# document

id: 190d4ae70f2c09cde740b3bdf4f25311
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: 1624
content:
"[...] surface soil moisture projections. Based on surface soil moisture \r projections, more than 40% of global land areas (excluding \r Antarctica and Greenland) are expected to experience robust year\u0002round drying, even under lower emissions scenarios (Cook et al., \r 2020). The percentage of land area experiencing drying is slightly \r lower when runoff is used as an aridity metric instead (20-30%); \r taking this into consideration, it is estimated that about a  third \r of global land areas will experience at least moderate drying in \r response to anthropogenic emissions, even under SSP1-2.6 (medium \r confidence) (Cook et al., 2020)."


id: 568e1a267a5122f0d89e93f075e71f53
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: 12481
content:
"In summary, there is medium confidence that global runoff will \r increase with global warming, but with large regional and seasonal \r variations. There is high confidence that runoff will increase in the \r northern high latitudes and decrease in the Mediterranean region and \r southern Africa. There is medium confidence that runoff will increase \r in regions of central and eastern Africa, and decrease in Central \r America and parts of southern South America, with the magnitude \r of the change increasing with emissions. There is medium confidence\r that the seasonality of runoff and streamflow will increase with \r global warming in the subtropics. In snow-dominated regions, there \r is high confidence that peak flows associated with spring snowmelt \r will occur earlier in the year and medium confidence that snowmelt\u0002induced runoff will decrease with reduced snow, except in glacier-fed \r basins where runoff may increase in the near term. There is medium \r [...]"


id: ccf7a38be39954c27336c59c2605fdbf
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: 33404
content:
"[...] confidence that flooding in general will increase, although with \r considerable variation based on geographic region and flood type. \r These projected climate-related changes will occur in the context of \r human-caused land-use and land-cover changes, which may also \r have a large influence."


id: b05019f1e9bf24c6b711b70e1c4047f1
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:
"southern Africa, and south-western Australia, on the order of \r 0.5  to  1  month for a  moderate emissions scenario (SSP2-4.5) and \r two months for a high-emissions scenario (SSP5-8.5; Ukkola et al., \r 2020). Drought intensity is projected to increase in the tropics, mainly \r in the Amazonian basin, Central Africa, and southern Asia, as well as \r in Central America and south-western South America (Ukkola et al., \r 2020). The CORDEX South Asia multi-model ensemble projections \r indicate an increase in the frequency and severity of droughts over \r central and northern India during the 21st century, under the RCP4.5 \r and RCP8.5 scenarios (medium confidence) (Mujumdar et al., 2020). \r Under intermediate or high-emissions scenarios, the likelihood of \r extreme droughts (events that have magnitudes equal to or less than \r the 10th percentile of the 1851-1880 baseline period) increases by \r 200-300% in the Amazonian basin, south-western North America, \r [...]"


id: b36979c3bd30fe22182a797cf2aa8868
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: 28805
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"[...] Central America, the Mediterranean, southern Africa, and south\u0002western South America (Cook et al., 2020). Even under a  low\u0002emissions scenario (SSP1-2.6), the likelihood of extreme droughts \r increases by 100% in south-western North America, south-western \r South America, the Amazon, the Mediterranean, and southern Africa \r (Cook et al., 2020). Thus, there is high confidence that drought \r severity and intensity will increase in the Mediterranean, southern "


id: ebc683fcadc4ec0e4e6721bb695ccee0
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: 38848
content:
"Paleoclimate records provide context for these future expected changes \r in drought and aridity. In the Mediterranean, western North America, \r and Central Chile, there is high confidence that climate change will shift \r soil moisture (as represented by the Palmer Drought Severity Index) \r outside the range of observed and reconstructed values spanning the \r last millennium (Figure  8.20; Cook et al., 2014; Otto-Bliesner et al., \r 2016). Warmer temperatures, leading to increased evaporative losses, \r are clearly implicated in the projected future drying in these semi-arid \r regions (Dai et al., 2018), emphasizing the central role that warming \r plays in driving increased evaporative demand (Vicente-Serrano et al., \r 2020). In contrast, future trajectories are more uncertain in regions \r like Central Asia and eastern Australia-New Zealand where projected \r changes in precipitation and soil moisture are less coherent (Figure 8.19 \r [...]"


id: 45c9107297223fd84668641c78549c2d
created_at: 2023-04-19 13:22:39
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vector_id: 9494
content:
"[...] and 8.20; Hessl et al., 2018). More information on projected changes \r in drought, including specific categories or drought, can be found in \r Section 11.6.5 and Section 12.4."


id: 2f2a24e2881130fcb72ec59218587886
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
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"Groundwater projections were not assessed in AR5. Groundwater \r processes are not explicitly included in most current CMIP6 models \r and so must be calculated separately with hydrologic models \r (e.g.,  R.G. Taylor et al., 2013; Cuthbert et al., 2019a). A  range of \r factors are important in assessing groundwater projections, including \r the mean difference between precipitation and evaporation, the \r intensity of precipitation (R.G. Taylor et al., 2013a), and in changes \r in snow (Tague and Grant, 2009), glaciers (Gremaud et al., 2009), \r and permafrost (Okkonen and Klove, 2011). Climate impacts on \r groundwater are occurring in the context of severe and growing \r human-caused groundwater depletion (WGII; Konikow and Kendy, \r 2005; Rodell et al., 2018; Bierkens and Wada, 2019), and water \r scarcity issues (Mekonnen and Hoekstra, 2016). Climate-related \r changes to the water cycle can influence water demand (for example, \r [...]"


id: 83d88ff23b92825aa9bdcc5e27957726
created_at: 2023-04-19 13:22:39
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"[...] precipitation decreases in an irrigated area), and anthropogenic \r groundwater depletion can influence the water cycle through \r interactions with surface energy fluxes, surface water, and vegetation \r (Cuthbert et al., 2019a), although uncertainties in estimates of future \r groundwater depletion are large (Smerdon, 2017; Bierkens and Wada, \r 2019). Some aspects of groundwater change will be irreversible, \r including the increase of saltwater intrusion into coastal aquifers \r with sea level rise (Werner and Simmons, 2009), and depletion of \r fossil aquifers and aquifers with very long recharge times (Bierkens \r and Wada, 2019). "


id: dbba7652218f2e858cc86e1556f0d69c
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
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"CMIP5 projections show no consistent shift in the zonal mean \r position of the ITCZ (Donohoe et al., 2013; Donohoe and Voigt, 2017; \r Byrne et al., 2018). The ITCZ position is strongly connected to cross\u0002equatorial energy transport (Kang et al., 2008; Bischoff and Schneider, \r 2014), which also shows no consistent change in future projections \r (Donohoe et al., 2013). Since AR5 it has been reported that most \r CMIP5 models project a narrowing of the ITCZ in response to surface \r warming together with intensified ascent in the core region and \r weakened ascent on the ITCZ edges (Lau and Kim, 2015; Byrne et al., \r 2018), implying a narrowing of precipitation regions influenced by \r the ITCZ. Modelled changes in the width and intensity of the zonal \r mean ITCZ are strongly anti-correlated, for example, narrowing is \r associated with increased intensity while broadening with decreased \r intensity. Such changes are associated with changes in tropical high \r [...]"


id: a3f1e43ca7c59f30134cb2ec52b80b4b
created_at: 2023-04-19 13:22:39
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"Since AR5, the paradox between the projected weakening and the \r observed strengthening of the Walker circulation since the 1990s \r (Section  8.3.2.2) has triggered debate about the drivers of these \r changes (England et al., 2014; McGregor et al., 2014; Kociuba and \r Power, 2015; Vilasa et al., 2017; Chung et al., 2019). Projected changes \r in equatorial SST gradients are not entirely consistent with observed \r trends (Coats and Karnauskas, 2017; Seager et al., 2019a), and one \r CMIP5 model that projects a  future strengthening of the Walker \r circulation is more consistent with observations than other models \r (Kohyama et al., 2017). Other studies suggest that these differences \r arise from the dominant influence of internal climate variability to \r the observed trends (Chung et al., 2019), or as a  consequence of \r a  systematic cold bias of most CMIP5 models in their Equatorial \r Pacific cold tongues (Seager et al., 2019a). However, the latter \r [...]"


id: c70f819dee85d70686cc478c488499e5
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
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"[...] hypothesis is based on a simplified model of tropical Pacific dynamics \r and is not consistent with the current physical understanding of the \r tropical circulation response to increasing CO2 levels (Section 8.2.2.2) \r or with independent paleoclimate evidence suggesting a  weaker \r Walker circulation under warmer climates (Tierney et al., 2019; \r McClymont et al., 2020). Different time scales of the tropical Pacific "


id: 490ddfaf55b19efac766120d312ccdd1
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:
"and Ajayamohan, 2015). Greater warming over the Asian land region \r compared to the ocean contributes to intensification of the monsoon \r low-level south-westerly winds and precipitation (Endo et al., 2018), \r even though the combined effect of upper and lower tropospheric \r warming makes the Asian monsoon circulation response rather \r complicated. A  high resolution model projection, based on the \r RCP8.5 scenario, indicates that a  northward shift of the low-level \r jet and associated weakening of the large-scale monsoon circulation \r can induce a large reduction in the genesis of monsoon low pressure \r systems by the late 21st century (Sandeep et al., 2018). Experiments \r with constant forcing indicate that at 1.5degC and 2degC global warming \r levels, mean precipitation and monsoon extremes are projected to \r intensify in summer over India and South Asia (Chevuturi et al., 2018; \r D. Lee et al., 2018) and that a 0.5degC difference would imply a 3% \r [...]"


id: 95ac6bf8ed0a539300e539da96b59065
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
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"[...] increase of precipitation (Chevuturi et al., 2018). CMIP5 models \r project an increase in short intense active days and decrease in long \r active days, with no significant change in the number of break spells \r for India (Sudeepkumar et al., 2018)."


id: 53a8a80c259bda288aeb9238f8e97b37
created_at: 2023-04-19 13:22:39
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content_type: text
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vector_id: 11991
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"Inter-hemispheric mass exchange can act as a  bridge connecting \r SH circulation with EAsiaM rainfall, however this inter-hemispheric \r link is projected to weaken in a future warmer climate as seen from \r a  CCSM4 projection using the RCP8.5 scenario (Yu et al., 2018). \r A  comparison of 1.5degC and 2degC global warming levels reveals \r how a  0.5degC difference could result in precipitation enhancement \r over large areas of East Asia (D. Lee et al., 2018; J. Liu et al., 2018; \r Chen et al., 2019), with substantial increases in the frequency and \r intensity of extremes (Chevuturi et al., 2018; D. Li et al., 2019). Future \r monsoon projections from the CMIP6 models show increase of \r EAsiaM precipitation across all the scenarios (Z. Chen et al., 2020b), \r though with a large model spread mostly on the long-term and in \r the higher emissions scenarios (Figure 8.22). Considering all the five \r scenarios used across the report, changes in precipitation, runoff \r [...]"


id: b85eb518c65264f931a3b50a54a4a392
created_at: 2023-04-19 13:22:39
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"[...] and P-E over the EAsiaM are positive and become larger for highest \r emissions scenarios and for the long-term mean, except for the mid\u0002term SSP1-1.9 scenario where the changes are close to zero or even \r negative (Table 8.2). Additionally, CMIP6 models confirm a projected \r increased length of the EAsiaM season due to early onset and late \r retreat (Ha et al., 2020)."


id: 368d971269f793399803d1e7cde2715
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
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"CMIP5 projections indicated a possible intensification of the EAsiaM \r circulation during the 21st century, in addition to precipitation \r increase, although there is a  lack of consensus on changes in the \r western North Pacific subtropical high, this is an important feature \r of the EAsiaM circulation (Kitoh, 2017). Furthermore, the EAsiaM \r precipitation enhancements in the CMIP5 projections are prominent \r over the southern part of the Baiu rainband by the late 21st century, \r with no significant changes in the Meiyu precipitation over central\u0002eastern China (Horinouchi et al., 2019). It was also shown that the \r Baiu precipitation response in CMIP5 projections is accompanied by \r a southward retreat of the western North Pacific subtropical high and \r a southward shift of the East Asian subtropical jet (Horinouchi et al., \r 2019). According to the high-resolution MRI-AGCM global warming \r experiments, future summer precipitation could potentially increase \r [...]"


id: 14921f512f4a5ed459de58cab7abee80
created_at: 2023-04-19 13:22:39
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"[...] on the southern side and decrease on the northern side of the present\u0002day Baiu location in response to downward-motion tendencies which \r can offset the 'wet-gets-wetter' effect, but is subject to large model \r uncertainties (Ose, 2019). Future projections of land warming over \r the Eurasian continent (Endo et al., 2018) and intensified land-sea \r thermal contrast (Z. Wang et al., 2016; Tian et al., 2019) can potentially "


id: 5c218b0dbad49471b65e831efa160e4b
created_at: 2023-04-19 13:22:39
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vector_id: 13464
content:
"and demise (Cook et al., 2013). It is recognized that CMIP5 models \r are generally too coarsely-resolved to simulate the Gulf of California \r and the moisture surges associated with the NAmerM (Pascale et al., \r 2017). Under different RCPs, CMIP5 models tend to project a reduction \r in NAmerM precipitation but an increase in extreme precipitation \r events (Torres-Alavez et al., 2014; Bukovsky et al., 2015; Pascale \r et al., 2019). The almost unchanged or slight decrease in NAmerM \r total precipitation amount under global warming projections is at \r odds with paleoclimate records that suggest increased monsoon \r precipitation under past warm conditions (D'Agostino et al., 2019; \r Seth et al., 2019). However, there is low agreement on how those \r changes and the mechanisms that drive them are affected under \r different RCPs since most simulations are model-dependant (Cook \r and Seager, 2013; Geil et al., 2013; Pascale et al., 2019). Projections \r [...]"


id: ed7923f86aacd251efd34a3967a72c64
created_at: 2023-04-19 13:22:39
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"[...] from six CMIP6 models show a  shortening of the NAmerM under \r the SSP5-8.5 scenario due to earlier demises (Moon and Ha, \r 2020). In addition, CMIP6 projections show a decrease in NAmerM \r precipitation under SSP2-4.5 and SSP5-8.5 scenarios by the end of \r the 21st century with large inter-model spread (Figure  8.22). This \r result is also supported by the analysis of 31 CMIP6 models under \r the SSTP5-8.5 scenario for the 2080-2099 period (Almazroui et al., \r 2021). Non-linearities and uncertainties in the NAmerM projected \r changes are valid for many water cycle variables, like precipitation, \r runoff and P-E (Table 8.2). "


id: d2e1049e2fc22ee4ff3c2e6c3f988e45
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
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"increases in rainfall in eastern-central Sahel and a decrease in the \r west for the end of the 21st century (Roehrig et al., 2013; Biasutti, \r 2019; Monerie et al., 2020). However, the magnitude of WAfriM \r projected precipitation depends on the convective parametrization \r used (Hill et al., 2017), and large uncertainties remain in WAfriM \r projections because of large inter-model spread, particularly over \r the western Sahel (Roehrig et al., 2013; Biasutti, 2019; Monerie \r et al., 2020). CMIP6 models show a  general increase of WAfriM \r precipitation across all future scenarios but with a substantial model \r spread for the SSP5-8.5 scenario (Figure 8.22). This sensitivity arises \r from the combined and contrasting influences of anthropogenic \r greenhouse gas and aerosol forcing that affect WAfriM precipitation \r (particularly over the Sahel) directly and also indirectly through \r subtropical North Atlantic SST changes (Giannini and Kaplan, 2019). \r [...]"


id: 73984d851e161d909895b70520e052c7
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: 17592
content:
"[...] The large model spread and associated uncertainties in projected \r precipitation changes is reflected also in runoff and P-E changes \r (Table 8.2). Regional climate models (RCMs) ensembles (e.g., Klutse \r et al., 2018) agree with CMIP5 projected rainfall trends but some \r individual models show rainfall declines (e.g.,  Sylla et al., 2015; \r Akinsanola et al., 2018), highlighting the existing large uncertainties \r in RCMs WAfriM rainfall projections."


id: 63ac720139e7a6f016006220d6032172
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: 14827
content:
"Since AR5, some studies indicate that the SAmerM would experience \r changes in its seasonal cycle, with delayed monsoon onsets under \r increasing GHG emissions associated to different RCPs (Fu et al., \r 2013; Reboita et al., 2014; Boisier et al., 2015; Pascale et al., 2016; \r Seth et al., 2019; Sena and Magnusdottir, 2020). In contrast, other \r studies indicate projected earlier onsets and delayed retreats of the \r SAmerM under the RCP8.5 scenario based on six CMIP5 models \r (Jones and Carvalho, 2013). These differences have been linked to \r the methodology used to determine monsoon timing, and sensitivity \r to the monsoon domain considered (Section 8.3.2.4.5; Correa et al., \r 2021). Recent studies provide further evidence for the projection \r of delayed SAmerM onsets by the late 21st century (Sena and \r Magnusdottir, 2020). An analysis of six CMIP6 models under the \r SSP5-8.5 scenario confirm the projections of delayed SAmerM onsets \r [...]"


id: 51bb5eda7f1249294a38dba82c0055f1
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
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"[...] by the end of the 21st century (Moon and Ha, 2020). In addition, \r projected changes in the intensity and length of the SAmerM season \r have been found to be model-dependent (Pascale et al., 2019). The \r analysis of CMIP5 projections of total monsoon rainfall indicate mixed \r signals in the Amazon and SAmerM regions (Jones and Carvalho, \r 2013; Marengo et al., 2014), with some studies suggesting increased "


id: 8a58c5783e6afad09b4e86f43d58a4fe
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: 21513
content:
"summer precipitation in the core SAmerM region (Kitoh et al., 2013; \r Seth et al., 2013). Dynamical downscaling of CMIP5 projections \r under the RCP4.5 and RCP8.5 scenarios with the Eta RCM suggests \r reductions of austral summer precipitation over the SAmerM region \r throughout the 21st century (Chou et al., 2014). Further analysis \r using 15 different CMIP6 models for the SSP2-4.5 scenario suggest \r reductions in total SAmerM rainfall (B. Wang et al., 2020). However, \r other analyses of CMIP6 projections under different SSP scenarios \r do not report clear changes in the SAmerM precipitation throughout \r the 21st century (Figure 8.22; Z. Chen et al., 2020b; Jin et al., 2020). \r Similar uncertainties for all the SSP scenarios used across the report \r are found for other water cycle variables, including runoff and P-E \r (Table 8.2). Furthermore, there is disagreement in projected extreme \r precipitation in the region, with some CMIP5-based studies suggest \r [...]"


id: 796d8e9318ffaa8021b17d6971b39201
created_at: 2023-04-19 13:22:39
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"[...] reductions (Marengo et al., 2014), while others indicate increases \r based on CMIP5 and CMIP6 models (Kitoh et al., 2013; Sena and \r Magnusdottir, 2020). "


id: 5d4d3f0c13916a9d43f9df730558e5be
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:
"Blocking projections are assessed in this Report in \r Chapter  4  (Section  4.5.1.6), and model performance in simulating \r blocking is also discussed in Chapter  3  (Section  3.3.3.3). CMIP5 \r projections suggest a complex response in blocking frequencies with \r an eastward shift in NH winter blocking, mid-latitude decreases in \r boreal summer except in eastern Europe-western Russia, and SH \r decreases in the Pacific sector during austral spring and summer. \r CMIP6 projections (Figure. 4.28) show a notable decrease in blocking \r activity over Greenland and the North Pacific for the SSP3-7.0 and \r SSP5-8.5 scenarios. However, the continued large differences among \r current models as well as the sensitivity to blocking detection methods \r limits confidence in projected regional changes in blocking (see also \r Section 10.3.3.3.1). The influence of blocking on multiple elements of \r the water cycle means that the uncertainty in blocking projections adds \r [...]"


id: 80b9487af35bc2613c05c76906275f86
created_at: 2023-04-19 13:22:39
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"While the potential role of increasing hydrologic extremes with \r quasi-resonant stationary waves during NH summer has received \r considerable attention (see Section 8.3.2.6), as yet there is no clear \r evidence in model projections that this variability will increase (Teng \r and Branstator, 2019). The influence of the Arctic on mid-latitude \r circulation is assessed in Cross-Chapter Box 10.1, which reports that \r there is low confidence in the dominant contribution of Arctic warming \r compared to other drivers in future projections. Potential changes to \r the stratospheric polar vortex in CMIP5 models have a substantial \r influence on tropospheric stationary waves and associated hydrologic \r impacts in both the NH (Zappa and Shepherd, 2017) and SH (Mindlin \r et al., 2020). CMIP5 models have some important limitations in their \r representation of stationary waves (Lee and Black, 2013; Simpson \r et al., 2016; Garfinkel et al., 2020) and this aspect of CMIP6 models \r [...]"


id: 9427c70edbddfc19fd2977d7196abdd8
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
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content:
"Biases remain in cyclone locations, intensities, cloud features, and \r precipitation (Catto, 2016, Chang et al., 2016). Uncertainties in \r projected precipitation changes in many mid-latitude regions can \r be explained to a large degree by uncertainties in projected storm \r track or ETC changes. Multiple studies (Chang et al., 2013; Zappa \r et al., 2015; Chang, 2018) have shown strong relationships between \r model-projected precipitation change in many regions and model\u0002projected change in storm track activity near that regions. While front \r frequency is well represented, frontal precipitation frequency is too \r high and the intensity is too low (Catto et al., 2015). Some of the \r bias in storm tracks appears to be related to limitations in model \r realization of blocking (Zappa et al., 2014). The CMIP6 generation \r of models has improved representation of storm tracks in both \r hemispheres (Bracegirdle et al., 2020a; Harvey et al., 2020). Simulation \r [...]"


id: 473e07ce98485b2d1e61e16082cc408f
created_at: 2023-04-19 13:22:39
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"[...] of storm tracks and their associated precipitation generally improve \r with increasing resolution beyond that used in most current climate \r models (Jung et al., 2006; Michaelis et al., 2017; Barcikowska et al., \r 2018). In terms of projections, the decreases in cyclone occurrence \r over the Mediterranean were replicated in a higher resolution model \r (Raible et al., 2018)."


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vector_id: 18218
content:
"In AR5, several factors were identified as relevant to the uncertainties \r in projections of cyclone intensity, frequency, location of storm tracks \r and precipitation associated with ETCs. These include horizontal \r resolution, resolution of the stratosphere, and how changes in the \r Atlantic meridional overturning circulation (AMOC) were simulated. \r Since AR5, projections of extratropical cyclones and storm tracks have \r been examined further, largely confirming previous assessments. In \r particular, extratropical cyclone precipitation scales with the product \r of cyclone intensity (as measured by near-surface wind speed) \r and atmospheric moisture content (Pfahl and Sprenger, 2016). \r Booth et al. (2018) showed that the fraction of rainfall generated \r by the convection scheme in simulated extratropical cyclones is \r highly model- and resolution-dependent, which may be a source of \r uncertainty regarding their precipitation response to anthropogenic \r [...]"


id: 7b01d219f8dd2c081072a9c9abe154aa
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: 18844
content:
"[...] forcings. Also, increased moisture availability may increase the \r maximum intensity of individual storms while reducing the overall \r frequency as poleward energy transport becomes more efficient."


id: 761844fdf40dbfcea69713fc28094ac3
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: 18005
content:
"CMIP6 projections indicate that the amplitude of ENSO (Annex IV.2.3) \r variability will not substantially change during the 21st century (high \r confidence) (Section  4.4.3.2). However, rainfall variability related \r to ENSO is projected to increase significantly by the second half of \r the 21st century, regardless of ENSO amplitude (Section  4.5.3.2). \r Regional precipitation variability associated with ENSO increases \r due to increases in atmospheric moisture, regardless of changes in \r ENSO variability itself (Pendergrass et al., 2017). In many regions, \r the magnitude of the projected changes related to ENSO is small \r compared with historical interannual variability (Bonfils et al., \r 2015; Power and Delage, 2018; Perry et al., 2020). Uncertainties \r in precipitation projections related to ENSO depend on internal \r variability associated with the mode (Section 8.5.2), hence the need \r to have relatively large ensembles (about 15 members) to adequately \r [...]"


id: 98adf054f03b49247817aeddbf139cba
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: 24078
content:
"[...] estimate uncertainty (Deser et al., 2018; N. Maher et al., 2018; C. Sun \r et al., 2018; Zheng et al., 2018)."


id: d6f195748636febc518baa8a5f8b5151
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: 35122
content:
"Under continued global warming, more intense moisture transport \r within atmospheric river events is projected to increase the magnitude \r of heavy precipitation events on the west coast of the USA (Ralph \r and Dettinger, 2011; Lavers et al., 2015; Warner and Mass, 2017), \r in Western Europe (Lavers et al., 2015; Ralph et al., 2016; Ramos \r et al., 2016), and in East Asia (very likely) (Kamae et al., 2019). All \r CMIP5 models analysed agreed under a range of scenarios, except \r over the Iberian Peninsula (Ramos et al., 2016) where there is only \r low confidence in projected changes. Kamae et al. (2019) reported \r a 1% increase per degC warming in the frequency of atmospheric rivers \r affecting East Asia, but this is strongly affected by SST changes. \r Emerging evidence of possible regional changes due to dynamical \r factors are uncertain (Lavers et al., 2013; Gao et al., 2015; Payne \r and Magnusdottir, 2015). The frequency, magnitude and duration of \r [...]"


id: 90e9b8d5dc0f6f728e226f395889a026
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: 22704
content:
"[...] atmospheric rivers making landfall along the North American west \r coast are projected to increase (Gershunov et al., 2019). In contrast, \r V. Espinoza et al. (2018) suggest that the number of atmospheric river \r events is projected to slightly decrease globally. "


id: bb0b8419cdc87b7ce5d9e58d292372ad
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: 30130
content:
"Sensitivity studies generally project increases in Madden Julian \r Oscillation (MJO, Annex IV.2.8) precipitation amplitude in a warmer \r climate, with increases of up to 14% degC-1 of warming (Arnold et al., \r 2013, 2015; Caballero and Huber, 2013; Liu and Allan, 2013; Maloney \r and Xie, 2013; Schubert et al., 2013; Subramanian et al., 2014; \r Carlson and Caballero, 2016; Pritchard and Yang, 2016; Adames \r et al., 2017a; Wolding et al., 2017; Haertel, 2018). However, in CMIP5 \r models with realistic historical MJO behaviour, the precipitation \r amplitude over the Indo-Pacific warm pool region changes from \r -4% to +8% degC-1 in the RCP8.5 scenario relative to the end of the \r 20th century (Bui and Maloney, 2018; Maloney et al., 2019). When \r simulated MJO precipitation amplitude increases with warming, the \r leading factor for such change is the intensification of the lower \r tropospheric vertical moisture gradient, that supports stronger \r [...]"


id: ffa0f2db6b30c70ba60571f8f4b40265
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: 42371
content:
"[...] vertical moisture advection per unit diabatic heating (Arnold et al., \r 2015; Adames et al., 2017a, b; Wolding et al., 2017). In idealized \r simulations with constant CO2 forcing with El  Nino-like patterns, \r the MJO activity penetrates farther east into the central and east \r Pacific with increased warming (Subramanian et al., 2014; Adames \r et al., 2017a). Increased MJO convective variability in a  warmer \r climate does not reflect into increased ability of the MJO to force the \r extratropics (Wolding et al., 2017)."


id: b36471a5491754f7ea3f3654f024d1b7
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: 28801
content:
"Understanding the limits to projecting water cycle changes are \r fundamental for refining climate and hydrological models needed to \r develop successful climate change adaptation strategies. Regional \r water cycle projections depend on a  range of model-dependent \r responses (Section 8.5.1) and are also strongly influenced by internal \r variability, especially in the near term (Section 8.5.2; Hawkins and \r Sutton, 2012; Rowell, 2012; Orlowsky and Seneviratne, 2013; Kent \r et al., 2015; Fatichi et al., 2016; Greve et al., 2018; Chegwidden \r et al., 2019). CMIP6 models show that different model responses \r to the same forcing scenario remain the main source of uncertainty \r for projected changes in regional precipitation (Figure 8.23; Lehner \r et al., 2020). Section  8.5.3 assesses the potential for non-linear \r responses when shifting from low- to high-global warming levels \r (Section  8.4.2.4; James et al., 2017). While regional uncertainties \r [...]"


id: 53844a0f5e2fcc1af233397b4aed43a4
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: 11967
content:
"[...] related to downscaling methods (Section 10.3.3) and impact models \r (WGII Chapter 4) are not covered here, the added value of regional \r climate models is briefly discussed (Section 8.5.1.2.2) with a focus on \r water cycle changes."


id: 7512fb9663bd58e1cdfad34fd60a1f47
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: 17835
content:
"The AR5 Chapter  7  recognized that the simulation of clouds and \r precipitation remains challenging for state-of-the-art GCMs. \r Model development and evaluation have continued since \r AR5, with a  particular emphasis on the representation of new \r model components, like interactive vegetation, aerosols and \r biogeochemical cycles. For example, the comparison of simulated \r tropical precipitation across three successive generations of CMIP \r models (including CMIP6) indicates overall little improvement for the \r summer monsoons, the double-ITCZ bias, the diurnal cycle and the \r frequency of precipitation (Fiedler et al., 2020). Some of these issues \r are related to inherent model limitations in three specific areas: \r atmospheric convection, cloud-aerosol interactions and land surface \r processes (ocean and cryosphere-related processes are addressed \r in Chapter 9). These limitations do not weaken the overall progress \r [...]"


id: 43a29d2d5d5e8fc964963b2b671e779d
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: 9122
content:
"[...] made in the large-scale simulation of present-day climate (FAQ 3.3 \r and Section  3.3.2.3), even though the improvement of CMIP6 \r compared with CMIP5 models is limited (Figure 3.12) and is generally \r less systematic or obvious at the regional scale (e.g., Gusain et al., \r 2020; Monerie et al., 2020; Oudar et al., 2020a). Instead, they call for \r a careful interpretation of hydrological projections with the full range \r of plausible outcomes, rather than only considering the most likely \r scenarios (Sutton, 2018, 2019). "


id: d1c5f7ee2b0d368c1ba2cd99d339b99b
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: 34259
content:
"Since AR5, there have been improvements in the representation of \r convective clouds and related precipitation in GCMs. For instance, \r the drizzle issue (too light and too frequent rainfall events) has led \r to modifications in the deep convection triggering scheme (Rochetin \r et al., 2014b; Han et al., 2017; Xie et al., 2018; Wu et al., 2019). \r Although high-resolution studies have highlighted these limitations, \r most GCMs still rely on a convective available potential energy (CAPE) \r closure which has been adapted to various cloud regimes (Bechtold \r et al., 2014; Han et al., 2017; Walters et al., 2019) or evaluated \r against convection-permitting models (CPMs; J. Chen et al., 2020a). \r To increase the sensitivity of convection to tropospheric humidity, \r several models now include a  representation of deep convective \r entrainment dependent on relative humidity (Bechtold et al., 2008; \r Han et al., 2017; M. Zhao et al., 2018; Walters et al., 2019). Other \r [...]"


id: 52ab0cfd03eb0b37ae4df9d53284db63
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: 11824
content:
"[...] efforts have focused on the improvement of shallow convection and \r low-level cloudiness due to their major contribution to uncertainty \r in climate sensitivity (Section 7.4.2.4). A cloud-regime-based study \r however highlights an apparent disconnection between cloud \r and precipitation processes in GCMs (Tan et al., 2018), suggesting \r that a  good representation of clouds does not lead to systematic \r improvement in simulated precipitation. A global simulation in which \r the parametrized convection is switched off shows a strong influence \r of parametrized convection on daily precipitation extremes (P. Maher \r et al., 2018). Regional simulations at a 25km resolution suggest that \r an explicit deep convection can be beneficial even at such a relatively \r coarse resolution (Vergara-Temprado et al., 2020). Perturbed physics \r ensembles (PPE, Section 1.4.4) make it possible to identify parameters \r in the convection scheme that are most important in determining \r [...]"


id: d934e92359853ec18d2604d373e3b963
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: 35512
content:
"Since AR5, spatial aggregation of tropical convection has also received \r growing attention in both observational (Holloway et al., 2017) and \r modelling studies (Muller and Bony, 2015; Wing et al., 2017; Tan \r et al., 2018). The changing degree of convective organization was \r highlighted as a  key mechanism for dynamic changes in extreme \r precipitation (Pendergrass, 2020a). Yet, convective parametrizations \r do not represent all aspects of mesoscale convective systems (Hourdin \r et al., 2013; Park et al., 2019). This is related to the complexity of \r mechanisms involved from synoptic to mesoscale dynamics, which \r are only partially resolved by models. Cloud-resolving models (CRMs, \r Section  8.5.1.2.2) represent a  useful benchmark for improving the \r parametrization of mesoscale convective systems. Machine learning \r can also be used to parametrize moist convection after training \r the model with a conventional or a super parametrization scheme \r [...]"


id: df777ae7cf1ede76b575e4c43201978a
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: 36563
content:
"[...] (Gentine et al., 2018; O'Gorman and Dwyer, 2018), but has not yet \r been used in the CMIP framework."


id: 199b4652d5e26d574e5101f239a1a76
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: 1713
content:
"improvements in their simulation of the tropical precipitation \r climatology compared to CMIP5 (Figure  3.10; Fiedler et al., 2020). \r For instance, the double-ITCZ syndrome is still prominent (Tian and \r Dong, 2020) despite being reduced in some models (e.g., Qin and \r Lin, 2018). This systematic bias was shown to arise from atmospheric \r processes including cloud feedbacks (Tian, 2015; Dixit et al., 2018; \r Talib et al., 2018) and the SST threshold at which deep convection \r occurs in the tropics (Oueslati and Bellon, 2015; Xiang et al., 2017; \r Adam et al., 2018). Such biases can also arise from a  too weak \r sensitivity of seasonal tropical precipitation to local SSTs compared \r with observations (Good et al., 2021). These biases are large enough \r to alter forced precipitation changes, and consequently limit our \r confidence in projected precipitation changes (Samanta et al., 2019; \r Aadhar and Mishra, 2020). Observational constraints can be used to \r [...]"


id: 66a9283a04fbff7e28a3793657d866f6
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: 15319
content:
"[...] narrow model response uncertainties (DeAngelis et al., 2015; G. Li \r et al., 2017; Ham et al., 2018; Watanabe et al., 2018), although there \r is still no consensus that model selection or weighting is a reliable \r alternative to the 'one-model-one-vote' approach used in Section 8.4 \r (Box  4.1). The detrimental influence of model errors can also be \r mitigated by focusing on phenomena or events (Polson and Hegerl, \r 2017; Weller et al., 2017), implementing bias adjustment techniques \r (Section 10.2.3.2), or adopting a non-probabilistic storyline approach \r (Zappa and Shepherd, 2017). "


id: 2540b7c51ecf384b44659735d26a161a
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: 3744
content:
"A major challenge in representing convective clouds and related \r precipitation events in GCMs is a  lack of sophisticated cloud \r microphysics in convective parametrization schemes (e.g., Fan et al., \r 2016). Most of these schemes only include simple microphysical \r treatments, such as direct partition between cloud condensation and \r precipitation, and do not include advanced treatment of conversion \r among different types of hydrometeors. As such these schemes are \r unable to simulate microphysical cloud and precipitation responses \r to aerosol-related perturbations in cloud droplet concentration \r and ice crystals (see Box 8.1), or perturbations in thermodynamical \r states from global warming. Efforts have been made to include more \r advanced cloud microphysical treatment in cumulus parametrizations \r (Song and Zhang, 2011; Grell and Freitas, 2014; Berg et al., 2015) or \r to use explicit cloud microphysics schemes in climate models with \r [...]"


id: 8b4cc5d84928ce0138c33f5ab17686ce
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: 21668
content:
"[...] a  'super parametrization' (Wang et al., 2015), which have been \r shown to improve the performance in simulating cloud properties \r and precipitation. However, few of these improvements have been \r incorporated into CMIP6 climate models so the projected precipitation \r response to anthropogenic perturbation may still be hindered by the \r inadequate microphysical treatment in cumulus parametrization \r (Smith et al., 2020). "


id: 9a3296f29e12ea85b88dbe03324e22be
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: 24368
content:
"Land surface processes determine the partitioning of net surface \r radiation into sensible, latent and ground heat fluxes, the partitioning \r of precipitation into evapotranspiration and runoff, and the net \r terrestrial carbon flux at the Earth's surface. They are relevant for \r simulating the terrestrial water cycle responses to climate change, as \r well as the response to land use change (FAQ 8.1). Even basic land \r surface properties such as albedo (Terray et al., 2018) or the ratio of \r transpiration to total evaporation (Chang et al., 2018) still need to \r be improved in state-of-the-art coupled GCMs. Runoff sensitivities \r are also not well constrained in these models, which display a large \r spread for the present-day climate, influencing simulated changes \r under global warming (Lehner et al., 2019). Earth System Models \r (ESMs) incorporate some combined biophysical and biogeochemical \r processes to a  limited extent, and many relevant processes about \r [...]"


id: ee3d6a82dd3b61d1bcb94d1d76eb865f
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: 39287
content:
"[...] how plants and soils interactively respond to climate changes are yet \r to be considered (e.g., Y. Liu et al., 2020). Consequently, land surface \r processes and their atmospheric coupling contribute to the range in \r water cycle projections (Jia et al., 2019). "


id: dfbd6b2eadc76f3253202f0ab7b6a9fd
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: 36612
content:
"In AR5 Chapter 7, there was low confidence in the representation \r of cloud-aerosol interactions in climate models. Despite progresses \r in this field since AR5, cloud-aerosol interactions remain a  major \r obstacle to understanding climate and severe weather (Varble, 2018). \r High aerosol concentrations have been observed to suppress rain in \r water clouds (Campos Braga et al., 2017; Fan et al., 2020). However, \r such aerosol effects are muted in GCMs, which tend to produce \r precipitation from shallow clouds too frequently at the expense \r of rain intensity (Suzuki et al., 2015; Jing et al., 2017). This arises \r from incomplete knowledge of how clouds adjust to aerosol primary \r effects such as cloud condensation nuclei (CCN). The adjustment \r occurs mainly as a dynamic response to the impacts of CCN on cloud \r droplet size and number concentrations on precipitation-forming \r processes (Rosenfeld et al., 2008; Goren and Rosenfeld, 2014; Koren \r [...]"


id: ce6478087aeb05ae6d203f6878cafd0f
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: 33653
content:
"[...] et al., 2014; Camponogara et al., 2018). Uncertainties are large \r for deep clouds, as their processes are much more complex and "


id: a4753d1166b9536c336652e88c27b34
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: 26145
content:
"Progress in the representation of land surface heterogeneity has \r been made, in the form of improved mapping of root zone storage \r capacity (Wang-Erlandsson et al., 2016), improved vegetation \r stand, disturbance and fire dynamics (F.  Li et al., 2013; Fisher \r et al., 2018; Haverd et al., 2018; Yue et al., 2018; Zou et al., 2019), \r better representation of urban surfaces (Box 10.3), and the explicit \r representation of inland water bodies (Gu et al., 2015; Verseghy and \r MacKay, 2017). The representation of realistic snow and vegetation \r cover significantly affects the simulation of the land surface energy \r and water budgets at multiple time scales (Loranty et al., 2014; \r Bartlett and Verseghy, 2015; Thackeray et al., 2015; Qiu et al., \r 2016; Thackeray and Fletcher, 2016; L. Wang et al., 2016; Alessandri \r et al., 2017). Groundwater remains inadequately represented in \r [...]"


id: fd9646b905879d1076c36d149fcdfdfa
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: 41965
content:
"[...] many models, which limits our current understanding of the two\u0002way interactions between groundwater and the rest of the hydrologic \r cycle (R.G. Taylor et al., 2013a; Leng et al., 2014; Vergnes et al., 2014; \r Pokhrel et al., 2015; Maxwell and Condon, 2016; Collins, 2017; \r Scanlon et al., 2018; Condon et al., 2020). Land management exerts \r an increasing influence on the water cycle (Abbott et al., 2019) whose \r representation in the current-generation climate models is generally \r incomplete (Section 10.3.3.7.2)."


id: 31855fc038ab8b1b10b07b5f223f73ad
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: 5959
content:
"The physiological response of plants to increasing atmospheric CO2 is \r generally accounted for, but only using empirical models of stomatal \r conductance that are characterized by a  single critical parameter \r of intrinsic water-use efficiency (Franks et al., 2017, 2018). This \r reflects a lack of structural diversity and caution about the consensus \r of the photosynthesis response to increasing CO2 (Knauer et al., \r 2015; Huang et al., 2016), which has implications for the ability of \r the current-generation models to account for uncertainty in future \r evapotranspiration changes. Most CMIP5 models underestimate the \r ratio of plant transpiration to total terrestrial evapotranspiration, \r which may suggest that they also underestimate the impact of plant \r physiology on the water cycle (Lian et al., 2018). Plant hydraulics \r are not explicitly considered in many land surface models, which \r may lead to an underestimation of the influence of the increasing \r [...]"


id: c93839dfd0fea09465da71194d3edb72
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: 32729
content:
"[...] atmospheric moisture stress on plant transpiration under climate \r change (Massmann et al., 2019; Grossiord et al., 2020; Y.  Liu \r et al., 2020). Most ESMs underestimate the water use efficiency \r measured at many sites and, consequently overestimate the ratio of \r evapotranspiration to precipitation (J. Li et al., 2018). "


id: 5edf0018e571f1c16159d4ec7afbd7a3
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: 13986
content:
"Aside from land surface models (LSMs), global hydrological models \r (GHMs) have been further developed for off-line simulations of the \r hydrological impacts of both climate change and water management \r (Jimenez Cisneros et al., 2014; Schewe et al., 2014; Doll et al., 2016, \r 2018; Pokhrel et al., 2016, 2017; Veldkamp et al., 2018). GHMs can \r equal or outweigh the contribution of GCMs to uncertainties in \r hydrological projections at the regional scale (Giuntoli et al., 2015). \r Historical GHM simulations are currently not sufficient to improve \r regional water cycle projections, due to modelling uncertainties in \r both the driving GCMs and land surface hydrology (Pechlivanidis \r et al., 2017; Samaniego et al., 2017; Hattermann et al., 2018; \r Krysanova et al., 2018). Biophysical vegetation processes are still \r not accounted for in many GHMs, which may lead to inadequate \r projections of terrestrial runoff and water resources. However, \r [...]"


id: 6472eb67dd4c1317b5bedb37fb7d0986
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: 14947
content:
"[...] hydrological models that do simulate these effects often disagree \r (Prudhomme et al., 2014), so do not necessarily provide the added \r value of a more sophisticated representation of vegetation processes \r and land surface conditions (Doll et al., 2016). "


id: c10cad9416e8dd38b3307351aa318971
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: 31268
content:
"Since AR5, horizontal resolution has increased in most global climate \r models, which has led to several improvements in the simulation of \r the water cycle (see also Section 10.3.1.1), not only in areas with \r steep or complex orography, but also over the tropical oceans and \r within the North Pacific and North Atlantic storm tracks (Piazza \r et al., 2016; Roberts et al., 2018; Bui et al., 2019; Chen and Dai, 2019; \r Vanniere et al., 2019). Yet, the added value of higher resolution global \r climate models is not systematic (Johnson et al., 2016; Ogata et al., \r 2017; D. Huang et al., 2018; Mahajan et al., 2018; Vanniere et al., \r 2019) and needs careful assessment (Haarsma et al., 2016; Caldwell \r et al., 2019). Several AGCM studies suggest that increased spatial \r resolution leads to better simulation of the atmospheric moisture \r transport from ocean to land, the geographical distribution of \r annual mean precipitation (Demory et al., 2014), and the frequency \r [...]"


id: 2cadb4c0d118befdc890a2fbb4a94483
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: 5125
content:
"[...] distribution of daily precipitation intensities (L. Zhang et al., 2016; \r Chen and Dai, 2019) including extremes in many (Jacob et al., 2014; \r Westra et al., 2014), but not all cases (Bador et al., 2020). "


id: 660856a31fa5adcc2ab53942be21ae85
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updated_at: 2023-04-19 13:22:39
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vector_id: 15209
content:
"Part of the improvement in simulated precipitation accuracy is related \r to improved simulation of the frequency and/or mean intensity of \r tropical (Roberts et al., 2015; Walsh et al., 2015) and extratropical \r (Hawcroft et al., 2016) cyclones. Idealized regional experiments also \r show that the North Atlantic storm track response to global warming \r can be amplified in higher resolution models (Willison et al., 2015). \r Increased atmospheric horizontal resolution can be also important \r for simulating Northern Hemisphere (NH) blockings (Davini et al., \r 2017; Schiemann et al., 2017) and synoptic features of the East \r Asian summer monsoon (Yao et al., 2017; Kusunoki, 2018). Variable \r resolution based on grid stretching may be a valuable alternative for \r simulating regional phenomena like monsoons (Sabin et al., 2013; \r Krishnan et al., 2016) or tropical cyclones (Harris et al., 2016; Chauvin \r et al., 2017), while avoiding inconsistencies in the forcings or physics \r [...]"


id: 3d8b0c534f3df22bcff0d8d785ba4465
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: 8002
content:
"Regional Climate Models (RCMs) are used to dynamically downscale \r global model simulations for a particular region (usually at a spatial \r resolution of the order of 10 to 50 km; see Section 10.3.3). The AR5 \r reported that RCMs are useful for regions with variable topography \r and for small-scale phenomena. However, they inherit biases from \r their driving GCMs and thus may lack physical consistency with \r them. Since AR5, the application of RCMs has largely increased due \r to international model intercomparison projects such as CLARIS\u0002LPB (Sanchez et al., 2015). Many studies have focused on present\u0002day climatological precipitation, showing with high confidence\r improvements in its monthly to seasonal accumulation and spatial \r distribution (Dosio et al., 2015; Giorgi et al., 2016; Bozkurt et al., 2019; \r Falco et al., 2019; Di Virgilio et al., 2020), although the modelling of \r precipitation remains the 'Achilles heel' of both GCMs and RCMs and \r [...]"


id: 7fe8f007ce48f4fce4d9601718b4b7dd
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: 19642
content:
"[...] should be considered cautiously when informing regional climate \r change adaptation strategies (Tapiador et al., 2019b)."


id: d605ab5911a696c19ecf9ad0b4a4bc61
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: 34970
content:
"Estimating internal variability is an important challenge in the \r assessment of human-induced changes in the water cycle since its \r magnitude and range of variability can exceed the anthropogenic \r signal, at least at the regional scale and for near-term projections \r or low-emissions scenarios (Sections  4.4.1.4 and 8.4.2.9; Deser \r et al., 2012; Shepherd, 2014; Xie et al., 2015; Sarojini et al., 2016; \r Dai and Bloecker, 2019; Lehner et al., 2020). Underestimating \r internal variability in models may result in the overestimation of \r anthropogenic climate change because the 'noise' in the signal-to\u0002noise ratio is underestimated (Knutson and Zeng, 2018). There is \r medium confidence that this underestimation affects global water \r cycle projections, for instance, in terms of drought persistence and \r severity in the south-western USA, eastern Australia, southern Africa, \r the Mediterranean, the southern Amazonian basin and China (Ault \r [...]"


id: b89a8e23280caf495ddcd3ae21dfab76
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: 29687
content:
"[...] et al., 2014; Cook et al., 2018; Gu et al., 2018). In CMIP6 models, \r the uncertainty in future projections of 20-year mean precipitation \r changes attributable to internal variability ranges from 41% in \r the near term (2021-2040) to 5% in the long term (2081-2100) \r (Figure 8.23). For decadal-mean precipitation changes, the relative \r contribution of internal variability is even larger when using large \r ensembles (Lehner et al., 2020)."


id: 540f19f558a0acccfe2f18e21cc5b14
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: 12062
content:
"Over the 20th century, CMIP5 models show a realistic magnitude of \r decadal precipitation variability, if not a slight overestimation in some \r regions (Knutson and Zeng, 2018). However, the relatively short and \r human-influenced instrumental record limits our ability to quantify the \r magnitude of internal variability in the water cycle, particularly over \r long time scales (decadal and beyond). Global extended reanalyses \r (Section 1.5.2) have been used to derive long-term variability in the \r regional water cycle components (Caillouet et al., 2017), merged with \r historical meteorological and hydrological local observations (Bonnet \r et al., 2017; Devers et al., 2020). Specific assessment of these types \r of methodology and related uncertainties is provided in Chapter 10 \r (Sections  10.2 and 10.3). Paleoclimate archives (tree rings, corals, \r ice core, speleothems, lake and ocean sediments) provide extended \r reconstructions of key water cycle metrics and large-scale circulation \r [...]"


id: 77d203746ffb34ca0ea894645260a780
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: 18290
content:
"[...] features. Some studies have suggested that CMIP5 models \r underestimate internal variability at decadal and longer time scales, \r and therefore may be missing important processes in the climate \r system (Ault et al., 2012, 2013; Bunde et al., 2013; Franke et al., 2013; \r Cheung et al., 2017; Hope et al., 2017; Kravtsov, 2017; Cassou et al., \r 2018). However, recent assessments using paleoclimate records have \r found that CMIP5 models are able to reproduce decadal-to-centennial \r variability, including the severity, persistence and spatial extent of \r megadroughts (Coats et al., 2015; Stevenson et al., 2015; PAGES \r Hydro2K Consortium, 2017), once signal reddening (autocorrelation) \r in proxy archives is accounted for (Dee et al., 2017; PAGES Hydro2K \r Consortium, 2017). Implementation of proxy system models, that \r is, functions that transform model variables into proxy units, has \r reduced model-proxy disagreement, although some differences in \r [...]"


id: 420d27c889f25b7fd69ce5411a265afa
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: 8820
content:
"Beyond modelling uncertainties, internal variability and unpredictable \r natural forcings may also lower the degree of confidence in projected \r water cycle changes, especially in the near term (2021-2040) and \r regional-scale projections (Hawkins and Sutton, 2011; Kent et al., \r 2015; Thompson et al., 2015; Fatichi et al., 2016; McKinnon and \r Deser, 2018; Chen and Brissette, 2019; Lehner et al., 2020). Although \r there is low confidence that the main modes of climate variability \r (Annex IV) are altered in a  warmer climate (Sections  4.4.3 and \r 4.5.3), increasing contrast between wet and dry weather regimes \r (Section 8.2.2.1) will amplify their influence on water cycle variability \r (Section 8.4.2.9) and therefore contribute to uncertainties in near\u0002term precipitation changes (Figure  8.23). The role of internal \r variability as source of uncertainties in regional climate projections \r is assessed in Section 10.3.4.3. Here we assess the role of internal \r [...]"


id: 5a37860efdb1c15c487ed8fb5d448ba2
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: 13124
content:
"[...] variability in influencing water cycle projections using paleoclimate \r reconstructions, pre-industrial model simulations, and large single \r model ensembles (Section 8.5.2.1). Implications for the predictability \r of near-term water cycle changes are specifically assessed, as \r they show significant but model-dependent regional hydrological "


id: 97e0684a3994a4d972a3d2a49c55d12e
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: 23933
content:
"In AR5, decadal prediction of precipitation over some land areas \r showed improved skill due to specified radiative forcing, with almost \r no added value from ocean initialization. Since AR5, more studies \r have been devoted to understanding the potential or effective \r water cycle predictability related to ocean multi-decadal variability. \r Decadal hindcast experiments based on large ensembles highlight \r increasing skill scores in annual mean precipitation three to seven \r years ahead, at least over the Sahel and Europe (Yeager et al., 2018). \r There is relatively high predictability of the AMV impacts over the \r Mediterranean basin, Central Asia and the Americas (from the USA to \r northern South America) during boreal summer, but in boreal winter \r the signal-to-noise ratio shows only weak predictability over land \r (Yamamoto and Palter, 2016; Ruprich-Robert et al., 2017). The link \r between South Asian summer monsoon changes and the AMOC \r [...]"


id: ad8fe4ca46191dbe5b9c560dd1838f9d
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: 27777
content:
"[...] and the decadal variability in the Pacific Ocean open the possibility \r of increased predictability for the near future (Kushnir et al., 2017; \r X. Huang et al., 2020b; Sandeep et al., 2020)."


id: 3f49c309a25a0ac1ded8f03746b83835
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: 8326
content:
"Since AR5, SMILEs have helped quantify the time of emergence of \r climate change signals (see Sections  1.4.2.2 and 10.4.3). Results \r from SMILEs indicate that by 2000-2009 (compared to 1950-1999), \r simulated anthropogenic shifts in mean annual precipitation \r already emerged over 36-41% of the globe including high latitudes \r (Frankcombe et al., 2018; Kumar and Ganguly, 2018), the eastern \r subtropical oceans, and the tropics (Zhang and Delworth, 2018). \r By 2050 (2100), more than 60% (85%) of the globe is projected to \r show detectable anthropogenic shifts in mean annual precipitation \r (Zhang and Delworth, 2018). Other SMILE results for the 1950-2100 \r period (Kay et al., 2015; Sigmond and Fyfe, 2016) indicate that \r internal variability can obscure the detection of the anthropogenic \r hydroclimatic signal until the middle to late 21st century in many \r parts of the world for both mean and extreme precipitation \r (Martel et al., 2018; Dai and Bloecker, 2019). A  common finding \r [...]"


id: f7cbd9097c64622c9e5cec23b4a40a89
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: 40969
content:
"[...] is that changes in the characteristics of wet extreme events will \r emerge earlier than changes in average conditions (Gaetani et al., \r 2020; Hawkins et al., 2020; Kusunoki et al., 2020). An assessment "


id: f0358f18e8589eda6dd58a9f0529ef21
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: 39647
content:
"Volcanic eruptions can affect climate projections in the near term \r (2021-2040; Section  4.4.4 and Cross-Chapter Box  4.1). In this \r chapter, they are of interest because they can trigger a  transient \r departure from the water cycle response to anthropogenic radiative \r forcing. Major volcanic eruptions temporarily reduce total global and \r wet tropical region precipitation (high confidence) (Iles and Hegerl, \r 2014), can weaken or shift the ITCZ (Iles and Hegerl, 2014; Colose \r et al., 2016; Liu et al., 2016), and reduce summer monsoon rainfall \r (medium confidence) (Pausata et al., 2015b; Zambri and Robock, \r 2016; Zambri et al., 2017; Zuo et al., 2019; M. Singh et al., 2020). \r Monsoon precipitation in one hemisphere can be enhanced by the \r remote volcanic forcing occurring in the other hemisphere (medium \r confidence) (Pausata et al., 2015a; Liu et al., 2016; Zuo et al., 2019). \r Over the Sahel, the sign of hydrological changes depend on the \r [...]"


id: 372f14d55b51cde353d588d04253be92
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: 6925
content:
"[...] hemisphere where the volcanic eruptions occur (J.M.  Haywood \r et al., 2013). Out of phase changes in the Sahel and the Amazonian \r basin are expected from the effect of volcanic aerosols on tropical \r Atlantic SST and the ITCZ (Hua et al., 2019). Over the last millennium, \r uncertainties remain in the symmetry/asymmetry of the monsoon \r response because it is difficult to estimate the exact latitude and \r season of past volcanic eruptions further back in time (Colose et al., \r 2016; Fasullo et al., 2019)."


id: 6d19c8ff37c810b1c2786c442ae17f1e
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: 16471
content:
"Since AR5, there is further evidence that the pattern-scaling technique \r has limitations (Lopez et al., 2014; Wartenburger et al., 2017; Tachiiri \r et al., 2019), and that alternative approaches, such as multiple \r regressions using the land-sea warming contrast as an additional \r predictor, offer added value (Joshi et al., 2013). The  simplest \r traditional pattern-scaling approach approximates future changes by \r the product of a time-evolving global surface temperature change \r and a  pattern that varies spatially but is constant across time, \r scenarios, and models. This technique was shown to be more robust \r across scenarios rather than across models, with better results for \r temperature compared with precipitation (Tebaldi and Arblaster, \r 2014; see also Section 4.2.4). One approach which avoids scaling is to \r consider a period in a different scenario with the same global surface \r temperature change (Herger et al., 2015). It is attractive as it provides \r [...]"


id: 718051ae935cfdb197533fae73d79fd
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: 17217
content:
"[...] patterns of any temporal resolution that are consistent across \r variables. Nonetheless, this technique is still only based on global \r surface temperature and is not necessarily suitable for precipitation \r changes projected in stabilized versus transient scenarios (at the \r same global warming level) given the fast-atmospheric adjustment \r to GHG radiative forcing (Sections 8.2.1 and 8.4.1.1)."


id: 5d7b98440dce0873b113928b4fb1bd87
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: 13732
content:
"abrupt quadrupling of atmospheric CO2 (Webb et al., 2017). Preliminary \r analyses based on CMIP5 models showed that annual precipitation \r changes following a doubling step change in CO2 from pre-industrial \r levels are not necessarily consistent with the response to the step \r from doubling to quadrupling despite a  similar change in radiative \r forcings (Good et al., 2016a; Ceppi and Shepherd, 2017). Beyond the \r visual comparison of the climate response at various global warming \r levels (e.g., Figure 4.35), the linearity across global warming levels can \r be assessed by using the highest emissions scenario and comparing \r seasonal mean relative precipitation changes at +2degC versus +4degC \r above pre-industrial (1850-1900) temperatures (Figure  8.25). The \r results support the previous finding (Good et al., 2016b) that a second \r 2degC warming does not necessarily lead to the same precipitation \r anomaly pattern as the first 2degC, especially in the tropics where regional \r [...]"


id: cf1eaff8df418d12c5aafb2edbc03e52
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: 33786
content:
"[...] differences can be large but not necessarily consistent among different \r models. They are also consistent with a  recent analysis of CMIP5 \r models showing that the projected drying in the Mediterranean and \r in Chile is substantially faster than the increase in GSAT, and therefore \r does not scale linearly with global warming (Zappa et al., 2020). "


id: 211ab5ecafa140e48bd315f7b9dbc702
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: 3043
content:
"Non-linearities in the climate response are thought to arise from \r multiple factors. These include state-dependent ice-albedo feedback \r and its potential influence on Northern Hemisphere (NH) storm \r tracks (Peings and Magnusdottir, 2014; Semenov and Latif, 2015; see \r also Cross-Chapter Box 10.1 and Section 8.6.1.2); a state-dependent \r sensitivity of tropical precipitation to increased SST (Schewe and \r Levermann, 2017; He et al., 2018); a complex response of the Atlantic \r meridional overturning circulation (AMOC; Sections  9.2.4.1 and \r 8.6.1.1) and its model- and magnitude dependent teleconnections \r with regional temperature and precipitation (Kageyama et al., \r 2013; Jackson et al., 2015; Qasmi et al., 2017, 2020); and other \r atmospheric and terrestrial (Section 8.5.3.2) processes such as cloud \r and land surface feedbacks (Ceppi and Gregory, 2017; King, 2019). \r The response of convective precipitation may exhibit non-linearities \r [...]"


id: 306d5934d3f7923d8b26ff9666e28843
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: 5775
content:
"[...] because it is itself modulated by both dynamics and atmospheric \r water content, each responding independently to warming (Chadwick \r and Good, 2013; Neupane and Cook, 2013). "


id: 2be32f5ea2bb9a47ce3e6d4713b97329
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: 4971
content:
"Land surface responses and feedbacks represent a potential source of \r non-linearity for the water cycle response, at least at regional and local \r scales. The forced response of soil moisture and freshwater resources \r not only depends on precipitation, but also on evaporation (Laine \r et al., 2014), snowmelt (Thackeray et al., 2016), and runoff (X. Zhang \r et al., 2018) which are intrinsically non-linear processes depending \r on soil moisture or temperature thresholds. Bare ground evaporation \r is, for instance, usually estimated as a non-linear function of surface \r soil moisture (Jefferson and Maxwell, 2015). Plant transpiration \r requires more complex formulations with non-linear dependencies \r on multiple environmental factors including root-zone soil moisture \r and atmospheric CO2 concentration (Franks et al., 2017). Globally, \r land surface evaporation is both energy and soil-moisture limited, \r but one of these limitations can become dominant depending on \r [...]"


id: 747bfb000bf400ef6d69082e3ddeb4e4
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: 17729
content:
"[...] regions and seasons. Non-linearities may be particularly strong in \r transitional regimes where and when soil moisture limitation plays \r a major role (Berg and Sheffield, 2018b)."


id: 2798a74b42196c19b1f1af882cd7eac7
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: 4146
content:
"Compared to atmospheric circulation and seasonal mean precipitation, \r extreme precipitation has been found to scale more accurately with \r local and global mean temperature (Chou et al., 2012; Pendergrass \r et al., 2015). The projected increase in the magnitude of extreme \r precipitation is generally proportional to the global warming level, \r with an increase of around 7%  per  1degC warming (Section  11.4.5) \r although this rate shows seasonal and geographical variations and \r is slightly less for five-day than for one-day precipitation maxima. \r Projected changes in extreme precipitation are the result of both \r thermodynamical and more model-dependent and potentially less \r linear dynamical contributions (Pfahl et al., 2017). Projected changes \r in precipitation extremes are also potentially sensitive to a  non\u0002linear response of spatial convective organization (Pendergrass et al., \r 2016), and can exhibit a  quadratic rather than linear response to \r [...]"


id: 6719a0e3dd324c0e535193bbc88596c1
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: 15411
content:
"Land surface runoff and groundwater recharge are highly non\u0002linear process, depending for instance on rainfall intensity, soil \r infiltration capacity, vertical profile of soil moisture and water table \r depth. A non-linear relationship between rainfall and groundwater \r recharge was observed in the tropics where intense seasonal \r rainfalls associated with internal climate variability contribute \r disproportionately to recharge (R.G. Taylor et al., 2013a; Cuthbert \r et al., 2019a). Groundwater fluxes in arid regions are generally less \r responsive to climate variability than in humid regions, which can \r temporarily buffer climate change impacts on water resources or lead \r to a long, initially hidden, hydrological responses to global warming \r (Cuthbert et al., 2019a). Hydrological model simulations driven by \r individual and combined forcing show that decreased precipitation \r can cause larger deficits in soil moisture, streamflow and water table \r [...]"


id: 131a9b5c17d7c86f7017d41285c4c6a0
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: 574
content:
"[...] depth than other forcings, but also that these factors are not linearly \r cumulative when applied in combination (Hein et al., 2019). Surface \r runoff was found to scale only approximately with global warming \r (Tanaka et al., 2017). Significant non-linearities were found in the \r projected annual mean runoff response to global warming in CMIP5 \r projections, which could not be entirely explained by precipitation \r changes (X.  Zhang et al., 2018). Similar non-linear behaviours are \r found in CMIP6 models over the Amazon, Yangtze, Niger, Euphrates \r and Mississippi river basins (Figure 8.26), highlighting the need to \r reassess the assumption of linearity when estimating regional water \r cycle changes. "


id: 3b607c8f8807db25714f4c94ceb15be4
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: 7634
content:
"Multiple lines of evidence, including both paleoclimate reconstructions \r and simulations, suggest that a  severe weakening or collapse of \r Atlantic Meridional Overturning Circulation (AMOC, see Glossary) \r causes abrupt and profound changes in the global hydrological cycle \r (Chiang and Bitz, 2005; Broccoli et al., 2006; Chiang and Friedman, \r 2012; Jackson et al., 2015; Renssen et al., 2018). Deep water \r formation in the North Atlantic is dependent on a delicate balance of \r heat and salt fluxes (Buckley and Marshall, 2016); disruption in either \r of these due to melting ice sheets, a  change in precipitation and \r evaporation, or ocean circulation can force AMOC to cross a tipping \r point (SROCC; Drijfhout et al., 2015). During the last deglacial \r transition, one such slowdown in AMOC - during the Younger Dryas \r event (12,800-11,700 years ago) - caused worldwide changes in \r precipitation patterns. These included a southward migration of the \r [...]"


id: 84fee85940f5f99776638a83004907ab
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: 20539
content:
"[...] tropical ITCZ (Peterson et al., 2000; McGee et al., 2014; Schneider \r et al., 2014; Mohtadi et al., 2016; Reimi and Marcantonio, 2016; \r P.X.  Wang et al., 2017) and systematic weakening of the African \r and Asian monsoons (Tierney and DeMenocal, 2013; Otto-Bliesner \r et al., 2014; Cheng et al., 2016; Grandey et al., 2016; Wurtzel et al., \r 2018). Conversely, the Southern Hemisphere (SH) monsoon systems \r intensified (Cruz et al., 2005; Ayliffe et al., 2013; Strikis et al., 2015, \r 2018; Campos et al., 2019). Drying occurred in Meso-America \r (Lachniet et al., 2013) while the North American monsoon system \r was largely unaffected (Bhattacharya et al., 2018). The mid-latitude \r region in North America was wetter (Polyak et al., 2004; Grimm et al., \r 2006; Wagner et al., 2010; Voelker et al., 2015), while Europe was \r drier (Genty et al., 2006; Rach et al., 2017; Naughton et al., 2019). \r A transient coupled climate model simulation was able to reproduce \r [...]"


id: c2d1e75e6a24b837ec8235c7976c65b7
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: 31551
content:
"[...] the large-scale precipitation response to such an event (Figure 8.27a; \r Liu et al., 2009). "


id: 29efee03e0e7ac22d8602c1514bfd597
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: 4602
content:
"These patterns of past hydroclimatic change are relevant for future \r projections because it is very likely that AMOC will weaken by 2100 \r in response to increased greenhouse gas emissions (Section 9.2.3.1; \r Weaver et al., 2012; Drijfhout et al., 2015; Bakker et al., 2016; \r Reintges et al., 2017). Furthermore, there is medium confidence\r that the decline in AMOC will not involve an abrupt collapse before \r 2100 (Section 9.2.3.1). The response of precipitation to hypothetical \r AMOC collapse under elevated greenhouse gases bears resemblance \r to the paleoclimate response during the Younger Dryas event, with \r some important differences due to effects of increased CO2 on global \r precipitation patterns (Figure  8.27b). As with the paleoclimate \r events, AMOC collapse results in a southward shift in the ITCZ that \r is most pronounced in the tropical Atlantic. This could cause drying \r in the Sahel region (Defrance et al., 2017) as well as Meso-America \r [...]"


id: abac5759f9b29279072314c0100a66d6
created_at: 2023-04-19 13:22:39
updated_at: 2023-04-19 13:22:39
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vector_id: 27443
content:
"This close association between the land surface and the water cycle \r makes the Amazon a  potential hotspot for abrupt change (Torres \r and Marengo, 2014). Both deforestation and drying are projected to \r increase by 2100, resulting in a worst-case scenario of up to a 50% \r loss in forest cover by 2050 (Soares-Filho et al., 2006; Boisier et al., \r 2015; ter Steege et al., 2015; Gomes et al., 2019). Deforestation in \r the Amazon also raises the probability of catastrophic fires (Brando \r et al., 2014). The combination of deforestation, drier conditions, \r and increased fire can push the rainforest ecosystem past a tipping \r point, beyond which there is rapid land surface degradation, a sharp \r reduction in atmospheric moisture recycling, an increase in the \r fraction of precipitation that runs off, and a  further shift towards \r a drier climate (Staal et al., 2015; Boers et al., 2017; Zemp et al., 2017; \r Ruiz-Vasquez et al., 2020). A rapid drop in precipitation has a direct \r [...]"


id: bea466adf6ba5fe8239b24c09ec18bc7
created_at: 2023-04-19 13:22:39
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index: climate_gpt_v2
vector_id: 30799
content:
"[...] impact on river flows, driving basin-scale shifts from a regulated to \r unregulated state (Salazar et al., 2018). Regional climate modeling \r experiments confirm that increased deforestation leads to a  drier \r climate, although not all models show a true tipping point, at least \r under present-day climatic conditions (Lejeune et al., 2015; Spracklen \r and Garcia-Carreras, 2015)."


id: d8f06524f49fb542a5cc86a9ab8c4bdc
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: 35460
content:
"CMIP5 and CMIP6 models, some of which include dynamic \r vegetation schemes, cannot simulate the magnitude, nor the spatial \r extent, of greening and precipitation change associated with the \r last Green Sahara under standard mid-Holocene (6,000 years ago) \r boundary conditions (high confidence) (Figure 3.11; Harrison et al., \r 2014; Tierney et al., 2017; Brierley et al., 2020). This result remains \r unchanged since AR4 (Jansen et al., 2007). This may be due to \r climatological biases in the models (Harrison et al., 2015) or could \r imply that the strength of the feedbacks between vegetation and \r the water cycle in the models is too weak (Hopcroft et al., 2017). To \r date, climate models still only produce the amount and spatial extent \r of rainfall that is needed to sustain a Green Sahara if they are given \r prescribed changes in the land surface, such as albedo, soil moisture, \r vegetation cover and/or dust emissions (Pausata et al., 2016; Skinner \r [...]"


id: 58f49d23f4865840438e9aa618a04b7b
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: 12890
content:
"Greening of the Sahara and Sahel regions in North Africa, in \r response to an increase in precipitation, has long been considered \r an amplifying mechanism that can lead to abrupt change. Although \r the high surface albedo of the desert stabilizes the energy balance \r of the system (Charney, 1975), greening can induce strong, positive \r feedbacks between the land surface and precipitation that can shift \r the region into a 'Green Sahara' state. The fact that the transition \r phase between a Desert Sahara and Green Sahara is not theoretically \r stable (Brovkin et al., 1998) creates a tipping point and allows for the \r possibility of an abrupt shift between dry and wet climate regimes. \r Paleoclimate reconstructions provide evidence of past Green Sahara \r states (DeMenocal and Tierney, 2012), under which rainfall rates \r increased by an order of magnitude (Tierney et al., 2017), leading \r to a vegetated landscape (Jolly et al., 1998) with large lake basins \r [...]"


id: 76839bcc08c541eabc73f7d267fc2361
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: 18069
content:
"[...] (Gasse, 2000; Drake and Bristow, 2006). The underlying driver of the \r Green Sahara is the periodic increase in summer insolation associated \r with the orbital precession cycle (Kutzbach, 1981). In this sense, Green \r Saharas are not direct analogues for a  response to anthropogenic \r greenhouse gas emissions (GHGs), as these past states were forced \r by natural, seasonal changes in solar radiation. However, the climate \r dynamics of Green Sahara periods (which have global impacts, \r Pausata et al., 2020), and the speed of the transitions between Desert \r Saharas and Green Saharas, are relevant for future projections."


id: f22bb181c9adfbfe848066490089e1a2
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: 39999
content:
"Since AR5, paleoclimatic studies have improved our view of the \r timing, spatial extent, and speed of transitions associated with the \r early Holocene (11,000-5,000 years ago) Green Sahara. Observed \r transitions into and out of Green Sahara states are always \r faster than the underlying forcing, in agreement with theoretical \r considerations (high confidence) (Tierney and DeMenocal, \r 2013; Shanahan et al., 2015; Tierney et al., 2017). However, \r there is low confidence in the duration of the transition because \r sedimentary records cannot typically resolve changes on decadal \r to multi-decadal time scales (Tierney and DeMenocal, 2013). Both \r paleoclimate data and modelling experiments suggest that the \r timing and speed of the transition was spatially heterogeneous \r (high confidence), with northern Saharan locations becoming drier \r thousands of years before more equatorial locations (Shanahan \r et al., 2015; Tierney et al., 2017; Dallmeyer et al., 2020). These \r [...]"


id: 5cf1f15c8b51c3b855c8e41727181cca
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: 13619
content:
"[...] observations are consistent with theoretical studies suggesting \r that spatial heterogeneity and diversity in ecosystems can mitigate \r the probability of catastrophic change (Van Nes and Scheffer, 2005; "


id: ba7f04317ceb2452bf830c4738649593
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: 30028
content:
"increasing stability in the atmosphere (Cook et al., 2013; Huang et al., \r 2014) can drive positive feedbacks (see also Section  6.3.6). Thus \r there is strong potential for dust to contribute to abrupt changes in \r the water cycle, especially in semi-arid regions where wind erosion \r is highly sensitive to vegetation cover and drought variability (Yu \r et al., 2015). One such event occurred over the Central USA during \r the 1930s: the Dust Bowl drought, an iconic event characterized by \r widespread land degradation and historically unprecedented levels \r of dust storm activity (Hansen and Libecap, 2004; Lee and Gill, \r 2015). While initialized by warm sea surface temperatures in the \r North Atlantic, modeling work indicates that land cover changes and \r resulting dust emissions contributed to the severity and spatial extent \r of the drought by further suppressing precipitation (Cook et al., 2009; \r Hu et al., 2018; Cowan et al., 2020). There is also increasing evidence \r [...]"


id: 19cf11849cf00f759b9cad7d39391884
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: 1736
content:
"[...] that dust aerosol feedbacks are necessary to explain the magnitude \r of rainfall increase during the mid-Holocene Green Sahara (Pausata \r et al., 2016; Tierney et al., 2017)."


id: d54beb0304f9c4b77fc9a27af54d5b48
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: 34849
content:
"Solar radiation modification (SRM) techniques seek to reduce the \r impacts of climate change by modifying the Earth's radiation budget, \r either by reflecting incoming solar radiation or increasing the amount \r of heat lost to space. Note that, following SR1.5, the definition of \r SRM in this Report refers to changes in both solar and longwave \r radiation (Section 4.6.3.3 and Glossary). A variety of methods have \r been proposed, including injection of aerosols or their precursors \r into the stratosphere, cloud brightening, and cirrus cloud thinning \r (Table 4.8). Since SRM alters the planetary energy balance, changes in \r the hydrological cycle are theoretically expected (Section 8.2). These \r changes can be abrupt if the initial magnitude of SRM is large, rather \r than increased gradually. Since AR5, a diversity of SRM techniques \r have been tested using climate model simulations, with an increasing \r focus on consequences for regional water availability. Techniques \r [...]"


id: 67d1d6b32820dba7fb15eb7a3acf71b2
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: 15537
content:
"[...] targeting shortwave radiation (sulfate injection, surface albedo \r modification, cloud brightening) are likely to reduce global mean "


id: 1e88b63a43a047781c73ea0c0b5a4a60
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 2576
content:
"However, heavier rainfall does not always lead to greater flooding. This is because flooding also depends \r upon the type of river basin, the surface landscape, the extent and duration of the rainfall, and how wet the \r ground is before the rainfall event (FAQ 8.2, Figure 1). Some regions will experience a drying in the soil as the \r climate warms, particularly in subtropical climates, which could make floods from a rainfall event less probable \r because the ground can potentially soak up more of the rain. On the other hand, less frequent but more intense \r downpours can lead to dry, hard ground that is less able to soak up heavy rainfall when it does occur, resulting \r in more runoff into lakes, rivers and hollows. Earlier spring snowmelt combined with more precipitation falling \r as rain rather than snow can trigger flood events in cold regions. Reduced winter snow cover can, in contrast, \r [...]"


id: 44f5cecfa04e4e51cf735d1c2b07cc24
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 9363
content:
"[...] decrease the chance of flooding arising from the combination of rainfall and rapid snowmelt. Rapid melting of \r glaciers and snow in a warming climate is already increasing river flow in some regions, but as the volumes of ice \r diminish, flows will peak and then decline in the future. Flooding is also affected by changes in the management \r of the land and river systems. For example, clearing forests for agriculture or building cities can make rainwater \r flow more rapidly into rivers or low-lying areas. On the other hand, increased extraction of water from rivers can \r reduce water levels and the likelihood of flooding. "


id: 75292404a36719140baa20285aa0e068
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 17847
content:
"A drought is broadly defined as drier than normal conditions; that is, a moisture deficit relative to the average \r water availability at a given location and season. Since they are locally defined, a drought in a wet place will \r not have the same amount of water deficit as a drought in a dry region. Droughts are divided into different \r categories based on where in the water cycle the moisture deficit occurs: meteorological drought (precipitation), \r hydrological drought (runoff, streamflow, and reservoir storage), and agricultural or ecological drought (plant \r stress from a combination of evaporation and low soil moisture). Special categories of drought also exist. For \r example, a snow drought occurs when winter snowpack levels are below average, which can cause abnormally \r low streamflow in subsequent seasons. And while many drought events develop slowly over months or years, \r [...]"


id: 25910bc21d5e5f059d7b7d0b6b628326
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 3797
content:
"[...] some events, called flash droughts, can intensify over the course of days or weeks. One such event occurred \r in 2012 in the Midwestern region of North America and had a severe impact on agricultural production, with \r losses exceeding $30 billion US dollars. Droughts typically only become a concern when they adversely affect \r people (reducing water available for municipal, industrial, agricultural, or navigational needs) and/or ecosystems \r (adverse effects on natural flora and fauna). When a drought lasts for a very long time (more than two decades) \r it is sometimes called a megadrought."


id: c429caa53a835f9bb3f3ea70a428cedf
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 31797
content:
"Most droughts begin when precipitation is below normal for an extended period of time (meteorological \r drought). This typically occurs when high pressure in the atmosphere sets up over a  region, reducing cloud \r formation and precipitation over that area and deflecting away storms. The lack of rainfall then propagates \r across the water cycle to create agricultural drought in soils and hydrological drought in waterways. Other \r processes act to amplify or alleviate droughts. For example, if temperatures are abnormally high, evaporation \r increases, drying out soils and streams and stressing plants beyond what would have occurred from the lack of \r precipitation alone. Vegetation can play a critical role because it modulates many important hydrologic processes \r (soil water, evapotranspiration, runoff). Human activities can also determine how severe a  drought is. For \r example, irrigating croplands can reduce the socio-economic impact of a drought; at the same time, depletion of \r [...]"


id: f049f51d0bb11a704c6be63d4611f1a1
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 39662
content:
"The effect of climate change on drought varies across regions. In the subtropical regions like the Mediterranean, \r southern Africa, south-western Australia and south-western South America, as well as tropical Central America, \r western Africa and the Amazon basin, precipitation is expected to decline as the world warms, increasing the \r possibility that drought will occur throughout the year (FAQ 8.3, Figure 1). Warming will decrease snowpack, \r amplifying drought in regions where snowmelt is an important water resource (such as in south-western South \r America). Higher temperatures lead to increased evaporation, resulting in soil drying, increased plant stress, and \r impacts on agriculture, even in regions where large changes in precipitation are not expected (such as central \r and northern Europe). If emissions of greenhouse gases are not curtailed, about a third of global land areas \r [...]"


id: 5bf1f40888651fa487db93a8fa2f45c2
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 13438
content:
"[...] are projected to suffer from at least moderate drought by 2100. On the other hand, some areas and seasons \r (such as high-latitude regions in North America and Asia, and the South Asian monsoon region) may experience \r increases in precipitation as a result of climate change, which will decrease the likelihood of droughts. FAQ 8.3, \r Figure 1 highlights the regions where climate change is expected to increase the severity of droughts."


id: 58dfb5a683a3a3176eb72e6306ccaaa8
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 12877
content:
"Cecile Agosta (France), Kyle Armour (United States of America), Mathias Aschwanden (Switzerland), \r Jonathan L. Bamber (United Kingdom), Sophie Berger (France/Belgium), Fabio Boeira Dias (Finland/\r Brazil), Jason E. Box (Denmark/United States of America), Eleanor J. Burke (United Kingdom), \r Kevin D. Burke (United States of America), Xavier Capet (France), John A.  Church (Australia), \r Lee de Mora (United Kingdom), Chris Derksen (Canada), Catia M. Domingues (Australia, United \r Kingdom/Brazil), Jakob  Dorr (Norway/Germany), Paul J. Durack (United States of America/\r Australia), Thomas L. Frolicher (Switzerland), Thian Y. Gan (Canada/Malaysia), Gregory G. Garner \r (United States of America), Sebastian  Gerland (Norway/Germany), Heiko  Goelzer (Norway/\r Germany), Natalya  Gomez (Canada), Irina  V.  Gorodetskaya (Portugal/Belgium, The Russian \r Federation), Jonathan M. Gregory (United Kingdom), Robert Hallberg (United States of America), \r [...]"


id: d6bed8722dff0866ae03cdf29e85d12a
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 35089
content:
"[...] F. Alexander Haumann (United States of America/Germany), Tim H. J. Hermans (The Netherlands), \r Emma M. Hill (Singapore/United States of America, United Kingdom), Regine Hock (United States of \r America, Norway/Germany), Stefan Hofer (Norway/Austria), Romain Hugonnet (France, Switzerland/\r France), Philippe Huybrechts (Belgium), A.K.M. Saiful Islam (Bangladesh), Laura C. Jackson (United \r Kingdom), Nicolas C. Jourdain (France), Andreas Kaab (Norway/Germany), Nicole S. Khan (China/\r United States of America), Shfaqat Abbas  Khan (Denmark), Matthew Kirwan (United States of \r America), Roxy Mathew Koll (India), James Kossin (United States of America), Anders Levermann \r (Germany), Sophie Lewis (Australia), Shiyin Liu (China), Daniel Lowry (New Zealand/United States \r of America), Marta  Marcos (Spain), Ben  Marzeion (Germany), Matthew  Menary (France/United \r Kingdom), Sebastian H. Mernild (Norway, Denmark/Norway), Philip Orton (United States of America), \r [...]"


id: 204498d323136836961ab5e1b15a5629
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 2894
content:
"[...] Matthew D. Palmer (United Kingdom), Frank Pattyn (Belgium), Brodie Pearson (United States of \r America/United Kingdom), Cecile Pellet (Switzerland), Chris Perry (United Kingdom), Mark D. Pickering "


id: c256c9e081e6cc6ec2a9c4ccca112a9
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 31474
content:
"This chapter assesses past and projected changes in the ocean, \r cryosphere and sea level using paleoreconstructions, instrumental \r observations and model simulations. In the following summary, we \r update and expand the related assessments from the IPCC Fifth \r Assessment Report (AR5), the Special Report on Global Warming of \r 1.5degC (SR1.5) and the Special Report on Ocean and Cryosphere in \r a Changing Climate (SROCC). This chapter covers major advances since \r SROCC, including the synthesis of extended and new observations. \r These advances allow for improved assessment of past change, \r processes and budgets for the last century, and the use of a hierarchy \r of models and emulators, which provide improved projections and \r uncertainty estimates of future change. In addition, the systematic use \r of model emulators makes our projections of ocean heat content, land \r ice loss and sea level rise fully consistent with each other and with \r [...]"


id: 97837765d0541f2d8990ae5070ffc9e7
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
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vector_id: 23870
content:
"[...] the assessed equilibrium climate sensitivity and projections of global \r surface air temperature across the entire report. In this executive \r summary, uncertainty ranges are reported as very likely ranges and \r expressed by square brackets, unless otherwise noted."


id: bca6baa9ac25587f0ea09fc00912972d
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 30439
content:
"The heat content of the global ocean has increased since at \r least 1970, and will continue to increase over the 21st century \r (virtually certain). The associated warming will likely continue \r until at least 2300, even for low-emissions scenarios, because \r of the slow circulation of the deep ocean. Ocean heat content \r has increased from 1971 to 2018 by 0.396 [0.329 to 0.463, likely\r range] yottajoules and will likely increase until 2100 by two to four \r times that amount under SSP1-2.6 and four to eight times that amount \r under SSP5-8.5. The long time scale also implies that the amount of \r deep-ocean warming will only become scenario-dependent after about \r 2040 (medium confidence), and that the warming is irreversible over \r centuries to millennia (very high confidence). On annual to decadal \r time scales, the redistribution of heat by the ocean circulation dominates \r spatial patterns of temperature change (high confidence). At longer time \r [...]"


id: 30058d018b182e04683de7fc94548ca7
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updated_at: 2023-04-19 13:22:40
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content:
"[...] scales, the spatial patterns are dominated by additional heat, primarily \r stored in water masses formed in the Southern Ocean, and by weaker \r warming in the North Atlantic where heat redistribution caused by \r changing circulation counteracts the additional heat input through the \r surface (high confidence). {9.2.2, 9.2.4, 9.6.1, Cross-Chapter Box 9.1}"


id: f50879725fc216ccb38ea6c21a5e50be
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 40475
content:
"The Arctic Ocean will likely become practically sea ice free1\r during the seasonal sea ice minimum for the first time before \r 2050 in all considered SSP scenarios. There is no tipping \r point for this loss of Arctic summer sea ice (high confidence). \r The practically ice-free state is projected to occur more often with higher \r greenhouse gas concentrations, and it will become the new normal for \r high-emissions scenarios by the end of this century (high confidence). \r Based on observational evidence, Coupled Model Intercomparison \r Project Phase 6 (CMIP6) models and conceptual understanding, the \r substantial satellite-observed decrease of Arctic sea ice area over \r the period 1979-2019 is well described as a linear function of global \r mean surface temperature, and thus of cumulative anthropogenic \r carbon dioxide (CO2) emissions, with superimposed internal variability \r (high confidence). According to both process understanding and \r [...]"


id: a2f5b62bbcfe210dd04245add32ea7ec
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updated_at: 2023-04-19 13:22:40
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index: climate_gpt_v2
vector_id: 25887
content:
"[...] CMIP6 simulations, a  practically sea ice-free state will likely be \r observed some years before additional (post-2020) cumulative \r anthropogenic CO2 emissions reach 1000 GtCO2. {4.3.2, 9.3.1}"


id: 34877a50895cf717ceb10201f5fa823a
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 6480
content:
"Both the Greenland Ice Sheet (virtually certain) and the \r Antarctic Ice Sheet (likely) will continue to lose mass \r throughout this century under all considered SSP scenarios. \r The related contribution to global mean sea level rise until \r 2100 from the Greenland Ice Sheet will likely be 0.01 to \r 0.10 m under SSP1-2.6, 0.04 to 0.13 m under SSP2-4.5 and \r 0.09-0.18 m under SSP5-8.5, while the Antarctic Ice Sheet will \r likely contribute 0.03 to 0.27 m under SSP1-2.6, 0.03 to 0.29 m \r under SSP2-4.5, and 0.03 to 0.34 m under SSP5-8.5. The loss of \r ice from Greenland will become increasingly dominated by surface \r melt, as marine margins retreat and the ocean-forced dynamic \r response of ice-sheet margins diminishes (high confidence). In the \r Antarctic, dynamic losses driven by ocean warming and ice-shelf \r disintegration will likely continue to outpace increasing snowfall this \r century  (medium confidence). Beyond 2100, total mass loss from \r [...]"


id: 683036c4e2cad977855abbd6e427c579
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
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index: climate_gpt_v2
vector_id: 15599
content:
"[...] both ice sheets will be greater under high-emissions scenarios than \r under low-emissions scenarios (high confidence). The assessed likely\r ranges consider those ice-sheet processes in whose representation \r in current models we have at least medium confidence, including \r surface mass balance and grounding-line retreat in the absence of \r instabilities. Under high-emissions scenarios, poorly understood \r processes related to marine ice sheet instability and marine ice cliff \r instability, characterized by deep uncertainty, have the potential to \r strongly increase Antarctic mass loss on century to multi-century time \r scales. {9.4.1, 9.4.2, 9.6.3, Box 9.3, Box 9.4}"


id: 8272be25122214c866a04a9b3789b3af
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updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 20073
content:
"Regional sea level change has been the main driver of changes \r in extreme still water levels across the quasi-global tide gauge \r network over the 20th century (high confidence) and will be \r the main driver of a  substantial increase in the frequency \r of extreme still water levels over the next century (medium \r confidence). Observations show that high-tide flooding events that \r occurred five times per year during the period 1960-1980 occurred, \r on average, more than eight times per year during the period \r 1995-2014 (high confidence). Under the assumption that other \r contributors to extreme sea levels remain constant (e.g., stationary \r tides, storm-surge, and wave climate), extreme sea levels that \r occurred once per century in the recent past will occur annually or \r more frequently at about 19-31% of tide gauges by 2050 and at \r about 60% (SSP1-2.6) to 82% (SSP5-8.5) of tide gauges by 2100 \r (medium confidence). In total, such extreme sea levels will occur \r [...]"


id: 6c493d2b834a8058dd320d7956918f78
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updated_at: 2023-04-19 13:22:40
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index: climate_gpt_v2
vector_id: 16345
content:
"[...] about 20 to 30 times more frequently by 2050 and 160 to 530 times \r more frequently by 2100 compared to the recent past, as inferred \r from the median amplification factors for SSP1-2.6, SSP2-4.5, and \r SSP5-8.5 (medium confidence). Over the 21st century, the majority \r of coastal locations will experience a  median projected regional \r sea level rise within +-20% of the median projected GMSL change \r (medium confidence). {9.6.3, 9.6.4}"


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vector_id: 13758
content:
"At sustained warming levels between 3degC and 5degC, the Arctic \r Ocean will become practically sea ice free throughout several months \r in most years (high confidence); near-complete loss of the Greenland \r Ice Sheet and complete loss of the West Antarctic Ice Sheet will occur \r irreversibly over multiple millennia (medium confidence); substantial \r parts or all of Wilkes Subglacial Basin in East Antarctica will be lost \r over multiple millennia (low confidence); 60 to 75% of current glacier \r mass outside Antarctica will disappear (low confidence); nearly all \r glacier mass in low latitudes, Central Europe, Caucasus, western \r Canada and the USA, North Asia, Scandinavia and New Zealand \r will  likely disappear; Northern Hemisphere spring snow cover \r extent will decrease by up to 50% relative to 1995-2014 (medium \r confidence); permafrost volume in the top 3 m will decrease by up \r to 90% compared to 1995-2014 (medium confidence). Committed \r [...]"


id: 1b72aefdab7755b1f942098d782a4831
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 2005
content:
"[...] GMSL rise over 2000 years will be about 12 to 16 m with 4degC of \r peak warming and 19 to 22 m with 5degC of peak warming (medium \r agreement, limited evidence). {9.3.1, 9.4.1, 9.4.2, 9.5.1, 9.5.2, \r 9.5.3, 9.6.3}"


id: c762f75f39f3cfcc4f3d35b33ca057d5
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 32396
content:
"At sustained warming levels between 1.5degC and 2degC, the Arctic \r Ocean will become practically sea ice-free in September in some \r years (medium confidence); the ice sheets will continue to lose mass \r (high confidence), but will not fully disintegrate on time scales of \r multiple centuries (medium confidence); there is limited evidence\r that the Greenland and West Antarctic ice sheets will be lost almost \r completely and irreversibly over multiple millennia; about 50 to 60% \r of current glacier mass excluding the two ice sheets and the glaciers \r peripheral to the Antarctic Ice Sheet will remain, predominantly in \r the polar regions (low confidence); Northern Hemisphere spring \r snow cover extent will decrease by up to 20% relative to 1995-2014 \r (medium confidence); the permafrost volume in the top 3 m will \r decrease by up to 50% relative to 1995-2014 (medium confidence). \r Committed GMSL rise over 2000 years will be about 2 to 6 m \r [...]"


id: 47eb8f96ec29e2cbc1caef3febd19620
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 9862
content:
"[...] with 2degC of peak warming (medium agreement, limited evidence). \r {9.3.1, 9.4.1, 9.4.2, 9.5.1, 9.5.2, 9.5.3, 9.6.3}"


id: 171698f00dce9a185b8434fc5c76882c
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 1283
content:
"Changes in Earth gravity, Earth rotation and viscoelastic solid Earth deformation (GRD) - result from the redistribution of \r mass between terrestrial ice and water reservoirs and the ocean. Contemporary terrestrial mass loss leads to elastic solid Earth uplift \r and a nearby RSL fall. (For a single source of terrestrial mass loss, this is within about 2000 km; for multiple sources, the distance \r depends on the interaction of the different RSL patterns.) Farther away (around more than 7000 km for a single source of terrestrial \r mass loss), RSL rises more than the global average, due to first-order gravitational effects. Earth deformation associated with adding \r water to the ocean and a shift of the Earth's rotation axis towards the source of terrestrial mass loss leads to second-order effects \r that increase spatial variability of the pattern globally. GRD effects due to the redistribution of ocean water within the ocean itself are \r [...]"


id: 1431c157c7d10e94eb2946048f2782bc
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 770
content:
"[...] referred to as self-attraction and loading effects. There is high confidence in the understanding of GRD processes."


id: 10fc4fae8cecafa67ecadcf780642412
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 181
content:
"Extreme sea level is an exceptionally low or high local sea surface height arising from combined short-term phenomena (e.g., storm \r surges, tides and waves). RSL changes affect extreme sea levels directly by shifting the mean water levels, and indirectly by modulating \r the depth for propagation of tides, waves and/or surges. Extreme sea levels can be influenced by changes in the frequency, tracks, \r or strength of weather systems, or anthropogenic changes such as dredging. Extreme still water level refers to the combined \r contribution of RSL change, tides and storm surges. Wind-generated waves also contribute to coastal sea level. Extreme total water \r level is the extreme still water level plus wave setup (time-mean sea level elevation due to wave energy dissipation). When considering \r coastal impacts, swash (vertical displacement up the shore-face induced by individual waves) is also important and included in Extreme \r [...]"


id: a8fee90739c8a6e6bac95e89a7c407f0
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 26941
content:
"[...] coastal water level. There is low to medium confidence in the understanding of extreme sea level processes (Sections 9.6.4 and 12.4)."


id: 1a639870d59bb7714cd06201eb7abbad
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 1844
content:
"Figure 9.3 | Sea surface temperature (SST) and its changes with time. (a) Time series of global mean SST anomaly relative to 1950-1980 climatology. Shown \r are paleoclimate reconstructions and PMIP models, observational reanalyses (HadISST) and multi-model means from the Coupled Model Intercomparison Project (CMIP) \r historical simulations, CMIP projections, and HighResMIP experiment. (b) Map of observed SST (1995-2014 climatology HadISST). (c) Historical SST changes from observations. \r (d) CMIP 2005-2100 SST change rate. (e) Bias of CMIP. (f) CMIP change rate. (g) 2005-2050 change rate for SSP5-8.5 for the CMIP ensemble. (h) Bias of HighResMIP (bottom \r left) over 1995-2014. (i) HighResMIP change rate for 1950-2014. (j) 2005-2050 change rate for SSP5-8.5 for the HighResMIP ensemble. No overlay indicates regions with \r high model agreement, where >=80% of models agree on sign of change. Diagonal lines indicate regions with low model agreement, where <80% of models agree on sign of \r [...]"


id: cc4b9dabd37df1a15b0e27d6dc267b17
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 33262
content:
"[...] change (see Cross-Chapter Box Atlas.1 for more information). Further details on data sources and processing are available in the chapter data table (Table 9.SM.9)."


id: 439a3c0b15a6cc9ad042ddebe6f2661e
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 9115
content:
"It is  virtually certain  that SST will continue to increase in the \r 21st  century, at a  rate depending on future emissions scenarios. \r The  future global mean SST increase projected by CMIP6 models \r for the period 1995-2014 to 2081-2100 is 0.86 [5-95% range: \r 0.43-1.47] degC under SSP1-2.6, 1.51 [1.02 to 2.19] degC under SSP2-4.5, \r 2.19 [1.56 to 3.30] degC under SSP3-7.0, and 2.89 [2.01 to 4.07] degC under \r SSP5-8.5 (Figure  9.3). While under SSP1-2.6, the CMIP6 ensemble \r consistently projects that it is very likely at least 83% of the world \r ocean surface will have warmed by 2100, and under SSP5-8.5, at least \r 98% of the world ocean surface will have warmed. The spatial pattern \r of future change is consistent with observed SST change over the \r 20th century, though with notable regional differences (Figure 9.3). \r Long-term change in SST patterns is important for regional impacts \r but also affects radiative feedbacks, and therefore long-term change \r [...]"


id: e04c4358338bd9906e864638cfda3eaa
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 36711
content:
"[...] in climate sensitivity (Section 7.4.4.3). In the Southern Ocean, CMIP6 \r models project that SSTs will eventually consistently increase in the \r 21st  century, at a  rate dependent on future scenarios (Figure  9.3 \r and Section 9.2.3.2; Bracegirdle et al., 2020). Yet, there is only low \r confidence that this Southern Ocean warming will emerge by the \r end of the century (Section 7.4.4.1), due to the inconsistent historical \r and near-term simulations and observations over the 20th  century \r (Figure  9.3). Furthermore, the equilibrium SST pattern from proxy \r records or simulated by climate models under CO2 forcing stand in \r contrast with the cooling trends in the Southern Ocean observed over \r the past decades (Section 7.4.4.1.2). Similarly, the SST change pattern \r observed in the tropical Pacific Ocean will transition on centennial \r time scales to a mean pattern resembling the El Nino pattern (medium \r confidence) (Annex IV). However, it is difficult to delineate a climate \r [...]"


id: 751c1fc8e1666f906c74e64329cdff
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 17841
content:
"[...] change trend ressembling an El Nino pattern and El Nino variability \r (Wittenberg, 2009; Collins et  al., 2010) without large ensembles \r (Kay et  al., 2015). Several Pliocene SST reconstructions indicate \r enhanced warming in the centre of the eastern Pacific equatorial \r cold tongue upwelling region, consistent with reconstruction of \r enhanced subsurface warming and enhanced warming in coastal \r upwelling regions (Section  7.4.4.2.2). The North Atlantic subpolar \r gyre is projected to continue to warm more slowly than surrounding \r regions (Suo et  al., 2017), as the Gulf Stream concurrently warms \r rapidly (Figure 9.3; Cheng et al., 2013) and the Atlantic Meridional \r Overturning Circulation further declines under greenhouse gas forcing, \r although models disagree about the rate of change (Figure 9.3 and \r Section 9.2.3.1). In summary, CMIP6 models show a future pattern of \r SST change comparable to historical trends with intensity depending "


id: 8ef4807550452794cab6f0128164bdb9
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 22339
content:
"In AR5 (Flato et  al., 2013), a  marginal improvement was noted in \r Coupled Model Intercomparison Project Phase 5 (CMIP5) climate \r model SST biases compared to Phase 3 (CMIP3) models in AR4, with \r a reduction in the magnitude of biases. The AR5 noted that, in several \r regions, large SST biases are symptomatic of errors in the representation \r of important processes, such as dynamics in the equatorial Pacific and \r North Atlantic, and Southern Ocean. Common regional biases in SST \r or historical SST trends are not exclusively linked to the representation \r of the ocean (high confidence), but can have multiple causes, \r including: errors in the representation of long-term historical trends \r in equatorial winds (Section 9.2.1.2); misrepresentation of the forced \r equatorial ocean response (Karnauskas et al., 2012; Kohyama et al., \r 2017; Coats and Karnauskas, 2018); thermocline depth errors (Linz \r et  al., 2014); errors in atmospheric model cloud-related shortwave \r [...]"


id: bb6d2edcc555ff2f6f67763ea4f20345
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 30194
content:
"[...] radiation (Hyder et al., 2018); biases in ocean circulation variability (C. \r Wang et al., 2014); and deficiencies in upper ocean (Q. Li et al., 2019) \r and atmospheric (Bates et al., 2012) boundary layer parametrizations. \r In CMIP6, the mid-latitude biases in the Northern Hemisphere are \r improved in the  multi-model mean, and the inter-model standard \r deviation of the zonal mean SST error is significantly decreased \r in the northern Hemisphere south of 50degN compared to CMIP5, \r though biases in equatorial regions remain essentially unchanged \r (Section 3.5.1.1 and Figures 3.23, 3.24 and 9.3). Some long-standing \r ocean model biases have been reduced through increases in model \r resolution in CMIP6 (Bock et al., 2020) and improved parametrizations \r (Fox-Kemper et al., 2011; Q. Li et al., 2016; Qiao et al., 2016; Reichl \r and Hallberg, 2018). The High Resolution Model Intercomparison \r Project (HighResMIP) ensemble (Figure 9.3) has smaller cold biases \r [...]"


id: 20024a14051ba0d9dc1f06e4d0b19d5e
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 2844
content:
"[...] in the North Atlantic and the tropical Pacific, and smaller warm biases \r in the upwelling regions off the western coasts of Africa, North and \r South America  (Roberts et  al., 2018, 2019; Caldwell et  al., 2019; \r Docquier et al., 2019). In summary, CMIP6 models show persistent \r regional biases in representing the climatological SST state (very high \r confidence), but higher resolution reduces some biases, particularly \r in the North Atlantic and eastern boundary upwelling systems \r (Figure 9.3; high confidence).\r The CMIP6 models represent the observed trends in SST patterns with \r greater fidelity than CMIP5, with the ocean area that is inconsistent \r with the observed trends decreasing by about three  quarters from \r CMIP5 to CMIP6 (Olonscheck et  al., 2020). In some regions, the "


id: d3ee779e27f4578cb00f880192103bed
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 34619
content:
"The SROCC (Abram et al., 2019) and AR5 (Rhein et al., 2013) assessed \r that observations of air-sea fluxes had not yet reached the density \r or accuracy to directly detect trends beyond the noise. New evidence \r since SROCC confirms that direct heat and freshwater flux trends \r have not emerged yet as spatial (Figure 9.4), annual (Yu, 2019), and \r decadal (Zanna et al., 2019) variability overwhelm detection. Since \r AR5, comprehensive comparisons (Bentamy et al., 2017; Valdivieso \r et al., 2017; Yu et al., 2017) have used updated and new surface \r flux products to improve surface flux uncertainty estimates, and \r these comparisons note that implied global energy imbalances \r often exceed the observed ocean warming. Flux estimates using \r top of atmosphere observations and atmospheric fluxes from \r reanalysis have improved over past products (Trenberth and Fasullo, \r 2018) but require consistency adjustments (Trenberth et al., 2019) \r [...]"


id: 76b0a9309be3c907aa588583683a17c
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 18097
content:
"[...] as the energy budget is not closed. Adjustments are needed for \r all flux products, and they remain less accurate than direct ocean \r heat content change measurements (Cheng et  al., 2017). Some \r regional changes are likely robust in both satellite observations and \r projections (Figure 9.4). Recent satellite-based surface flux products \r with improved retrieval algorithms and new satellites, for example, \r J-OFURO3 (Tomita et al., 2019) and OAFlux-HR (Yu, 2019), provide "


id: 58bee8ce022542d2043ce45329980590
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 12854
content:
"Air-sea flux biases result from common causes in most models, and \r many are the same as during AR5 (Rhein et al., 2013). Important \r currents (e.g., Gulf Stream, Kuroshio, Antarctic Circum-polar Current \r patterns) are often found in erroneous locations in models, affecting \r SST and flux signatures (Bates et al., 2012; Beadling et al., 2020; J.-\r L.F. Li et al., 2020), but their locations are improved in high-resolution \r ocean models (Chassignet et  al., 2017, 2020; Hewitt et  al., 2020), \r and high-resolution coupled models reduce the mean air-sea flux \r biases (Delworth et  al., 2012; Sakamoto et  al., 2012; Small et  al., \r 2014; Haarsma et  al., 2016; Caldwell et  al., 2019; L.C Jackson \r et al., 2020). Oceanic variability stems either from internal chaotic \r variability or atmospheric forcing (Hasselmann, 1976; Serazin \r et  al., 2016, 2017). Large-scale variability in the ocean tends to \r [...]"


id: 70dbbae71ffa4055aa279856f9d0f627
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 17118
content:
"[...] follow atmospheric forcing in low-resolution models, while in high\u0002resolution coupled models ocean variability drives atmospheric \r variability on small scales (Bishop et al., 2017; Small et al., 2019), \r allowing these high-resolution models to mimic the coupling with \r clouds, precipitation, and atmospheric and oceanic boundary layers \r apparent in observations (Chelton and Xie, 2010; Frenger et  al., \r 2013). Even coarse-resolution models, such as the ocean and sea ice \r components used in CMIP6, show significant sensitivity in the mean \r and variability of SST and sea ice to modest changes in flux forcing \r (Tsujino et al., 2020). Finally, there is still considerable disagreement \r between different parametrizations of air-sea fluxes used in models \r and strong scatter in direct observations (Renault et  al., 2016; \r Brodeau et al., 2017). In summary, there is very high confidence that \r air-sea heat flux and stress biases are reduced in coupled models \r [...]"


id: 8d2c4e948091899f45ed8afd2fba974d
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 22022
content:
"[...] with high ocean resolution over coarse-resolution models, although \r the effect on trends remain unclear."


id: 5f3ffa6111997efedbff4b8fb9de20ce
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 14066
content:
"a complete suite of turbulent fluxes including heat, moisture, and \r momentum. When combined with satellite-based surface radiation \r from Clouds and the Earth's Radiant Energy System (CERES) Energy \r Balanced and Filled (EBAF; Kato et al., 2018) and precipitation from \r Global Precipitation Climatology Project (GPCP; Adler et al., 2003), \r full ocean-surface forcing is available since 1987 (Figure 9.4). These \r products agree with sparse buoy and ship observations within \r 30 W m-2 (Bentamy et al., 2017; Cronin et al., 2019). While patterns \r agree between models and satellites in net fluxes (Figure  9.4), \r the trend magnitudes are substantially weaker in models. The \r fluxes tending to warm the North Atlantic and Southern Ocean \r are consistent with the largest changes observed in the surface \r properties and water masses (Sections 9.2.1.1, 9.2.2.1 and 9.2.2.3). \r The observed trend toward a  saltier Atlantic Ocean and a  fresher \r [...]"


id: d4f8c50935c6e3a2176d750349a868a9
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 34794
content:
"[...] Indian Ocean, as well as trends in evaporation minus precipitation \r (E-P) patterns in the equatorial Pacific (see also Section  8.3.1) \r enhance the present mean pattern of wetting and drying. Elsewhere \r patterns are less clear, with only partial, large-scale agreement \r with the 'wet gets wetter' simplification (Sections 3.3.2.3, 4.4.1 \r and 4.5.1). In summary, globally integrated and large-scale fluxes \r are more reliably inferred from heat content and salinity change, \r while regional trends are rarely robust in observations; where they \r are robust, they tend to be underestimated or in disagreement \r in models (very high confidence)."


id: ad99b10ad01288b358eb7ee952ef0200
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 27784
content:
"There is low confidence in long-term wind stress trends in most \r regions, but a few locations have likely trends over the scatterometer \r era and in projections, as shown in Figure  9.4 (Desbiolles et  al., \r 2017; Young and Ribal, 2019; Yu, 2019). The AR5 (Rhein et al., 2013) \r assessed with medium confidence that zonal wind stress over the \r Southern Ocean increased from the early 1980s to the 1990s (medium \r confidence) (Figure  9.4). Over 1995-2014, the zonal wind  stress \r over the Southern Ocean continued to increase, westerly winds in \r the North Pacific and North Atlantic weakened, while the easterly \r equatorial Pacific winds of the Walker circulation strengthened \r (Figure  9.4). In historical simulations, CMIP5 models projected \r annular modes (Annex IV) to move poleward and strengthen in both \r hemispheres (Yang et al., 2016), while in CMIP6 models westerlies \r only strengthen over the Southern Ocean, with a weaker trend than \r [...]"


id: 6f2980ef42d679966b0e87155bd9adca
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 16826
content:
"[...] recently observed (Figure 9.4 and Sections 4.5.1 and 4.5.3). In the \r tropical Pacific Ocean, a  weakening trend in easterly winds and \r Walker circulation in the 20th century has been inferred based on \r observed sea level pressure data (Vecchi et  al., 2006; Vecchi and \r Soden, 2007) and coral proxies (Carilli et al., 2014) and is projected \r to continue by CMIP6 models (Figure  9.4). Yet, over 1995-2014 \r observed winds have strengthened (Figure  9.4). The observed \r strengthening may have been influenced by a combination of factors \r (Section 7.4.4.2.1), but there is low confidence in the attribution of \r this signal to anthropogenic warming (Section 3.3.3.1) and medium \r confidence  that it reflects internal variability (Section  8.3.2.3). \r Near-term projected changes over the Southern Ocean result from \r ozone recovery and greenhouse gases (Sections 4.3.3 and 4.4.3). \r Overall, there is only low confidence in observed and projected wind \r [...]"


id: 6e9c4b8a469cd2729bfefd03cae2853d
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 16726
content:
"[...] stress trends in most regions because trends in oceanic wind stresses \r during the satellite era have not emerged or are inconsistent with \r historical simulated changes."


id: 1e847d2203fc6f95db3a00cb5bc2815e
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 2571
content:
"The density difference from surface to deep ocean is the upper-ocean \r stratification. The AR5 (Rhein et  al., 2013) assessed that it is very \r likely that the thermal contribution to stratification over the fixed \r 0-200 m layer increased by about 1% per decade between 1971 and \r 2010 (based on linear trend consistently across reports). The SROCC \r (Bindoff et  al., 2019) found it very likely that density stratification \r increased by 0.46-0.51% per decade between 60degS and 60degN from \r 1970 to 2017). New published estimates based on a  variety of \r different interpolated observations show that SROCC assessed rate \r is too low, even using the same data and methods (Li et al., 2020). \r The 1960-2018 stratification increase is estimated at 1.2  +-  0.1% \r per decade from the IAP dataset, 1.2 +- 0.4% per decade from the \r Ishii product, 0.7 +- 0.5% per decade from the EN4 dataset, 0.9 \r +- 0.5% per decade from ORAS4, and 1.2 +-0.3% per decade from the \r [...]"


id: 6ec1e2208d5c79a858aae85c09f04846
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 16754
content:
"[...] National Centers for Environmental Information (NCEI) dataset (G. Li \r et al., 2020). The improved methodology for computing stratification \r change on individual profiles before gridding yields a global annual \r mean increase of 0-200 m stratification change of 0.8 +- 0.2% per \r decade between 1960 and 2018 (Yamaguchi and Suga, 2019) and \r a  global summer mean increase of 0-200 m stratification change \r of 1.3 +- 0.3% per decade between 1970 and 2018 (Sallee et  al., \r 2021) is of a similar magnitude to the long-term trend (Yamaguchi \r and Suga, 2019; G. Li et  al., 2020). In summary, there is limited \r evidence that focusing on changes over a fixed depth range might "


id: 7c089e5c78a6cec66cdfee4278a2d618
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 19029
content:
"The SROCC assessed that upper-ocean stratification will continue \r to increase in the 21st century under increased radiative forcing (high \r confidence), due to increased surface temperature and high-latitude \r surface freshening (Bindoff et  al., 2019). New climate model \r simulations concur with SROCC assessment of a future increase of \r the 0-200 m stratification under increased radiative forcing in all \r regions of the world ocean (Kwiatkowski et al., 2020). In addition, \r CMIP6 climate models project a  shallowing of the mixed-layer \r in summer and winter by the end of the century under increased \r radiative forcing (Figure  9.5; Kwiatkowski et  al., 2020), with the \r exception of the Arctic showing deepening of the mixed layer as \r a result of sea ice retreat (Figure 9.5; Lique et al., 2018). The regions \r of largest shallowing are associated with the deepest climatological \r mixed layer, in both winter and summer, particularly affecting the \r [...]"


id: a57ef2b3c2ea0fe5346e3459570f33c3
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 26347
content:
"[...] North Atlantic and the Southern Ocean basins (Figure  9.5). While \r CMIP6 models tend to project shallowing mixed layers under \r a warming climate, except at high latitudes (Figure 9.5; Lique et al., \r 2018; Kwiatkowski et al., 2020), a deepening in the summer mixed\u0002layer depth by intensification of the surface winds and storms may \r explain inconsistency among models in many regions (Figure  9.5; \r Young and Ribal, 2019), although model mixed-layer biases are \r large in the summer in the Southern Ocean (Belcher et  al., 2012; \r Sallee et al., 2013a; Q. Li et al., 2016; Tsujino et al., 2020). Lack of \r observed ocean turbulence and climate model limitations do not allow \r for direct assessment of ocean surface turbulence change and limit \r confidence in past and future mixed-layer change. Understanding \r of turbulent processes, their representation in ocean and climate \r models, and their effect on mixed-layer biases have been an active \r [...]"


id: 45b2b79dae35b23172cf27eb74c81099
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 9475
content:
"[...] and rapidly evolving topic of research since AR5 (Buckingham et al., \r 2019; Q. Li  et  al., 2019). Small-scale mixed-layer processes are "


id: 78f564945da5066a29d31a49cef922a1
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 18471
content:
"While AR5 and SROCC did not assess change in mixed-layer depth, the \r reported changes in stratification can modulate the surface mixed\u0002layer depth, which is set by a balance between fluxes and dynamical \r mixing (winds, tides, waves, convection) acting against the background \r stratification and restratification processes (solar and dynamical). \r Despite the large stratification increase observed at a  global scale, \r new evidence shows that summer mixed-layer depth deepened \r consistently over the globe at a rate of 2.9 +- 0.5% per decade from \r 1970 to 2018, with the largest deepening observed in the Southern \r Ocean, corresponding to overall deepening from 3-15 m per decade \r depending on region (Somavilla et al., 2017; Sallee et al., 2021). While \r the shorter observational record in winter (compared to summer) \r does not allow global winter mixed-layer trends to be reliably \r assessed (Sallee et al., 2021), winter mixed-layer depths deepening \r [...]"


id: a6785e7646f8757bb3bfbfe3db70b7c1
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 26509
content:
"[...] at rates of 10  m per decade have been reported at individual \r long-term mid-latitude monitoring sites (Somavilla et  al., 2017). \r Projections agree that shoaling of mixed-layer depth is expected in \r the 21st  century, but only for strong emissions scenarios, and only \r in some regions (Figure 9.5). In summary, there is limited observational\r evidence that the mixed layer is globally deepening, while models \r show no emergence of a trend until later in the 21st century under \r strong emissions."


id: 4e647cba50a21b3e54c1d86c9a7eeec
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 11064
content:
"The SROCC established that MHWs have occurred in all basins over the last decades. Additional evidence documenting widespread \r occurrence of marine heat waves in all basins and marginal seas continues to accumulate (Y. Li et al., 2019; Yao et al., 2020). The SROCC \r highlighted the role of large-scale climate modes of variability in amplifying or suppressing MHW occurrences, which has since \r been further corroborated, increasing confidence in climate modes as important drivers of MHWs (Holbrook et al., 2019; Sen Gupta \r et al., 2020). More generally, understanding of processes leading to MHWs has increased since SROCC, including air-sea heat flux \r (Section 9.2.1.2), increased horizontal heat advection, shoaling of the mixed-layer and suppressed mixing processes (Section 9.2.1.3), \r reduced coastal upwelling and Ekman pumping (Section 9.2.3.5), changes in eddy activities and planetary waves, and the re-emergence \r [...]"


id: 62bd22457391fa7efa88ccc5ff26e6b6
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 14664
content:
"The SROCC reported with high confidence that MHWs - defined as days exceeding the 99th percentile in sea surface temperature \r (SST) from 1982 to 2016 - have very likely doubled in frequency between 1982 and 2016. Additional observation-based evidence \r and acquisition of longer observation time series since SROCC have confirmed and expanded on this assessment: since the 1980s \r MHWs have also become more intense and longer (Frolicher and Laufkotter, 2018; Smale et al., 2019; Laufkotter et al., 2020). Satellite \r observations and reanalyses of SST show an increase in intensity of 0.04degC per decade from 1982 to 2016, an increase in spatial \r extent of 19% per decade from 1982 to 2016, and an increase in annual MHW days of 54% between the 1987-2016 period compared \r to 1925-1954 (Frolicher et al., 2018; Oliver, 2019). The SROCC assessed that 84-90% of all MHWs that occurred between 2006 and \r [...]"


id: 9d7333f68e1e0d8277c84400c37f2f29
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 24936
content:
"[...] 2015 are very likely caused by anthropogenic warming. There is new evidence since SROCC that the frequency of the most impactful \r marine heatwaves over the last few decades has increased more than 20-fold because of anthropogenic global warming (Laufkotter \r et al., 2020). In summary, there is high confidence that MHWs have increased in frequency over the 20th century, with an approximate \r doubling from 1982 to 2016, and medium confidence that they have become more intense and longer since the 1980s."


id: 391475bd43a4b5b03e250c378c500e0d
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 7246
content:
"Consistent with SROCC, future MHWs are defined with reference to the historical climate conditions. The SROCC assessed that MHWs will \r very likely further increase in frequency, duration, spatial extent and intensity under future global warming in the 21st century. The CMIP6 \r projections allow us to confirm this assessment and quantify future change based on global mean probability ratio change (Box 9.2, \r Figure 1): they project MHWs will become four times (5-95% range: 2-9 times] more frequent in 2081-2100 compared to 1995-2014 \r under SSP1-2.6, or eight times (3-15 times) more frequent under SSP5-8.5. The SROCC highlighted that future change of MHWs will \r not be globally uniform, with the largest changes in the frequency of marine heatwaves being projected to occur in the western tropical \r Pacific and the Arctic Ocean (medium confidence). New evidence from the latest generation of climate models confirms and complements \r [...]"


id: f9f62910d0e6eeb4a7206eb79890f173
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 41341
content:
"[...] SROCC assessment (Box 9.2, Figure 1). Moderate increases are projected for mid-latitudes, and only small increases are projected for \r the Southern Ocean (medium confidence) (Hayashida et al., 2020). While under the SSP5-8.5 scenario, permanent MHWs (more than \r 360 days per year) are projected to occur in the 21st century in parts of the tropical ocean, the Arctic Ocean and around 45degS, the \r occurrence of such permanent MHWs can largely be avoided under the SSP1-2.6 scenario (Frolicher et al., 2018; Oliver et al., 2019; Plecha \r and Soares, 2020). The resolution of current climate models (CMIP5 and CMIP6) capture the broad features of MHWs, but they may have \r a bias towards weaker and longer MHWs in the historical period (medium confidence) (Frolicher et al., 2018; Pilo et al., 2019; Plecha and \r Soares, 2020) and greater intensification in western boundary current regions (Hayashida et al., 2020)."


id: cf881de02db60e0b5fdd0fa4ca08fd87
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 33875
content:
"Ocean warming is not uniform with depth. The AR5 (Rhein  et  al., \r 2013) assessed that, since 1971, ocean warming was virtually \r certain for the upper 700 m and likely for the 700-2000  m layer. \r Both AR5 and SROCC (Bindoff et al., 2019) assessed that the deep \r ocean below 2000  m had likely warmed since 1992, especially \r in the Southern Ocean. Section  2.3.3.1 provides an updated \r assessment of ocean temperature change for different depth layers, \r time periods and observation-based reconstructions (Table  2.7). \r Section 2.3.3.1 confirms the previous assessment that it is virtually \r certain that the upper ocean (0-700  m) has warmed since 1971, \r that ocean warming at intermediate depths (700-2000 m) is very \r likely since 2006, and that it is likely that ocean warming has \r occurred below 2000 m since 1992. Section 3.5.1.3 assessed that it \r is extremely likely that human influence was the main driver of the \r [...]"


id: 68f99c0643fc7db1be5c2fd25922118f
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 15736
content:
"[...] ocean heat content increase observed since the 1970s, which extends \r into the deeper ocean (very high confidence), and shows that biases \r in potential temperature have a complex pattern (Figure 3.25). In the \r present section, we assess the regional patterns of this warming and \r associated processes driving regional ocean warming."


id: a14ba6e63039bfdd605865d08ef5474e
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 25599
content:
"Ocean warming - that is, increasing ocean heat content (OHC) - is \r an important aspect of energy on Earth: SROCC (Bindoff et al., 2019) \r reported that there is high confidence that ocean warming during \r 1971-2010 dominated the increase in the Earth's energy inventory, \r which is confirmed by the Box 7.2 assessment that the ocean has \r stored 91% of the total energy gained from 1971 to 2018. As reported \r in Sections 2.3.3.1, 3.5.1.3 and 7.2.2.2, Box 7.2 and Cross-Chapter \r Box 9.1, confidence in the assessment of global OHC change since \r 1971 is strengthened compared to previous reports, and extended \r backward to include likely warming since 1871. Table  7.1 updates \r the estimates of total ocean heat gains from 1971 to 2018, 1993 to \r 2018 and 2006 to 2018. Section 3.5.1.3 assesses that it is extremely \r likely that anthropogenic forcing was the main driver of the OHC \r increase over the historical period. Section  2.3.3.1 reports that \r [...]"


id: bdc932017dfb641573d4802624324ca6
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 30652
content:
"[...] current multi-decadal to centennial rates of OHC gain are greater \r than at any point since the last deglaciation (medium confidence)."


id: c519793fd8c379e86cea39aacf4fc6c3
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 31987
content:
"Below 2000 m, direct observations of full-depth ocean temperature \r change are limited to ship-based, high-quality deep-ocean \r temperature measurements. Such high-quality full-depth ship-based \r sampling has improved from 1990 to the present due to the World \r Ocean Circulation Experiment (WOCE) and the Global Ocean \r Ship-based Hydrographic Investigations Program (GO-SHIP; Sloyan \r et al., 2019). The SROCC (Bindoff et al., 2019) assessed that the likely\r warming of the ocean since the 1990s below 2000 m is associated \r with a marked regional pattern, with larger warming in the Southern \r Ocean. In the deep North Atlantic, warming has reversed to cooling \r over the past decade, possibly due to internal variability fed by North \r Atlantic Deep Water (Section  9.2.2.3). Over the past decade, the \r warming rate of Antarctic Bottom Water (AABW; Section 9.2.2.3) has \r been dependent on origin: slower from the Weddell Sea and faster \r [...]"


id: b326ef57993a61a633e449dae05772ee
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 28763
content:
"[...] from the Ross Sea and Adelie Land. One new study (Purkey et al., \r 2019) strengthens confidence in AABW warming: below 4000  m \r a  monotonic, basin-wide, and multi-decadal temperature change \r is found in the southern Pacific basin, with larger warming rates \r near the bottom water formation sites than further downstream. \r New analysis of one model provides limited evidence that the sparse "


id: fc061a42e2c8c4fd3237f2ac4e60ba91
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 41700
content:
"Different processes drive OHC patterns over a range of time scales. \r Recent literature has highlighted the role of ocean circulation \r variability in driving OHC patterns by decomposing the global pattern \r of OHC change into a  combination of added heat due to climate \r change taken up under fixed ocean circulation ('added heat'), and \r redistribution of heat associated with changing ocean currents \r ('redistributed heat'; Gregory et al., 2016; Bronselaer and Zanna, 2020; \r Couldrey et al., 2021). Redistributed heat alters regional patterns of \r heat storage and carbon storage (Cross-Chapter Box 5.3; Bronselaer \r and Zanna, 2020; Todd et al., 2020; Couldrey et al., 2021) but does \r not affect the global OHC. There is medium confidence that decadal \r variability of the ocean circulation strengthened the rate of ocean \r warming in the Southern Hemisphere compared to the Northern \r Hemisphere in the decade from 2005 (Rathore et al., 2020; L. Wang \r [...]"


id: 5a42030c0640b85d7fc5c2c6b619edb8
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 13127
content:
"pattern observed is predominantly due to heat redistribution with \r regions of both warming and cooling (Figure 9.6; Zika et al., 2021); \r however, extending analysis back to 1972 shows the importance of \r added heat setting a large-scale warming pattern with mid-latitude \r maxima consistent with subduction of water masses, particularly in \r Southern Hemisphere Mode Waters (Section 9.2.2.3, and Figures 9.6 \r and 9.8; Bronselaer and Zanna, 2020). The longer the analysis \r window, the more added heat dominates over redistributed heat. \r This  translates into more ocean area with statistically significant \r warming trends and less area with statistically significant cooling \r trends (Johnson and Lyman, 2020). The region where added heat \r is most compensated for by redistributed cooling is in the northern \r North Atlantic basin, where changes in the subpolar gyre circulation \r and Atlantic Meridional Overturning Circulation (AMOC) result in \r [...]"


id: 34417af7decdcea2acf215a18cf3118
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 6430
content:
"[...] cooling (Section 9.2.3.1; Williams et al., 2015; Piecuch et al., 2017; \r Zanna et al., 2019; Bronselaer and Zanna, 2020). In summary, and \r strengthening SROCC assessment, ocean warming is not globally \r uniform due to patterns of uptake predominantly along known water \r mass pathways, and due to changing ocean circulation redistributing \r heat within the ocean (high confidence)."


id: 358611710d3701117beef6c241f8548
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 6671
content:
"(Muilwijk et al., 2018; Q. Wang et al., 2019; Tsubouchi et al., 2021), \r similar to SROCC assessment, and consistent with observed increase \r in OHC in the ice-free Arctic ocean (Mayer et al., 2019). It is estimated \r that an increase of 0.021 PW of OHT occurred after 2001 into the \r Arctic, which is sufficient to account for the recent OHC change in the \r northern seas (Tsubouchi et al., 2021). However, these trends cannot \r yet be attributed to anthropogenic forcing due to potential internal \r variability (Muilwijk et al., 2018; Wang et al., 2019). New evidence \r strengthens the case that El Nino-Southern Oscillation (ENSO) and the \r Northern Annular Mode affect interannual OHT variability (Trenberth \r et  al., 2019) and shows that a  slowing AMOC reduces northward \r OHT in the Atlantic at 26.5degN (Section 9.2.3.1 and Figure 9.8; Bryden \r et al., 2020). Despite a decrease of AMOC northward heat (0.17 PW) \r and mass (2.5 Sverdrup (Sv); 1 Sv = 109 kg s-1) transport, OHT has \r [...]"


id: dde036b4591da5b56e2553e396e6855b
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 36295
content:
"[...] increased toward the Arctic through increased upper northern North \r Atlantic temperatures and stronger wind-driven gyres (medium \r confidence) (Section  9.2.3.4 and Figure  9.11; Singh et  al., 2017; \r Oldenburg et al., 2018). In summary, OHT has increased toward the \r Arctic in recent decades, which at least partially explains the recent \r OHC change in the Arctic (medium confidence)."


id: 1213353abcd879aec7a8584baa57eb64
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 368
content:
"0-2000 m layer will increase from 2017 to 2100 by 0.900 +- 0.345 YJ \r (1 YJ = 1024 Joules) under RCP2.6 and 2.150 +- 0.540  YJ under \r RCP8.5. Updating SROCC estimates with CMIP6 projections gives \r heat content increases and 17-83% ranges in the 0-2000 m layer \r between 1995-2014 and 2081-2100 of 1.06 (0.80-1.31) YJ, \r 1.35 (1.08-1.67) YJ, 1.62 (1.37-1.91) YJ, 1.89 (1.60-2.29) YJ under \r scenarios SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5, respectively \r (Figure 9.6 and Table 9.1). The two-layer model used here to calculate \r thermosteric sea level rise (9.SM.4) and tuned for AR6-assessed \r equilibrium climate sensitivity (ECS; Section  7.SM.2), provides \r consistent 17-83% ranges of 1.18 (0.99-1.42) YJ, 1.56 (1.33-1.86) YJ, \r 1.90 (1.63-2.21) YJ, 2.23  (1.92-2.64) YJ under scenarios SSP1-2.6, \r SSP2-4.5, SSP3-7.0, and SSP5-8.5, respectively (Table  9.1). Based \r on CMIP6 models and the two-layer model, it is likely that, between \r [...]"


id: a564f5d178f2ef5dadfa72550ee7df80
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 26327
content:
"[...] 1995-2014 and 2081-2100, OHC will increase two to four times the \r amount of the 1971-2018 OHC increase under SSP1-2.6, and four \r to eight times that amount under SSP5-8.5. The CMIP6 models show \r that OHC dependence on scenarios begins only after about 2040 \r (Figure 9.6)."


id: b9acea17908d1c1c7babeb6fefaf7e81
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 29879
content:
"CMIP5 and CMIP6 models simulate OHC changes that are consistent \r with the updated observational and improved estimates of OHC \r over the period 1960 to 2018 (Figures 9.6, 9.7 and 9.8), and they \r replicate the vertical partitioning of OHC change for the industrial \r era, although with a  tendency to underestimate OHC gain \r shallower than 2000 m and overestimate it deeper than 2000 m \r (Section 3.5.1.3). The AR5 (Flato et al., 2013) assessed that climate \r models transport heat downward more than the real ocean. Since \r AR5, studies have shown that increasing the horizontal resolution of \r ocean models tends to increase agreement of vertical heat transport \r with observations as the dependency on ad-hoc choices of eddy \r parametrizations is relaxed (Griffies et al., 2015; Chassignet et al., \r 2020). The magnitude of the AMOC and Indonesian Throughflow \r affect future OHC change  - for example, through overestimated \r modelled downward heat pumping (Kostov et al., 2014) - and there \r [...]"


id: 83eaee1b999e770b56b8ac01604ddde
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 20358
content:
"[...] are indications of greater model consistency in these transports at \r higher resolution (Figure 9.10; Chassignet et al., 2020; L.C. Jackson \r et al., 2020). Climate models tend to reproduce the observed added \r heat, but redistributed heat is less well represented (Figure  9.8; \r Bronselaer and Zanna, 2020; Dias et al., 2020; Couldrey et al., 2021). \r Since redistributed heat dominates historical OHC change, historical \r simulations poorly reproduce regional patterns, but as future OHC \r change will become dominated by added heat, more skill in future \r modelled OHC patterns is expected (Bronselaer and Zanna, 2020). \r In summary, climate models have more skill in representing OHC \r change from added heat than from ocean circulation change (high \r confidence). Since added heat dominates over redistributed heat \r on a  centennial scale (especially under high-emissions scenarios) \r confidence in future modelled OHC patterns at the end of the \r [...]"


id: 341b0f0f50e8f9a476e3a9eaade5df24
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 6408
content:
"The OHC patterns projected by CMIP6 models (Figures 9.6 and 9.7) \r are similar to the CMIP5 projections assessed in SROCC (Bindoff et al., \r 2019): faster warming in all water mass subduction regions \r (e.g., subtropical cells and mode waters); deeper penetration in the \r centre of subtropical gyres; slower northern North Atlantic warming \r due to slowing AMOC; and slower subpolar Southern Ocean \r warming  due upwelled pre-industrial water masses. Decreased \r aerosol forcing will allow Northern Hemisphere ocean warming \r to be faster and less dominated by Southern Hemisphere change \r (Shi  et  al., 2018; Irving et  al., 2019). Since SROCC, distinguishing \r between added and redistributed heat has aided in understanding \r projections (Bronselaer and Zanna, 2020; Dias et al., 2020; Couldrey \r et  al., 2021). The near-term decades will feature patterns strongly \r influenced by heat redistribution and internal variability (Rathore \r [...]"


id: e0a536195aa171dd3c5946a807bb73ba
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 36780
content:
"[...] et  al., 2020). Strengthening Southern Hemisphere westerlies are \r projected, except for stringent mitigation scenarios (Bracegirdle et al., \r 2020), and will cause a northward and downward OHT. There is low \r agreement in future Southern Ocean warming across model results \r due to uncertainties in the magnitude of westerly wind changes \r (Figure 9.4; Liu et al., 2018; He et al., 2019; Dias et al., 2020; Lyu et al., \r 2020b) and the degree of eddy compensation of overturning across \r different parametrizations and resolutions (Section 9.2.3.2; Beal and \r Elipot, 2016; Mak et al., 2017; Roberts et al., 2020). By 2100, however, \r the OHC change will be dominated by the added heat response, \r particularly for strong warming scenarios (Garuba and Klinger, 2018; \r Bronselaer and Zanna, 2020) with added heat following unperturbed \r water mass pathways in the North Atlantic and Southern Ocean \r (Figure  9.8; Dias et  al., 2020; Couldrey et  al., 2021). There is high \r [...]"


id: ec6788425264d487fa14d0bbefdeb0c5
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 38950
content:
"[...] confidence that projected weakening of the AMOC (Section 9.2.3.1) \r will cause a decrease in northward OHT in the Northern Hemisphere \r mid-latitudes (Figure 9.8 and Sections 9.2.3.1 and 4.3.2.3; Weijer et al., \r 2020) associated with a dipole pattern of Atlantic OHC redistributed \r from northern to low latitudes that may override added heating in \r the northern North Atlantic (Figures 9.6, 9.7 and 9.8). Variations \r in  the degree of AMOC redistributed heat (Menary  and Wood, \r 2018) causes large intermodel spread in SST (Figure  9.3) and  OHC \r change (Figure 9.6; Kostov et al., 2014; Bronselaer and Zanna, 2020; "


id: 19901b575297e13ef55df291b24e78ce
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 1707
content:
"from ice core rare gas elemental and isotopic ratios document a rise \r in global OHC relative to the Last Glacial Maximum of >17,000 ZJ \r (change in mean ocean temperature >3.1degC; 1 ZJ = 1021 Joules) \r (Figure 9.9; Bereiter et al., 2018; Baggenstos et al., 2019; Shackleton \r et al., 2019, 2020). This temperature increase is significantly larger \r than the modelled OHC changes associated with collapse of AMOC \r alone, and tracks rising Southern Ocean SST (Uemura et al., 2018), \r strengthening of the deep abyssal overturning cell (Du et al., 2020) \r and increased North Atlantic water in the Southern Ocean (Wilson \r et  al., 2020). This underscores the importance of Antarctic abyssal \r ventilation on long-term oceanic heat budgets (Section  9.2.3.2). \r An  ensemble of four intermediate-complexity models project \r 10,000-year future responses to CO2 emissions (Clark et al., 2016) with \r SST change peaking around 2300 and a varying scenario-dependent \r [...]"


id: f06ede3a51585837176cad4311c5d404
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
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vector_id: 39681
content:
"[...] magnitude approaching the scale of glacial-to-interglacial changes \r in paleodata (Figure 9.9). Long-term OHC commitments relative to \r 1850-1900 conditions are 2.6, 9.7, 15.2, 21.6, and 28.0 YJ (with \r mean ocean temperature change as much as 5.1degC) for emissions \r of 0, 1280, 2560, and 3840 and 5120 Gt after 2000 CE respectively, \r with OHC peaking near 4000 CE, reflecting whole-ocean warming \r lagging SST by thousands of years. The exact timing is uncertain, \r subject to rates of high-latitude meltwater input (Van Breedam et al., \r 2020) and circulation time (Gebbie and Huybers, 2019). In summary, \r there is very high confidence that there is a long-term commitment \r to increased OHC in response to anthropogenic CO2 emissions, which \r is essentially irreversible on human time scales."


id: d8eaddb10e0451403335a8ee17832144
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 35457
content:
"Figure 9.9 | Long-term trends of ocean heat content (OHC) and surface temperature. (a, b) Ice-core rare gas estimates of past mean OHC (ZJ), scaled to global \r mean ocean temperature (degC), and to steric global mean sea level (GMSL) (m) per CCB-2 (red dashed line), compared to surface temperatures (black solid line, gold solid \r line; degC rightmost axis). Southern Ocean sea surface temperature (SST) from multiple proxies in 11 sediment cores and from ice core deuterium excess (Uemura et  al., \r 2018). (a) Penultimate glacial interval to last interglacial, 150,000-100,000 yr B2K (before 2000) (Shackleton et al., 2020). (b) Last glacial interval to modern interglacial, \r 40,000-0 yr B2K (Baggenstos et al., 2019; Shackleton et al., 2019). Changes in OHC (dashed lines) track changes in Southern Ocean SST (solid lines). (c) Long-term projected \r [...]"


id: 5bb575d9714d1428e40d730dbd026f22
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 13389
content:
"[...] (2000 to 12000 CE) changes of OHC (dashed lines) in response to four greenhouse gas emissions scenarios (Clark et al., 2016) scale similarly to large-scale paleo changes but \r lag projected global mean SST (solid lines). (d) model simulated 1500-1999 OHC (Gregory et al., 2006) and 1955-2019 observations (Levitus et al., 2012) updated by NOAA \r NODC. All data expressed as anomalies relative to pre-industrial time. Further details on data sources and processing are available in the chapter data table (Table 9.SM.9)."


id: 1d04046b9204ff496623a403c74e771c
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 2288
content:
"Ocean circulation changes also affect salinity, largely on annual \r to decadal time scales (Du et  al., 2019; Liu et  al., 2019; Holliday \r et al., 2020). For instance, in the subpolar North Atlantic, increasing \r northward transport of Atlantic waters entering the subpolar gyre \r from the South have compensated the salinity decrease expected \r from increased Greenland meltwater flux since the early 1990s \r (Dukhovskoy et  al., 2016, 2019; Stendardo et  al., 2020). After \r the mid-2010s the trend reversed towards a  broad freshening, \r the  largest in 120 years, in the North Atlantic (Holliday et  al., \r 2020). The long-term freshening in the Pacific Ocean has also been \r subject to decadal variability, such as a marked salinification since \r 2005 associated with increased surface fluxes (G. Li et al., 2019). \r Local salinity anomalies forced by water cycle intensification can \r be weakened by rapid exchange between basins with opposing \r [...]"


id: 6ad3c563937ec3c461bebe248a5f9a40
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 16078
content:
"[...] trends, such as by water mass exchange in shallow wind-driven cells \r between the tropics and the subtropics (Levang and Schmitt, 2020). \r Similarly, eddy exchanges between neighbouring gyres can partly \r counterbalance decadal time scale long-term subpolar freshening \r and affect deep convection (Levang and Schmitt, 2020). There is high \r confidence that, at annual to decadal time scales, regional salinity \r changes are driven by ocean circulation change superimposed on \r longer-term trends."


id: abadd148090a4af1df4bd3f372017e43
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 27446
content:
"Globally the mean salinity contrast at near-surface between high- and \r low-salinity regions increased 0.14 [0.07 to 0.20] from 1950 to \r 2019 (Section  2.3.3.2). At regional scale, SROCC (Meredith et  al., \r 2019) assessed an Arctic liquid freshwater trend of 600 +- 300 km3 yr-1\r (600+-200 Gt yr-1) between 1992 and 2012, reflecting changes \r associated with continental freshwater imports that affect ocean \r mass (land ice, rivers) as well as changes in sea ice volume. Since AR5, \r regional observation-based analyses not assessed in SROCC further \r confirm the long-term, large-scale and regional patterns of salinity \r change, both at the ocean surface and in the subsurface ocean, \r including almost 120 years of changes in the North Atlantic (Friedman \r et al., 2017) and 60 years of monitoring in the subpolar North Pacific \r (Cummins and Ross, 2020). These longer time series also provide \r context to detect large multi-annual change from 2012 to 2016 in \r [...]"


id: 1a429c8373a94b7f80c2111da9bc392d
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 1815
content:
"[...] the subpolar North Atlantic, unprecedented over the centennial \r record (Holliday et al., 2020). In summary, there is high confidence\r that salinity trends have extended for more than 60 to 100 years in \r the regions with long historical observation records, such as the North \r Pacific and the North Atlantic basin."


id: 4b6939533ef503c84839e0c02c14f3e5
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 10517
content:
"While there is low confidence in direct estimates of trends in \r surface freshwater fluxes (Sections 2.3.1.3.5, 8.3.1.1 and 9.2.1.2), \r as discussed in SROCC (Meredith et al., 2019), observational studies \r coupled with modelling studies suggest that surface flux changes \r drive many observed near-surface salinity changes, on top of \r changes specific to polar regions. Advances in salinity observations - \r for example, the Argo program (Riser et al., 2016); Soil Moisture and \r Ocean Salinity (SMOS), Aquarius and Soil Moisture Active Passive \r (SMAP; Supply et  al., 2018; Vinogradova et  al., 2019)  - combined \r with process studies (SPURS-1/2; Lindstrom et  al., 2015; SPURS-2 \r Planning Group 2015) and methodological and numerical advances, \r have increased understanding of how subsurface salinity anomalies \r link to surface fluxes, and thus increase confidence that near-surface \r and subsurface salinity pattern changes since the 1950s are linked \r [...]"


id: 89fe832fca29f769bd38141a07d55fa3
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 21446
content:
"[...] to changing surface freshwater fluxes (Zika et al., 2018; Cheng et al., \r 2020) with an additional contribution from changes in sea ice and \r land ice discharge at high latitudes (Haumann et  al., 2016; Purich \r et  al., 2018; Dukhovskoy et  al., 2019; Rye et  al., 2020). There is \r therefore medium confidence in the processes linking surface fluxes \r to surface and subsurface salinity change."


id: 88905573453ff004890283db64345cca
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 21190
content:
"The SROCC (Bindoff et al., 2019) assessed that projected salinity \r changes in the subsurface ocean reflect changes in the rates of \r formation of water masses or their newly formed properties. \r Additional consistent newer evidence based on CMIP5 and regional \r climate models confirms that 21st  century projections adhere \r to the 'fresh gets fresher, salty gets saltier' paradigm, through \r subduction of freshening high-latitude waters into the ventilated \r water masses in both hemispheres in the Pacific, Indian and \r Southern Ocean - especially the Arctic and upper Southern Ocean, \r and saltier subtropical and Mediterranean surface waters - lead to \r saltier pycnoclines and North Atlantic mode water (Metzner et al., \r 2020; Parras-Berrocal et al., 2020; Silvy et al., 2020; Soto-Navarro \r et  al., 2020). Overall, projections confirm SROCC assessment \r that fresh ocean regions will continue to get fresher and salty \r ocean regions will continue to get saltier in the 21st  century \r [...]"


id: 66c5a25b544cc7a4d671be1db41483b7
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 15347
content:
"Climate models from CMIP3 to CMIP5 generally simulated shallower \r and lighter SAMW and AAIW than is observed (Flato et al., 2013). \r New analysis of CMIP5 models suggests that the freshening of \r these water masses is one of the most prominent projected salinity \r changes in the world ocean, and that this freshening emerged from \r internal variability as early as the 1980s to 1990s (Silvy et al., 2020).\r Trends in North Atlantic Deep Water (NADW) are obscured by decadal \r variability (Rhein et al., 2013; Bindoff et al., 2019). The AR5 (Rhein \r et al., 2013) assessed that it is very likely that the temperature, salinity, \r and formation rate of the Upper NADW (formed by deep convection \r in the Labrador and Irminger Seas) is dominated by strong decadal \r variability related to the North Atlantic Oscillation (NAO) and it is \r likely that Lower NADW (formed in the Nordic Seas and supplied to \r the North Atlantic by deep overflows over the sills between Scotland \r [...]"


id: 9f34b13dee1e2616c9f586f3f69acaec
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 25231
content:
"[...] and Greenland) cooled from 1955 to 2005. New insights from \r observations have emphasized the stability of the deep overflows \r associated with Lower NADW (Hansen et al., 2016; Jochumsen et al., \r 2017; Osterhus et al., 2019) and even slight warming in the Faroe \r Bank Channel (Hansen et al., 2016). As a result, the AR5 assessment \r that Lower NADW likely cooled between 1955 and 2005 is revised \r to: it is likely that any observed changes in temperature, salinity, \r and formation rate of the Lower NADW are dominated by decadal \r variability. For CMIP5 models, it was shown that AMOC variability is \r linked to variability in NADW formation (Heuze, 2017) and projected \r AMOC decline to decreased NADW formation (both Lower NADW \r and Upper NADW; Heuze et al., 2015). For CMIP6 models, projected \r AMOC decline is also associated with a decline in NADW formation \r (Reintges et al., 2017; Weijer et al., 2020). The link between AMOC \r [...]"


id: 3f1567be3c5fe29f20d4016fea9ed33a
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 8288
content:
"[...] and NADW formation appears insensitive to the large range in \r model bias in NADW water mass characteristics (Heuze, 2017). Many \r models may overestimate deep water formation in the Labrador Sea, \r but at least one new model is consistent with recent Overturning in \r the Subpolar North Atlantic Program (OSNAP) observations showing \r very weak overturning in the western subpolar gyre, where Labrador \r Sea water is formed (Menary et al., 2020a). The CMIP6 models show \r a reduced bias in NADW properties compared to CMIP5 models, but \r still feature varying locations of deep convection in the subpolar \r gyre: some convect only in the Labrador Sea (6/35 models), most in \r both the Labrador and Irminger Seas (26/35 models; as is observed), \r and some only in the Irminger Sea (3/35 models), but in general, the \r area where deep convection takes place has expanded relative to \r CMIP5, which appears unrealistic (Heuze, 2021). Models with most \r [...]"


id: 84a02fefe6bca3b186f1b33a9539373c
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 20479
content:
"[...] deep convection in the subpolar gyre feature the smallest bias in \r NADW characteristics, partly associated with NADW formed in the \r Nordic Seas (as observed) being largely unable to leave the area \r (Heuze, 2021) due to inaccurate overflows (Danabasoglu et al., 2010; \r Deshayes et al., 2014; Wang et al., 2015). Despite the wide range in \r model bias, it remains very likely that any long-term (multi-decadal \r or longer) decrease in AMOC is accompanied by a decline in NADW \r formation, associated with lighter densities in the northern North \r Atlantic and Arctic basins.\r The SROCC (Meredith et al., 2019) assessed that the global volume \r of Antarctic Bottom Water (AABW) had decreased and warmed \r since the 1980s, most noticeably near Antarctica. The SROCC also \r noted freshening in the Indian and Pacific sectors of the Southern "


id: 2f7e8c98c52ca3e418e1cfb94bdd19b2
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 5616
content:
"Subtropical mode waters (STMW) ventilate the main thermocline of \r the ocean at mid- to low-latitudes and have circulation time scales \r away from the surface of the order of years to decades. The SROCC \r (Bindoff et al., 2019) reported that warming in the subtropical gyres \r penetrates deeper than in other gyres, following the density surfaces \r in these gyres. Consistently, we assess that STMW have deepened \r worldwide, with greatest deepening in the Southern Hemisphere \r (high confidence) (Hakkinen et al., 2016; Desbruyeres et al., 2017). \r Subsurface warming in the Northern Hemisphere STMW is larger \r than at the surface (Sugimoto et al., 2017) because they are formed in \r winter western boundary current extensions, where surface warming \r is larger than the global average (Section  9.2.1.1). Variability \r in STMW thickness or temperature has a  large imprint on OHC \r (Section 9.2.2.1; Kolodziejczyk et al., 2019). STMW are observed to be \r [...]"


id: a34bd3418818a0c4e2dd9c2d31601c90
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 25940
content:
"[...] freshening in the North Pacific and associated with increased salinity \r in the North Atlantic (Oka et al., 2017; Silvy et al., 2020), with large \r decadal variability (Oka et al., 2019; Wu et al., 2020). Anthropogenic \r temperature and salinity changes in the STMW layer are projected \r to intensify in the future, with emergence from natural variability \r around 2020 to 2040 (Silvy et al., 2020)."


id: b207ba012643018ec889ee447065c143
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 28544
content:
"Subantarctic mode water (SAMW) and Antarctic intermediate water \r (AAIW) form at the Southern Ocean surface directly north of the \r Antarctic Circumpolar Current and ventilate the upper 1000 m of \r the Southern Hemisphere subtropics. The SROCC (Meredith et  al., \r 2019) reported a freshening of these water masses between 1950 \r and 2018, and they are projected to have the largest subsurface \r temperature increase of the Southern Hemisphere oceans, along with \r a continued freshening, in the 21st century. The SROCC connected \r SAMW and AAIW to Southern Ocean temperature changes as the \r large Southern Ocean surface heat uptake is circulated and mixed \r along with these water masses (high confidence). Close to its \r formation region, SAMW is predominantly affected by air-sea flux \r changes, while further northward it is influenced by wind-forced \r changes (Meredith et al., 2019). New evidence shows that a change \r in SAMW heat content over the last decade is primarily attributable \r [...]"


id: 68d496d2ddd7ac14128c32dd012a2908
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 15705
content:
"[...] to its thickening (Kolodziejczyk et al., 2019). Over the past decade, the \r SAMW and AAIW volumes have changed by thickening of the lighter \r and thinning of the denser parts of SAMW and AAIW, leading to \r lightening of these ventilated ocean layers overall (Hong et al., 2020; \r Portela et al., 2020). Over the last decade, there is limited evidence\r of increased subduction of SAMW due to deepening mixed layers \r in the SAMW formation region (Section  9.2.1.3; Qu et  al.,  2020). "


id: ef16b0c902465858056c1886c8b85594
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 39433
content:
"Ocean and a higher rate of freshening in the Indian Sector from the \r 2000s to 2010s than from the 1990s to 2000s (low confidence). \r Since SROCC, freshening of Indian Ocean AABW from 1974 to 2016 \r has been revealed (Aoki et  al., 2020). Additionally, interannual to \r decadal variability in AABW has been quantified to be larger than \r previously thought in terms of temperature, salinity and thickness, \r and in volume transport (Abrahamsen et  al., 2019; Purkey et  al., \r 2019; Gordon et  al., 2020; Silvano et  al., 2020). Multi-decadal to \r centennial modes of variability could have driven the observed trends \r of the lower cell over the past decades via the opening of a Weddell \r Sea Polynya (L. Zhang et  al., 2019), although other studies find it \r contributed minimally to the observed abyssal warming (Zanowski \r et al., 2015; Zanowski and Hallberg, 2017). Therefore, there is limited \r evidence and low agreement in the role of open ocean polynyas in \r [...]"


id: 6941dbc3464124df941aae9d854f590e
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 15784
content:
"[...] driving past decadal observed trends of AABW. Beyond variability, all \r observational, theoretical, and numerical evidence supports SROCC \r assessment that formation and export of AABW will continue to \r decrease due to warming and freshening of surface source waters \r near the Antarctic continent. Consistent with Section  9.2.3.2, \r confidence in this assessment is increased to medium confidence\r compared to SROCC."


id: 3f7a743fc10844574a046e4980b9c564
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 8355
content:
"Circumpolar Deep Water (CDW) lies in the Southern Ocean and forms \r by the mixing of NADW and AABW (Talley, 2013). The SROCC (Meredith \r et  al., 2019) assessed with low confidence that mean southward \r and upward CDW transport is linked to decadal wind variability \r (Section 9.2.3.2), and that CDW has warmed south of the Antarctic \r Circumpolar Current (ACC) in the past decades. New  evidence \r reinforces SROCC assessment: changes in Southern Ocean wind stress \r have been confirmed to drive variability and increase the large-scale \r southward CDW transport (Waugh et al., 2019). In addition, growing \r evidence suggests that the upper-ocean stratification increase in \r the subpolar Southern Ocean since the 1970s (Section 9.2.1.3) has \r reduced the volume of CDW that is mixed to the surface, causing \r subsurface CDW warming (Bronselaer et al., 2020; Haumann et al., \r 2020; Jeong et al., 2020; Moorman et al., 2020). Large regions of the \r [...]"


id: ad952d361bb7a8d9c4049e0ee0fa86c0
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
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index: climate_gpt_v2
vector_id: 27781
content:
"[...] Antarctic shelves are currently isolated from warm CDW (Thompson \r et al., 2018; Jourdain et al., 2020). The SROCC (Meredith et al., 2019) \r assessed that subsurface warming extends close to Antarctica and has \r co-occurred with shoaling of the CDW since the 1980s, influencing \r the continental shelf most in the Amundsen-Bellingshausen Seas, \r Wilkes Land, and the Antarctic Peninsula. New evidence since SROCC \r reinforces confidence in the importance of the role of winds in \r transporting heat associated with CDW to continental shelves and \r ice cavities in the Amundsen-Bellingshausen Seas (Dotto et al., 2019) \r and via variable small-scale undercurrents to the Shirase Glacier \r Tongue in East Antarctica (Hirano et al., 2020; Kusahara et al., 2021). \r There is limited evidence that increased greenhouse gas forcing has \r caused a slight mean change of the local winds from 1920 to 2018, \r facilitating CDW heat intrusion onto the Amundsen-Bellingshausen \r [...]"


id: 9a30193cdc5c2d583d781ae9b96d48a7
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 24366
content:
"[...] continental shelf and ice shelf melt (Holland et al., 2019). Multiple \r lines of observational, numerical, theoretical, and paleo evidence \r provide high confidence that changes in wind pattern (Spence et al., \r 2014; Dotto et  al., 2019;  Holland et  al., 2019), increased ice-shelf \r melt (Golledge et al., 2019; Moorman et al., 2020), reduction in sea \r ice production (Timmermann and Hellmer, 2013; Obase et al., 2017), \r and eddies (Stewart and Thompson, 2015; Thompson et al., 2018) can "


id: 1ddd8242243713b44505b7f0339dd9d7
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 2446
content:
"The AR5 (Flato et  al., 2013) found that the mean AMOC strength \r in CMIP5 models ranges from 15 to 30 Sv for the historical period. \r The multi-model mean overturning at 26degN in CMIP5 and CMIP6 is \r comparable to the RAPID array measurements (Reintges et al., 2017), \r but the inter-model spread in CMIP6 is as large (10-31 Sv) as in \r CMIP5 (Section 3.5.4; Weijer et al., 2020). Biases in simulations of the \r present-day AMOC and associated deep convection in the subpolar \r gyre and Nordic Seas were large in CMIP5 models, with many models \r exhibiting ocean convection that is too deep, over too large an area, \r too far south, and occurring too frequently (Section  9.2.1.3 and \r Figure 9.5; Heuze, 2017) related to biases in sea ice extent, overflows, \r and freshwater forcing (Deshayes et al., 2014; H. Wang et al., 2015). \r As a result, the AMOC in CMIP5 was nearly always too shallow, with too \r weak a temperature contrast between the northward and southward \r [...]"


id: 9647088a0de5a7f8de202c00dff82075
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 23633
content:
"[...] flowing branches. Deep convection errors are still large in CMIP6, and \r the shallow bias in AMOC persists (Weijer et al., 2020; Heuze, 2021). \r Since AR5, there is emerging evidence that enhancing horizontal \r resolution can reduce long-standing climate model biases in AMOC \r strength, where the magnitude and profile of northward heat transport \r at 26degN become more comparable to observations (Chassignet et al., \r 2020; Roberts et  al., 2020). The sensitivity of the AMOC to ocean \r resolution, however, is model-dependent and can be positive as well as \r negative (Roberts et al., 2020). An increase in AMOC strength at 26degN, \r with higher resolution in the ocean component, has been associated \r with too strong (deep) convection in the subpolar gyre and too deep \r winter mixed layers (L.C. Jackson et al., 2020), which occurs in most \r CMIP6 models that are unable to overflow deep water formed in the "


id: 56de4b2a8bf42cdfc65986bc36603ab2
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 12534
content:
"The AMOC is a potential driver of Atlantic Multi-decadal Variability \r (AMV), but there is new evidence that anthropogenic aerosol \r changes have contributed to observed AMV changes, and that \r underestimation of the magnitude and duration of AMV changes \r in CMIP5 is tempered in CMIP6 (Section  3.7.7 and Annex IV.2.7). \r Comparison of observed AMOC variability at the RAPID section with \r modelled variability reveals that CMIP5 models appear to largely \r underestimate the interannual and decadal time scale variability \r (Roberts et al., 2014; Yan et al., 2018), and similar results are found \r when analysing CMIP6 models (Section 3.5.4.1). By underestimating \r the multi-decadal AMOC-AMV link and other low-frequency AMOC \r variability, climate models also underestimate internal variability \r in subpolar SSTs that feed back on the North Atlantic Oscillation \r (NAO). This causes the NAO to lack variability on multi-decadal time \r [...]"


id: 74c1d1f177902c40164bd59aa38d5b80
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 17777
content:
"[...] scales (Kim et al., 2018). Despite the role of the AMOC in generating \r AMV through subsurface temperatures in antiphase with SST and \r downward heat fluxes into the ocean that anticorrelate with SSTs \r (R. Zhang et al., 2019), it is generally accepted that AMOC forcing \r of SST variability exists alongside stochastic wind forcing and \r external  forcing by aerosols (Bellomo et  al., 2018; Haustein et  al., \r 2019; O'Reilly et al., 2019; Wills et al., 2019)."


id: d15ab899d85f79741a3aa89f59735792
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vector_id: 34185
content:
"Nordic Seas across the Greenland-Iceland-Scotland Ridge. Models \r with a correct AMOC strength may do so by compensating a lack of \r deep-water outflow from the Nordic Seas through too much deep \r convection and deep-water formation in the Labrador and Irminger \r Seas (Heuze, 2021).\r Models and paleoreconstructions have often assumed a close relation \r between the AMOC and deep convection in the Labrador Sea; the \r Labrador Sea convection variability has been interpreted as connecting \r to AMOC variability. Observational studies have been inconclusive on \r whether this relation exists (Buckley and Marshall, 2016). New insight \r from observed overturning in the eastern and western subpolar \r gyre in the North Atlantic in OSNAP (Lozier et al., 2019; Petit et al., \r 2020) reveals that 15.6 +- 3.1Sv takes place north of the OSNAP array \r between Greenland and Scotland, with only 2.1 +- 0.9 Sv of overturning \r occurring across the Labrador Sea, as found with the OSNAP 53degN \r [...]"


id: 336eca12d643a9576d945f0b7b16d2c
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
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index: climate_gpt_v2
vector_id: 6301
content:
"[...] array spanning the mouth, calling into question the validity of the \r Labrador Sea convection-AMOC link (Lozier et al., 2019). Although \r these results are derived from only the first 21 months of data from \r monitoring since 2014, hydrographic observations during 1990-1997 \r previously found small overturning (1-2 Sv) in the Labrador Sea \r (Pickart and Spall, 2007). However, previous estimates of Labrador \r Sea Water formation (obtained with different techniques) suggest \r larger overturning (Haine et al., 2008). Part of this controversy could \r be explained if a large fraction of newly formed Labrador Sea Water \r is not exported from the Labrador Sea. The OSNAP observations are \r supported by previous hydrographic measurements in showing strong \r east-west symmetry in isopycnal slope in the Labrador Sea in periods \r of both strong and weak convection; this implies compensating \r northward and southward transport above and below the potential \r [...]"


id: 9ff323d517b7cfa38074ce4b1ec833fe
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
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index: climate_gpt_v2
vector_id: 25352
content:
"[...] density surface that separates the upper and lower overturning \r limbs (Lozier et al., 2019), despite large deep convection variability \r (Yashayaev, 2007; Yashayaev and Loder, 2016). New observations \r of deep winter mixing in the Irminger Basin (de  Jong et  al., 2018; \r Josey et  al., 2019) support the assertion that the Irminger Sea, in \r addition to the Nordic Seas (Chafik and Rossby, 2019), are the main \r sources of overturning in the eastern subpolar gyre, consistent with \r OSNAP (Petit et al., 2020). It is unclear to what extent models are in \r disagreement with this view of overturning in the subpolar gyre, as \r a direct comparison with OSNAP of model analyses partitioning the \r overturning into a western and eastern part is mostly lacking, with \r a notable exception (Menary et al., 2020a). Other results give rise to \r considerable uncertainty over veracity of the models in simulating \r the overturning partitioning between east and west and the role of \r [...]"


id: 68d9f55ca3dbbe18bb06932126a255c2
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
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index: climate_gpt_v2
vector_id: 15714
content:
"[...] various drivers of AMOC variability, including: the analysis of water \r mass formation in CMIP6 models (Heuze, 2021); the analysis between \r Labrador Sea Water formation and AMOC in a  suite of ocean-only \r models (Danabasoglu et al., 2014); and the fact that when the OSNAP \r observing system design was tested in an eddy-permitting ocean \r model comparable amounts of overturning in the western and eastern \r subpolar gyre were found (Susan Lozier et al., 2017). Disagreement \r between models and OSNAP observations may decrease in higher\u0002resolution models (Menary et  al., 2020a). In summary, multiple \r lines of evidence provide medium agreement between models and \r observations on drivers of change and variability in the AMOC and, in \r particular, the role of Labrador Sea deep convection in constituting \r AMOC variability."


id: 3960a2bce53cf4c8c356251ea33ddb7
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 7306
content:
"The SROCC (Collins et al., 2019) assessed that in situ observations \r (2004-2017) and sea surface temperature reconstructions indicate \r that AMOC has weakened relative to 1850-1900 (medium \r confidence). However, SROCC also assessed that there is insufficient \r data to quantify the magnitude of the weakening, or to properly \r attribute it to anthropogenic forcing, due to the limited length of \r the observational record. Here, this assessment is adjusted to low \r confidence in the weakening (as also discussed in Sections 2.3.3.4.1 \r and 3.5.4.1). The CMIP5 multi-model mean showed no 20th century \r trend in AMOC (Cheng et al., 2013). The CMIP6 multi-model mean \r slightly opposes the reconstructed decline due to a strong increase \r in the 1940-1985 period (Menary et al., 2020b; Weijer et al., 2020), \r thought to be in response to aerosol forcing (Section  3.5.4.1), \r followed by a  smaller decline since the 1990s. Also, agreement \r [...]"


id: 1dcf802c6f4c98d3aefe6d54c6659ad7
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 2433
content:
"[...] between different proxy-based reconstructions is weak in many \r details (Moffa-Sanchez et  al., 2019) and questions can be raised \r regarding various proxies used in reconstructions (Section 2.3.3.4.1). \r For instance, SST-based proxies can be influenced by atmospheric \r and other processes acting on different time scales (Moffa-Sanchez \r et  al., 2019; Jackson and Wood, 2020). In addition, many proxies \r are indirect and based on AMOC-related processes assumed to be \r similar to those found in models, such as the link between AMOC \r and Labrador Sea convection, which has been questioned recently \r (see above). In addition, the subpolar gyre from which many AMOC \r proxies are taken may vary independently of AMOC, with similar \r patterns in SST and OHC driven by wind variability (Williams et al., \r 2014; Piecuch et  al., 2017). Finally, a  new dynamic reconstruction \r of the Atlantic inflow to the Nordic Seas suggests no slowdown \r [...]"


id: 8851b9ee3f184cc8e301ca77ff970dcb
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
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index: climate_gpt_v2
vector_id: 21142
content:
"[...] over the past 70 to 100 years (Rossby et al., 2020), in contrast to \r a  new compilation of proxy reconstructions which suggests that \r AMOC is presently in its weakest state in the last millennium (Caesar \r et  al., 2021), reinforcing the evidence that motivated the previous \r SROCC assessment. Section  3.5.4.1 also questions the veracity of \r the models' forced AMOC response during the 20th century. Given \r the  large discrepancy between modelled and reconstructed AMOC "


id: fb15fa4948fbf09aae8bd3711c24a406
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 41551
content:
"The strength of AMOC has been measured directly since 2004 using \r the RAPID Array (Section 2.3.3.4.1; Smeed et al., 2018). RAPID-based \r estimates show a  large amount of variability compared to CMIP \r models (Roberts et  al., 2014). Observed changes since 2004 are \r too short for the evaluation of a long-term trend given the decadal \r scale internal variability (Section  2.3.3.4.1). Nevertheless, Smeed \r et al. (2018) argue that, between 2007 and 2011, AMOC shifted to \r a state of reduced overturning - decreasing from 18.8 Sv between \r 2004 and 2008 to 16.1 Sv after 2008. A shift in AMOC strength of \r this magnitude is not captured by CMIP5 and CMIP6 models, which \r generally underestimate interannual to decadal AMOC variability \r (Section  3.5.4.1). Additional evidence since SROCC also raises the \r inconsistency between the RAPID weakening in the 3000-5000 m \r depth range and the relative constancy of deep overflows from the \r [...]"


id: 28b99ea9b8a18bc3e45d73befc42181d
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
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index: climate_gpt_v2
vector_id: 4379
content:
"[...] Arctic (Osterhus et al., 2019), implying that the recent decrease in \r AMOC at 26.5degN (Smeed et  al., 2018) is not caused by overflow \r weakening or reduced overturning in the Nordic Seas, although the \r weakening occurred almost exclusively in the 3000-5000 m depth \r range associated with a reduction of Lower NADW (Section 9.2.2.3). "


id: 544ef29faf2aa154e03806b514eb34d2
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 12109
content:
"Figure 9.10 | Atlantic Meridional Overturning Circulation (AMOC) strength in simulations and sensitivity to resolution and forcing. (Top left) AMOC \r magnitude (units: Sverdrup (Sv) = 109 kg s-1) in Paleoclimate Modelling Intercomparison Project (PMIP) experiments. (Top right) Time series of AMOC from Coupled Model \r Intercomparison Project Phase 5 and 6 (CMIP5 and CMIP6) based on (Menary et al., 2020b). (Bottom left) Percent change in AMOC strength per year at different resolutions \r over the 1950-2050 period with colours for model families (Roberts et al., 2020). (Bottom right) A compilation of percentage changes in the simulated AMOC after applying \r an additional freshwater flux in the subpolar North Atlantic at the surface for a limited time (de Vries and Weber, 2005; Stouffer et al., 2006; Yin and Stouffer, 2007; Jackson, \r [...]"


id: 687080d23b836d5d332db3baf76b3ee0
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
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index: climate_gpt_v2
vector_id: 15641
content:
"[...] 2013; Liu and Liu, 2013; Jackson and Wood, 2018; Haskins et al., 2019). Symbols indicate whether the AMOC recovers within 200 years (circles), is starting to recover (upwards \r arrow), or does not recover within 200 years (downwards arrow). Symbol size indicates rate of freshwater input. Further details on data sources and processing are available \r in the chapter data table (Table 9.SM.9)."


id: e0f16198b3b85c03159a11c651892e4b
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 36837
content:
"climate models to represent the Southern Ocean persist due to most \r CMIP6 models still using parameterized mesoscale eddy processes, \r which are limited in projecting the future response of the horizontal \r and vertical circulation under climate warming, and also because \r of the continued absence of active ice-shelf and ice-sheet coupling \r in the CMIP6 model suite, therefore ignoring basal meltwater \r and calving feedback on the circulation (Meredith et al., 2019). In \r addition, two important limitations of CMIP6 models of the Southern \r Ocean involve processes that were not assessed in SROCC. First, \r the poor representation of dense overflows causes most of the \r Antarctic Bottom Water (AABW) to be formed by spurious open \r ocean convection rather than by dense overflows from the Antarctic \r continental shelves that feed the lower overturning cell (Snow et al., \r 2015; Dufour et al., 2017; Heuze, 2021). Second, Antarctic continental \r [...]"


id: 1fc1a94c2e0e3a2f58136cb10aa36ada
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
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index: climate_gpt_v2
vector_id: 2792
content:
"[...] shelf waters are poorly simulated because potentially important \r controlling mechanisms tend to be too small and transient to \r observe and resolve in CMIP ocean models. These small processes \r include: the heterogeneity of observed sub-ice-shelf melt with \r warm water driving narrow basal channels that cut underneath \r the ice (Drews, 2015; Alley et al., 2016; Marsh et al., 2016; Milillo \r et al., 2019); eddies and tides (Stewart et al., 2018; Jourdain et al., \r 2019; Hausmann et  al., 2020), which can drive Circumpolar Deep \r Water (CDW) onto the continental shelves or dynamically increase \r melting (Section  9.2.3.6); and feedback mechanisms between \r ocean, atmosphere and cryosphere that can weaken or amplify \r initial perturbations (Donat-Magnin et al., 2017; Spence et al., 2017; \r Turner et al., 2017; Silvano et al., 2018; Webber et al., 2019; Hazel \r and Stewart, 2020). In addition, the Southern Ocean in CMIP5 and \r [...]"


id: da37f827576d7cd6f7fd708e1a015a3f
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 35689
content:
"[...] CMIP6 models exhibit surface temperature biases (Section 9.2.1.1), \r which have been linked in CMIP5 models to errors in atmospheric \r model cloud-related shortwave radiation (Hyder et  al., 2018) and \r are somewhat improved in High Resolution Model Intercomparison \r Project (HighResMIP) models (Figure 9.3). In summary, there is high \r confidence that future change in the subpolar Southern Ocean region, \r including sea ice cover and ocean temperature change on Antarctic \r continental shelves, depends on feedback mechanisms involving the \r ocean, atmosphere and cryosphere that are poorly understood and \r not represented in the current generation of climate models. This \r results in large uncertainty and low confidence in the future sea \r ice cover (Section 9.3.2) and in ocean temperature change on the \r Antarctic continental shelf (Section 9.4.2.3)."


id: 89d7ad02db8197c2c8d5539ca1e61aaa
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
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index: climate_gpt_v2
vector_id: 21417
content:
"the models. In theoretical models of the thermohaline circulation, the \r circulation strength is proportional to a density or pressure difference \r between the subpolar North Atlantic and subtropical South Atlantic \r (Kuhlbrodt et al., 2007; Weijer et al., 2019). In all models, the north\u0002south pressure gradient decreases in the 21st century, as subpolar \r waters warm faster than subtropical waters, and an enhanced \r hydrological cycle drives freshening at subpolar latitudes, while \r subtropical latitudes feature more evaporation and salinification \r (Section  9.2.1). As a  result, surface waters at subpolar latitudes \r become more buoyant and more stable, so that deep water formation \r driving the AMOC declines (Section 9.2.1.3). Projected AMOC decline \r by 2100 ranges from 24 [4 to 46] % in SSP1-2.6 to 39 [17-55] % \r in SSP5-8.5 (medium  confidence) (Section  4.3.2.3). Note that \r these ranges are based on ensemble means of individual models, \r [...]"


id: af1a3e07974146c6fcb91abcc92d05c6
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
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index: climate_gpt_v2
vector_id: 28052
content:
"[...] largely smoothing out internal variability. If single realizations are \r considered, the ranges become wider, especially by lowering the low \r end of the range (Section 4.3.2.3). In summary, it is very likely that \r AMOC will decline in the 21st century, but there is low confidence in \r the model's projected timing and magnitude. In addition, freshwater \r from the melting of the Greenland Ice Sheet (Sections 9.4.1.3 and \r 9.4.1.4) could further enhance the future weakening of AMOC in the \r 21st century (Collins et al., 2019; Golledge et al., 2019)."


id: c7854a6e2f4a101ce187d1a04ea0ba32
created_at: 2023-04-19 13:22:40
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index: climate_gpt_v2
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content:
"Both AR5 (Collins et  al., 2013) and SROCC (Collins et  al., 2019) \r assessed that an abrupt collapse of AMOC before 2100 was very \r unlikely, but SROCC added that, by 2300, an AMOC collapse was \r as likely as not for high-emissions scenarios. The SROCC also \r assessed that model bias may considerably affect the sensitivity of \r the modelled AMOC to freshwater forcing. Tuning towards stability \r and model biases (Valdes, 2011; Liu et  al., 2017; Mecking et  al., \r 2017; Weijer et al., 2019) provides CMIP models a tendency toward \r unrealistic stability (medium confidence). By correcting for existing \r salinity biases, Liu et al. (2017) demonstrated that AMOC behaviour \r may change dramatically on centennial to millennial time scales, \r and that the probability of a collapsed state increases. None of the \r CMIP6 models features an abrupt AMOC collapse in the 21st century, \r but they neglect meltwater release from the Greenland Ice Sheet. \r [...]"


id: ed391b8cd9c041e311f4a5277f0ab49d
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
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index: climate_gpt_v2
vector_id: 39116
content:
"[...] Also, a  recent process study reveals that a  collapse of AMOC \r can be induced, even by small-amplitude changes in freshwater \r forcing (Lohmann and Ditlevsen, 2021). As a result, we change the \r assessment of an abrupt collapse before 2100 to medium confidence \r that it will not occur."


id: 268ff386207712a72b17dc7909ae060e
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 3949
content:
"Despite these challenges, the CMIP6 ensemble does represent the \r main Southern Ocean circulation characteristics: the simulated \r Antarctic Circumpolar Current (ACC) transport is generally lower than \r observation-based values but consistent when considering ensemble \r spread, and the inter-model spread in ACC transport has greatly \r reduced from previous generations of climate models from CMIP3 \r to CMIP6 (Beadling et  al., 2019, 2020). The structure (but not the \r magnitude) of the two-cell zonally averaged overturning is captured \r by most CMIP6 models (Russell et al., 2018; Beadling et al., 2019). \r In addition, while issues remain, CMIP6 climate models show clear \r improvements in their representation of AABW compared to CMIP5: \r several models correctly represent or parameterize Antarctic shelf \r processes, fewer models exhibit Southern Ocean deep convection, \r bottom density biases are reduced, and abyssal overturning is more \r [...]"


id: b1f600acac3be17874b05095361e820a
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
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index: climate_gpt_v2
vector_id: 28530
content:
"models show an improvement compared to CMIP5 models, with \r an overall reduction in the equatorward bias of the annual mean \r westerly jet from 1.9deg in CMIP5 to 0.4deg in CMIP6, but in contrast, \r they show no such overall improvements for their representation of \r the Amundsen Sea Low (Bracegirdle et al., 2020; Lyu et al., 2020a), \r which can be critical in driving variability of water masses on the \r Antarctic continental shelf in west Antarctica, the Weddell Sea or \r the Ross Sea (Holland et al., 2019; Silvano et al., 2020).\r The SROCC (Meredith et al., 2019) established that, while trends in \r the atmospheric forcing of the Southern Ocean have been dominated \r by a  strengthening of the Southern Hemisphere westerly winds in \r recent decades, there is medium confidence that ACC transport is \r weakly sensitive to changes in winds. It also reported that, instead of \r increasing the mean ACC transport, additional energy input associated \r [...]"


id: 71d0273e5c040e6586bb183a247705a0
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
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index: climate_gpt_v2
vector_id: 17265
content:
"[...] with increased wind stress cascades into the eddy field (medium \r confidence). In contrast with the AR5 assessment (Rhein et al., 2013), \r SROCC evaluated that it was unlikely that there has been a  net \r southward migration of the mean ACC position over the past 20 years. \r There is no additional evidence to revisit SROCC assessment on wind \r sensitivity. However, new evidence does suggest that air-sea buoyancy \r forcing associated with idealized 4xCO2 forcing leads to an increase in \r ACC transport (limited evidence) (Shi et al., 2020). The SROCC noted \r that, if the general strengthening in westerly winds is sustained, then \r it is very likely that the eddy field will continue to increase in intensity, \r and it is likely that the mean position and strength of the ACC will \r remain only weakly sensitive to winds. In the future, the strength of \r the Southern Hemisphere westerly wind jet results from a competition \r [...]"


id: c45bd26c21e72bd8b75255516dc2a9f5
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
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index: climate_gpt_v2
vector_id: 31844
content:
"[...] between decrease due to ozone hole recovery and increase due to \r increased radiative forcing (Section 4.3.3.1). This competition results \r in an increased atmospheric jet by 2100 compared to present day \r under SSP2-4.5, SSP3-7.0, and SSP5-8.5, but a decreased jet by 2100 \r under SSP1-2.6 (Bracegirdle et  al., 2020). There is little inter-model \r spread in the CMIP6 future response of the atmospheric westerly \r jet, providing high confidence in this assessment (in contrast, CMIP6 \r models show no consistency in their future projection of easterly wind \r change along the Antarctic continental shelf break; Bracegirdle et al., \r 2020). Paleo-oceanographic evidence suggests that ACC flow through \r Drake Passage was consistently stronger during warm intervals of the \r past (both during interstadials and interglacials), but with relatively \r little change and no consensus on the sign of change in other regions \r [...]"


id: 2cf7193ea1d2a930eedeb11a38f93a8f
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
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index: climate_gpt_v2
vector_id: 5182
content:
"[...] (Lamy et al., 2015; Toyos et al., 2020). In summary, additional evidence \r since SROCC confirms that there is medium confidence that the ACC \r has been weakly sensitive to Southern Hemisphere atmospheric jet \r increase in the past decades. New evidence since SROCC suggests that \r there is high confidence that the Southern Hemisphere atmospheric jet \r will increase in the 21st century for all scenarios (except for SSP1-1.9 \r and SSP1-2.6; Section  4.3.3.1) with a  greater increase for larger \r radiative forcing. An increase in westerly winds will very likely force \r an increase of the eddy field in the ACC, and while there is medium \r confidence that the ACC is weakly sensitive to wind change, new \r advances since SROCC provide limited evidence that the ACC transport \r will nevertheless increase in response to wind and buoyancy fluxes.\r For the upper cell overturning circulation, SROCC concluded that: \r its transport has experienced significant inter-decadal variability in \r [...]"


id: 20e2e0ec8090ff73b6545062fe1594a1
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 3007
content:
"in the assessments of a long-term increase in upper-ocean overturning. \r Consistent with SROCC, the importance of eddy processes and winds \r in driving long-term change and variability have been reinforced, with \r a potential fast wind response partially counteracted by a slower eddy \r response (Doddridge et al., 2019; Waugh et al., 2019; Stewart et al., \r 2020). Eddy parametrizations affect the strength of overturning, its \r sensitivity to winds and the ACC transport (Mak et al., 2017). Even \r in eddy-resolving simulations, sub-gridscale dissipation affects the \r overturning and ACC (Pearson et  al., 2017). In addition, there has \r been progress in understanding the importance of Antarctic Ice Shelf \r meltwater and sea ice, in driving the observed changes in the near \r surface and in the upper overturning cell over the past decades, on \r top of changes induced by winds and eddies (Bronselaer et al., 2020; \r Haumann et al., 2020; Jeong et al., 2020; Rye et al., 2020). In particular, \r [...]"


id: 99404b5d2c28f309f9cfd980dcab2b6a
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
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index: climate_gpt_v2
vector_id: 24178
content:
"[...] increased stratification caused by increased freshwater flux to the \r surface ocean (Section 9.2.1.3) can cause a shoaling and warming of \r the CDW layer, and create a positive feedback, enhancing basal melt \r of the Antarctic Ice Sheet (Section 9.4.2.1; Bronselaer et al., 2018; \r Golledge et al., 2019; Schloesser et al., 2019; Sadai et al., 2020). There \r is medium confidence in the existence of this feedback mechanism \r but low agreement on the magnitude of the feedback. The SROCC \r reported that CMIP5 models project that the  overall transport \r of upper-ocean overturning cell will increase by up to 20% in the \r 21st century, and no new studies alter that assessment."


id: 7a858c13f79a68412ceb440921fd81f0
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 18749
content:
"For the lower cell overturning circulation, SROCC assessed that \r a slowdown of its transport is consistent with the observed decrease \r in volume (medium confidence) of AABW in the global ocean \r (Section  9.2.2.3). Additional evidence since SROCC strengthens \r confidence that increased glacial meltwater flux will reduce the \r density of bottom waters during the 21st century. It will eventually \r reach a  point where deep convection will be curtailed, and shelf \r water will become too buoyant to sink to the ocean interior, thereby \r slowing the lower cell overturning circulation (Bronselaer et al., 2018; \r Golledge et al., 2019; Lago and England, 2019; Moorman et al., 2020). \r While such changes are consistent with the observed freshening \r and decreased volume of the AABW layer reported in SROCC \r (as discussed in Section 9.2.2.3), new observation-based studies have \r highlighted how the lower cell overturning can episodically increase \r [...]"


id: e953077413b8128defde7cb2a6af0419
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 38406
content:
"[...] as a  response to climate anomalies, temporally counteracting the \r tendency for melt to reduce AABW formation (Abrahamsen et  al., \r 2019; Castagno et al., 2019; Gordon et al., 2020; Silvano et al., 2020). \r In addition, while the opening of open ocean polynyas can affect \r the lower cell on decadal to centennial time scales, there is limited \r evidence and low agreement in the role of open ocean polynyas \r in driving observed trends of the lower cell in the last decade \r (Section 9.2.2.3). Based on CMIP5 models, SROCC reported with low \r confidence that formation and export of AABW associated with the \r lower overturning cell will decrease in the 21st century, and there \r is no new evidence to revisit that assessment from climate models. \r However, additional paleo evidence from marine sediments suggests \r that AABW formation/ventilation was vulnerable to freshwater \r fluxes during past interglacials (Hayes et  al., 2014; Huang et  al., \r [...]"


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vector_id: 15454
content:
"[...] 2020; Turney et  al., 2020) and that AABW formation was strongly \r reduced (Skinner et al., 2010; Gottschalk et al., 2016; Jaccard et al., \r 2016) or possibly totally curtailed (Huang et  al., 2020) during the \r Last Glacial Maximum (LGM) and transient cold intervals of marine "


id: 491b07a7432e269cf4e257cc925bc250
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 10084
content:
"isotope stages 2 and 3 (MIS2 and MIS3). Specifically, sedimentary \r reconstructions show a  transient reduction in AABW ventilation \r in the Atlantic sector of the Southern Ocean during MIS5e, which \r is assessed to have been warmer than modern climate (Thomas \r et  al., 2020). However, long multi-centennial or millennial model \r runs under higher-than-pre-industrial CO2 concentrations show that, \r after 500-1000  years, ventilation in the Southern Ocean resumes, \r and possibly overshoots with enhanced convection in the Weddell \r and Ross seas, leading to enhanced bottom water ventilation globally \r (Yamamoto et  al., 2015; Frolicher et  al., 2020). AABW ventilation \r increased at the onset of the last deglacial transition, promoting \r the release of previously sequestered CO2 to the atmosphere on \r centennial to millennial time scales (Bauska et al., 2016; Jaccard et al., \r 2016; Rae et al., 2018), concomitant with a southward shift of the \r [...]"


id: 185ec23dd6918a847cd243fc985e1be9
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 1498
content:
"[...] Southern Hemisphere westerly wind belt (Denton et al., 2010; Jaccard \r et al., 2016) and reduced sea ice cover (Ferrari et al., 2014; Stein et al., \r 2020). In summary, the combination of observational, numerical and \r paleoclimate evidence provides us with medium confidence that the \r lower cell will continue decreasing in the 21st century as a result of \r increased basal melt from the Antarctic Ice Sheet."


id: 5c9c248dfbc60be7bcb155e796da470f
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 13554
content:
"The AR5 (Rhein et al., 2013) assessed with medium to high confidence\r that the North Pacific subpolar gyre, the South Pacific subtropical gyre, \r and the subtropical cells have intensified. They also reported that the \r North Pacific subtropical gyre had expanded since the 1990s, and \r that, overall, the changes in gyre systems were likely predominantly \r due to interannual-to-decadal variability. The SROCC (Meredith et al., \r 2019) complemented the AR5 assessment by reporting that the polar \r Beaufort Gyre in the Arctic expanded to the north-west between \r 2003 and 2014, contemporaneous with changes in its freshwater \r accumulation and alterations in wind forcing. Consistent with the \r reported change over the gyres, both AR5 and SROCC (Bindoff et al., \r 2019; Collins et al., 2019) reported that western boundary currents \r (WBCs) have intensified (Figure  9.11), and expanded poleward, \r except for the Gulf Stream and the Kuroshio. Section  2.3.3.4 \r [...]"


id: 30a4bd206149776956051cc37712bc20
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 5810
content:
"[...] provides an overall assessment of gyres and WBCs, including an \r assessment of change from paleoclimate archives. Section  2.3.3.4 \r assesses that, while WBC strength is highly variable at multi-decadal \r scale (high confidence), WBCs and subtropical gyres have shifted \r poleward since 1993 (medium confidence), at a rate on the order of \r 0.04-0.1 degree per decade during 1993-2018. Figure 9.11 shows \r that CMIP5 and CMIP6 models agree in projecting a  weaker Gulf \r Stream and Gulf Stream Extension, while the Kuroshio changes less \r (Sen Gupta et al., 2016)."


id: 8ceade6d4496944cef32222df461d71b
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 21974
content:
"The effect of tropical modes of variability on climate and their \r long-term changes are reviewed in detail in Annex IV, while changes \r to the tropical ocean are assessed throughout the report and \r briefly summarized here. Section  2.4 concludes that a  sustained \r shift beyond multi-centennial variability has not been observed \r for El  Nino-Southern Oscillation (ENSO) (medium confidence) and \r that there is limited evidence and limited agreement about the \r long-term behaviour of other tropical modes. Section  3.7 assesses \r with high confidence that human influence has not affected the \r principal tropical modes of interannual climate variability and their \r associated regional teleconnections beyond the range of internal \r variability. Section  4.3.3.2 assesses with medium confidence\r that there is no consensus from models for a  systematic change \r in the amplitude of ENSO sea surface temperature variability over \r [...]"


id: 32aaeff010bbc2ded28cb3fe0f76bb3f
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 6178
content:
"[...] the 21st century. The related change in tropical SSTs is covered in \r Section 9.2.1.1. The projected changes in SST have implications for \r marine heat wave characteristics, which are assessed in Box  9.2. \r SST changes in the tropics are related to changes in the atmospheric \r circulation, including surface equatorial easterly trade winds and \r Walker Circulation (Section 4.5.3.2), and the weakening Indonesian \r Throughflow and strengthening Agulhas Extension and leakage \r (Section  9.2.3.4). Weakening trade winds under climate change \r (Vecchi and Soden, 2007) will tend to decrease upwelling, along \r isopycnals in the eastern Pacific and diapycnal upwelling in  the "


id: ce2c6bfbae09a645eb3bfabab2b13ff4
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 33607
content:
"Although the observed wind stress curl shows systematic \r poleward shift in each basin as a result of anthropogenic warming \r (Section 2.3.1.4; Chen and Wu, 2012; Wu et al., 2012; Zhai et al., 2014), \r which has caused a  systematic shift of the WBCs and subtropical \r gyres since 1993 (Wu et  al., 2012; Yang et  al., 2016, 2020), the \r response of current strength is more complex and inconsistent across \r regions (Sloyan and O'Kane, 2015; Y.-L. Wang et al., 2016; Elipot and \r Beal, 2018; McCarthy et al., 2018; Wang and Wu, 2018; Dong et al., \r 2019). The strength of WBCs and gyres exhibit inconsistent responses \r because they are dependent on wind stress forcing and because \r multi-scale interaction and air-sea interaction have an important \r role in their long-term trends and variability (Zhang et  al., 2020). \r Observed changes in gyre circulation are dominated by interannual \r and decadal modes of variability globally (Qiu and Chen, 2012; Melzer \r [...]"


id: 6ebf82b66bb5a6606d0ebaf8b941a86d
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 16750
content:
"[...] and Subrahmanyam, 2017; McCarthy et al., 2018; Hu et al., 2020). \r The North Atlantic subpolar gyre is strongly modulated by variability \r associated with the NAO and AMV (Annex IV; Robson et al., 2016). "


id: 7b2d7d932a00bafa86ed262c9fb50936
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 18864
content:
"Subpolar gyre systems can change abruptly due to a  positive \r feedback between convective mixing and salinity transport (Born \r et  al., 2013, 2016) and air-sea interaction (Moffa-Sanchez et  al., \r 2014; Moreno-Chamarro et al., 2017) within the gyre. In the Arctic, \r both the Beaufort gyre and mesoscale eddies strengthened between \r 2003 and 2014 (Armitage et al., 2017), which might be partly due \r to increased wind stress (Oldenburg et al., 2018) or reduced sea ice \r thickness and changes in sea ice pack morphology (van der Linden \r et  al., 2019). Presently, there is limited evidence in attributing \r causality to these changes for any of the proposed mechanisms. \r In the North Pacific, there has been an increasing trend in the Alaska \r Gyre from 1993 to 2017 (Cummins and Masson, 2018), which might \r be attributed to Pacific Decadal Oscillation (low confidence) (Hristova \r et al., 2019). In the Southern Ocean, limited evidence indicates that \r [...]"


id: 7bf0e98711ff9e4d1db5dc55fa60a02f
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 19013
content:
"[...] the subpolar gyres respond to Southern Hemisphere atmospheric \r modes of variability at interannual time scale (Armitage et al., 2018; \r Dotto et al., 2018)."


id: f963a75c92ba1269fe7ef4fb82f43d23
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 41236
content:
"mesoscale eddies, and also improves simulation of the strength and \r position of WBCs such as the Kuroshio Current, Gulf Stream, and \r East Australian Current (very high confidence) (Sasaki et al., 2004; \r Chassignet and Marshall, 2008; Delworth et al., 2012; Yu et al., 2012; \r Small et  al., 2014; Haarsma et  al., 2016; Chassignet et  al., 2017, \r 2020; Hewitt et  al., 2020). Improved boundary current location \r relates to improved recirculation regions (Jayne et al., 2009), mean \r path and variability, and existence of multiple stable paths (Qiu \r et al., 2005; Delman et al., 2015), air-sea fluxes (Small et al., 2014), \r and related coastal weather patterns (Kaspi and Schneider, 2011). \r The wind-current feedback, implemented by considering relative \r velocity of currents and wind, realistically dampens mesoscale \r eddies and WBCs, through mesoscale air-sea interaction (Ma et al., \r [...]"


id: 98112edfd9850304a4e110d9f5e7c46c
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 23964
content:
"[...] 2016; Renault et al., 2016, 2019), even though sub-mesoscale wind\u0002current damping feedback is missing in these models (medium \r confidence) (Z. Zhang et al., 2016). As eddies potentially play a role \r in determining the strength of gyre circulations and their low\u0002frequency variability (Fox-Kemper and Pedlosky, 2004; Berloff et al., \r 2007), it is expected that eddy-present and eddy-rich models will \r differ in their decadal variability and sensitivity to changes in the \r wind stress of gyres from eddy-parameterized models (medium \r confidence). Nonetheless, important aspects of gyre strength \r depend primarily on forcing and not resolution, allowing long-term \r changes in gyre strength to be investigated with low-resolution \r climate models (Hughes and de Cuevas, 2001; Yeager, 2015)."


id: be128770bcfc1dbc21e9f2c20002fc4c
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 30709
content:
"Eastern boundary upwelling systems (EBUS) exist where trade winds \r draw cold and generally low-pH/low-oxygen waters upward. Coastal \r upwelling plays a key role in supplying the food chain with nutrients, \r hence the richness and productivity of EBUS (Bindoff et al., 2019). \r The SROCC (Bindoff et al., 2019) assessed with high confidence that \r three out of the four major EBUS have experienced large-scale wind \r intensification in the past 60 years (only the trend for the Canary \r Current is considered uncertain). However, it also emphasized that \r various processes can also modulate, or even reverse, wind trends \r locally (Bindoff et  al., 2019). Here we revisit SROCC assessment \r (Bindoff et  al., 2019) based on evidence showing low agreement\r between studies that have investigated trends over past decadess of \r upwelling-favourable winds (Varela et al., 2015). This low agreement\r has been related to differences in wind products, season of interest, \r [...]"


id: e6908a90feb90dc35fa80423fd70cc61
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 37860
content:
"[...] and length of the considered time series (Varela et al., 2015). Based \r on this, we assess that only the California Current system has \r experienced large-scale upwelling-favorable wind intensification over \r the period 1982-2010, albeit with regional differences (Garcia-Reyes \r and Largier, 2010; Seo et  al., 2012). In the Benguela, Canary, and \r Humboldt systems, large-scale, upwelling-favourable wind trends \r are ambiguous, owing to low confidence in long-term in situ \r marine wind data (Cardone et al., 1990; Bakun et al., 2010) and low \r agreement among available studies (Narayan et al., 2010; Sydeman \r et  al., 2014; Varela et  al., 2015). Our assessment confirms SROCC \r assessment (Bindoff et al., 2019) in that high natural variability of \r EBUS and their inadequate representation by most climate models \r gives low confidence in attribution of observed changes, while \r anthropogenic changes are projected to emerge primarily in the \r [...]"


id: 9e73355272fdd63664b91ee25d2ac5e
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 25099
content:
"[...] second half of the 21st century (limited evidence: one model and one \r study) (Brady et al., 2017)."


id: ab8c94977fcd06fa3e292d098309c955
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 27416
content:
"The SROCC (Collins et al., 2019) concluded with high confidence that \r Indonesian Throughflow (ITF) transport from the Pacific Ocean to \r the Indian Ocean has increased in the past two decades as a result \r (medium confidence) of an unprecedented intensification of the \r equatorial Pacific trade wind system. Section 2.3.3.4 assesses that \r there is high confidence that the increase in the ITF over the past \r two decades is linked to multi-decadal scale variability rather than \r a  longer-term trend. Consistently, in the future, as winds change \r under increased radiative forcing, most models project a decline of \r the ITF on the centennial time scale (Figure 9.11). One of the clearest \r changes of ocean current transport simulated by climate models is \r a  weakening of the Indonesian Throughflow, projected in CMIP5 \r simulations under RCP4.5 and RCP8.5 scenarios (Sen Gupta et al., \r 2016; Stellema et  al., 2019), and in CMIP6 simulations under the \r [...]"


id: dfa72b24cfe59d2475ddf72119c18844
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 36597
content:
"The SROCC reports with high confidence that the Agulhas leakage \r from the Indian to the Atlantic Ocean has increased in the past two \r decades (Collins et  al., 2019), and there is no additional evidence \r since then allowing this assessment to be revisited (Biastoch et al., \r 2015; Loveday et  al., 2015; Lubbecke et  al., 2015). There is low \r confidence in future projections of Agulhas leakage because most \r CMIP models cannot directly simulate it, due to coarse resolution. \r However, there is medium evidence that the strength of the Southern \r Hemisphere westerlies controls Agulhas leakage  (Durgadoo et  al., \r 2013; Biastoch et al., 2015; Loveday et al., 2015), and high confidence\r that the strength of the Southern Hemisphere westerlies will increase \r under increased radiative forcing, except in lower warming scenarios \r (SSP1-1.9, SSP1.2-6; Section 4.3.3.1; Bracegirdle et al., 2020). There \r is also evidence that increasing Agulhas leakage is consistent with \r [...]"


id: b97b871736584489616d8156aee0e187
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 29844
content:
"[...] observed change of the temperature and salinity structure in the \r Atlantic ocean, and with variability of the AMOC (Section  9.2.3.1; \r Biastoch et al., 2015). This range of indirect evidence provides medium \r confidence that the Agulhas leakage will increase in the 21st century, \r except for the strongest mitigation scenario (Figure 9.11)."


id: 9f518a9e94b42567c8261988d26fef39
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 25253
content:
"challenged. Changes in sea level pressure and wind fields in EBUS \r appear to be primarily tied  to those affecting subtropical highs \r (Garcia-Reyes et al., 2013). Poleward expansion of the Hadley cell \r (Section  2.3.1.4.1;  Staten et  al., 2018) and the related poleward \r migration of subtropical highs (He et al., 2017; Cherchi et al., 2018), \r produce robust patterns of changes of reduced upwelling at low \r latitude and enhanced upwelling at high latitude (Echevin et  al., \r 2012; Belmadani et al., 2014; Bettencourt et al., 2015; Rykaczewski \r et al., 2015; Sousa et al., 2017; Lamont et al., 2018; Sylla et al., 2019). \r These patterns are most apparent in summer in both hemispheres. \r Synoptic variability of upwelling winds, important to the functioning \r of upwelling ecosystems (Garcia-Reyes et  al., 2014), may also be \r affected by climate change (Aguirre et al., 2019). However, coarse \r resolution model projections of winds in upwelling regions may be \r [...]"


id: 3b7d07dfc39f6d0c42d03d3b74bcbe93
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 7652
content:
"[...] more consistent than higher-resolution projections, as these regions \r are highly sensitive to resolution (Small et al., 2015)."


id: 657782feb46d38b2d59b7fe87b3ffb30
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 15117
content:
"circulations and mixing, leading to indirect connections between \r the inner shelves and coastlines and offshore conditions. Coastal \r processes link to large-scale metrics of climate and regional effects, \r from changing rivers and estuaries, melt and runoff to deep water, \r to how changes offshore affect regional and coastal conditions.\r Shelf-deep ocean exchanges involve eddying, tidal, or turbulent \r motions and small-scale topography such as submarine canyons; \r high-resolution observations and models are needed to capture \r these effects (Greenberg et al., 2007; Capet et al., 2008; Allen and \r Durrieu de Madron, 2009; Colas et  al., 2012; Trotta et  al., 2017). \r Example coastal processes that introduce uncertainty into large-scale \r projections are exchange of CDW across the Antarctic shelf-break, \r which affects AABW formation and Antarctic ice-shelf-ocean \r interaction (Sections  9.2.2.3 and 9.2.3.2; Stewart and Thompson, \r [...]"


id: 233e315bc715745d1fe60bbb72b61610
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 3403
content:
"[...] 2013, 2015), river and estuarine plumes and their responses to water \r level and hydrology change (Banas et al., 2009; Sun et al., 2017), \r fjord dynamics linked to glacial outflows (Straneo and Cenedese, \r 2015; Torsvik et al., 2019), and changing formation of water masses \r in marginal seas (Kim et al., 2001; Greene and Pershing, 2007; Giorgi \r and Lionello, 2008; Renner et  al., 2009). Downscaling projections \r to the local level allows process detail (Foreman et  al., 2014; \r Mathis and Pohlmann, 2014; Meier, 2015; Tinker et al., 2016). Some \r processes can only be simulated when coastal models are forced by \r larger-scale models of the atmosphere, cryosphere, or hydrosphere \r (Seo et al., 2007, 2008; Somot et al., 2008; Oerder et al., 2015; Renault \r et al., 2016; Y. Zhang et al., 2016; Wahlin et al., 2020), including the \r addition of tides (Janekovic and Powell, 2012; Timko et  al., 2013; \r Tinker et al., 2015; Pickering et al., 2017; Hausmann et al., 2020). \r [...]"


id: 69900cb0c208e36f3dd12098c9659a7c
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 15838
content:
"[...] Due to coastal process complexity and small scale, linking the \r effects of coastal ocean changes to global ocean changes requires \r high-resolution modelling (Holt et al., 2017, 2018), two-way nesting, \r or local mesh refinement (Fringer et al., 2006; Zhang and Baptista, \r 2008; Mason et al., 2010; Dietrich et al., 2012; Hellmer et al., 2012; \r Ringler et al., 2013; Q. Wang et al., 2014; Zangl et al., 2015; Y.J. Zhang \r et al., 2016; Soto-Navarro et al., 2020). Coarse climate models and \r HighResMIP models do not represent some coastal phenomena such \r as cross-shelf exchanges and sub-mesoscale eddies, which require \r 1 km or finer resolution. Thus, there is low confidence in projecting \r centennial scale coastal climate change where regional downscaling \r or refinement is lacking. There is high confidence in the ability of \r regional coupled models to improve coastal climate change process \r understanding and provide regional information (Section 12.4), but \r [...]"


id: ef3ddb0f6ee8e1539d12fbe42b19bb25
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 39462
content:
"Projected future annual cumulative upwelling wind changes at most \r locations, and seasons remain within  +-10-20% of present-day \r values in the 21st  century, even in the context of high-end \r emissions scenarios (4xCO2 or RCP8.5)  (medium confidence). \r Changes due to wind stress curl and alongshore pressure gradients \r tend to agree with alongshore wind changes (Oerder et al., 2015; \r Sylla et  al., 2019). Direct  estimation of oceanic upward transport \r (Oyarzun and Brierley, 2019; Sylla et  al., 2019) and nutrient flux \r into the euphotic layer (Jacox et  al., 2018) provide a  meaningful \r estimator of upwelling, integrating all relevant processes, including \r changes in wind stress curl. However,  there is limited evidence\r from vertical velocity of climate models and missing processes in \r coarse-resolution climate models that presently limit this approach. \r Change in upper-ocean stratification (Section 9.2.1.3) is projected \r [...]"


id: 24a90983866426988ed15801985ba9a2
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 3642
content:
"[...] to increase confinement of upwelling vertical velocities to near the \r ocean surface (high confidence) (Oerder et al., 2015; Oyarzun and \r Brierley, 2019)."


id: 4e58fdec7dc0c4a7fdca1f0c5e0f22fb
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 11058
content:
"Ocean dynamic sea level change is strongly affected by internal \r variability (Section 9.6.1.4), partly from interannual to decadal coupled \r atmosphere-ocean modes of variability via wind-driven redistribution \r (Annex IV; Griffies et  al., 2014; Han et  al., 2017) and partly from \r intrinsic ocean variability, particularly in higher-resolution simulations \r (such as HighResMIP), which statistically resemble observations, even \r on short time scales (Figure 9.12; Griffies et al., 2014; Serazin et al., \r 2016; Llovel et  al., 2018; Chassignet et  al., 2020). High-resolution \r simulations are not used in relative sea level projections (Section 9.6.3) \r due to the limited range of forcing scenarios. The most marked feature \r of long-term regional sea level change in the continuous satellite \r altimetry record, beginning in 1992, is the east-west dipole in the \r Pacific Ocean (rising more rapidly in the east, see also Section 9.6.1.3), \r [...]"


id: 54a9f53a21ec83dc38cc430c1dec1626
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 12170
content:
"[...] which persisted until 2015, and can be explained by anomalously \r strong trade winds (Merrifield et al., 2012; England et al., 2014; Griffies \r et al., 2014; Takahashi and Watanabe, 2016; Han et al., 2017) together \r with associated changes in surface heat flux (Piecuch et al., 2019). \r The most notable features of sub-annual variability in altimetry are \r eddies and tides, which are directly simulated only in high-resolution \r models (Haigh et al., 2019; Chassignet et al., 2020)."


id: b01e3a5e2fb7805de0144956272915ab
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 28221
content:
"Projections of ocean dynamic sea level change (Box  9.1) on \r multi-annual time scales resemble the patterns of steric sea level \r change in the open ocean (Figures 9.11 and 9.12; Lowe and Gregory, \r 2006; Pardaens et al., 2011; Couldrey et al., 2021). On shorter time \r scales, especially in extratropical coastal areas, there may be an \r important barotropic component (also called bottom pressure change) \r due mostly to changes in wind-driven circulation and eddies apparent \r in the variance of ocean dynamic sea level (Figure 9.12; Roberts et al., \r 2016; Hughes et al., 2018). This component is highly sensitive to ocean \r model resolution (Chassignet et al., 2020). Steric sea level change is \r associated with local changes in temperature and salinity, which \r come about through changes in surface fluxes of heat and freshwater \r (Section 9.2.1.2) and through redistribution of existing water masses \r by changed ocean circulation and mixing processes (Figure  9.12\r [...]"


id: 194b94749ea376a2955c3f16e5570c06
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 1661
content:
"[...] and Sections 9.2.2.1 and 9.2.3). Redistribution of water masses \r often involves anticorrelated thermosteric and halosteric changes \r (Figure 9.12), especially in the Atlantic (Pardaens et al., 2011; Bouttes \r et al., 2014; Durack et al., 2014; Griffies et al., 2014; Han et al., 2017)."


id: 7fe1495fc48fe280c28dd2595b192222
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
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index: climate_gpt_v2
vector_id: 19637
content:
"Figure 9.12 | (a-f) Coupled Model Intercomparison Project Phase 6 (CMIP6) multi-model mean projected change contributions to relative sea level change \r in (a, d) steric sea level anomaly, (b, e) thermosteric sea level anomaly, and (c, f) halosteric sea level anomaly between 1995-2014 and 2081-2100 using \r a method that does not require a reference level (Landerer et al., 2007). Global mean change has been removed from these figures, consistent with the methods \r in Sections 9.6.3 and 9.SM.4 and the definitions of Gregory et al. (2019). (Gregory et al., 2019). See Figure 9.27 for global mean sea level (GMSL). (g-i) Standard deviation \r of ocean dynamic sea level change from (g) Aviso observations (10-day high-pass filter); (h) five-day mean of high-resolution Ocean Model Intercomparison Project phase 2 \r (OMIP-2) models forced with observed fluxes; and (i) five-day mean of low-resolution OMIP-2 models which are comparable in resolution to the models in (a-f). No overlay \r [...]"


id: 2614eeafdb63c88649e72f03dd061b5e
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 3871
content:
"[...] indicates regions with high model agreement, where >=80% of models agree on the sign of change. Diagonal lines indicate regions with low model agreement, where <80% \r of models agree on the sign of change (see Cross-Chapter Box Atlas.1 for more information). Further details on data sources and processing are available in the chapter data \r table (Table 9.SM.9)."


id: 86779bccdc6404daad21455dbfcc10e6
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 20823
content:
"Projections of the pattern and amplitude of regional ocean dynamic \r sea level change in CMIP6 and previous model generations show \r a large model spread, of a similar size to the geographical spread \r (Figure  9.12). The model spread derives from model dependence \r of changes both in surface fluxes (Section  9.2.1.2) and in the \r ocean response (Section 9.2.2). The spread is similar in CMIP6 and \r CMIP5, and is largest in regions with large projected variations in \r ensemble-mean ocean dynamic sea level change (Lyu et al., 2020a), \r such as the Southern Ocean Dipole with an ocean dynamic sea level \r rise north of the ACC and a  fall to the south, the Atlantic Dipole \r with a  sea level rise north of 40degN and a  fall in 20degN-40degN, the \r Northwest Pacific Dipole, and the large sea level rise in the Arctic \r (Church et  al.,  2013b; Slangen et  al., 2014a, 2015; Bilbao et  al., \r 2015; Gregory et al., 2016; C. Chen et al., 2019; Lyu et al., 2020a; \r [...]"


id: c92299575816f376a5162d5b19c6b788
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 32711
content:
"[...] Couldrey et  al., 2021). Patterns of change are consistent between \r model simulations and observations (medium confidence). The major \r model ensemble-mean features resemble thermosteric sea level \r change, as expected from altered input of heat to the ocean \r without changing circulation, while model spread results from the \r diversity in redistribution of the heat content of the unperturbed \r ocean (Section 9.2.2.1; Bouttes and Gregory, 2014; Gregory et  al., \r 2016; Huber and Zanna, 2017; Lyu et al., 2020b; Todd et al., 2020; \r Couldrey et al., 2021)."


id: 9277d217dbf7a48bf4fc8898b2632050
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 22989
content:
"The observed decrease of Arctic sea ice area is a  key indicator of \r large-scale climate change (Section 2.3.2.1.1, Cross-Chapter Box 2.2). \r The SROCC (Meredith et al., 2019) assesses that sea ice extent, which \r is the total area of all grid cells with at least 15% sea ice concentration, \r has declined since 1979 in each month of the year (very high \r confidence). In contrast to SROCC, we assess changes in sea ice area \r (the actual area of the ocean covered by sea ice) rather than sea ice \r extent, because sea ice area is geophysically more relevant and not \r grid-dependent (Notz, 2014; Ivanova et al., 2016; Notz et al., 2016; \r Notz and SIMIP Community, 2020). Arctic sea ice area is calculated \r based on measurements by passive microwave satellite sensors that \r provide near-continuous measurements of gridded, pan-Arctic sea \r ice concentration from 1979 onwards. Irreducible uncertainties in the \r conversion of thermal microwave brightness temperature to sea ice \r [...]"


id: 744d53f2bc112135e11f1c7a8ff25700
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 17696
content:
"[...] concentration, and choices in algorithm design, cause uncertainties in \r observed Arctic sea ice area, which are far smaller than the observed \r sea ice loss (e.g., Comiso et al., 2017a; Niederdrenk and Notz, 2018; \r Alekseeva et al., 2019; Kern et al., 2019; Meier and Stewart, 2019). \r Sea ice area has decreased in every month of the year from 1979 to \r the present (very high confidence) (Figure  9.13). The absolute and \r the relative ice losses are highest in late summer-early autumn (high \r confidence) (Figure 9.13). Averaged over the decade 2010-2019, the \r monthly Arctic sea ice area from August to October has been around \r 2 million km2 (or about 25%) smaller than during 1979-1988 (high \r confidence) (Figure 9.13)."


id: 74cc67def20dfef8325ad6e49baea548
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 17787
content:
"The North Atlantic sea level change dipole is forced by a reduction \r in heat loss from the ocean north of 40degN (i.e., net heat uptake), \r which in all Earth system models leads to a weakening of the AMOC, \r although the magnitude has a large model spread (Section 9.2.3.1; \r Gregory et al., 2016; Huber and Zanna, 2017). The reduced northward \r transport of warm, salty water (Section 9.2.2) causes further ocean \r dynamic sea level change, whose details are model-dependent. North \r of 40degN, this redistribution leads to a sea level rise, predominantly \r halosteric, reinforcing the thermosteric effect of heat uptake (Couldrey \r et al., 2021). Comparison of observed Atlantic OHC for 1955-2017 \r with a  reconstruction assuming no change in circulation indicates \r that the thermosteric sea level change resulting from southward \r redistribution of heat may be detectable (Zanna et  al., 2019). This \r redistribution causes a tendency for SST cooling north of 40degN and \r [...]"


id: 8907a8680cba2f0bcfd1ea3d403f6ca0
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 21272
content:
"[...] anomalous heat input from the atmosphere, and thus a  positive \r feedback on AMOC weakening (Winton et al., 2013; Gregory et al., \r 2016; Todd et  al., 2020; Couldrey et  al., 2021). Many climate and \r ocean models agree that the AMOC weakening is associated with "


id: 8632d0cb348701afca8d5f33018411b9
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 20761
content:
"reconstructions  (SSIA,Sep = 0.2 million km2) (medium confidence\r because of limited reliability of longer-term sea ice reconstructions) \r (Brennan et  al., 2020). Internal variability has been estimated to \r have contributed 30 to 50% of the observed Arctic summer sea ice \r loss since 1979 (Kay et al., 2011; Stroeve et al., 2012; Ding et al., \r 2017, 2019; England et  al., 2019). However, this estimate from \r models might be biased towards internal over forced variability \r because of the models' high internal variability and because the \r CMIP5 simulated September sea ice sensitivity to forcing is lower \r than observed, even if internal variability is taken into account (Notz \r and Stroeve, 2016; Rosenblum and Eisenman, 2017). Most CMIP6 \r models fail to simulate the observed sensitivity of sea ice loss to CO2\r emissions (as a proxy for time) and to temperature simultaneously. \r However, they better capture the observed sensitivity of sea ice loss \r [...]"


id: 46b5a81f8844da54f621673448224d1d
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 9646
content:
"[...] to CO2 emissions than CMIP5 models (Section 3.4.1; Figure 9.14h; \r Notz and SIMIP Community, 2020). "


id: 8b8e68930da854f916c4dc204f5fd950
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 21721
content:
"The SROCC assessed that approximately half of the satellite-observed \r Arctic summer sea ice loss is driven by increased concentrations \r of atmospheric greenhouse gases (medium confidence). Recent \r attribution studies now allow the strengthened assessment that it \r is very likely that more than half of the observed Arctic sea ice loss \r in summer is anthropogenic (Section  3.4.1.1). This assessment is \r confirmed by process-based analyses of Arctic sea ice loss not assessed \r by SROCC. Similar to the paleorecord, the satellite record of Arctic \r sea ice area from 1979 onwards is strongly and linearly correlated \r with global mean temperature on decadal and longer time scales \r (Figures 9.14a,e) (e.g., Gregory et al., 2002; Rosenblum and Eisenman, \r 2017). The correlation holds across all months with R2 ranging from \r 0.61 to 0.81 (Niederdrenk and Notz, 2018). However, in contrast \r to paleorecords, sea ice fluctuations during the satellite period are \r [...]"


id: 484ef80058763299315524748387b318
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 9935
content:
"[...] only weakly correlated with Northern Hemisphere insolation (Notz \r and Marotzke, 2012); modern Northern Hemisphere sea ice area \r is more strongly correlated with atmospheric carbon dioxide (CO2) \r concentration (Johannessen, 2008; Notz and Marotzke, 2012) and \r cumulative anthropogenic CO2 emissions (Figures 9.14b,f; Zickfeld \r et al., 2012; Herrington and Zickfeld, 2014; Notz and Stroeve, 2016). \r The R2 values of the correlation between sea ice area and cumulative \r CO2 emissions range across all months from 0.76 to 0.92 (Stroeve \r and Notz, 2018). In summary, there is high confidence that satellite\u0002observed Arctic sea ice area is strongly correlated with global mean \r temperature, CO2 concentration and cumulative anthropogenic \r CO2 emissions."


id: 1560e36e4f819351406726f69c64a7f8
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 974
content:
"In examining temperature thresholds for the loss of Arctic summer \r sea ice, the Special Report on Global Warming of 1.5degC (SR1.5; \r Hoegh-Guldberg et  al., 2018) and SROCC assess that a  reduction \r of September mean sea ice area to below 1 million km2, practically \r a  sea ice-free Arctic Ocean, is more probable for a  global mean \r warming of 2degC compared to global mean warming of 1.5degC \r (high confidence). Analyses of CMIP6 simulations (Notz and SIMIP \r Community, 2020) confirm this result, as they show that, on decadal \r and longer time scales, Arctic summer sea ice area will remain highly \r correlated with global mean temperature until the summer sea \r ice has vanished (Figure  9.14a,e). Quantitatively, existing studies \r (Screen and Williamson, 2017; Jahn, 2018; Ridley and Blockley, 2018; \r Sigmond et al., 2018; Notz and SIMIP Community, 2020) also show \r that, for a  warming between 1.5 and 2degC, the Arctic will only be \r [...]"


id: 8a083e292bb069b08a3990da365f8269
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 21456
content:
"[...] practically sea ice free in September in some years, while at 3degC \r warming, the Arctic is practically sea ice free in September in most \r years, with longer practically sea ice-free periods at higher warming \r levels (medium confidence). However, because of the CMIP5 and \r CMIP6 models' generally too low sensitivity of sea ice loss to global \r warming, there is only low confidence regarding the specific warming \r level at which the Arctic Ocean first becomes practically sea ice free \r (Section 4.3.2.1; Notz and SIMIP Community, 2020)."


id: 1f76a84f9c32f1aebdef9ed434e89ba9
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 2746
content:
"In addition to changes in the external forcing, internal variability \r substantially affects Arctic sea ice, evidenced from both paleorecords \r (e.g., Chan et al., 2017; Horner et al., 2017; Kolling et al., 2018) and \r satellites after 1979 (e.g.,  Notz and Stroeve, 2018; Roberts et  al., \r 2020) (high confidence). Most of the internal variability on annual \r time scales is related to atmospheric temperature fluctuations, \r for example linked to cyclone activities (Wernli and Papritz, 2018; \r Olonscheck et  al., 2019), while multi-decadal internal variability \r is primarily related to changes in oceanic heat transport (Zhang, \r 2015; Halloran et  al., 2020). These mechanisms are represented \r in current climate models (Olonscheck et al., 2019; Halloran et al., \r 2020), but the resulting internal variability of September sea \r ice area in CMIP5 and CMIP6 models, as given by the ensemble \r mean standard deviation SSIA,Sep = 0.5 million km2 (Olonscheck \r [...]"


id: 3532934e293f0f8181100ae72977573
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 6608
content:
"[...] and Notz, 2017; Notz and SIMIP Community, 2020), exceeds the \r estimated internal variability for the period 1850 to 1979 from \r both reanalyses (SSIA,Sep = 0.3 million km2) and direct observational "


id: 90c7832fc064cfb02279f8c763125c92
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 22681
content:
"There is an indication that CMIP6 simulations of Arctic sea ice \r have improved relative to CMIP5 (Section  3.4.1.1), but detailed \r evaluation studies exist mainly for CMIP5 models. These studies \r found that CMIP5 model projections and reanalyses show a  large \r spread of simulated regional Arctic sea ice concentration (Laliberte \r et al., 2016; Chevallier et al., 2017), which remains true for CMIP6 \r models (Shu et al., 2020; Wei et al., 2020). In addition, both CMIP5 \r and CMIP6 models show a large spread in the simulated seasonal \r cycle of Arctic sea ice area, with too high a sea ice area in March in \r the ensemble mean (Notz and SIMIP Community, 2020). The CMIP5 \r models have also had difficulty simulating realistic landfast sea ice \r (Laliberte et  al., 2018). These findings imply that both CMIP5 and \r CMIP6 models do not realistically capture the regional and seasonal \r processes governing observed Arctic sea ice evolution, causing low \r [...]"


id: 83e44a658ddcb97b80a10dd17bf6affd
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 20351
content:
"[...] confidence in the models' projections of future regional sea ice \r evolution, including updated projections for shipping routes across \r the Northern Sea Route and Northwest Passage (Wei et al., 2020)."


id: 55912b03dcb4540a224190a4ecab63c7
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 12324
content:
"The CMIP5 models also have issues with capturing the seasonal \r cycle of observed changes in Arctic sea ice drift speed, which affects \r their simulation of regional sea ice concentration patterns. Direct \r measurements of Arctic sea ice from drift buoys and satellites show \r that drift speed of Arctic sea ice has increased over the satellite \r period in all seasons (e.g., Rampal et al., 2009; Docquier et al., 2017). \r In summer, CMIP5 models show a slowdown of Arctic sea ice drift \r rather than the observed acceleration (Tandon et al., 2018). In winter, \r CMIP5 models generally capture the observed acceleration of Arctic \r drift speed. The drift acceleration is primarily caused by the decrease \r in concentration and thickness in the observational record (Rampal \r et al., 2009; Spreen et al., 2011; Olason and Notz, 2014; Docquier \r et al., 2017) and, for winter, in CMIP5 models (Tandon et al., 2018). \r Changes in wind speed are less important for the observed large-scale \r [...]"


id: 68a1b5ba1002db4e51e23ab608dde5ec
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 15677
content:
"[...] changes (Spreen et al., 2011; Vihma et al., 2012; Olason and Notz, \r 2014; Docquier et al., 2017; Tandon et al., 2018). In summary, there \r is high confidence that Arctic sea ice drift has accelerated because of \r the decrease in sea ice concentration and thickness."


id: dc12e08fa9721335472399148654ffbf
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 35998
content:
"Observations of regional changes in sea ice thickness vary in quality. \r Analysis of submarine data in the central Arctic Ocean suggests that \r its sea ice has thinned by about 75 cm compared to the mid-1970s \r (Section 2.3.2.1.1). For smaller regions, data are too sparse to allow \r for quantitative estimates of long-term trends (King et  al., 2017; \r Rosel et al., 2018), but a clear thinning signal over 10 to 20 years has \r been found for sea ice in the Fram Strait (Spreen et al., 2020), north of \r Canada (Haas et al., 2017) and for landfast ice in the Kongsfjorden/\r Svalbard Arctic border (Pavlova et al., 2019). The CMIP5 models and \r reanalyses fail to capture the observed distribution (Stroeve et al., \r 2014; Shu et al., 2015) and evolution (Chevallier et al., 2017) of Arctic \r sea ice thickness. Most CMIP6 models do not capture the observed \r spatial distribution of sea ice thickness realistically (Wei et al., 2020). \r [...]"


id: 5d7bfdacf111eb806ddd0d7036490bbb
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 13734
content:
"[...] This leads to low confidence in estimates of thickness from reanalyses \r and from CMIP5 and CMIP6 models and in their projections of sea \r ice volume."


id: a452d5946fe97221987237390f122035
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 26114
content:
"The SR1.5 assessed with high confidence that there is no hysteresis \r in the loss of Arctic summer sea ice. In addition, there is no tipping \r point or critical threshold in global mean temperature beyond which \r the loss of summer sea ice becomes self-accelerating and irreversible \r (high confidence). This is because stabilizing feedbacks during winter \r related to increased heat loss through thin ice and thin snow, and \r increased emission of longwave radiation from open water, dominate \r over the amplifying ice albedo feedback (see Section 7.4.2 for details \r on the  individual feedbacks; e.g.,  Eisenman, 2012; Wagner and \r Eisenman, 2015; Notz and Stroeve, 2018). Observed and modelled \r Arctic summer sea ice and global mean temperature are linked \r with little temporal delay, and the summer sea ice loss is reversible \r on decadal time scales (Armour et  al., 2011; Ridley et  al., 2012; \r Li et al., 2013; Jahn, 2018). The loss of winter sea ice is reversible \r [...]"


id: a6b315557ea9e058ef9f2b2e043098f8
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 26545
content:
"[...] as well, but the loss of winter sea ice area per degree of warming \r in CMIP5 and CMIP6 projections increases as the ice retreats from \r the continental shore lines, because these limit the possible areal \r fluctuations (high confidence) (Section 4.3.2.1; Bathiany et al., 2016, \r 2020; Meccia et al., 2020)."


id: 2f9103683357b8d2305442d17f7a5abe
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 5631
content:
"The SROCC assessed that the regional trends are closely related \r to meridional wind trends (high confidence). This is the case as the \r regional trends in the maximum northward extent of the ice cover \r (Figure 9.15) are determined by the balance between the northward \r advection of the ice that is formed in polynyas near the continental \r margin, and the lateral and subsurface melting through oceanic heat \r fluxes. The advection of the sea ice is strongly correlated with winds \r and cyclones (Schemm, 2018; Vichi et al., 2019; Alberello et al., 2020). \r Accordingly, the increasing sea ice area in the Ross Sea can be linked \r to a  strengthening of the Amundsen Sea low (e.g.,  Holland et  al., \r 2017b, 2018), while other regional sea ice trends in the austral autumn \r can be linked to changes in westerly winds, cyclone activity and the \r Southern Annular Mode (SAM) in summer and spring (Doddridge and \r Marshall, 2017; Holland et al., 2017a; Schemm, 2018). In addition to \r [...]"


id: 27a0e34c53c9957c76400981b88e9c3b
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 4151
content:
"[...] the wind-driven changes, increased near-surface ocean stratification \r (Section 9.2.1.3) has contributed to the observed increase in sea ice \r coverage (e.g., Purich et al., 2018; L. Zhang et al., 2019) as it tends \r to cool the surface ocean (Sections 9.2.1.1 and 9.2.3.2). The changes \r in stratification result partly from surface freshening (De  Lavergne \r et al., 2014), associated with increased northward sea ice advection \r (Haumann et al., 2020) and/or melting of the Antarctic ice sheet (medium \r confidence) (e.g.,  Haumann et  al., 2020; Jeong et  al., 2020; Mackie \r et al., 2020), and amplified by local ice-ocean feedbacks (Goosse and \r Zunz, 2014; Lecomte et al., 2017; Goosse et al., 2018). In the Amundsen \r Sea, strong ice shelf melting can cause local sea ice melt next to the \r ice shelf front by entraining warm circumpolar deep water to the ice \r shelf cavity and surface ocean (medium confidence) (Sections 9.2.3.2 \r [...]"


id: cbb9b923273babb86bb29072a26fa83c
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 33167
content:
"[...] and 9.4.2.2; Jourdain et al., 2017; Merino et al., 2018). It has also been \r suggested that the observed regional increase in sea ice coverage since "


id: 133d052eba6c8ec3a7970d0f39eede6
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 598
content:
"Figure 9.15 | Antarctic sea ice historical records and Coupled Model Intercomparison Project Phase 6 (CMIP6) projections. (Left) Absolute anomaly of \r observed monthly mean Antarctic sea ice area during the period 1979-2019 relative to the average monthly mean Antarctic sea ice area during the period 1979-2008. \r (Right) Sea ice coverage in the Antarctic as given by the average of the three most widely used satellite-based estimates for September and February, which usually are the \r months of maximum and minimum sea ice coverage, respectively. First column: Mean sea ice coverage during the decade 1979-1988. Second column: Mean sea ice coverage \r during the decade 2010-2019. Third column: Absolute change in sea ice concentration between these two decades, with grid lines indicating non-significant differences. Fourth \r [...]"


id: 1c7bc8f6c4c9476ad9c207621191854b
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 2198
content:
"[...] column: Number of available CMIP6 models that simulate a mean sea ice concentration above 15% for the decade 2045-2054. The average observational record of sea ice area \r is derived from the UHH sea ice area product (Doerr et al., 2021), based on the average sea ice concentration of OSISAF/CCI (OSI-450 for 1979-2015, OSI-430b for 2016-2019) \r (Lavergne et al., 2019), NASA Team (version 1, 1979-2019) (Cavalieri et al., 1996) and Bootstrap (version 3, 1979-2019) (Comiso, 2017) that is also used for the figure panels \r showing observed sea ice concentration. Further details on data sources and processing are available in the chapter data table (Table 9.SM.9)."


id: 8d8db036d2185a24215a7969714b23bc
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 22092
content:
"Ice Sheet (Bronselaer et  al., 2018). Compared to the interannual \r variability during the satellite record from 1979 onwards, models \r simulate too much variability in both CMIP5 (Zunz et al., 2013) and \r CMIP6 (Roach et al., 2020). The seasonal cycle in sea ice coverage is \r misrepresented in most CMIP5 (e.g., Holmes et al., 2019) and CMIP6 \r models (Roach et al., 2020), but the multi-model mean seasonal cycle \r in CMIP5 and CMIP6 agrees well with observations (Shu et al., 2015; \r Roach et al., 2020). Most CMIP5 models do not realistically simulate \r the evolution of Antarctic sea ice volume (Shu et  al., 2015) and \r consistently overestimate the amount of low concentration sea ice, \r and underestimate the amount of high concentration sea ice (Roach \r et al., 2018a). In contrast, CMIP6 models simulate a more realistic \r distribution of regional sea ice coverage (Roach et al., 2020). Most \r CMIP5 models poorly represent Antarctic sea ice drift (e.g., Schroeter \r [...]"


id: 6b4116c52a20e5be8b6144a5aa38c2b8
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 16168
content:
"[...] et  al., 2018; Holmes et  al., 2019), affecting simulated historical \r trends, with models that simulate a strong sea ice motion showing \r more variability in sea ice coverage than models with weaker sea ice \r motion (Schroeter et al., 2018). Owing to limited agreement between \r model simulations and observations, limited reliable observations on \r a process level, and a lack of process understanding of the substantial \r spread in CMIP5 and CMIP6 model simulations, there remains low \r confidence in existing future projections of Antarctic sea ice decrease \r and lack of decrease."


id: c604cbed9f663eed5ee152102399aaa4
created_at: 2023-04-19 13:22:40
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index: climate_gpt_v2
vector_id: 32148
content:
"The analysis and understanding of the long-term evolution of the \r Antarctic sea ice cover is hindered by the scarcity of observational \r records before the satellite period, and the scarcity of paleorecords \r (see Section  2.3.2.1.2 for further details). Such long records are \r particularly relevant given that the Southern Ocean response \r to external forcing takes longer than the length of the available \r direct observational record (Goosse and Renssen, 2001; Armour \r et al., 2016). There is only limited evidence for large-scale decadal \r fluctuations in sea ice coverage caused by large-scale temperature \r and wind forcing. Sparse direct pre-satellite observations suggest \r a decrease in sea ice coverage from the 1950s to the 1970s (Fan \r et  al., 2014). Paleo-proxy data indicate that, on multi-decadal to \r multi-centennial time scales, sea ice coverage of the Southern \r Ocean follows large-scale temperature trends (e.g.,  Crosta et  al., \r [...]"


id: 3e2d3aadd67b3d0a510435b1b22a3fb4
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"[...] 2018; Chadwick et  al., 2020; Lamping et  al., 2020), for example \r linked to fluctuations in the El  Nino-Southern Oscillation and \r Southern Annular Mode (Crosta et al., 2021), and that during the \r Last Glacial Maximum, Antarctic sea ice extended to about the \r polar front latitude in most regions during winter, whereas the \r extent during summer is less well understood (e.g.,  Benz et  al., \r 2016; Xiao et al., 2016; Nair et al., 2019)."


id: d074283eb61d4e13520eb40c8d28e6de
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
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index: climate_gpt_v2
vector_id: 34026
content:
"The SROCC assessed that observations are too sparse to reliably \r estimate long-term trends in Antarctic sea ice thickness. This remains \r true, and only qualitative statements on prevailing thicknesses are \r possible. Data from ICESat-1 laser altimetry (Kurtz and Markus, 2012), \r from Operation IceBridge (Kwok and Kacimi, 2018), and long-term \r shipboard observations collected in the Antarctic Sea Ice Processes \r and Climate (ASPeCt) dataset (Worby et  al., 2008) suggest that \r sea ice thicker than 1 m prevails in regions of multi-year ice along \r the eastern coast of the Antarctic Peninsula in the Weddell Sea, in the \r high-latitude embayment of the Weddell Sea, and along the coast \r of the Amundsen Sea, with remaining regions dominated by thinner \r first-year sea ice (high confidence). Regional patterns in ice thickness \r are affected by areas of high snow deposition and resulting snow-ice \r formation (Massom et  al., 2001; Maksym and Markus, 2008), and \r [...]"


id: 31d00eba0b6f8f4d9f64307a608bf56
created_at: 2023-04-19 13:22:40
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index: climate_gpt_v2
vector_id: 6013
content:
"[...] deformation, ridging, and rafting that regionally cause formation of \r very thick sea ice (Massom et  al., 2006; G. Williams et  al., 2015). \r In addition, near ice shelves a sub-ice platelet layer from supercooled \r water can significantly increase sea ice thickness (Hoppmann et al., \r 2020; Haas et  al., 2021). Regarding snow thickness, observations "


id: a8f38be7bb782ee365d09ede73ba4a59
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
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index: climate_gpt_v2
vector_id: 26935
content:
"There is low confidence in the long-term trend of Antarctic sea ice \r thickness. Both ASPeCt and ICESat-1 measurements are biased low \r in regions with thick ice (Kern and Spreen, 2015), compared to results \r from reanalyses (Massonnet et al., 2013; Haumann et al., 2016) and \r observations with autonomous vehicles under sea ice (G. Williams \r et al., 2015). Estimates of sea ice thickness from CryoSat-2 do not \r substantially reduce uncertainty, primarily because of the unknown \r snow thickness and radar scattering above the snow-ice interface \r (Bunzel et  al., 2018; Kwok and Kacimi, 2018; Kacimi and Kwok, \r 2020). Isolated in situ time series show no clear long-term trend \r in landfast ice thickness in the Weddell Sea (Arndt et  al.,  2020). \r Reanalyses suggest overall increasing sea ice thickness and volume \r between 1980 and 2010 (Holland et  al., 2014; Zhang, 2014; \r Massonnet et  al., 2015), while CMIP5 (Shu et  al., 2015; Schroeter \r [...]"


id: 99376c881be42fe239c445e597136d2b
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index: climate_gpt_v2
vector_id: 24169
content:
"[...] et al., 2018) and CMIP6 models simulate a decrease in Antarctic sea \r ice volume over the historical period. Because of this discrepancy, \r and the unclear reliability of the reanalyses (Uotila et al., 2019), there \r is low confidence in CMIP5 and CMIP6 simulated future Antarctic \r sea ice thickness."


id: 52e718f07c31eef35e298a6ea46cfe0a
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
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index: climate_gpt_v2
vector_id: 11851
content:
"Post-1992, SROCC stated that it is extremely likely that the rate \r of mass change of Greenland Ice Sheet was more negative during \r 2012-2016 than during 1992-2001, with very high confidence\r that summer melting has increased since the 1990s to a  level \r unprecedented over at least the last 350 years. Since SROCC, the \r updated synthesis of satellite observations by the Ice Sheet Mass \r Balance Intercomparison Exercise (The IMBIE Team, 2020) and the \r GRACE Follow-On (GRACE-FO) Mission (Abich et al., 2019; Kornfeld \r et al., 2019), have confirmed the mass change record, and the record \r has been extended to 2020 (The IMBIE Team, 2021) as presented \r in 2.3.2.4. The Greenland Ice Sheet lost 4890 [4140-5640] Gt of ice \r between 1992 and 2020, causing sea level to rise by 13.5 [11.4 to \r 15.6]  mm (The IMBIE Team, 2021; see also Section  2.3.2.4.1, \r Figure 9.16 and Table 9.5). The IMBIE Team's (2020) estimates are \r consistent with other post-AR5 reviews (Figure 9.17, Table 9.SM.1; \r [...]"


id: 794007edc5a9a898c18f078d088c1e53
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
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index: climate_gpt_v2
vector_id: 18526
content:
"[...] Bamber et al., 2018a; Cazenave et al., 2018; Mouginot et al., 2019; \r Slater et  al.,  2021). Recent GRACE-FO data (Sasgen et  al., 2020; \r Velicogna et al., 2020) show that, after two cold summers in 2017 \r and 2018, with relatively moderate mass change of about -100 \r Gt yr-1, the 2019 mass change (-532 +- 58 Gt yr-1) was the largest \r annual mass loss in the record. The high agreement across a variety \r of methods confirms SROCC and Chapter 2 assessments. The mass\u0002loss rate was, on average, 39 [-3 to 80] Gt yr-1 over the period \r 1992-1999, 175 [131 to 220] Gt yr -1 over the period 2000-2009 \r and 243 [197 to 290] Gt yr-1 over the period 2010-2019 (see Table 9.\r SM.1)."


id: e75c6e9765eee97bd2e69e19405ce935
created_at: 2023-04-19 13:22:40
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index: climate_gpt_v2
vector_id: 38003
content:
"The SROCC assessed with high confidence that surface mass balance \r (SMB), rather than discharge, has started to dominate the mass loss \r of the Greenland Ice Sheet (due to increased surface melting and \r runoff), increasing from 42% of the total mass loss for 2000-2005 \r to 68% for 2009-2012. While these estimates have been confirmed \r since SROCC (Mouginot et  al., 2019), the new longer record, as \r well as further comprehensive studies (Khan et  al., 2015; Colgan \r et  al., 2019; Mottram et  al., 2019; The IMBIE Team, 2020) and \r detailed discharge records (King et al., 2020; Mankoff et al., 2020)\r reveal a  more complex picture than the continuous trajectory this \r statement may have implied. Discharge was relatively constant from \r 1972-1999, varying by around 6% for the whole ice sheet, while SMB \r varied by a factor of over two interannually, leading to either mass \r gain or loss in a given year (Figure 9.16). During 2000-2005, the rate \r [...]"


id: e31a4280f7eb3779e8d9d545986b6cfb
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index: climate_gpt_v2
vector_id: 37252
content:
"[...] of discharge increased by 18%, then remained fairly constant again \r (increasing by 6% from 2006-2018). After 2000, SMB decreased "


id: c4812ac863cd282a20c936d2f9855122
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
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index: climate_gpt_v2
vector_id: 31877
content:
"of western Greenland, reducing meltwater retention capacity. \r Moreover, meltwater infiltration into firn can be strongly limited \r by low-permeability ice slabs created by refreezing of infiltrated \r meltwater (Machguth et al., 2016). Recent observations and modelling \r efforts indicate that rapidly expanding low-permeability layers have \r led to an increase in runoff area since 2001 (MacFerrin et al., 2019). \r In summary, there is medium confidence that meltwater storage and \r refreezing can temporarily buffer a  large-scale melt increase, but \r limiting factors have been identified.\r The SROCC reported that there was medium confidence that ocean \r temperatures near the grounding zone of tidewater glaciers are \r critically important to their calving rate, but there was low confidence \r in understanding their response to ocean forcing. The increase in ice \r discharge in the late 1990s and early 2000s (Mouginot et al., 2019; \r [...]"


id: 42d29ce8385fa09ebb402f140462f4c6
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
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vector_id: 8948
content:
"[...] King et  al., 2020; Mankoff et  al., 2020) has been associated with \r a period of widespread tidewater glacier retreat (Murray et al., 2015; \r Wood et al., 2021) and speed up (Moon et al., 2020). Since SROCC, \r new studies provide strong evidence for rapid submarine melting \r at tidewater glaciers (Sutherland et al., 2019; Wagner et al., 2019; \r Bunce et al., 2020; R.H. Jackson et al., 2020). Changes in submarine \r melting and subglacial meltwater discharge can trigger increased \r ice discharge by reducing the buttressing to ice flow and promoting \r calving (Benn et al., 2017; Todd et al., 2018; Ma and Bassis, 2019; \r Mercenier et  al., 2020); through undercutting (Rignot et  al., 2015; \r D.A.  Slater et  al., 2017; Wood et  al., 2018; Fried et  al., 2019) and \r frontal incision (Cowton et al., 2019). Warming ocean waters have \r been implicated in the recent thinning and breakup of floating ice \r tongues in north-eastern and north-western Greenland (Mouginot \r [...]"


id: d7265456158ec83dddb01231ca3b6ad
created_at: 2023-04-19 13:22:40
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index: climate_gpt_v2
vector_id: 35161
content:
"[...] et al., 2015; Wilson et al., 2017; Mayer et al., 2018; Washam et al., \r 2018; An et al., 2021; Wood et al., 2021). On decadal time scales, \r tidewater glacier terminus position correlates with submarine melting \r (Slater et al., 2019). Over shorter time scales, individual glaciers or \r clusters of glaciers can behave differently and asynchronously (Bunce \r et  al., 2018; Vijay et  al., 2019; An et  al., 2021), and there are not \r always clear associations between water temperature and glacier \r calving rates (Motyka et al., 2017), retreat or speed-up (Joughin et al., \r 2020; Solgaard et al., 2020). Variations in ice melange at the front \r of a glacier, associated with changes in ocean and air temperature, \r have also emerged as a plausible control on calving (Burton et al., \r 2018; Xie et  al., 2019; Joughin et  al., 2020). In summary, there is \r high confidence that warmer ocean waters and increased subglacial \r discharge of surface melt at the margins of marine-terminating \r [...]"


id: 1841bc25775a443d8fffae36788aff75
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
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index: climate_gpt_v2
vector_id: 1470
content:
"[...] glaciers increase submarine melt, which leads to increased ice \r discharge. There is medium confidence that this contributed to the \r increased rate of mass loss from Greenland, particularly in the period \r 2000-2010 when increased discharge was observed in the south-east \r and north-west."


id: 539537b85cb42d69cd376607e0d39106
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 11979
content:
"The SROCC did not assess the role of cloud changes in detail. Studies \r since AR5 have shown that higher incident shortwave radiation in \r conjunction with reduced cloud cover leads to increased melt rates, \r particularly over the low-albedo ablation zone in the southern \r part of the Greenland Ice Sheet (Hofer et al., 2017; Niwano et al., \r 2019; Ruan et  al., 2019). Conversely, an increase in cloud cover \r over the high-albedo central parts of the ice sheet, leading to \r higher downwelling longwave radiation, was shown to lead either \r to increased melt (Bennartz et  al., 2013) or reduced refreezing of \r meltwater (van Tricht et al., 2016). The elevation dependence of the \r cloud radiative effect and its control on surface meltwater generation \r and refreezing (W. Wang et al., 2019; Hahn et al., 2020) can induce \r a spatially consistent response of the integrated Greenland Ice Sheet \r melt to dominant patterns of cloud and atmospheric variability. \r [...]"


id: cc9568dec6bbe1db9ed03580209227d8
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
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content:
"[...] The shortwave and longwave radiation effects on surface melt by \r clouds have been shown to compensate for each other during strong \r atmospheric river events, and the increase in melt is caused by \r increased sensible heat fluxes during such events (Mattingly et al., \r 2020). In summary, there is medium confidence that cloud cover \r changes are an important driver of the increasing melt rates in the \r southern and western part of the Greenland Ice Sheet."


id: 5fe224f05be2712217319e400ee5e43d
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
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index: climate_gpt_v2
vector_id: 14178
content:
"The SROCC stated with high confidence that positive albedo feedbacks \r contributed substantially to the post-1990s Greenland Ice Sheet melt \r increase. Several (mostly positive) feedbacks involving surface albedo \r operate on ice sheets (e.g.,  Fyke et  al., 2018). Melt amplification \r by the observed increase of bare ice exposure through snowline \r migration to higher parts of the ice sheet since 2000 (Shimada et al., \r 2016; Ryan et al., 2019) was five times stronger than the effect of \r hydrological and biological processes that lead to reduced bare \r ice albedo (Ryan et al., 2019). Impurities, in part biologically active \r (Ryan et al., 2018), have been observed to lead to albedo reduction \r (Stibal et al., 2017) and are estimated to have increased runoff from \r bare ice in the southwestern sector of the Greenland Ice Sheet by \r about 10% (Cook et al., 2020). In summary, new studies confirm that \r there is high confidence that the Greenland Ice Sheet melt increase \r [...]"


id: 5168031c2cc3c5b489bda5aec647a3b5
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
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index: climate_gpt_v2
vector_id: 11599
content:
"[...] since about 2000 has been amplified by positive albedo feedbacks, \r with the expansion of bare ice extent being the dominant factor, and \r albedo in the bare ice zone being primarily controlled by distributed \r biologically active impurities (see also Section 7.3.4.3)."


id: 33cd66f8387f701615e1557257ad9ef5
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
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vector_id: 6356
content:
"energy balance models (Punge et  al., 2012; Cullather et  al., 2014; \r van  Kampenhout  et  al.,  2017,  2020; Alexander et  al., 2019) or use \r elevation classes to compensate for their coarser resolution (Lipscomb \r et al., 2013; Sellevold et al., 2019; Gregory et al., 2020; Muntjewerf \r et  al., 2020a, b). Resulting SMB simulations compare better with \r regional climate models and observations (Alexander et  al., 2019; \r van Kampenhout et  al., 2020), but the remaining shortcomings \r lead to problems reproducing a  present-day ice-sheet state close to \r observations. In summary, there is medium confidence in quantitative \r simulations of the present-day state of the Greenland Ice Sheet in ESMs.\r The SROCC (Meredith et al., 2019) stated that there is low confidence\r in understanding coastal glacier response to ocean forcing because \r submarine melt rates, calving rates, bed and fjord geometry and \r the roles of ice melange and subglacial discharge are poorly \r [...]"


id: c7a172bf31770fc9f890e0ca69fa0898
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
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vector_id: 32433
content:
"[...] understood. Ice-ocean interactions remain poorly understood and \r difficult to model, with parametrizations often used for calving of \r marine-terminating glaciers (Mercenier et al., 2018) and submarine \r and plume-driven melt (Beckmann et  al., 2019). Due to the \r difficulties of modelling the large number of marine-terminating \r glaciers and limited availability of high-resolution bedrock data, the \r majority of recent modelling work on Greenland outlet glaciers is \r focused on individual or a  limited number of glaciers (Krug et  al., \r 2014; Bondzio et al., 2016, 2017; Morlighem et al., 2016b; Muresan \r et al., 2016; Choi et al., 2017; Beckmann et al., 2019), or a specific \r region (Morlighem et al., 2019). Since SROCC, using a flowline model \r that includes calving and submarine melting, Beckmann et al. (2019) \r concluded that the AR5 upscaling of contributions from four of the \r largest glaciers (Nick et  al., 2013) overestimated the total glacier \r [...]"


id: f85f3733ddcebf1ec99cc8a26d63285f
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
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index: climate_gpt_v2
vector_id: 41063
content:
"[...] contribution from the Greenland Ice Sheet, due to differences in \r response between large and small glaciers. The regional study of \r Morlighem et al. (2019) confirms that ice-ocean interactions have \r the potential to trigger extensive glacier retreat over decadal time \r scales, as indicated by observations (Section  9.4.1.1). One focus \r of  continental ice-sheet models has been the improved treatment \r of marine-terminating glaciers via the inclusion of calving processes \r and freely moving calving fronts (Aschwanden et  al., 2019; Choi \r et al., 2021). An improved bedrock topographic dataset (Morlighem \r et al., 2017) allows for ice discharge to be better captured for outlet \r glaciers in continental ice-sheet models, and simulations indicate \r that bedrock topography controls the magnitude and rate of retreat \r (Aschwanden et al., 2019; Ruckamp et al., 2020). Overall, although \r there is high confidence that the dynamic response of Greenland \r [...]"


id: d5cb103bfa720e51d619ef06e1d28f09
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 34924
content:
"[...] outlet glaciers is controlled by bedrock topography, there is low \r confidence in quantification of future mass loss from Greenland \r triggered by warming ocean conditions, due to limitations in the \r current understanding of ice-ocean interactions, its implementation \r in ice-sheet models, and knowledge of bedrock topography."


id: 9232651ee662b60cf9974d5e335f1f32
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
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index: climate_gpt_v2
vector_id: 22940
content:
"The SROCC stated that climate model simulations of Greenland SMB \r had improved since AR5, giving medium confidence in the ability of \r climate models to simulate changes in Greenland SMB. Since SROCC, \r a multi-model intercomparison study (Fettweis et al., 2020) of regional \r and global climate models has shown that the greatest inter-model \r spread occurs in the ablation zone, due to deficiencies in an accurate \r model representation of the ablation zone extent and processes \r related to surface melt and runoff, confirming SROCC statement that \r there is large uncertainty in the bare ice model (Ryan et al., 2019). \r This intercomparison showed that simple, well-tuned SMB models \r using positive degree day melt schemes can perform as well as more \r complex physically based models (Figure Atlas 30). Furthermore, the \r ensemble mean of the models produced the best estimate of the \r present-day SMB relative to observations (particularly in the ablation \r [...]"


id: bd91e4287de015e84830eb040d2e3f04
created_at: 2023-04-19 13:22:40
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vector_id: 30611
content:
"[...] zone). Further assessment of Greenland Ice Sheet regional SMB can \r be found in Section Atlas.11.2.3. Recent progress confirms SROCC \r assessment that there is medium confidence in the ability of climate \r models to simulate changes in Greenland SMB."


id: a6cd1bd4c415764c3c8f3b34d9398716
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 26560
content:
"Since SROCC, projections of the Greenland Ice Sheet are also \r available from The Ice Sheet Model Intercomparison Project for \r CMIP6 (ISMIP6) (Box  9.3; Annex II; Figure  9.17; Nowicki et  al., \r 2016, 2020a). ISMIP6 multi-model projections are corrected with an \r assessment of the historical dynamical response to pre-2015 climate \r forcing (Box 9.3). For the period 2015-2100, the ISMIP6 uncorrected \r multi-model ensemble projects sea level contributions ranging from \r 0.01 to 0.05 m under RCP2.6, 0.04 to 0.14 m under RCP8.5 (Goelzer \r et  al., 2020), 0.02 to 0.06 m under SSP1-2.6, and 0.08 to 0.25 m \r under SSP5-8.5 (Table 9.2; Payne et al., 2021). The higher mass loss \r in the SSPs is attributed to a larger decrease in SMB due to the high \r climate sensitivity of the models used (Payne et al., 2021). This finding \r is confirmed by Choi et al. (2021), where CMIP6 SSP5-8.5 SMB leads \r to larger ice loss than CMIP5 RCP8.5, while ice discharge is similar. \r [...]"


id: 55a44bc8392d8a7f18596fd637ab8e4c
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 12340
content:
"[...] As the ISMIP6 framework considers a  subset of the RCPs/SSPs \r and CMIP models, SSP-based projections have been inferred from \r multiple approaches. First, the ISMIP6 CMIP5-forced (Goelzer et al., \r 2020) and CMIP6-forced (Payne et  al., 2021) combined ensemble \r projections were corrected with the historical trend (Box 9.3) using \r bootstrapping. Second, an emulator of the ISMIP6 projections \r (Box 9.3; Edwards et al., 2021) is forced by distributions of global \r surface air temperature for each SSP from a two-layer energy budget \r emulator (Supplementary Material 7.SM.2) and then corrected with \r the historical trend in the same way. These two approaches result \r in projections that are similar in their median values to AR5 and \r SROCC projections (Table 9.2), but differ in their range. Similar results \r are obtained when the AR5 parametric fit is applied to the ISMIP6 \r models (Table 9.2, Supplementary Material 9.SM.4.4), which is used \r [...]"


id: edfe3a8274b81ec768c18107254ea197
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 39245
content:
"The AR5 and SROCC projected that changes in Greenland SMB will \r contribute to sea level in 2100 by 0.03 (0.01 to 0.07) m sea level \r equivalent (SLE) under RCP2.6, and 0.07 (0.03 to 0.16) m SLE under \r RCP8.5. New since SROCC are the projections of SMB obtained by \r an ESM, two regional climate models, and reconstructions based on \r temperature from the CMIP5 and CMIP6 ensembles (Hofer et al., 2020; \r Noel et al., 2021). The range of sea level contribution from Greenland \r SMB in Noel et al. (2021) is comparable to the AR5 assessment when \r either CMIP5 or CMIP6 models are used, while Hofer et al. (2020) \r find a greater mass loss across all CMIP6 emissions scenarios when \r compared to CMIP5 scenarios. Using SSP5-8.5 instead of RCP8.5 \r increases the mean projected sea level from 2005-2100 by up to \r 0.06 m in the regional climate model simulations of Hofer et al. (2020) \r who attribute the difference mainly to a greater Arctic amplification \r [...]"


id: f6e4573cc848df3880d0612b89ee406b
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 40806
content:
"[...] and associated cloud and sea ice feedbacks in the CMIP6 SSP5-8.5 \r simulations. In summary, these new projections with fixed ice-sheet \r topography do not provide sufficient evidence to change the AR5 and \r SROCC assessments."


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vector_id: 22413
content:
"Reviewing modelling studies since AR5 (Church et al., 2013b), SROCC \r (Oppenheimer et  al., 2019) assessed Greenland's contribution to \r future sea level to be relatively similar to AR5 (Table 9.2). The baseline \r for projections has shifted from 1986-2005 in SROCC, to 1995-2014 \r in this Report. Adjusted to the new 1995-2014 baseline by subtracting \r 0.01 m, SROCC projected a likely contribution of 0.07 (0.0-0.11) m \r SLE under RCP2.6, and 0.14 (0.08-0.27) m SLE under RCP8.5 by 2100. \r Since SROCC, new projections for the 21st  century have included \r dynamic ice sheets coupled to ESMs (Muntjewerf et  al., 2020a; \r Van Breedam et al., 2020) or regional atmospheric models (Table 9.2; \r Le  clec'h et  al., 2019). The coupled ESM-ice-sheet model CESM2-\r CISM2 (Community Earth System Model Version 2 and Community Ice \r Sheet Model 2) projects a sea level rise of 0.109 m in 2100 relative \r to 2015 under SSP5-8.5 (Muntjewerf et  al., 2020a) and a  similar \r [...]"


id: b6d5dece46d863f56de6037d4a6ad645
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 29365
content:
"[...] contribution under the idealized 1% yr-1 increase in CO2 scenario \r (Muntjewerf et  al., 2020b). The CESM2-CISM2 simulations include \r ice-sheet-atmosphere interactions and ice-sheet surface meltwater \r routed to the ocean. The coupled regional atmospheric model and \r ice-sheet model MAR-GRISLI (Modele Atmospherique Regional and \r Grenoble ice sheet and land ice model) projects a sea level rise of \r 0.079 m in 2100 relative to 2000 under RCP8.5 (Le Clec'h et al., 2019). \r An  ESM of lower complexity coupled to an ice-sheet model gives \r a sea level contribution of 0.025 to 0.064 m under RCP2.6 and 0.056 \r to 0.12 m under RCP8.5 (the range is due to four simulations with \r different parameter sets for the atmosphere model) (Van  Breedam \r et  al., 2020). Van Breedam et  al. (2020) identify a  simulation with \r a  preferred parameter set that projects 0.034 m for RCP2.6 and "


id: 425ae04f8c80a6f77037f00decd45fc1
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 8872
content:
"The SROCC noted that the study by Aschwanden et al. (2019) projects \r a significantly higher Greenland contribution to sea level than the \r assessed likely range in AR5 and SROCC. Under RCP8.5, Aschwanden \r et al. (2019) found that Greenland could contribute up to 0.33 m to \r sea level by 2100 relative to 2000 (the ensemble member that best \r reproduces the 2000-2015 mean SMB from a regional climate model \r projects Greenland mass losses of 0.08 m SLE under RCP2.6 and \r 0.18 m SLE under RCP8.5). The SROCC noted that the potentially high \r sea level contribution in this study could be due to the assumption \r of spatially uniform warming, which can overestimate surface melt \r rates. However, it also reflects the deep uncertainty surrounding \r atmospheric forcing, surface processes, submarine melt, calving \r and ice dynamics. Goelzer et al. (2020) ascribe 40% of the ISMIP6 \r multi-model ensemble spread to ice-sheet model uncertainty, 40% to \r [...]"


id: cba7e47d995e1afef4c008455c4598a9
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 33154
content:
"[...] climate model uncertainty and 20% to ocean forcing uncertainty. \r We note that this finding reflects the current challenges associated \r with the representation of ice-ocean interactions in models, and \r the uncertainty in basal conditions (Section 9.4.1.2). However, this \r finding is consistent with the work of Aschwanden et  al. (2019) "


id: ae7ac5e0b012a7ba235de8e2403ff88
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 27932
content:
"In summary, it is virtually certain that the Greenland Ice Sheet will \r continue to lose mass this century under all emissions scenarios, \r and high confidence that total mass loss by 2100 will increase with \r cumulative emissions. The sea level assessment (Section  9.6.3.3) is \r based on the emulated ISMIP6 projections, allowing a more consistent \r approach to a wider range of climate and ocean forcings. The Greenland \r Ice Sheet is likely to contribute 0.06 (0.01 to 0.10) m under SSP1-2.6 and \r 0.13 (0.09 to 0.18) m under SSP5-8.5 by 2100 relative to 1995-2014. \r These projections (as well as those of AR5 and SROCC) are lower \r than the study of Aschwanden et al. (2019) or the range of possible \r sea level changes resulting from Structured Expert Judgement (SEJ; \r Section 9.6.3.2; Bamber et al., 2019), contributing to the deep uncertainty\r in projected sea level (Box 9.4). There is, however, high confidence that \r the loss from Greenland will become increasingly dominated by SMB \r [...]"


id: b19dbcbe4e97fc86e4bcbd58b1b1a028
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 28471
content:
"[...] and surface melt, as the ocean-forced dynamic response of glaciers will \r diminish as marine margins retreat to higher grounds."


id: 92b9c6c55a56b20c95a7aee3fff9a90e
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 23032
content:
"The SROCC stated that surface processes, rather than ice \r discharged into the ocean, will dominate Greenland ice loss \r over the 21st  century, regardless of the emissions scenario (high \r confidence). This is confirmed by the ISMIP6 projections (Goelzer \r et al., 2020; Payne et al., 2021). The projected mass loss of Greenland \r is predominantly due to increased surface meltwater and loss in \r refreezing capacity resulting in decreasing SMB (high confidence), \r concurrent with rising temperatures and darkening of the ice-sheet \r surface (Fettweis et al., 2013; Vizcaino et al., 2015; Le Clec'h et al., \r 2019; Muntjewerf et  al., 2020a, b; Sellevold and Vizcaino, 2020). \r Mass changes due to SMB and outlet glacier dynamics are linked \r (Goelzer et  al., 2013; Furst et  al., 2015; Ruckamp et  al., 2020), as \r mass loss by one process decreases mass loss by the other  - for \r example, SMB removes ice before it can reach the marine glacier \r [...]"


id: 2abd70d1182f8e46a7c8ca08353e5924
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 4747
content:
"[...] terminus. There is medium confidence that the mass loss through ice \r discharge will decrease in the future (Furst et al., 2015; Aschwanden \r et al., 2019; Golledge et al., 2019), because an increase in mass loss \r (via increased discharge or surface runoff) leads, in most areas, to \r a retreat of the glacier margin onto land above sea level, isolating the \r ice sheet from marine influence."


id: 86782e871b6d1a804a177400cc18c083
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 20824
content:
"2015; Calov et  al., 2018; Aschwanden et  al., 2019). Since SROCC, \r a new study gives a sea level contribution of 0.11 to 0.20 m in low\u0002emissions scenarios and 0.61 to 1.29 m in high-emissions scenarios \r (Van Breedam et  al., 2020). The low-emissions projections by Van \r Breedam et al. (2020) encompass AR5's assessed contribution, while \r the high emissions projections are higher than that from AR5. The \r 'optimal' ensemble member of Aschwanden et  al. (2019) (see also \r Section  9.4.1.3) indicates that Greenland could contribute 0.25  m \r under RCP2.6 and 1.74 m under RCP8.5. Structured expert judgement \r (Bamber et al., 2019) projects Greenland losses of 0.54 (0.28-1.28) m \r under 2degC warming and 0.97 (0.4-2.23) m under 5degC warming. These \r studies therefore agree that the AR5 and SROCC assessments are \r at the low end of the range of projections. In addition, observations \r suggest that Greenland Ice Sheet losses are tracking the upper range \r [...]"


id: b2aa5b5dfa59575988fbef32aa411ea9
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 28670
content:
"[...] of AR5 projections (T. Slater et al., 2020). Therefore, we update the likely\r range for the contribution of the Greenland Ice Sheet to global mean \r sea level (GMSL) by 2300 to 0.11-0.25 m under RCP2.6/SSP1-2.6 and \r 0.31-1.74 m under RCP8.5/SSP5-8.5. However, given the uncertainty in \r climatic drivers used to project ice-sheet change over the 21st century \r (Goelzer et  al., 2020; Hofer et  al., 2020; Noel et  al., 2021) and the \r large range in simulations since AR5 extending beyond 2100, we only \r have low confidence in the contribution to GMSL by 2300 and beyond. "


id: e584dae25ce8cd88ddc081fc82d458b2
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 37688
content:
"Accounting for more detailed feedbacks between the atmosphere \r and the ice sheet (Gregory et al., 2020) found a gradual relationship \r between sustained global mean warming and the corresponding near\u0002equilibrium ice-sheet volume, in contrast to a sharp threshold as found \r by Robinson et al. (2012). Rather than a climatically controlled tipping \r point for irreversible loss of the Greenland Ice Sheet, Gregory et  al. \r (2020) found a threshold of irreversibility linked to ice-sheet size, similar \r to previous work (Ridley et al., 2010). The results of Gregory et al. (2020) \r show that, if the ice sheet loses mass equivalent to about 3-3.5 m of \r sea level rise, it would not regrow to its present state, and 2 m of the sea \r level rise would be irreversible. The point in time at which the current \r ice sheet might reach this critical volume depends on oceanic and \r atmospheric conditions, ice dynamics, and climate-ice sheet feedbacks \r [...]"


id: 514d45af5203a1ab71fc9a239c0da522
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 11580
content:
"[...] (Gregory et al., 2020; Van Breedam et al., 2020). Therefore, projections \r differ in the magnitude and rate of temperature change to cross the \r threshold for irreversible loss. Projections from a  large ensemble \r indicate that the mass threshold may be reached in as early as 400 years \r under extended RCP8.5 if warming reaches 10degC or more above present \r levels (Aschwanden et al., 2019). In summary, there is high confidence\r in the existence of threshold behaviour of the Greenland Ice Sheet in \r a warmer climate; however, there is low agreement on the nature of the \r thresholds and the associated tipping points."


id: 3cde8dd0a38e784a4f233d4f0fc672e7
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 7892
content:
"The ISMIP6 initial state intercomparison projects (initMIP) for the Greenland (Goelzer et al., 2018) and Antarctic (Seroussi et al., \r 2019) ice sheets were designed to understand the uncertainty in sea level projections resulting from the choice of initialization \r procedures used for projections of sea level (Nowicki et al., 2016). Participating modelling groups (Annex II) were free to decide \r on the initialization method used to bring ice-sheet models to a  present-day state, with the effect of these choices captured in \r a control simulation (starting from the present-day state, with no further climate forcing applied), which measures intrinsic model drift. \r Compared to the earlier SeaRISE intercomparison project (Bindschadler et al., 2013; Nowicki et al., 2013), the modelled present-day \r ice sheets are in closer agreement with observations, and the model drift has been reduced (Goelzer et al., 2018; Seroussi et al., \r [...]"


id: c1ce4f83f25e1efc73e7e54b8e6e7b13
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 31399
content:
"[...] 2019). Nonetheless, historical simulations remain challenging for ice-sheet models, due to limited ice-sheet observations prior to the \r satellite era and biases in the historical atmospheric and oceanic forcings from climate models (Nowicki and Seroussi, 2018). ISMIP6 \r and LARMIP-2 therefore did not provide a protocol for the historical runs used to bring the ice sheets to present day, nor criteria for \r sub-selecting models from the multi-model ensemble based on the ability to reproduce historical changes (Levermann et al., 2020; \r Nowicki et al., 2020a)."


id: 7ce024ff753f371b2a23bf1eb12f79b3
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 19165
content:
"ISMIP6 projections for the Greenland and Antarctic ice sheets\r The ISMIP6 projection protocol (Nowicki et al., 2016, 2020a) was designed to sample the uncertainty in future sea level due to climate \r scenarios (via the use of high- and low-emissions scenarios and multiple climate models), ice-ocean interactions and inland response \r to ice-shelf collapse, and ice-sheet model diversity. The participating ice-sheet models are listed in Annex II. For each ice sheet, forcing \r was selected (Barthel et al., 2020) from the CMIP5 (Taylor et al., 2012) and CMIP6 (Eyring et al., 2016) models. Atmospheric forcing fields \r consisted of anomalies in SMB and surface air temperatures; these were generated directly from the CMIP models for the Antarctic \r Ice Sheet and downscaled using the regional climate model (MAR) for the Greenland Ice Sheet (Hofer et al., 2020). To sample the \r [...]"


id: ea77494cb8e45bede962f8cea9f52116
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 38614
content:
"[...] uncertainty due to ocean forcings, models used either a model-specific scheme with the ISMIP6-provided oceanic dataset or a standard \r ISMIP6 approach. For the Greenland Ice Sheet, the oceanic dataset consists of thermal forcing (temperature minus freezing temperature) \r extrapolated into fjords and subglacial runoff. The standard approach uses timelines of tidewater glacier retreat (D.A. Slater et al., 2019, \r 2020). For the Antarctic Ice Sheet, the oceanic dataset consists of salinity, thermal forcing and temperature added to an observationally \r derived climatology and extrapolated under ice shelves. The standard approach is a basal melt rate that depends quadratically on thermal \r forcing, adapted from Favier et al. (2019), with two different calibrations (Figure 9.19, Jourdain et al., 2020) that reproduce observed \r basal melt rates across Antarctica or Pine Island Glacier, respectively (Sections 9.4.2.2, 9.4.2.3). Antarctic ice-shelf disintegration datasets \r [...]"


id: ad664ff6d1036488cedf753f5a147a32
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 27744
content:
"[...] (Nowicki et al., 2020a) assume that ice shelves disintegrate when annual surface melt reaches a threshold (Trusel et al., 2015)."


id: ec7954e1e98d6a45de2d9b8aa4a45280
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 38964
content:
"The ISMIP6 projections (Goelzer et al., 2020; Seroussi et al., 2020; Payne et al., 2021) are reported as experiment minus control and \r represent the sea level resulting from future climate change only. The control simulation, which has constant climate conditions \r starting in 2015 from the historical run, captures drift associated with the choices made for the initialization method and historical \r run. Subtraction of this control removes any long-term dynamic response of the ice sheet to pre-2015 climate change. This response \r has been assessed using dynamic discharge derived from observations over the last 40 years (Mouginot et al., 2019; Rignot et al., \r 2019), under an assumption that it persists at the past rate until 2100, rather than diminishing. The dynamic response to historical \r forcing is estimated as 0.19 +- 0.10 mm yr-1 for the Greenland Ice Sheet (Section 9.4.1.3) and 0.33 +- 0.16 mm yr-1 for the Antarctic \r [...]"


id: adc2bdc995d08fe12850fe21acc38099
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 27813
content:
"[...] Ice Sheet (Section 9.4.2.5). Over the period 2015-2100, this leads to an additional sea level contribution of 1.7 cm for Greenland and \r 2.8 cm for Antarctica."


id: 19343b88619d66997bce4bf96b3022ad
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 1651
content:
"LARMIP-2 is focused on the uncertainty in the ocean forcing and associated ice-shelf melting (Levermann et al., 2014, 2020) with \r the majority of the models also participating in ISMIP6 (Annex II). The experiments start from present day and impose an additional \r basal ice-shelf melting of 8 m yr -1 at the beginning of the 100-year simulation. A control run is used to remove drift resulting from \r initialization. The time derivative of the ice-sheet response yields a linear response function, which is then convoluted with a forcing of \r basal shelf melt time series for five Antarctic regions. The forcing time series for RCP2.6, 4.5, 6.0 and 8.5 were obtained from a random \r combination of global mean temperature for each Representative Concentration Pathway (RCP) from MAGICC-6.0 (Meinshausen \r et  al., 2011), a  scaling factor and time delay for the relationship between global surface air temperature and subsurface ocean \r [...]"


id: 21165748cb5030f45f37d05b91ec2ef0
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 3041
content:
"[...] warming in a given sector of the Southern Ocean from one of 19 CMIP5 models (Taylor et al., 2012) and a basal melting sensitivity \r from the interval [7-16] m yr-1 degC-1 to convert the regional subsurface warming into basal ice-shelf melting. This process is repeated \r 20,000 times to obtain a probability distribution of the sea level contribution for five Antarctic sectors. The linear response framework \r captures complex temporal responses of the ice sheets resulting from an increase in basal ice-shelf melting, but neglects the response \r to SMB and any self-dampening or self-amplifying processes, such as marine ice shelf instability (MISI). The LARMIP-2 method is"


id: aa463f21ea3ef5bedb26c89b4c995af
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 27188
content:
"The ISMIP6 and GlacierMIP projections are primarily based on a limited number of CMIP5 RCPs and CMIP6 SSPs, and a limited sampling \r of ice-ocean interaction parameters and ice-shelf collapse simulations. Emulators provide a method for expanding these projections \r to a range of SSPs with more comprehensive sampling of climate, ice-sheet and glacier modelling uncertainties. Sections 9.4.1.3, \r 9.4.2.5 and 9.5.1.3 show estimates from the emulator of Edwards et al. (2021). This is a Gaussian Process, rather than a physically \r based (Cross-Chapter Box 7.1) model derived from the ISMIP6 and GlacierMIP simulations; projections use distributions of global \r surface air temperature (GSAT) from the two-layer emulator (Supplementary Material 7.SM.2) and ice-sheet parameters as inputs, \r and include estimates of the emulator uncertainty. Therefore, probability intervals are not inflated by a further factor, as is often the \r [...]"


id: e01a86d429991cc54db205f66c052893
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 36676
content:
"[...] case for multi-model ensemble projections, to account for missing uncertainties (Section 9.6.3.2). The emulator is used in Section 9.6.3 \r to provide projections of the land ice contribution to sea level that are fully consistent with each other, ocean heat content, and the \r assessed equilibrium climate sensitivity and projections of GSAT across the entire report."


id: 3385da36a61bbeb72cf28c93c0022f34
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 6315
content:
"The SROCC reported with very high confidence that the acceleration, \r retreat and thinning of the principal West Antarctic outlet glaciers \r has dominated the observed Antarctic mass loss over the last \r decades, and stated with high confidence that these losses were \r driven by melting of ice shelves by warm ocean waters. The average \r West Antarctic Ice Sheet (WAIS) mass loss of 82 +- 9 Gt yr-1 between \r 1992 and 2017 (The IMBIE Team, 2021) leads to substantial observed \r surface lowering (e.g., Schroder et al., 2019; Shepherd et al., 2019), \r particularly in coastal regions (Figure  9.18). Recent studies using \r satellite altimetry (Schroder et al., 2019) and the input-output method \r (Rignot et  al., 2019) consistently show mass loss in these coastal \r regions since the late 1970s (Figure  9.16). Because of consistent \r multiple lines of evidence, there is high confidence in mass loss of the \r Totten Glacier in East Antarctica (Miles et al., 2013; X. Li et al., 2016; \r [...]"


id: 54904cbb6252eb7f9caac7d012718762
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 12156
content:
"[...] Mohajerani et al., 2018; Rignot et al., 2019; Schroder et al., 2019; \r Shepherd et al., 2019) since about 2000, dominated by changes in \r coastal ice dynamics (X. Li et al., 2016). It is currently unclear whether \r mass loss of the EAIS over the last three decades has been significant \r (Rignot et al., 2019) or, at 5 +- 46 Gt yr-1 between 1992 and 2017, \r essentially zero within uncertainties (The IMBIE Team, 2018). \r In summary, WAIS losses, through acceleration, retreat and thinning \r of the principal outlet glaciers, dominated the AIS mass losses over \r the last decades (very high confidence) and there is high confidence"


id: 9815eba31eb0a9d25816852ce26b5046
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 23969
content:
"As stated in Section 2.3.2.4, satellite observations by Ice Sheet Mass \r Balance Intercomparison Exercise (IMBIE) combining multi-team \r estimates based on altimetry, gravity anomalies (GRACE) and the \r input-output method, already presented in SROCC (Meredith et al., \r 2019), are updated and extended to 2020 (The IMBIE Team, 2021). \r The Antarctic Ice Sheet (AIS) lost 2670 [1800 to 3540] Gt mass over \r the period 1992-2020, equivalent to 7.4 [5.0 to 9.8] mm GMSL rise \r (for contribution to sea level budget, see Figures 9.16 and 9.18, and \r Table 9.5). Within uncertainties, this estimate agrees with a review of \r post-AR5 studies up to 2016 (Bamber et al., 2018b) and is consistent \r with recent single studies based on satellite laser altimetry (Smith \r et  al., 2020), the input-output method (Rignot et  al., 2019) and \r gravimetry (Velicogna et al., 2020). The mass-loss rate was on average \r 49 [-2 to 100] Gt yr-1 over the period 1992-1999, 70 [22 to 119] \r [...]"


id: 625d915e1e6e93f4d2b6054939a2bcc
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 14587
content:
"[...] Gt yr-1 over the period 2000-2009, and 148 [94 to 202] Gt yr -1 over \r the period 2010-2016 (see Figures 9.16 and 9.18, and Table 9.SM.1). \r However, recent work suggests that the mass loss has not further \r increased since 2016 because of regional mass gains in Dronning \r Maud Land (Velicogna et al., 2020). Mass loss of the West Antarctic \r and Antarctic Peninsula ice sheets has increased since about 2000 \r (very high confidence), essentially due to increased ice discharge \r (Harig and Simons, 2015; Paolo et al., 2015; Forsberg et al., 2017; "


id: f3198429f2d26be131dad781393f8a
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 40153
content:
"As stated in SROCC, snowfall and glacier flow are the largest \r components determining AIS mass changes, with glacier flow \r acceleration (dynamic thinning) on the WAIS and the Antarctic \r Peninsula driving total loss trends in recent decades (very high \r confidence), and a partial offset of the dominating dynamic-thinning \r losses by increased snowfall (high confidence). The SROCC attributed \r medium confidence to estimates of 20th-century snowfall increases \r equivalent to a sea level change of -7.7 +- 4.0 mm on the EAIS, and \r -2.8 +- 1.7 mm on the WAIS, respectively (Medley and Thomas, 2019). \r Loss of buttressing, which can be caused by ice-shelf thinning, gradual \r ice-shelf front retreat or ice-shelf disintegration, has been linked to \r instantaneous ice velocity increases, and thus dynamic thinning, since \r the early 1990s. This link is clearly evident in the Amundsen and, to \r a lesser degree, Bellingshausen sectors (Gudmundsson et al., 2019), \r [...]"


id: 521a2765628d68f17438632066d2b538
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 11717
content:
"[...] where passive shelf ice (ice that can be removed without major \r effects on the ice-shelf dynamics) is very limited or absent (Furst \r et  al., 2016). Surface mass balance (SMB) changes, dominated by \r snowfall, exhibit strong regional and temporal variability, for example "


id: 3083f2d67697ac380c3adc57f300523e
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
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vector_id: 5790
content:
"Figure 9.18 | Antarctic Ice Sheet cumulative mass change and equivalent sea level contribution. (a) A p-box (Section 9.6.3.2) based estimate of the range of values of \r paleo Antarctic ice sheet mass and sea level equivalents relative to present day and the median over all central estimates (Bamber et al., 2009; Argus and Peltier, 2010; Dolan et al., \r 2011; Mackintosh et al., 2011; Golledge et al., 2012, 2013, 2014, 2015, 2017b; K.G. Miller et al., 2012; Whitehouse et al., 2012; Ivins et al., 2013; Argus et al., 2014; Briggs et al., \r 2014; Maris et al., 2014; de Boer et al., 2015, 2017; Dutton et al., 2015; Pollard et al., 2015; DeConto and Pollard, 2016; Gasson et al., 2016; Goelzer et al., 2016; Yan et al., 2016; \r Kopp et al., 2017; Simms et al., 2019); (b left) cumulative mass loss (and sea level equivalent) since 2015, with satellite observations shown from 1993 (Bamber et al., 2018a; The \r [...]"


id: 4151629097b7beebb269e6932f1dc9d1
created_at: 2023-04-19 13:22:40
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content:
"[...] IMBIE Team, 2018; WCRP Global Sea Level Budget Group, 2018) and observations from 1979 (Rignot et al., 2019), and projections from Ice Sheet Model Intercomparison Project \r for CMIP6 (ISMIP6) to 2100 under RCP8.5/SSP5-8.5 and RCP2.6/SSP1-2.6 scenarios (thin lines from Seroussi et al., 2020; Edwards et al., 2021; Payne et al., 2021) and ISMIP6 \r emulator under SSP5-8.5 and SSP1-2.6 to 2100 (shades and bold line; Edwards et al., 2021); (b, right) 17th-83rd, 5th-95th percentile ranges for ISMIP6, ISMIP6 emulator, and \r LARMIP-2 including surface mass balance (SMB) at 2100. (c-e) Schematic interpretations of individual reconstructions (Anderson et al., 2002; Bentley et al., 2014; de Boer et al., \r 2015; Goelzer et al., 2016) of the spatial extent of the Antarctic Ice Sheet are shown for the: (c) mid-Pliocene Warm Period, (d) Last Interglacial; and (e) Last Glacial Maximum \r [...]"


id: 9d688655f0050f0db6e846908eb0a80a
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 24930
content:
"[...] (Fretwell et al., 2013): grey shading shows extent of grounded ice. (f-g) Maps of mean elevation changes (f) 1978-2017 derived from multi-mission satellite altimetry (Schroder \r et al., 2019) and (g) ISMIP6: 2061-2100 projected changes for an ensemble using the Norwegian Climate Center's Earth System Model (NorESM1-M) climate model under the \r RCP8.5 scenario (Seroussi et al., 2020). Further details on data sources and processing are available in the chapter data table (Table 9.SM.9)."


id: 5be2753532fa2d9c8bbd4dbb688e651f
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 13425
content:
"in  this context. Antarctic ice-shelf basal meltwater flux varied \r between about 1100  +- 150 Gt yr-1 in the mid-1990s and about \r 1570  +- 140  Gt  yr-1 in the late 2000s before decreasing to \r 1160 +- 150 Gt yr-1 in 2018, and basal melt rates strongly vary with \r geographical position and depth, as a function of the surrounding \r water temperature (Adusumilli et al., 2020). Section 9.2.2.3 assesses \r that the intrusion of warm Circumpolar Deep Water (CDW), which \r has warmed and shoaled since the 1980s, has been at least partially \r controlled by forcing with significant decadal variability. Limited \r evidence suggests that, beyond strong internal decadal wind \r variability, increased greenhouse gas forcing has slightly modified the \r mean local winds between 1920 and 2018, facilitating the intrusion \r of CDW heat on the Amundsen-Bellingshausen continental shelf, \r and increased ice shelf melt (Section 9.2.2.3). However, theoretical \r [...]"


id: 50b55eb1410b3aeb7136ec5356ed170c
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
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index: climate_gpt_v2
vector_id: 11473
content:
"[...] understanding is still incomplete and in situ measurements within \r the ice-ocean boundary layer are sparse (Wahlin et  al., 2020). \r Modelling, and therefore attribution of ice shelf basal melt, remains \r challenging because of insufficient process understanding, required \r spatial resolution, the paucity of in situ observations (Dinniman et al., \r 2016; Asay-Davis et al., 2017; Turner et al., 2017), and uncertainties \r of bathymetric datasets under ice-shelf cavities (Goldberg et  al., \r 2019, 2020; Morlighem et al., 2020). In summary, ice-shelf thinning, \r mainly driven by basal melt, is widespread around the Antarctic coast \r and particularly strong around the WAIS (high confidence), although \r basal melt rates show substantial spatio-temporal variability."


id: ec29e8b4c15db4180b586db8b22c6745
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 38922
content:
"Satellite observations suggest that changes in sea ice coverage and \r thickness can modulate iceberg calving, ice shelf flow and glacier \r terminus position around Antarctica (Miles et al., 2013, 2016, 2017; \r Massom et al., 2015; Greene et al., 2018; Bevan et al., 2019), either \r through mechanical coupling or via changes to ocean stratification, \r influencing basal melting. A combined observational and modelling \r study (Massom et al., 2018) showed that regional loss of a protective \r sea ice buffer played a role in the rapid disintegration events of the \r Larsen A  and B and Wilkins ice shelves in the Antarctic Peninsula \r between 1995 and 2009, by exposing damaged (rifted) outer ice shelf \r margins to enhanced flexure by storm-generated ocean swells. One \r observational study (Sun et al., 2019) suggests that the absence of sea \r ice in front of ice shelves, which leads to strengthened topographic \r waves, favours higher ice-shelf basal melt rates by increasing the \r [...]"


id: bcab6cf09f8a4811116c3a90bc45fce
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 30441
content:
"[...] baroclinic (depth varying) ocean heat flux which can enter the cavity \r (Wahlin et al., 2020). Paleo evidence for sea ice control on ice sheets \r is lacking, but geologic evidence shows a  concordance between \r periods of ice-sheet growth and the expansion of sea ice (Patterson \r et al., 2014; Levy et al., 2019), both being favoured by reduced sea \r surface temperatures. Modelling confirms that sea ice controls the \r strength of ice melange (Robel, 2017; Schlemm and Levermann, 2021) \r and thus influences ice-shelf flexure and calving rates and stability of \r floating ice margins, but one model shows this had negligible effect \r on AIS retreat rates during past warm periods (Pollard et al., 2018). \r Loss of ice-shelf-proximal sea ice is also associated with increased \r solar heating of surface waters and increased sub-shelf melting \r (Bendtsen et al., 2017; Stewart et al., 2019). In summary, although \r in some cases sea ice decrease and glacier and ice-shelf flow and \r [...]"


id: 3813950b813500e2fed9844f75bce64e
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 7067
content:
"[...] terminus position changes can have the same common cause, there \r is medium confidence that sea ice decrease ultimately favours the \r mass loss of nearby ice shelves through a variety of processes."


id: 4a61266ebde60e0d46c077299f4191
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 10325
content:
"The SROCC stated with medium confidence that sustained mass \r losses of several major glaciers in the Amundsen Sea Embayment \r (ASE) are compatible with the onset of marine ice sheet instability \r (MISI). However, whether unstable WAIS retreat had begun, or was \r imminent, remained a  critical uncertainty. New publications since \r SROCC have not substantially clarified this question. One study \r that combined satellite measurements with a numerical model and \r prescribed ice-shelf thinning (Gudmundsson et  al., 2019) suggests \r that MISI is not required to explain the observed current mass \r loss rates of the WAIS, because they are consistent with external \r climate drivers. Furthermore, the fast grounding line retreat of the \r Pine Island Glacier in the ASE, which was triggered in the 1940s \r (Smith et al., 2017), observed after 1992 (Rignot et al., 2014) and \r previously interpreted as a sign of MISI (Favier et al., 2014), seems to \r [...]"


id: 9e0b1416989f8d4b2dd72b67bcde192b
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 25043
content:
"[...] have stabilized recently (Milillo et al., 2017; Konrad et al., 2018), and \r its current flow patterns do not suggest ongoing or imminent MISI \r (Bamber and Dawson, 2020). However, sustained fast grounding line \r retreat has been observed for the Smith Glacier in the ASE (Scheuchl \r et  al., 2016), and an analysis of flow patterns and grounding line \r retreat of the ASE Thwaites Glacier between 1992 and 2017 (Milillo \r et  al., 2019) showed sustained, albeit spatially heterogeneous, \r grounding line retreat, highlighting ice-ocean interactions that \r lead to increased basal melt. In addition, Denman Glacier in East \r Antarctica was shown to hold potential for unstable retreat (Brancato \r et al., 2020). In summary, the observed evolution of the ASE glaciers \r is compatible with, but not unequivocally indicating an ongoing MISI \r (medium confidence)."


id: 3b240756a06fefa72edc319e019dcd9d
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 7592
content:
"the advantage of connecting melt rates to emissions scenarios, \r a large variety of melt parametrizations exist (DeConto and Pollard, \r 2016; Lazeroms et al., 2018; Reese et al., 2018; Hoffman et al., 2019; \r Pelle et al., 2019; Jourdain et al., 2020), and there is low agreement\r due to limited observational constraints (ocean temperature, salinity, \r velocity, and ice shelf draft)(Jourdain et al., 2020), uncertainty in the \r physics of parametrized processes, missing processes (e.g.,  tides), \r and uncertainty in the treatment of ice-sheet-climate feedbacks \r (Donat-Magnin et al., 2017; Bronselaer et al., 2018; Golledge et al., \r 2019). Parametrizations are usually calibrated to present-day melt \r rates, but can respond differently to projected ocean warming (Favier \r et  al., 2019; Jourdain et  al., 2020). Two different calibrations were \r used in ISMIP6 (Box 9.3; Jourdain et al., 2020; Nowicki et al., 2020b): \r one reproducing melt rates averaged around the whole continent \r [...]"


id: f359276e5476a114d0317b27c50ee85
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 40190
content:
"[...] (MeanAnt: Figure 9.19), and the other reproducing melt rates near the \r grounding line of Pine Island Glacier (PIGL; see Figure 9.19), leading to \r large differences in melt rates. Evaluation with observations and two \r cavity-resolving models suggests that the MeanAnt parametrization \r better reproduces observed melt rates and projected increases in both \r the warm Amundsen Sea Embayment and cold Ronne-Filchner shelf \r cavity, as well as total Antarctic melting (Jourdain et al., 2020). The \r PIGL calibration represents the upper end for increased basal melt \r sensitivity that would be caused by continent-wide changes to ocean \r water properties and circulation under strong future forcing (Jourdain \r et  al., 2020). The basal sliding law also has a  strong influence \r on grounding line retreat and glacier acceleration in response to \r perturbations, and varies spatially (Sun et  al., 2020). Sliding laws \r (Joughin et al., 2019) can only be constrained with observations in \r [...]"


id: 2040386fb1c974afb8c9ee7c4f46ac0e
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
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index: climate_gpt_v2
vector_id: 2891
content:
"[...] regions experiencing significant change, and with sufficiently long \r observational records."


id: 16d4daad450f169d26bae33802436ba6
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 1232
content:
"Section 9.2.3.2 assesses that there is low confidence in simulations \r of Southern Ocean temperature. Few ocean models resolve ice-shelf \r cavities, and biases in present-day melt rates can be substantial in \r some sectors, including the key region of the Amundsen Sea (e.g., an \r exception is the FESOM simulation in Figure  9.19 includes ice\u0002shelf cavities and simulates ice-shelf basal melting and refreezing) \r (Naughten et  al., 2018). An  increasing number of observational \r studies from which basal melt rates are calculated (Huhn et al., 2018; \r Adusumilli et al., 2020; Das et al., 2020; Hirano et al., 2020; Stevens \r et al., 2020), combined with improved understanding of influences \r specific to water-masses and modes of melting or dissolving (Silvano \r et al., 2018; Adusumilli et al., 2020; Malyarenko et al., 2020; Wahlin \r et al., 2020), may help to refine these models in the future. However, \r given the limited number of available models and their biases, there \r [...]"


id: 849b42fa783153e74b5fffd219b70d02
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 20476
content:
"The SROCC noted that AIS simulations are increasingly evaluated \r or formally calibrated with modern observations and/or paleodata - \r to obtain more realistic initial conditions (ice-sheet geometry, \r velocity and forcing) and to constrain uncertainty in probabilistic \r projections. This trend continues (Nias et  al., 2019; Gilford et  al., \r 2020; Hamlington et  al., 2020b; Wernecke et  al., 2020). However, \r while the large-scale characteristics of the initial ice-sheet state \r have improved significantly (Box  9.3), capturing the smaller-scale \r rates of change, including mass trends, remains challenging for \r many models (Goldberg et  al., 2015; Reese et  al., 2020; Seroussi \r et  al., 2020; Siegert et  al., 2020). This  increases uncertainty in \r projections, especially for the 21st  century (Section  9.4.2.5). \r However, uncertainties in ice-sheet model simulations have been \r much better quantified since AR5, through model intercomparison \r [...]"


id: 9657c29bc6bd5932422b76fc65883da0
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 23650
content:
"[...] projects (in  particular, ISMIP6 and LARMIP-2; see Box  9.3), \r perturbed parameter ensembles, and increasing use of statistical \r emulation (Gilford et  al., 2020; Levermann et  al., 2020; Wernecke \r et al., 2020; DeConto et al., 2021; Edwards et al., 2021) to better \r sample the parameter space. By exploring uncertainties more fully, \r these methods have the potential to identify better simulations of \r the historical period."


id: f3aca482012ed09835c7e09c13d2b2ce
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
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index: climate_gpt_v2
vector_id: 40243
content:
"Improvements in the representation of grounding line evolution in \r ice-sheet models since AR5 (such as sub-grid schemes for basal friction \r and ice-shelf melt, and local grid refinement) means that most of the \r model simulations presented in SROCC were dominated by physical \r processes. Since then, these advances have been applied in several \r model intercomparison projects  - such as ISMIP6 and LARMIP-2 \r (see Box 9.3); MISMIP+ (Cornford et al. 2020); and ABUMIP (Sun et al. \r 2020). All models participating in ISMIP6 and LARMIP-2 simulate \r ice-shelf and grounding-line evolution, and include sub-shelf melt \r parametrization, which was not the case in the Sea-level Response to \r Ice Sheet Evolution (SeaRISE) project intercomparison (Bindschadler \r et  al., 2013; Nowicki et  al., 2013). Simulations of grounding line \r evolution (Seroussi et al., 2017, 2020) have benefitted from improved \r bedrock topography (Morlighem et al., 2020). Treatment of sub-shelf \r [...]"


id: d95803276330ea641c288a2157e848fa
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
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index: climate_gpt_v2
vector_id: 35539
content:
"[...] melting, however, remains one of the causes of large differences in \r AIS models, particularly for partially floating grid cells in models with \r coarse resolution (Levermann et al., 2020; Edwards et al., 2021). Due \r to the limitations in resolving cavities in ocean models, as described \r above, basal melt rates are generally parameterized at the ice shelf \r base, based on ocean model simulations of temperatures and salinity \r instead (Nowicki et al., 2020b; Seroussi et al., 2020). While this has "


id: abb0bba6d3eca3063124a51e3a8a6470
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
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index: climate_gpt_v2
vector_id: 27454
content:
"The AR5 projected a  negative contribution from Antarctic surface \r mass balance (SMB) changes to sea level over the 21st  century \r (i.e., mitigating sea level rise), due to increased snowfall associated \r with warmer air temperatures. Sensitivity of SMB to Antarctic \r surface air temperature change varied from 3.7 to 7% degC-1, and \r the sea level projections assumed a sensitivity of 5.1 +- 1.5% degC-1\r from CMIP3 era models (Gregory and Huybrechts, 2006) to estimate \r SMB changes from Antarctic temperatures in the CMIP5 ensemble. \r Since the AR5, analyses of CMIP5 and CMIP6 models have found \r Antarctic temperature sensitivity for accumulation (precipitation \r minus sublimation) of 3.5 to 8.7% degC-1 (Frieler et al., 2015), for SMB \r of 6.0 to 9.9% degC-1 (Previdi and Polvani, 2016) and for precipitation of \r around 4 to 9% degC-1 (+-1 standard deviation ranges; Bracegirdle et al., \r 2020). An accumulation sensitivity estimate derived from ice core \r [...]"


id: 95d5b69a16a6c200ced13222b7bd4147
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
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index: climate_gpt_v2
vector_id: 23559
content:
"[...] data lies in the middle of the range, around 6% degC-1 (Frieler et al., \r 2015). These are consistent, within uncertainties, with each other and \r AR5, under the approximation that SMB is dominated by snowfall."


id: c14109bfb8f9a16e7d915872320cdd42
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 31301
content:
"The AR5 found that the median and likely sea level contributions \r due to SMB from 1986-2005 to 2100 were -0.05 (-0.09 to \r -0.02) m under RCP8.5 and -0.02 (-0.05 to 0.00) m under RCP2.6. \r The SROCC did not present a  separate SMB contribution, instead \r showing total Antarctic projections derived from ice-sheet models \r (Section 9.4.2.5). Projections of the SMB contribution to sea level \r tend to be slightly more negative since AR5, due at least in part to \r the higher range in equilibrium climate sensitivity values in CMIP6 \r (Payne et  al., 2021). Mean and +-1 standard deviation ranges for \r grounded Antarctic Ice Sheet SMB changes from 2000 to 2100 \r computed from CMIP5 models are -0.08 (-0.13 to -0.04) m sea \r level equivalent (SLE) for RCP8.5 and, similarly for CMIP6 models, \r are -0.07 (-0.11 to -0.03) m for SSP5-8.5 (Gorte et al., 2020). The \r general circulation models (GCMs) used to drive ice-sheet models \r in ISMIP6 (Box 9.3) project mean grounded AIS SMB changes from \r [...]"


id: 2ae9405d8222c2f74e90f58e9ef0ba41
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 4784
content:
"[...] 2005 to 2100 of -0.06 (range -0.08 to -0.03) m SLE under RCP8.5 for \r the six CMIP5 models (Seroussi et al., 2020) and -0.09 (range -0.10 \r to -0.07) m SLE under SSP5-8.5 for the four CMIP6 models, which \r have climate sensitivity values of 4.8degC -5.3degC (Payne et al., 2021). \r We apply the AR5 parametric AIS SMB model (Section 9.6.3.2) to \r updated projections of global mean temperature from a two-layer \r energy budget emulator (Supplementary Material  7.SM.2), which \r gives a  median -0.05 (5-95% range -0.07 to -0.02) m SLE for \r SSP5-8.5 (Section  9.4.2.5, Table  9.3), that is, similar to the AR5 \r assessment and slightly smaller than the CMIP6 estimate. This \r estimate is used to augment the LARMIP-2 dynamic projections \r (Box 9.3) in Sections 9.4.2.5 and 9.4.2.6. Overall, CMIP5 and CMIP6 \r GCM simulations of sea level fall by 2100 due to Antarctic SMB \r increases are around 2-4 cm greater than estimates derived with \r [...]"


id: dcd55cee626dabf4c37dcc4893926237
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 36113
content:
"[...] the statistical method used in AR5. Further details about projections \r of Antarctic temperature, precipitation and SMB are provided in \r Section Atlas.11.1.4, which assesses that, due to the challenges of \r model evaluation (Section 9.4.2.2) and the possibility of increased \r meltwater runoff (Kittel et  al., 2021), there is only medium \r confidence that the future contribution of Antarctic SMB to sea level \r this century will be negative under all greenhouse gas emissions \r scenarios. Longer time scales are discussed in 9.4.2.6."


id: be28a792a6c88272624779fe7f9cbdb1
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 30722
content:
"The response of sub-shelf melting to ocean warming is also poorly \r constrained. A  key unknown is whether, and when, cold ice-shelf \r cavities might become more similar to the Amundsen Sea Embayment, \r not only in ocean temperature but also ice-ocean  heat exchange, \r which depends on the cavity geometry and ocean circulation \r (Little et al., 2009). Only two ocean models with ice-shelf cavities have \r been used to make sub-shelf basal melting projections for Special \r Report on Emissions Scenarios and Representative Concentration \r Pathway (RCP) scenarios (Hellmer et  al., 2012; Timmermann and \r Hellmer, 2013; Timmermann and Goeller, 2017; Naughten et  al., \r 2018). The FESOM simulation, forced by a CMIP5 multi-model mean \r under RCP8.5, projects a  90% increase in melting (Figure  9.19), \r although this could be overestimated due to an underestimation \r of present-day melt  rates (Section  9.4.2.2; Naughten et  al., 2018). \r [...]"


id: fe9d38329881c3a9ddc2593089c0052d
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 42175
content:
"[...] The temperature-melt relationship was parameterized by ISMIP6 in \r terms of heat exchange velocity in m a-1, and by LARMIP-2 as basal \r melt sensitivity in m a-1 degC-1 (Box 9.3; Jourdain et al., 2020; Levermann \r et  al., 2020; Reese et  al., 2020), and both vary widely around the \r continent, depending on cavity type. Median values of ISMIP6 heat \r exchange velocity vary by a  factor of 5-10 when calibrating to \r either mean Antarctic or high Pine Island Glacier observed melt rates \r (Section 9.4.2.2; Box 9.3; Jourdain et al., 2020). Basal melt sensitivities \r near the grounding line estimated by Reese et al. (2020) with a box \r model of ocean overturning range from 3.9 m a-1 degC-1 for the Weddell \r Sea to 10.5 m a-1 degC-1 for the Amundsen Sea region, with a continental \r mean of 5.3 m a-1 degC-1. Similarly high Amundsen Sea sensitivities \r are estimated in coupled ice-ocean simulations of Thwaites Glacier \r [...]"


id: a0cfe48a5924b932e93feafd49160b9e
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 25507
content:
"[...] (mean 9.4 m a-1 degC-1; range 6-16 m a-1 degC-1) (Seroussi et al., 2017). \r These large variations lead to large differences in basal melt rates and \r projected sea level contributions when applied to the whole ice sheet \r in ISMIP6 and LARMIP-2 (Box 9.3). Projections of melt rates from the \r two ISMIP6 calibrations are higher than those from FESOM, driven \r by a  CMIP5 multi-model mean (Figure  9.19; Jourdain et  al., 2020). \r The ISMIP6 ensemble mostly uses the mean Antarctic calibration, but \r includes some simulations with the Pine Island Glacier calibration, \r and the ISMIP6 emulator samples more of these higher values; \r LARMIP-2 uses basal melt sensitivities (7-16 m a-1 degC-1) consistent \r with estimates for the Amundsen Sea Embayment. Due to the limited \r availability of cavity-resolving ocean models, and the wide regional \r variation in estimates of basal melt sensitivity to ocean temperature, \r there is only low confidence in projected future sub-ice-shelf melt \r [...]"


id: 31998bcdc6cbcb22acc34e2a330abc1e
created_at: 2023-04-19 13:22:40
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vector_id: 5974
content:
"[...] rates. The impact of this uncertainty on AIS model projections to 2100 \r is discussed in Section 9.4.2.5."


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content:
"The SROCC highlighted that an important ongoing deficiency in \r projections of Antarctic sub-shelf melting is the lack of ice-ocean \r coupling in most continental-scale studies. Increased basal melting is \r mainly caused by warmer CDW (Section 9.2.2.3) on the continental \r shelves, and warming surface waters intruding under ice shelves \r (Naughten et al., 2018). Predicting whether or not open ocean water \r masses will freely penetrate ice shelf cavities, or will be partially \r blocked by ocean density gradients, is complex (Wahlin et al., 2020); \r while melting related to CDW inflow is currently dominant in the \r Amundsen Sea Embayment, melt in other embayments is limited \r by deep inflows of high-salinity shelf water or seasonally warmed \r shallow incursions of Antarctic Surface Water (Stewart et al., 2019; \r Adusumilli et  al., 2020). There is little consensus regarding future \r change in CDW (Section 9.2.2.3), and more generally low confidence\r [...]"


id: 174aab9b74f76229897118295e44865e
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
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index: climate_gpt_v2
vector_id: 1321
content:
"Since SROCC, further studies have highlighted the modelling \r uncertainties in this area. Coastal surface air temperature projections \r in CMIP6 models show large inter-model differences driven by \r sea ice retreat and exhibit more warming relative to global mean \r temperature under low emissions than high, due to delayed response \r of the Southern Ocean to stabilized emissions and stratospheric ozone \r recovery (Bracegirdle et  al., 2020). The updated study of DeConto \r et  al. (2021) includes improvements to the climate simulations \r relative to those in DeConto and Pollard (2016), and the resulting \r surface meltwater projections are now consistent with Trusel et al. \r (2015). However, the net effect of meltwater feedbacks on ice shelves \r is uncertain. Ice discharge is expected to lead to surface ocean and \r atmosphere cooling: this increases ocean stratification and sub-shelf \r melting, but also reduces ice-shelf surface melting and delays \r [...]"


id: cf234a36538754404b652c1152538085
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
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index: climate_gpt_v2
vector_id: 33789
content:
"[...] hydrofracturing (Golledge et al., 2019; Sadai et al., 2020; DeConto \r et al., 2021). The new studies are insufficient to change SROCC's low \r confidence assessment on ice-shelf loss. The consequence of this \r uncertainty on projections is discussed in Section 9.4.2.5 and Box 9.4."


id: 89a311876389d2f6a5d2fefbc3ebb0fd
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
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index: climate_gpt_v2
vector_id: 21390
content:
"The SROCC noted limited evidence from geological records and \r ice-sheet modelling, suggesting that parts of the AIS experienced \r rapid (centennial) retreat likely due to MISI between 20,000 and \r 9,000 years ago, and also described more uncertain evidence for the \r Last Interglacial (LIG) and mid-Pliocene Warm Period (MPWP). Recent \r support for past MISI is provided by model simulations of the WAIS \r during the LIG (Clark et al., 2020), the British Ice Sheet during the last \r termination (Gandy et al., 2018) and the Laurentide Ice Sheet during \r the Younger Dryas (Pico et al., 2019), which show progressive retreat \r despite declining temperatures, indicative of a  true (ice  dynamic) \r instability. Direct observational evidence of rapid paleo ice-sheet \r grounding line retreat is rare but, on the Larsen continental shelf, \r retreat rates of >10 km yr-1 during the deglaciation have been \r estimated (Dowdeswell et al., 2020). MISI has also been inferred from \r [...]"


id: 9114a0ab71a8fdda093077b141805206
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
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index: climate_gpt_v2
vector_id: 22727
content:
"[...] sedimentological evidence of ice loss from Wilkes Subglacial Basin, \r East Antarctica (Bertram et al., 2018; Wilson et al., 2018; Blackburn \r et al., 2020) but these reconstructions cannot unambiguously identify \r unstable from progressive retreat. Therefore, there is limited evidence\r to identify the operation of instability mechanisms such as MISI \r in paleo ice-sheet retreat."


id: 193b9430ba8d38f61d194ac872b0381f
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 1655
content:
"MISI is a  proposed self-reinforcing mechanism within marine ice \r sheets that lie on a  bed that slopes down towards the interior of \r the ice sheet, whereby, in the absence of ice-shelf buttressing, \r the position of the grounding line is inherently unstable until \r reaching an upward sloping bed. The SROCC (Meredith et al., 2019) \r noted advances in modelling MISI since AR5, but that 'significant \r discrepancies' remained in projections due to poor understanding \r of mechanisms, and lack of observational data to constrain the \r models. Since SROCC, modelling uncertainties have been more \r thoroughly explored, rather than constrained (compatibility of \r current observations in the Amundsen Sea Embayment with MISI is \r assessed in Section 9.4.2.1). Internal climate variability might either \r slow (Hoffman et al., 2019) or amplify (Robel et al., 2019) MISI, and \r stable grounding line positions can be reached on downward sloping \r [...]"


id: 9cdb8f01fdd1fb1c33a5c96d692ec2de
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
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vector_id: 24831
content:
"[...] beds if ice shelves provide buttressing (Sergienko and Wingham, \r 2019; Cornford et al., 2020). Ice-sheet model simulations that remove \r all Antarctic ice shelves (and prevent them from reforming) show \r 2-10 m SLE Antarctic mass loss after 500 years due to MISI, of which \r WAIS collapse contributes 2-5 m (Sun et al., 2020), with the majority \r of the mass loss in the first one to two centuries. Much of the multi\u0002model variation is due to the sliding law (Section 9.4.2.2). However, \r it is not expected that widespread ice-shelf loss will occur before the \r end of the 21st century (Section 9.4.2.3; Box 9.4). A recent update \r of bed topography that unveiled large and overdeepened subglacial "


id: 940942f0bc4ccc1d8a9eed834a473700
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 23249
content:
"occurrence in the present or the past (Section 9.4.2.2), its potential \r to affect future sea level rise was very uncertain. Since SROCC, new \r simulations show later ice-shelf disintegration, in agreement with \r other models (Section 9.4.2.3; DeConto et al., 2021), and therefore \r lower projections at 2100 (Section  9.4.2.5). New theoretical \r evidence suggests that ice-cliff collapse may only occur after very \r rapid ice shelf disintegration caused by unusually high meltwater \r production (Clerc et al., 2019; Robel and Banwell, 2019), and that \r the subsequent rate of retreat depends on the terminus geometry \r (Bassis and Ultee, 2019). As SROCC noted, only Crane Glacier on \r the Peninsula has shown retreat consistent with MICI, after the \r Larsen B ice shelf collapsed, and MICI-style behaviour at Jakobshavn \r and Helheim Glaciers in Greenland might not be representative of \r wider Antarctic glaciers. Observations from Greenland show that \r [...]"


id: 3a5de99355f968992ad49d0b937683d9
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
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index: climate_gpt_v2
vector_id: 7472
content:
"[...] steep cliffs commonly evolve into short floating extensions, rather \r than collapsing catastrophically (Joughin et al., 2020). As assessed \r in Section  9.4.2.2 and 9.4.2.3, there is therefore low confidence\r in simulating mechanisms that have the potential to cause \r widespread, sustained and very rapid ice loss from Antarctica this \r century through MICI, and low confidence in projecting the driver of \r ice-shelf disintegration."


id: 8f0f4bab99925ee2d90d3d17a356c4a
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
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index: climate_gpt_v2
vector_id: 22369
content:
"Since SROCC, new projections have arisen from multi-model \r intercomparison projects ISMIP6 and LARMIP-2 (Box 9.3) and one \r model that includes MICI (Section 9.4.2.4; Table 9.3; DeConto et al., \r 2021). Corrections are added to allow comparison: all ISMIP6-derived \r projections have an estimate of the historical dynamical response \r to pre-2015 climate forcing added, which increases contributions \r (Box  9.3; Figure  9.18); the LARMIP-2 dynamic projections are \r combined with an estimate of SMB, which decreases contributions \r (Sections 9.4.2.3 and 9.6.3.2); and the ISMIP6 emulated and LARMIP-2 \r projections were re-estimate using the global surface air temperature \r distributions from the two-layer energy budget emulator described in \r Supplementary Material 7.SM.2. The majority of the new projections \r indicate that, under all emissions scenarios, the AIS will lose mass \r overall and contribute to sea level rise. Most thinning occurs in the \r [...]"


id: d16a0d975caff96de69bc3a07fd74eed
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
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vector_id: 34197
content:
"[...] Amundsen Sea sector in WAIS and Totten Glacier in EAIS (Figure 9.18). \r The most negative contribution is -0.02 m (5th percentile of ISMIP6 \r combined RCP8.5 and SSP5-8.5 projections after correction) and \r the largest contribution is 0.57 m SLE (95th percentile; Levermann \r et al., 2020), or 0.63 m SLE with MICI (95th percentile; DeConto et al., \r 2021). ISMIP6 ensemble ranges are wider for the high scenarios \r (RCP8.5/SSP5-8.5) than the low (RCP2.6/SSP1-2.6), in part because \r more simulations were available. The ISMIP6 simulations that apply \r an ice-shelf collapse scenario based on exceedance of a  surface \r meltwater threshold (Trusel et al., 2015), driven by CMIP5 models, \r show only a small increase in mass loss (around 0-0.04 m), mostly \r from the Peninsula, due in part to the small number of ice shelves \r predicted to collapse this century (Seroussi et al., 2020). Simulations \r driven by the CMIP5 model HadGEM2-ES, which has unusually \r [...]"


id: 554b3937249e823db2ab8eafe38c9aae
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
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index: climate_gpt_v2
vector_id: 12286
content:
"[...] extreme warming in the Ross Sea (Barthel et al., 2020), show a larger \r mass loss (up to about 0.05 m) in East Antarctica under ice-shelf \r collapse (Edwards et al., 2021). The ISMIP6 projections do not include \r the efficient meltwater drainage or atmospheric feedbacks that could \r reduce mass loss further (Seroussi et al., 2020)."


id: 70282b8cb9b98b25cb7889bc7cb8db13
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 16986
content:
"The AR5 assessed the median and likely (66-100% probability) sea \r level contributions of the AIS in 2100 relative to 1986-2005 to be \r 0.06 (-0.04 to +0.16) m SLE under RCP2.6 and 0.04 (-0.08 to +0.14) m \r SLE under RCP8.5 (Table 9.3; no change when using the AR6 baseline). \r The AR5 stated that only the collapse of the marine-based sectors of \r the AIS, if initiated, could cause GMSL to rise substantially above the \r likely range during the 21st century, with medium confidence that \r this would not exceed several tenths of a metre during this period. \r The assessment of the dynamical contribution had no dependence on \r emissions scenarios, due to the lack of literature, so the decrease in \r sea level contribution in the higher-emissions scenario was solely due \r to increased SMB (Section 9.4.2.3). The SROCC (Oppenheimer et al., \r 2019) assessed the total contribution based on five new ice-sheet \r modelling studies that incorporated marine ice-sheet dynamics, \r [...]"


id: 992c5c2244e0d7d18c1af48b90e9f6fc
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
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content:
"[...] combining their estimates and interpreting the 5-95th percentile \r range of the resulting distribution as the likely range (17-83% \r probability interval, i.e., not open-ended as in the AR5). The median \r and likely range contributions by 2100 were 0.04 (0.01-0.11) m \r under RCP2.6 and 0.12 (0.03-0.28)  m under RCP8.5 (Table  9.3). \r The positive scenario-dependence in SROCC  - where increases in \r dynamic losses driven by ocean warming and ice-shelf disintegration \r under higher emissions (Section 9.4.2.3) dominate over increases in \r SMB - arose from a combination of physical processes and model \r limitations. Modelling improvements in these studies included \r improved representations of grounding line response to drivers, more "


id: e7a47be5da6706d0940373ab953c06c9
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
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vector_id: 38059
content:
"noted that deep uncertainty remained beyond 2100: while solid \r Earth feedbacks could reduce ice loss over multi-century time scales, \r MICI (Section 9.4.2.4) might give contributions higher than the likely\r ranges. The SROCC also presented structured expert judgement \r (SEJ) projections for comparison (Bamber et al., 2019), which give \r higher values. Since SROCC, three studies have made projections \r to 2300: (i) Rodehacke et  al. (2020) assessed two methods for \r implementing precipitation changes (based on repeating 2071-2100 \r forcings beyond 2100), which both gave negative projections at \r 2300 because the dynamic response was very small (-0.11 to \r -0.01 m SLE for RCP2.6; -0.25 to -0.07 m for RCP8.5 forcing); (ii) In \r contrast, simulations forced by 2081-2100 ocean-only projections \r under RCP8.5/SSP5-8.5 beyond 2100, using two implementations \r of the ISMIP6 'non-local' basal melt parametrizations (Box 9.3 and \r [...]"


id: 4cc02ef49d27258a02425952a22f4cc6
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 10770
content:
"[...] Section 9.4.2.2) and two sliding laws, are all positive (0.08 m to 0.96 m \r SLE by 2300), though these do not include the negative contribution \r from SMB changes (Lipscomb et al., 2021); (iii) Finally, DeConto et al. \r (2021) update projections for the MICI hypothesis (Section 9.4.2.4) \r using the extensions of the RCPs to 2300, and obtain far higher \r contributions: median (17-83%) ranges of 1.09 (0.71-1.35) m SLE \r under RCP2.6 and 9.60 (6.87-13.54) m SLE under RCP8.5. These \r are larger than previous estimates (DeConto and Pollard, 2016), \r particularly at the upper end: 0.68 (0.29-1.13) m SLE for RCP2.6 \r and 8.40 (7.47-9.76) m for RCP8.5 (Edwards et al., 2019), which can \r largely be explained by the higher maximum ice cliff calving rate. \r LARMIP-2 dynamic projections (Box  9.3) are also estimated under \r the extended SSPs and corrected with SMB (as in Section 9.4.2.5), \r giving median (17-83%) ranges of 0.40 (0.18-0.78)  m SLE at \r [...]"


id: 8c1a75dd78f827d61bef7fd62c1784fe
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
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index: climate_gpt_v2
vector_id: 21823
content:
"[...] 2300 under SSP1-2.6 and 1.57 (0.68-3.14) m under SSP5-8.5. The \r longer time scale may invalidate the linear response assumption of \r LARMIP-2, which neglects any self-dampening or self-amplifying \r processes. The ranges of projections for 2300 without MICI (Golledge \r et al., 2015; Bulthuis et al., 2019; Levermann et al., 2020; Rodehacke \r et al., 2020; Lipscomb et al., 2021; 'assessed ice-sheet contributions' \r in Section 9.6.3.5 are -0.14 to +0.78 m SLE under RCP2.6/SSP1-2.6, \r and -0.27 to 3.14 m SLE under RCP8.5/SSP5-8.5). The lower bounds \r are the 5th percentile of Bulthuis et al. (2019) and the lowest mean/\r median from Rodehacke et al. (2020), respectively; the upper bounds \r are the 83% percentiles of the LARMIP-2 estimates. These ranges \r are wider than SROCC likely ranges, and more consistent with the \r SEJ (Bamber et al., 2019). However, projections in which Antarctica \r contributes much more than the assessed ranges under sustained \r [...]"


id: 71a6c2d810d7986b10f0626724b31040
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
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index: climate_gpt_v2
vector_id: 17242
content:
"[...] very high greenhouse gas emissions - that is, around 7-14 m to GMSL \r by 2300 (DeConto et al., 2021), cannot be ruled out, and are taken \r as a sensitivity case (Section 9.6.3.5; Table 9.11). In summary, there \r is high confidence that Antarctic mass loss will be greater beyond \r 2100 under high greenhouse gas emissions, but the large range \r of projections mean we have only low confidence in the likely AIS \r contribution to GMSL by 2300 for a given scenario. Deep uncertainty\r remains in the role of AIS instabilities under very high emissions."


id: 4ff82c1bdf511334d244604e34f242b6
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 11362
content:
"It is not possible to distinguish which of ISMIP6 and LARMIP-2 is \r more realistic, due to limitations in historical simulations (Box 9.3) \r and understanding of basal melting (Section  9.4.2.3.2), so the \r projections are combined using a 'p-box' approach (Section 9.6.3.2). \r The mean of the ISMIP6 emulated and LARMIP-2 medians gives the \r assessed median projections, and the outer edges of the 17-83% \r ranges give the outer edges of the assessed likely (17-83%) ranges - \r that is, encompassing the structural and parametric uncertainties \r of both methods, giving medium confidence in their combined \r projections. The main difference between this assessment and SROCC \r is to increase the medians of the lower scenarios by 0.05-0.07 m, \r so that all SSPs are similar to SROCC assessment of RCP8.5, and \r to substantially increase the upper ends of the likely ranges: by \r 0.14-0.16 m for RCP2.6/SSP1-2.6 and RCP4.5/SSP2-4.5, and 0.06 m \r [...]"


id: 8d341c0fab22f3edfccaf9abc583c9
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
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index: climate_gpt_v2
vector_id: 22026
content:
"[...] for RCP8.5/SSP5-8.5. The increase relative to SROCC is partly due to \r the increase in LARMIP-2 projections relative to the original LARMIP \r study (Levermann et  al., 2014), arising from the larger number of \r participating ice-sheet models (Levermann et al., 2020). The historical \r dynamic response to pre-2015 climate forcing applied to the ISMIP6 \r emulator could be overestimated, due to the assumption of a constant \r future rate (Box 9.3). This assessment encompasses SROCC and all \r projections since, except the 83rd percentiles of projections that \r allow MICI under RCP8.5 (DeConto et al., 2021) and the Structured \r Expert Judgement (SEJ) under 5degC shown in SROCC (Bamber et al., \r 2019). Both are used in further p-box estimates to give the outer \r limits of low confidence assessments (Section 9.6.3.2)."


id: a4e03b24e7263bac4d71a19d60c36cac
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
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index: climate_gpt_v2
vector_id: 26229
content:
"of a  further study (Bulthuis et  al., 2019), SROCC confirmed that \r low emissions would limit Antarctic ice loss over multi-century \r time scales (high confidence), but it was not possible to determine \r whether this was sufficient to prevent substantial ice loss (medium \r confidence). Since SROCC, new studies have revisited this topic \r (Garbe et  al., 2020; Rodehacke et  al., 2020; Van Breedam et  al., \r 2020; DeConto et al., 2021; Lipscomb et al., 2021), allowing a more \r complete assessment along with other studies (Feldmann and \r Levermann, 2015; Clark et al., 2016; Golledge et al., 2017a; Edwards \r et  al., 2019) and the extension to LARMIP-2 above. The majority \r project 0-1.3 m SLE on multi-century time scales under scenarios of \r 1degC-2degC warming. Projections can increase up to 2 m SLE under high \r basal melt sensitivity to ocean warming (Section 9.4.2.3; Lipscomb \r et al., 2021) or MICI (Section 9.4.2.4). On multi-millennial time scales \r [...]"


id: b148aeeacc5287e179e8c64e44daf4fb
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
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vector_id: 28419
content:
"[...] (>=2,000 years), many projections remain below 1.6 m SLE under \r 1degC-2degC warming - that is, less than about half of the WAIS in SLE \r (see also Section  9.6.3.5 and Figure  9.30). Other studies project \r majority or total loss of WAIS under 1degC-2degC warming, exceeding \r 2 m SLE, under the higher end of the warming range (>=1.5degC), or high \r ocean warming (>=0.5degC) and/or high basal melting around WAIS, or \r MICI. All but two of these multi-millennial studies use variants of \r the same ice-sheet model, though different modelling choices mean \r they can be considered quasi-independent. Simulations of previous \r interglacial periods often show near or total WAIS disintegration, with \r mass loss exceeding 3 m SLE (e.g. Figure 9.18), although limitations \r of these studies or inferences that can be drawn under different \r forcings limit confidence in the robustness of these as quantitative \r analogues (Sections 9.4.2.4 and 9.6.2). Overall, increased evidence \r [...]"


id: 4bc38c068e94a78612ccffbb83fee7ce
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
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index: climate_gpt_v2
vector_id: 10590
content:
"[...] and agreement on the time scales and drivers of mass loss confirm \r the SR1.5 assessment that a threshold for WAIS instability may be \r close to 1.5degC-2degC (medium confidence), and that the probability \r of passing a  threshold is larger for 2degC warming than for 1.5degC \r (medium confidence), particularly under strong ocean warming. New \r projections agree with previous studies that only part of WAIS would \r be lost on multi-century time scales if warming remains less than \r 2degC (medium confidence). There is limited agreement about whether \r complete disintegration would eventually occur at this level of \r warming, but medium confidence this would take millennia."


id: 500a9d8041967537b7900a4c767d9a93
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
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index: climate_gpt_v2
vector_id: 11374
content:
"The SROCC (Meredith et al., 2019; Oppenheimer et al., 2019) assessed \r that Antarctic mass losses could be irreversible over decades to \r millennia (low confidence). Garbe et  al. (2020) show that the AIS \r is always volumetrically smaller when regrowing under a  given \r warming level than when it retreats under the same forcing. Even if \r retreat followed by regrowth results in a net zero change in volume, \r the spatial distribution of mass may be altered, especially in parts of \r West Antarctica vulnerable to MISI. Projections that start reducing \r CO2 concentrations from 2030 onwards, reaching pre-industrial \r levels around 2300, show sea level contributions exceeding 1 m \r by 2500 when including MICI (DeConto et al., 2021). New research \r therefore confirms SROCC assessment that mass loss from the AIS is \r irreversible on decadal to millennial time scales (low confidence) (FAQ \r 9.1), and suggests that reducing atmospheric CO2 concentrations or \r [...]"


id: 17a76df65f58b015b70c53e0ee8e5e64
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
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index: climate_gpt_v2
vector_id: 1370
content:
"[...] temperatures to pre-industrial levels may not be sufficient to prevent \r or reverse substantial Antarctic mass losses (low confidence)."


id: 37bd893fde5cfb57ddb81f947250017e
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
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index: climate_gpt_v2
vector_id: 7016
content:
"The IPCC's Fifth Assessment Report (AR5; Vaughan et  al., 2013) \r assessed glacier changes from studies based on the regions defined \r in the Randolph Glacier Inventory (RGI; RGI version 2.0): a satellite \r observation-based, global inventory of glacier outlines for the \r year 2000. Following Special Report on the Ocean and Cryosphere \r in a  Changing Climate (SROCC; Hock et  al., 2019b; Meredith \r et  al., 2019), we report on studies based on RGI version 6.0 (RGI \r Consortium, 2017). Increased volume of satellite observations and \r the inclusion of detailed regional glacier inventories has resulted in \r an improved inventory (RGI Consortium, 2017). A  new consensus \r estimate for the ice thickness distribution of all glaciers in RGI 6.0 \r was obtained from an ensemble of five numerical models. However, \r only one out of five models covered all regions (Farinotti et al., 2019), \r and was, where possible, calibrated and validated with the worldwide \r [...]"


id: e62d404f9c2e34faa0ec29a9f373f7e2
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
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index: climate_gpt_v2
vector_id: 37799
content:
"[...] Glacier Thickness Database (GlaThiDa 3.0: GlaThiDa Consortium, \r 2019; Welty et  al., 2020). The updated inventory shows decreases \r in estimated glacier volume in the Arctic, High Mountain Asia and \r Southern Andes, partially compensated by increases in Antarctica. \r 15% of the total glacier volume is estimated to be below sea level \r and would not contribute to sea level rise if melted (Farinotti et al., \r 2019). Supplementary Material Table  9.SM.2 shows the inventory \r glacier area and mass for each region in the year 2000."


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"Under around 2degC-3degC peak warming, complete or near-complete \r loss of the WAIS is projected in most studies after multiple millennia \r (low confidence), with continent-wide mass losses of around 2-5 m \r SLE or more; this could occur on multi-century time scales under very \r high basal melting (Lipscomb et al., 2021) or widespread ice-shelf \r loss and/or MICI (low confidence) (Sun et al., 2020; DeConto et al., \r 2021). Mass losses under around 2degC-3degC warming could be less \r than 2 m SLE, particularly for multi-century time scales, low basal \r melting, or less responsive sliding laws. If warming exceeds around \r 3degC above pre-industrial, part of the EAIS (typically the Wilkes \r Subglacial Basin) is projected to be lost on multi-millennial time \r scales (low confidence), with total AIS mass loss equivalent to around \r 6-12 m or more sea level rise; mass loss could be much smaller if the \r dynamic response is small (Bulthuis et al., 2019; Rodehacke et al., \r [...]"


id: 1407e3e5adef5ad4d667260eac621da
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"[...] 2020), or much faster under widespread ice-shelf loss and/or MICI \r (Sun et al., 2020; DeConto et al., 2021). A study by Garbe et al. (2020) \r suggests that 6degC sustained warming and associated mass loss of \r about 12 m SLE may be a  critical threshold beyond which the ice "


id: 20532ded325dd9713fa597d763a2bb83
created_at: 2023-04-19 13:22:40
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content:
"The SROCC found a  globally coherent trend of glacier decline in \r the last decades, despite large annual variability and regional \r differences (very high confidence). Section  2.3.2.3 assesses the \r global glacier mass changes for the whole 20th  century (see \r Table  9.5 for contribution to the sea level budget. Note that the \r peripheral glaciers in Greenland and Antarctica are added to the ice \r sheets for the budget). The AR6 assessment is based on Marzeion \r et  al. (2015), using glacier-length reconstructions (Leclercq et  al., \r 2011) and a glacier model forced by gridded climate observations \r (Marzeion et  al., 2012), and not considering the estimated mass \r loss of uncharted glaciers (100 +- 50 Gt yr-1; Parkes and Marzeion, \r 2018). The time series are assumed independent, resulting in larger \r uncertainty than presented in SROCC (see also Section 9.6.1). The rate \r of global glacier mass loss (excluding the periphery of ice sheets) for \r [...]"


id: 9dc847268bc1071149cec5e34c9be867
created_at: 2023-04-19 13:22:40
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"[...] the period 1901-1990 is estimated to be very likely 210 +- 90 Gt yr -1, \r representing 16 [28 to 7] % of the glacier mass in 1901, in agreement \r with SROCC within uncertainty estimates."


id: 65254b5186ea1202c923472107bc9986
created_at: 2023-04-19 13:22:40
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content:
"The glacier mass loss rate from geodetic mass balance \r assessments in the Southern Andes during 2006-2015 was smaller \r (720  +-  70  kg  m-2  yr -1; Braun et  al., 2019; Dussaillant et  al., \r 2019; Hugonnet et  al., 2021) than previously assessed in SROCC \r (860 +- 160 kg m-2 yr -1), though within uncertainties. In the Central \r and Desert regions of the Southern Andes, an increase in mass loss \r from 2000-2009 to 2010-2018, and a high loss rate in Patagonia for \r the whole period, are observed (Dussaillant et al., 2019). Records of \r glacier mass loss in Peru (Seehaus et al., 2019a) and Bolivia (Seehaus \r et al., 2019b) in the period 2000-2016 show an increase in mass \r loss towards the end of the observation period. In western North \r America, outside of Alaska and western Yukon, there was a fourfold \r increase in mass loss for 2009-2018 (860 +- 320 kg  m-2  yr -1) \r compared to 2000-2009 (203 +- 214 kg m-2 yr -1; Menounos et al., \r [...]"


id: d778fc6880f24bd521b50b67eca77720
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"[...] 2019), and in the Canadian Arctic there was a  doubling of mass \r loss in the last two decades compared with pre-1996 (Noel et al., \r 2018; Cook et  al., 2019). The peripheral glaciers in NE Greenland \r experienced a 23% increase in mass loss in 1980-2014 compared \r to the period 1910 to 1978-1987 (Carrivick et al., 2019). In Iceland, \r 16 +- 4% of the around 1890 glacier mass has been lost; about half \r of that loss occurred in the period 1994-2019 (Adalgeirsdottir et al., \r 2020). Glacier records starting in 1960 in Norway show that half of \r the observed glaciers advanced in the 1990s but all have retreated \r since 2000 (Andreassen et  al., 2020). In Svalbard, glaciers have \r been losing mass since the 1960s, with a tendency towards more \r negative mass balance since 2000 (Deschamps-Berger et al., 2019; \r Van Pelt et al., 2019; Morris et al., 2020; Noel et al., 2020; Schuler \r et al., 2020). A similar increase in mass loss has been observed for \r [...]"


id: 4404baee5c455f57e5a8cc8ed526e533
created_at: 2023-04-19 13:22:40
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index: climate_gpt_v2
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"[...] Franz Josef Land in the Russian Arctic (Zheng et al., 2018). Rapid \r retreat and downwasting throughout the European Alps in the \r early 21st century is reported (Sommer et al., 2020) and long-term \r records, although limited, indicate sustained glacier mass loss in \r High Mountain Asia since around 1850, with increased mass loss \r in recent decades (Shean et  al., 2020). In summary, although \r interannual variability is high in many regions, glacier mass records \r throughout the world show with very high confidence that the \r loss rate has been increasing in the last two decades (see also \r Section 8.3.1.7.1 and 12.4 for regional glacier assessment)."


id: 73bcc2cbd1b7abbcd226ea55375bc8d0
created_at: 2023-04-19 13:22:40
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content:
"Since SROCC, new regional estimates for the Andes (Dussaillant \r et  al., 2019), High Mountain Asia (Shean et  al., 2020), Iceland \r (Adalgeirsdottir et al., 2020), the European Alps (Davaze et al., 2020; \r Sommer et al., 2020) and Svalbard (Schuler et al., 2020), two new \r global (Ciraci et  al., 2020; Hugonnet et  al., 2021) and an ad  hoc \r estimate for the latest glaciological observations (Zemp et al., 2020) \r have extended the glacier mass change time series up to 2018-2019 \r (Figure 9.21 and Supplementary Material Table 9.SM.3). A reconciled \r global estimate for the period 1962-2019 has been compiled by Slater \r et al. (2021). However, in contrast to Slater et al. (2021), after 2000 \r this assessment is based on the first globally complete and consistent \r estimate of 21st-century glacier mass change from differencing \r of digital elevation models (Hugonnet et  al., 2021) covering \r 94.7% of glacier area with glacier mass change for each glacier in \r [...]"


id: 5538d31d98ab597b0922298bc403495b
created_at: 2023-04-19 13:22:40
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"[...] the inventory produced with unprecedented accuracy. The estimates \r from Hugonnet et al. (2021) agree within uncertainties with new and \r previous estimates at global (Hock et al., 2019b; Wouters et al., 2019; \r Zemp et al., 2019; Ciraci et al., 2020; Slater et al., 2021) and regional \r scale (Dussaillant et  al., 2019; Adalgeirsdottir et  al., 2020; Schuler \r et al., 2020; Shean et al., 2020). Excluding peripheral glaciers of ice \r sheets (RGI regions 5 and 19), glacier mass loss rate was very likely\r 170 +- 80 Gt yr -1 for the period 1971 to 2019 (8 [4 to 14] % of 1971 \r glacier mass), 210 +- 50 Gt yr -1 over the period 1993-2019 (6 [4 to \r 8] % of 1993 glacier mass) and 240 +- 40 Gt yr-1 over the period \r 2006-2019 (3 [2 to 4] % of 2006 glacier mass; Sections  2.3.2.3 \r and 9.6.1, Table  9.5,4 and Cross-Chapter Box  9.1). Including the \r peripheral glaciers of the ice sheets, the global glacier mass loss rate \r [...]"


id: 83f24bdb4311cf21c835a26230a23139
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"[...] in the period 2000-2019 is very likely 266 +- 16 Gt yr -1 (4 [3 to 6] % \r of glacier mass in 2000) with an increase in the mass loss rate from \r 240 +- 9 Gt yr-1 in 2000-2009 to 290 +- 10 Gt yr-1 in 2010-2019 (high \r confidence). These estimates are in agreement with SROCC estimate \r and extend the period to 2018-2019. In summary, new evidence \r published since SROCC shows that, during the decade 2010-2019, \r glaciers lost more mass than in any other decade since the beginning \r of the observational record (very high confidence) (Section 8.3.1.7.1 \r and Figure 9.20)."


id: 6f1d14a607d2d371ea218103c4cd5435
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"Section 2.3.2.3 assesses that the rate and global character of glacier \r retreat in the latter part of 20th century, and finds that the first decades \r of the 21st century appear to be unusual in the context of the Holocene \r (medium confidence) and the global glacier recession in the beginning \r of the 21st  century to be unprecedented in the last 2000 years \r (medium confidence). These assessments are supported by regional \r evidence. New reconstructions of the Patagonian Ice Sheet suggest \r that 20th-century glacial recession occurred faster than at any time \r during the Holocene (Davies et al., 2020). The reconstructions of glacier \r variations show that the glaciers in some regions are now smaller \r than previously recorded: since the mid-16th  century in the Mont \r Blanc and Grindelwald regions of the European Alps (Nussbaumer and \r Zumbuhl, 2012), since the 9th century in Norway (Nesje et al., 2012), \r and for the past 1800 years in north-west Iceland (Harning et  al., \r [...]"


id: 8dd718fe5630a7e2eee2f068dea2b6e2
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"[...] 2016, 2018). In Arctic Canada and Svalbard, many glaciers are now \r smaller than they have been in at least 4000 years (Lowell et al., 2013; \r Miller et al., 2013, 2017; Schweinsberg et al., 2017, 2018) and more \r than 40,000 years in Baffin Island (Pendleton et al., 2019). Although \r the millennial glacier length variation records are incomplete and \r discontinuous, and glacier fluctuations depend on multiple factors \r (e.g., temperature, precipitation, topography, internal glacial dynamics), \r there is a coherent relationship between rising temperatures, negative \r mass balance and glacier retreat on centennial time scales across most \r of the world. Glaciological and geodetic observations show that the \r rates of early 21st-century mass loss are the highest since 1850 (Zemp \r et al., 2015). For all regions with long-term observations, glacier mass \r in the decade 2010-2019 was the smallest since at least the beginning \r of the 20th century (medium confidence)."


id: 2e12dadba19e58504cdc710595d6b40
created_at: 2023-04-19 13:22:40
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content:
"In the Northern Hemisphere, where summer insolation decreased \r during the Holocene (Section 2.2.1), glaciers generally waxed (Briner \r et al., 2016; Kaufman et al., 2016; Lecavalier et al., 2017; Zhang et al., \r 2017; Axford et al., 2019; Geirsdottir et al., 2019; Larsen et al., 2019; \r Luckman et al., 2020). Conversely, in the Southern Hemisphere, where \r summer insolation increased during the Holocene, glaciers generally \r waned (Solomina et al., 2015; Kaplan et al., 2016; Reynhout et al., \r 2019). However, these general global trends were modulated by \r regional climate variations in temperature and precipitation (Murari \r et al., 2014; Kaplan et al., 2016; Batbaatar et al., 2018; Saha et al., \r 2018) and there are a number of examples of this. A precipitation \r increase led to a local early Holocene (7-8 ka) glacier maximum in arid \r Mongolia (Gichginii Range). Glacier advances at about 9 ka in south\u0002west Greenland have been suggested to be a consequence of the \r [...]"


id: 4ef7c819a893f55208574e8a7ba8e506
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"[...] freshwater pulse from the Laurentide Ice Sheet, which led to cooling \r in the Baffin Bay area (Schweinsberg et al., 2018). Lake sediments \r indicate that the glaciers in the region were smaller than today, or \r absent between 8.6 and 1.4 ka (Larocca et  al., 2020). Glaciers on \r the Antarctic Peninsula and in Patagonia during the Holocene were \r strongly affected by the southern westerly winds, sea ice extent, \r and ocean circulation (Garcia et al., 2020). Recent studies indicate \r that explosive volcanism can drive glacier advances (Solomina et al., \r 2015, 2016; Schweinsberg et  al., 2018; Bronnimann et  al., 2019). \r In summary, on millennial time scales over the Holocene, there is \r high confidence that orbital forcing drove hemispheric-scale glacier \r variations, but new studies provide a nuanced picture of responses to \r a variety of regional-scale forcings."


id: 6124b52e74a5b4ca891848be7aa6e29b
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"In contrast to the global glacier mass decline (Figure 9.21, Table 9.5, and \r Supplementary Material 9.SM.2), a few glaciers have gained mass or \r advanced due to internal glacier dynamics or locally restricted climatic \r causes. The SROCC discusses the 'Karakoram anomaly' (centred on the \r western Kunlun range (at about 80degE, 35degN), but also covering part \r of the Pamir and Karakoram ranges), where glaciers have been close \r to balance since at least the 1970s, and had a slightly positive mass \r balance since the 2000s. Since SROCC, new evidence suggests that this \r anomaly is related to a combination of low-temperature sensitivity of \r debris-covered glaciers, a decrease of summer air temperatures (Cross\u0002Chapter Box 10.3), and an increase in snowfall, possibly caused by \r increases in evapotranspiration from irrigated agriculture (Bonekamp \r et al., 2019; de Kok et al., 2020; Farinotti et al., 2020; Shean et al., \r [...]"


id: 3acbc1d6bc1a1c99e70463ca32cdf1b9
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"[...] 2020). However, a recent geodetic mass balance estimate suggests \r substantially increased thinning rates of High Mountain Asian glaciers \r after about 2010 (Hugonnet et al., 2021). There is limited evidence to \r assess whether the Karakoram anomaly will persist in coming decades \r but, due to the projected increase in air temperature throughout the \r region, its long-term persistence is unlikely (high confidence) (Cross\u0002Chapter Box  10.3; Kraaijenbrink et  al., 2017; de Kok et  al., 2020; \r Farinotti et al., 2020)."


id: 67abe3fe1c03cccf54f783b03f0d4dfe
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"had a  significant effect on observed long-term glacier changes. \r Several studies have shown melt increases due to the deposition \r of light-absorbing particles (Schmale et  al., 2017; Wittmann et  al., \r 2017; Sigl et al., 2018; Di Mauro et al., 2019, 2020; Magalhaes et al., \r 2019; Constantin et al., 2020). Conversely, increasingly thick debris \r cover (>2-5 cm) on retreating glaciers can slow down glacier melt \r (Pratap et al., 2015; Brun et al., 2016). Although debris covers only \r about 4-7% of the total glacier area globally (Scherler et al., 2018; \r Herreid and Pellicciotti, 2020), many glaciers are heavily debris\u0002covered in their lower reaches, especially in High Mountain Asia, the \r Caucasus, the European Alps, Southern Andes and Alaska, resulting \r in different responses to warming than similar clean-ice glaciers. A \r shift in regional meteorological conditions, driven by the location and \r strength of the upper level zonal wind, has been found to have forced \r [...]"


id: 2d15427ed075878bae0afe93440e72c5
created_at: 2023-04-19 13:22:40
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vector_id: 5209
content:
"[...] recent high mass loss rates in Western North America (Menounos \r et al., 2019). High geothermal heat flux areas underneath glaciers and \r high energy dissipation in the flow of water and ice causes additional \r mass loss of the glaciers in Iceland (Johannesson et  al., 2020), \r accounting for 20% of the mass loss since 1994 (Adalgeirsdottir \r et al. 2020). Glacier lake volume in front of retreating glaciers, has \r increased globally by around 48% between 1990 and 2018 (Shugar \r et al., 2020), which can increase both subaqueous melt and calving. \r In summary, there is high confidence that non-climatic drivers have \r and will continue to modulate the first-order temperature response \r of glaciers in some regions."


id: cc39289f00ab2bded2b1dffece68bee9
created_at: 2023-04-19 13:22:40
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content:
"Secondary processes such as debris-cover thickening (e.g.,  Herreid \r and Pellicciotti, 2020), albedo changes due to light-absorbing \r particles (e.g., Magalhaes et al., 2019; Williamson et al., 2019), trends \r of refreezing and water storage in firn (e.g., Ochwat et al., 2021), \r dynamic instabilities such as surges (e.g., Thogersen et  al., 2019) \r or glacier collapse (e.g.,  Kaab et  al., 2018), are not represented \r in global glacier models, resulting in both underestimated and \r overestimated sensitivity to warming that is currently not possible to \r quantify. Furthermore, challenges for future projections are caused \r by the low-resolution and high-spatial variability at sub-grid scale \r of the precipitation amount provided by general circulation models \r (GCMs), which requires downscaling to the spatial scale of a glacier \r (Maussion et al., 2019; Zekollari et al., 2019; Marzeion et al., 2020). \r In summary, in agreement with SROCC, progress in global scale \r [...]"


id: bfaabc5be27789c08770366daddc62bf
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"[...] glacier modelling efforts allows medium confidence in the capability \r of current-generation glacier models to simulate the magnitude and \r timing of glacier mass changes as a response to climatic forcing."


id: f4620e1c0d6b553a2fa9e02fc7744952
created_at: 2023-04-19 13:22:40
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content:
"The AR5 (Vaughan et  al., 2013) and SROCC (Hock et  al., 2019b) \r stated with high confidence that the world's glaciers are presently \r in imbalance due to the warming of recent decades. The observed \r retreat of glaciers is only a partial response to the already realized \r warming (Christian et al., 2018), and they are committed to losing \r considerable mass in the future, even without further change in \r air temperature (Mernild et al., 2013; Trussel et al., 2013; Zekollari \r and Huybrechts, 2015; Huss and Fischer, 2016; Marzeion et  al., \r 2018; Jouvet and Huss, 2019). One model estimates that 36 +- 8 % \r of global glacier mass is already committed to be lost due to \r past greenhouse gas emissions (Marzeion et  al., 2018). Although \r accumulation and ablation instantly determine the SMB, the glacier \r geometries adjust to changed atmospheric conditions over a longer \r time (Zekollari et  al., 2020). The adjustment time, often referred \r [...]"


id: 2a7bd6756ca3b9560a91e82a56b56891
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content:
"[...] to as the response time, is variable from one glacier to another, \r depending on the glacier geometry (thickness and steepness), SMB \r and gradient (e.g., Johannesson et al., 1989; Harrison et al., 2001; \r Luthi, 2009; Zekollari et al., 2020). Response time is variable: years \r for smaller and steeper glaciers (Beedle et  al., 2009; Luthi and \r Bauder, 2010; Rabatel et al., 2013), up to tens or hundreds of years \r for larger and gentle-sloped glaciers (e.g., Burgess and Sharp, 2004; "


id: e0d1e0b50ccf0c2ab4de026b96536e1
created_at: 2023-04-19 13:22:40
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vector_id: 36813
content:
"mass loss compared to the RCP forced simulations, although with \r fewer global glacier models. To enable the glacier contribution to \r future sea level rise to be estimated under the full range of SSP \r scenarios (Section  9.6.3.3), the GlacierMIP results are emulated \r using a Gaussian process model (Box 9.3 and Table 9.4; Edwards \r et al., 2021). The emulated projections show a narrower range than \r the roughly equivalent RCP projections, which may be explained \r by not accounting for covariance in the regional uncertainties \r (Marzeion et al., 2020) and by the fact that the emulator caps sea \r level contribution for each region at the volume above floatation \r estimated by Farinotti et al. (2019) (Table 9.SM.2). Comparison of \r simulated and emulated regional sea level contributions support this \r explanation. Rates of change and post-2100 sea level projections \r are estimated with the AR5 parametric fit (Supplementary \r [...]"


id: bb81ff0e079befe52c506b9ccd34d2e2
created_at: 2023-04-19 13:22:40
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"[...] Material 9.SM.4.5; Church et al., 2013b) applied to the GlacierMIP \r results (Marzeion et al., 2020), and these are also shown in Table 9.4 \r for comparison."


id: 2dd99bfabc6d5c3854a71f13726db14c
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"The SROCC assessed that global glacier mass loss by 2100, relative \r to 2015 will be 18 [likely range 11 to 25] % for scenario RCP2.6 \r and 36 [likely range 26 to 47] % for RCP8.5, and that many glaciers \r will disappear regardless of the emissions scenario (very high \r confidence). Since SROCC, new results from Marzeion et al. (2020) \r have been published (Box 9.3, Figure 9.21 and Table 9.4, including \r peripheral glaciers in Greenland and Antarctica). Glaciers will lose \r 29,000 [9000 to 49,000] Gt and 58,000 [28,000 to 88,000] Gt \r over the period 2015-2100 for RCP2.6 and RCP8.5, respectively \r (medium confidence), which represents 18 [5 to 31] % and 36 [16 to \r 56] % of their early 21st  century mass, respectively (Table  9.4). \r Within  uncertainties, these agree with SROCC estimates, although \r with a slightly smaller mass loss due to the inclusion of models with \r lower sensitivity to changing climate conditions (Marzeion et  al., \r [...]"


id: bf5972d63a961c2752dc1265cde5250b
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content:
"[...] 2020). The greatest source of uncertainty in glacier mass loss until \r the middle of the 21st century is the disagreement between glacier \r models, with emissions scenario becoming the dominant cause of \r uncertainty by the end of the 21st century (Marzeion et al., 2020)."


id: a5faada25b918b7df3efed573e79aa9d
created_at: 2023-04-19 13:22:40
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index: climate_gpt_v2
vector_id: 26422
content:
"Permafrost changes mostly refer to changes in extent, temperature \r and active layer thickness (ALT). The SROCC (Hock et  al., 2019b; \r Meredith et  al., 2019) reported with very high confidence that \r record high permafrost temperatures at the depth of the zero \r annual amplitude (the depth about 10-20 m below the surface \r where the seasonal soil temperature cycle vanishes) were attained \r in recent decades in the Northern circumpolar permafrost region, \r high confidence that permafrost has warmed over recent decades in \r many mountain ranges, and overall very high confidence that global \r warming over the last decades has led to widespread permafrost \r warming. As reported in SROCC, the global (polar and mountain) \r permafrost temperature has increased at 0.29degC  +-  0.12degC near \r the depth of zero annual amplitude between 2007 and 2016 \r (Biskaborn et  al., 2019). Stronger warming has been observed in \r the continuous permafrost zone (0.39degC +- 0.15degC) compared to \r [...]"


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vector_id: 15279
content:
"[...] the discontinuous zone (0.20degC +- 0.10degC), consistent with the fact \r that, near the melting point, a large amount of energy is required \r for melting the ice (Figure 9.22), and because of the reduced effect \r of Arctic amplification in more southerly locations (Romanovsky \r et  al., 2017). This is consistent with longer-term Arctic trends \r from deep boreholes shown in Figure 2.22. Mountain permafrost \r temperature trends are heterogeneous, reflecting variations in local \r conditions such as topography, surface type, soil texture and snow \r cover, but again, generally weaker warming rates are observed \r in warmer permafrost at temperatures close to 0degC, particularly \r when ice content is high (e.g., Mollaret et al., 2019; Noetzli et al., \r 2019; PERMOS, 2019). In summary, strong variability in recent \r permafrost temperature trends is linked to local conditions, \r regionally varying temperature trends, and the thermal state of \r [...]"


id: a9ecaef36af4d300af32f4f6388ce78f
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 27116
content:
"[...] permafrost itself. However, as discussed in Section  2.3.2.5, there \r is overall high confidence in the observed increases in permafrost \r temperature over the past three to four decades throughout the \r permafrost regions."


id: 1f5ff8f775837e0905f7cff979f1673
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 2724
content:
"In AR5 and SROCC, glacier mass loss beyond 2100 was calculated \r using a parametric fit to available model simulations. In section 9.6.3.5, \r that same parametric fit is applied to Marzeion et al. (2020) projections, \r resulting in complete glacier mass loss at year 2300 under SSP5-8.5 \r and 40-100% mass loss under SSP1-2.6. Clark et al. (2016) simulate \r glacier mass evolution, not including glaciers peripheral to the Antarctic \r Ice Sheet (AIS), for different warming levels for the next 10,000 years. \r There is limited evidence and low confidence that, at sustained \r warming levels between 1.5 and 2degC, about 50-60% of glacier mass \r will remain, predominantly in the polar regions. At sustained warming \r levels between 2 and 3degC, about 50-60% of glacier mass outside \r Antarctica will be lost and, at sustained warming levels, between 3 and \r 5degC, 60-75% of glacier mass outside Antarctica will disappear. Based \r [...]"


id: 781d61b0cd719e6c8d17390a9c24d3f6
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 18342
content:
"[...] on Marzeion et al. (2020), there is medium confidence that nearly all \r glacier mass in low latitudes, Central Europe, the Caucasus, western \r Canada and the USA, North Asia, Scandinavia and New Zealand will \r disappear at this high warming level."


id: f72296869dce61ae0ccedcad9b8ec54c
created_at: 2023-04-19 13:22:40
updated_at: 2023-04-19 13:22:40
content_type: text
index: climate_gpt_v2
vector_id: 40850
content:
"As stated in AR5 (Flato et al., 2013), coupled models contributing to \r CMIP5 showed large inter-model variability of permafrost extent due \r to deficiencies in reproducing surface characteristics and processes \r (Koven et  al., 2013), particularly thermal properties of the ground \r and snow. These deficiencies led SROCC (Meredith et al., 2019) to \r express only medium confidence in the models' capacity to correctly \r project the magnitude of future permafrost changes, in spite of high \r confidence in the models' projection of a general thaw depth increase \r and a substantial loss of shallow permafrost. The SROCC further noted \r that several types of physical 'pulse' disturbances, in particular fire \r and thermokarst formation, are usually not represented in coupled \r climate models. This has been discussed in detail in SROCC, which \r assessed that there is high confidence that permafrost degradation \r through fire (Jones et  al., 2015; Gibson et  al., 2018) is currently \r [...]"


