylor \r (United  Kingdom/Canada, United  Kingdom), Laurent  Terray (France), Natalia  Tilinina \r (The Russian Federation), Bart van den Hurk (The Netherlands), Sergio M. Vicente-Serrano (Spain), \r Michael  Wehner (United  States of  America), Laura J.  Wilcox (United  Kingdom), Cunde  Xiao \r (China), Prodromos Zanis (Greece), Xuebin Zhang (Canada)"


id: f97b1a614a1fa88d1b6ed8c1682846d4
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 41256
content:
"Modifications of Earth's energy budget by anthropogenic \r radiative forcings drive substantial and widespread changes \r in the global water cycle. There is high confidence that global \r mean precipitation and evaporation increase with global warming, \r but the estimated rate is model-dependent (very likely range of \r 1-3% per 1degC). The global increase in precipitation is determined by \r a robust response to global mean surface air temperature (very likely\r 2-3% per 1degC) that is partly offset by fast atmospheric adjustments \r to atmospheric heating by greenhouse gases and aerosols. The overall \r effect of anthropogenic aerosols is to reduce global precipitation and \r alter large-scale atmospheric circulation patterns through their well\u0002understood surface radiative cooling effect (high confidence). Land\u0002use and land-cover changes also drive regional water cycle changes \r through their influence on surface water and energy budgets (high \r [...]"


id: 6b4a75ad1a47a5c2733c0927d47e879e
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 16177
content:
"Anthropogenic aerosols have driven detectable large-scale \r water cycle changes since at least the mid-20th century (high \r confidence). Shifts in the tropical rain belt are associated with \r the inter-hemispheric temperature response to the time-evolving \r radiative influence of anthropogenic aerosols and the ongoing \r warming influence of greenhouse gases (high confidence). Cooling in \r the Northern Hemisphere by sulphate aerosols explained a southward \r shift in the tropical rain belt and contributed to the Sahel drought \r from the 1970s to the 1980s (high confidence), subsequent recovery \r from which has been linked with greenhouse gas warming (medium \r confidence). Observed changes in regional monsoon precipitation, \r especially over South Asia, East Asia and West Africa, have been \r limited over much of the 20th century due to increases driven by \r warming from greenhouse gases being counteracted by decreases \r due to cooling from anthropogenic aerosols (high confidence). \r [...]"


id: 30c2574639053c177bbdd0109bcc5ac1
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 5834
content:
"Without large-scale reduction in greenhouse gas emissions, \r global warming is projected to cause substantial changes \r in the water cycle at both global and regional scales (high \r confidence). Global annual precipitation over land is projected to \r increase on average by 2.4 [-0.2 to +4.7] %  (likely  range) in the \r SSP1-1.9 low-emissions scenario and by 8.3 [0.9 to 12.9] % in the \r SSP5-8.5 very high-emissions scenario by 2081-2100, relative to \r 1995-2014. It is virtually certain that evaporation will increase over \r the oceans and very likely that evapotranspiration will increase over \r land with regional exceptions in drying areas. There is low confidence\r in the sign and magnitude of projected changes in global land runoff \r in all Shared Socio-economic Pathway scenarios. Projected increases \r in precipitation amount and intensity will be associated with \r increased runoff in the northern high latitudes (high confidence). \r [...]"


id: 803817a931be8aa2b71d2b997182d025
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 19689
content:
"[...] There is high confidence that mountain glaciers will diminish in \r all regions and that seasonal snow cover duration will generally \r decrease. Runoff from small glaciers will typically decrease through \r loss of ice mass, while runoff from large glaciers is likely to increase \r with increasing global warming until glacier mass becomes depleted \r (high confidence). {4.5.1, 8.4.1}"


id: adb9cc04d968c0c73f2361cc9c5b7c6d
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 27806
content:
"Abrupt human-caused changes to the water cycle cannot be \r excluded. There is evidence of abrupt change in some high-emissions \r scenarios, but there is no overall consistency regarding the magnitude \r and timing of such changes. Positive land surface feedbacks, including \r vegetation and dust, can contribute to abrupt changes in aridity, \r but there is only low confidence that such changes will occur during \r the 21st century. Continued Amazon deforestation, combined with \r a  warming climate, raises the probability that this ecosystem will \r cross a  tipping point into a  dry state during the 21st century (low \r confidence). The paleoclimate records show that a  collapse in the \r Atlantic Meridional Overturning Circulation (AMOC) causes abrupt \r shifts in the water cycle (high confidence), such as a southward shift in \r the tropical rain belt, weakening of the African and Asian monsoons, \r strengthening of Southern Hemisphere monsoons, and drying in \r [...]"


id: b767715a1b6194c63baa3a26c969fabe
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
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index: climate_gpt_v2
vector_id: 29478
content:
"[...] Europe. There is medium confidence that AMOC will not collapse \r before 2100, but should it collapse, it is very likely that there would be \r abrupt changes in the water cycle. {8.6.1, 8.6.2}"


id: 37829d3cbbff711778185674067e6f7c
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 6978
content:
"Water is vital to all life on Earth. Seventy-one percent of the Earth \r is covered by water, with saline ocean water accounting for around \r 97% of total water availability (Figure  8.1). Terrestrial freshwater \r represents less than 2% of all water on Earth, and the remainder \r (around 1-2%) is primarily made up of saline groundwater and saline \r lakes (Durack, 2015; Abbott et al., 2019). Ice sheets, glaciers and \r snow pack account for approximately 96% of all freshwater, with less \r than 4% of freshwater considered easily accessible and available for \r essential ecosystem functioning and human society's water resource \r needs (Durack, 2015; Abbott et al., 2019). This very small fraction of \r freshwater represents a total volume of about 835,000 km3, mostly \r contained in groundwater (630,000 km3), the remaining 205,000 km3 \r being stored in lakes, rivers, wetlands and soils (Abbott et al., 2019). \r Although the natural cycling rate of this amount is theoretically \r [...]"


id: 427a64e6cef4a84207a2c988c3e396de
created_at: 2023-04-19 13:22:39
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content:
"[...] enough to meet global human and ecosystem needs, there are large \r geographical and seasonal differences that influence the availability \r of freshwater to meet regional demands. "


id: 923a611dea266f4c0023e433fbe68b11
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 22945
content:
"The consequences of climate change on terrestrial ecosystems and \r human societies are primarily experienced through changes to \r the global water cycle (Jimenez Cisneros et al., 2014). Changes in \r the quantity and seasonality of water due to climate change have \r long been recognized by IPCC and global development agencies as \r heavily influencing the food security and economic prosperity of many \r countries, particularly in the arid and semi-arid areas of the world \r including Asia, Africa, Australia, Latin America, the Mediterranean, \r and small island developing states (Bates et al., 2008; Schewe \r et al., 2014; Mekonnen and Hoekstra, 2016). Having too much or \r too little water increases the likelihood of flooding and drought, \r as precipitation variability increases in a  warming climate (Stocker \r et al., 2013; Hoegh-Guldberg et al., 2018). Climate change poses \r a threat to both regional water availability and global water security. \r [...]"


id: 45c34796c6b581e5f7a3fa876b4e321b
created_at: 2023-04-19 13:22:39
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index: climate_gpt_v2
vector_id: 9489
content:
"[...] Changes in precipitation and glacier runoff and snowmelt influence \r other hydroclimate variables like surface and subsurface runoff, and \r groundwater recharge, which are critical to the water, food and energy \r security of many regions (Oki and Kanae, 2006; Jimenez Cisneros \r et al., 2014; Schewe et al., 2014; Mekonnen and Hoekstra, 2016). "


id: cebe925bfc153ed2ea0c1d61750727
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 33713
content:
"Freshwater is the most essential natural resource on the planet \r (Mekonnen and Hoekstra, 2016; Djehdian et al., 2019) and underpins \r almost all Sustainable Development Goals (SDGs), which require \r access to adequate and safe resources for drinking and sanitation \r (SDG 6) and many other purposes. Freshwater supports a range of \r human activities from irrigation to industrial processes including \r the generation of hydro-electricity and the cooling of thermoelectric \r power plants (Bates et al., 2008; Schewe et al., 2014). These activities \r require sufficient quantities of freshwater that can be drawn from \r rivers, lakes, groundwater stores, and in some cases, desalinated sea \r water (Schewe et al., 2014). Recent estimates of global water pools \r and fluxes suggest that half of global river discharge is redistributed \r each year by human water use (Abbott et al., 2019). This emphasizes \r the need to consider both anthropogenic climate change and \r [...]"


id: 31cec4cbf812f1bd9084e13c10f410be
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 6011
content:
"[...] direct human influences, such as population increase or migration, \r economic development, urbanization, and land use change, when \r planning water-related mitigation or adaptation strategies (Jimenez \r Cisneros et al., 2014)."


id: eb1088ed612f5124c31d4ef3d17bade4
created_at: 2023-04-19 13:22:39
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content_type: text
index: climate_gpt_v2
vector_id: 38711
content:
"Based on long-term observational evidence (Hartmann et al., 2013), \r AR5 concluded it was likely that anthropogenic influence has affected \r the water cycle since the 1960s (IPCC, 2018). Detectable human \r influence on changes to the water cycle were found in atmospheric \r moisture content (medium confidence), global-scale changes of \r precipitation over land (medium confidence), intensification of heavy \r precipitation events over land regions where sufficient data networks \r exist (medium confidence), and very likely changes to ocean salinity \r through its connection with evaporation minus precipitation change \r patterns (Sections  2.5, 2.6, 3.3, 7.6, 10.3 and 10.4; Stocker et al., \r 2013). The AR5 also reported that it is very likely that global surface air \r specific humidity increased since the 1970s. There was low confidence\r in the observations of global-scale cloud variability and trends, medium \r confidence in reductions of pan-evaporation, and medium confidence\r [...]"


id: 92272a388121f0a2b2674c065b4a8754
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
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index: climate_gpt_v2
vector_id: 22929
content:
"[...] in the non-monotonic changes of global evapotranspiration since the \r 1980s. In terms of streamflow and runoff, AR5 identified that there \r is low confidence in the observed increasing trends of global river \r discharge during the 20th century. Similarly, AR5 concluded that there \r is low confidence in any global-scale observed trend in drought or \r dryness (lack of rainfall) since the mid-20th century. Yet, the frequency \r and intensity of drought likely increased in the Mediterranean and \r West Africa, while they likely decreased in central North America and \r north-western Australia since 1950. "


id: a7d67af4106c6545397d86d9423d3705
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
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vector_id: 26738
content:
"Understanding the interactions between the water and energy \r cycles is one of the four core projects of the World Climate Research \r Programme (WCRP). Latent heat fluxes, released by condensation of \r atmospheric water vapour and absorbed by evaporative processes, are \r critical to driving the circulation of the atmosphere on scales ranging \r from individual thunderstorm cells to the global circulation of the \r atmosphere (Stocker et al., 2013; Miralles et al., 2019). Water vapour \r is the most important gaseous absorber in the Earth's atmosphere, \r playing a key role in the Earth's radiative budget (Schneider et al., 2010). \r As atmospheric water vapour content increases with temperature, it \r has a  considerable influence on climate change (Section  7.4.2.2). \r Additionally, a  small fraction of the atmospheric water content is \r liquid or solid and has a major effect on both solar and longwave \r radiative fluxes, from the Earth's surface to the top of the atmosphere. \r [...]"


id: 33f1259c63b26f5c5079c5ddee181006
created_at: 2023-04-19 13:22:39
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index: climate_gpt_v2
vector_id: 6383
content:
"[...] The cloud response to anthropogenic radiative forcings, both in the \r tropics and in the extratropics (Zelinka et al., 2020), is therefore also \r crucial for understanding climate change (Section 7.4.2.4)."


id: 2ff1bea2b1d058bf57c840864ef2d9ac
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 5693
content:
"The terrestrial water and carbon cycles are also strongly coupled (Cross\u0002Chapter Box 5.1). As atmospheric carbon dioxide (CO2) concentration \r increases, the physical environment in which plants grow is altered, \r including the availability of soil moisture necessary for plants' CO2\r uptake and, potentially, the effectiveness of CO2 removal techniques \r to mitigate climate change (Section  5.6.2.1.2). Rising surface CO2\r concentrations also modify stomatal (small pores at the leaf surface) \r regulation as well as the plants' biomass, thus affecting ecosystem \r photosynthesis and transpiration rates and leading generally to \r a net increase in water use efficiency (Lemordant et al., 2018). These \r coupled changes have profound implications for the simulation of the \r carbon and water cycles (Gentine et al., 2019; see also Section 5.4.1), \r which can be better assessed with the new generation Earth system \r models, although both the carbon concentration and carbon-climate \r [...]"


id: 79066091d335be9ffbb6d609ffe5824
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 18481
content:
"[...] feedbacks remain highly uncertain over land (Section  5.4.5; Arora \r et al., 2020). The water constraints on the terrestrial carbon sinks \r are a matter of debate regarding the feasibility or efficiency of some \r land-based CO2 removal and sequestration techniques requested to \r comply with the Paris Agreement (Section 5.6.2.2.1; Fuss et al., 2018; \r Belyazid and Giuliana, 2019)."


id: 66b62b1d2e0319f5fcc7326b3913f091
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 15330
content:
"Water cycle projections in AR5 (Collins et al., 2013) were \r considered primarily in terms of water vapour, precipitation, surface \r evaporation, runoff, and snowpack. Globally-averaged precipitation \r was projected to increase with global warming with virtual certainty \r (Chapter 12 Executive Summary and Section 12.4.1.1). Regionally, \r precipitation in some areas of the tropics and polar regions could \r increase by more than 50% by the end of the 21st century under \r the RCP8.5 emissions scenario, while precipitation in large areas \r of the subtropics could decrease by 30% or more (AR5 FAQ 12.2, \r Figure  12.22). Overall, the contrast of annual mean precipitation \r between dry and wet regions and between dry and wet seasons \r ('wet get wetter, dry get drier') was projected to increase over \r most of the globe with high confidence (Chapter  12 Executive \r Summary and Section 12.4.5.2). Globally, the frequency of intense \r [...]"


id: ef26fbb83816fc019afc3a37c41f117e
created_at: 2023-04-19 13:22:39
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content_type: text
index: climate_gpt_v2
vector_id: 39447
content:
"[...] precipitation events was projected to increase while the frequency \r of all precipitation events was projected to decrease, leading to \r the contradictory-seeming projection of a  simultaneous increase \r in both droughts and floods (12.2 and Section  12.4.5.5 in AR5 \r WGI). Surface evaporation change was projected to be positive \r over most of the ocean and to generally follow the pattern of \r precipitation change over land (Chapter  12 Executive Summary, \r and Section  12.4.5.4). Near-surface relative humidity reductions \r over many land areas were projected to be likely, with medium \r confidence (Section 12.4.5.1). General decreases in soil moisture in \r present-day dry regions were considered likely, and projected with "


id: 523fc30751cfd81452d6ba719207ae40
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
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index: climate_gpt_v2
vector_id: 11742
content:
"The Special Report on climate change, desertification, land \r degradation, sustainable management, food security, and \r greenhouse gas (GHG) fluxes in terrestrial ecosystems (SRCCL) has \r clear connections with the water cycle. This Report indicates that since \r 1850-1900, land surface temperature has risen nearly twice as much \r as global surface temperature (high confidence), with an increase \r in dry climates (high confidence). Land surface processes modulate \r the likelihood, intensity and duration of many extreme events \r including droughts (medium confidence) and heavy precipitation \r (medium confidence). The direction and magnitude of hydrological \r changes induced by land use change and land surface feedbacks \r vary with location and season (high confidence). Desertification \r exacerbates climate change through feedbacks involving vegetation \r cover, greenhouse gases and mineral dust aerosol (high confidence). \r [...]"


id: 9a724fce043475f3f8a79a637913f466
created_at: 2023-04-19 13:22:39
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index: climate_gpt_v2
vector_id: 24419
content:
"[...] Urbanization increases extreme rainfall events over or downwind of \r cities (medium confidence). Intensification of rainy events increase \r their consequences on land degradation. "


id: de33f034f898c4e6154852100a555951
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 36356
content:
"The SR1.5 assessed the impacts of global warming of 1.5degC above \r pre-industrial levels. The dominant human influence on observed \r global warming and related water cycle changes was confirmed. \r Further evidence that anthropogenic global warming has caused \r an increase in the frequency, intensity and/or amount of heavy \r precipitation events at the global scale (medium confidence), as \r well as in drought occurrence in the Mediterranean region (medium \r confidence) was also reported. Chapter 3 of SR1.5 (Hoegh-Guldberg \r et al., 2018) highlights that each half degree of additional global \r warming influences the climate response. Heavy precipitation shows \r a  global tendency to increase more at 2degC compared to 1.5degC, \r though there is low confidence in projected regional differences \r in heavy precipitation at 1.5degC compared to 2degC global warming, \r except at high latitudes or at high altitude where there is medium \r [...]"


id: 8b2c4aec6151ff6cd35cb6a3d93a0c27
created_at: 2023-04-19 13:22:39
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index: climate_gpt_v2
vector_id: 21649
content:
"[...] confidence. A key finding is that 'limiting global warming to 1.5degC \r compared to 2degC would approximately halve the proportion of the \r world population expected to suffer water scarcity, although there \r is considerable variability between regions (medium confidence)' \r (SR1.5). This is consistent with greater adverse impacts found at \r 2degC compared to 1.5degC for a number of dryness or drought indices \r (Schleussner et al., 2016; Lehner et al., 2017; Greve et al., 2018). There \r is also medium confidence that land areas with increased runoff and \r exposure to flood hazards will increase more at 2degC compared to \r 1.5degC of global warming."


id: b23dfb31089ebcaa77c0eb5b3c177e3a
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 28589
content:
"The AR5 report was a major step forward in the assessment of the \r human influence on the Earth's water cycle, yet regional projections \r of precipitation and water resources often remained very uncertain \r for a range of reasons including modelling uncertainty and the large \r influence of internal variability (Sections  1.4.3 and 8.5.2; Hawkins \r and Sutton, 2011; Deser et al., 2012). Since AR5, longer and more \r homogeneous observational and reanalysis datasets have been \r produced along with new ensembles of historical simulations driven \r by all or individual anthropogenic forcings. These factors, together \r with improved detection-attribution tools, has enabled a  more \r comprehensive assessment and a  better understanding of recent \r observed water cycle changes, including the competing effects of \r GHGs and aerosol emissions. New paleoclimate reconstructions \r have been also developed, particularly from the SH, that were not \r [...]"


id: 5f45372ae2f5cf85f0a6d40ea08eee4b
created_at: 2023-04-19 13:22:39
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index: climate_gpt_v2
vector_id: 14070
content:
"[...] available at the time of AR5. There have also been advances in \r modelling clouds, precipitation, surface fluxes, vegetation, snow, \r floodplains, groundwater and other processes relevant to the water \r cycle. Convection permitting and cloud-resolving models have been \r implemented over increasingly large domains and can be used as \r benchmarks for the evaluation of the current-generation climate \r models. The added value of increased resolution in global or regional \r climate models can be also assessed more thoroughly based on \r dedicated model intercomparison projects (Sections 10.3.3 and 8.5.1). \r Ongoing research activities on decadal predictions and observational \r constraints are aimed at narrowing the plausible range of near-term \r (2021-2040) to long-term (2081-2100) water cycle changes."


id: 336f35337ebc91f5dc341a573da90ef5
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
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vector_id: 6302
content:
"Chapter  8  has multiple links across all AR6 WGI chapters, so \r necessarily includes references to other chapter subsections and \r figures. Model evaluation of large-scale circulation and precipitation \r is mostly covered by Chapter  3, while hydrological extremes are \r covered by Chapter 11. Chapter 8 focuses on key processes relevant \r to the water cycle and their resolution-dependent representation in \r models. Observed and projected changes in large-scale circulation and \r precipitation are primarily assessed in Chapters 2, 3 and 4. Beyond \r global and regional mean precipitation amounts, Chapter  8  also \r focuses on other precipitation properties (e.g.,  frequency, intensity \r and seasonality) and other water cycle variables (evapotranspiration, \r runoff, soil moisture and aridity, solid and liquid freshwater reservoirs). \r Key regional phenomena (e.g.,  tropical overturning circulations, \r monsoons, extratropical stationary waves and storm tracks, modes \r [...]"


id: cb1fc1f75d439566d99b36f06b1ea0e5
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 33076
content:
"[...] of variability and related teleconnections) are also assessed given \r their major dynamical contribution to regional water cycle changes. \r Although the biosphere and the cryosphere are key components of \r the water cycle, a more comprehensive assessment of their responses \r can be found in Chapters 5 and 9, respectively. Further assessment "


id: 8326a704daddeccf648e4e671ebce182
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 20197
content:
"The chapter starts with theoretical evidence that link small-scale \r processes and drivers, as well as global energy budget and large\u0002scale circulation constraints to physically-understood changes in the \r global water cycle (Section 8.2). Observed and projected water cycle \r changes (Sections 8.3 and 8.4, respectively) are assessed in separate \r sections, but with a  parallel structure to facilitate comparison of \r a specific topic across sections. Projections are primarily assessed on \r the basis of contrasted emissions scenarios to emphasize the water \r cycle response to mitigation. Unless otherwise specified, projected \r anomalies are estimated relative to the 1995-2014 baseline \r climatology and are assessed over 20-year time slices, 2021-2040, \r 2041-2060 and 2081-2100 for near-, mid- and long-term changes \r respectively. Beyond multi-model ensemble means, model response \r uncertainty, the influence of natural climate variability, and the \r [...]"


id: 7c21a06111a330276fe7c2b4fc9803d5
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updated_at: 2023-04-19 13:22:39
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index: climate_gpt_v2
vector_id: 19048
content:
"[...] potential non-linearities in the regional water cycle response are \r also considered (Section 8.5). Low likelihood but physically plausible "


id: 12b54830f9268c4961fa5176760f7a59
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 493
content:
"Figure 8.3 | Schematic representation of fast and slow responses of the atmospheric energy balance and global precipitation to radiative forcing.\r ('Baseline' panel) The atmospheric energy budget: (1) responds instantaneously to radiative forcings; (2) leading to rapid atmospheric adjustments; and (3) slower semi-rapid \r adjustments involving the land surface and vegetation that further modify atmospheric circulation patterns. (4) This slow precipitation response to global mean surface air \r temperature is quantified as (a) the hydrological sensitivity, e, and the total precipitation response, including initial rapid adjustments, is termed the apparent hydrological \r sensitivity, ea. (b-d) The slow precipitation response over land and ocean develops over time. Large, filled arrows (in panels from 'baseline' to 4) depict fluxes or circulation \r [...]"


id: 837582667539f469639cc96f393eb578
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
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index: climate_gpt_v2
vector_id: 20263
content:
"[...] change while small arrows (1-4) denote increases (|) or decreases (|) in variables (P is precipitation; L is atmospheric longwave radiative cooling; S is solar radiation absorption \r by the atmosphere; H is sensible heat flux; E is surface evaporative heat flux; and T is temperature). Adapted from Allan et al. (2020) with statistics taken from Figures 7.2 and \r Figure 8.1."


id: 14a418ce4cd12930b1c9e6e4aac87c9e
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 855
content:
"Observed estimates of hydrological sensitivity (e = 3.2 +- 0.8% degC-1) \r based on interannual variability (Allan et al., 2020) or responses to \r El Nino-Southern Oscillation (ENSO) of 9% degC-1 (Adler et al., 2017) are \r not suitable to assess the magnitude of e (Figure 8.4). This is because \r these relationships depend on amplifying feedbacks associated with \r ENSO-related cloud changes (G.L. Stephens et al., 2018) that may not \r be relevant for longer term climate change. However, there is robust \r evidence and high agreement across observations, modelling and \r supporting physics that precipitation increases at a lower % degC-1 rate \r than water vapour content in the global mean (Held and Soden, 2006; \r Collins et al., 2013; Allan et al., 2020), implying an increased residence \r time of atmospheric water vapour (Hodnebrog et al., 2019b; Dijk et al., \r 2020). Increasing global precipitation, evaporation and moisture fluxes \r [...]"


id: 7ac4febdeff8670a163be3f90574b14c
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
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index: climate_gpt_v2
vector_id: 18799
content:
"[...] with warming thereby drive an intensification but not acceleration of \r the global water cycle (Sections 8.3.1.1 and 8.4.1.1). "


id: 62d2c2b9b1973d134876458acc37fc15
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 14682
content:
"While thermodynamics exert a  strong control on water vapour \r changes, global mean precipitation and evaporation are constrained \r by the balance of energy fluxes in the atmosphere and at the surface \r (Figure 8.3). Global mean precipitation increases of 1-3% per 1 degC of \r warming, as estimated in AR5 (Collins et al., 2013), are explained as \r a combination of rapid (or fast) atmospheric adjustments and slow \r temperature-driven responses (Figure 8.3, panels 1-4) to radiative \r forcings (Andrews et al., 2010; Bala et al., 2010; Cao et al., 2012). Fast \r atmospheric adjustments are caused by near-instantaneous (hours \r to days) changes in the atmospheric energy budget (Figure  8.3, \r panels 1-3) and atmospheric properties (e.g.,  temperature, clouds \r and water vapour) in direct response to the radiative effects of \r a forcing agent (Sherwood et al., 2015). A further relatively fast (days \r to months) adjustment of the climate system involves interactions \r [...]"


id: 6a031f400e9ffbed5f1f829762d2ef44
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 15919
content:
"[...] with vegetation and land surface temperature (Figure 8.3, panel 3), \r which respond more rapidly than ocean temperature to a radiative \r forcing (Cao et al., 2012; Dong et al., 2014). The slower temperature\u0002dependent precipitation response is driven by the increased \r atmospheric radiative cooling rate of a  warming atmosphere. \r Warming drives increases in precipitation intensity while frequency is \r dominated by rapid atmospheric adjustments to the radiative forcing \r based on abrupt 4xCO2 CMIP6 simulations (Douville and John, 2021). \r Since AR5, many new studies applying the dual rapid adjustment and \r slow response framework show that global precipitation responses \r to different forcing agents are physically well understood (Flaschner \r et al., 2016; MacIntosh et al., 2016; Samset et al., 2016; Myhre et al., \r 2018a). Further confidence in the coupled processes involved are \r provided by simple models representing the energy budget and \r [...]"


id: e83a09abd90f6a07c6087b968795cab9
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 38187
content:
"[...] thermodynamic constraints that limit global mean evaporation to \r around 1.5% degC-1 (Siler et al., 2019). This strengthens the physical \r link between energy budget and thermodynamic drivers of the global \r water cycle (Section 8.2.2.1)."


id: abd0f818e7e49134fe059b1b0179608a
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 27472
content:
"The overall global mean rate of precipitation change per 1 degC of \r GSAT increase, apparent hydrological sensitivity (ea), is reduced \r compared to hydrological sensitivity by the direct influence of \r radiative forcing agents on the atmospheric energy balance. Rapid \r atmospheric adjustments that alter precipitation are primarily \r caused by GHGs and absorbing aerosols, with high agreement and \r medium evidence across idealized simulations (Flaschner et al., 2016; \r Samset et al., 2016). A range of rapid precipitation adjustments to \r CO2 between models are also attributed to vegetation responses \r leading to a re-partitioning of surface latent and sensible heat fluxes \r (DeAngelis et al., 2016). Values obtained from six CMIP5 models \r simulating the Last Glacial Maximum (LGM; 21,000-19,000 years \r ago) and pre-industrial period (ea = 1.6-3.0%degC-1) are larger than for \r each corresponding abrupt 4xCO2 experiment (ea = 1.3-2.6% degC-1) \r [...]"


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vector_id: 1708
content:
"[...] due to differences in the mix of forcings, vegetation and land surface \r changes and a  higher thermodynamic %  degC-1 evaporation scaling \r in the colder state (Figure  8.4, Section  8.4.1.1; G.  Li et al., 2013). \r Updated estimates across comparable experiments from 22 CMIP5/\r CMIP6 models (Rehfeld et al., 2020) display a  consistent range \r (ea = 1.7 +- 0.6% degC-1). Confirming ea in observations (Figure 8.4) is \r difficult due to measurement uncertainty, varying rapid adjustments \r to radiative forcing and unforced variability (Dai and Bloecker, 2019; \r Allan et al., 2020)."


id: 29143c13ee21d4e66ec0abf87911b305
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 4435
content:
"aerosol) produce only a small rapid adjustment of the global water \r cycle and therefore larger ea than drivers that immediately modulate \r the atmospheric energy budget such as GHGs and absorbing aerosol \r (Salzmann, 2016; Samset et al., 2016; Lin et al., 2018; F. Liu et al., \r 2018). Thus, global precipitation appears more sensitive to radiative \r forcing from sulphate aerosols (2.8 +- 0.7% degC-1; eae) than GHGs \r (1.4 +- 0.5% degC-1; ea<e) while the response to black carbon aerosol \r can be negative (-3.5 +- 5.0% degC-1; ea<<e) due to strong atmospheric \r solar absorption (Samset et al., 2016). Therefore, artificially reducing \r surface-absorbed sunlight through solar radiation modification \r strategies to mitigate GHG warming will not mitigate precipitation \r changes (see Sections  4.6.3.3, 6.4.7 and 8.6.3). Aerosol-induced \r precipitation changes depend upon the type of aerosol species \r and their spatial distribution. Global mean precipitation increases \r [...]"


id: c6eb155ad0fdcd71f9c0e8d0010515f9
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 32307
content:
"[...] after complete removal of present-day anthropogenic aerosol \r emissions (see also Section 4.4.4) in four different climate models \r (ea = 1.6-5.5% degC-1) are mainly attributed to sulphate aerosol as \r opposed to other aerosol species (Samset et al., 2018b). Idealized \r modelling studies show that sulphate aerosol increases over Europe \r produce a  larger global precipitation response than an equivalent \r increase in aerosol burden or radiative forcing over Asia, explained \r by differences in cloud climatology and cloud-aerosol interaction \r (Kasoar et al., 2018; L. Liu et al., 2018). The vertical profiles of black \r carbon and ozone further influence the magnitude of the rapid global \r precipitation response, yet are difficult to observe and simulate (Allen \r and Landuyt, 2014; MacIntosh et al., 2016; Stjern et al., 2017; Sand \r et al., 2020). "


id: b8bbb87fe82652dc72eda9a7ac0c0320
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 29709
content:
"In summary, there is high confidence that global mean evaporation \r and precipitation increase with global warming, but the estimated \r rate is model-dependent (very likely range of 1-3 % degC-1). The global \r increase in precipitation is determined by a robust response to global \r surface temperature only (very likely 2-3% degC-1) that is partly offset \r by fast atmospheric adjustments to the vertical profile of atmospheric \r heating by GHGs and aerosols. Global precipitation increases due to \r GHGs are offset by the well-understood overall surface radiative \r cooling effect by aerosols (high confidence). Over land, the average \r warming-related increase in precipitation is expected to be smaller \r than over the ocean due to increasing land-ocean thermal contrast \r and surface feedbacks, but the overall precipitation increase over \r land is generally reinforced by fast atmospheric responses to GHGs \r that strengthens convergence of winds (medium confidence). Global \r [...]"


id: c28e0391eb03baf6cd465aeafa75d7f4
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 31515
content:
"[...] mean precipitation and evaporation increase at a  lower rate than \r atmospheric moisture per 1degC of global warming (high confidence), \r leading to longer water vapour lifetime in the atmosphere and driving \r changes in precipitation intensity, duration and frequency and an \r overall intensification but not acceleration of the global water cycle."


id: 60301e41b3907e2fa6f91aeb8fdca4e1
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 14226
content:
"Hydrological sensitivity is generally lower over land but with a large \r uncertainty range (e = -0.1 to 3.0%  degC-1) relative to the oceans \r (e = 2.3 to 3.3% degC-1) based on multi-model 4 x CO2 CMIP6 simulations \r (Pendergrass, 2020b), broadly consistent with comparable CMIP5 \r experiments (T.B.  Richardson et al., 2018a; Samset et al., 2018a). \r Suppressed hydrological sensitivity over land (Figures 8.3d and 8.4) \r is associated with greater warming compared with the oceans, which \r alters atmospheric circulation and precipitation patterns (Saint-Lu \r et al., 2020). Also, since oceans supply much of the moisture to fuel \r precipitation over land, the slower ocean warming rate means there \r is insufficient moisture supplied to maintain continental relative \r humidity levels (Byrne and O'Gorman, 2018), which can inhibit \r convection (J. Chen et al., 2020a). Land surface feedbacks involving \r soil-vegetation-atmosphere coupling further drive continental drying \r [...]"


id: 76468da0fee889ed58a89f239782075e
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 18029
content:
"[...] (Berg et al., 2016; Kumar et al., 2016; Chandan and Peltier, 2020). \r The suppressed hydrological sensitivity is counteracted by rapid \r precipitation responses in most GHG-forced simulations, explained \r by increases in surface downward longwave radiation due to CO2\r increases that rapidly warm the land, destabilize the troposphere and \r strengthen vertical motion in the short term (Chadwick et al., 2014; \r T.B. Richardson et al., 2016, 2018a). There is medium understanding \r of how land-sea warming contrast governs rapid precipitation \r responses based on idealized modelling that shows similar spatial \r patterns of precipitation response to radiative forcing from GHGs, \r solar forcing and absorbing aerosols (Xie et al., 2013; Samset et al., \r 2016; Kasoar et al., 2018). Rapid precipitation adjustments to CO2\r have been counteracted by cooling from anthropogenic aerosol \r increases over land (Box 8.1) but this compensation is expected to \r [...]"


id: 350e7ac297de5062c1a90cecdba8b9ef
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 6578
content:
"Since AR5, numerous studies have confirmed that changes in P-E \r with warming over land cannot be interpreted simply as a  'wet \r regions get wetter, dry regions get drier' response (Chadwick et al., \r 2013; Greve et al., 2014; Roderick et al., 2014; Byrne and O'Gorman, \r 2015; Scheff and Frierson, 2015). Firstly, P-E is a simplistic diagnostic \r of the water cycle that inadequately describes 'dryness' or aridity (Fu \r and Feng, 2014; Roderick et al., 2014; Greve and Seneviratne, 2015; \r Scheff and Frierson, 2015; Greve et al., 2019; Vicente-Serrano et al., \r 2020). Secondly, terrestrial P-E is generally positive and balanced \r by surface runoff and percolation into subsurface soils and \r aquifers (Figure 8.1). As a result, the simple thermodynamic scaling \r (Figure 8.5b) predicts that P-E over land will become more positive \r (wetter) with warming (Greve et al., 2014; Roderick et al., 2014; \r Byrne and O'Gorman, 2015). This is not necessarily true, however, \r [...]"


id: 67c784d6a21e7987847fb16d9688ad20
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 15531
content:
"[...] in the dry seasons and regions where terrestrial water is lost to \r the atmosphere and exported (Sheffield et al., 2013; Kumar et al., \r 2015; Keune and Miralles, 2019). Thirdly, regional P-E patterns over \r land are affected by changes in atmospheric circulation, oceanic \r moisture supply and land surface feedbacks. As the land warms \r more than oceans, spatial gradients in temperature and relative \r humidity influence moisture supply and reduce P-E over some land \r regions, such as southern Chile and Argentina around 30degS-50degS \r as captured by an extended thermodynamic scaling (Figure 8.5b). \r Drying of soils can be amplified by vegetation responses (Berg et al., \r 2016; Byrne and O'Gorman, 2016; Lambert et al., 2017) but limited \r by atmospheric circulation feedbacks (Zhou et al., 2021). Changes \r in soil moisture and rainfall intensity (Sections 8.2.3.2 and 8.2.3.3) \r can alter the partitioning of precipitation between evaporation and \r [...]"


id: e04f87198aac7213850704d9519fd2f6
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 36714
content:
"2013). These can dominate regional water cycle changes, affecting \r the availability of freshwater and the occurrence of climate extremes. \r Atmospheric circulation changes generally dominate the spatial \r pattern of rapid precipitation adjustments (Section 8.2.1) to different \r forcing agents in the tropics (Bony et al., 2013; He and Soden, 2015; \r T.B. Richardson et al., 2016, 2018a; Tian et al., 2017; X. Li et al., 2018). \r Radiative forcing with heterogeneous spatial patterns such as ozone \r and aerosols (including cloud interactions; Section 6.4.1 and Box 8.1) \r drive substantial responses in regional atmospheric circulation \r through uneven heating and cooling effects (L. Liu et al., 2018; Dagan \r et al., 2019b; Wilcox et al., 2019). Changes in atmospheric circulation \r are also driven by slower, evolving patterns of warming and associated \r changes in temperature and moisture gradients (Bony et al., 2013; \r [...]"


id: 9427f534591d387b02e357fd11f42c8f
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 23274
content:
"[...] Samset et al., 2016, 2018a; Ceppi et al., 2018; Ma et al., 2018). There is \r strong evidence that large regional water cycle changes arise from the \r atmospheric circulation response to radiative forcings and associated \r SST pattern evolution but low agreement in the sign and magnitude \r (Chadwick et al., 2016b). The role of prolonged weather regimes in \r determining wet and dry extremes is also better understood since AR5 \r (Kingston and McMecking, 2015; Schubert et al., 2016; D. Richardson \r et al., 2018; Barlow et al., 2019). Advances in knowledge of expected \r large-scale dynamical responses of the water cycle are further \r assessed in this section (see also Figure 8.21)."


id: dd1c60aa5ce2de57fb89a4e8188834e
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 36165
content:
"Long-term weakening of the tropical atmospheric overturning \r circulation is expected as climate warms in response to elevated \r CO2 (Collins et al., 2013). A  weaker circulation is required \r to reconcile  global mean low-level water vapour increases \r (around  7%  degC-1) with the smaller global precipitation responses \r of about 1-3% degC-1 (Section 8.2.1). The slowdown can occur in both \r the Hadley and Walker circulations, but occurs preferentially in the \r Walker circulation in most climate models (Vecchi and Soden, 2007) \r but this response has been questioned on the basis of model bias \r in east Pacific SST (Seager et al., 2019a). Weakening is expected \r to drive P-E decreases over the western Pacific and increases over \r the eastern Pacific. However, the driving mechanisms for Walker \r circulation weakening differ to those involved in determining ENSO \r variability, so it is too simplistic to interpret changes as an El Nino \r [...]"


id: bc40f87be617678c846a3c2e3be18861
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 30365
content:
"[...] pattern of regional hydrological cycle extremes (Sohn et al., 2019). \r Internal variability is also capable of temporarily strengthening the \r Walker circulation (Section 2.3.1.4.1; L'Heureux et al., 2013; Chung \r et al., 2019) while regional responses depend on the pattern of \r warming (Sandeep et al., 2014). "


id: fcf30d980a0e77b23bd040c455623593
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 41863
content:
"Monsoons are key components of the tropical overturning circulation \r that can be understood as a  balance between net energy input \r (e.g., radiative and turbulent fluxes) and the export of moist static energy. \r This is determined by contrasting surface heat capacity between ocean \r and land and modified through changes in atmospheric dynamics, \r tropical tropospheric stability and land surface properties (Jalihal et al., \r 2019). Thermodynamic increases in moisture transport are expected to \r increase monsoon strength and area (Christensen et al., 2013). Since \r AR5, evidence continues to demonstrate that monsoon circulation is \r sensitive to spatially varying radiative forcing by anthropogenic aerosols \r (Hwang et al., 2013; R.J. Allen et al., 2015; Z. Li et al., 2016b) and GHGs \r (Dong and Sutton, 2015). Changes in SST patterns also play a role (Guo \r [...]"


id: edc6c95cd1123362d494f35910e7cf94
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 39190
content:
"[...] et al., 2016; W. Zhou et al., 2019; Cao et al., 2020) by altering cross\u0002equatorial energy transports and land-ocean temperature contrasts. \r This evidence continues to support a thermodynamic strengthening of \r monsoon precipitation that is partly offset by slowing of the tropical \r circulation but with weak evidence and low agreement for regional \r aspects of circulation changes. Disagreement between paleoclimate \r and modern observations, physical theory and numerical simulations \r of global monsoons have been partly reconciled (Section  3.3.3.2) \r through improved understanding of regional processes (Harrison et al., \r 2015; R. Bhattacharya et al., 2017; Bhattacharya et al., 2018; Biasutti \r et al., 2018; D'Agostino et al., 2019; Jalihal et al., 2019; Seth et al., \r 2019), although interpreting past changes in the context of future \r projections requires careful account of differing forcings and feedbacks \r (D'Agostino et al., 2019). Assessment of past changes and future \r [...]"


id: 805daf7f3d68d94eb43e77112ebdf110
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 19713
content:
"[...] projections in regional monsoons are provided in Sections 2.3.1.4.2, \r 8.3.2.4 and 8.4.2.4. "


id: 9dd1a0db4f02d93c7f7026306a6f4802
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 25002
content:
"The regional Inter-tropical Convergence Zone (ITCZ) position, width \r and strength determine the location and seasonality of the tropical \r rain belt. Since AR5, multiple studies have linked cross-equatorial \r energy transport to the mean ITCZ position (Donohoe et al., 2013; \r Frierson et al., 2013; Bischoff and Schneider, 2014; Boos and Korty, \r 2016; Loeb et al., 2016; Adam et al., 2018; Biasutti and Voigt, 2019). \r Multi-model studies agree that aerosol cooling in the NH led to \r a southward shift in the ITCZ and tropical precipitation after the 1950s \r up to the 1980s that is linked with the 1980s Sahel drought (Box 8.1; \r Section 8.3.2.4 and 10.4.2.1). In particular, aerosol-cloud interaction \r was identified as a potentially important driver of this shift (Chung \r and Soden, 2017) but this is uncertain since observations suggest that \r models may overestimate (Malavelle et al., 2017; Toll et al., 2017) or \r underestimate (Rosenfeld et al., 2019) the aerosol cloud-mediated \r [...]"


id: c4b993d429d57ce2e3e50680d047e3f5
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 31925
content:
"[...] cooling effects. In addition, greenhouse gas forcing has been invoked \r in explaining much of the increase in Sahel precipitation since the \r 1980s through enhanced meridional temperature gradient, with only \r a secondary role for aerosol (Dong and Sutton, 2015)."


id: c34d33886f9270ba05ad282834f0a053
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 31630
content:
"Since AR5, understanding of poleward expansion of the Hadley Cells \r has improved (Section 2.3.1.4.1) but its role in subtropical drying is \r limited to the zonal mean and dominated by ocean regions (Byrne \r and O'Gorman, 2015; Grise and Polvani, 2016; He and Soden, 2017; \r Schmidt and Grise, 2017; Siler et al., 2018; Chemke and Polvani, 2019; \r Grise and Davis, 2020). Over subtropical land, evolving SST patterns \r and land-ocean warming contrasts, that are partly explained by rapid \r responses to CO2 increases, can dominate aspects of the atmospheric \r circulation response (Byrne and O'Gorman, 2015; He and Soden, 2015; \r Chadwick et al., 2017; H. Yang et al., 2020) and resultant regional \r water cycle changes, particularly for projected drying in semi-arid, \r winter-rainfall dominated subtropical climates (Deitch et al., 2017; \r Brogli et al., 2019; Seager et al., 2019b; Zappa et al., 2020). Poleward \r expansion of the tropical belt is expected to drive a corresponding \r [...]"


id: d8ae2ebdeb9bea43c784499f27d83fa9
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 35414
content:
"[...] shift in mid-latitude storm tracks, but the controlling mechanisms \r differ between hemispheres. Southern Hemisphere expansion is driven \r by GHG forcing and amplified by stratospheric ozone depletion, while "


id: c6b73211f9242aaf99b3dc725af067f5
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 32258
content:
"Understanding of how ITCZ width and strength respond to a warming \r climate has improved since AR5 (Byrne and Schneider, 2016; Harrop and \r Hartmann, 2016; Popp and Silvers, 2017; Dixit et al., 2018; Zhou et al., \r 2020). Studies suggest that convection gets stronger and more focused \r within the core of the ITCZ (Lau and Kim, 2015; Byrne et al., 2018). This \r leads to drying on the equatorward edges of the ITCZ and a moistening \r tendency in the ITCZ core (Byrne and Schneider, 2016). Feedbacks \r involving clouds have been identified as an important mechanism \r leading to tightening and strengthening of the ITCZ (Popp and Silvers, \r 2017; Su et al., 2017, 2019, 2020; Talib et al., 2018). Stronger ascent \r in the core amplifies the 'wet get wetter' response while reduced \r moisture inflow near the ITCZ edges reduces this response below the \r 7%  degC-1 thermodynamic increase in moisture transport. Thus, there \r is a  range of evidence and medium agreement for strengthening \r [...]"


id: 7bf264344f26fcdeecd34717e2167105
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 19014
content:
"[...] and contraction of the ITCZ with warming that sharpens contrasts \r between wet and dry regimes. However, understanding of how the \r regional ITCZ location responds in a  warming climate is not robust "


id: 5d36366c692166ca2be729c636156ed3
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 13673
content:
"Declining ice-sheet mass, glacier extent and Northern Hemisphere \r (NH) sea ice, snow cover and permafrost (Collins et al., 2013; \r Vaughan et al., 2013) is an expected consequence of a  warming \r climate (Sections  2.3.2, 3.4, 4.3.2.1 and 9.3-9.5). A  decline in \r mountain snow cover and increased snow and glacier melt will \r alter the amount and timing of seasonal runoff in mountain regions \r (Sections  3.4.2, 3.4.3 and 9.5). Earlier and more extensive winter \r and spring snowmelt (X. Zeng et al., 2018) can reduce summer and \r autumn runoff in snow-dominated river basins of mid-high latitudes \r of the NH (Rhoades et al., 2018; Bloschl et al., 2019). Since AR5, \r an earlier but less rapid snowmelt has been explained by reduced \r winter snowfall and less intense solar radiation earlier in the season \r (Musselman et al., 2017; Wu et al., 2018; Grogan et al., 2020). \r Reduced snow cover also increases energy available for evaporation, \r [...]"


id: 98850c6bdab665c2ca6d9a5904320c2
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 24046
content:
"[...] which can dominate declining river discharge based on modelling \r of the Colorado River (Milly and Dunne, 2020). An increase in the \r fraction of precipitation falling as rain compared with snow can lead \r to declines in both streamflow and groundwater storage in regions \r where snowmelt is the primary source of recharge (Earman and \r Dettinger, 2011; Berghuijs et al., 2014). Such regions include western \r South America and western North America, semi-arid regions which \r rely on snowmelt from high mountain chains (Ragettli et al., 2016; \r Milly and Dunne, 2020). Rain-on-snow melt events reduce at lower \r altitudes due to declining snow cover but increase at higher altitudes \r where snow tends to be replaced by rain based on observations and \r modelling (Musselman et al., 2018; Pall et al., 2019), thereby altering \r seasonal and regional characteristics of flooding (Section 11.5)."


id: 106061457522d20ca0aa0a14e40c179d
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 70
content:
"The influence of amplified Arctic warming on mid-latitude regional \r water cycles is not well understood based on simple physical \r grounds due to the large number of competing physical processes \r (Cross-Chapter Box 10.1). The thermal gradient between polar and \r lower latitude regions decreases at low levels due to Arctic warming \r amplification. However, at higher altitudes, the corresponding \r thermal gradient increases with warming due to cooling of the Arctic \r stratosphere and this is consistent with a strengthening of the winter \r jet stream in both hemispheres, yet there is low agreement on the \r precise mechanisms (Vallis et al., 2015; Vihma et al., 2016). Changes \r in the strength of the polar stratospheric vortex can also alter the \r mid-latitude circulation in winter, but responses are not consistent \r across models (Oudar et al., 2020a). Nevertheless, thermodynamic \r strengthening of moisture convergence into weather systems and \r [...]"


id: 3c694087c13c1090bc62adea4f696615
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 7820
content:
"[...] polar regions is robust (Section 8.2.2.1) and remains valid despite \r weak understanding of atmospheric circulation change."


id: 317bd69dc1821867679b2bf02b5ba787
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 5946
content:
"In summary, there is high confidence that altered atmospheric \r wind patterns in response to radiative forcing and evolving \r surface temperature patterns will affect the regional water cycle \r in most regions. Mean tropical circulation is expected to slow with \r global warming (high confidence) but temporary multi-decadal \r strengthening is possible due to internal variability (medium \r confidence). Slowing of the tropical circulation reduces the meridional \r P-E gradient over the Pacific and can partly offset thermodynamic \r amplification of P-E patterns and strengthening of monsoons (high \r confidence) but regional characteristics of tropical rain belt changes \r are not well understood. There is medium confidence in processes \r driving strengthening and tightening of the ITCZ that increase \r the contrasts between wet and dry tropical weather regimes and \r seasons. There is high confidence in understanding of how radiative \r [...]"


id: 4ce71d0a44294f56a5f3a690bdb62f7f
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 10800
content:
"[...] forcing and global warming drive a  poleward expansion of the \r subtropics and mid-latitude storm tracks but only low confidence in \r how poleward expansion influences drying of subtropical and mid\u0002latitude climates. There is low confidence in understanding how Arctic \r warming amplification affects mid-latitude regional water cycles but \r high confidence that thermodynamic strengthening of precipitation \r within weather systems and in monsoons and polar regions is robust \r to large-scale circulation changes."


id: ca0d25d9e5fdf2974580584ef7d36740
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 32874
content:
"Seasonal melt water from high mountain glaciers in Asia (see Cross\u0002Chapter Box 10.4) supply the basic needs of 221 +- 97 million people \r (Pritchard, 2019; Immerzeel et al., 2020). Glacier-melt in response to \r warming can initially lead to increased runoff volumes, especially in \r peak summer flows, but they will eventually decline as most glaciers \r continue to shrink. SROCC concluded there is high confidence that \r the peak runoff has already been passed for some smaller glaciers \r (Hock et al., 2019a). Increased precipitation and glacier-melt can also \r contribute to rising lake levels and flood hazards in regions such as \r the inner Tibetan Plateau, Patagonia, Peru, Alaska and Greenland \r (Lei et al., 2017; Shugar et al., 2020; Stuart-Smith et al., 2020). Since \r AR5, evidence from multiple locations (New Zealand, Greenland, \r Antarctica) shows that intrusions of warm, moist air are important in \r controlling glacier mass balance, the likelihood of extreme ablation \r [...]"


id: 2dc39ac565a4fefb8160a3771126d4fb
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 5340
content:
"[...] or snowfall events depending on air temperature (Gorodetskaya \r et al., 2014; Mackintosh et al., 2017; Mattingly et al., 2018; \r Little et al., 2019; Oltmanns et al., 2019; Wille et al., 2019; Adusumilli \r et al., 2021). Sensible heating from warm air and increased longwave \r radiation from atmospheric moisture and low clouds drive melt \r events (Stuecker et al., 2018). "


id: 53748bbd6bd36b8bed31e88b1ac2a9d2
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
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vector_id: 11962
content:
"evidence from simplified calculations, convection resolving models and \r observations that thermodynamics drives an increase in convective \r available potential energy (CAPE) with warming and therefore the \r intensity of convective storms (Singh and O'Gorman, 2013; Romps, \r 2016; Barbero et al., 2019). Also, declining relative humidity over land \r (Sections 2.3.1.3.2 and 8.2.2.1) increases lifting condensation level, \r thereby delaying but intensifying convective systems (Louf et al., \r 2019; J. Chen et al., 2020a). Larger systems are linked with increasing \r tropopause height (Lenderink et al., 2017) that can also amplify storm \r precipitation (Prein et al., 2017). However, the heaviest rainfall is not \r necessarily associated with the most intense (deepest) storms based \r on satellite data (Hamada et al., 2015; Hamada and Takayabu, 2018). \r Precipitation intensification can exceed thermodynamic expectations \r where and when additional latent heating invigorates individual \r [...]"


id: ade09e3dee9030b030f37d88a22bf2f8
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
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index: climate_gpt_v2
vector_id: 27834
content:
"[...] storms (Section  11.4.1) as implied by medium agreement across \r modelling and observational studies (Berg et al., 2013; Molnar et al., \r 2015; Scoccimarro et al., 2015; Prein et al., 2017; Zhou and Wang, \r 2017; Nie et al., 2018; Kendon et al., 2019; Z.  Zhang et al., 2019). \r This intensification depends on time of day, based on convection\u0002permitting simulations (E.P. Meredith et al., 2019)."


id: d2d89f2296df16a06ce2b42017bacd7a
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 34447
content:
"Intensification of sub-daily rainfall is inhibited in regions and seasons \r where available moisture is limited (Prein et al., 2017). However, \r a fixed threshold temperature above which precipitation is limited \r by moisture availability is not supported by modelling evidence \r (Neelin et al., 2017; Prein et al., 2017). Enhanced latent heating \r within storms can also suppress convection at larger scales due to \r atmospheric stabilization as demonstrated with high resolution, \r idealized and large ensemble modelling studies (Loriaux et al., 2017; \r Chan et al., 2018; Nie et al., 2018; Tandon et al., 2018; Kendon et al., \r 2019). Stability is also increased by the direct radiative heating effect \r of higher CO2 concentrations (Baker et al., 2018) and  influenced \r by aerosol effects on the atmospheric energy budget and cloud \r development (Box  8.1). Since AR5, modelling evidence shows \r increases in convective precipitation extremes are limited by droplet/\r [...]"


id: fe628f955ce3b87f5d51287b8a5ab0a
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
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index: climate_gpt_v2
vector_id: 42128
content:
"[...] ice fall speeds (Singh and O'Gorman, 2014; Sandvik et al., 2018) \r but these processes are only crudely represented (Tapiador et al., \r 2019a). Idealized regional and coupled global models combined \r with limited observational evidence shows that instantaneous \r precipitation extremes are sensitive to microphysical processes, while \r daily extremes are determined more by the degree of convective \r aggregation (Bao and Sherwood, 2019; Pendergrass, 2020a). "


id: efea0b59bb0ebf08e334800d5fef0748
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
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index: climate_gpt_v2
vector_id: 39592
content:
"Evidence that heavy precipitation events (from sub-daily up to \r seasonal time scales) intensify as the planet warms has strengthened \r since AR5 (Section 11.4, Box 11.1 and Cross-Chapter Box 3.2) based \r on improved physical understanding, extensive modelling and \r increasing observational corroboration (O'Gorman, 2015; Fischer \r and Knutti, 2016; Neelin et al., 2017). There is robust evidence, with \r medium agreement across a range of modelling and observational \r studies, of thermodynamic intensification of wet seasons (Chou \r et al., 2013; Liu and Allan, 2013; Dunning et al., 2018; Lan et al., \r 2019; Zhang and Fueglistaler, 2019). Extreme daily precipitation is \r expected to increase at close to the 7% degC-1 increase in the near\u0002surface atmospheric moisture-holding capacity determined by the \r Clausius-Clapeyron equation (Section 11.4, Figure 8.4), with limited \r evidence that higher rates apply for shorter duration precipitation \r [...]"


id: 542efdeed948a712068c9ccc596bf7bc
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 12088
content:
"[...] events (Formayer and Fritz, 2017; Lenderink et al., 2017; Ali et al., \r 2018; Guerreiro et al., 2018; Burdanowitz et al., 2019; W.  Zhang \r et al., 2019a). However, observed estimates sample multiple synoptic \r weather states, mixing thermodynamic and dynamic factors, so are \r not directly relatable to climate change responses (Bao et al., 2017; \r Drobinski et al., 2018). The contrasting spatial scales sampled by the \r observations and models (from global to cloud resolving) explain \r the large range of daily and sub-daily precipitation scaling with \r temperature assessed in Figure 8.4."


id: e9fb658749b4d1a162b064946b35936f
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 38517
content:
"Dynamical changes modify and can dominate thermodynamic drivers \r of local rainfall and flood hazard change (Box  11.1). For example, \r increased land-ocean temperature gradients (Section 8.2.2.2) explain \r more intense rain from convective systems over the Sahel based on \r satellite data since the 1980s (Taylor et al., 2017) and dynamical \r feedbacks can invigorate active to break phase transition over India \r (Karmakar et al., 2017; Roxy et al., 2017). Satellite data shows long-lived, \r organized mesoscale convective systems contribute disproportionally \r to extreme tropical precipitation (Roca and Fiolleau, 2020). Since AR5, \r the spatial variability in soil moisture has been linked with the timing \r and location of convective rainfall by altering the partitioning between \r latent and sensible heating. This was demonstrated for the Sahel, \r Europe and India in observations (C.M. Taylor et al., 2013; Taylor, 2015; \r [...]"


id: 43ce174d2e20bafd2f651256511add5f
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 9159
content:
"The amount and intensity of rainfall within extratropical storms is \r expected to increase with atmospheric moisture. This is particularly \r evident for atmospheric rivers (see Glossary) and research since AR5 \r has confirmed their link with flooding and terrestrial water storage \r (Froidevaux and Martius, 2016; Paltan et al., 2017; Waliser and \r Guan, 2017; Adusumilli et al., 2019; Ionita et al., 2020; Payne et al., \r 2020). There is robust evidence based on simple physics and detailed \r modelling that extratropical cyclone rainfall, including atmospheric \r river events, will intensify through increased atmospheric moisture \r flux (Lavers et al., 2013; Ramos et al., 2016; Yettella and Kay, 2017; \r V. Espinoza et al., 2018; Algarra et al., 2020; Xu et al., 2020; Zavadoff \r and Kirtman, 2020; Zhao, 2020), although changes in dynamical \r aspects will modify responses regionally (Section  8.4.2.8). For \r [...]"


id: cce9bcb273fb8b5ef869543aff53f080
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 33392
content:
"[...] example, stronger latitudinal temperature gradients in the high\u0002latitude upper troposphere drive increased extratropical storm \r speed around 30degN-70degN based on CMIP5 simulations (Dwyer and \r O'Gorman, 2017), causing reduced precipitation accumulation."


id: 5482ffcf22d09583bc215b4f8890706
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 12144
content:
"In summary, there is very high confidence that heavy precipitation \r events will become more intense in a  warming climate. There is \r high confidence that increased moisture and its convergence within \r extratropical and tropical cyclones and storms will increase rainfall \r totals during wet events at close to the 7%  degC-1 thermodynamic \r response, with low confidence of higher rates for sub-daily intensities. \r There is medium confidence that more intense but less frequent \r rainfall increases the proportion of rainfall leading to surface runoff \r and focused groundwater recharge from temporary water bodies. \r There is low confidence in how the frequency of flooding will change \r regionally as it is strongly dependent on catchment characteristics, \r antecedent conditions and how atmospheric circulation systems \r respond to climate change, which is less certain than thermodynamic \r drivers (Section 11.5). However, there is high confidence that increases \r [...]"


id: 1ddb58bd57b1fa0cb9559e11f34bb65c
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 2441
content:
"[...] in precipitation intensity and amount during very wet events (from \r sub-daily up to seasonal time scales) will intensify severe flooding \r when these extremes occur."


id: 78eab7a8ac338368674e453694585034
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 18463
content:
"The response of flood hazard to changing rainfall characteristics \r depends on time and space scale and the nature of the land surface \r (Section 11.5.1 and FAQ 8.2). Sustained and heavy rainfall can lead \r to widespread flooding and landslides while intensification of short\u0002duration intense rainfall can increase the severity and frequency \r of flash flooding (Marengo et al., 2013; Chan et al., 2016; Gariano \r and Guzzetti, 2016; Sandvik et al., 2018). Flooding events in many \r tropical regions (e.g., north-western South America, southern Africa \r and Australasia) are associated with ENSO variability (Emerton et al., \r 2017; Takahashi and Martinez, 2019; Pabon-Caicedo et al., 2020) and \r amplified by thermodynamic increases in water vapour. Flood hazard \r from heavy rainfall is modulated by snowmelt (Section  8.2.3.1), \r vegetation characteristics (Page et al., 2020; Murphy et al., 2021) and \r direct human intervention (Sections  8.2.3.4 and FAQ 8.2) but also \r [...]"


id: 5cdf5e1e4e30dfa403daa9bb82978a40
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 13606
content:
"[...] can be compounded by sea level rise (Sections 4.3.2.2 and 9.6.4) in \r coastal and delta regions (Bevacqua et al., 2019; Ganguli and Merz, \r 2019; Eilander et al., 2020). Antecedent soil moisture conditions are \r an important modulator of flooding (Section 11.5.1) but become less \r important for smaller catchments and for more severe floods (Wasko \r and Nathan, 2019). Depleted soil moisture after more intense dry \r seasons (Section  8.2.2.1) can allow greater uptake of wet season \r rainfall before soils saturate. Since AR5, evidence confirms that more \r intense rainfall increases the proportion of runoff and reservoir \r recharge relative to infiltration into the soil (Eekhout et al., 2018; Yin "


id: bfaa2e8c23a958d5710584bade5e96e0
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 30996
content:
"Regional changes in aridity  - broadly defined as a  deficit of \r moisture  - are expected to occur in response to anthropogenic \r forcings as a consequence of shifting precipitation patterns, warmer \r temperatures, changes in cloudiness (affecting solar radiation), \r declining snowpack, changes in winds and humidity, and vegetation \r cover (Figure  8.6). Evapotranspiration (see Annex VII: Glossary) \r is a  key component of aridity, and is composed of two main \r processes: evaporation from soil, water and vegetation surfaces; \r and transpiration, the exchange of moisture between plants and \r atmosphere through plant stomata. On a  global level, warmer \r temperatures increase evaporative demand in the atmosphere, and \r thus (assuming sufficient soil moisture is available) increase moisture \r loss from evapotranspiration (high confidence) (Dai et al., 2018; \r Vicente-Serrano et al., 2020). On a regional level, aridity is further \r [...]"


id: 21d205d473b37db20531517dfab70d40
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 3168
content:
"[...] modulated by seasonal rainfall patterns, runoff, water storage, and \r interactions with vegetation. "


id: 4771e37a1c43ae7d5153bcb5120f9113
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 9781
content:
"et al., 2020). Societies have developed a  variety of strategies to \r manipulate the water cycle to increase resiliency in the face of water \r scarcity, including irrigation, creation of artificial reservoirs, and \r groundwater pumping. While potentially buffering water resource \r capacity, in some cases these interventions may unexpectedly \r increase vulnerability (medium confidence). For example, while \r increased irrigation efficiency may ensure more water is available \r to crops, the corresponding reduction in runoff and subsurface \r recharge may exacerbate hydrologic drought (Grafton et al., 2018). \r Furthermore, while building dams and increasing surface reservoir \r capacity can boost water resources, they may actually increase \r drought vulnerability if demands rise to take advantage of the \r increased supply or if over-reliance on these surface reservoirs is \r encouraged (Di Baldassarre et al., 2018). Interactions between \r [...]"


id: 9eb29697265aa4ecf6ef45db37a6f00a
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 25145
content:
"[...] adaptation, vulnerability, and drought impacts are discussed further \r in WGII (Chapters 2 and 4)."


id: ddc7f821d5ca535b80b8c1d5623156e8
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 36282
content:
"A drought is a period of abnormally dry weather that persists for long \r enough to cause a serious hydrological imbalance (Glossary; Wilhite \r and Glantz, 1985; Wilhite, 2000; Cook et al., 2018). Most droughts \r begin as persistent precipitation deficits ('meteorological drought') \r that propagate over time into deficits in soil moisture, streamflow, \r and water storage (Figure 8.6), leading to a reduction in water supply \r ('hydrological drought'). Increased atmospheric evaporative demand \r increases plant water stress, leading to 'agricultural and ecological \r drought' (Williams et al., 2013; C.D. Allen et al., 2015; Anderegg et al., \r 2016; McDowell et al., 2016; Grossiord et al., 2020). Evaporative \r demand affects plants in two ways. It increases evapotranspiration, \r depleting soil moisture and stressing plants through lack of water \r (Teuling et al., 2013; Sperry et al., 2016), and also directly affects plant \r physiology, causing a decline in hydraulic conductance and carbon \r [...]"


id: 5e5b387032a91c3159c55bbd4d0e28c6
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 13894
content:
"[...] metabolism, leading to mortality (Figure 8.6; Breshears et al., 2013; \r Hartmann, 2015; McDowell and Allen, 2015; Fontes et al., 2018). \r While droughts are traditionally viewed as 'slow moving' disasters \r that typically take months or years to develop, rapidly evolving and \r often unpredictable flash droughts can also occur (Otkin et al., 2016, \r 2018). Flash droughts can develop within a  few weeks, causing \r substantial disruption to agriculture and water resources (Pendergrass \r et al., 2020). Conversely, droughts that persist for a long time (usually \r a decade or more) are called megadroughts. Droughts span a large \r range of spatial and temporal scales, arise through a  variety of \r climate system dynamics (e.g., internal atmospheric variability, ocean \r teleconnections), and can be amplified or alleviated by a variety of \r physical and biological processes. As such, droughts occupy a unique \r space within the framework of extreme climate and weather events, \r [...]"


id: 301e1271e8018c70dace32c9772ff8b1
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 5726
content:
"Human activities influence the regional water cycle directly through \r modifying and exploiting stores and flows from rivers, lakes and \r groundwater and by altering land cover characteristics. These \r actions alter surface energy and water balances through changes in \r permeability, surface albedo, evapotranspiration, surface roughness \r and leaf area. Direct redistribution of water by human activities for \r domestic, agricultural and industrial use of about 24,000  km3 yr-1\r (Figure 8.1) is equivalent to half the global river discharge or double \r the global groundwater recharge each year (Abbott et al., 2019). Since \r AR5, both modelling studies and observations have demonstrated \r that land use change can drive local and remote responses in \r precipitation and river flow by altering the surface energy balance, \r moisture advection and recycling, land-sea thermal contrast and \r associated wind patterns (Alter et al., 2015; Wey et al., 2015; De Vrese \r [...]"


id: 421ad2b6c661be665d45aa7299961a4f
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index: climate_gpt_v2
vector_id: 8825
content:
"[...] et al., 2016; Pei et al., 2016; Wang-Erlandsson et al., 2018; Vicente\u0002Serrano et al., 2019). There is robust evidence that a warming climate \r combined with direct human demand for groundwater will deplete \r groundwater resources in already dry regions (Wada and Bierkens, \r 2014; D'Odorico et al., 2018; Jia et al., 2019)."


id: af573dd62b852f67708646fe9e31351
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 28082
content:
"While the role of precipitation in droughts is obvious, other climatic \r drivers are also important, such as temperature, radiation, wind, \r and humidity (Figure  8.6). These factors have a  strong influence on \r atmospheric evaporative demand, which affects evapotranspiration \r and soil moisture (Figure  8.6). In snow-dominated regions, high \r temperatures increase the fraction of precipitation falling as rain \r instead of snow and advance the timing of spring snowmelt (high \r confidence) (Vincent et al., 2015; Mote et al., 2016, 2018; Berg and \r Hall, 2017; Solander et al., 2018). This can result in lower than normal \r snowpack levels (a 'snow drought'), and thus reduced streamflow, even \r if total precipitation is at or above normal for the cold season (Harpold \r et al., 2017). Plants also affect the severity of droughts by modulating \r evapotranspiration (Figure  8.6). As discussed above, the  effect of \r elevated CO2 on plants has the potential to both increase and reduce \r [...]"


id: 3b375c39554cbfc44f5b91ea3af161ce
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
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index: climate_gpt_v2
vector_id: 7607
content:
"[...] water loss through evapotranspiration via enhanced WUE and plant \r growth, respectively (Figure 8.6), but there is low confidence in whether \r one process dominates over another at the global scale. "


id: b7f79122194c3e9e3d22a48da877ca02
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
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index: climate_gpt_v2
vector_id: 29581
content:
"Changes in land use from forest to agriculture can exert profound \r regional effects on the water cycle (FAQ 8.1) by modifying the \r surface energy balance and moisture recycling (Krishnan et al., 2016; \r Paul et al., 2016; Llopart et al., 2018; Singh et al., 2019). There is \r medium evidence from modelling and observations over the Amazon \r and East Africa that deforestation drives increased streamflow \r (Dos Santos et al., 2018; Guzha et al., 2018; Levy et al., 2018) but \r limited evidence that increases in global runoff due to deforestation \r are counterbalanced by decreases resulting from irrigation (Hoegh\u0002Guldberg et al., 2018). Total Amazon deforestation drives reductions \r in precipitation but with a large 90% confidence range (-38 to +5 %) \r based on 44 primarily pre-AR5 climate model simulations (Spracklen \r and Garcia-Carreras, 2015) with smaller reductions (-2.3 to -1.3 %) \r attributed to observed Amazon deforestation up to 2010. Climate \r [...]"


id: 3ea4e27cc5b75facbadbc04277b841af
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
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index: climate_gpt_v2
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content:
"[...] model development has reduced this uncertainty range but has not \r altered the median change (Lejeune et al., 2015). Large-scale global \r deforestation (20 million km2) simulated by 9 CMIP6 models confirms \r a large range in precipitation amount reduction of -37 +- 54 mm yr-1\r over the deforested regions (Boysen et al., 2020). However, small\u0002scale deforestation can increase precipitation locally (Lawrence and \r Vandecar, 2015). A 50-60% deforestation rate corresponded to a wet \r season delay of about one week and greater chance of dry spells \r of eight days or longer based on correlation analysis of rain gauge \r and land-use data for South America (Leite-Filho et al., 2019). Forest \r and grassland fires can also modify hydrological response at the \r watershed scale (Havel et al., 2018). Afforestation or reforestation \r aimed at removing CO2 from the atmosphere can also alter the water \r cycle at the regional scale (Section 8.4.3 and Cross-Chapter Box 5.1)."


id: d72f3867e79bbf2b0be21da2d140e1c2
created_at: 2023-04-19 13:22:39
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index: climate_gpt_v2
vector_id: 35166
content:
"and atmospheric water vapour locally (Jia et al., 2019; Mishra \r et al., 2020). Irrigation can explain declining groundwater storage \r in some regions, including north-western India and North America \r (Asoka et al., 2017; G. Ferguson et al., 2018). Simulations spanning \r 1960-2010 indicate that approximately 30% of the present human \r water consumption is supplied from non-sustainable water resources \r (Wada and Bierkens, 2014). However, there is only limited evidence \r that groundwater extraction is lowering streamflow (Mukherjee \r et al., 2018; de Graaf et al., 2019). Model experiments show that \r irrigation can either aggravate or alleviate climate-induced changes \r of surface or subsurface water (Leng et al., 2015). Widespread \r extraction of water from rivers can reduce flows and decrease the \r level and area of inland seas and lakes (Wurtsbaugh et al., 2017; \r Torres-Batllo et al., 2020; X. Wang et al., 2020). Between 1985 and \r [...]"


id: b1b0ca6c242924e2d703508b727f35a9
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index: climate_gpt_v2
vector_id: 28479
content:
"[...] 2015, about 139,000 km2 of inland water areas have become land, \r while creation of dams has converted about 95,000 km2 of land to \r water, particularly in the Amazon and Tibetan Plateau (Donchyts et al., \r 2016). Direct management of river flow is comparable in magnitude \r to climate change effects for snow-fed rivers at a continental scale \r based on a global analysis and a study of 96 Canadian catchments \r (Tan and Gan, 2015; Arheimer et al., 2017). "


id: af8716953a5fed9e301ada5f2675db7f
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 28115
content:
"The SRCCL assessed with medium confidence that mean and extreme \r precipitation is increased over and downwind of urban areas (Jia \r et al., 2019). There is medium confidence that altered thermodynamic \r and aerodynamic properties of the land surface from urbanization \r affects evaporation and increases precipitation over or downwind of \r cities (Box 10.3) due to altered stability and turbulence (Han et al., \r 2014; Pathirana et al., 2014; Jiang et al., 2016; D'Odorico et al., 2018; \r Sarangi et al., 2018; Boyaj et al., 2020). However, reduced biogenic \r aerosol, but increased anthropogenic aerosol emissions modify \r cloud microphysics and precipitation processes (Box  8.1; Schmid \r and Niyogi, 2017; D'Odorico et al., 2018; Fan et al., 2020; Zheng \r et al., 2020). Urbanization also decreases permeability of the surface, \r leading to increased surface runoff (Chen et al., 2017; Jia et al., 2019). \r Large-scale infrastructure, such as the construction and operation of \r [...]"


id: c0bca6132c0b4d95c9f71df7bb18f6c1
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index: climate_gpt_v2
vector_id: 31207
content:
"[...] dikes, weirs, and hydropower plants, also alters surface energy and \r moisture fluxes, potentially influencing the regional water cycle. \r Limited modelling evidence suggests that large-scale solar and wind \r farms can increase precipitation locally (over the Sahel and North \r America) when dynamic vegetation responses are represented \r (Y. Li et al., 2018; Pryor et al., 2020), with remote effects also possible \r (Lu et al., 2021). "


id: decbc86f52fd14133d68f0c55c2891ad
created_at: 2023-04-19 13:22:39
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content_type: text
index: climate_gpt_v2
vector_id: 36464
content:
"Dimming over the NH causes a relative cooling, compared to the Southern Hemisphere (SH), which induces a southward shift of the \r northern edge of the tropical rain belt (Section 3.3.2.3; Allen et al., 2014; Bronnimann et al., 2015). CMIP5 simulations show that most of the \r cooling is caused by the aerosol cloud-mediated effect (Chung and Soden, 2017). Dimming also weakens monsoon flow and precipitation, \r offsetting or even overcoming the expected precipitation increase due to increased GHGs (Ayantika et al., 2021). The oceanic response to \r a weakened monsoon cross-equatorial flow can further weaken the South Asian monsoon through an amplifying feedback loop (Swapna \r et al., 2012; Krishnan et al., 2016; Patil et al., 2019). These processes partially explain (medium confidence) the southward shift of the NH \r tropical edge of the tropical rain belt from the 1950s to the 1980s (Allen et al., 2014; Bronnimann et al., 2015) and the severe drought in \r [...]"


id: 7cb287dda225dc79c0ab0cdbfb9f7de7
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index: climate_gpt_v2
vector_id: 19135
content:
"[...] the Sahel that peaked in the mid-1980s (Rotstayn et al., 2002; Undorf et al., 2018b). These processes also explain (high confidence) the \r observed decrease of South East Asian monsoon precipitation during the second half of the 20th century (Figure 8.7; Bollasina et al., 2011; \r Sanap et al., 2015; Krishnan et al., 2016; Lau and Kim, 2017; Lin et al., 2018; Undorf et al., 2018b). "


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content_type: text
index: climate_gpt_v2
vector_id: 11528
content:
"Cloud droplets nucleate on pre-existing aerosol particles which act as cloud condensation nuclei (CCN). Anthropogenic aerosols \r add CCN, compared to a pristine background, and produce clouds with more numerous and smaller droplets, slower to coalesce \r into raindrops and to freeze into ice hydrometeors at temperatures below 0degC. Adding CCN suppresses light rainfall from shallow \r and short-lived clouds, but it is compensated by heavier rainfall from deep clouds. Adding aerosols to clouds in extremely clean air \r invigorates them by more efficient vapour condensation on the added drop surfaces (Koren et al., 2014; Fan et al., 2018). Clouds \r forming in more polluted air masses (hence with more numerous and smaller drops) need to grow deeper to initiate rain (Freud \r and Rosenfeld, 2012; Konwar et al., 2012; Campos Braga et al., 2017). This leads to larger amount of cloud water evaporating aloft \r [...]"


id: 4807649bdfc16bda5f151a2d11f6fa01
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
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index: climate_gpt_v2
vector_id: 9891
content:
"[...] while cooling and moistening the air there at the expense of the lower levels, which leads to convective invigoration (Dagan et al., \r 2017; Chua and Ming, 2020), followed by convergence, air mass destabilization and added rainfall in an amplifying feedback loop "


id: 9741ea0562db864656b388b4d31fd27f
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 23807
content:
"Ice nucleating particle (INP) initiate ice precipitation from persistent supercooled water clouds that have cloud droplets too small for \r efficient warm rain, or expedite mixed-phase precipitation in short-lived supercooled rain clouds (Creamean et al., 2013). Most INPs \r are desert and soil dust particles, rather than air pollution aerosols (DeMott et al., 2010). Biogenic particles from terrestrial and marine \r origin are more rare, but important at temperatures above about -15degC (Murray et al., 2012; DeMott et al., 2016). Dust particles \r from long-range transport across the Pacific were found to enhance snow-forming processes over the Sierra Nevada in California \r (Creamean et al., 2013; Fan et al., 2014). The impact of INPs was demonstrated by glaciogenic cloud seeding experiments, which \r enhanced orographic supercooled clouds with medium confidence of success (French et al., 2018; Rauber et al., 2019; Friedrich et al., \r [...]"


id: b128cdc5cebbc79004cd4ad0db66a8c6
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 28392
content:
"[...] 2020). There are still major gaps in understanding the effects of INPs mainly on deep convective clouds (Kanji et al., 2017; Stanford \r et al., 2017; Korolev et al., 2020)."


id: 21af89705bd1ad6f012e2479e2290c54
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 3140
content:
"This section focuses on the evaluation and attribution of past and \r recent water cycle changes using observational datasets, theoretical \r understanding and model simulations. Paleoclimate records and \r historical observations provide evidence for past water cycle changes \r caused both by natural variability and human activities (Haug et al., \r 2003; Buckley et al., 2010; Pederson et al., 2014). Key elements of \r the observed water cycle changes are assessed in this section, \r including flux and storage variations across the atmosphere, the \r continents and to a lesser extent the ocean and cryosphere, as well \r as related changes in large-scale atmospheric circulation and modes \r of variability. Particular emphasis is placed on assessing changes \r across regions and seasons (Box 8.2). Detailed regional assessments \r are presented in Chapters 10, 11, 12 and Atlas. Further information \r concerning large-scale observed water cycle changes and their \r [...]"


id: 80d52d49111e143980be724a2c02f039
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 19800
content:
"Declining near-surface relative humidity over land areas (e.g.,  the \r USA, Mediterranean, South Asia, South America and southern Africa) \r is evident in surface observations (Willett et al., 2014, 2020; Dunn \r et al., 2017). This is consistent with a faster rate of warming over \r land than ocean (Sections 2.3.1.3 and 8.2.2.1; Byrne and O'Gorman, \r 2018). CMIP5 simulations underestimate the observed decreases \r in relative humidity over much of global land during 1979-2015 \r (Douville and Plazzotta, 2017; Dunn et al., 2017) even when observed \r SSTs are prescribed (-0.05 to -0.25% per decade compared with an \r observed rate of -0.4 to -0.8% per decade). It is not yet clear if this \r discrepancy is related to internal variability or can be explained by \r deficiencies in models (Vanniere et al., 2019; Douville et al., 2020)\r or observations (Willett et al., 2014). Over the NH mid-latitude \r continents, there is medium confidence that human influence has \r [...]"


id: fe3d2172d414afef10b9eb2adc3ee5fd
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 42097
content:
"[...] contributed to a decrease in near-surface relative humidity in summer\r (Sections 2.3.1.3 and 3.3.2.3). "


id: 8454363950e9943fcc286161373c47b8
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 20431
content:
"Water vapour transport (or convergence) estimates from observations \r have substantial uncertainties even in regions of high quality \r radiosonde data. Consequently many studies use reanalyses for \r water transport estimates instead of instrumental observations. For \r example, increases in low-level (800-1000 hPa) moisture convergence \r into the tropical wet regime with a smaller outflow increase in the \r mid-troposphere (400-800 hPa) with warming was detected in one \r reanalysis (ERA-Interim; Allan et al., 2014). Modelling evidence \r combined with statistical analysis demonstrate consistency between \r reanalysis moisture convergence and P-E over land (Robertson et al., \r 2016). Advances in reanalysis representation of atmospheric moisture \r and winds in addition to new observational isotope analysis have \r improved the ability to identify the main sources of water vapour for \r key continental regions and quantify the relative contributions from \r [...]"


id: 53ef2e92af3e93c6ee254f6152fafe95
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 12036
content:
"[...] moisture advection and recycling (Gimeno et al., 2012; van Der Ent \r et al., 2014; Joseph et al., 2016). "


id: f22c496bac46ff6e3f65efcf52d863b9
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 40000
content:
"Observed changes in moisture transport can also arise from changes \r in atmospheric circulation as well as thermodynamics. For instance, \r moisture transport into the Arctic region estimated from reanalyses \r datasets is consistent with radiosonde data (Dufour et al., 2016),\r with increases since 1979 linked to atmospheric circulation (Nygard \r et al., 2020). Moisture transport into the Eurasian Arctic was \r identified to increase by 2.6% per decade during 1948-2008 based \r on a reanalysis estimate (X. Zhang et al., 2013). More intense moist \r intrusions associated with atmospheric rivers affecting the Arctic and \r Europe have been documented since 1979, but with a  substantial \r influence from decadal internal variability (Ummenhofer et al., 2017; \r Mattingly et al., 2018). A recent strengthening of tropical circulation \r and associated moisture convergence has been identified since \r around 2000 for the Amazon region (Arias et al., 2015; Barichivich \r [...]"


id: 2254ec9501c39f6fa8e0c170bc856857
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 3262
content:
"[...] et al., 2018; J.C. Espinoza et al., 2018; X.Y. Wang et al., 2018). This was \r also strenghtened by increased moisture transport from the North \r Atlantic, driving more abundant latent heat release (Segura et al., \r 2020) and leading to an increased frequency of extreme floods in the \r northern Amazon (Barichivich et al., 2018; Heerspink et al., 2020). \r Overall, increased moisture transport has been linked to increased "


id: 46ee9e5542c52699721749724c105cf2
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 9683
content:
"Since AR5, it is very likely that increases in global atmospheric water \r vapour were observed based on in situ, satellite and reanalysis data \r (with medium confidence in the magnitude; Section 2.3.1.3). Satellite \r records show increases in upper tropospheric water vapour (constant \r relative humidity while temperatures have increased) since 1979 \r (E.-S. Chung et al., 2014; Blunden and Arndt, 2020), to which human \r influence has likely contributed (Section 3.3.2.2). Combined satellite \r and reanalysis estimates and CMIP6 atmosphere-only simulations \r (1988-2014) show global mean precipitable water vapour increases \r of 6.7 +-  0.3 %  degC-1, very close to the Clausius-Clapeyron rate \r (Allan et al., 2020). Satellite-based products show increases close \r to the Clausius-Clapeyron rate over the ice-free oceans (about 7 to \r 9 % degC-1; 1998-2008), but reanalysis estimates outside this range \r (Schroder et al., 2019) are an expected consequence of their changing \r [...]"


id: 3c7e04a3627200200fd431459523b9c
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 7833
content:
"[...] observing systems (Allan et al., 2014; Parracho et al., 2018). Increases \r in precipitable water vapour are found over the central and sub\u0002Arctic based on multiple reanalyses with some corroboration from "


id: 37ff5cb5b638b3e521d1c6524e31113d
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 7058
content:
"The AR5 concluded that it is likely there has been an overall increase \r in annual mean precipitation amount over mid-latitude land areas in \r the NH, with low confidence since 1901, but medium confidence after \r 1951. There is further evidence of a faster increase since the 1980s \r (medium confidence) (Sections 2.3.1.3.4 and 3.3.2.2). Precipitation \r has increased from 1950 to 2018 over mid-high latitude Eurasia, \r most of North America, south-eastern South America, and north\u0002western Australia, while it has decreased over most of Africa, eastern \r Australia, the Mediterranean region, the Middle East, and parts of \r East Asia, central South America, and the Pacific coasts of Canada, as \r simulated by the CMIP5 multi-ensemble mean (Dai, 2021). Since AR5, \r there have been updates of several precipitation datasets, including \r satellite estimates, reanalysis and merged products (Adler et al., \r 2017; Roca, 2019). However, observational uncertainties remain \r [...]"


id: 525d8e67ad98b804e2a8f8b6a39debf7
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 11768
content:
"[...] an issue for assessing regional trends in seasonal or annual mean \r precipitation amount (Hegerl et al., 2015; Maidment et al., 2015; \r Sarojini et al., 2016; Beck et al., 2017), as well as the convective and \r stratiform types of precipitation (e.g., Ye et al., 2017). Precipitation \r trends at regional scales are dominated by internal variability across \r much of the world (Knutson and Zeng, 2018). Regional changes in \r precipitation amounts can also be obscured by contrasting responses \r to GHG compared with aerosol forcings (Wu et al., 2013; Hegerl \r et al., 2015; Xie et al., 2016; Zhao and Suzuki, 2019; Zhao et al., \r 2020) and changes in precipitation intensity versus frequency (Shang \r et al., 2019). "


id: 22fdcc0a892041171e26590b9f8a4e46
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 3354
content:
"In the tropics, increases in precipitation amount are observed in \r convergence zones and decreases in the descending branches \r of the atmospheric circulation since 1979 (Chou et al., 2013; Liu \r and Allan, 2013; Gu et al., 2016; Polson et al., 2016; Polson and \r Hegerl, 2017), consistent with increased moisture transports with \r warming (Gimeno et al., 2020). Over tropical land areas, there is \r substantial variability in the 'wet convergent regimes get wetter' \r and 'dry divergent regimes get drier' pattern of trends observed \r since 1950 that are modulated by decadal changes in ENSO (Liu \r and Allan, 2013; Gu and Adler, 2018). CMIP6 models indicate an \r increased contrast between wet and dry regions in the tropics and \r subtropics (Figure 8.7; Schurer et al., 2020). This provides further \r evidence that rainfall has increased in wet regimes, and slightly \r decreased in dry regimes over the period 1988-2019 (Figure 3.14). \r [...]"


id: cdccadb27200ddfd4d220410ece9fe0a
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 33536
content:
"[...] This greater contrast is primarily attributable to greenhouse gas \r forcings, although the observed trends are statistically larger than \r the model responses (Section 3.3.2.3)."


id: 24a69f1440c7cd903b04cfbbaf576eb9
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 3638
content:
"of 0.12  mm  day-1 per decade, while the number of days with \r precipitation exceeding 0.1  mm  day-1 significantly decreased at \r a rate of 0.9 days per decade (Shang et al., 2019). There is consistency \r in trend estimates during 1998-2015 over mainland China among \r satellite-based products and station data, which show increased \r precipitation amounts in autumn and winter and decreases in summer \r (Chen and Gao, 2018), consistent with a decreased intensity of East \r Asian monsoon precipitation (Lin et al., 2014; Deng et al., 2018). \r Further assessment of precipitation changes over the South and \r South East Asian and the East Asian monsoon regions is presented in \r Section 8.3.2.4. An increasing trend in the frequency of heavy rainfall \r occurrences at the expense of low and moderate rainfall occurrences \r is found over central India (Krishnan et al., 2016; Roxy et al., 2017) \r and over eastern China with the latter due to increasing high aerosol \r [...]"


id: 5c5aeb2314b6ea2e2df1043127bec70d
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 13501
content:
"[...] levels (Qian et al., 2009; J. Guo et al., 2017; Xu et al., 2017; Day et al., \r 2018), consistent with the effects of absorbing aerosol on stability \r and convective inhibition (Box 8.1)."


id: 19365fe7fc8a877de4ec259236d84e4
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 1654
content:
"Observed precipitation records since the early 1900s show \r increases in precipitation totals over central and north-eastern \r North America that are attributable to anthropogenic warming but \r larger in magnitude than found in CMIP5 simulations (Knutson and \r Zeng, 2018; Guo et al., 2019). Decreases in precipitation amount \r over the central and south-western USA and increases over the \r north-central USA during 1983-2015 (Cui et al., 2017; P. Nguyen \r et al., 2018), are not clearly associated with forced responses in \r CMIP6 simulations (Figure  8.7; see also Section  10.4.2.3). Over \r Europe, precipitation trends since 1979 do not show coherence \r across datasets (Zolina et al., 2014; P. Nguyen et al., 2018). Longer \r records since 1910 show increases for much of Scandinavia, north\u0002western Russia, and parts of north-western Europe/United Kingdom \r and Iceland (Knutson and Zeng, 2018). Records since 1930 show \r increases of annual preciptation amount over western Russia (see \r [...]"


id: 84105e0b7a2007a53610835f720f19a1
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 20389
content:
"[...] also Atlas.8.2). Widespread increases in daily precipitation intensity \r appear clearly over regions with a  high density of rain gauges, \r such as Europe and North America over the 1951-2014 period \r (Alexander, 2016). Observations during 1966-2016 over northern \r Eurasia show increases in the contribution of heavy convective \r showers to total precipitation by 1-2% on average (with local \r trends of up to 5%) for all seasons except for winter (Chernokulsky \r et al., 2019). Increases in convective precipitation intensity have \r been identified, particularly on sub-daily time scales, using a range \r of modelling and observational data (Berg et al., 2013; Kanemaru \r et al., 2017; Pfahl et al., 2017)."


id: c6626ae16db00c5283fef68006835cfd
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 32214
content:
"Over South America, there is observational and paleoclimate \r evidence of declining precipitation amount during the past 50 years \r over the Altiplano and central Chile, primarily explained by the PDO \r but with at least 25% of the decline attributed to anthropogenic \r influence (Morales et al., 2012; Neukom et al., 2015; Boisier et al., \r 2016; Seager et al., 2019b; Garreaud et al., 2020). In contrast, \r a significant rainfall increase has been detected over the Peruvian-\r Bolivian Altiplano (from observational data and satellite-based \r estimations) since the 1980s (Figure 8.7; Imfeld et al., 2020; Segura \r et al., 2020). Long-term (1902-2005) precipitation data indicate \r positive trends over south-eastern South America and negative \r trends over the southern Andes, with at least a partial contribution \r from anthropogenic forcing (Gonzalez et al., 2014; Vera and Diaz, \r 2015; Diaz and Vera, 2017; Boisier et al., 2018; Knutson and Zeng, \r [...]"


id: d769538594130127a3a7558d6f67a267
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 35204
content:
"[...] 2018; see further assessment in Section 10.4.2.2 and Atlas.7.2.2). \r The Peruvian Amazon has exhibited significant rainfall decreases \r during the dry season since 1980 (Lavado et al., 2013; Ronchail \r et al., 2018). Increases in wet season rainfall in the northern and \r central Amazon since the 1980s and decreases during the dry \r season in the southern Amazon (Barreiro et al., 2014; Gloor et al., \r 2015; Martin-Gomez and Barreiro, 2016; J.C. Espinoza et al., 2018; \r X.Y. Wang et al., 2018; Haghtalab et al., 2020) are not explained \r by radiative forcing based on CMIP6 experiments (Figure 8.7) and \r trends are insignificant over longer periods since 1930 (Kumar \r et al., 2013) or more recently, since 1973 (Almeida et al., 2017). \r See Section 8.3.2.4.5 for monsoon-related changes. For the tropical \r Andes region, trends in annual precipitation show heterogenous \r patterns, ranging between -4% per decade and +4% per decade \r [...]"


id: 654bacc33f7126e6294ebd0d3ae8cf16
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 15097
content:
"[...] in the northern and southern tropical Andes for a 30-year period at \r the end of the 20th century, although increases during 1965-1984 \r and decreases since 1984 have been registered in Bolivia (Carmona \r and Poveda, 2014; Pabon-Caicedo et al., 2020). \r Over China, annual precipitation totals changed little from 1973 \r to 2016, but precipitation intensity significantly increased at a rate "


id: bbc32d1bad81a0057ef670396b2e0923
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 30263
content:
"In summary, regional changes in precipitation amounts can be \r obscured by the contrasting responses to GHG and aerosol forcings \r across much of the 20th century and can thus be dominated \r by internal variability at decadal to multi-decadal time scales \r (high confidence). There is, however, a  detectable increase in \r northern high-latitude annual precipitation over land which has \r been primarily driven by human-induced global warming (high \r confidence) (Section  3.3.2). Human influence has strengthened \r the zonal mean precipitation contrast between the wet tropics \r and dry subtropics since the 1980s (medium confidence), although \r regional studies suggest a more complex precipitation response to \r evolving anthropogenic forcings. There is high confidence that daily \r mean precipitation intensities have increased since the mid-20th \r century in a majority of land regions with available observations \r and it is likely that such an increase is mainly due to GHG forcing \r [...]"


id: 9d6609b0aaa8880babb8e168fc1eff98
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 24927
content:
"Plant water use efficiency (WUE) is expected to rise with CO2 levels \r (high confidence) (Section 8.2.3.3 and Box 5.2), and can in theory \r counteract rising evapotranspiration in a  warmer atmosphere \r (Section  8.2.3.3). However, observational studies suggest that this \r may not be the case in some ecosystems. For example, Frank et al. \r (2015) found that while the WUE increased in European forests across \r the 20th century, transpiration also increased due to more plant \r growth, a  lengthened growing season, and increased evaporative \r demand. Likewise Guerrieri et al. (2019) observed that while WUE \r and photosynthesis increased in North American forests, stomatal \r conductance experienced only modest declines that were restricted \r to moisture-limited forests. Other studies further suggest that in \r many ecosystems increased WUE will not compensate for increased \r plant growth, amplifying declines in surface water availability \r [...]"


id: 9e2525d81b85b4899fe8f409b9b263a9
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 25055
content:
"[...] (De Kauwe et al., 2013; Ukkola et al., 2016b; A. Singh et al., 2020), \r while drought conditions can also offset the CO2 fertilization effect \r and lead to a  decline in WUE (N.  Liu et al., 2020). There is low \r confidence regarding the impact of plant physiological effects on \r observed trends in evapotranspiration."


id: 132fca7d486390aa6384aa2cee2d4bf5
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 586
content:
"The AR5 assessed that there was medium confidence that pan \r evaporation declined in most regions over the last 50 years, yet \r medium confidence that evapotranspiration increased from the early \r 1980s to the late 1990s. Since AR5, these conflicting observations \r have been attributed to internal variability and by the fact that \r evapotranspiration is less sensitive to trends in wind speed and is partly \r controlled by vegetation greening (K.  Zhang et al., 2015; Y.  Zhang \r et al., 2016; Z.  Zeng et al., 2018b). Observation-based estimates \r show a robust positive trend in global terrestrial evapotranspiration \r between the early 1980s and the early 2010s (Miralles et al., 2014b; \r Z.  Zeng et al., 2014, 2018b; K.  Zhang et al., 2015; Y.  Zhang et al., \r 2016). The rate of increase varies among datasets, with an ensemble \r mean terrestrial average rate of 7.6 +- 1.3 mm yr-1 per decade for \r 1882-2011 (Z. Zeng et al., 2018a). In addition, a decreasing trend \r [...]"


id: 88ad97d238924c60cbea995b374cc2be
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 21210
content:
"[...] in pan evaporation plateaued or reversed after the mid-1990s \r (C.M. Stephens et al., 2018) has been reported as due to a shift from \r a dominant influence of wind speed to a dominant effect of water \r vapour pressure deficit, which has increased sharply since the 1990s \r (Yuan et al., 2019). The absence of a  trend in evapotranspiration \r in the decade following 1998 was shown to be at least partly an \r episodic phenomenon associated with ENSO variability (Miralles \r et al., 2014b; K. Zhang et al., 2015; Martens et al., 2018). Thus, there \r is medium confidence that the apparent pause in the increase in \r global evapotranspiration from 1998 to 2008 is mostly due to internal \r variability. In contrast to AR5, there are now consistent trends in \r pan evaporation and evapotranspiration at the global scale, given \r the recent increase in both variables since the mid-1990s (medium \r confidence). Given the growing number of quantitative studies, there \r [...]"


id: bb145900c0b90068f2fd0ce413799009
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 30138
content:
"[...] is high confidence that global terrestrial annual evapotranspiration \r has increased since the early 1980s."


id: 1566b08b09f4ea7770e682741ded6593
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 978
content:
"The AR5 reported low confidence in the assessment of trends in \r global river discharge during the 20th century. This is because many \r streamflow observations have been impacted by land use and dam \r construction, and the largest river basins worldwide differ in many \r characteristics, including geography and morphology. In regions \r with seasonal snow storage, AR5 WGII assessed that there is robust \r evidence and high agreement that warming has led to earlier spring \r discharge maxima and robust evidence of earlier breakup of Arctic \r river ice, as well as indications that warming has led to increased \r winter flows and decreased summer flows where streamflows are \r lower and that the observed increases in extreme precipitation led \r to greater probability of flooding at regional scales with medium \r confidence. The SROCC found robust evidence and high agreement\r that discharge due to melting glaciers has already reached its \r [...]"


id: a528b9158265cf276719475f146b6fbe
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 26288
content:
"[...] maximum point and has begun declining with smaller glaciers, but \r only low confidence that anthropogenic climate change has already \r affected the frequency and magnitude of floods at the global scale. "


id: 31c16555e56436120236fd4c854f15f8
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
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content:
"Significant trends in streamflow and continental runoff were observed \r in 55 out of 200 large river basins during 1948-2012, with an even \r distribution of increasing and decreasing trends (Section 2.3.1.3.6; \r Dai, 2016). A global detection and attribution study shows that the \r simulation of spatially heterogeneous historical trends in streamflow \r is consistent with observed trends only if anthropogenic forcings are \r considered (Gudmundsson et al., 2019). Section  3.3.2.4 assesses \r with medium confidence that anthropogenic climate change has \r altered regional and local streamflows, although a significant trend \r has not been observed in the global average (Sections 2.3.1.3.6 and \r 3.3.2.3). Multiple human-induced and natural drivers have been \r shown to play an important but variable role in observed regional \r trends of streamflow for several different areas (Fenta et al., 2017; \r Ficklin et al., 2018; Glas et al., 2019; Vicente-Serrano et al., 2019). \r [...]"


id: ef470e6dbd9c7182a5a06ed38fcb967f
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content:
"[...] For instance, decreasing runoff during the dry season has been \r observed over the Peruvian Amazon since the 1980s (Lavado et al., \r 2013; Ronchail et al., 2018). Up to 30-50% of the recent multi\u0002decadal decline in streamflow across the Colorado River Basin can \r be attributed to anthropogenic warming and its impacts on snow and \r evapotranspiration (Woodhouse et al., 2016; McCabe et al., 2017; \r Udall and Overpeck, 2017; Xiao et al., 2018; Milly and Dunne, 2020). \r In the Upper Missouri River basin, Martin et al. (2020) found that \r warming temperatures have contributed to streamflow reductions \r since at least the late 20th century. Cold regions in the NH have \r experienced an earlier occurrence of snowmelt floods, an overall \r increase in water availability and streamflow during winter, and \r a  decrease in water availability and streamflow during the warm \r season (Aygun et al., 2019). "


id: 9b840d35c6dfb54b3924aef31d83aac2
created_at: 2023-04-19 13:22:39
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content:
"forcing (Marvel et al., 2019), but then emerges again from 1981 to \r present, although it is not detectable in the GLEAM nor MERRA-2 \r soil moisture reanalysis products. Likewise, Bonfils et al. (2020) \r investigated changes in precipitation, temperature and continental \r aridity in CMIP5 historical simulations and found that the dominant \r multivariate fingerprint, an amplification of wet-dry latitudinal \r patterns and progressive continental aridification, was associated \r with greenhouse gas emissions (Figure  8.9a,d), and the second \r leading fingerprint was associated with anthropogenic aerosols \r (Figure 8.9e,h). This study found that the anthropogenic greenhouse \r gas signal is statistically detectable in reanalyses over the 1950-2014 \r period (signal-to-noise ratio above 1.96). Gu et al. (2019) found that \r a global trend in declining soil moisture is detectable in the GLDAS-2 \r reanalysis product and is attributable to greenhouse gas forcing. \r [...]"


id: 4fb5f7d4dd0c14debac654de39ee4599
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
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index: climate_gpt_v2
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content:
"[...] Padron et al. (2020) reconstructed the global patterns of dry season \r water availability from 1902-2014, and found it extremely likely\r (99% range) that trends in the last three decades of the analysis \r period could be attributed to anthropogenic forcing, mainly due to \r increases in evapotranspiration. It is very likely (>90% range) that \r anthropogenic forcing has affected global patterns of soil moisture \r over the 20th century."


id: 4c2d52cc752ef0306744a5c1eba817e6
created_at: 2023-04-19 13:22:39
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content_type: text
index: climate_gpt_v2
vector_id: 10662
content:
"The Mediterranean region has experienced notable changes \r in drought and aridity. A number of studies have identified a decline \r in precipitation since 1960 and attributed this to anthropogenic \r forcing (Hoerling et al., 2012; Gudmundsson and Seneviratne, 2016; \r Knutson and Zeng, 2018; Seager et al., 2019b). Kelley et al. (2015) \r showed that climate change caused a  three-fold increase in the \r likelihood of the 2007-2010 meteorological drought in the eastern \r Mediterranean. However, historical trends in precipitation across \r the Mediterranean are spatially variable and contain substantial \r decadal variability, such that an anthropogenic influence may not \r be detectable in all areas (Zittis, 2018; Vicente-Serrano et al., 2021). \r Records of soil moisture provide a  clearer signal, indicating that \r higher temperatures and increased atmospheric demand have played \r a strong role in driving Mediterranean aridity (Vicente-Serrano et al., \r [...]"


id: faf7232b5327162cdd90daf70bf56ccc
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content:
"[...] 2014). Hydrological modeling suggests that the recent decline in \r soil moisture in the Mediterranean is unprecedented in the last \r 250 years (Hanel et al., 2018). Paleoclimate evidence extends this \r view, additionally indicating that dryness in the Mediterranean is \r approaching an extreme condition compared to the last millennium \r (Markonis et al., 2018) and that the 15-year drought in the Levant \r (1998-2012) has an 89% likelihood of being the driest of the last \r 900 years (Cook et al., 2016a). Marvel et al. (2019) found that the \r Mediterranean region contributes strongly to the anthropogenic \r warming component of the global trend in aridity. There is high \r confidence that anthropogenic forcings are causing increased aridity \r and drought severity in the Mediterranean region."


id: 44c94c33b40f4ce69aeaf38efa5fb3c0
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
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index: climate_gpt_v2
vector_id: 9328
content:
"Several studies have analyzed CMIP5 and land surface models and \r detected a significant summer drying trend in the NH across the late \r 20th century that is attributable to anthropogenic forcings (Mueller \r and Zhang, 2016; Douville and Plazzotta, 2017). This trend is mainly \r driven by dryland areas such as the western USA and west-central \r Asia, where both reanalysis products and satellite data confirm \r there has been a persistent decline in soil moisture since 1990 (Y. Liu \r et al., 2019a). In the western USA, snow deficits have very likely\r contributed to recent drying (Mote et al., 2018). Spring snow water \r equivalent across the Sierra Nevada Mountains reached a  record \r low in 2015 (Margulis et al., 2016; Mote et al., 2016), possibly the \r lowest of the last five hundred years (Belmecheri et al., 2016). Over \r the longer California drought (2011-2015) anthropogenic warming \r alone reduced snowpack levels in the Sierras by 25% (Berg and Hall, \r [...]"


id: 4729b79bdc86f747fdbbd32b905fbc8a
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index: climate_gpt_v2
vector_id: 9721
content:
"[...] 2017). The north-western USA also experienced snow drought in \r 2015, despite near-normal levels of total cold season precipitation \r (Mote et al., 2016; Marlier et al., 2017). There is high confidence that \r anthropogenic warming contributed to these recent snow droughts \r (Belmecheri et al., 2016; Mote et al., 2016). "


id: 2d4bee91aca68112282cc7d348ed9bcc
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
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index: climate_gpt_v2
vector_id: 5255
content:
"Millennium drought in south-eastern Australia was the worst \r experienced since 1783 (Gergis et al., 2012), and that the spatial \r extent and duration of cool season (April to September) rainfall \r anomalies were either very much below average or unprecedented \r over at least the last 400 years (Freund et al., 2017). Other \r paleoclimate studies suggest that the Millennium drought in \r eastern Australia was not unusual in the context of natural \r variability reconstructed over the past millennium (Palmer et al., \r 2015; Cook et al., 2016c; Kiem et al., 2020). While there is currently \r low confidence that recent droughts in eastern Australia can be \r clearly attributed to human influence (Cai et al., 2014; Delworth \r and Zeng, 2014; Rauniyar and Power, 2020), there is emerging \r evidence that declines in April to October rainfall in south-eastern \r Australia since the 1990s would not have been as large without the \r influence of increasing levels of atmospheric GHGs (Rauniyar and \r [...]"


id: 676d8ca721867b0d5e8499dbe3b51987
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content:
"intense in recent decades, continuing across rainy seasons (Hoell \r et al., 2017b; Nicholson, 2017), and this trend appears to be unusual \r in the context of the last 1500 years (Tierney et al., 2015). Knutson \r and Zeng (2018) attribute decreased annual precipitation over the \r Sudan to anthropogenic forcing, but other studies argue that the \r recent trend cannot yet be distinguished from natural variability, at \r least over parts of this region (Hoell et al., 2017b; Philip et al., 2018). \r There remains low confidence due to limited evidence that drying \r the north-eastern Africa is attributable to human influence. In the \r Western Cape region of South Africa, human influence increased \r the likelihood of the severe 2015-2017 drought by a  factor of \r 3-6, depending on the analysis (Otto et al., 2018; Pascale et al., \r 2020). Anthropogenic forcing also contributed to the 2018 drought, \r mainly by increasing evapotranspiration (Nangombe et al., 2020). \r [...]"


id: 25a51d8877427330ce28e3f7db1dfdf
created_at: 2023-04-19 13:22:39
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index: climate_gpt_v2
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content:
"[...] While some analysis of instrumental precipitation data in this region \r detect a slight long-term drying trend consistent with the simulated \r anthropogenic response (Seager et al., 2019b), there is strong \r multi-decadal variability in the data (Wolski et al., 2021). However, \r a  study of streamflow in southern Africa detected a  significant \r decline (Gudmundsson et al., 2019; see also Section 10.6.2). There \r is medium confidence in the long-term drying trend in this region \r and its attribution to anthropogenic forcing, and medium confidence\r that anthropogenic warming has contributed to recent severe \r drought events."


id: 8ae72940b1dc4911b85b6ded8b64bc61
created_at: 2023-04-19 13:22:39
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index: climate_gpt_v2
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content:
"The AR5 and SROCC found, with very high confidence, a  general \r decline in glaciers due to climate change in recent decades. There \r is very high confidence that during the decade 2010-2019 glaciers \r lost more mass than in any other decade since the beginning of the \r observational record (Sections 2.3.2.3 and 9.5.1). Human influence \r is very likely the main driver of the global, near-universal retreat of \r glaciers since the 1990s (Section 3.4.3.1). In Table 9.5, the contribution \r of glaciers to sea level rise for different periods is presented; in \r 1971-2018 glacier mass loss contributed 20.9 [10.0  to 31.7] mm \r or 22.2% of the sea level rise during that period. The highest mass \r loss rates are observed in the southern Andes, New Zealand, Alaska, \r Central Europe and Iceland while the largest mass loss are observed in \r Alaska, the periphery of Greenland and Arctic Canada (Section 9.5.1 \r and Figure 9.20). Predominantly, runoff from small glaciers such as in \r [...]"


id: df64b9d1e59823cfd371419f1185d138
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content:
"[...] Canada has decreased because of glacier mass loss, while runoff from \r larger glaciers such as in Alaska has typically increased (Bolch et al., \r 2010; Thomson et al., 2011; Tennant et al., 2012; WGMS, 2017; Huss \r and Hock, 2018). Asia contains the largest concentration of glaciers \r outside the polar regions where the total glacier mass change is \r -16.3 +- 3.5 Gt yr-1 over 2000-2016 with considerable intra-regional \r variability (Brun et al., 2017). Mass losses of glaciers in Asia between "


id: f2f8bb97e0a42abb0f4c4d0360c2a441
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content:
"autumn and winter seasons (Barry and Gan, 2020). Such recent \r positive trends in snow cover extent are however at odds with \r other surface and satellite datasets and with the negative trends \r simulated by most CMIP5 and CMIP6 models (Mudryk et al., 2017, \r 2020). Hernandez-Henriquez et al. (2015) also detected positive \r trends in October to November SCE in in the NOAA SCE Climate \r Data Record (NOAA-CDR), which are not replicated in other datasets \r (Section 9.5.3). Wu et al. (2018) found slower snowmelt rates over \r the NH in 1980-2017, with higher ablation rates in locations with \r deep snow water equivalent (SWE), but due to the reduction of \r SWE in deep snowpacks, moderate/high ablation rates showed \r decreasing trends. Santolaria-Otin and Zolina (2020) reported \r weak but significant decline in SCE in autumn over northern \r Eurasia and North America during 1979-2005, and similarly for \r spring, except for northern Siberia which showed higher spring \r [...]"


id: 51ccddabbed3e0e49421cf441db7b2ab
created_at: 2023-04-19 13:22:39
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index: climate_gpt_v2
vector_id: 11678
content:
"[...] SCE.  Kapnick and Hall (2012) detected significant loss of spring \r mountain snowpack in western USA in 1950-2008. For Canada, \r extensive decreasing snow depths, SCE and duration were detected \r since mid-1970s, especially in western Canada during winter and \r spring (DeBeer et al., 2016). Berghuijs et al. (2014) show that across \r the continental USA, catchments with more snowfall than rainfall \r generally have higher mean streamflow, which will probably \r decrease with smaller fractions of precipitation falling as snow \r because of climate warming."


id: 3b4aabea37eeff660612dabe622c54a2
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 7617
content:
"The AR5 assessed that Northern Hemisphere (NH) snow cover \r extent (SCE) has decreased since the late 1960s, especially in \r spring (very high confidence). This is confirmed by recent studies \r (Section 2.3.2.2; Kunkel et al., 2016). AR6 assesses that NH spring \r snow cover has been decreasing since 1978 (very high confidence) \r and  that this trend extends back to 1950 (high confidence) \r (Section  9.5.3). Human-caused global warming is the dominant \r driver of this observed decline (Section 3.4.2; Estilow et al., 2015). \r Model simulations suggest that surface temperature responses at \r hemispheric/regional scales explain between 40% and 85% of the \r SCE trend variability (Mudryk et al., 2017). A decreasing trend in \r snowfall has also been detected in the NH (Figure 8.1; Rupp et al., \r 2013). Snowfall as a  proportion of precipitation has decreased \r [...]"


id: 8b52832da9784aaa093544c38fdb720a
created_at: 2023-04-19 13:22:39
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vector_id: 21673
content:
"[...] significantly in recent years (Berghuijs et al., 2014). However, a late\u000220th-century increase in snowfall in West Antarctica observed in ice \r cores has been linked to a  combination of factors including the \r anthropogenically forced deepening of the Amundsen Sea Low \r (Thomas et al., 2015, 2017)."


id: e2741ffbb4b6705eaa62ca1a56c41222
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index: climate_gpt_v2
vector_id: 37139
content:
"Increasing global freshwater withdrawals, primarily associated \r with the expansion of irrigated agriculture in drylands, have led \r to global groundwater depletion that has an estimated range of \r about 100 and about 300 km3 yr-1 from hydrological models and \r volumetric-based calculations (Bierkens and Wada, 2019). The \r magnitude of this change is such that its estimated contribution \r to global sea level rise is in the order of 0.3 to 0.9 mm yr-1 (Wada \r et al., 2010; Konikow, 2011; Doll et al., 2014; Pokhrel et al., 2015; \r de Graaf et al., 2017; Hanasaki et al., 2018). Groundwater depletion \r has been observed regionally in The USA High Plains, California's \r Central Valley (Scanlon et al., 2012), north-west India (Rodell et al., \r 2009; Asoka et al., 2017), Upper Ganges in India (MacDonald \r et al., 2016), North China Plain (Feng et al., 2013), north-central \r Middle East region of Tigris-Euphrates-Western Iran (Voss et al., \r [...]"


id: 429c823048414a5aaac54c801866fb6c
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vector_id: 8913
content:
"[...] 2013), Central Asia (Hu et al., 2019), and North Africa (Bouchaou \r et al., 2013). The regional contribution of agricultural irrigation to \r groundwater depletion was previously highlighted by SRCCL but \r no formal assessment of observed changes in global or regional \r groundwater featured in AR5. "


id: 76587fa982713a60bb9fbdb49a10c5fb
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
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index: climate_gpt_v2
vector_id: 18043
content:
"Following a  global review of groundwater and climate change \r (R.G. Taylor et al., 2013a) and AR5 WGII, evidence of an association \r between heavy or extreme precipitation and groundwater recharge \r has continued to grow, especially in tropical (Asoka et al., 2018; \r Cuthbert et al., 2019a; Kotchoni et al., 2019) and subtropical regions \r (Meixner et al., 2016). Stable-isotope ratios of oxygen and hydrogen \r at 14 of 15 sites across the tropics trace groundwater recharge to \r intensive monthly rainfall, commonly exceeding the 70th intensity \r percentile, approximately (Jasechko and Taylor, 2015). Further, heavy \r rainfall recharging groundwater resources is often influenced by \r climate variability such as ENSO and PDO (R.G. Taylor et al., 2013b; \r Kuss and Gurdak, 2014; Asoka et al., 2017; Cuthbert et al., 2019b; \r Kolusu et al., 2019; Shamsudduha and Taylor, 2020). Additionally, \r increases in groundwater storage estimated from GRACE for 37 \r [...]"


id: f1044bea6f03bf8239cc92d3c34fad89
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index: climate_gpt_v2
vector_id: 39773
content:
"[...] of the world's large-scale aquifer systems from 2002 to 2016 are \r generally found to result from episodic recharge associated with \r extreme (>90th percentile) annual precipitation. "


id: a43e92e3d185dc8db424b05b8a51badd
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 26088
content:
"The AR5 concluded it is likely that the tropical belt, as delimited by \r the Hadley circulation, has widened since the 1970s. Observations \r in the satellite era indicate precipitation increases in the core of \r the Pacific Inter-tropical Convergence Zone (ITCZ) and decreases on \r the ITCZ margins (Gu et al., 2016; Su et al., 2017). As the satellite \r period has lengthened, observations have increasingly been used to \r assess trends in the ITCZ and tropical rain belt. Since AR5, significant \r narrowing and strengthening of the Pacific ITCZ after 1979 have been \r identified in atmospheric reanalyses (Wodzicki and Rapp, 2016), but \r no change in the ITCZ location (Byrne et al., 2018). Atmospheric \r model simulations suggest that with a narrower ITCZ, the subtropical \r jet becomes baroclinically unstable at a  lower latitude and allows \r mid-latitude eddies to propagate farther equatorward (Watt-Meyer \r and Frierson, 2019). Observational analyses also show that the \r [...]"


id: 36a1f8fe8ba474a58519682b8341543f
created_at: 2023-04-19 13:22:39
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index: climate_gpt_v2
vector_id: 6844
content:
"[...] ITCZ narrowing (Zhou et al., 2020) is associated with increased \r precipitation in the ITCZ core region that is strongly coupled to \r increasing Outgoing Longwave Radiation (OLR) in the expanding \r dry zones, particularly over land regions in the subtropics and mid\u0002latitudes (Lau and Tao, 2020). In addition, an eastward movement of \r the South Pacific Convergence Zone (SPCZ) between 1977 and 1999 \r has been reported, with associated significant precipitation trends in \r the South Pacific regions (Salinger et al., 2014). "


id: 282b8f1ab0c885d3e42ea3857896d4bb
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
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index: climate_gpt_v2
vector_id: 4266
content:
"The poleward shift of the HC is closely related to migration of the \r location of tropical cyclone trajectories in both hemispheres (Sharmila \r and Walsh, 2018; Studholme and Gulev, 2018), with a  very likely\r poleward shift over the western North Pacific Oceans since the 1940s \r (Section  11.7.1.2). Moreover, the Western North Pacific Subtropical \r High has extended westward since the 1970s, resulting in a monsoon \r rain band shift over China, with excessive rainfall along the middle \r and lower reaches of the Yangtze River valley along about 30degN over \r eastern China. At the same time, the effect of anthropogenic aerosols \r dominated the response to GHG increases over East Asia, resulting in \r a weakening of the East Asian summer monsoon and causing a drying \r trend in north-eastern China (Hu, 2003; Yu and Zhou, 2007; T. Wang \r et al., 2013; Z. Li et al., 2016b; Lau and Kim, 2017) and northern parts of \r South Asia (Section 8.3.2.4.2; Preethi et al., 2017). During 1977-2007, \r [...]"


id: 65b11e90aafc561bab03503367f48ab7
created_at: 2023-04-19 13:22:39
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index: climate_gpt_v2
vector_id: 15154
content:
"[...] the precipitation variability over the eastern USA increased due to \r changes in the intensity and position of the western ridge of the North \r Atlantic Subtropical High (Li et al., 2011; Diem, 2013). "


id: bcf92e2deaeab28701873bfe210a0651
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updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 30488
content:
"Reconstructions in the Sahel (Carre et al., 2019) and Belize (Ridley \r et al., 2015) support the southward displacement of the tropical \r rain belt since 1850 and the narrowing trend of the tropical rainbelt \r detected in observations (Rotstayn et al., 2002; Hwang et al., 2013). \r Decreasing precipitation trends in the NH during the 1950s to 1980s \r have been attributed to anthropogenic aerosol emissions from North \r America and Europe, which peaked during the late1970s and declined \r thereafter following improved air quality regulations, causing dimming \r (brightening) through reduced (increased) surface solar radiation \r (Box 8.1 Figure 1), in agreement with model simulations (Chiang et al., \r 2013; Hwang et al., 2013). This is consistent with energetic constraints \r where tropical precipitation shifts are anti-correlated with cross\u0002equatorial energy transport (Section 6.3.3, Box 8.1). It also provides \r a physical mechanism for the severe drought in the Sahel that peaked \r [...]"


id: 21078334987434e97ccbe217a0482c05
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vector_id: 3033
content:
"[...] in the mid-1980s (Sections 8.3.2.4.3 and 10.4.2.1) and the southward \r shift of the NH tropical edge from the 1950s to the 1980s (Allen et al., "


id: 90de1b6fedf8e1b6d01f8c49cb4c6470
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content_type: text
index: climate_gpt_v2
vector_id: 22693
content:
"Since AR5, an improved understanding of the key drivers of the \r recent HC expansion has been achieved, identifying the role of \r both internal variability and anthropogenic climate change. Part of \r the recent expansion (1979-2005) of the HC has been driven by \r a swing from warm to cold phase of the Pacific Decadal Variability \r (PDV; Meehl et al., 2016; Grise et al., 2019). The presence of large \r multi-decadal variability in 20th-century reanalyses means there \r is limited evidence on the human influence on the recent HC \r strengthening, yet the southward shift of the southern edge and \r widening of the SH HC appeared as robust features in all reanalysis \r datasets, and their trends have accelerated during 1979-2010 \r (D'Agostino and Lionello, 2017). As assessed in Section  3.3.3.1, \r GHG increases and stratospheric ozone depletion have contributed \r to the expansion of the zonal mean HC in the SH since around \r 1980, and the expansion of the NH HC has not exceeded the range \r [...]"


id: bfa51641751b2ae4b30adb2b5888904a
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updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 30988
content:
"[...] of internal variability (medium confidence). Moreover, Antarctic \r ozone depletion can cause a poleward shift in the SH mid-latitude \r jet and HC (Sections 3.3.3 and 6.3.3.2). Further assessment of the \r attribution of recently observed changes in the HC extent and \r intensity is found in Section 3.3.3.1."


id: caff6048862ef01dc219b94e8b03ee4a
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 33053
content:
"Large human populations in the monsoon regions of the world \r heavily depend on freshwater supply for agriculture, water resources, \r industry, transport and various socio-economic activities. The effects \r of GHG forcing combined with water vapour feedback (R.J. Allen et al., \r 2015; Dong and Sutton, 2015; Evan et al., 2015; Dunning et al., 2018) \r and cloud feedbacks (Stephens et al., 2015; Potter et al., 2017) are \r fundamental to monsoon precipitation changes in a warming world. \r Since AR5 there has been improved understanding of precipitation \r changes associated with regional monsoons. Sections  2.3.1.4.2 \r and 3.3.3.2 provide an assessment of observed changes and \r attribution for the global monsoon. Here we provide an assessment \r of the observed changes in regional monsoons (see Annex V  and \r Figure 8.11) and underlying causes. In AR6, the definition of regional \r monsoons slightly differs from AR5 and the rationale for it is provided \r [...]"


id: d482713f7b107c433c2679f09718c62c
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content:
"Figure 8.11 | Regional monsoon precipitation changes from observations and model attribution. Precipitation changes during 1951-2014 are shown as least\u0002square linear trends in box-whisker plots (first and fourth rows) over the six regional monsoons, for example, North American monsoon (NAmerM, July-August-September, JAS), \r West African monsoon (WAfriM, June-July-August-September, JJAS), South and South East Asian monsoon (SAsiaM, June-July-August-September, JJAS), East Asian monsoon \r (EAsiaM, June-July-August), South American monsoon (SAmerM, December-January-February, DJF), Australian and Maritime Continent monsoon (AusMCM, December-\r January-February, DJF), and over the two land domains, for example, equatorial America (EqAmer, June-July-August, JJA) and South Africa (SAfri, December-January-February, \r DJF), as identified in the map shown in the middle and as described in Annex V. Precipitation changes are computed from observations and from Detection and Attribution Model \r [...]"


id: 6493929119c05fea47018b612af4f14b
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
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content:
"[...] Intercomparison Project (DAMIP) CMIP6 experiments over the historical period with all-forcing (ALL), GHG-only forcing (labelled GHG), Aerosol-only (AER) and Natural (NAT) \r forcings prescribed. Observations are based on the CRU (light green) and GPCC (light blue) datasets and the APHRODITE (light orange) dataset for SAsiaM and EAsiaM. CMIP6 \r simulations are taken from nine CMIP6 models contributing to DAMIP, with at least three members. Ensembles are weight-averaged for the respective model ensemble size. \r Observed trends are shown as coloured circles and the simulated trends from the CMIP6 multi-model experiments are shown as box-whisker plots. Precipitation anomaly time\u0002series are shown in the second and third row. The thick black line is the multi-model ensemble-mean precipitation anomaly time-series from the ALL experiment and the grey \r [...]"


id: 503205a415469937377bb55294e7d25b
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
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index: climate_gpt_v2
vector_id: 11398
content:
"[...] shading shows the spread across the multi-model ensembles. An 11-year running mean has been applied on the precipitation anomaly time-series prior to calculating the \r multi-model ensemble mean. Further details on data sources and processing are available in the chapter data table (Table 8.SM.1)."


id: eecf7cfbd6f69645f372e02b0700b41e
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 39389
content:
"Evidence from several climate modelling studies indicates that the \r observed decrease in the regional monsoon precipitation during the \r second half of the 20th century is dominated by the radiative effects \r of NH anthropogenic aerosols, with smaller contributions due to \r volcanic aerosols from the Mount Pinatubo (1991) and El Chichon \r (1982) eruptions (Bollasina et al., 2011; Polson et al., 2014; Sanap \r et al., 2015; Krishnan et al., 2016; Liu et al., 2016; Lau and Kim, 2017; \r Lin et al., 2018; Takahashi et al., 2018; Undorf et al., 2018a, b; Patil \r et al., 2019; M. Singh et al., 2020; see Box 8.1, Figure 1 and Figure 8.11). \r Land-use changes over South and South East Asia and  the rapid \r warming trend of the equatorial Indian Ocean during the recent few \r decades also appear to have contributed to the observed decrease \r in monsoon precipitation (Roxy et al., 2015; Krishnan et al., 2016; \r Singh, 2016). Overall, the magnitude of the precipitation response \r [...]"


id: 9d5ca92411ee7582946292391fc43b31
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
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index: climate_gpt_v2
vector_id: 24916
content:
"[...] to anthropogenic forcing exhibits large spread across CMIP5 models \r pointing to the strong internal variability of the regional monsoon \r (Saha et al., 2014; Salzmann et al., 2014; Sinha et al., 2015), \r including variations linked to phase changes of the Pacific Decadal \r Variability (Section AVI.2.6; X. Huang et al., 2020a), uncertainties in \r representing aerosol-cloud interactions (Takahashi et al., 2018), and \r the effects of local compared with remote aerosol forcing (Bollasina \r et al., 2014; Polson et al., 2014; Undorf et al., 2018b). CMIP3 and \r CMIP5 models do not accurately reproduce the observed seasonal \r cycle of precipitation over the major river basins of South and South \r East Asia, limiting the attribution of observed regional hydroclimatic \r changes (Hasson et al., 2014, 2016; Biasutti, 2019). While warm \r rain processes and organized convection are known to dominate \r the heavy orographic monsoon rainfall over the Western Ghats \r [...]"


id: ef7b80122f4573e410a7d739adcb1708
created_at: 2023-04-19 13:22:39
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content:
"[...] mountains (Shige et al., 2017; Choudhury et al., 2018), in various \r parts of India (Konwar et al., 2012) and East Asia (Section 11.7.3.1), \r there are uncertainties in representing the regional physical processes \r of the monsoon environment, including cloud-aerosol interactions \r (Sarangi et al., 2017), land-atmosphere (e.g., Barton et al., 2020) and \r ocean-atmosphere coupling (Annamalai et al., 2017), in state-of\u0002the-art climate models (see also Section 8.5.1)."


id: 746573b7b79c7481c7b73f124e3eaf7c
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
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index: climate_gpt_v2
vector_id: 17709
content:
"The AR5 reported a  decreasing trend of global land monsoon \r precipitation over the last half-century, with primary contributions \r from the weakened summer monsoon systems in the Northern \r Hemisphere (NH). Since AR5, several studies have documented \r long-term variations and changes in the South and South East \r Asian summer monsoon (SAsiaM) rainfall. The SAsiaM strengthened \r during past periods of enhanced summer insolation in the NH, \r such as the early-to-mid Holocene warm period around 9000 to \r 6000 years before the present (BP) (Masson-Delmotte et al., 2013; \r Mohtadi et al., 2016; Braconnot et al., 2019) and weakened during \r cold periods (high confidence), such as the Last Glacial Maximum \r (LGM) and Younger Dryas (Shakun et al., 2007; Cheng et al., 2012; \r Dutt et al., 2015; Chandana et al., 2018; Hong et al., 2018; E. Zhang \r et al., 2018). These long-time scale changes in monsoon intensity are \r tightly linked to orbital forcing and changes in high-latitude climate \r [...]"


id: e8c179781e0393d634da086048c5841a
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 38298
content:
"[...] (Braconnot et al., 2008; Battisti et al., 2014; Araya-Melo et al., 2015; \r Rachmayani et al., 2016; Bosmans et al., 2018; E. Zhang et al., 2018). \r A weakening trend of the SAsiaM during the last 200 years has been \r documented based on tree ring oxygen isotope chronology from the \r northern Indian subcontinent (Xu et al., 2018) and South East Asia \r (Xu et al., 2013), oxygen isotopes in speleothems from northern India \r (Sinha et al., 2015), and tree ring width chronologies from the Indian \r core monsoon region (Shi et al., 2017). Nevertheless, the detection \r of century-long decreases in regional monsoon rainfall is obscured \r by the presence of multi-decadal time scale precipitation variations \r (Turner and Annamalai, 2012; Knutson and Zeng, 2018) which are \r evident in long-term rain guage records extending back to the early \r 1800s (Sontakke et al., 2008) and emerge in long-term climate \r simulations (Braconnot et al., 2019)."


id: 2d0b085e4053a9af331779f576baaa95
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 5199
content:
"A significant decline in summer monsoon precipitation is observed \r over India since the mid-20th century, which is accompanied by \r a weakening of the large-scale monsoon circulation (Mishra et al., \r 2012; Abish et al., 2013; Krishnan et al., 2013, 2016; Saha et al., 2014; \r Roxy et al., 2015; Guhathakurta et al., 2017; Samanta et al., 2020). \r This precipitation decline is corroborated by a  decreasing trend in \r the frequency of monsoon depressions that form over Bay of Bengal \r (Prajeesh et al., 2013; Vishnu et al., 2016), an increasing trend in \r the frequency and duration of monsoon breaks or 'dry spells' (Singh \r et al., 2014), significant decreases in soil moisture and increases in \r drought severity across different parts of India post-1950 (Niranjan \r Kumar et al., 2013; Ramarao et al., 2015, 2019; Krishnan et al., 2016; \r Ganeshi et al., 2020; Mujumdar et al., 2020). While recent studies \r have reported an apparent recovery of the Indian summer monsoon \r [...]"


id: 1a87fb8c249e8dab2f074b007b29c6ab
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
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index: climate_gpt_v2
vector_id: 1869
content:
"[...] over a relatively short period since 2003 (Jin and Wang, 2017; Hari \r et al., 2020), long-term trends for the period 1951-2015 indicate \r an overall decrease in the regional monsoon precipitation (Kulkarni \r et al., 2020; Ayantika et al., 2021). A case study on the Indian summer \r monsoon is provided in Section 10.6.3."


id: 98666179c8c516bcd22346ee71211c9a
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 24023
content:
"During the recent past, long-term rain gauge observations display \r substantial variability in the WAfriM precipitation over the \r 20th century (Section 10.4.2.1). The WAfriM experienced the wettest \r decade of the 20th century during the 1950s and early 1960s (high \r confidence), over much of the western and central Sahel region, \r followed abruptly by the driest years during 1970-1989 (Ali and \r Lebel, 2009; Nicholson, 2013; Descroix et al., 2015). The percentage \r deficit in the annual rainfall during 1970-1989, relative to the long\u0002term mean, ranged from 60% in the north of Sahel to 25-30% in \r the south (Le Barbe et al., 2002; Lebel et al., 2003). The long decline \r in annual rainfall is related to a decrease of rain occurrence over the \r Sahel (Le Barbe and Lebel, 1997; Frappart et al., 2009; Bodian et al., \r 2016) and the Soudano-Guinean sub-region of West Africa (Le Barbe \r et al., 2002), even though the interannual variability pattern is \r [...]"


id: 68ed8b0e340b9c4c86a5e2d9c1891de5
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
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index: climate_gpt_v2
vector_id: 15731
content:
"[...] more complex (Balme et al., 2006). Decrease of rainfall occurrences \r resulted from decreases in large convective events in the core of the \r rainy season (Bell et al., 2006), that modulate interannual variability \r of the WAfriM (Panthou et al., 2018)."


id: 564edcabdbbd6b3f6e2e176f949a80dc
created_at: 2023-04-19 13:22:39
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index: climate_gpt_v2
vector_id: 12449
content:
"Internal variability and volcanic eruptions also contributed to the \r weakened EAsiaM (Hsu et al., 2014; Qian and Zhou, 2014; Zhou et al., \r 2017a; Knutson and Zeng, 2018). Since the late 1970s, the EAsiaM \r weakening has been also linked to SST changes in the Pacific Ocean \r with warm conditions in the central-eastern tropical part and cold \r ones in the north, similar to a positive phase of the Pacific Decadal \r Variability (PDV; Section AVI.2.6; Z. Li et al., 2016b; Zhou et al., 2017a). \r In the late 1990s the transition from a positive to a negative PDV has \r been associated with the recent recovery observed in the EAsiaM \r strength (Zhou et al., 2017a). Atlantic Multi-decadal Variability (AMV) \r also has an influence on the EAsiaM via the global teleconnection \r pattern propagating from the North Atlantic through the westerly jet \r (Zuo et al., 2013; Wu et al., 2016a, b). This North Atlantic influence \r [...]"


id: 939ba2383b0980ef4d585bfe4dbad6b4
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index: climate_gpt_v2
vector_id: 23174
content:
"[...] has contributed to the increase of precipitation over the Huaihe\u0002Huanghe valley since the late 1990s (Y. Li et al., 2017). When PDV \r and AMV are in opposite phase, the former has a larger influence in \r driving the southern flooding and northern drought pattern over the \r region (Q. Yang et al., 2017)."


id: 994ae91e752673352e50679b94ffdb71
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
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index: climate_gpt_v2
vector_id: 24189
content:
"SST variations in the tropical Atlantic, Pacific and Mediterranean Sea \r (Rodriguez-Fonseca et al., 2015; Diakhate et al., 2019). Increased \r frequency of extreme rainfall events impacts high flow occurrences \r of the large Sahelian rivers as well as small to meso-scale catchments \r (Wilcox et al., 2018). Overall, extreme intense precipitation events are \r more frequent in the Sahel since the beginning of the 21st century \r (Giannini et al., 2013; Panthou et al., 2014, 2018; Sanogo et al., \r 2015; Taylor et al., 2017). Intensification of mesoscale convective \r systems associated with extreme rainfall in the WAfriM is favoured \r by enhancement of meridional temperature gradient by the warming \r of the Sahara desert (Taylor et al., 2017) at a pace that is two to four \r times greater than that of the tropical-mean temperature (K.H. Cook \r et al., 2015; Vizy et al., 2017). Periods of monsoon-breaks and the \r persistence of low rainfall events are still prominent, particularly \r [...]"


id: 873076d4a9f5e068b0a4e2e5395d20d
created_at: 2023-04-19 13:22:39
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index: climate_gpt_v2
vector_id: 20946
content:
"[...] after the onset, thus exposing West Africa simultaneously to the \r potential impacts of dry spells (W.  Zhang et al., 2017b) and also \r extreme localized rains and floods (Engel et al., 2017; Lafore et al., \r 2017). Occurrence of extreme events is compounded by land use and \r land cover changes leading to increased runoff (Bamba et al., 2015; \r Descroix et al., 2018). "


id: 662773d0eff4356b4713527f05d116ee
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 15235
content:
"The Sahel drought from the 1970s until the early 1990s was related to \r anthropogenic emissions of sulphate aerosols in the Atlantic, which \r led to an inter-hemispheric pattern of SST anomalies and associated \r regional precipitation changes (Section 6.3.3.2 and Box 8.1). Also the \r combined effects of anthropogenic aerosols and GHG forcing appear \r to have contributed to the late twentieth century drying of the Sahel \r through their effect on SST, by cooling the North Atlantic and warming \r the tropical oceans (Giannini and Kaplan, 2019; Hirasawa et al., \r 2020). Subsequent aerosol removal led to SST warming of the North \r Atlantic, shifting the ITCZ further northward and strengthening the \r WAfriM (Giannini and Kaplan, 2019). The recent recovery has been \r ascribed to prevailing positive SST anomalies in the tropical North \r Atlantic potentially associated with a positive phase of the Atlantic \r Multi-decadal Oscillation (Diatta and Fink, 2014; Rodriguez-Fonseca \r [...]"


id: d53557bf977fed7920e2ad5d7be7e8f3
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 34834
content:
"[...] et al., 2015). The Sahel rainfall recovery has also been attributed to \r higher levels of GHG in the atmosphere and increases in atmospheric \r temperature (Dong and Sutton, 2015). "


id: 2f2e2383f5a8fa99cb3db66639a517e6
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 5571
content:
"An increase in intense rainfall and severe weather events has been \r observed in several locations, especially in south-western Arizona \r since 1991, resulting from increases in atmospheric moisture content \r and instability; a  change that has been confirmed by convective\u0002permitting model simulations (Luong et al., 2017; Pascale et al., \r 2019). A dense network of 59 rain gauges located in south-eastern \r Arizona suggests an intensification of monsoon sub-daily rainfall \r since the mid-1970s (Demaria et al., 2019), as expected by a stronger \r global warming signature for sub-daily rather than daily or monthly \r precipitation accumulation (Section 11.4). Section 10.4.2.3 provides \r further details on changes in precipitation in south-western North \r America. Evidence from multiple reanalyses suggests that increases \r in NAmerM rainfall have contributed to the increasing trend of global \r monsoon precipitation (Section 2.3.1.4.2; Lin et al., 2014). In addition, \r [...]"


id: 1e9f75071d79486b9975b03b4e87f953
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
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index: climate_gpt_v2
vector_id: 2590
content:
"[...] more frequent occurrence of earlier retreats of the NAmerM since \r 1979 is documented (Arias et al., 2012, 2015), in association with the \r positive phase of the Atlantic Multi-decadal Variability (AMV) and \r a westward expansion of the North Atlantic Subtropical High (W. Li \r et al., 2011, 2012). "


id: 2a5e601f58977c2d495dae21477008b2
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 4678
content:
"Since AR5, there has been improved understanding of changes in the \r South American monsoon (SAmerM) as evidenced from paleoclimate \r records, instrumental observations and climate model simulations. \r However, general circulation models (GCMs) still exhibit difficulties \r in reproducing SAmerM precipitation amount (Rojas et al., 2016; \r D'Agostino et al., 2020b). Paleoclimate evidence suggests a relatively \r stronger SAmerM during the 1400-1600 period (Bird et al., 2011b; \r Vuille et al., 2012; Ledru et al., 2013; Apaestegui et al., 2014; Novello \r et al., 2016; Wortham et al., 2017). Last millennium GCM simulations \r are able to reproduce stronger SAmerM during the 1400-1600 \r period in comparison with warmer epochs such as the 900-1100 \r period (Rojas et al., 2016) or the current warming period (Diaz and \r Vera, 2018). PMIP3/CMIP5 simulations indicate a consistent weaker \r [...]"


id: e5b2d13079a22cfd13a92f09a1af0cee
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index: climate_gpt_v2
vector_id: 37714
content:
"[...] SAmerM during the mid-Holocene (6000 years ago; see Cross\u0002Chapter Box  2.1) in comparison to current conditions (Bird et al., \r 2011a; Mollier-Vogel et al., 2013; Prado et al., 2013a; D'Agostino \r et al., 2020b), thus favouring savannah/grassland-like vegetation \r (Smith and Mayle, 2018), in agreement with climate reconstructions \r from different proxies (Prado et al., 2013b). Signals of weak and \r strong SAmerM during mid-Holocene and LGM, respectively, are \r evident also in high-resolution long-term (i.e., more than about \r 22,000 years) rainfall reconstructions based on oxygen isotopes in \r speleothems from Brazil (Novello et al., 2017; Strikis et al., 2018; \r Campos et al., 2019)."


id: 960384443cc9f8458b340f0e7d404844
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
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index: climate_gpt_v2
vector_id: 23589
content:
"Significant increases in precipitation have been observed over \r south-eastern Brazil during 1902-2005 while non-significant \r decreases have been found over central Brazil (Vera and Diaz, \r 2015). In Bolivia, increases were observed during 1965-1984, while \r reductions have occurred since then (Seiler et al., 2013). However, \r the Peruvian Amazon does not reveal significant changes in mean \r rainfall during 1965-2007 (Lavado et al., 2013; Ronchail et al., 2018). \r Historical simulations from CMIP5 ensembles adequately capture \r the observed summer precipitation amount over central and south\u0002eastern Brazil, thereby providing high confidence in interpreting the \r observed variability of SAmerM for the period 1960-1999 (Gulizia \r and Camilloni, 2015; Pascale et al., 2019). Also, CMIP5 simulations \r indicate that the anthropogenic forcing associated with increased \r [...]"


id: adb1cd48e2ec8333f84006baae8df069
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
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index: climate_gpt_v2
vector_id: 27803
content:
"[...] GHG emissions is necessary to explain the positive trends in upper\u0002troposphere zonal winds observed over the South American Altiplano \r (Vera et al., 2019). However, the detection of anthropogenically\u0002induced signals for precipitation is still ambiguous in monsoon \r regions, like the SAmerM (Hoegh-Guldberg et al., 2018)."


id: 1ba31ca674f3b36f32a1d57e616462a2
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
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index: climate_gpt_v2
vector_id: 2048
content:
"In contrast, it has been shown that observed trends in blocking are \r sensitive the choice of the blocking index, and that there is a large \r internal variability that complicates the detection of forced trends \r (Barnes et al., 2014; Cattiaux et al., 2016; Woollings et al., 2018), \r compromising the attribution of any observed changes in blocking. \r Many climate models still underestimate the occurrence of blocking, \r at least in winter over north-eastern Atlantic and Europe (Dunn\u0002Sigouin and Son, 2013), which leads to caution in the interpretation \r of their results for these regions. However, over the Pacific Ocean \r there have been large improvements in the simulation of blocking \r for the last 20 years (Davini and D'Andrea, 2016; Patterson et al., \r 2019). In the SH, increases in blocking frequency have occurred in the \r South Atlantic in austral summer (Dennison et al., 2016) and in the \r southern Indian Ocean in austral spring (Schemm, 2018). A reduced \r [...]"


id: 7259b04a8d8c36bc1bce7de282e6502b
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vector_id: 17361
content:
"[...] blocking frequency has been found over the south-western Pacific \r in austral spring (Sections 2.3.1.4.3 and 3.4.1.3.3; Schemm, 2018)."


id: 7f3e4429ed623701b284d412d5faa8f9
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 19541
content:
"number of cyclones from 1979 show a large spread of trends across \r different estimates (Section 2.3.1.4.3; Neu et al., 2013; Z. Li et al., \r 2016a; Grieger et al., 2018) resulting in low confidence in any clear \r increase of in the total number of cyclones. However, starting from \r the early 1990s, most reanalyses show increases in the total cyclone \r number by about 2-5% per decade (Figure 8.12). Increasing trends \r in the total number of cyclones are dominated by the increase in \r the number of shallow and moderate cyclones (which are more \r dependent on the datasets and identification methods used) than \r with decreasing number of deep cyclones since the early 1990s \r (Tilinina et al., 2013; Chang, 2018). In the SH the variability of the \r total number of cyclones is characterized by strong inter-decadal \r variability preventing a  clear assessment of trends. However, in \r contrast to the NH,there is a  significant increasing trend in the \r [...]"


id: f7297b646dceb3614ddfa1847df54c32
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updated_at: 2023-04-19 13:22:39
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index: climate_gpt_v2
vector_id: 40858
content:
"[...] number of deep cyclones (about 10% over 1979-2018) in ERA5, \r ERA-Interim, JRA55 and MERRA, and in the CFSR dataset after 2000 \r (Figure 8.12; Reboita et al., 2015; X.L. Wang et al., 2016)."


id: ec8130d8b9d3b5b32acb4c3aee8b3048
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 38971
content:
"Changes in the number of deep storms, which are often associated \r with heavier precipitiation over the North Atlantic and North \r Pacific, exhibit strong seasonal differences and decadal variability \r (Colle et al., 2015; Chang et al., 2016; Matthews et al., 2016; Priestley \r et al., 2020a). An increase in the number of summer cyclones over the \r Atlantic-European sector (Tilinina et al., 2013) is consistent with \r the  increase in the strength of the strongest fronts over Europe \r (Schemm et al., 2018). Chang et al. (2016) reported a decrease in the \r number of strong summer storms in the latitudinal band 40degN-75degN \r over the last decades, however, the assessment of seasonal trends in \r the Atlantic-European sector is complicated by the choice of region, \r attribution of tracks to the region selected, and thresholds used to \r identify trajectories, leading to low confidence on regional seasonal \r trends. For the SH, Grieger et al. (2018) reported a growing number \r [...]"


id: e8043410444548be522e31a036df9b7e
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index: climate_gpt_v2
vector_id: 38132
content:
"[...] of cyclones over sub-Antarctic region in the austral-summer during \r 1979-2010, while statistically significant trends were absent \r during the austral winter. "


id: 6a02f53b0f4cfff5073c8025134fd16c
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updated_at: 2023-04-19 13:22:39
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content:
"Atmospheric rivers (ARs) are long, narrow (up to a  few hundred \r kilometres wide), shallow (up to few kilometres deep) and transient \r corridors of strong horizontal water vapour transport that are typically \r associated with a low-level jet stream ahead of the cold front of an \r extratropical cyclone (Ralph et al., 2018). Atmospheric rivers were \r not assessed in AR5. ARs are associated with atmospheric moisture \r transport from the tropics to the mid- and high latitudes  (Zhu \r and Newell, 1998), although the drivers of moisture transport \r relative to the different airstreams within extratropical cyclones \r remains a subject of current study (Dacre et al., 2019). While much \r previous research has focused on the west coast of North America, \r ARs occur throughout extratropical and polar regions (e.g.,  Guan \r and Waliser, 2015) and are often associated with locally-heavy \r precipitation, including a  substantial fraction of all mid-latitude \r [...]"


id: 2cf1e3228d5d0226470d5b9b2385cbb
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
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index: climate_gpt_v2
vector_id: 5177
content:
"[...] extreme precipitation events (e.g., Waliser and Guan, 2017). ARs \r also affect East Asia strongly during the period from late spring to \r summer (Kamae et al., 2017). ARs can be related to warming/melt \r events trough the intrusions of warm and moist air in Antarctica, "


id: a399effb919091c37b0545da23a89e9b
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 25989
content:
"Tropical SSTs and associated global circulation may increase rainfall \r in West Africa, as observed in some years during 1950-2015, despite \r the presence of El  Nino (Pomposi et al., 2020). During an El  Nino \r summer, equatorial convective systems and the associated Walker \r circulation tend to shift eastward, leading to decreases in Indian \r summer monsoon rainfall (Li and Ting, 2015; Roy et al., 2019). This \r teleconnection is modulated by Indian Ocean Variability (Terray et al., \r 2021), as observed during the extreme positive IOD event in 2019 \r (Ratna et al., 2021). Since the end of the 19th century, synchronous \r hydroclimate changes (medium confidence) have been identified \r over south-eastern Australia and South Africa (Gergis and Henley, \r 2017) modulated by ENSO, as well as other regional fluctuations like \r the Botswana High over southern Africa (Driver and Reason, 2017). \r Over southern South America, the ENSO influence on precipitation \r [...]"


id: d948de2c6267563149c2fd16eed88668
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 35528
content:
"[...] (Cai et al., 2020; Poveda et al., 2020) interacts with the influence of \r SAM (Pedron et al., 2017), exhibiting large multi-decadal variations \r because of changes in the correlation between the two large-scale \r modes (Vera and Osman, 2018). Other processes underlying ENSO \r teleconnections of relevance for water cycle changes include water \r vapour and moisture transports, like over the Middle East (Sandeep \r and Ajayamohan, 2018), south-eastern China (S.  Yang et al., \r 2018b), or central Asia (X. Chen et al., 2018), south-eastern South \r America (Martin-Gomez et al., 2016; Martin-Gomez and Barreiro, \r 2016), Australia (Rathore et al., 2020) and southern USA (Okumura \r et al., 2017). "


id: a55bd3233c3a24534c18c93fc40cdd4f
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 26321
content:
"There is no evidence of a trend in the Indian Ocean Dipole (IOD; Section \r AIV.2.4) mode and associated anthropogenic forcing (Sections 2.4.3 \r and 3.7.4). The AR5 concluded that the IOD is likely to remain active, \r affecting climate extremes in Australia, Indonesia and East Africa. \r Since the AR5, IOD teleconnections have been identified extending \r further to the Middle East (Chandran et al., 2016), to the Yangtze \r river (Xiao et al., 2015), where in boreal summer and autumn positive \r IOD events tend to increase the precipitation in the south-eastern \r and central part of the basin, and to the southern Africa extreme \r wet seasons (Hoell and Cheng, 2018). During the last millenium, \r the combined effect of a positive IOD and El Nino conditions have \r caused severe droughts over Australia (Abram et al., 2020). In the \r satellited period, it is found more effective in inducing significant \r decrease of rainfall over Indonesia, with the opposite occurring for \r [...]"


id: 2ca85fd0ab450787a9292d9587ccc0c7
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 5119
content:
"[...] negative IOD events (As-syakur et al., 2014; Nur'utami and Hidayat, \r 2016; Pan et al., 2018). Similarly, over the Ganges and Brahmaputra \r river basins major droughts have been recorded during co-occurring \r El Nino and positive IOD, while floods occurred during La Nina and \r negative IOD conditions (Pervez and Henebry, 2015). Over equatorial \r East Africa the IOD affects the short rain season (medium confidence) \r exacerbating flooding and inundations independently of ENSO \r (Behera et al., 2005; Conway et al., 2005; Ummenhofer et al., 2009; \r Hirons and Turner, 2018). Extreme conditions, like the 2019 Australian \r bushfires and African flooding, have been associated with strong \r positive IOD conditions (Cai et al., 2021)."


id: 7ecbe56b0999b3785d995c1b0ed6d41c
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 19461
content:
"A positive trend has been observed in the Northern Annular Mode \r (NAM; Section AIV.2.1) in the second half of the 20th century, which \r partially reversed since the 1990s (Section 2.4.5.1), but the detection \r and attribution of these changes remain difficult (Section  3.7.1). \r The linkages of the NAM with weather and climate extremes in the \r northern extratropics are still unclear in models and observations \r (Vihma, 2014; Overland et al., 2016; Screen et al., 2018). However, \r robust links are identified between precipitation trends and \r variability in Europe and the phases of the Atlantic component of \r the NAM, that is, the NAO (Moore et al., 2013; Comas-Bru and \r McDermott, 2014). Reduced winter precipitation is well correlated \r with the NAO over Southern Europe and Mediterranean countries \r (Kalimeris et al., 2017; Corona et al., 2018; Vazifehkhah and Kahya, \r 2018; Neves et al., 2019). NAO teleconnections in those regions \r [...]"


id: c968ebbdd4e79e003288b6412cf3c90d
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 32761
content:
"[...] include influences on groundwater and streamflow (Zamrane et al., \r 2016; Massei et al., 2017; Jemai et al., 2018). Remote teleconnections \r of the NAO have been identified over Northern China, the Yangtze \r River valley and India (Jin and Guan, 2017; Di Capua et al., 2020). The \r summer phase of the NAO is significantly correlated with variations \r in summer rainfall in East China, with the thermal forcing of the \r Tibetan Plateau providing a  link to this Eurasian teleconnection \r (Z. Wang et al., 2018). "


id: f0dda3666a5d1462ad82c1dcaf91e2e3
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 39742
content:
"Simulated changes in MJO precipitation amplitude are extremely \r sensitive to the pattern of SST warming (Takahashi et al., 2011; \r Maloney and Xie, 2013; Arnold et al., 2015) and ocean-atmosphere \r coupling (DeMott et al., 2019; Klingaman and Demott, 2020). In \r agreement with results from previous model generations, most \r CMIP5 models still underestimate MJO amplitude, and struggle \r to generate a  coherent eastward propagation of precipitation \r and wind (Hung et al., 2013; Jiang et al., 2015; Ahn et al., 2017), \r affecting regional surface climate in the tropics and extratropics. \r In addition, most CMIP5 models simulate an MJO that propagates \r faster compared with observations, with a poorly represented intra\u0002seasonal precipitation variability (Ahn et al., 2017). Over the Indian \r Ocean, the propagation speed of convection in some CMIP5 models \r tends to be slower than observed due to a strong persistence of \r [...]"


id: e0ccfcb09d7444ad8e896ce0b28cf7b9
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 36809
content:
"[...] equatorial precipitation (Hung et al., 2013; Jiang et al., 2015). Among \r other processes, improving the moisture-convection coupling, \r the  representation of moist convection, the interaction between \r lower tropospheric heating and boundary layer convergence, and the \r topography of the Maritime Continent improve simulations of \r the MJO (Ahn et al., 2017, 2020a; Kim and Maloney, 2017; Yang \r and Wang, 2019; H. Tan et al., 2020; Y.-M. Yang et al., 2020). In \r fact, CMIP6 models reproduce the amplitude and propagation of \r the MJO better than CMIP5 models due to increased horizontal \r moisture advection over the Maritime Continent (Ahn et al., 2020b). \r Despite the diverse theories of MJO evolution and processes that "


id: 2ad057c18ea528662273976c587db8a9
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 4761
content:
"In the Southern Hemisphere (SH), an observed positive trend is \r identified in the strength of the Southern Annular Mode (SAM, \r Section AIV.2.2) since 1950, especially in austral summer (high \r confidence, Section  2.4.1.2). While stratospheric ozone depletion \r and GHG increases largely contributed to this change, climate \r models still have trouble simulating the SAM and its response to \r ozone and GHGs (Section 3.7.2). Shifts in the south-westerly winds \r (Fletcher et al., 2018) and the expansion of the SH Hadley cell (Kang \r and Polvani, 2011; H.  Nguyen et al., 2018) influence SAM-related \r rainfall anomalies in in southern South America and southern \r Australia during the austral spring-summer. Over New Zealand, \r large-scale SLP and zonal wind patterns associated with SAM \r phases modulate regional river flow (Li and McGregor, 2017). The \r SAM also influences precipitation and water vapour changes over \r Antarctica via moisture fluxes (Marshall et al., 2017; Oshima and \r [...]"


id: 5ed043310726e85fef38ef523367cfe2
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 13975
content:
"[...] Yamazaki, 2017; Grieger et al., 2018) but CMIP5 models are limited \r in their ability to simulate these regional teleconnections (Marshall \r and Bracegirdle, 2015; Palerme et al., 2017). SAM and its interaction "


id: aa518088ba3168e34087de3725a1b7fc
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 27194
content:
"In this Report, Chapter 4 provides an updated assessment of global \r annual precipitation (Section 4.3.1), finding that it is very likely that \r annual precipitation averaged over all land regions continuously \r increases as global surface temperatures increase in the 21st \r century (high confidence). CMIP6 projections for long-term changes \r in P-E (Figure 8.13) show that, for all scenarios, P-E increases over \r the tropics and high latitudes and decreases over the subtropics, \r resulting from a  thermodynamically driven amplification of P-E \r patterns (Section  8.2.2.1). Both the intensity of changes and the \r spread among the models is larger for the higher emissions scenarios. \r A  less coherent latitudinal pattern and smaller magnitude of P-E \r changes over land reflect the complex influence of land-ocean \r warming contrast, atmospheric circulation change and vegetation \r feedbacks (Section  8.2.2.1). However, stronger atmospheric \r [...]"


id: d1b486d1dc9072ae712648ab09a56583
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 34246
content:
"[...] moisture transport, increases in precipitation and evaporation over \r global land and ocean and larger continental runoff that is in part \r fed by melting of glaciers characterizes a more intense water cycle \r with global warming."


id: ad8347b30740e21bd5aaeb9f034c5fe5
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 27768
content:
"Most projected changes in the water cycle are not expected to be \r uniform in space or time. They are driven by both dynamical and \r thermodynamical processes (Section  8.2) and have not necessarily \r emerged yet in the recent observational record (Section 8.3) as they \r are superimposed on substantial natural fluctuations in weather and \r climate. Therefore, projecting regional water cycle changes remains \r challenging. However, a number of physically understood responses \r can be evaluated using both CMIP5 and CMIP6 models, which are \r important for guiding decision making that anticipates, prepares for, \r and responds to water cycle changes. In this section, global maps of \r projected changes in water cycle variables are assessed using the \r WGI AR6 'simple method' (see Cross-Chapter Box Atlas1), which uses \r hatching to highlight where less than 80% of the models agree on \r the sign of projected changes. This choice differs from Section 4.2.6 \r [...]"


id: e74f863c70478b98c03cdbe96330188d
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 37987
content:
"[...] for a  number of reasons. These include the weak signal-to-noise \r ratio of projected hydrological changes in low to medium emissions \r scenarios, the sensitivity of their statistical significance to the \r baseline reference period, and the non-Gaussian distribution of many \r water cycle variables (see Cross-Chapter Box Atlas.1 for more details \r on strengths and limitations of the hatching methods implemented \r within AR6)."


id: 6ff9f5d769bc00f84d635f3487de5526
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 16956
content:
"Global and global land mean water cycle changes from CMIP6 \r projections are shown in Table 8.1. Increases in global and continental \r precipitation, P-E and runoff in both the mid-term and long-term \r illustrate the future intensification of the water cycle, with the \r magnitude of change increasing with emissions scenarios. Consistent \r with AR5, CMIP6 simulations of global mean precipitation show \r a systematic multi-model mean increase of 1.6 to 2.9 % degC-1 warming \r (apparent hydrological sensitivity; Section  8.2.1) by 2081-2100 \r relative to present day across the new SSP scenarios (using global \r surface air temperature change from Table 4.1). It is well understood \r that rising concentrations of CO2 drive a long-term increase in global \r precipitation with warming, but with the increase partly offset by \r rapid atmospheric adjustments to the direct atmospheric heating \r from radiative forcing agents (Section  8.2.1). The largest apparent \r [...]"


id: 98da149cbde09f1c4a5503227094c5f
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 24110
content:
"[...] hydrological sensitivity is found for SSP1-1.9, where the suppressing \r effects on precipitation from atmospheric heating by greenhouse \r gases (GHGs) rapidly reduce as their concentration falls. Additional \r warming due to reduced aerosol loadings under the SSP scenarios \r (Lund et al., 2019) further increases global precipitation (Rotstayn \r et al., 2013; Wu et al., 2013; Salzmann, 2016; T.B. Richardson et al., "


id: 9176946eaff1ec21fd2c02e712c92382
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 22802
content:
"While projections of water vapour are well understood due to the \r constraints of the Clausius-Clapeyron relationship, projections \r of water vapour transport are complicated regionally by the role of \r changes in the wind field, which is influenced by a  wide variety \r of  factors. Additionally, there has been relatively little general \r evaluation of moisture transport in models. In CMIP5 models, \r both the mean and variability of the vertically-integrated moisture \r transport is projected to increase, largely due to increases in water \r vapour (Lavers et al., 2015), with substantial regional differences \r (Levang and Schmitt, 2015). Single-model studies have illustrated \r projected increases in low-altitude moisture transport into \r convergence regions (Allan et al., 2014) and from ocean to land \r (Zahn and Allan, 2013) that are consistent with present day trends. \r Increases in moisture transport have been linked to increases in large \r [...]"


id: 4447a8366960ef6f37a92a85ed2bcf3d
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 9251
content:
"[...] precipitation accumulations over land (Norris et al., 2019). Based \r on robust physics and supported by modelling studies, it is well \r understood that moisture transport increases into convergent parts \r of the atmospheric circulation such as storm systems, the tropical rain \r belt and high latitudes (Section 8.2.2.1), but changes in atmospheric \r circulation that are less well understood alter moisture transport \r regionally (Section 8.2.2.2). Therefore, given the limited examination \r of moisture transport in models, regional projections should be \r considered with caution. Changes in moisture transport specifically \r associated with monsoons, atmospheric rivers, and other specific \r circulation features are discussed further in the following sections."


id: efa4d9200e25b7125e0cc185235c4b2f
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 39543
content:
"Geographical patterns of projected precipitation changes show \r substantial seasonal contrasts and regional differences, including \r over land (Figure 8.14 and Figure 4.27). Projections for 2081-2100 \r under the SSP2-4.5 scenario suggest increased precipitation over \r the tropical oceans, north-eastern Africa, the Arabian Peninsula, \r India, south-eastern Asia and the Polar regions while decreased \r precipitation is projected mainly over the subtropical regions \r (Section  4.5.1.4). Precipitation changes contrast regionally in the \r tropics with wetter wet seasons over South Asia, central Sahel and \r eastern Africa, but less precipitation over Amazonia and coastal \r West Africa (Section  8.4.2.4). These large-scale responses are \r associated with stronger moisture transports in a warmer climate \r that are modulated by the greater warming over land than ocean, \r atmospheric circulation responses and land surface feedbacks \r (Section  8.2.2). There is agreement across CMIP5 and CMIP6 \r [...]"


id: eacc5594561afe67986d23766e3afad2
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 38668
content:
"[...] modelling studies that precipitation increases in wet parts of the \r atmospheric circulation and decreases in dry parts (Liu and Allan, \r 2013; Kumar et al., 2015; Deng et al., 2020; Schurer et al., 2020) \r although these regions shift with atmospheric circulation changes. \r The overall pattern is robust across different model scenarios and \r time horizons (Tebaldi and Knutti, 2018), but some deviations \r from the mean pattern cannot be excluded due to the multiple \r time scales and non-linear atmospheric or land surface processes \r involved (Section 8.5.3). Near-term regional changes in precipitation \r are more uncertain because of a  stronger sensitivity to natural \r variability (Section  8.5.2) and non-GHG anthropogenic forcings \r (Section 4.4.1.3 and 8.4.3.1)."


id: ec194193c870f0d3eaf08719818ef725
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 38904
content:
"Projected changes in regional precipitation also arise as a response \r to changes in large-scale atmospheric circulation (Section  8.2.2.2 \r and 8.4.2), both in the tropics (Chadwick et al., 2016b; Byrne et al., \r 2018) and extratropics (Shaw, 2019; Oudar et al., 2020b). Despite \r variability in simulated changes, CMIP5 climate models consistently \r project large rainfall changes (of varying sign) over considerable \r proportions of tropical land during the 21st century (Chadwick \r et al., 2016b). Since AR5, some robust responses in large-scale \r circulation patterns have been identified. For example, and as further \r assessed in Section 8.4.2, CMIP6 models project a northward shift \r in the tropical rain belt over eastern Africa and the Indian Ocean \r and a  southward shift in the eastern Pacific and Atlantic oceans \r (Mamalakis et al., 2021). A projected strengthening and tightening \r of the tropical rain belt increases the contrasts between wet and \r [...]"


id: 8b8db0ca1b73169120e8c05d5d65bc4c
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 21720
content:
"[...] dry tropical weather regimes and seasons. It is less clear how the \r well understood poleward expansion of the subtropics and mid\u0002latitude storm tracks influences precipitation over subtropical and \r mid-latitude continents (Section 8.2.2.2). "


id: c4a37709d933c4bf4beb5dd6f8de619c
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 31901
content:
"An ensemble of 31 CMIP6 models under the SSP5-8.5 scenario \r projects increases precipitation by 10-30% over much of the USA \r and decreases by 10-40% over Central America and the Caribbean \r by 2080-2099 (Almazroui et al., 2021). This CMIP6 ensemble also \r projects an increase in annual precipitation over the southern \r Arabian Peninsula and a  decrease over the northern Arabian \r Peninsula, as also projected by CMIP3 and CMIP5 models (Almazroui \r et al., 2020a). Annual mean precipitation is projected to increase over \r South Asia during the 21st century under all scenarios, although the \r rate of change varies within the region based on 27 CMIP6 models \r (Almazroui et al., 2020c). CMIP6 projections also display a reduction \r in annual mean precipitation over northern and southern Africa \r while increases are projected over Central Africa, under the SSP1-2.6, \r SSP2-4.5 and SSP5-8.5 scenarios (Almazroui et al., 2020b). The AR6 \r [...]"


id: 6cf22248719b61d36877bc7d1ab252f
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 16448
content:
"[...] Atlas assesses that regions where annual mean rainfall is likely to \r increase include the Ethiopian Highlands, East, South and North Asia, \r south-eastern South America, northern Europe, northern and eastern \r North America, and the Polar Regions. In contrast, regions where "


id: 7c055a02f0cb02dbc32ea52cfd323dcf
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 19027
content:
"In summary, it is virtually certain that global precipitation will \r increase with warming due to increases in GHG concentrations \r and decreases in air pollution. There is high confidence that total \r precipitation will increase in the high latitudes, with a  shift from \r snowfall to rainfall except in the coldest regions and seasons. \r There is also high confidence that precipitation will decrease over \r the Mediterranean, southern Africa, Amazonia, Central America, \r south-western South America, south-western Australia and coastal \r West Africa and that monsoon precipitation will increase over South \r Asia, East Asia and central-eastern Sahel. See Section  8.4.2.4 for \r a more detailed assessment of changes in regional monsoons. Daily \r mean precipitation intensities, including extremes, are projected to \r increase over most regions (high confidence). The number of dry days \r is projected to increase over the subtropics, Amazonia, and Central \r [...]"


id: e5b45a8a98f52345a11cdb6f8d292293
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 37718
content:
"[...] America (medium confidence). There is high confidence in an overall \r increase in precipitation variability over most land areas."


id: 991daa3034429918ab934b31d5cfa5d3
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 24152
content:
"The AR5 did not highlight observed changes in water cycle seasonality and SRCCL mostly emphasized changes in vegetation \r seasonality. Since AR5, a  number of relevant studies have been published, but often with conflicting results. Based on three in \r situ datasets, reduced precipitation seasonality was identified over 62% of the terrestrial ecosystems analysed from 1950-2009 \r (Murray-Tortarolo et al. 2017). In contrast, both in situ and satellite data show a general increase in the annual range of precipitation \r from 1979 to 2010, which is dominated by wetter wet seasons (Chou et al., 2013). This paradox may be partly explained by a larger \r aerosol radiative forcing in the middle of the 20th century as well as by internal variability (Kumar et al., 2015; see also Box 8.1). For \r instance, the 'long rains' over East Africa experienced declining trends in the 1980s and 1990s (Nicholson, 2017), which was linked \r [...]"


id: 120b69f80f631aad7c7e5a15c1257b9d
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 364
content:
"[...] to anthropogenic aerosols and SST patterns (Rowell et al., 2015), followed by a recent recovery that was linked to internal variability \r (Wainwright et al., 2019). Two satellite datasets revealed decreased rainfall seasonality in the tropics but an increased seasonality in \r the subtropics and mid-latitudes since 1979, without clear attribution (Marvel et al., 2017)."


id: e5eb499fea8bb5125fa91ffd9f8daa04
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 37748
content:
"Additional changes in seasonality may manifest in the timing and duration of wet seasons. A later monsoon onset trend was reported \r throughout India from 1901 to 2013 (Sahany et al., 2018). Conversely, an earlier rainfall onset was implicated in increased springtime \r rainfall over the Tibetan Plateau in recent decades (W. Zhang et al., 2017a). Winter and early spring precipitation over the north-western \r Himalaya for the period 1951-2007 shows an increasing trend of daily precipitation extremes in association with enhanced amplitude \r variations of extratropical synoptic-scale systems known as 'Western Disturbances'(Madhura et al., 2014; Cannon et al., 2015; Krishnan \r et al., 2019). In China, an earlier onset was observed during 1961-2012 (Deng et al., 2019). In the African Sahel, rainfall has been most \r concentrated in the peak of the rainy season since the end of the 20th century (Biasutti, 2019). A shift in the seasonality of Sahelian \r [...]"


id: d005a67e98ca4c8b8de7520e7352f404
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
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content:
"[...] rainfall, including delayed cessation has also been reported (Section 10.4.2.1; Nicholson, 2013; Dunning et al., 2018). Over southern Africa, \r an observed earlier onset (1985-2007) is in contrast to a simulated historical and projected future delay in the wet season (Maidment \r et al., 2015; Dunning et al., 2018). An increasingly early onset of the North American monsoon has been observed from 1978 to 2009 \r (Arias et al., 2015). Seasonality changes in the South American monsoon indicate delayed onsets since 1978 (Fu et al., 2013; Yin et al., \r 2014; Arias et al., 2015; Debortoli et al., 2015; Arvor et al., 2017; Giraldez et al., 2020; Haghtalab et al., 2020; Correa et al., 2021). "


id: e6e0a14e447774107479756ee6978428
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
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"Since AR5, several studies have further documented a projected increase in rainfall seasonality and the understanding of the underlying \r mechanisms has been improved (Sections 8.2.1 and 8.3.2). CMIP5 models show that the seasonal concentration of annual precipitation \r will increase over many regions by the end of the 21st century, with robust model agreement in most subtropical regions where an \r increase in the mean number of dry days was also reported in the RCP8.5 scenario (Pascale et al., 2016). The semi-arid, winter rainfall \r dominated subtropical climate is projected to shift poleward and eastward, with the equatorward margins replaced by a more arid \r climate type. However, evolving SST patterns and land-ocean warming contrasts cause more complex responses (Alessandri et al., \r 2015; Polade et al., 2017; Brogli et al., 2019; Zappa et al., 2020). Projections over California show a stronger and shorter wet season \r [...]"


id: 615175c395941b0f318e13e0011993a2
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
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"[...] (Polade et al., 2017; Dong et al., 2019). Decreases in future winter and spring rainfall are projected over south-western Australia (Hope \r et al., 2015). Central Asia is projected to experience wetter winters, associated with an increase in snow depth in the north-eastern \r regions (Y. Li et al., 2019). Even in a +2degC climate, both extreme precipitation and dryness will increase significantly in the extratropics, \r amplifying the seasonal precipitation range (Fujita et al., 2019). A single-model study shows that the annual range of precipitation \r increases globally by 2.6%  per 1degC of global warming in stabilized low-warming scenarios (Z. Chen et al., 2020a)."


id: f7d2d4773db7cec0802151d4a388f6c9
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
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index: climate_gpt_v2
vector_id: 40975
content:
"In the tropics, an amplified annual cycle (by about 3-5% degC-1) of global land monsoon hydroclimates (precipitation, precipitation minus \r evaporation (P-E), and runoff) is projected by CMIP5 models under the RCP8.5 scenario, mostly due to a more intense wet season \r (W. Zhang et al., 2019b). A longer rainy season is projected by CMIP6 models over most regional monsoon areas except in the Americas \r (Moon and Ha, 2020). A delayed onset and cessation of the wet season over West Africa and the Sahel (Dunning et al., 2018) and \r a slightly delayed onset of South Asian monsoon rainfall (Hasson et al., 2016) are projected by CMIP5 models. CMIP5 projections suggest \r a strengthening of the annual cycle and a lengthening of the dry season in Southern Amazonia (Fu et al., 2013; Reboita et al., 2014; Boisier \r et al., 2015; Pascale et al., 2016; Sena and Magnusdottir, 2020). This is further verified by the projections from six CMIP6 models (Moon \r [...]"


id: f7d8ec4ac6de96c6b1e3b5332769ccc1
created_at: 2023-04-19 13:22:39
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content:
"[...] and Ha, 2020). A wet season shorter by 5-10 days by the end to the 21st century is projected for southern Africa (Dunning et al., 2018). "


id: c7989fd194b943f5c803a697ccf6c57f
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
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"An increase in streamflow seasonality is projected over several large rivers in the low-mitigation RCP8.5 scenario, but with only small \r changes in the seasonality timing, except in northern high latitudes due to the earlier but potentially slower snowmelt in a warmer world \r (Eisner et al., 2017; Musselman et al., 2017). At the end of the century in a high-emissions scenario, peak snowmelt timing is projected to \r occur one month earlier and peak water volume is 79% lower in the eastern USA (Rhoades et al., 2018). Earlier snowmelt is projected, for \r example, by 30 days at the end of the 21st century in RCP4.5 for the Sierra Nevada in the western USA (F. Sun et al., 2018). Sub-seasonal \r changes in water availability were found in many regions in the RCP8.5 scenario. However, these should be considered with caution given \r the magnitude of model errors (C.R. Ferguson et al., 2018). Increases in the seasonality of water availability has been found to be more \r [...]"


id: 1f3e3f87535bd21581b37ef0fd03c7fd
created_at: 2023-04-19 13:22:39
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"[...] pronounced in areas with high atmospheric evaporative demand, giving rise to a pattern of seasonally variable regimes becoming even \r more variable (Konapala et al., 2020). RCP4.5 and RCP8.5 projections show a pronounced soil drying in summer and autumn over western \r Europe, and a springtime drying over northern Europe due to an earlier snowmelt (Ruosteenoja et al., 2018)."


id: 46f8941ce0ad0d16c166eba057487047
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
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content:
"A simple relative seasonality metric (Walsh and Lawler, 1981) applied to global projections based on CMIP6 models and SSP scenarios \r supports previous CMIP5 findings, especially the amplified seasonality of precipitation around the Mediterranean, and across southern \r Africa, California, southern Australia and the Amazon (Box 8.2, Figure 1). While such changes are not significant in the low-emissions \r SSP1-2.6 scenario, they are consistent with the increased frequency of dry days projected over the same regions (Figure  8.16). \r In monsoon regions outside the Americas, rainfall seasonality does not show a significant increase even in high-emissions scenarios. \r This challenges previous CMIP5 findings based on the difference between maximum and minimum monthly precipitation in a year \r (W. Zhang et al., 2019b) and higher sensitivity to the projected increase in precipitation extremes (Section 11.4.5). In the northern high \r [...]"


id: f8ad6750f4bcd482bc7e1d64a8fbcc94
created_at: 2023-04-19 13:22:39
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content:
"[...] latitudes, milder winters are associated with wetter conditions and a decrease in precipitation seasonality."


id: 1b6e656d0565d0aa76d4274888d8ea62
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 2003
content:
"Box 8.2, Figure 1 | Projected long-term changes in precipitation seasonality. Global maps of projected changes in precipitation seasonality (simply defined \r as the sum of the absolute deviations of mean monthly rainfalls from the overall monthly mean, divided by the mean annual rainfall as in Walsh and Lawler, 1981) \r averaged across available CMIP6 models (number provided at the top right of each panel) in the SSP1-2.6 (b), SSP2-4.5 (c) and SSP5-8.5 (d) scenario respectively. \r The simulated 1995-2014 climatology is shown in panel (a). All changes are estimated in 2081-2100 relative to 1995-2014. Uncertainty is represented using the \r simple approach. No overlay indicates regions with high model agreement, where >=80% of models agree on sign of change. Diagonal lines indicate regions with \r low model agreement, where <80% of models agree on sign of change. For more information on the simple approach, please refer to the Cross-Chapter Box Atlas.1. \r [...]"


id: cc59f9feaf19d8720305f0b6dad2367b
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 33268
content:
"[...] Further details on data sources and processing are available in the chapter data table (Table 8.SM.1)."


id: c043689d8e4dee277f929b571978e68
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 31127
content:
"in evapotranspiration can also be influenced by changes in soil \r moisture and vegetation, which modulate the moisture flux from the \r land to the atmosphere. Several studies of CMIP5 projections suggest \r that increases in plant water use efficiency will limit or counteract \r rising evapotranspiration (Milly and Dunne, 2016; Swann et al., \r 2016; Lemordant et al., 2018; Y. Yang et al., 2018). However, other \r studies have found that transpiration increases due to the impact of \r climate change on growing season length, leaf area, and evaporative \r demand (Section  8.2.3.3; Frank et al., 2015; Mankin et al., 2017, \r 2018, 2019; Guerrieri et al., 2019; S.  Zhou et al., 2019; Vicente\u0002Serrano et al., 2020). The parametrizations accounting for these \r complex physiological processes in global climate models may also \r be insufficient (Franks et al., 2017; Peters et al., 2018; Peano et al., \r 2019). Thus, there is currently low confidence in the role of vegetation \r [...]"


id: 7362ae7db645d8c6956ae5adf2a8b729
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
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vector_id: 17556
content:
"Since AR5, studies confirm that global mean annual runoff increases \r with global surface temperature increase (X.  Zhang et al., 2014, \r 2018; Lehner et al., 2019), but varies regionally (Chen et al., 2017; \r H. Yang et al., 2017; Cook et al., 2020). CMIP5 models display a large \r spread in the ratio of runoff to precipitation for the present-day \r climate, which applies also to future runoff changes under global \r warming (Lehner et al., 2019). In studies of CMIP6 projections, runoff \r increases in most parts of the northern high latitudes and Asia and \r north and eastern Africa, and decreases in the Mediterranean region, \r southern Africa, southern Australia and in parts of western Africa, \r as well as in Central and South America (Greve et al., 2018; Cook \r et al., 2020). Projected changes in runoff also vary seasonally. In the \r Northern Hemisphere (NH), runoff increases during winter since more \r precipitation falls as rain than snow and decreases in the summer as \r [...]"


id: 62c50dcc4a2de086c529da69a3979b71
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
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vector_id: 14675
content:
"[...] less snow is available to contribute to runoff during the warm season \r (Cook et al., 2020). Global maps of projected changes for December-\r January-February and June-July-August are shown in Figure 8.18, \r showing projected changes becoming larger and more consistent \r in the higher emissions scenarios. Runoff projections for CMIP6 are \r also shown in Figure 8.16 for tropical and extratropical averages at \r a range of global mean warming levels and in Table 8.1 for global \r land in different future scenarios. In the tropics, both the mean and \r interannual variability of runoff increase with warming. The increase \r in variability is roughly twice as large as the increase in the mean, \r and has a large spread across models. In the extratropics, changes \r are small in the summer but there are large increases in the winter, \r with the mean increasing much more than the variability, in contrast \r to the tropics. "


id: b7a6fbb4da58b1408acf75eece98a59f
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
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content:
"The AR5 assessed that projected changes in runoff had low confidence\r over the period 2016-2035; however, under the RCP8.5 scenario, \r runoff increases by 2100 are likely in high northern latitudes. This is \r consistent with projected regional precipitation increases, based on \r consistency of changes across different generations of models and \r different forcing scenarios, and with runoff decreases being likely\r in southern Europe, the Middle East and southern Africa. There was \r considerable uncertainty in the magnitude and direction of change \r for some regions, largely driven by the uncertainty in projected \r precipitation changes, particularly across south Asia. For flooding, \r AR5 assessed with medium confidence that flooding would increase \r over parts of South and South East Asia, tropical Africa, north-east \r Eurasia, and South America, and decrease for parts of Northern \r and Eastern Europe, Anatolia, Central Asia, Central North America, \r [...]"


id: 6a894bf0884b58710c7882b20dfdd433
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 16029
content:
"complicating factors for projecting both pluvial (overland) and fluvial \r (river) flooding that limit confidence in their assessment. In addition to \r precipitation, flooding also depends on basin and river characteristics \r such as permeability, antecedent soil moisture, and antecedent flow \r levels for river flooding, so projections of extreme precipitation and \r flooding are not always closely linked (Section  8.2.3.2). Possible \r changes in water resources management and land use add another \r layer of complexity to future changes. There is medium confidence\r in a general increase in pluvial and fluvial flooding, although there \r are large regional variations, discussed further in Sections  11.5.5, \r and 12.4. There is medium confidence in a  substantial increase \r in the frequency of extreme sea level events for coastal regions \r (Section  9.6.4.2) and the associated coastal flooding is regionally \r assessed in Section  12.4. The risk of glacier lake outburst floods \r [...]"


id: 51f1256f1385490ec781a4950f3cd3fe
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 11697
content:
"[...] (GLOFs) is expected to increase with glacier melting in some high \r mountain regions (Section 12.4)."


id: c61fccf0fc7f284172764ac5bd7aa9d7
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
content_type: text
index: climate_gpt_v2
vector_id: 32165
content:
"Soil moisture in the top soil layer (10 cm) shows more widespread \r drying than total soil moisture, reflecting a  greater sensitivity of \r the upper soil layer to increasing evaporative demand (Figure 8.19; \r Berg et al., 2017). Conversely, total column soil moisture represents \r the carry-over of moisture from previous seasons deeper in the soil \r column, and potentially higher sensitivity to vegetation processes \r (Berg et al., 2017; Kumar et al., 2019). Central America, the \r Amazonian basin, the Mediterranean region, southern Africa, and \r south-western Australia are projected to experience significant \r declines in total soil moisture, whereas declines in Europe (north \r of the Mediterranean), western Siberia, and north-eastern North \r America are limited to the surface (Figure 8.19). It should be noted \r that because models differ in their number of hydrologically active \r layers, there is less confidence in total soil moisture projections than \r [...]"


