# document

id: e9ad5abe0a59807e72055d538363b007
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 38464
content:
"derived directly from global climate models (GCMs) or produced \r\nby 'offline' using global hydrological models (GHMs) driven by \r\nthe same GCMs (Orlowsky and Seneviratne, 2013; Giuntoli et al., \r\n2015, 2018; Chegwidden et al., 2019). Although some of the model \r\nresponse uncertainty is related to climatological biases (Grose \r\net al., 2017; G. Li et al., 2017; Lehner et al., 2019; Samanta et al., \r\n2019), model biases are not the only way to assess the reliability of \r\nclimate projections (compare with Box 4.1). Therefore, our focus here \r\nis on the representation of key processes that are not completely \r\nresolved in current-generation GCMs (Section  8.5.1.1) and on the \r\nmodel improvements associated with increased horizontal resolution \r\n(Section 8.5.1.2)."


id: 811399f862d52d0da0c23014220d6458
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 19846
content:
"Model response uncertainty is typically estimated as the inter-model \r\nspread (range) projected by a  set of climate models for a  given \r\nemissions scenario. It is best estimated at the end of a high-emissions \r\nscenario when internal variability has a  limited contribution to \r\ntotal uncertainty (Figure 8.23). Even for aggregated quantities, like \r\ndecadal-mean precipitation averaged over relatively large domains, \r\nmodel response uncertainty is substantial and can exceed scenario \r\nuncertainty (Hawkins and Sutton, 2011; Lehner et al., 2020, 1.5.4, \r\n4.4.1.3). This can also be true for other water cycle variables such \r\nas soil moisture, runoff and streamflow at the regional scale, either "


id: 53bf25dea6ca8a972158da84b604507d
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 12003
content:
"the diurnal cycle of precipitation over land (Couvreux et al., 2015), \r\nthe rainfall intensity distribution associated with the West African \r\nmonsoon (Roehrig et al., 2013), and the intensity of tropical cyclones \r\n(Sections 10.3.3.4 and 11.7.1.3), phenomena for which atmospheric \r\nconvection also plays a key role."


id: 72bc814017c3ebcf515ea0befd25155f
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 17427
content:
"Moist convection is fundamental to the water cycle through its vertical \r\ntransport of momentum, heat, and moisture across the atmosphere. \r\nIt is particularly active in the tropics where it contributes to more \r\nthan half of annual precipitation and to the development of severe \r\nweather events. Given limitations in computing resources, the current\u0002generation GCMs cannot yet represent small-scale cloud processes \r\nand consequently shallow and deep convection is determined by \r\nsub-grid-scale parametrizations. While such parametrizations can be \r\nevaluated against field observations (e.g., Abdel-Lathif et al., 2018), \r\nit remains challenging to estimate convective entrainment that is \r\nvalid for both shallow and deep convection (G.J. Zhang et al., 2016). \r\nComparisons between regional projections with explicit compared \r\nwith parametrized convection also highlight the limitations of \r\nparametrized convection for assessing climate change (Kendon et al., \r\n2019; Jackson et al., 2020)."


id: 5059d4b55f78de3e0886cedabf3583a
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 11418
content:
"Atmospheric convection is particularly important for a  realistic \r\nsimulation of tropical precipitation intensities (Pendergrass and \r\nHartmann, 2014a; Kendon et al., 2019). Many CMIP5 models \r\nproduce rainfall at water vapour amounts lower than in observations \r\n(Takahashi, 2018), as well as too light and too frequent precipitation \r\nevents (Sun et al., 2015; Trenberth et al., 2017). Such biases can \r\nbe explained by a  lack of convective inhibition (Rochetin et al., \r\n2014a, b) and by too much convective and too little non-convective \r\nprecipitation (Chen and Dai, 2019). Tropical convection controls the \r\namount of precipitable water simulated over the equatorial Indian \r\nOcean, which has been identified as a key metric for differentiating \r\nmodel skill in simulating South Asian monsoon precipitation (Hagos \r\net al., 2019). Many models have difficulty in adequately simulating "


id: d5a52dd02870fafc9af2127e68dfdc3c
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 34902
content:
"While some global modelling centres have reported progress in their \r\nparametrization of convection and in their simulation of seasonal, \r\ndaily and sub-daily precipitation (e.g.,  Danabasoglu et al., 2020; \r\nRoehrig et al., 2020), CMIP6 models as a whole only show limited "


id: 5363ea8013ee310148a9b8d6f424eec2
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 11943
content:
"include also the impacts of aerosols on ice-precipitation processes. \r\nAerosols can substantially invigorate (Rosenfeld et al., 2008; Koren \r\net al., 2014; Fan et al., 2018) and electrify (Thornton et al., 2017; \r\nQ. Wang et al., 2018) deep tropical convective clouds. High-resolution \r\natmospheric simulations suggest that high aerosol concentrations \r\ncan increase environmental humidity by producing clouds that \r\nmix more condensed water into the surrounding air, which in turn \r\nfavours large-scale ascent and strong convective events (Abbott and \r\nCronin, 2021). Further assessment of uncertainties in aerosol-cloud \r\ninteractions for shallow water clouds is provided in Section 7.3.3.2."


id: 3f11909f4413e82ea8b9a23397338f8b
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 8284
content:
"In summary, since AR5 empirical convective parametrization schemes \r\nand associated precipitation biases have improved in some but not \r\nall global climate models. There is still low confidence in their ability \r\nto accurately simulate the spatio-temporal features of present-day \r\nprecipitation, especially in the tropics where a  double-ITCZ bias is \r\nstill apparent in many models. While such biases limit the reliability of \r\nprecipitation projections in some cases, there is currently only medium \r\nconfidence that model selection or weighting is a better alternative \r\nto the one-model-one-vote approach (Box 4.1). Improved water cycle \r\nprojections can be achieved by focusing on phenomena or weather \r\nevents, such as a thermodynamic intensification of convective events \r\n(high confidence, Section  8.2.2.1), however accurate quantitative \r\nestimates are currently hampered by complex, model-dependent \r\ndynamical responses (Section 8.2.2.2)."


id: dd847068920a95fb3b9817df5da593c4
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 36240
content:
"In summary, there is still low confidence in the simulated influence \r\nof the aerosol microphysical effects on future precipitation changes."


id: 317ed382da763c57c22c2228534a9eda
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 5950
content:
"2016; Berg and Sheffield, 2018a; Santanello et al., 2018). This has \r\nled to the development of dedicated field campaigns (Song et al., \r\n2016; Phillips et al., 2017; Dirmeyer et al., 2018), remotely sensed \r\nobservations (Ferguson and Wood, 2011; Roundy and Santanello, \r\n2017), and tailored diagnostics (Tawfik et al., 2015a, b; Miralles et al., \r\n2016, 2019; Dirmeyer and Halder, 2017). Dynamic vegetation models \r\nhave been introduced in global ESMs but they need further evaluation \r\n(Medlyn et al., 2015; Prentice et al., 2015; Cantu et al., 2018; Franks \r\net al., 2018) to provide valuable information on potential vegetation \r\nfeedbacks. Plant migration and mortality, increased disturbances \r\nfrom wild fires, insects and extreme events, interactive nitrogen cycle, \r\nor the impact of increased levels of tropospheric ozone are often \r\nignored or poorly represented in the current-generation of ESMs \r\n(Bonan and Doney, 2018; Fisher et al., 2018). "


id: 95698c548e9516609b3c9b83101981bf
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 23488
content:
"Since AR5, development of new and existing processes in land surface \r\nmodels (LSMs) have been evaluated. These include soil freezing and \r\npermafrost (Vergnes et al., 2014; Chadburn et al., 2015; K. Yang et al., \r\n2018; Gao et al., 2019), soil and snow hydrology (Brunke et al., 2016; \r\nDecharme et al., 2016), glaciers (Shannon et al., 2019), surface waters \r\nand rivers (Decharme et al., 2012), as well as vegetation (Bartlett and \r\nVerseghy, 2015; Betts et al., 2015; Knauer et al., 2015; Tang et al., \r\n2015) and the representation of hydraulic gradients throughout the \r\nsoil-plant-atmosphere continuum (Bonan et al., 2014). Such land \r\nsurface model developments have led to significant improvements \r\nin global offline hydrological simulations driven by observed \r\natmospheric forcings (e.g., C. Li et al., 2017; Decharme et al., 2019)."


id: 447b589ce9a5011692f3a450ad5cfeba
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 9277
content:
"In summary, since AR5 substantial advances have been made in \r\nthe representation of land surface processes in current-generation \r\nEarth System Models (ESMs). Offline hydrological models allow \r\nthe application of bias-adjusted atmospheric forcings, but there \r\nis low confidence of an improved response compared to coupled \r\nclimate models, given their inherent limitations (Box  10.2). While \r\nimprovements in the representation of complex land surface \r\nfeedbacks relevant to the water cycle are needed, there is currently \r\nlow confidence that they will systematically improve the reliability of \r\nwater cycle projections. "


id: 7a50f90458b81c654a654e2fa7ada6ea
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 18719
content:
"Coarse spatial resolution of climate models has often been considered \r\na key limitation in global climate projections (Di Luca et al., 2015; \r\nRoberts et al., 2018). Proposed and tested solutions include a uniform \r\nor regional increase in the resolution of GCMs, or the use of regional \r\nclimate models (RCMs). The increase in computing resources has \r\nalso led to the development of convection-permitting models (Prein \r\net al., 2015), which have been integrated over larger domains, but \r\nare still unsuitable for CMIP simulations. Statistical downscaling \r\ntools are also widely used to generate fine-scale regional climate "


id: b80a5dd4457a4fab09557f57af172753
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 29598
content:
"Since AR5, there has been increasing recognition of the need to \r\nbetter understand the role of land-atmosphere coupling and related \r\nfeedbacks (Joetzjer et al., 2014; Berg et al., 2016; Catalano et al., "


id: f98bef44955573c4544ab6a0409eb48
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 41273
content:
"information necessary for climate impacts and adaptation studies. \r\nA  comprehensive assessment of the added value of increased \r\nspatial resolution and of the benefits and shortcomings of statistical \r\ndownscaling tools are addressed in Chapter 10 (Section 10.3.3)."


id: c044209f0442e348168c26b2f1fd4469
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 31130
content:
"orographic precipitation, snow, runoff and streamflow in complex \r\ntopography areas (Zhao and Li, 2015). However, the added value \r\nmay be easier to assess in offline rather than online land surface \r\nsimulations (Doll et al., 2016) given the possible use of bias-corrected \r\natmospheric forcings. Offline high-resolution GHMs are routinely used \r\nto monitor water resources or to assess the hydrological impacts of \r\nbias-adjusted global climate projections (Davie et al., 2013; S. Huang \r\net al., 2017, 2018). Yet, the development and calibration of 'hyper\u0002resolution' hydrological models, with gridcells of typically 100 m to \r\n1  km, raises a  number of issues given the lack of comprehensive \r\nsurface or subsurface information (Bierkens et al., 2015) and the lack \r\nof coupling with the atmosphere (Berg and Sheffield, 2018a). "


id: ac73297870754a67b0aa512eea513d0a
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 27580
content:
"In summary, there is high confidence that increasing horizontal \r\nresolution in GCMs can reduce a  number of systematic model \r\nerrors of relevance for the water cycle, including synoptic circulation \r\nand the statistics of daily precipitation. High-resolution GCMs and \r\nGHMs provide improved representation of land surfaces, including \r\ntopography, vegetation and land use change, which are required to \r\naccurately simulate changes in the terrestrial water cycle. However, \r\nthere is low confidence that the higher horizontal resolution \r\nsimulations currently available provide more accurate projections of \r\nthe large-scale features of the water cycle."


id: 38665447d296278ea19da2a68c32083e
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 7133
content:
"Increasing horizontal model resolution in CMIP5 and CMIP6 models \r\nleads to a systematic increase in global mean precipitation, enhanced \r\nmoisture advection to land in close connection with increased \r\norographic precipitation, and a partial reduction of the long-standing \r\ndouble ITCZ bias (Demory et al., 2014; Caldwell et al., 2019; Vanniere \r\net al., 2019). Recent studies based on HighResMIP simulations \r\n(Haarsma et al., 2016) confirm the added value of increased \r\nhorizontal resolution (at least 50 km in the atmosphere and 25 km \r\nin the ocean) for the simulation of tropical (Roberts et al., 2020) and \r\nextratropical cyclones (Priestley et al., 2020b). CMIP6 model biases \r\nin annual mean precipitation are only slightly reduced at higher \r\nresolution (Figure 3.10). "


id: 9c1ff6eac5b22b758231438c11d1e2c9
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 24723
content:
"Regional Convective Permitting Models (CPMs), typically run at \r\na resolution less than 10 km, have been implemented over increasingly \r\nlarge domains. Compared to models with parametrized convection \r\n(Section 8.5.1.1.1), they generally show improved simulation of key \r\nfeatures of the water cycle such as orographic precipitation, sea \r\nbreeze dynamics, the diurnal cycle in precipitation, soil-moisture-\r\nprecipitation feedbacks, daily precipitation persistence, sub-daily to \r\ndaily precipitation intensities and related extremes (Section 8.2.3.2; \r\nBirch et al., 2015; Prein et al., 2015; Kendon et al., 2017; Leutwyler \r\net al., 2017; Willetts et al., 2017; Hohenegger and Stevens, 2018; \r\nBerthou et al., 2019b; Takahashi and Polcher, 2019; Fumiere et al., \r\n2020; Scaff et al., 2020; Caillaud et al., 2021). A growing number of \r\nstudies have also assessed the potential added value of using CPMs "


id: 53a28ede1656212ae464921c7656387a
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
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vector_id: 11986
content:
"fingerprints over land (Section 8.5.2.2). The role of volcanic eruptions \r\nis also briefly assessed in terms of consequences and uncertainties in \r\nwater cycle projections (Section 8.5.2.3)."


id: 4c8647e62e134598e6c0f88aefc16379
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 10722
content:
"for regional climate projections (Ban et al., 2015; Giorgi et al., 2016; \r\nFosser et al., 2017; Kendon et al., 2017, 2019; C.  Liu et al., 2017; \r\nLenderink et al., 2019; Rasmussen et al., 2020; see also Atlas 5.6.3). \r\nAlthough projected changes in rainfall occurrence in CPMs are broadly \r\nand qualitatively consistent with the results of GCMs and RCMs \r\n(Kendon et al., 2017), there is a tendency towards stronger changes \r\nin both wet and dry extremes (Berthou et al., 2019a; Kendon et al., \r\n2019; Lenderink et al., 2019; Finney et al., 2020a). While both GCMs \r\nand RCMs project an overall decrease in summer precipitation over \r\nthe Alps, RCMs simulate an increase over the high Alpine elevations \r\nthat is not present in the global simulations (Giorgi et al., 2016). "


id: 7469f104f96d11ff3f54b47c95023364
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 17713
content:
"Recent studies based on both GCMs and CPMs indicate that both \r\nCAPE and convective inhibition will increase in a  warmer climate \r\n(Section 8.2.3.2; J. Chen et al., 2020a), consistent with a shift from \r\nmoderate to less frequent but stronger convective events (Rasmussen \r\net al., 2020). If underestimated by models with parametrized \r\nconvection, such a mechanism could explain the underestimation of \r\nboth projected increase in precipitation extremes (Borodina et al., \r\n2017; Yin et al., 2018) and land surface drying (Douville and Plazzotta, \r\n2017) in the extratropics. CMIP5 models with a  larger increase in \r\nextreme precipitation also exhibit larger declines or smaller increases \r\nin light to moderate events (Thackeray et al., 2018)."


id: 1c3556e2ccb75b06cd6e0a8b43ad58ff
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 2155
content:
"In summary, there is high confidence that dynamical downscaling \r\nusing limited area models adds value in simulating precipitation \r\nand related water cycle processes at the regional scale, especially \r\nin complex orography areas (Section  10.3.3.5.1). There is high \r\nconfidence that the explicit simulation of atmospheric convection can \r\nimprove the representation of weather phenomena, including the life \r\ncycle of convective storms and related precipitation extremes. Even \r\nwith an improved simulation of small-scale processes, there is only \r\nmedium confidence that there will be an improvement in RCM-based \r\nwater cycle projections as they rely on GCM boundary conditions."


id: e41bb638ea374b140acebaf33d88fe67
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 37433
content:
"time scales (Dee et al., 2017; Parsons et al., 2017). It is unclear \r\nwhether remaining discrepancies represent limitations of the climate \r\nmodels, or limitations of the proxy system models. Therefore, there \r\nis medium to high confidence (i.e.,  depending on the region) that \r\nclimate models do not underestimate water cycle internal variability. "


id: fc64be130e9f67cde0e9662ec404c8ea
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 41769
content:
"between eastern Australian rainfall and the central equatorial Pacific \r\nSSTs with varying skill, and also display periods when the ENSO \r\nteleconnection weakens substantially for several decades (Brown \r\net al., 2016a). Differences in simulated internal variability have been \r\nfound to be responsible for the inter-model spread in predicted shifts \r\nin subtropical dry zones for a given shift in the Hadley cell (Seviour \r\net al., 2018). CMIP5 models show that both internal variability \r\nand anthropogenic forcings are responsible for the drying over \r\nthe South Atlantic Convergence Zone region, though with large \r\nuncertainties (Zilli and Carvalho, 2021). Moreover, the detection of \r\nthe anthropogenic forcing on the South Atlantic Convergence Zone \r\nis strongly dependent on the characterization of model internal \r\nvariability (Talento and Barreiro, 2012). "


id: 4d747e6a5b029e45bf9dc48d6af22a84
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 10906
content:
"The mechanisms driving internal variability in the water cycle in \r\nclimate model simulations varies. While models indicate that cool \r\nSSTs in the eastern tropical Pacific (La Nina or the cool phase of \r\nthe PDO) are associated with drought in south-western North \r\nAmerica, they also show that atmospheric internal variability may \r\nbe a  more prominent driver (Coats et al., 2015, 2016; Stevenson \r\net al., 2015; Parsons et al., 2018). Simulations of the last millennium \r\nfrom CMIP5-PMIP3 reproduce the observed negative correlation "


id: 680b3ecea5d3f8d8aaf743327cfcf650
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 15573
content:
"Figure 8.24 | Impact of the North Atlantic Oscillation (NAO) on 2016-2045 climate trends. (a) Regressions of winter sea level pressure (SLP) and precipitation \r\ntrends upon the normalized leading principal component (PC) of winter SLP trends in the CESM1 Large Ensemble, multiplied by two to correspond to a two standard deviation \r\nanomaly of the PC (as internal climate variability component); (b) CESM1 ensemble-mean winter SLP and precipitation trends (as forced climate variability component); \r\n(c) b - a (forced minus internal climate variability component); (d) b + a (forced plus internal climate variability component). Precipitation in colour shading (mm day-1 per \r\n30 years) and SLP in contours (interval = 1 hPa per 30 years with negative values dashed). Figure adapted from Deser et al. (2017), https://creativecommons.org/licenses/by/4.0/; \r\nfurther details on data sources and processing are available in the chapter data table (Table 8.SM.1)."


id: a74113fc2bedb65d9740ba69e57d6b75
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 26643
content:
"of the methods used to estimate time of emergence is presented \r\nin Chapter 10 (Section 10.3.4.3). For specific regional examples of \r\nclimate change attribution and emergence of anthropogenic signal, \r\nsee Section 10.4.2."


id: 2b4556b2c2fc4181a1d01d34dd448de0
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 4857
content:
"Beyond the tropics, North Pacific decadal variability (Annex IV.2.6, \r\n2.4.5, 3.7.6) exerts a  strong modulation of extratropical ENSO \r\nteleconnections, but also influences low-frequency variability of the \r\nWalker circulation, which is underestimated by most CMIP5 models \r\n(England et al., 2014). Atlantic Multi-decadal Variability (Annex IV.2.7, \r\n2.4.6, 3.7.7) teleconnections show a high model spread among CMIP5 \r\nmodels, both in terms of persistence and spatial coherence (Qasmi \r\net al., 2017), which has potential consequences for the water cycle \r\nvariability simulated over Europe. For example, internal variability \r\nwill continue to play an important role in the variability of river flows \r\nover France in coming decades (medium confidence) (Giuntoli et al., \r\n2013; Boe and Habets, 2014; Bonnet et al., 2017)."


id: 4fba2a3baa20fdeb7cf15a3248419f24
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 11320
content:
"In summary, there is medium confidence that climate models \r\nreproduce the general magnitude and character of internal variability \r\nthat influences water cycle variables. There is high confidence that \r\ninternal variability will continue to be a major source of uncertainty, \r\nat least for near-term water cycle projections at the regional scale. \r\nThere is low confidence in the region-dependent time of emergence \r\nof water cycle changes (see also Section 10.4.3), but there is medium \r\nconfidence that changes in wet extreme events will emerge earlier \r\nthan changes in average conditions."


id: cc9d4ceb0dab0dc4b286e1d14788cbb5
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 33323
content:
"Ensembles of atmosphere-only simulations driven by observed or \r\nreconstructed SST are useful for evaluating the ability of models to \r\ncapture the circulation and/or precipitation variability observed over \r\nthe historical period (Zhou et al., 2016; Deng et al., 2018; Douville \r\net al., 2019). However, limitations of such AGCM-based attribution \r\nmethods, that is, related to the lack of air-sea interactions in the \r\nresponse, may lead to erroneous attribution conclusions in some \r\nregions for local circulation and mean and extreme precipitation \r\n(Dong et al., 2017). Other methods to measure the portion of \r\nprecipitation variability include the partitioning into dynamical \r\nas opposed to thermodynamical components (Saffioti et al., 2016; \r\nFereday et al., 2018; Lehner et al., 2018), the analysis of variance \r\n(Dong et al., 2018a) and direct characterization of stochastic weather\u0002noise (Short Gianotti et al., 2014). "


id: 3e646e4790f3842b4e601b3bdeca674d
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 8158
content:
"Adapting water resource management in the face of climate change \r\nwill greatly benefit from improved prediction of land surface hydrology \r\nat the decadal time scale. Climate predictions (Section 1.4.4) differ \r\nfrom climate projections by constraining the initial state of the slow \r\ncomponents of the climate system (i.e., the ocean, the cryosphere \r\nand the terrestrial hydrology) as well as volcanic aerosols and ozone \r\ndepleting substances with observations. Anthropogenic and natural \r\nradiative forcing and low-frequency modes of variability (e.g., AMV \r\nand PDV, Annex IV.2.7 and IV.2.6) suggest the possible predictability \r\nof climate in the first decade or so of the 21st century, in addition to \r\nthe projected response to the anthropogenic forcing (Sections 4.2.3 \r\nand 4.4.1.3). "


id: bc5fd96aaefd87e4d6f435d25e7c7645
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 30386
content:
"Single-model initial condition large ensembles (SMILEs) are a powerful \r\ntool for estimating the magnitude of internal variability in historical \r\nand future climates (Section  1.4.4). Using SMILEs, it has been \r\nshown, for example, that internal NAO variability imparts substantial \r\nuncertainty to future changes in European precipitation (Figure 8.24; \r\nDeser et al., 2017). For the South Asian summer monsoon, internal \r\nvariability can overshadow the forced monsoon rainfall trend, thereby \r\nincreasing near-term projection uncertainties (X. Huang et al., 2020a). \r\nSpecific regional applications of the use of large ensembles are further \r\nassessed in Sections 10.3.4.3 and 10.3.4.4. "


id: 4769c605c5f637411c70434a231f53ef
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 9775
content:
"The additional skill associated with the initialization of the cryosphere \r\nand the land surface has received limited attention. However, there \r\nis observational evidence that oceanic decadal variations can \r\npropagate into the atmosphere and, consequently accumulate into \r\nterrestrial land surface reservoirs (e.g.,  Bonnet et al., 2017) and \r\nvegetation (e.g., Zeng et al., 1999). This land surface memory, like in \r\nsoil moisture (Alessandri and Navarra, 2008; Catalano et al., 2016) \r\nor snow (Loranty et al., 2014), may also contribute to the decadal \r\npredictability of the terrestrial component of the water cycle, but "


id: 434d3f9f998ca8c3874c49181e4ab5df
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 9052
content:
"remains difficult to assess given the limitations of observational \r\nrecords. Vegetation initialization seems to generate as much noise as \r\nsignal and does not necessarily translate into improved skill in early \r\ndecadal predictions based on ESMs (Weiss et al., 2014). "


id: a93af96a3a05fb9c4682bc64aca8f42d
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 26995
content:
"single large events or clustered smaller ones, can alter the water cycle \r\n(see also Cross-Chapter Box 4.1), and regional drought events may \r\nbe enhanced by co-occurring volcanic (Liu et al., 2016; Gao and Gao, \r\n2017; Zambri et al., 2017) and GHG (e.g., Cook et al., 2018) forcing \r\n(low confidence). Volcanic eruptions may also lead to widespread \r\nprecipitation anomalies up to several years following an eruption \r\nthrough their potential influence on the El Nino Southern Oscillation \r\n(low confidence) (Stevenson et al., 2016; Dee et al., 2020; McGregor \r\net al., 2020)."


id: 25b78067c6e93d795ea9c78172d4066d
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 3819
content:
"Decadal hydrological predictability in an idealized setting has also \r\nbeen investigated through offline land surface hindcast experiments, \r\ndriven by observed atmospheric forcing and/or initial conditions, \r\nsuggesting the potential for skilful predictions for terrestrial water \r\nstorage, deep soil moisture, and groundwater (Yuan and Zhu, 2018). \r\nYet, a real-world assessment is hampered by the lack of observations \r\nand is only feasible when multi-decadal records of satellite estimates \r\nof terrestrial water storage, snow mass or soil moisture are available."


id: 9b759386644d968eb3142c1c1967bac0
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 24604
content:
"In summary, large volcanic eruptions reduce global mean precipitation, \r\nas well as precipitation in tropical wet regions (high confidence). \r\nThere is low confidence in specific regional and seasonal responses, \r\nprimarily due to the limitations of the observational record."


id: 369abb5334517ad0b8a259bb708f3707
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 6840
content:
"In summary, there is high confidence that the water cycle changes \r\nthat have already emerged from internal variability will become more \r\npronounced in near-term (2021-2040) projections. However, there \r\nis low confidence in decadal predictions of precipitation changes, \r\nparticularly over most land areas, because internal variability \r\nremains difficult to predict and can offset or amplify the forced water \r\ncycle response. "


id: ad15709cf5d6694eb68ac9d53d976292
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 27688
content:
"The AR5 concluded that annual and seasonal mean precipitation \r\nchanges can be estimated by linear pattern-scaling techniques (Santer \r\nand Wigley, 1990; Arnell and Gosling, 2016; Greve et al., 2018), \r\nwhich represent regional changes in precipitation as a linear function \r\nof global mean temperature change. However, there are a number of \r\ncaveats when pattern-scaling is applied to low-emissions scenarios \r\nor to scenarios where localized forcing (e.g., anthropogenic aerosols) \r\nare significant and vary in time (Collins et al., 2013). Here the focus is \r\nin on non-linear water cycle responses to increasing global warming \r\nlevels, as estimated for instance from the difference between the first \r\n2degC of global warming, and the next 2degC of warming (Figure 8.25), \r\nand their possible underlying mechanisms."


id: 3e1bf85f0e72a4bcc171f4025564d445
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 8098
content:
"Data for six major eruptions over the last century along with \r\nCMIP5 historical experiments indicate that volcanic eruptions cause \r\na  detectable decrease in streamflow in northern South America, \r\nCentral Africa, high-latitude Asia and in wet tropical-subtropical \r\nregions, and a detectable increase in south-western North America \r\nand southern South America (Iles and Hegerl, 2015). Attempts \r\nto include volcanic forcing in future projections show enhanced \r\nprecipitation variability on annual to decadal time scales with \r\nsmall reductions in Asian monsoon rainfall (Bethke et al., 2017). \r\nThe occurrence of volcanic eruptions in the coming century, either as "


id: e6a1acaaea09e2a12327f5718f3ed8d2
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 37870
content:
"and the rate of change (Ceppi et al., 2018). The usual distinction \r\nbetween the fast adjustment to increased GHG concentrations and \r\nthe slower response to SST warming (Section 8.2.2.2) may, however, \r\nnot be sufficient to explain the time evolution of the hydroclimatic \r\nresponse at the regional scale, especially in subtropical land areas "


id: d387945604d18f66aa269d5e20637ade
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 34555
content:
"response of different parts of the Earth system cause evolving spatial \r\nwarming patterns (Good et al., 2016a). This occurs primarily because \r\ndifferent feedbacks occur at different time scales (Armour et al., 2013; \r\nAndrews et al., 2015), which in turn implies that the atmospheric \r\ncirculation and water cycle is dependent both on the level of warming "


id: ef335904908c619412f89381b845f5dc
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 39452
content:
"where this response critically depends on shifts in atmospheric \r\ncirculation associated with distinct 'fast' (typically five to ten years, \r\nthat is however much slower than the atmospheric adjustment \r\nassessed in Section  8.2.1) and slow SST warming patterns (Zappa \r\net al., 2020). The changing balance between the water cycle response \r\nto anthropogenic GHG and aerosol forcings is another source of non\u0002linearity across time and global warming levels (Ishizaki et al., 2013; \r\nRowell et al., 2015; Y. Liu et al., 2019b; Wilcox et al., 2020). "


id: 530e013c6f5c05e5fec777b2fcb7d709
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 11878
content:
"In summary, there is high confidence that continued global warming \r\nwill further amplify GHG-induced changes in large-scale atmospheric \r\ncirculation and precipitation. Nonetheless, there are cases where \r\nregional water cycle changes are not linearly related to global \r\nwarming due to the interaction of multiple forcings, feedbacks and \r\ntime scales (medium confidence, see also Sections 4.2.4, 7.4.3 and \r\n8.2.1). Aridity in subtropical regions is highly sensitive to fast shifts \r\nin large-scale atmospheric circulation so are particularly susceptible \r\nto such non-linearities."


id: be5c6f11093f28dc2d2e720fa6982acd
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 30754
content:
"Based on a  simple model, it was also suggested that the Indian \r\nsummer monsoon may exhibit a moisture-advection feedback which \r\nallows multiple stable states as boundary conditions change (Zickfeld \r\net al., 2005). However, limitations of this theory and comprehensive \r\nGCMs suggest a  near-linear monsoon response to a  broad range \r\nof radiative forcings (Boos and Storelvmo, 2016). Non-linear \r\nprecipitation responses to global warming have been reported in \r\nthe Indo-Pacific, where a  linear increase in SSTs can trigger non\u0002linear changes in precipitation and a  shift in the ITCZ depending \r\non the relative amplitudes of uniform and structured SST anomalies \r\n(C.T.Y. Chung et al., 2014; Toda and Watanabe, 2018)."


id: 516a71f4a7f19e078ba90f996d99cf51
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 11605
content:
"Snowmelt is a nonl-inear process and projected changes in snowfall \r\nare also a  non-linear combination of changes in total precipitation \r\nand in the fraction of solid precipitation. In cold regions, snowfall may \r\nfirst increase because of the increased water capacity of a  warmer \r\natmosphere and then decrease because snow falls as rain in an even \r\nwarmer atmosphere. Such non-linearities can contribute to elevation, "


id: af8381d19428205f0bb81982e5adb1f
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 28111
content:
"latitudinal and seasonal contrasts in the observed and projected \r\nretreat of the Northern Hemisphere (NH) snow cover (Shi and Wang, \r\n2015; Thackeray et al., 2016). Mountain glaciers also represent source \r\nof non-linear runoff responses since the annual runoff can first increase \r\ndue to additional melting and then decrease as the glaciers shrink \r\n(Kraaijenbrink et al., 2017; Shannon et al., 2019). Section  9.5.1.3 \r\nconcludes with high confidence that the average annual runoff from "


id: 32f19259b402ded521c21ce5bda53da4
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 6227
content:
"glaciers will generally reach a peak at the latest by the end of the 21st \r\ncentury, and decline thereafter. This peak may have already occurred for \r\nsmall catchments with little ice cover, but tends to occur later in basins \r\nwith large glaciers. Permafrost thawing is another mechanism which \r\ncan trigger a non-linear hydrological response in the high latitudes of \r\nthe NH (Walvoord and Kurylyk, 2016), whose magnitude and potential \r\nabruptness is assessed in Section 5.4.3.3."


id: 698f29e1737e95ca3bc8772b07157451
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 15836
content:
"linear interactions between the ocean, atmosphere, and land surface \r\ncan result in rapid shifts between wet and dry states (Sections 8.6.1 \r\nand 8.6.2). Cessation of solar radiation modification could also result \r\nin abrupt changes in the water cycle (Section  8.6.3). This section \r\nreviews these types of abrupt shifts and assesses the likelihood that \r\nthey will occur by 2100."


id: 41ec247ee9d2df8bed7e5617a630cf3
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 8792
content:
"Beyond changes in land surface water fluxes, non-linearities in the \r\nresponse of soil moisture and freshwater reservoirs have not been \r\nwell documented in global climate projections but deserve further \r\nattention given the complex interactions between the water, energy \r\nand carbon cycles (Berg and Sheffield, 2018a), the growing direct \r\nhuman influence on rivers and groundwater (Abbott et al., 2019), \r\nand a possible offset between the linear components of changes in \r\nprecipitation and evapotranspiration. Significant non-linearities were \r\nfound in water scarcity projections, as seen by the stronger sensitivity \r\nto the first 2degC increase in global warming (Gosling and Arnell, 2016)."


id: 1f25c0e7834af3cca7da6566a5ad678d
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 2688
content:
"In summary, there is both numerical and process-based evidence \r\nthat terrestrial water cycle changes can be non-linear at the regional \r\nscale (high confidence). Non-linear regional responses of runoff, \r\ngroundwater recharge and water scarcity have been documented \r\nbased on both CMIP5 and CMIP6 models, and highlight the limitations \r\nof simple pattern-scaling techniques (medium confidence). Water \r\nresources fed by melting glaciers are particularly exposed to such \r\nnon-linearities (high confidence)."


id: e6f574ae068e706827aa338e9a9e56b0
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 37920
content:
"In this Report, abrupt change is defined as a regional-to-global scale \r\nchange in the climate system that occurs faster than the  typical \r\nrate of changes in its history, implying non-linearity in the climate \r\nresponse (see Glossary). Often, abrupt change arises from positive \r\nfeedbacks in the climate system that cause the current state to \r\nbecome unstable, and cross a 'tipping point' (Lenton et al., 2008); \r\nthat is, a rapid shift from one climate state to another. The water cycle \r\nhas several attributes with potential to produce abrupt change. Non\u0002"


id: 95d7f0c32484111a032791d444ff44a2
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 23562
content:
"AMOC collapse also causes the Asian monsoon systems to weaken \r\n(Figure 8.27b; W. Liu et al., 2017) counteracting the strengthening \r\nexpected in response to elevated greenhouse gases (see \r\nSection 8.4.2). Europe is projected to experience moderate drying in \r\nresponse to AMOC collapse (Jackson et al., 2015). "


id: 50d4bc7e5b55b0cc41383993ca8ea433
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 11498
content:
"In summary, given that there is medium confidence that the decline \r\nin AMOC will not involve an abrupt collapse before 2100, there is low \r\nconfidence that an AMOC-driven abrupt change in the water cycle \r\nwill occur by 2100. However, if AMOC collapse does occur, it is very \r\nlikely that there would be large regional impacts on the water cycle."


id: eb1e6efc8bb71201868209ad4cd0cefd
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 38720
content:
"Changes in the land surface, including vegetation cover and dust \r\nemissions, can trigger abrupt changes in the water cycle. Plants \r\nregulate the exchange of water and energy between the land surface \r\nand the atmosphere (Section  8.2.3.3), such that sudden shifts in \r\nplant functions, types, or biomes can trigger feedbacks that have \r\nthe potential to cause abrupt changes in the regional water cycle. \r\nDust emissions, from either climatic or land use changes, affect the \r\nradiation budget and can regionally exacerbate dry extremes. Below, \r\nwe assess the likelihood of abrupt changes in the water cycle for the \r\nwell-studied regions of the Amazon and the Sahel, and the potential \r\nfor dust emissions to amplify drought and aridity."


id: 5f1a50a14853c4af59fe5735b63b8eef
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 14034
content:
"In AR5, some simulations using a  coupled climate-carbon cycle \r\nmodel exhibited an abrupt dieback of the Amazon forest in future \r\nclimate scenarios (Oyama and Nobre, 2003; Cox et al., 2004; Malhi \r\net al., 2008). However, subsequent work demonstrated that abrupt \r\nAmazon dieback does not occur consistently across, or even within, \r\nEarth system models (Lambert et al., 2013; Boulton et al., 2017). \r\nThe occurrence of dieback is highly dependent on both how dry the \r\nsimulated climate is in the present day (Malhi et al., 2009) as well \r\nas the representation of forest structure and competitive dynamics \r\n(Levine et al., 2016). Models with a low diversity of plant characteristics \r\nand types have a higher tendency for abrupt change (Sakschewski \r\net al., 2016). Abrupt shifts and ecosystem disruptions can occur on \r\nthe sub-regional level (Pires and Costa, 2013), highlighting the need "


id: 14016366e4149349032cedf98e469976
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 735
content:
"The Amazon forest plays an active role in driving atmospheric moisture \r\ntransport and generating precipitation in the South American region \r\n(SRCCL; Drumond et al., 2014; Poveda et al., 2014; Yin et al., 2014; \r\nStaal et al., 2018, 2020; Agudelo et al., 2019; Espinoza et al., 2019). "


id: ff6cd6ad9b823bd1b2ccef4fcda950c4
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 42341
content:
"for higher-resolution modelling studies. Since AR5, CMIP6 projections \r\nsuggest that a tipping point in the Amazon system may be crossed on \r\na local or regional scale (Staal et al., 2020) but continue to be highly \r\ndependent on model biases in precipitation and the simulation of the \r\nland surface. Consequently, the timing, and probability, of an abrupt \r\nshift remains difficult to ascertain. "


id: 6939c7b44a0da1af6ac63540fea18504
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 15777
content:
"Bathiany et al., 2013). Conversely, low ecosystem diversity can \r\nproduce local or regional 'hot spots' of abrupt change such as those \r\nseen in some paleoclimate records (Claussen et al., 2013). "


id: 274086525e9256d0f547462ec5f4bc4c
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 4074
content:
"In summary, while there is a  strong theoretical expectation that \r\nAmazon drying and deforestation can cause a rapid change in the \r\nregional water cycle, currently there is limited model evidence to \r\nverify this response, hence there is low confidence that such a change \r\nwill occur by 2100."


id: 490f199b61085d9e3628389fa33ee4ca
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 10076
content:
"Some climate model simulations suggest that under future high\u0002emissions scenarios, CO2 radiative forcing causes rapid greening in \r\nthe Sahel and Sahara regions via precipitation change (Claussen \r\net al., 2003; Drijfhout et al., 2015). For example, in the BNU-ESM \r\nRCP8.5 simulation, the change is abrupt with the percentage of bare \r\nsoil dropping from 45% to 15%, and percentage of tree cover rising \r\nfrom 50% to 75%, within 10 years (2050-2060; Drijfhout et al., \r\n2015). However, other modelling results suggest that this may be \r\na short-lived response to CO2 fertilization (Bathiany et al., 2014). "


id: f126ae8521f45c1070804910434bd109
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 39809
content:
"In summary, given outstanding uncertainties in how well the current \r\ngeneration of climate models capture land surface feedbacks in the \r\nSahel and Sahara, there is low confidence that an abrupt change to \r\na greener state will occur in these regions before 2100 or 2300."


id: 7de38ab788c3f9f8fffdaf1ee7980ddf
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 19327
content:
"Mineral dust aerosols in the climate system originate from both semi\u0002permanent and transient sources (Prospero et al., 2002; Ginoux et al., \r\n2012). The former are typically arid regions where significant alluvial \r\nsediments have accumulated over time, while the latter are often \r\nassociated with natural (e.g., droughts, wildfires) and anthropogenic \r\n(e.g.,  land use change, desertification) disturbances. Modern-day \r\ndust emissions are dominated by natural sources (Ginoux et al., \r\n2012), although human emissions may contribute 10-60% of the \r\nglobal atmospheric dust load (Webb and Pierre, 2018). Paleo-dust \r\nrecords suggest that human factors (land use change and landscape \r\ndisturbance) may have doubled global dust emissions between 1750 \r\nand the last quarter of the 20th century (Section 2.2.6; Hooper and \r\nMarx, 2018)."


id: 9fb7b57dc3bedc9d63894290f9ff7d72
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 25304
content:
"Dust aerosols influence the climate system and hydrologic cycle \r\nthrough both direct impacts on radiation (absorbing and scattering \r\nlongwave and shortwave) and via indirect effects on cloud and \r\nprecipitation processes (Box  8.1; Choobari et al., 2014; Kok et al., \r\n2018; Schepanski, 2018). The capacity of dust aerosols to suppress \r\nprecipitation by reducing humidity and energy availability, and "


id: fbab3e257a9de74e01e3db8f465621b5
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 41648
content:
"precipitation relative to future CO2-emissions scenarios (Bala et al., \r\n2008; A. Jones et al., 2013; Tilmes et al., 2013; Ferraro et al., 2014; \r\nCrook et al., 2015). In contrast, cirrus cloud thinning, a  longwave \r\nradiation technique, results in increased global precipitation as it \r\ncauses enhanced radiative cooling in the troposphere (medium \r\nconfidence) (Crook et al., 2015; Kristjansson et al., 2015; Jackson \r\net al., 2016)."


id: 8aa6bd7debbc8bfe0f684b398b21c7bb
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 21567
content:
"The magnitude of hydrological disruption for both the initiation and \r\ntermination of SRM depends on the method used, as well as the \r\nstrength and duration of its implementation (Ekholm and Korhonen, \r\n2016; Irvine et al., 2019). Under abrupt SRM implementation, \r\nhydrological shifts are rapid, occurring within the first decade \r\n(Crook et al., 2015). Artificial enhancement of albedo in Northern \r\nHemisphere desert regions causes a southward shift in the Hadley \r\nCell and ITCZ, and extreme drying in the northern tropics (Crook et al., \r\n2015). Uniform or tropical stratospheric sulfate injection weakens \r\nthe African and Asian summer monsoons and causes drying in the \r\nAmazon (Robock et al., 2008; Crook et al., 2015; Dagon and Schrag, \r\n2016). Changes in evapotranspiration can produce large deficits or \r\nsurpluses in soil moisture and runoff in different regions and seasons \r\n(Dagon and Schrag, 2016). "


id: 7b628d462ec2745e4937a7e8293aca29
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 18907
content:
"The importance of dust aerosol feedbacks in future abrupt climate \r\nevents, like droughts or rapid aridification, is unclear. In part, this is \r\nbecause the response of dust aerosol emissions and loading levels \r\nin the atmosphere to climate change is highly uncertain (Tegen and \r\nSchepanski, 2018; Webb and Pierre, 2018). This difficulty in predicting \r\nfuture dust responses is rooted in the fact that emissions depend on \r\nboth changes to the land surface (e.g., land use/land cover change, \r\naridification, ecological responses to climate change) and the state \r\nof the atmosphere (Tegen and Schepanski, 2018). While there is some \r\nevidence that global dust aerosol concentrations in the future will \r\nincrease (Allen et al., 2016; Tegen and Schepanski, 2018), it is highly \r\ndependent on changes in precipitation patterns and atmospheric \r\ncirculation (see the SRCCL, Section 2.4.1), and it is not clear what the \r\nradiative impact will be (Allen et al., 2016; Kok et al., 2018). "


id: 55a4cd8067cc42aeb46a79eb9a8d99c4
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 12341
content:
"Rapid changes (years to decades) in the hydrological cycle are \r\nalso expected if SRM is terminated abruptly, either purposefully or \r\nbecause of technical failure or political disagreement. We reiterate \r\nthe AR5 conclusion that if SRM 'were terminated for any reason, there \r\nis high confidence that surface temperatures would increase rapidly \r\n(within a decade or two) to values consistent with the GHG forcing'. \r\nThe additional global warming caused by SRM termination may result \r\nin a rapid increase in global mean precipitation (medium confidence) \r\n(A. Jones et al., 2013). Heterogenous regional and seasonal changes \r\nare also expected, but are model-dependent (A. Jones et al., 2013). \r\nAs with SRM initiation, the impact of SRM termination is expected to \r\nbe dependent on the technique deployed. "


id: dff719af21df2aeb009c2f592c7a7a5f
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 36646
content:
"In summary, due to limited evidence, there is low confidence\r\nregarding the role of dust in abrupt climate change events over the \r\nnext century."


id: 20a042f9ead78bdc0d72879f26af05f9
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 2967
content:
"In summary, it is very likely that abrupt water cycle changes will occur \r\nif SRM is abruptly initiated or halted, especially in tropical regions. \r\nFurther assessment of the potential side-effects of SRM is found in \r\nSection 4.6.3.3."


id: 8bcc2c207e7d6cd614e7864857de0bea
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 21768
content:
"interactions between climate change and internal variability and \r\nin the detection and attribution of observed water cycle changes."


id: 7115b5b2359004566b566f8fbf6b9432
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 17153
content:
"The authors are extremely grateful to the WGI Bureau and Technical \r\nSupport Unit for their outstanding support throughout the writing \r\nof the chapter. Special thanks to Sarah Connors for her exceptional \r\ncontribution to the development of this chapter. Her help with \r\nchapter meetings and organizational support was invaluable. Thanks \r\nto Sophie Berger, Melissa Gomis, Nigel Hawtin and Tom Maycock for \r\ntheir contributions to figures and tables. We must also make a special \r\nacknowledgment of our chapter scientists, Sabin Thazhe Purayil and \r\nStephane Senesi, without whom we could not have completed our \r\nassessment nor could we have produced the excellent figures that \r\nappear in the chapter. We would also like to thank all reviewers \r\nfor their useful comments. Finally, we are infinitely indebted to \r\nour families for their extended patience and support during this \r\ndemanding process. "


id: 6fe9f1a50dfd0c52e0aab99ab2f98d64
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 16945
content:
"The ways in which humans use and change land cover, for example by converting fields to urban areas or clearing \r\nforests, can affect every aspect of the water cycle. Land-use changes can alter precipitation patterns and how \r\nwater is absorbed into the ground, flows into streams and rivers, or floods the land surface, as well as how \r\nmoisture evaporates back into the air. Changes in any of these aspects of the interconnected water cycle can \r\naffect the entire cycle and the availability of freshwater resources."


id: 2a944d7ebae6d2e84cce467955a4530f
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 4720
content:
"Land use describes the combination of activities and ground cover defining each area of the Earth's continental \r\nsurface. Altering land use can modify the exchange of water between the atmosphere, soil and subsurface \r\n(FAQ 8.1, Figure 1). "


id: ccb9d8b4a8ef569d71712734b9edde7c
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 33347
content:
"For instance, changes in land cover can affect the ability of soils to soak up surface water (infiltration). When soil \r\nloses its capacity to soak up water, precipitation that would normally infiltrate and contribute to groundwater \r\nreserves will instead overflow, increasing surface water (runoff) and the likelihood of flooding. For example, \r\nchanging from vegetation to urban cover can cause water to flow rapidly over buildings, roads and driveways \r\nand into drains rather than soaking into the ground. Deforestation over wide areas can also directly reduce \r\nsoil moisture, evaporation and rainfall locally but can also cause regional temperature changes that affect \r\nrainfall patterns."


id: 5baec3683edecbd3237a12b87aaabb6
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 13380
content:
"Extracting water from the ground and river systems for agriculture, industry and drinking water depletes \r\ngroundwater and can increase surface evaporation because water that was previously in the ground is now in \r\ndirect contact with the atmosphere, being available for evaporation. "


id: 701b296e7247c8ee72c6f62b80f3c533
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 16974
content:
"Changing land use can also alter how wet the soil is, influencing how quickly the ground heats up and cools \r\ndown and the local water cycle. Drier soils evaporate less water into the air but heat up more in the day. This can \r\nlead to warmer, more buoyant plumes of air that can promote cloud development and precipitation if there is \r\nenough moisture in the air. "


id: 85cb7b6d26aed31738be197fd2f94225
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 20680
content:
"Changes in land use can also modify the amount of tiny aerosol particles in the air. For instance, industrial and \r\ndomestic activities can contribute to aerosol emissions, as do natural environments such as forests or salt lakes. \r\nAerosols cool down global temperature by blocking out sunlight but can also affect the formation of clouds and \r\ntherefore the occurrence of precipitation (see FAQ 7.2). "


id: a81777a32741b9407f7683fa4926639
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 26785
content:
"Vegetation plays an important role in soaking up soil moisture and evaporating water into the air (transpiration) \r\nthrough tiny holes (stomata) that allow the plants to take in carbon dioxide. Some plants are better at retaining \r\nwater than others, so changes in vegetation can affect how much water infiltrates into the ground, flows into \r\nstreams and rivers, or is evaporated. "


id: 374104bfd6ecd04ec3d7a8ff9b8e5f39
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 6936
content:
"More globally, land-use change is currently responsible for about 15% of the emissions of carbon dioxide \r\nfrom human activities, leading to global warming, which in turn affects precipitation, evaporation, and plant \r\ntranspiration. In addition, higher atmospheric concentrations of carbon dioxide due to human activities can \r\nmake plants more efficient at retaining water because the stomata do not need to open so widely. Improved \r\nland and water management (e.g., reforestation, sustainable irrigation) can also contribute to reducing climate \r\nchange and adapting to some of its adverse consequences. "


id: d8ad745a4520b9f13beafca671bc00b9
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 35413
content:
"In summary, there is abundant evidence that changes in land use and land cover alter the water cycle globally, \r\nregionally and locally, by changing precipitation, evaporation, flooding, groundwater, and the availability of \r\nfreshwater for a variety of uses. Since all the components of the water cycle are connected (and linked to the \r\ncarbon cycle), changes in land use trickle down to many other components of the water cycle and climate system. "


id: c69f451dba0674c4f6ab0fc3b4e47cf1
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 32247
content:
"A warmer climate increases the amount and intensity of rainfall during wet events, and this is expected to \r\namplify the severity of flooding. However, the link between rainfall and flooding is complex, so while the most \r\nsevere flooding events are expected to worsen, floods could become rarer in some regions. "


id: d6b0d5180f8f7ce44b1f4bcfe9d79fd6
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 35076
content:
"Floods are a natural and important part of the water cycle but they can also threaten lives and safety, disrupt \r\nhuman activities, and damage infrastructure. Most inland floods occur when rivers overtop their banks (fluvial\r\nflooding) or when intense rainfall causes water to build up and overflow locally (pluvial flooding). Flooding is \r\nalso caused by coastal inundation by the sea, rapid seasonal melting of snow, and the accumulation of debris, \r\nsuch as vegetation or ice, that stops water from draining away. "


id: a94e746d7459129804c22da000a895e2
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 27009
content:
"Climate change is already altering the location, frequency and severity of flooding. Close to the coasts, rising \r\nsea levels increasingly cause more frequent and severe coastal flooding, and the severity of these floods is \r\nexacerbated when combined with heavy rainfall. The heavy and sustained rainfall events responsible for most \r\ninland flooding are becoming more intense in many areas as the climate warms because air near Earth's surface \r\ncan carry around 7% more water in its gas phase (vapour) for each 1degC of warming. This extra moisture is drawn \r\ninto weather systems, fueling heavier rainfall (FAQ 8.2, Figure 1). "


id: 82f335d4084fc45dace1af561f473e38
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 20167
content:
"A warming climate also affects wind patterns, how storms form and evolve, and the pathway those storms \r\nusually travel. Warming also increases condensation rates, which in turn releases extra heat that can energize \r\nstorm systems and further intensify rainfall. On the other hand, this energy release can also inhibit the uplift \r\nrequired for cloud development, while increases in particle pollution can delay rainfall but invigorate storms. \r\nThese changes mean that the character of precipitation events (how often, how long lasting and how heavy they \r\nare) will continue to change as the climate warms. "


id: d7be3159a86f900e3f0f2b886f3b6e52
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 35264
content:
"In addition to climate change, the location, frequency and timing of the heaviest rainfall events and worst \r\nflooding depend on natural fluctuations in wind patterns that make some regions unusually wet or dry for \r\nmonths, years, or even decades. These natural variations make it difficult to determine whether heavy rainfall \r\nevents are changing locally as a result of global warming. However, when natural weather patterns bring heavy \r\nand prolonged rainfall in a warmer climate, the intensity is increased by the larger amount of moisture in the air."


id: e306f7eb232613781b6025fad3c2e457
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 37236
content:
"An increased intensity and frequency of record-breaking daily rainfall has been detected for much of the land \r\nsurface where good observational records exist, and this can only be explained by human-caused increases in \r\natmospheric greenhouse gas concentrations. Heavy rainfall is also projected to become more intense in the future \r\nfor most places. So, where unusually wet weather events or seasons occur, the rainfall amounts are expected to \r\nbe greater in the future, contributing to more severe flooding. "


id: f3689943e119c1b11ba508961014a2b4
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 40197
content:
"A mix of both increases and decreases in flooding have been observed in some regions and these changes have \r\nbeen attributed to multiple causes, including changes in snowmelt, soil moisture and rainfall. Although we \r\nknow that a warming climate will intensify rainfall events, local and regional trends are expected to vary in \r\nboth direction and magnitude as global warming results in multiple, and sometimes counteracting, influences. \r\nHowever, even accounting for the many factors that generate flooding, when weather patterns cause flood \r\nevents in a warmer future, these floods will be more severe. "


id: 8fb108c9a6cfe3ff927976fe7733fc3e
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 22491
content:
"Flooding presents a hazard but the link between rainfall and flooding is not simple.\r\nWhile the largest flooding events can be expected to worsen, flood occurrence may decrease in some regions. "


id: 37ec0dc35e925b0505232cc2f83e30b3
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 7044
content:
"Droughts usually begin as a deficit of precipitation, but then propagate to other parts of the water cycle (soils, \r\nrivers, snow/ice and water reservoirs). They are also influenced by factors like temperature, vegetation and \r\nhuman land and water management. In a  warmer world, evaporation increases, which can make even wet \r\nregions more susceptible to drought."


id: 309d67618582d949e1e89bb2007525f4
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 5803
content:
"Baylor Fox-Kemper (United States of America), Helene T. Hewitt (United Kingdom), Cunde Xiao (China)"


id: b3a16efc083c6cd6fdafcbafe410947e
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 28844
content:
"Gudfinna Adalgeirsdottir (Iceland), Sybren S. Drijfhout (The Netherlands), Tamsin L. Edwards \r\n(United Kingdom), Nicholas R. Golledge (New Zealand/United Kingdom), Mark Hemer (Australia), \r\nRobert E. Kopp (United States of America), Gerhard Krinner (France/Germany, France), Alan Mix \r\n(United States of America), Dirk Notz (Germany), Sophie Nowicki (United States of America/\r\nFrance, United States of America), Intan Suci Nurhati (Indonesia), Lucas Ruiz (Argentina), \r\nJean-Baptiste Sallee (France), Aimee B.A. Slangen (The Netherlands), Yongqiang Yu (China)"


id: 9afe69da69e8bd3c7ac025b4e5413d40
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 24514
content:
"(United Kingdom), Johannes Quaas (Germany), Roshanka Ranasinghe (The Netherlands/Sri Lanka, \r\nAustralia), Roelof Rietbroek (The Netherlands), Malcolm J. Roberts (United Kingdom), Alessio Rovere \r\n(Germany/Italy), Maria  Santolaria Otin (Spain, France/Spain), Abhishek Savita (Australia/India), \r\nAlex Sen Gupta (Australia/United Kingdom, Australia), Helene Seroussi (United States of America/\r\nFrance), Sharon L. Smith (Canada), Olga N. Solomina (The Russian Federation), Esther Stouthamer \r\n(The  Netherlands), Fiametta Straneo (United States of America/Italy, United States of America), \r\nWilliam V. Sweet (United States of America), Thomas Wahl (United States of America/Germany), Lisan Yu \r\n(United States of America), Jiacan Yuan (United States of America/China), Jan David Zika (Australia)"


id: 1f214a826e18eb4e2e5856de00b00f6c
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 2684
content:
"Unnikrishnan Alakkat (India), Benjamin P. Horton (Singapore/United Kingdom), Simon Marsland \r\n(Australia)"


id: 9a709bf2e5a169ea814016608c822277
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 24415
content:
"Gregory G. Garner (United States of America), Tim H. J. Hermans (The Netherlands), Lijuan Hua (China), \r\nTamzin Palmer (United Kingdom), Brodie Pearson (United States of America/United Kingdom)"


id: 8efeca6ecf4726f001dbe7d3add12b5e
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 22351
content:
"Fox-Kemper, B., H.T. Hewitt, C. Xiao, G. Adalgeirsdottir, S.S. Drijfhout, T.L. Edwards, N.R. Golledge, M. Hemer, R.E. Kopp, \r\nG.  Krinner, A. Mix, D. Notz, S. Nowicki, I.S. Nurhati, L. Ruiz, J.-B. Sallee, A.B.A. Slangen, and Y. Yu, 2021: Ocean, \r\nCryosphere and Sea Level Change. In Climate Change 2021: The Physical Science Basis. Contribution of Working \r\nGroup I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. \r\nZhai, A. Pirani, S.L.  Connors, C. Pean, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. \r\nLonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekci, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, \r\nCambridge, United Kingdom and New York, NY, USA, pp. 1211-1362, doi:10.1017/9781009157896.011."


id: b1341a3c6c92e583c3f5ee176dab7d1b
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 28410
content:
"Marine heatwaves  - sustained periods of anomalously high \r\nnear-surface temperatures that can lead to severe and persistent \r\nimpacts on marine ecosystems - have become more frequent \r\nover the 20th century (high confidence). Since the 1980s, they \r\nhave approximately doubled in frequency (high confidence) and \r\nhave become more intense and longer (medium confidence).\r\nThis  trend will continue, with marine heatwaves at global scale \r\nbecoming four times [2 to 9, likely range] more frequent in 2081-2100 \r\ncompared to 1995-2014 under SSP1-2.6, and eight times [3 to 15, likely\r\nrange] more frequent under SSP5-8.5. The largest changes will occur in \r\nthe tropical ocean and the Arctic (medium confidence). {Box 9.2}"


id: c451d8b37131899aeaf2d9be560fe3e2
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 31830
content:
"The upper ocean has become more stably stratified since \r\nat least 1970 over the vast majority of the globe (virtually \r\ncertain), primarily due to surface-intensified warming and \r\nhigh-latitude surface freshening (very high confidence). Changes \r\nin ocean stability affect vertical exchanges of surface waters with \r\nthe deep ocean and large-scale ocean circulation. Based on recent \r\nrefined analyses of the available observations, the global 0-200 m \r\nstratification is now assessed to have increased about twice as much \r\nas reported by SROCC, with a 4.9 +- 1.5% increase from 1970 to 2018 \r\n(high confidence) and even higher increases at the base of the surface \r\nmixed layer. Upper-ocean stratification will continue to increase \r\nthroughout the 21st century (virtually certain). {9.2.1}"


id: 9ba197aa3ac8129842471af4529ded22
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 24634
content:
"At the ocean surface, temperature has, on average, increased by \r\n0.88 [0.68 to 1.01] degC between 1850-1900 and 2011-2020, with \r\n0.60 [0.44 to 0.74] degC of this warming having occurred since 1980. \r\nThe ocean surface temperature is projected to increase between \r\n1995 to 2014 and 2081 to 2100 on average by 0.86 [0.43 to \r\n1.47, likely range] degC in SSP1-2.6 and by 2.89 [2.01  to 4.07, \r\nlikely range] degC in SSP5-8.5. Since the 1950s, the fastest surface \r\nwarming has occurred in the Indian Ocean and in western boundary \r\ncurrents, while ocean circulation has caused slow warming or surface \r\ncooling in the Southern Ocean, equatorial Pacific, North Atlantic, and \r\ncoastal upwelling systems (very high confidence). At least 83% of the \r\nocean surface will very likely warm over the 21st century in all Shared \r\nSocio-economic Pathways (SSP) scenarios. {2.3.3, 9.2.1}"


id: 98965a8f283f92e03212c73b160f88b8
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 24058
content:
"The Atlantic Meridional Overturning Circulation (AMOC) will \r\nvery likely decline over the 21st century for all SSP scenarios. \r\nThere is medium confidence that the decline will not involve \r\nan abrupt collapse before 2100. For the 20th century, there is low \r\nconfidence in reconstructed and modelled AMOC changes because of \r\ntheir low agreement in quantitative trends. The low confidence also \r\narises from new observations that indicate missing key processes in \r\nboth models and measurements used for formulating proxies and \r\nfrom new evaluations of modelled AMOC variability.  This results \r\nin low confidence in quantitative projections of AMOC decline in the \r\n21st  century, despite the  high confidence  in the future decline as \r\na qualitative feature based on process understanding. {9.2.3}"


id: 1b0ea12245834748182e48cc0f51e12
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 1960
content:
"Southern Ocean circulation and associated temperature \r\nchanges in Antarctic ice-shelf cavities are sensitive to changes \r\nin wind patterns and increased ice shelf melt (high confidence). \r\nHowever, limitations in understanding feedback mechanisms \r\ninvolving the ocean, atmosphere and cryosphere, which are not fully \r\nrepresented in the current generation of climate models, generally \r\nlimit our confidence in future projections of the Southern Ocean and \r\nof its forcing on Antarctic sea ice and ice shelves. {9.2.3, 9.3.2, 9.4.2}"


id: fd9ad7b994fc17c5330a926f1716a218
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 41967
content:
"Many ocean currents will change in the 21st  century as \r\na  response to changes in wind stress associated with \r\nanthropogenic warming (high confidence). Western boundary \r\ncurrents have shifted poleward since 1993 (medium confidence), \r\nconsistent with a poleward shift of the subtropical gyres. Of the four \r\neastern boundary upwelling systems, only the California Current \r\nsystem has experienced some large-scale upwelling-favourable "


id: 57e62295682adbe65ec6fd2dc35f23e3
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 12729
content:
"The Antarctic Ice Sheet has  lost 2670 [1800 to 3540] Gt \r\nmass over the period 1992-2020, equivalent to 7.4 [5.0  to \r\n9.8] mm global mean sea level rise. The mass-loss rate was, on \r\naverage, 49 [-2 to +100] Gt yr -1 over the period 1992-1999, \r\n70 [22 to 119] Gt yr-1 over the period 2000-2009 and 148 [94 to \r\n202] Gt yr -1 over the period 2010-2019. Mass losses from West \r\nAntarctic outlet glaciers outpaced mass gain from increased snow \r\naccumulation on the continent and dominated the ice-sheet mass \r\nlosses since 1992 (very high confidence). These mass losses from the \r\nWest Antarctic outlet glaciers were mainly induced by ice-shelf basal \r\nmelt (high confidence) and locally by ice-shelf disintegration preceded \r\nby strong surface melt (high confidence). Parts of the East Antarctic \r\nIce Sheet have lost mass in the last two decades (high confidence). \r\n{2.3.2, 9.4.2, Atlas.11.1}"


id: fba9d32502dc1eeb360e53779e381a6c
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 41646
content:
"wind intensification since the 1980s (medium confidence). In the \r\n21st century, consistent with projected changes in the surface winds, \r\nthe East Australian Current Extension and Agulhas Current Extension \r\nwill intensify, while the Gulf Stream and Indonesian Throughflow \r\nwill weaken (medium confidence). Eastern boundary upwelling \r\nsystems will change, with a dipole spatial pattern within each system \r\nof reduction at low latitude and enhancement at high latitude \r\n(high confidence). {9.2.1, 9.2.3}"


id: 1834feec2713a625282f20a7360ed7c7
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 1458
content:
"For Antarctic sea ice, regionally opposing trends and large \r\ninterannual variability result in no significant trend in satellite\u0002observed sea ice area from 1979 to 2020 in both winter and \r\nsummer (high confidence). The regionally opposing trends result \r\nprimarily from changing regional wind forcing (medium confidence). \r\nThere is low confidence in model simulations of future Antarctic sea \r\nice decrease, and lack of decrease, due to deficiencies of process \r\nrepresentation, in particular at the regional level. {2.3.2, 9.2.3, 9.3.2}"


id: 18643c4a28e505e66b78b66341fd53c7
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 1504
content:
"The Greenland Ice Sheet has  lost 4890 [4140 to 5640] Gt \r\nmass over the period 1992-2020, equivalent to 13.5 [11.4 to \r\n15.6] mm global mean sea level rise. The mass-loss rate was \r\non average 39 [-3 to +80] Gt yr-1 over the period 1992-1999, \r\n175 [131 to 220] Gt yr-1 over the period 2000-2009 and \r\n243 [197 to 290] Gt yr -1 over the period 2010-2019. This mass \r\nloss is driven by both discharge and surface melt, with the latter \r\nincreasingly becoming the dominating component of mass loss with \r\nhigh interannual variability in the last decade (high confidence). The \r\nlargest mass losses occurred in the north-west and the south-east of \r\nGreenland (high confidence). {2.3.2, 9.4.1}"


id: a2b8fd1c3d33a5e230e397e5f151a7f5
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 25843
content:
"Glaciers lost 6200 [4600 to 7800] Gt of mass (17.1 [12.7 to \r\n21.5] mm global mean sea level equivalent) over the period \r\n1993-2019 and will continue losing mass under all SSP scenarios \r\n(very high confidence). During the decade 2010-2019, glaciers \r\nlost more mass than in any other decade since the beginning of \r\nthe observational record (very high confidence). For all regions \r\nwith long-term observations, glacier mass in the decade 2010-2019 \r\nis the smallest since at least the beginning of the 20th  century \r\n(medium confidence). Because of their lagged response, glaciers \r\nwill continue to lose mass at least for several decades even if global \r\ntemperature is stabilized (very  high  confidence). Glaciers  will lose "


id: fa3a71fcc178c3556a0fe7c101578cd0
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 41395
content:
"29,000 [9000 to 49,000] Gt and 58,000 [28,000 to 88,000] Gt over \r\nthe period 2015-2100 for RCP2.6 and RCP8.5, respectively (medium \r\nconfidence), which represents 18 [5 to 31] % and 36 [16 to 56] % \r\nof their early-21st-century mass, respectively. {2.3.2, 9.5.1, 9.6.1, \r\n9.6.3, 12.4}"


id: 294e097f5dc031b3b1dce050c284d009
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 4486
content:
"Ocean thermal expansion (38%) and mass loss from glaciers (41%) \r\ndominate the total change from 1901 to 2018. The contribution of \r\nGreenland and Antarctica to GMSL rise was four times larger during \r\n2010-2019 than during 1992-1999 (high confidence). Because of \r\nthe increased ice-sheet mass loss, the total loss of land ice (glaciers \r\nand ice sheets) was the largest contributor to global mean sea level \r\nrise over the period 2006-2018 (high confidence). {2.3.3, 9.6.1, 9.6.2, \r\nCross-Chapter Box 9.1, Table 9.A.1, Box 7.2}"


id: a7e7f0aea025b17d9b4aec7f753756e4
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 26750
content:
"At the basin scale, sea levels rose fastest in the Western Pacific \r\nand slowest in the Eastern Pacific over the period 1993-2018 \r\n(medium confidence). Regional differences in sea level arise from: \r\nocean dynamics; changes in Earth gravity, rotation and deformation \r\ndue to land ice and land-water changes; and vertical land motion. \r\nTemporal variability in ocean dynamics dominates regional patterns \r\non annual to decadal time scales (high confidence). The anthropogenic \r\nsignal in regional sea level change will emerge in most regions \r\nby 2100 (medium confidence). {9.2.4, 9.6.1}"


id: 59cfd839612aeb33f7e324c453e19dc1
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 13043
content:
"Increases in permafrost temperature have been observed over \r\nthe past three to four decades throughout the permafrost \r\nregions (high confidence), and further global warming will \r\nlead to near-surface permafrost volume loss (high confidence).\r\nComplete permafrost thaw in recent decades is a  common \r\nphenomenon in discontinuous and sporadic permafrost regions \r\n(medium confidence). Permafrost warmed globally by 0.29 [0.17 to \r\n0.41, likely range] degC between 2007 and 2016 (medium confidence). \r\nAn increase in the active layer thickness is a pan-Arctic phenomenon \r\n(medium confidence), subject to strong heterogeneity in surface \r\nconditions. The volume of perennially frozen soil within the upper 3 m \r\nof the ground will decrease by about 25% per 1degC of global surface \r\nair temperature change (up to 4degC above pre-industrial temperature) \r\n(medium confidence). {9.5.2}"


id: dd0cd1ff86b1a0982c9fe2cd79b67437
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 36155
content:
"Northern Hemisphere spring snow cover extent has been \r\ndecreasing since 1978 (very high confidence), and there is\r\nhigh confidence that this trend extends back to 1950. Further \r\ndecrease of Northern Hemisphere seasonal snow cover \r\nextent is virtually certain under further global warming. The \r\nobserved sensitivity of Northern Hemisphere snow cover extent to \r\nNorthern Hemisphere land surface air temperature for 1981-2010 \r\nis -1.9 [-2.8 to -1.0, likely range] million km2 per 1degC throughout \r\nthe snow season. It is virtually certain that Northern Hemisphere \r\nsnow cover extent will continue to decrease as global climate \r\ncontinues to warm, and process understanding strongly suggests \r\nthat this also applies to Southern Hemisphere seasonal snow \r\ncover (high  confidence). Northern Hemisphere spring snow cover \r\nextent will decrease by about 8% per 1degC of global surface air \r\ntemperature change (up to 4degC above pre-industrial temperature) \r\n(medium confidence). {9.5.3}"


id: feef00304767701fc95782a7a81b22b0
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 42250
content:
"It is  virtually certain  that GMSL will continue to rise until \r\nat least 2100, because all assessed contributors to GMSL \r\nare likely to virtually certain  to continue contributing \r\nthroughout this century. Considering only processes for which \r\nprojections can be made with at least medium confidence, \r\nrelative to the period 1995-2014, GMSL will rise by 2050 \r\nbetween 0.18  [0.15  to 0.23, likely range] m (SSP1-1.9) and \r\n0.23  [0.20  to  0.29, likely range]  m (SSP5-8.5), and by 2100 \r\nbetween 0.38  [0.28 to  0.55, likely range] m (SSP1-1.9) and \r\n0.77 [0.63  to 1.01, likely range]  m (SSP5-8.5). This GMSL \r\nrise is primarily caused by thermal expansion and mass loss from \r\nglaciers and ice sheets, with minor contributions from changes in \r\nland-water storage. These likely range projections do not include \r\nthose ice-sheet-related processes that are characterized by deep \r\nuncertainty. {9.6.3}"


id: e5a0152441094a60af84be06dc61f119
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 37708
content:
"Global mean sea level (GMSL) rose faster in the 20th century \r\nthan in any prior century over the last three millennia \r\n(high confidence), with a  0.20 [0.15 to 0.25] m rise over \r\nthe period 1901-2018 (high confidence). GMSL rise has \r\naccelerated since the late 1960s, with an average rate of \r\n2.3 [1.6 to 3.1] mm yr-1 over the period 1971-2018 increasing \r\nto  3.7  [3.2  to 4.2]  mm  yr-1 over the period 2006-2018 \r\n(high confidence). New observation-based estimates published \r\nsince SROCC lead to an assessed sea level rise over the period \r\n1901-2018 that is consistent with the sum of individual components. "


id: c1d7524c18d881226de63a322be48c57
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 31404
content:
"At sustained warming levels between 2degC and 3degC, the Arctic \r\nOcean will be practically sea ice free throughout September in \r\nmost years (medium confidence); there is limited evidence that the \r\nGreenland and West Antarctic ice sheets will be lost almost completely \r\nand irreversibly over multiple millennia; both the probability of their \r\ncomplete loss and the rate of mass loss will increase with higher \r\ntemperatures (high confidence); about 50 to 60% of current glacier \r\nmass outside Antarctica will be lost (low confidence); Northern \r\nHemisphere spring snow cover extent will decrease by up to 30% \r\nrelative to 1995-2014 (medium confidence); permafrost volume \r\nin the top 3 m will decrease by up to 75% relative to 1995-2014 \r\n(medium confidence). Committed GMSL rise over 2000 years will be \r\nabout 4 to 10 m with 3degC of peak warming (medium agreement, \r\nlimited evidence). {9.3.1, 9.4.1, 9.4.2, 9.5.1, 9.5.2, 9.5.3, 9.6.3}"


id: ecc77ca7ba4b9f25c26a0ebeeba2425f
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 39024
content:
"Higher amounts of GMSL rise before 2100 could be caused \r\nby earlier-than-projected disintegration of marine ice shelves, \r\nthe abrupt, widespread onset of marine ice sheet instability \r\nand marine ice cliff instability around Antarctica, and faster\u0002than-projected changes in the surface mass balance and \r\ndischarge from Greenland. These processes are characterized by \r\ndeep uncertainty arising from limited process understanding, limited \r\navailability of evaluation data, uncertainties in their external forcing \r\nand high sensitivity to uncertain boundary conditions and parameters. \r\nIn a low-likelihood, high-impact storyline, under high emissions such \r\nprocesses could in combination contribute more than one additional \r\nmetre of sea level rise by 2100. {9.6.3, Box 9.4}"


id: d75952b10a04b748805ff9b2dbd8e1d5
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 35191
content:
"Beyond 2100, GMSL will continue to rise for centuries due \r\nto continuing deep-ocean heat uptake and mass loss of the \r\nGreenland and Antarctic ice sheets, and will remain elevated for \r\nthousands of years (high confidence). Considering only processes \r\nfor which projections can be made with at least medium confidence\r\nand assuming no increase in ice-mass flux after 2100, relative to the \r\nperiod 1995-2014, by 2150, GMSL will rise between 0.6 [0.4 to 0.9, \r\nlikely range] m (SSP1-1.9) and 1.4 [1.0 to 1.9, likely range] m (SSP5-8.5). \r\nBy 2300, GMSL will rise between 0.3 m and 3.1 m under SSP1-2.6, \r\nbetween 1.7 m and 6.8 m under SSP5-8.5 in the absence of marine ice \r\ncliff instability, and by up to 16 m under SSP5-8.5 considering marine \r\nice cliff instability (low confidence). {9.6.3}"


id: 688a39de0c7c6325c12c807b35c1d7f2
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 15661
content:
"There are two major advances of this chapter compared with AR5 \r\nand SROCC facilitated by community efforts. The first is the temporal \r\nand spatial increase in observations of both the ocean and the \r\ncryosphere (Section  1.5.1.1). In particular, extended observations \r\nhave allowed for improved assessment of past change and closure \r\nof both the energy and sea level budgets in a  consistent way \r\n(Cross-Chapter Box 9.1) and the sea level budget for the last century \r\n(Section 9.6.1.1). Higher resolution observations have revealed the \r\ndetails of the Atlantic Meridional Overturning Circulation (AMOC; \r\nSection 9.2.3.1) and globally resolved glacier changes for the first time \r\n(Section 9.5.1.1). Improved methodology has resulted in a doubling \r\nof the assessed level of observed increase in global ocean 0-200 m \r\nstratification compared to SROCC assessment (Section 9.2.1.3)."


id: 7ec4b2720a65b71b1af4dba0b4ad2e02
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 19456
content:
"This chapter provides a holistic assessment of the physical processes \r\nunderlying global and regional changes in the ocean, cryosphere and \r\nsea level, as well as improved understanding of observed, attributed \r\nand projected future changes since the IPCC Fifth Assessment Report \r\n(AR5) and the Special Report on the Ocean and Cryosphere in \r\na Changing Climate (SROCC; see outline in Figure 9.1). The ocean and \r\ncryosphere (defined as the frozen components of the Earth system \r\nsuch as sea ice, ice sheets, glaciers, permafrost and snow) exchange \r\nheat and freshwater with the atmosphere and each other (Figure 9.2). \r\nIn a warming climate, the combined effects of thermal expansion of \r\nseawater and melting of the terrestrial cryosphere result in global \r\nmean sea level rise (Box 9.1)."


id: 7218ee34397be2e1404c481c49c677b7
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 17319
content:
"The second advance is the use of a  hierarchy of models and \r\nemulators to update projections of oceanic, cryospheric and sea \r\nlevel change arising from Coupled Model Intercomparison Project \r\nPhase 6 (CMIP6) and related projects (Section  1.5.4.3, Table  1.3, \r\nand Annex  II).2 The CMIP6 included an ice-sheet modelling \r\nintercomparison for the  first time. Particular modelling advances \r\nrelevant to this chapter are the increase in ocean resolution in the \r\nHigh Resolution Model Intercomparison Project (HighResMIP) and \r\nOcean Model Intercomparison Project phase 2 (OMIP-2) experiments \r\n(Sections 1.5.3.1 and 9.2), projections of future glacier (GlacierMIP) \r\nand ice sheet (ISMIP6) and Linear Antarctic Response Model "


id: dc50ae19a4db97118c184a42b1dc5a6f
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 36035
content:
"Ocean acidification and deoxygenation are covered in Chapter  5, \r\nand regional changes to the ocean and cryosphere are covered in \r\nChapter 12 and the Atlas. Ecosystem range shifts and climate risk \r\nfor marine biodiversity associated with ocean change are assessed \r\nin AR6 Working Group II (WGII). The notion of 'climate velocity' often \r\nused in impact studies, which is defined as the speed and direction \r\nat which a  climate variable moves across a  corresponding spatial \r\nfield, is underpinned by the assessment of changes in the physical \r\ncharacteristics of the ocean provided in this chapter."


id: 3b4e9eb4a36d1cbd9a947d88e397500b
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 7622
content:
"3 The conversion of land ice mass loss to global mean sea level rise used in this Report - the sea level equivalent (SLE) - is 362.5 gigatonnes (Gt) of ice loss for 1 mm of sea level rise"


id: fdcf71c5ce94ed60e3c79dd4b0237eb9
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 42014
content:
"Intercomparison Project (LARMIP-2) response from multi-model \r\nstudies (Sections 9.5.1 and 9.4, and Box 9.3), and new methods to \r\nsynthesize ocean and cryosphere models into sea level projections for \r\nall Shared Socieo-economic Pathway scenarios (SSPs; Sections 1.6.1, \r\n9.4.1.3, 9.4.2.5 and 9.6.3, and Cross-Chapter Box 1.4) and warming \r\nlevels (Sections 9.6.3 and 1.6.2, and Cross-Chapter Box  11.1). \r\nIn  particular, sea level projections and the individual contributions \r\n(Section 9.6.3.3) are consistent with equilibrium climate sensitivity \r\nand surface temperature assessments across this Report (Box 4.1 and \r\nCross-Chapter Box 7.1)."


id: 85d193dd221703487c71705b921fb9a7
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 20685
content:
"There are other advances in scientific understanding. In the cryosphere, \r\nthis chapter assesses how fast-responding elements (sea ice, permafrost \r\nand snow; Sections 9.3, 9.5.2 and 9.5.3) track warming levels across \r\nobservations and projections independent of scenario, process \r\nunderstanding of uncertainty in Antarctic Ice Sheet projections \r\n(Section 9.4.2 and Box 9.4) and new insight into thresholds for Arctic \r\nsea ice (Section 9.3.1.1) and Greenland and West Antarctic ice sheets \r\n(Sections 9.4.1.4 and 9.4.2.6). In the ocean, process understanding \r\nof ocean heat uptake (Section 9.2.2.1 and Cross-Chapter Box 5.3) \r\nand observed changes in ocean stratification (Section 9.2.1.3) have \r\nimplications for ocean biogeochemistry are also important."


id: b57a8489100d47ff8a0818a8825b6c0a
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 29155
content:
"Sea level change arises from processes acting on a  range of spatial and temporal scales, in the ocean, cryosphere, solid Earth, \r\natmosphere and on land (Figure 9.2). Relative sea level (RSL) change is the change in local mean sea surface height relative to \r\nthe sea floor, as measured by instruments that are fixed to the Earth's surface (e.g., tide gauges). This reference frame is used when \r\nconsidering coastal impacts, hazards and adaptation needs. In contrast, geocentric sea level change is the change in local mean \r\nsea surface height with respect to the terrestrial reference frame, and is the sea level change observed with instruments from space. \r\nThis box provides a brief summary of sea level processes using standard terminology (Gregory et al., 2019). "


id: 7e3d45efce53295360e54ff900155c83
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 19380
content:
"Global mean sea level change (Sections 9.6 and 2.3.3.3) is the change in volume of the ocean divided by the ocean surface area. \r\nIt is the sum of changes in ocean density ('global mean thermosteric sea level change') and changes in the ocean mass as a result of \r\nchanges in the cryosphere or land-water storage ('barystatic sea level change'). "


id: 5e22636d24a029c956666361c1978144
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 13855
content:
"Steric sea level change is caused by changes in the ocean density and is composed of 'thermosteric sea level change' and 'halosteric \r\nsea level change'. Thermosteric sea level change (also referred to as 'thermal expansion') occurs as a result of changes in ocean \r\ntemperature: increasing temperature reduces ocean density and increases the volume per unit of mass. Halosteric sea level change\r\noccurs as a result of salinity variations: higher salinity leads to higher density and decreases the volume per unit of mass. Although \r\nboth processes can be relevant on regional to local scales, thermosteric changes contribute to global mean sea level change, whereas \r\nglobal mean halosteric change is negligible (Gregory et al., 2019). There is high confidence in the understanding of processes causing \r\nthermosteric sea level change (Section 9.2.4.1)."


id: 7749b769f585e118373773c55e36f9ce
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 18192
content:
"The Greenland and Antarctic ice sheets are the largest reservoirs of frozen freshwater and therefore potentially the largest contributors \r\nto sea level rise. Fluctuations in ice-sheet volume arise from the imbalance between accumulation (either at the ice-sheet surface or on \r\nthe underside of ice shelves) and loss from sublimation, surface and basal melting, and iceberg calving. Ice sheets discharge the majority \r\nof their mass through marine-terminating ice streams that are in some cases buttressed by floating ice shelves. Changes in the thickness \r\nand extent of the ice shelves due to melt from below, calving, or disintegration, as a result of surface meltwater penetrating crevasses, \r\ncan affect the flow of the inland ice streams. There is medium confidence in ice-sheet processes but low confidence in their forcing \r\n(ocean changes and ice-shelf collapse) and in instability processes (Sections 9.4.1 and 9.4.2).3"


id: 1e5a015cfcb19987df8d9da7ca2bac00
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 2535
content:
"Glaciers contribute to sea level change via an imbalance between mass gain and mass loss processes, which leads to adjustments \r\nin the glacier geometry over an extended period of time, called the response time. The response time may range from a few years to \r\na few hundred years. The glacial meltwater does not all flow immediately into the ocean: it can refreeze, feed rivers (where it may be \r\nextracted for domestic use), evaporate, or be stored in (proglacial) lakes or closed basins. There is medium to high confidence in the \r\nunderstanding of processes leading to sea level contributions from glaciers (Section 9.5.1)."


id: 86b998052c295e851f5592fdf30d770b
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 20865
content:
"Land-water storage includes surface water, soil moisture, groundwater storage and snow, but excludes water stored in glaciers \r\nand ice sheets. Changes in land-water storage can be caused either by direct human intervention in the water cycle (e.g., storage of \r\nwater in reservoirs by building dams in rivers, groundwater extraction for consumption and irrigation, or deforestation) or by climate \r\nvariations (e.g., changes in the amount of water in internally drained lakes and wetlands, the canopy, the soil, the permafrost and "


id: 4a292469e8fd7b3850781ba9ef6ab51c
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 10291
content:
"the snowpack). Land-water storage changes caused by climate variations may be indirectly affected by anthropogenic influences. \r\nIt is difficult to assign a single confidence level to land-water storage as understanding can vary from low confidence in groundwater \r\nrecharge processes to high confidence in water storage via snowpack changes (Sections 8.2.3 and 8.3.1.7)."


id: 5bbf83bec3da8124403a4311046599a5
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 13400
content:
"Ocean dynamic sea level change refers to the change in mean sea level relative to the geoid and is associated with the circulation \r\nand density-driven changes in the ocean. Ocean dynamic sea level change varies regionally but by definition has a  zero global \r\nmean. It includes the depression of the sea surface by atmospheric pressure. There is medium confidence in the understanding of \r\nocean processes leading to dynamic sea level change (Section 9.2.4.2)."


id: 11ad3b00716ed34e08f52cd430a3a94d
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 299
content:
"Glacial isostatic adjustment is ongoing GRD in response to past changes in the distribution of ice and water on Earth's surface. \r\nOn a time scale of decades to tens of millennia following mass redistribution, Earth's mantle flows viscously as it evolves toward \r\nisostatic equilibrium, causing solid Earth movement and geoid changes, which can result in regional to local sea level variations. There \r\nis medium confidence in the understanding of glacial isostatic adjustment processes."


id: 2e120daeb4e0ddcfb7961b112df0f8d
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 5378
content:
"Vertical land motion is the change in height of the land surface or the sea floor and can have several causes in addition to elastic \r\ndeformation associated with contemporary GRD and viscoelastic deformation associated with glacial isostatic adjustment. Subsidence \r\n(sinking of the land surface or sea floor) can occur through compaction of alluvial sediments in deltaic regions, removal of fluids such \r\nas gas, oil, and water, or drainage of peatlands. Tectonic deformation of the Earth's crust can occur as a result of earthquakes and \r\nvolcanic eruptions. There is medium confidence in the understanding of vertical land motion processes. "


id: dcb639ca13a80938910330f2c5f9042f
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 36091
content:
"with buoy-based observations and improved treatment of sea ice, \r\nhave had important consequences for key climate change indicators \r\nsuch as global mean surface temperature (GMST), global surface \r\nair temperature (GSAT), and SST (Cross-Chapter Box  2.3). The AR5 \r\nassessment is confirmed, and it is now very likely that global mean SST \r\nchanged by 0.88 [0.68 to 1.01] degC from 1850-1900 to 2011-2020, and \r\n0.60 [0.44 to 0.74] degC from 1980 to 2020 (Figure 9.3 and Table 2.4)."


id: 7281d2ec7cdab3c3aec274920bdae265
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 17385
content:
"The IPCC Fifth Assessment Report (AR5; Hartmann et  al., 2013) \r\nassessed that it is virtually certain that global sea surface temperature \r\n(SST) has increased since the beginning of the 20th century (very high \r\nconfidence). The Special Report on Ocean and Cryosphere in \r\na Changing Climate (SROCC) did not assess past SST change. Since \r\nAR5, improvements in the understanding of recent SST biases in the \r\nobservational records, especially extending ship-based observations "


id: 82d41d7664a241a2711375272f3335ba
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 20143
content:
"Regions vary in the rate of SST warming, with slight cooling in some \r\nregions (Figure 9.3). The SROCC (Collins et al., 2019) and Section 7.4.4 \r\nassess SST changes over specific regions, which are consistent with the \r\nchanges reported here. The tropical ocean has been warming faster \r\nthan other regions since 1950, with the fastest warming in regions "


id: c749ed563f332ced303e2b959b374de8
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 32377
content:
"of the tropical Indian and western Pacific oceans (Figure 9.3), due to \r\na combination of local atmosphere-ocean coupling, the Indonesian \r\nThroughflow (Section  9.2.3.4 and Figure  9.11), and trends in the \r\nWalker circulation (Sections 2.3.1.4.1 and 3.3.3.1, and Figure 3.16). \r\nThe western boundary currents of the subtropical gyres have warmed \r\nfaster than the global mean over the past century. There remains low \r\nagreement in the changes of the location and the dynamical changes \r\nin western boundary current extensions (Sections 2.3.3.4.2 and \r\n9.2.3.4, and Figure 9.3). In the Arctic, the mean SST increase over the \r\nlast two decades is similar to, or only slightly higher than, the global \r\naverage (J.-L Chen et al., 2019). In contrast, the eastern Pacific Ocean, "


id: 721300f4181c09a12e4178161df6cb4d
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 17316
content:
"subpolar  North Atlantic Ocean and Southern Ocean have warmed \r\nmore slowly than the global average or cooled (Figure 9.3). Surface \r\nwarming in the subpolar Southern Ocean has been slower than \r\nthe global average since the 1950s, and this pattern is consistent \r\nwith the upwelling around Antarctica renewing surface water with \r\npre-industrial, deeper water masses (Section  9.2.3.2; Frolicher \r\net al., 2015; J. Marshall et al., 2015; Armour et al., 2016). New evidence \r\nsince SROCC (Meredith et  al., 2019) confirms slight cooling since \r\nthe 1980s around the subpolar Southern Ocean, contrasting with \r\nmarked warming directly northward of it (Section 9.2.3.2; Haumann \r\net al., 2020; Rye et al., 2020; Auger et al., 2021). In eastern boundary "


id: c4e98521c3b1fa21e97f36ab04d6a7e6
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 31950
content:
"direction of SST changes in observations are consistent with CMIP6 \r\nonly when including internal variability (Olonscheck et  al., 2020). \r\nThis is notably the case in the equatorial Pacific, North Atlantic, \r\nand Southern Ocean, which are regions where SST is of known \r\nimportance in controlling heat uptake (Section 9.2.2.1) and the global \r\nradiative feedback parameter (Section 7.4.4.3). Overall, despite some \r\npersistent regional biases, CMIP6 coupled climate models reproduce \r\nthe observed SST trends or high internal variability over the past \r\ncentury over a range of different multi-decadal periods (Figure 9.3; \r\nOlonscheck et al., 2020; Watanabe et al., 2021), highlighting their skill \r\nto inform future large-scale SST changes at regional scale. Warming \r\nis projected at varying rates in all regions by 2050, except the North \r\nAtlantic Subpolar Region, the equatorial Pacific, and the Southern \r\nOcean where models disagree (high confidence)."


id: 544ffb072c0a0a3a05df47cfd463c64b
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 12110
content:
"upwelling systems, SROCC (Bindoff et  al., 2019) reported low \r\nagreement between SST trends in recent decades, due to varying \r\nspatio-temporal resolution and interannual to multi-decadal \r\nvariability. Satellite evidence not included in SROCC shows that \r\n92% of these regions warmed more slowly than neighbouring \r\noffshore locations between 1982 and 2015, so upwelling may buffer \r\nthe near shore from warming (Section 9.2.3.5; Varela et al., 2018). \r\nCoupled ocean-atmospheric modes of variability strongly affect \r\nregional SST (Cross-Chapter Box  3.1 and Annex IV). In  summary, \r\na positive SST trend since 1950 is evident globally, but there is very \r\nhigh confidence that the Indian Ocean, western equatorial Pacific \r\nOcean, and western boundary currents have warmed faster than the \r\nglobal average, while the Southern Ocean, the eastern equatorial \r\nPacific, and the North Atlantic Ocean have warmed more slowly, or \r\nhave slightly cooled."


id: 224f0626a0d6ac5af017e630be80e932
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 3256
content:
"on future emissions scenario, and some of the observed cooling trends \r\nover the 20th  century will eventually transition to a  warming SST \r\non centennial time scales, in particular in the Southern Ocean (high \r\nconfidence) and in the equatorial Pacific (medium confidence), while \r\nthe North Atlantic subpolar gyre will continue to warm more slowly \r\nthan the global average (high confidence)."


id: 7beebaafe4a1867e3cb9693578f848b9
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 19012
content:
"Air-sea fluxes of energy, freshwater, and momentum (wind stresses) \r\nare difficult to observe directly (Cronin et al., 2019), so estimates of \r\nthe global mean net air-sea heat flux are inferred from observed \r\nocean warming (Section 2.3.3.1, Box 7.2, and Cross-Chapter Box 9.1). \r\nAir-sea heat fluxes resemble the warming patterns of CMIP3 \r\n(Domingues et al., 2008; Levitus et al., 2012) and are consistent with \r\nthe ensemble mean warming rate of CMIP5 (Cheng et  al., 2017, \r\n2019) and CMIP6 models (Section 3.5.1.3). Regional air-sea fluxes \r\nin models remain a  key driver of uncertainty (Huber and Zanna, \r\n2017; Tsujino et  al., 2020). A  substantial part of the upper 700 m \r\nenergy increase is very likely attributed to anthropogenic forcing via \r\nincreasing radiative forcing (Sections 3.5.1.3, 7.2 and 7.3)."


id: a5955df474a2a3bc2db0d18185067974
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 26370
content:
"hide larger increases occurring at the seasonally and regionally \r\nvariable pycnocline depth. There is also limited evidence that summer \r\nstratification change within the pycnocline has occurred at a rate of \r\n8.9 +- 2.7% per decade from 1970 to 2018, and limited evidence of \r\na winter pycnocline stratification increase (Cummins and Ross, 2020; \r\nSallee et al., 2021)."


id: 624efc92604611fc25d1d8f968552d68
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 14572
content:
"Boucher et al., 2020; Danabasoglu et al., 2020; Dunne et al., 2020; \r\nKelley et al., 2020). In summary, the representation of upper-ocean \r\nstratification and mixed layers has improved in CMIP6 compared \r\nto CMIP5. While it is virtually certain that the global mean upper \r\nocean will continue to stratify in the 21st  century, there is only \r\nlow confidence in the future evolution of mixed-layer depth, \r\nwhich is projected to mostly shoal under high emissions, except in \r\nhigh-latitude regions where sea ice retreats."


id: 3527aec0719b6dd59830c6647608c792
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 6597
content:
"not resolved in climate models (D'Asaro, 2014; Buckingham et al., \r\n2019; McWilliams, 2019) and despite significant improvements \r\nin their parametrization over the last decade (Fox-Kemper et  al., \r\n2011; Jochum et al., 2013; Q. Li et al., 2016, 2019; Qiao et al., 2016) \r\nand significant improvement in some models (Li and Fox-Kemper, \r\n2017; Dunne et  al., 2020), biases in mixed-layer representation \r\ngenerally persist (Heuze, 2017; Williams et al., 2018; Cherchi et al., \r\n2019; Golaz et al., 2019; Voldoire et al., 2019; Yukimoto et al., 2019; "


id: 424fdf9176c49699a69ce433b89904e
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 8863
content:
"Marine heatwaves (MHW) are periods of extreme high sea temperature relative to the long-term mean seasonal cycle (Hobday et al., \r\n2016). Studies since the Special Report on the Ocean and Cryosphere in a Changing Climate (SROCC; Collins et al., 2019) confirm the \r\nassessment that MHW can lead to severe and persistent impacts on marine ecosystems - from mass mortality of benthic communities, \r\nincluding coral bleaching, changes in phytoplankton blooms, shifts in species composition and geographical distribution, and toxic algal \r\nblooms, to decline in fisheries catch and mariculture (Smale et al., 2019; Cheung and Frolicher, 2020; Hayashida et al., 2020; Piatt et al., \r\n2020). Unlike synoptic atmospheric heatwaves (Section 11.3), MHWs can extend for millions of square kilometres, persist for weeks to \r\nmonths, and occur at subsurface (Bond et al., 2015; Schaeffer and Roughan, 2017; Perkins-Kirkpatrick et al., 2019; Laufkotter et al., 2020)."


id: 6a1509909c808097cbe317b5f24a4217
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 15940
content:
"variability in redistributing heat, driving interhemispheric asymmetry \r\nof the recent rate of ocean warming (Rathore et al., 2020; L. Wang \r\net al., 2021). Since SROCC, one new study shows that the subpolar \r\nNorth Atlantic 'warming hole' observed since the 1980s has emerged \r\nfrom internal climate variability and can be attributed to greenhouse \r\ngas emissions (Chemke et  al., 2020). A  new analysis of a  suite of \r\nclimate models (Hobbs et  al., 2021) confirms SROCC assessment, \r\nbased on one paper (Swart et  al., 2018), attributing the observed \r\nSouthern Ocean warming to anthropogenic forcing. Given the large \r\nfraction of global ocean warming in the Southern Ocean and the \r\nsparse observations there before 2005, there is limited evidence that \r\nglobal OHC increase since 1971 might have been underestimated \r\n(Cheng and Zhu, 2014; Durack et al., 2014). Cross-Chapter Box 9.1 "


id: 217541cff1e772d5b3dbbb26267e56
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 3095
content:
"The rate of ocean warming varies regionally, with some regions having \r\nexperienced slight cooling (Figure  9.6). The SROCC (Bindoff  et  al., \r\n2019) assessed that ocean warming in the 0-700 m depth is \r\nglobally widespread, with slower than global average warming in \r\nthe subpolar North Atlantic. The SROCC (Meredith et al., 2019) also \r\nestimated that the Southern Ocean accounted for around 75% of \r\nglobal ocean heat uptake during 1870-1995 and that 35-43% of the \r\nupper 2000 m global ocean warming occurred in the Southern Ocean \r\nover 1970-2017 (45-62% for 2005-2017). The SROCC noted that this \r\ninterhemispheric asymmetry might (at least partially) be explained by \r\nhigh concentrations of aerosols in the Northern Hemisphere. Here, \r\nwe confirm these assessments, bring new evidence attributing these \r\nregional trends, and discuss the role of decadal ocean circulation "


id: 530bf254fcc71ebad533fb08990f364d
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 11873
content:
"accounts for an increased error before 2005 in global OHC change. \r\nIn summary, in the upper 2000 m since the 1970s, the subpolar \r\nNorth Atlantic has been slowly warming, and the Southern Ocean \r\nhas stored a disproportionally large amount of anthropogenic heat \r\n(medium confidence)."


id: 3a31bad8acf75528893a2874c2322857
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 7443
content:
"observational record may underestimate the rate of deep-ocean \r\nwarming from 1990 to 2010 by about 20% (Garry et  al., 2019) \r\nwhich is included in the assessed OHC error (Cross-Chapter Box 9.1). \r\nThere is still low agreement in deep-ocean changes from ocean data \r\nassimilation reanalyses (Palmer et  al., 2017) and low confidence\r\nin such inferences. In summary, while observational coverage \r\nbelow 2000 m is sparser than in the upper 2000 m, there is high \r\nconfidence that deep-ocean warming below 2000 m has been larger \r\nin the Southern Ocean than in other ocean basins due to widespread \r\nAABW warming."


id: 11567edac89800e68159c6325aff25ea
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 248
content:
"Major volcanic eruptions have caused interannual to decadal cooling \r\nphases within the marked long-term increase in global OHC  - \r\nMount Agung in 1963, El Chichon in 1982 and Mount Pinatubo \r\nin 1991 (Cross-Chapter Box 4.1; Church et al., 2005; Fasullo et al., \r\n2016; Stevenson et  al., 2016; Fasullo and Nerem, 2018). In  the \r\nfirst few years following an eruption, heat exchange with the \r\nsubsurface ocean allows atmospheric cooling to be sequestered \r\ninto the seasonal thermocline, therefore reducing the magnitude of "


id: 14192973e3773d1161832ab1e6c4f04e
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 751
content:
"While heat redistribution reflects changes in ocean circulation and is \r\na useful concept to understand the underlying processes driving OHC \r\npatterns, change in ocean heat transport (OHT) arises due to changes \r\nin ocean circulation and ocean temperature and affects regional \r\nOHC  change. The AR5 did not assess change in OHT  and SROCC \r\n(Meredith et al., 2019) only assessed projected OHT increases into \r\nthe Nordic Seas and the Arctic Ocean. New evidence of increasing \r\nnorthward OHT into the Arctic has been observed in recent decades "


id: 79fccacb79ae15de33c54a1e87c27cfb
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 18661
content:
"the  peak atmospheric temperature anomaly (Gupta and Marshall, \r\n2018). However, while explosive volcanic eruptions only disturb \r\nthe Earth's radiative budget and surface fluxes for a  few years, \r\nthe ocean preserves an anomaly in OHC in the upper 500 m (also \r\naffecting thermosteric sea level) many years after the eruption \r\n(Gupta and Marshall, 2018; Bilbao et al., 2019). The anomaly affects \r\nthe atmosphere through air-sea heat fluxes with surface conditions \r\nreturning to normal only after several decades (Gupta and Marshall, \r\n2018; Bilbao et al., 2019), or on centennial time scales in the case \r\nof repeated eruptions (G.H. Miller et al., 2012; Atwood et al., 2016; \r\nGupta and Marshall, 2018). In summary, there is medium confidence\r\nthat oceanic mechanisms buffer the atmospheric response to \r\nvolcanic eruptions on annual time scales by storing volcanic cooling \r\nin the subsurface ocean, affecting OHC and thermosteric sea level on \r\ndecadal to centennial time scales."


id: cf1159c8a5066548eb66f76acb46ab18
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 33779
content:
"The SROCC (Bindoff et al., 2019) assessed that the ocean will continue \r\nto take up heat in the coming decades for all plausible scenarios, \r\nand here this assessment is confirmed with very high confidence. \r\nThe SROCC reported that, compared with the observed changes since \r\nthe 1970s, the warming of the ocean by 2100 would very likely double \r\nto quadruple for low-emissions scenarios (RCP2.6) and increase five \r\nto seven times for high-emissions scenarios (RCP8.5). The SROCC also \r\nconcluded with high confidence that the overall warming of the ocean \r\nwould continue this century, even after radiative forcing and mean \r\nsurface temperatures stabilize. The SROCC projected that OHC in the "


id: ca87d086792ad81330f3368497ac93ad
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 32974
content:
"Todd et al., 2020; Couldrey et al., 2021). In the 700-2000 m depth \r\nrange, CMIP5 and CMIP6 models project the largest warming to be \r\nin the North Atlantic Deep Water and Antarctic Intermediate Water \r\n(Figure 9.7) while below 2000 m, the North Atlantic cools in many \r\nmodels, and Antarctic Bottom Waters warm (Sallee et  al., 2013b; \r\nHeuze et al., 2015). In summary, on decadal time scales, redistribution \r\nwill dominate regional patterns of OHC change without affecting the \r\nglobally integrated OHC; however, by 2100, particularly under strong \r\nwarming scenarios, there is high confidence that regional patterns \r\nof OHC change will be dominated by added heat entering the sea \r\nsurface, primarily in water mass formation regions in the subtropics; \r\nand reduced aerosols will increase the relative rate of Northern \r\nHemisphere heat uptake (medium confidence)."


id: 6b2a49fb7acc5190eefab12047668654
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 16151
content:
"The SROCC assessed that the warming of the deep ocean is slow \r\nto manifest, with multi-century or longer response times, so global \r\nOHC (and global mean thermosteric sea level) will continue to \r\nrise for centuries (Figures 9.9 and 9.30). New studies show that \r\nthis continuation persists, even after cessation of greenhouse gas \r\nemissions (Ehlert and Zickfeld, 2018). Ocean warming will continue, \r\neven after emissions reach zero because of slow ocean circulation \r\n(Larson et al., 2020). OHC will increase until at least 2300, even for \r\nlow-emissions scenarios, but with a scenario-dependent rate (Nauels \r\net  al., 2017; Palmer et  al., 2018) and depends on cumulative CO2\r\nemissions, as well as the time profile of emissions (Bouttes et  al., \r\n2013). Past long-term changes in total OHC illustrate adjustment \r\nrelevant to expected future changes (Figure 9.9). Observational data "


id: ebb34a53c516f43a45e3529e85a5a94
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 38829
content:
"The AR5 (Rhein et  al., 2013) assessed that it was very likely that \r\nsubsurface salinity changes reflect surface salinity change, and \r\nthat basin-scale regions of high salinity and evaporation had trended \r\nmore saline, while regions of low salinity and more precipitation had \r\ntrended fresher since the 1950s. The SROCC (Bindoff et  al., 2019) \r\nassessment was consistent with AR5. Section  2.3.3.2 strengthens \r\nevidence that subsurface salinity trends are connected to surface trends \r\n(very likely), which are, in turn, linked to an intensifying hydrological \r\ncycle (medium confidence). Increasing evidence from updated \r\nobservational records indicates that it is now virtually certain that \r\nsurface salinity contrasts are increasing. At basin scale, Section 2.3.3.2 \r\nand AR5 concur that it is very likely that the Pacific and Southern Ocean \r\nhave freshened, and the Atlantic has become more saline. Figures 3.25 \r\nand 3.27 compare CMIP6 models to salinity observations."


id: beed00eef7fc2dd85b214c1ca52997d7
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 30854
content:
"The CMIP5 historical simulations have patterns similar to, but \r\nwith greater spatial variability than, observed estimates and \r\ncorrespondingly smaller amplitudes in the multi-model mean (Durack, \r\n2015; Cheng et al., 2020; Silvy et al., 2020). Section 3.5.2.1 reports, \r\nhowever, that the fidelity of ocean salinity simulation has improved \r\nin CMIP6, and near-surface and subsurface biases have been reduced \r\n(medium confidence), though the structure of the biases strongly \r\nreflects those of CMIP5. At regional scale, salinity biases are at least \r\npartially a result of inaccurate ocean dynamics (Levang and Schmitt, \r\n2020). Despite the regional limitations, Section 3.5.2.2 assesses that, \r\nat the global scale, it is extremely likely that human influence has \r\ncontributed to observed surface and subsurface salinity changes \r\nsince the mid-20th  century (strengthened from the very likely \r\nAR5 assessment)."


id: 883d92031a1ffb96b171f534e6a2776b
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 21122
content:
"Water masses refer to connected bodies of ocean water, formed at the \r\nocean surface with identifiable properties (temperature, salinity, density, \r\nchemical tracers) resulting from the unique formation conditions of the \r\noverlying atmosphere and/or ice, before being transferred (subducted) \r\nto the deeper ocean below the surface  turbulent layer. As water \r\nmasses subduct, they ventilate the subsurface ocean, transferring \r\ncharacteristics acquired at the ocean surface to the subsurface. \r\nBy  integrating surface flux changes, water masses provide higher \r\nsignal-to-noise ratios for detecting and monitoring climate change \r\nthan surface fluxes (Bindoff and McDougall, 2000; Durack and Wijffels, \r\n2010; Silvy et al., 2020)."


id: f11eb9799ddc0c798c6a3c299553e1ab
created_at: 2023-04-19 13:21:38
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vector_id: 39803
content:
"facilitate access of CDW to the sub-ice-shelf cavities (Section 9.4.2.1). \r\nHowever, there is low confidence in the quantitification, importance \r\nand the ability of present models, especially at coarse resolution, to \r\nproject changes in each of these processes (Section 9.4.2.2). Some \r\nstudies have projected a possible shift from cold to warm sub-ice-shelf \r\ncavities causing a  sudden flush of warm water underneath ice \r\nshelves, but there is low confidence in the driving processes and the \r\nthreshold to trigger the shift (Box  9.4; Hellmer et  al., 2012, 2017; \r\nSilvano et al., 2018; Hazel and Stewart, 2020)."


id: 9f4b2a53c12c970462324bed8048f1cd
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 25251
content:
"Atlantic Meridional Overturning Circulation (AMOC) is the main \r\noverturning current system in the South and North Atlantic \r\noceans. It transports warm upper-ocean water northwards, \r\nand cold, deep water southwards, as part of the global ocean \r\ncirculation system (Section  2.3.3.4.1). Changes in AMOC influence \r\nglobal  ocean heat content (OHC) and transport (Section  9.2.2.1); \r\nglobal ocean  anthropogenic carbon uptake changes and climate \r\nsensitivity (Cross-Chapter Box  5.3); and dynamical sea level \r\nchange (Section  9.2.4). Since AR5/SROCC, confidence in modelled \r\nand reconstructed AMOC has decreased due to new observations \r\nand model disagreement. Confidence levels have been revisited in \r\nmodelled AMOC evolution during the 20th century, the magnitude of \r\n21st-century AMOC decline, and the possibility of an abrupt collapse \r\nbefore 2100."


id: 689fdd92f971c5ea55cbb324b49dfa13
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 15675
content:
"in the 20th  century, and the uncertainty over the realism of the \r\n20th  century modelled AMOC response (Section  3.5.4.1), we have \r\nlow confidence in both."


id: 6e989e053073ab3e0475b58b9f467af5
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 16724
content:
"It is unclear what causes a weakening of the deepest limb of AMOC \r\nat 26.5degN, if the main sources for this flow farther north remain \r\nconstant. Various estimates of AMOC and associated heat transport \r\nsuggest an increase since the 1940s with a  subsequent decrease \r\nsince the 1990s (Section 2.3.3.4.1), supported by ocean reanalysis \r\n(Jackson et al., 2019), forced ocean model simulations (Robson et al., \r\n2012; Danabasoglu et  al., 2016) and CMIP6 simulations (Menary \r\net al., 2020a). This suggests that the observed AMOC-shift between \r\n2007 and 2011 may be part of a  longer-term decrease (medium \r\nconfidence), which has been attributed to be part of multiannual \r\nvariability (Rhein et al., 2019)."


id: 19151f1d84ed76583f69f3f709153365
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 1629
content:
"The SROCC (Collins et  al., 2019) found that AMOC will very likely \r\nweaken over the 21st century. In CMIP6 projections, the modelled \r\ndecline starting in the 1990s continues in all future projections, almost \r\nindependent of the forcing scenario until about 2060, after which \r\nlow-emissions scenarios show stabilization, while high-emissions \r\nscenarios continue to exhibit AMOC decline (Figure  9.10; Menary \r\net al., 2020b; Weijer et al., 2020). Despite differences in overall AMOC \r\nstrength, location and latitude of deep convection, sea ice and SST \r\nbias and representation of deep overflows, the model projections are \r\nqualitatively similar. This agreement suggests that AMOC decline may \r\nbe governed by large-scale constraints independent of the details of "


id: 6ade38f1176cebabf78f26d825749445
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 16086
content:
"The changing Southern Ocean circulation system exerts a  strong \r\ninfluence on the global climate by modulating: (i) global OHC \r\n(Section  9.2.2.1); (ii) global ocean anthropogenic carbon uptake \r\n(Cross-chapter Box  5.3); global ocean overturning circulation \r\n(Section  9.2.3.1); (iii) climate sensitivity (Section  7.4.4 and \r\nCross-chapter Box 5.3); (iv) sea level through basal melt of ice shelves \r\n(9.4.2); and (v) Southern Hemisphere sea ice cover (Section 9.3.2)."


id: b7d52538d5d85f2b01fdc52803dea22a
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 29563
content:
"The SROCC (Meredith et  al., 2019) had low  confidence in all \r\nCMIP5-based model projections due to their inability to explicitly \r\nresolve eddy processes, and their inability to properly consider future \r\nmeltwater change from the Antarctic Ice Sheet. These limitations of "


id: b9e01c337846a01253b9fd590f70600e
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 29921
content:
"central Pacific, and thus the meridional temperature gradients that \r\ndrive tropical instability waves (Terada et  al., 2020), along with \r\na weakening, flattening and shoaling of the tropical thermocline and \r\nequatorial undercurrent (Luo and Rothstein, 2011). A weak or absent \r\nequatorial undercurrent (Kuntz and Schrag, 2020) and a too-diffuse \r\nand incorrectly sloped tropical thermocline (Zhu et al., 2020) remain \r\nissues in most CMIP6 models. In summary, while future changes in \r\ntropical modes of variability remain unclear, change in atmospheric \r\nand ocean circulation will drive continued change in tropical ocean \r\ntemperature in the 21st  century (medium confidence), with part \r\nof the region experiencing drastic marine heat wave conditions \r\n(high confidence)."


id: 91d232beea4439c92d012ff0f6e0fcd1
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 22876
content:
"The tropics are a  tightly coupled ocean-atmosphere system with \r\ntightly interconnected basins (Cai et al., 2019). The zonal atmospheric \r\nWalker Circulation and the Indonesian Throughflow (Figure 9.11) are \r\nkey connections between the Pacific and Indian oceans, and variations \r\nin the Walker and Hadley Circulations are tightly linked to the tropical \r\nPacific SST and currents. The tropics have a profound influence on the \r\nclimate system through the multiple modes of variability they host, \r\nwhich have widespread global influence at seasonal to annual time \r\nscale (Annex IV)."


id: e4aa98869b371ead5156ddcfd41749f6
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 37515
content:
"All climate models reproduce WBCs and gyres, but eddy\u0002present or eddy-rich models (roughly 10-25 km and about \r\n10 km resolution, respectively) represent these currents more \r\nrealistically than eddy-parameterized models (very high confidence) \r\n(Small  et  al.,  2014; Griffies et  al., 2015; Chassignet et  al., 2017, \r\n2020; Hewitt et  al., 2017, 2020; Roberts et  al., 2018). Compared \r\nto observations or to eddy-present and eddy-rich models, the eddy\u0002parameterized models from CMIP5 and CMIP6 simulate weaker and \r\nwider WBCs, as well as less realistic locations of subtropical and \r\nsubpolar gyre boundaries (Figure 9.11). Increased resolution admits "


id: 83898697228dd35e974cabc6608ae6f0
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 20283
content:
"Under future scenarios RCP4.5 and RCP8.5, AR5 (Collins et  al., \r\n2013) assessed an intensification and poleward extension of "


id: 934e7d07dff795e89666d5bb69759203
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 23120
content:
"1990-2015. Realistic representation of the Bering Strait transport \r\nin the current generation of climate models is challenging because \r\nthe strait is narrow compared to the resolution of climate models \r\n(Clement Kinney et al., 2014; Aksenov et al., 2016). For the Atlantic \r\nto Arctic transport, Section 2.3.3.4 reports that the major branches \r\nof Atlantic Water inflow across the Greenland-Scotland Ridge have \r\nremained stable, with only the smaller pathway of Atlantic Water \r\nnorth of Iceland showing a strengthening trend during 1993-2018. \r\nSection 2.3.3.4 also assesses that the Arctic outflow remained stable \r\nfrom the mid-1990s to the mid-2010s. Future changes in these \r\ncurrents have not yet been studied in CMIP6 models."


id: 1b9fde3aaa9dd870351bb7a5e982eaa2
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 2043
content:
"the southern Hemisphere subtropical gyres in the 21st  century. \r\nNew evidence since AR5 further reinforces their conclusions, which \r\nare now extended to all subtropical gyre systems in the Northern \r\nand Southern hemispheres (Yang et al., 2016, 2020). CMIP6 models \r\nproject changes in WBCs that are consistent with projected changes \r\nin the surface winds. Under strong radiative forcing, in scenario \r\nSSP5-8.5, CMIP6 models project that the East Australian Current \r\nExtension, Agulhas Current Extension and Brazil Current will intensify \r\nin the 21st century, while the Gulf Stream will weaken (Figure 9.11). \r\nAlthough CMIP5/CMIP6 are limited in resolution, medium confidence\r\nis given to changes in WBCs due to consistency across generations of \r\nclimate models, including CMIP6, despite changes in model structure, \r\nresolution and parametrizations."


id: a8e8a65b9fd9dcc8124ab8ae97b7a34d
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 26926
content:
"Under increased radiative forcing, SROCC (Bindoff et  al., 2019) \r\nassessed that climate models project, in the 21st  century, \r\na reduction of wind and upwelling intensity in EBUS at low latitudes, \r\nand enhancement at high latitudes, under scenario RCP8.5, with an \r\noverall reduction in either upwelling intensity or extension. It also \r\nhighlighted that coastal warming and wind intensification may lead \r\nto variable countervailing responses to upwelling intensification \r\nat local scales. Despite differences among EBUS (D. Wang et  al., \r\n2015), there is growing evidence since SROCC in this pattern of \r\nchange.  While it has long been hypothesized that, for upwelling \r\nwinds, change is linked to air temperature contrast between ocean \r\nand land (Bakun, 1990), this hypothesis has increasingly been "


id: eeaee41f0ab859998f66681dc3f67d86
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 39372
content:
"The SROCC assessed that the annual Bering Strait volume transport \r\nfrom the Pacific to the Arctic Ocean increased from 2001-2014, \r\nconsistent with an estimated increased northward heat transport of \r\nabout 60% from 2001-2014, and an increased freshwater transport \r\nof 30 +- 20 km3 yr-1 from 1991 to 2015 (Meredith et  al., 2019). \r\nSection  2.3.3.4 assesses that volume transport from the Pacific to \r\nthe Arctic has increased since the 1990s from 0.8 Sv to 1.0 Sv over "


id: 61f198f24ef0ff41871cb570d853c44d
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 14507
content:
"In summary, SROCC and this Report conclude that the California \r\nCurrent system has experienced some upwelling-favourable wind \r\nintensification since the 1980s (high confidence), while low agreement\r\namong reported wind changes in the Benguela, Canary, and Humboldt \r\nsystems prevent a  similar assessment. As in SROCC, there is low \r\nconfidence in attribution of observed changes to anthropogenic or \r\nnatural causes. New evidence reinforces our confidence in SROCC \r\nassessment that, under increased radiative forcing, EBUS winds will \r\nchange with a dipole spatial pattern within each EBUS of reduction \r\n(weaker and/or shorter) at low latitude, and enhancement (stronger \r\nand/or longer) at high latitude (high confidence). There is medium \r\nconfidence that, across all scenarios, upwelling wind changes in \r\nEBUS will remain moderate in the 21st  century, within +-10-20% \r\nfrom present-day values."


id: e90c7e47c49d839d86aa27496255c7eb
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 38360
content:
"Changes in globally averaged ocean heat content (OHC) cause \r\nglobal mean thermosteric sea level (GMTSL) change (Box 9.1). The \r\nobserved increased OHC for 1971-2018 of 325 to 546 ZJ (very likely\r\nrange) (Section 7.2, Box 7.2) has led to a GMTSL rise of 0.03 to 0.06 \r\nm out of a total global mean sea level (GMSL) of 0.07 to 0.15 m \r\n(very likely range) (Section 2.3.3.3, Tables 2.7 and 9.5, and Cross\u0002Chapter Box 9.1)."


id: bf42b84a095af2071c6c13e86ac608c1
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 30917
content:
"Beyond the world's coastlines lie the shoreline, shallow estuaries, \r\ncontinental shelves, and deeper fjords and slopes, where depths \r\nincrease rapidly from the shelves to the deep-ocean floor. It is more \r\ndifficult to transport fluid across (rather than along) the shelf-break \r\nor slope (Brink, 2016), and estuaries and shelves have complex "


id: 4b53e95c332a0b3e8688b8b2b6d5c14b
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 10501
content:
"pronounced thermosteric sea level rise along the American coast \r\naround 40degN (Figures 9.12 and 9.26), leading to a relatively large \r\nocean dynamic sea level rise in this region (Yin, 2012; Bouttes et al., \r\n2014; Slangen et al., 2014b; Little et al., 2019; Lyu et al., 2020a)."


id: ff0842c0b7996ca69a897fe05a0c4cc1
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 42269
content:
"In summary, ocean dynamic sea level change involves changes to \r\ntemperature and salinity and responses of currents to changing \r\nforcing, with significant variability driven by unforced oceanic \r\nvariability. Projections of dynamic sea level variability require fully \r\nthree-dimensional ocean models, and only high-resolution ocean \r\nmodels are statistically consistent on short time scales with satellite \r\naltimeter observations (very high confidence)."


id: c6619d71d8bfcec3faf803c90447a52d
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 32213
content:
"The Southern Ocean Meridional Dipole is driven by a  northward \r\nadvection of excess heat (from changes in surface fluxes) by the \r\nwind-driven circulation followed by subduction or diffusive uptake \r\nin mid-latitudes, northward redistribution of existing heat by the \r\nstrengthening of that circulation, and the meridional contrast in \r\nthermal expansivity due to its temperature-dependence (Armour et al., \r\n2016; Gregory et  al., 2016; Lyu et  al., 2020b; Todd  et  al.,  2020; \r\nCouldrey et al., 2021)."


id: 8ba618fa78bcfa36694bf5d175139279
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 21741
content:
"The positive Arctic ocean dynamic sea level change is driven by \r\nincreased freshwater input (Couldrey et  al., 2021). The Northwest \r\nPacific Dipole is driven by the intensification of the Kuroshio Current \r\nin response to reduced heat loss and in some models to wind stress \r\nchange (C. Chen et al., 2019; Couldrey et al., 2021)."


id: c80ce2f04da5907d576c020c1d783b6f
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 32505
content:
"The SROCC discussed the regional distribution of Arctic sea ice loss, \r\nand the findings remain valid for the updated time series covering \r\n2019 (Figure 9.13). Sea ice loss in winter is strongest in the Barents \r\nSea, while summer losses occur primarily at the summer sea ice \r\nregion margins, in particular in the East Siberian, Chukchi, Kara and \r\nBeaufort Seas (Frey et al., 2015; Chen et al., 2016; Onarheim et al., \r\n2018; Peng and Meier, 2018; Maksym, 2019). In the Bering Sea, \r\nexpanding winter sea ice cover was observed until 2017 (Frey et al., \r\n2015; Onarheim et al., 2018; Peng and Meier, 2018), but a marked \r\nreduction in sea ice concentration has occurred since then (high \r\nconfidence) (Stabeno and Bell, 2019)."


id: 4a9c962a307342e123c52ead81126504
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 10368
content:
"With respect to seasonal changes in the sea ice cover, the winter sea \r\nice loss causes a decrease in the average sea ice age and fraction of \r\nmulti-year ice, as assessed by SROCC (very high confidence), and also \r\nof the ocean area covered intermittently by sea ice (Bliss et al., 2019). \r\nIn contrast, the seasonal ice zone (covered by sea ice in winter but not \r\nin summer) has expanded regionally (Bliss et al., 2019) and over the \r\nwhole Arctic (Steele and Ermold, 2015), because the loss of summer \r\nsea ice area is larger than the loss of winter sea ice area. Arctic sea \r\nice retreat includes an earlier onset of surface melt in spring and \r\na later freeze up in autumn, lengthening the open water season in \r\nthe seasonal sea ice zone (Stroeve and Notz, 2018). However, there is \r\nlow agreement in quantification of regional trends of melt and freeze \r\nonset between different observational products (Bliss et  al., 2017; \r\nSmith and Jahn, 2019)."


id: e0997057da592f11720df4f6224089f2
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 36769
content:
"The observed fluctuations and trends of the Arctic sea ice cover \r\narise from a combination of changes in natural external forcing and \r\nanthropogenic forcing, internal variability and internal feedbacks \r\n(e.g., Notz and Stroeve, 2018; Halloran et al., 2020). New paleo-proxy \r\ntechniques indicate regional sea ice changes over epochs and \r\nmillennia and allow possible drivers to be assessed. Biomarker IP25 \r\n(Belt et  al., 2007) together with other sedimentary biomarkers \r\n(Belt, 2018) provide local temporal information on seasonal sea \r\nice coverage, permanent sea ice coverage and ice-free waters, with \r\noccasional ambiguous contrasting results (Belt, 2019). These records \r\nand other proposed paleo proxies, including bromine in ice cores \r\n(Spolaor et al., 2016), dinocyst assemblages (e.g., De Vernal et al., \r\n2013b) and driftwood (e.g., Funder et al., 2011), provide evidence of \r\nsea ice fluctuations that exceed internal variability (high confidence)."


id: 23a01ddf2a0ec39bdc654eec78fdf3d7
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 3465
content:
"The inferred sea ice fluctuations over millennia can be related \r\nto Northern Hemisphere temperature evolution and give rise to \r\nArctic-wide fluctuations in sea ice coverage in the paleorecord \r\n(Section 2.3.2.1.1). On a regional scale, fluctuations include decreased \r\nsea ice cover during the Allerod warm period (14.7-12.9 ka) in the \r\nLaptev (Horner et al., 2016) and Bering Seas (Meheust et al., 2018); \r\nan extensive sea ice cover during the Younger Dryas (around 12 ka) \r\nin the Bering (Meheust et  al., 2018), Kara (Horner et  al., 2018), \r\nLaptev (Horner et al., 2016) and Barents (Belt et al., 2015) Seas, and \r\nat the Yermak Plateau (Kremer et al., 2018); little sea ice during the \r\nearly Holocene, when Northern Hemisphere summer insolation was \r\nhigher than today (8000 to 9000 years before present), in the North \r\nIcelandic Shelf area (Cabedo-Sanz et al., 2016; Xiao et al., 2017), Sea \r\nof Okhotsk (Lo et al., 2018), Canadian Arctic (Spolaor et al., 2016), "


id: 5fcb6f15ba2a2188484df8a7188c06f
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 14157
content:
"Reconstructions of Arctic sea ice coverage put the satellite period \r\nchanges into centennial context. Direct observational data coverage \r\n(Walsh et al., 2017) and model reconstructions (Brennan et al., 2020) \r\nwarrant high confidence that the low Arctic sea ice area of summer \r\n2012 is unprecedented since 1850, and that the summer sea ice loss is \r\nsignificant in all Arctic regions except for the Central Arctic (Cai et al., \r\n2021). Direct winter observational data coverage before 1953 is too \r\nsparse to reliably assess Arctic sea ice area. Since 1953, the years \r\n2015 to 2018 had the four lowest values of maximum Arctic sea ice \r\narea, which usually occurs in March (high confidence) (Figure 2.20). \r\nReconstructions of Arctic sea ice area before 1850 remain sparse, \r\nand as in SROCC, there remains medium confidence that the current \r\nsea ice levels in late summer are unique during the past 1 kyr \r\n(Section 2.3.2.1.1; Kinnard et al., 2011; De Vernal et al., 2013b)."


id: 81dfd9fe75b1f46bf3dd1205a795f08f
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 19972
content:
"Barents (Berben et  al., 2017), Bering (Meheust et  al., 2018), and \r\nChukchi (Stein et  al., 2017) Seas, at the Yermak Plateau (Kremer \r\net al., 2018) and north of Greenland (Funder et al., 2011); increasing \r\nsea ice cover throughout much of the middle and late Holocene \r\naround Svalbard (Knies et  al., 2017), in the North Icelandic Shelf \r\narea (Cabedo-Sanz et al., 2016; Harning et al., 2019; Halloran et al., \r\n2020), north of Greenland (Funder et al., 2011), and in the Western \r\nGreenland (Kolling et  al., 2018), Barents (Belt et  al., 2015; Berben \r\net al., 2017), Chukchi (De Vernal et al., 2013a; Stein et al., 2017) and \r\nLaptev (Horner et al., 2016) Seas. The consistent, Arctic-wide changes \r\ngive high confidence in millennial-scale co-variability of the sea ice \r\ncover with temperature fluctuation."


id: 31ffe9d4f38d8c481ccf02b0d29fa42f
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 6050
content:
"The SROCC examined the different atmospheric and oceanic processes \r\nthat caused the observed sea ice loss, with recent studies providing \r\nnew evidence for the importance of variations in air temperature \r\n(Olonscheck et al., 2019; Dahlke et al., 2020), wind patterns (Graham \r\net al., 2019), oceanic heat flux (Docquier et al., 2021) and riverine \r\nheat influx (Park et al., 2020). As in SROCC, the relative contribution of \r\neach physical cause to the sea ice loss cannot be robustly quantified \r\nbecause of disagreement among models (Burgard and Notz, 2017), \r\nsparse observations, and limited understanding of the variation of \r\neach factor with global mean temperature. This is addressed by new \r\ndiagnostics available from CMIP6 simulations, which now allow for \r\nmore detailed analyses of the drivers of sea ice loss at a process level \r\n(Keen et al., 2021)."


id: 41bb564be1eda5fa91d0560b06037ef
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 8760
content:
"practically sea ice free in the September mean for the first time for \r\nfuture CO2 emissions of less than 1000 Gt and before the year 2050 \r\nin all SSP scenarios (Notz and SIMIP Community, 2020). This new \r\nassessment is consistent with an observation-based projection of \r\na  practically sea ice-free Arctic Ocean in September for additional \r\nanthropogenic CO2 emissions of 800 +- 330 GtCO2 beyond the \r\nyear 2018 (Notz and Stroeve, 2018; Stroeve and Notz, 2018). This \r\nestimate may, however, be too high due to neglecting possible \r\nfuture reduction in atmospheric aerosol load that would cause \r\nadditional warming (Gagne et  al., 2015a; Wang et  al., 2018), and \r\nis subject to the same constraints as the carbon budget analysis \r\nfor global mean temperature (see section 5.5 for details). Based on \r\nCMIP6 simulations, it is very likely that the Arctic Ocean will remain \r\nsea ice covered in winter in all scenarios throughout this century \r\n(Sections 4.3.2 and 4.4.2)."


id: abbceb64f63f6815c39d8752bbf7d860
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 27458
content:
"For the more recent past, ice thickness can be directly estimated from \r\nsatellite retrievals of sea ice freeboard (Kwok and Cunningham, 2015; \r\nKwok, 2018). Based on these retrievals, there is medium confidence\r\nthat Arctic sea ice volume has decreased since 2003. There is low \r\nconfidence in the amount of decrease over this period and over \r\nthe CryoSat-2 period from 2011 onwards, primarily because of \r\nsnow-induced uncertainties in the retrieval algorithms, the shortness \r\nof the record, and the small identified trend (e.g., Bunzel et al., 2018; \r\nPetty et al., 2018, 2020)."


id: 4d4ab6e3f3906eba6906d087b960bbb3
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 10880
content:
"The SROCC (Meredith et  al., 2019) assessed that there was no \r\nsignificant trend in annual mean Antarctic sea ice area over the period \r\nof reliable satellite retrievals starting in 1979 (high confidence). The \r\nupdated time series is consistent with this assessment. It includes \r\na  maximum sea ice area in 2014, then a  substantial decline until \r\nthe minimum sea ice area in 2017, and an increase in sea ice area \r\nsince 2017 (Figures 2.20 and 9.15; Schlosser et al., 2018; Maksym, \r\n2019; Parkinson, 2019). As assessed in Section 2.3.2.1.2, the possible \r\nsignificance of the increase in mean Antarctic sea ice area over \r\nthe shorter period 1979 to 2014 (Figure  2.20; Simmonds, 2015; \r\nComiso et  al., 2017b) is unclear. This is because of observational \r\nuncertainty (see Section  9.3.1.1), large year-to-year fluctuations in \r\nall months (Figure 9.15), and limited understanding of the processes \r\nand reliability of year-to-year correlation of Antarctic sea ice area \r\n(Yuan et al., 2017)."


id: 32c2e25b65124bc535f3622076e1ceea
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 6200
content:
"The SROCC assessed with very high confidence that Arctic sea ice \r\nhas become thinner over the satellite period from 1979 onwards, \r\nand this assessment is confirmed for the updated time series \r\n(Section  2.3.2.1.1). Sea ice area has also decreased substantially \r\nover this period (Section 9.3.1.1), leading to the assessment that \r\nArctic sea ice volume has also decreased with very high confidence \r\nover the satellite period since 1979. There is, however, only low \r\nconfidence in quantitative estimates of the sea ice volume \r\nloss over this period because of a  lack of reliable, long-term, \r\npan-Arctic observations and substantial spread in available \r\nreanalyses (Chevallier et  al., 2017). Current best estimates from \r\nreanalyses suggest a reduction of September Arctic sea ice volume \r\nof 55 to 65% over the period 1979-2010, and of about 72% over \r\nthe period 1979-2016, with the latter deemed a  conservative \r\nestimate (Schweiger et al., 2019)."


id: 353e9e1a11a276ec09074320b9fe40eb
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 6614
content:
"As assessed by SROCC, the evolution of mean Antarctic sea ice area \r\nis the result of opposing regional trends (high confidence), with \r\nslightly decreasing sea ice cover during the period 1979 to 2019 in \r\nthe Amundsen and Bellingshausen Seas, particularly during summer, \r\nand slightly increasing sea ice cover in the eastern parts of the \r\nWeddell and Ross Seas (Figure 9.15). With the exception of the Ross \r\nSea, these trends are not significant, considering the large variability \r\nof the time series (Yuan et al., 2017)."


id: 93efa44b3d6452167f13bc9b32e04339
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 23231
content:
"1979 results from a long-term Southern Ocean surface cooling trend \r\n(e.g., Kusahara et al., 2019; Jeong et al., 2020) but the importance of \r\nthis mechanism for the observed sea ice evolution is unclear owing \r\nto intricate feedbacks between sea ice  change and surface cooling \r\n(Haumann et  al., 2020). The importance of changing wave activity \r\n(Section 9.6.4.2; Kohout et al., 2014; Bennetts et al., 2017; Roach et al., \r\n2018b) on sea ice is unclear due to limited process understanding. In \r\nsummary, there is high confidence that regional Antarctic trends are \r\nprimarily caused by changes in sea ice drift and decay, with medium \r\nconfidence in a dominating role of changing wind pattern. The precise \r\nrelative contribution of individual drivers remains uncertain because \r\nof limited observations, disagreement between models, unresolved \r\nprocesses, and temporal and spatial remote linkages caused by sea ice \r\ndrift (Section 9.2.3.2; Pope et al., 2017)."


id: b3c5b93bdcdbf073b68863c2db7b7b54
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 28871
content:
"Recent research has confirmed SROCC assessment of atmospheric and \r\noceanic drivers of the sea ice decline from 2014 to 2017, which can be \r\nlinked to changes in both subsurface ocean heat flux (Meehl et al., 2019; \r\nPurich and England, 2019) and atmospheric circulation, with the latter \r\npartly related to teleconnections with the tropics (Meehl et al., 2019; \r\nPurich and England, 2019; G. Wang et al., 2019). In the Weddell Sea, \r\nthese changes caused in 2017 the re-emergence of the largest polynya \r\nover the Maud Rise since the 1970s (Section 9.2.3.2; Campbell et al., \r\n2019; Jena et al., 2019; Turner et al., 2020)."


id: 72fd2cdcac3f2f63dc72b97d3902f62e
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 17481
content:
"The AR5 (Collins et al., 2013) and SROCC found low confidence in future \r\nprojections of Antarctic sea ice. This includes the projected mitigation \r\nof the sea ice loss by stratospheric ozone recovery (Smith et al., 2012) \r\nand by an increased freshwater input from melting of the Antarctic "


id: 4f9351b1934d43582ade256d9cd97ed0
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 11287
content:
"Regionally, proxy data from ice cores consistently indicate that the \r\nincrease of sea ice area in the Ross Sea and the decrease of sea ice \r\narea in the Bellingshausen Sea are part of longer centennial trends \r\nand exceed internal variability on multi-decadal time scales (medium \r\nconfidence) (e.g.,  Thomas et  al., 2019; Tesi et  al., 2020). These \r\ncentennial trends are consistent with simulations from CMIP5 models \r\n(Hobbs et al., 2016b; J.M. Jones et al., 2016; Kimura et al., 2017)."


id: 65ac5622fb55ee091abec6100ecccb04
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 15150
content:
"There is low confidence in the attribution of the observed changes \r\nin Antarctic sea ice area (Section  3.4.1.2). Based on the available \r\nevidence, the lack of a  negative trend of Antarctic sea ice area, \r\ndespite substantial global warming in recent decades, has been \r\nattributed to internal variability in analyses of the observational \r\nrecord (Meier  et  al., 2013; Gallaher et  al., 2014; Gagne et  al., \r\n2015b), reconstructions from early observations (Fan et  al., 2014; \r\nEdinburgh and Day, 2016) and proxy data (Hobbs et al., 2016b) in \r\nmodel simulations (Turner et al., 2013; Zunz et al., 2013; L. Zhang \r\net al., 2019). Nonetheless, without accurate simulations of observed \r\nchanges, the possible contribution of anthropogenic forcing to the \r\nregional changes in sea ice area remains unclear (Hosking et  al., \r\n2013; Turner et al., 2013; Haumann et al., 2014; L. Zhang et al., 2019)."


id: a5a218579330750d6db369175d5d6faa
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 26374
content:
"The attribution of the observed trends in atmospheric and oceanic \r\nforcing is also uncertain because of limited observational records and \r\ndiscrepancies between modelled and observed evolution of the sea \r\nice cover. More specifically, there is contrasting evidence for a direct \r\nrole of stratospheric ozone depletion on the observed changes in \r\natmospheric circulation (Haumann et al., 2014; England et al., 2016; \r\nLandrum et  al., 2017). In contrast, there is high confidence that \r\nmulti-decadal variations in the tropical Pacific and in the Atlantic \r\naffect the Amundsen Sea low (Li et al., 2014; Kwok et al., 2016; Meehl \r\net al., 2016; Purich et al., 2016; Simpkins et al., 2016), while other \r\nmodes of climate variability (Annex IV) affect, for example, Southern \r\nOcean cyclone activity (Simpkins et al., 2012; Cerrone et al., 2017; \r\nSchemm, 2018)."


id: e56b03b459872520735a2cb603e827aa
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 37667
content:
"The discrepancy between the modelled and observed evolution \r\nof Antarctic sea ice has been related by SROCC to deficiencies in \r\nmodelled stratification, freshening by ice-shelf meltwater, clouds, and \r\nother wind- and ocean-driven processes. Recent studies highlight the \r\npossible mis-representation of freshwater fluxes from ice shelves \r\n(Jeong et al., 2020), and the possible effect of the low resolution of \r\nmost models (Sidorenko et al., 2019), even though lower-resolution \r\nmodels are, in principle, capable of a  realistic simulation of the \r\nseasonal sea ice budgets in the Southern Ocean (Holmes et al., 2019). \r\nThe relative importance of these possible reasons for the models' \r\nshortcomings remains unclear (see Section 3.4.1.2 for details)."


id: 94551453d51bae1e9ea236159909ce90
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 23305
content:
"are too sparse in space and time to reliably estimate changes across \r\nSouthern Ocean sea ice (Webster et al., 2018)."


id: b0fab7413a42a2061a643b4c3bbd73d2
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 28365
content:
"Andresen et  al., 2017; Khan et  al., 2020; Vermassen et  al., 2020). \r\nHistorical photographs (Khan et al., 2020) show large mass losses \r\nof Jakobshavn and Kangerlussuaq Glaciers in West Greenland \r\nfrom 1880 until the 1940s, exceeding their 21st-century mass loss, \r\nwhereas the Helheim Glacier in East Greenland remained stable, \r\ngained mass in the 1990s, then rapidly lost mass after 2000. Together, \r\nthese three large outlet glaciers, draining about 12% of the ice sheet \r\nsurface area, have lost 22 +- 3 Gt yr -1 in the period 1880-2012 (Khan \r\net al., 2020). Overall, these studies provide a variable picture of the \r\nGreenland Ice Sheet mass change in the 20th century. The updated \r\nmass loss of Greenland Ice Sheet, including peripheral glaciers for \r\nthe period 1901-1990, is 120 [70-170] Gt yr-1 (see Table 9.5 and \r\nFigures 9.16 and 9.17)."


id: ecb75a0d380819d5fd99bca21bafa112
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 39005
content:
"In this section we present regional mass change time series for the \r\nGreenland Ice Sheet and assess the different processes that are \r\ncausing the increase in mass loss. The vast increase in observational \r\nproducts from various platforms (e.g, GRACE, PROMICE, ESA\u0002CCI, NASA MEaSUREs) provide a  consistent and clear picture of \r\na shrinking Greenland Ice Sheet (Colgan et al., 2019; Mottram et al., \r\n2019; Mouginot et al., 2019; King et al., 2020; Mankoff et al., 2020; \r\nMoon et al., 2020; Sasgen et al., 2020; Velicogna et al., 2020; The \r\nIMBIE Team, 2020). Section 2.3.2.4.1 provides an updated estimate \r\nof the total Greenland Ice Sheet mass change in a  global context \r\n(Figure  2.24). The estimated ice-sheet extent at different times is \r\nshown in Figure 9.17, and the paleo perspective on Greenland Ice \r\nSheet evolution is presented in Section 9.6.2."


id: 17d3e042fbd3ecdc39265cac2b063529
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 1397
content:
"For the 20th  century, SROCC (Meredith et  al., 2019) presented \r\none reconstruction for 1900-1983 and estimated mass change for \r\nthe Greenland Ice Sheet and its peripheral glaciers for the period \r\n1901-1990. Since SROCC, a comprehensive new study has extended \r\nthe satellite record back to 1972 (Figure  9.16; Mouginot et  al., \r\n2019). The rate of ice-sheet mass change was positive (i.e., it gained \r\nmass) in 1972-1980 (47 +- 21 Gt yr-1) and then negative (i.e., it lost \r\nmass; -51 +- 17 Gt yr -1 and -41 +- 17 Gt yr -1) in 1980-1990 and \r\n1990-2000, respectively. Other ice discharge time series starting in \r\n1985 (King et al., 2018, 2020; Mankoff et al., 2019, 2020) agree with \r\nMouginot et al. (2019) (see also Figure 9.16). There is limited evidence\r\nof temporally and spatially heterogeneous Greenland outlet glacier \r\nevolution during the 20th century (Lea et al., 2014; Luthi et al., 2016; "


id: 86a29e804d5e58cdd257a03e79cedddf
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 20851
content:
"more rapidly than discharge increased. In summary, the consistent \r\ntemporal pattern in these longer datasets leads to high confidence\r\nthat the Greenland Ice Sheet mass losses are increasingly dominated \r\nby SMB, but there is high confidence that mass loss varies strongly, \r\ndue to large interannual variability in SMB."


id: 2d61a3d6fed53dfc9921db319335a3f9
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 5268
content:
"show that SMB has been gradually decreasing in all regions, while \r\nthe increase in discharge in the south-east, central east, north-west \r\nand central west has been linked to retreating tidewater glaciers \r\n(Figure  9.16). In summary, the detailed regional records show an \r\nincrease in mass loss in all regions after the 1980s, caused by both \r\nincreases in discharge and decreases in SMB (high confidence), \r\nalthough the timing and patterns vary between regions. The largest \r\nmass loss occurred in the north-west and the south-east of Greenland \r\n(high confidence)."


id: 5f3a093aa8a9cb770059914702fc96cc
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 14061
content:
"On a regional scale, the surface elevation is lowering in all regions, and \r\nwidespread terminus and calving front retreats have been observed \r\n(with no glaciers advancing; Mottram et al., 2019; Moon et al., 2020). \r\nThe largest mass losses have occurred along the west coast and in \r\nsouth-east Greenland (Figure  9.16), concentrated at a  few major \r\noutlet glaciers (Mouginot et al., 2019; Khan et al., 2020). This regional \r\npattern is consistent with independent Global Navigation Satellite \r\nSystem (GNSS) observations from the Greenland Global Positioning \r\nSystem (GPS) network which show elastic bedrock uplift of tens of \r\ncentimetres between 2007-2019 as a result of ongoing ice mass loss \r\n(Bevis et al., 2019). The regional time series (Figures 9.16; Atlas.30) "


id: f861995e4ceb9e3f7c0c4715171009ee
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 41065
content:
"The SROCC stated with high confidence that variability in large-scale \r\natmospheric circulation is an important driver of short-term SMB \r\nchanges for the Greenland Ice Sheet. This effect of atmospheric \r\ncirculation variability on both precipitation and melt rates (and SROCC \r\nassessment) is confirmed by more recent publications (Valisuo et al., \r\n2018; B. Zhang et al., 2019; Velicogna et al., 2020). The strong mass \r\nloss in 2019 (Cullather et  al., 2020; Hanna et  al., 2020; Tedesco "


id: a3139d83b01265c2083f6de77524143e
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 25897
content:
"and Fettweis, 2020) was driven by highly anomalous atmospheric \r\ncirculation patterns, both on daily (Cullather et al., 2020) and seasonal \r\ntime scales (Tedesco and Fettweis, 2020). Although surface melt \r\nis anticorrelated with the summer North Atlantic Oscillation Index \r\n(Valisuo et al., 2018; Ruan et al., 2019; Sherman et al., 2020), especially \r\nin West Greenland (Bevis et al., 2019), Greenland Ice Sheet melt is \r\nmore strongly correlated with the Greenland Blocking Index (Hanna \r\net al., 2016, 2018) than with the summer North Atlantic Oscillation \r\nindex (Huai et al., 2020)."


id: ef121cebef74e8e2704c69e4dc95b963
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 39431
content:
"The SROCC reported that accurate bedrock topography is required for \r\nunderstanding and projecting the glacier response to ocean forcing. \r\nAccurate bathymetry is essential for establishing which water masses \r\nenter glacial fjords, and for reliable estimates of the submarine melt \r\nrates experienced by tidewater glaciers (Schaffer et al., 2020; T. Slater \r\net al., 2020; Wood et al., 2021). Subglacial and lateral topography \r\nis known to strongly modulate tidewater glacier dynamics and the \r\nsensitivity of tidewater glaciers to climatic forcing (Enderlin et  al., "


id: afaa9ba31a90faa45bc984e4405dabbb
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 28143
content:
"The SROCC reported with medium confidence that around half of \r\nthe 1960-2014 Greenland Ice Sheet surface meltwater ran off, while \r\nmost of the remainder infiltrated firn and snow, where it either \r\nrefroze or accumulated in firn aquifers. Studies since SROCC show \r\na decrease of firn air content between 1998-2008 and 2010-2017 \r\n(Vandecrux et  al., 2019) in the low-accumulation percolation area "


id: 81e54f8e622315e4166a9f3677e43fe0
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 19979
content:
"2013; Catania et  al., 2018). Bathymetric mapping around the ice \r\nsheet has greatly improved with direct and gravimetric surveys \r\n(Millan et al., 2018; An et al., 2019a, b; Jakobsson et al., 2020) leading \r\nto the improvement of Greenland-wide bathymetric and topographic \r\nmapping (e.g., Morlighem et al., 2017). However, large uncertainties \r\nin ice thickness remain for around half of the outlet glaciers \r\n(Mouginot et al., 2019; Wood et al., 2021) and sea ice covered and \r\niceberg-packed regions remain poorly sampled near glacier termini \r\n(Morlighem et al., 2017). There is high confidence that bathymetry \r\n(governing the water masses that flow into fjord cavities) and \r\nfjord geometry and bedrock topography (controlling ice dynamics) \r\nmodulate the response of individual glaciers to climate forcing."


id: ba7e0cd3f50f6b1498456d458c2e68d6
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 30026
content:
"The AR5 assessed that it is likely that anthropogenic forcing has \r\ncontributed to the surface melting of Greenland since 1993 (Bindoff \r\net al., 2013). Section 3.4.3.2 assesses that it is very likely that human \r\ninfluence has contributed to the observed surface melting of the \r\nGreenland Ice Sheet over the past two decades. There is medium \r\nconfidence of an anthropogenic contribution to recent mass loss \r\nfrom Greenland."


id: e6c9832baf274e5d1a8496bd06a6e6c0
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 37897
content:
"The SROCC (Oppenheimer et  al., 2019) stated that substantial \r\nchallenges remained for modelling of the Greenland SMB and the \r\ndynamical ice sheet. Since SROCC, further insights into modelling of \r\nthe Greenland ice sheet has come from model intercomparison studies \r\nof the SMB (Fettweis et al., 2020) and dynamical ice sheets (Goelzer \r\net al., 2020; Payne et al., 2021). Further aspects relevant to the forcing \r\nof the ice sheet from large scale global climate models and regional \r\nclimate models are discussed in Box 9.3 and Section Atlas.11.2."


id: d6ea2ef3d28e801a1dd93b8535f40e7d
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 35115
content:
"The SROCC (Oppenheimer et  al., 2019) noted the progress made \r\nin Greenland Ice Sheet models since AR5. New since SROCC is \r\na focus on improved representation of the present-day state of the \r\nice sheet (Box  9.3; Goelzer et  al., 2018, 2020). Improvements are \r\nclosely linked to the growing number and quality of observations \r\n(Section 9.4.1.1), new techniques to generate internally consistent \r\ninput datasets (Morlighem et  al., 2014, 2016a), wider use of data \r\nassimilation techniques (Larour et  al., 2014, 2016; Perego et  al., "


id: a216f03e00cd2fa0354caa891d2b96b9
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 25752
content:
"The SROCC noted increased use of coupled climate-ice sheet \r\nmodels for simulating the Greenland ice sheet, but it also noted \r\nthat remaining deficiencies in coupling between models of climate \r\nand ice sheets (e.g.,  low spatial resolution) limited the adequate \r\nrepresentation of the feedbacks between them. Some Earth system \r\nmodels (ESMs) now incorporate multi-layer snow models and full "


id: 7fd78628629ac2e0e1ea552e7cedc524
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 19630
content:
"2014; Goldberg et al., 2015; Lee et al., 2015; Schlegel et al., 2015; \r\nMosbeux et al., 2016), increased model resolution (Aschwanden et al., \r\n2016) and tuning of key processes such as calving (Choi et al., 2021). \r\nA  remaining challenge is low confidence in reproducing historical \r\nmass changes of the Greenland Ice Sheet (Box 9.3). However, there \r\nis medium confidence in ice-sheet models reproducing the present \r\nstate of the Greenland Ice Sheet, leading to medium confidence in \r\nthe current ability to accurately project its future evolution."


id: bc6a354285548cde36f466f403905beb
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 30391
content:
"0.073 m for RCP8.5. Although the ocean does not directly force the \r\nice-sheet models in these simulations, the new coupled models allow \r\nfor interactions between ice-sheet dynamics, SMB and local climate. \r\nThe coupled projections fall within the lower bounds of AR5 and \r\nSROCC and, as these studies do not prescribe ocean forcing directly, it \r\nis possible that the dynamic response is underestimated."


id: a27c1133691724ba53bc6c01beb11b58
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
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index: climate_gpt_v2
vector_id: 25805
content:
"Table 9.2 | Projected sea level contributions in metres from the Greenland Ice Sheet by 2100 relative to 1995-2014, unless otherwise stated, for \r\nselected Representative Concentration Pathway (RCP) and Shared Socio-economic Pathways (SSP) scenarios. Italics denote partial contributions. Historical \r\ndynamic response omitted from ISMIP6 simulations is estimated to be 0.19 +- 0.10 mm yr-1 (0.02 m +- 0.01 m in 2100 relative to 2015). The climate forcing is described in \r\nAppendix 7.SM.2."


id: a4c8fc3159ce4e6377ff0aa0157f610b
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 26220
content:
"and thus, there is medium confidence that uncertainty in mass loss \r\nfrom the Greenland Ice Sheet is dominated by uncertainty in climate \r\nscenario and surface processes, whereas uncertainty in calving and \r\nfrontal melt play a minor role."


id: e97b2d68f1925c9204ca11e3b225fb71
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 38433
content:
"The AR5 (Church et  al., 2013b) assessed the contribution from \r\nGreenland to sea level projections in 2300 as 0.15 m SLE in \r\nlow-emissions scenarios (about RCP2.6) and 0.31-1.19 m in high \r\nscenarios (approximately RCP6.0/RCP8.5). The SROCC (Oppenheimer \r\net  al., 2019) did not update AR5 estimates, given  limited \r\nevidence and low agreement from three new studies (Vizcaino et al., "


id: 1acf4e0b1c30385b7d034c7ddb71e20b
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 1914
content:
"consistent with future-focused studies (Aschwanden et al. 2019, Le \r\nClec'h et al., 2019, Gregory et al., 2020)."


id: a9e1ae4aa5cf2e87efe11e69f3855801
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 27110
content:
"The SROCC adopted the AR5 assessment that complete loss of \r\nGreenland ice, contributing about 7 m to sea level, over a millennium \r\nor more would occur for a sustained global mean surface temperature \r\n(GMST) between 1degC (low confidence) and 4degC (medium confidence) \r\nabove pre-industrial levels. New studies since SROCC (Gregory et al., \r\n2020; Van Breedam et al., 2020) confirm this assessment (see also \r\nFigure 9.30). Clark et al. (2016) estimate a complete loss to take about \r\n8000 years at 5.5degC and about 3000 years at 8.6degC. Based on the \r\nagreement between new and previous studies, there is therefore high \r\nconfidence that the rate at which Greenland Ice Sheet commitment is \r\nrealized depends on the amount of warming."


id: 24890194e1889d814c1cbfafebfcbfcd
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 3618
content:
"The role of the elevation-mass feedback for future projections of \r\nGreenland can be assessed from paleo simulations. Ice-sheet model \r\nsimulations of the Laurentide (Gomez et al., 2015; Gregoire et al., \r\n2016) and Eurasian (Alvarez-Solas et al., 2019) ice sheets invoke at \r\nleast some contribution to last glacial termination mass loss from SMB \r\nreduction, as a consequence of an elevation-mass balance feedback \r\n(Levermann and Winkelmann, 2016). In a model spanning Meltwater \r\nPulse 1A, this mechanism increased mass loss by approximately 66% \r\n(Gregoire et al., 2016) but in Last Interglacial simulations, the effect \r\nof this feedback is shown to depend on the surface scheme of the \r\nclimate model employed (Plach et al., 2019). Given the agreement \r\nbetween theoretical analyses and paleo-ice-sheet model experiments, \r\nthere is high confidence that the elevation-mass balance feedback \r\nis most relevant at multi-centennial and millennial time scales, "


id: 77e68ec72903019e919fb9f0ca4f2e4f
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 18298
content:
"Projections of ice sheets and glaciers in AR5 (Church et al., 2013b) and SROCC (Oppenheimer et al., 2019) were assessed by collecting \r\nsingle model studies - with the exception of glaciers in SROCC (Hock et al., 2019b). Community benchmark experiments (ISMIP-HOM; \r\nPattyn et al., 2008) or Marine Ice Sheet Model Intercomparison Projects (MISMIP; Pattyn et al., 2012); MISMIP3d, (Pattyn and Durand, \r\n2013); MISMIP+ (Asay-Davis et al., 2016; Cornford et al., 2020) have substantially advanced ice-sheet modelling since AR5. Model \r\nIntercomparison Projects (MIPs) now inform projections of both ice sheets and glaciers: the Ice Sheet MIP for CMIP6 (ISMIP6; \r\nSections 9.4.1.3 and 9.4.2.5), the Linear Antarctic Response MIP (LARMIP-2; Section 9.4.2.5) and GlacierMIP (Section 9.5.1.3)."


id: cb1f8777a207650553d152989e72f325
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 33075
content:
"Simulations of ice sheets and glaciers are dependent on forcing provided by atmosphere and ocean models.  Despite progress in \r\nrepresenting processes, reducing biases and increasing resolution, regional and global models still have difficulties reproducing observed \r\nregional air temperature, surface mass balance (SMB) and ocean changes (Sections 9.4.1.2 and 9.4.2.2, and Atlas.11). An assessment of \r\nCMIP5 and CMIP6 climate models, as forcing for land ice models, has been undertaken (Walsh et al., 2018; Barthel et al., 2020; Marzeion \r\net al., 2020; Nowicki et al., 2020b) with the aim of selecting the best available historical forcings and sampling potential regional future \r\nclimate changes. Despite improvement in simulation of atmospheric forcing, persistent biases remain in CMIP5 and CMIP6, which reduces \r\nthe fidelity of historical and future simulations of land ice."


id: ecd9d4546e41ecf808e8600fc0100c6
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 39040
content:
"applied to temperature projections for the Shared Socio-economic Pathways (SSPs; Supplementary Material 7.SM.2) and an estimate \r\nof SMB change from the AR5 parametric Antarctic Ice Sheet SMB model (Church et al., 2013b) is added to the results (Sections 9.4.2.4, \r\n9.4.2.5 and 9.6.3.2). It is not necessary to add a long-term dynamic response to the LARMIP-2 projections, as this is incorporated in \r\nthe basal melt time series."


id: 7aa180420b1430ecbd4023e4bf0da74b
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 18771
content:
"GlacierMIP (Marzeion et al., 2020) was designed to estimate the glacier contribution to sea level rise, including from peripheral \r\nglaciers in Greenland and Antarctica that can be considered to be dynamically decoupled, or entirely separate, from the ice sheets. \r\nGlacier models are described in Annex II. Initial conditions were based on Randolph Glacier Inventory Version 6 (RGI Consortium, 2017) \r\nand initial ice thickness and volume were provided from an update of Huss and Farinotti (2012), although some glacier models used \r\ntheir own estimates. Forcings were taken from 10 different CMIP5 general circulation models, selected based on availability of multiple \r\nRCPs, the choice in a previous model intercomparison (Hock et al., 2019a), and performance in glacier-covered regions according to \r\nWalsh et al. (2018). In addition, two global glacier models performed the same experiment with 13 CMIP6 models (Section 9.5.1.3)."


id: e09a569f576eef28767718da5ddadc3c
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 36772
content:
"that this is the case since the late 1970s. Furthermore, parts of the \r\nEAIS have lost mass in the last two decades (high confidence)."


id: bf4a014a1aaf8adc09a46c0af38d4e0
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 30923
content:
"with multi-decadal increases in the Antarctic Peninsula inferred since \r\nthe 1930s (Medley and Thomas, 2019), and dominate the interannual \r\nto decadal variability of the AIS mass balance (Rignot et al., 2019). \r\nHowever, no significant continent-wide SMB trend is inferred since \r\n1979 (The IMBIE Team, 2018; Medley and Thomas, 2019; regional \r\nchanges of Antarctic SMB are assessed further in Atlas Section 11.1). \r\nIn summary, there is very high confidence that the observed AIS mass \r\nloss since the early 1990s is primarily linked to ice-shelf changes."


id: 9617348c702e3f0899c0880a2cb777cb
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 23598
content:
"The SROCC stated with high confidence that melting of ice shelves by \r\nwarm ocean waters, leading to reduction of ice-shelf buttressing, has \r\ndriven the observed ongoing thinning of major WAIS outlet glaciers. \r\nSince SROCC, digitized radar measurements have shown that the \r\neastern ice shelf of Thwaites Glacier in the Amundsen Sea Embayment \r\nthinned between 10 and 33% during the three decades after 1978 \r\n(Schroeder et al., 2019), and the role of basal ice-shelf melting has \r\nbeen emphasized (Smith et  al., 2020). Strong surface meltwater \r\nproduction has been noted as a precursor of ice-shelf disintegration \r\nin and since SROCC (Bell et al., 2018), and recent work placed strong \r\nmeltwater production events (Lenaerts et  al., 2017; Nicolas et  al., \r\n2017; Wille et  al., 2019) and seasons (Robel  and  Banwell,  2019) "


id: eed7c7a05f8ff39800a4702ce11af69b
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 39394
content:
"The SROCC stated with high confidence that ice-shelf disintegration \r\nhas driven dynamic thinning in the northern Antarctic Peninsula over \r\nrecent decades, and expressed high confidence in current ongoing \r\nmass loss from glaciers that fed now-disintegrated ice shelves. \r\nHowever, the mass loss rate has decreased in the 20 years since \r\nthe immediate speed-up following ice-shelf disintegration in 1995 \r\nand 2002. Observed flow speed of these tributary glaciers is still \r\n26% higher than before the ice shelf disintegration (Seehaus et al., \r\n2018). Conversely, one study interpreted the increased flow speed \r\nof the Scar Inlet Ice Shelf's tributary glaciers as a sign of evolving \r\ninstability of the currently intact ice shelf (Qiao et al., 2020)."


id: e935a1ddde45ddf623085caeeb48c071
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 38384
content:
"Ongoing grounding line retreat, indicating dynamic thinning, \r\nis observed with high confidence in many areas of Antarctica, \r\nand particularly on the WAIS, with the highest rates being in the \r\nAmundsen and Bellingshausen Sea areas, and around Totten Glacier \r\nin East Antarctica, as stated in SROCC. Research published since \r\nSROCC has evidenced grounding line retreat of the West Antarctic \r\nBerry Glacier on the Getz Coast (Millan et al., 2020) and on the East \r\nAntarctic Denman Glacier (Brancato et al., 2020), both since 1996. \r\nFurthermore observed grounding line retreat in excess of 1.5 km \r\nbetween 2003 and 2015 has been reported for parts of Marie Byrd \r\nLand (Christie et  al., 2018). In summary, there is high confidence\r\nthat grounding lines of marine-terminating glaciers are currently \r\nretreating in many areas around Antarctica, particularly around \r\nthe WAIS, and additional areas of grounding line retreat have been \r\nevidenced since SROCC."


id: ab8b45310753ea35fcdfcfebc8922d34
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 27414
content:
"The SROCC reported limited evidence and medium agreement for \r\nanthropogenic forcing of the observed AIS mass balance changes. \r\nAs  stated in Section  3.4.3.2, there remains low confidence in \r\nattributing the causes of the observed mass of loss from the AIS since \r\n1993, in spite of some additional process-based evidence to support \r\nattribution to anthropogenic forcing."


id: ca7df3b0d60b198f28777be134b110f3
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 32965
content:
"The AR5 (Church et al., 2013b; Flato et al., 2013) stated that regional \r\nclimate models and global models with bias-corrected SST and sea \r\nice concentration tended to produce more accurate simulations of \r\nAntarctic SMB than coupled climate models. It also noted strong \r\nclimate model temperature biases over the Antarctic, though the \r\nlatter may reflect known biases in the reanalysis used (Freville et al., \r\n2014). Section Atlas.11.1 assesses that there is medium confidence\r\nin the capacity of climate models to simulate Antarctic climatology \r\nand SMB changes."


id: bbde5c3bed085525c122251c9c04d6d0
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 30285
content:
"An important difficulty is how to evaluate simulations of processes \r\nthat are: not currently observed; or rare; or indirectly deduced - in \r\nparticular, the ice-shelf disintegrations and cliff failures that would \r\ndrive the proposed marine ice cliff instability (MICI; Section 9.4.2.4 "


id: f941c7c8c449cee529dc8e2dc5894980
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 41217
content:
"and Box 9.4; DeConto and Pollard, 2016; DeConto et al., 2021). Models \r\nof ice-cliff failure can only be indirectly and partially evaluated, using \r\nexisting (i.e., static) cliffs and laboratory experiments (Clerc et  al., \r\n2019). The SROCC stated that there was low agreement on the exact \r\nMICI mechanism and limited evidence of its occurrence in the present \r\nor the past, and that the validity of MICI remains unproven. Only one \r\nice-sheet model represents MICI (Pollard et al., 2015; DeConto and \r\nPollard, 2016; DeConto et al., 2021). The mechanism has not been \r\nfound to be essential for reproducing Mid Pliocene Warm Period and \r\nLast Interglacial reconstructions or satellite observations, though \r\nLast Interglacial data slightly favours it in this model (Edwards et al., \r\n2019; Gilford et al., 2020; DeConto et al., 2021)."


id: fef69980b24c42cfb425489ce619443c
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 42255
content:
"In summary, there is now medium confidence in many ice-sheet \r\nprocesses in ice-sheet models, including grounding line evolution. \r\nHowever, there remains low confidence in the ocean forcing affecting \r\nthe basal melt rates, and low confidence in simulating mechanisms \r\nthat have the potential to cause widespread, sustained and very \r\nrapid ice loss from Antarctica through MICI."


id: 4991c56e1ba787e7034ce50d6a939b22
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 10172
content:
"Antarctic ice shelves modulate grounded ice flow through buttressing, \r\nso their weakening or disintegration is crucial for the timing and \r\nmagnitude of ice loss and onset of instabilities (Section  9.4.2.4; \r\nBox 9.4). Projections of ice-shelf disintegration are uncertain in terms \r\nof atmospheric warming and the response of the shelf surface  - \r\nthat is, surface melting, and whether shelves then disintegrate due \r\nto hydrofracturing and flexing, or are resilient through refreezing or \r\ndrainage (Bell et al., 2018). The SROCC stated it is not expected that \r\nwidespread ice-shelf loss will occur before the end of the 21st century, \r\nbut this was based on only one study, using a regional climate model \r\nforced by five GCMs (Trusel et al., 2015), so there was low confidence "


id: a735401468e62337b7e69a98bca2f3ff
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 26632
content:
"troughs in East Antarctica potentially vulnerable to MISI (Morlighem \r\net al., 2020) has only been used by a few models (Seroussi et al., \r\n2020; Sun et al., 2020), so current projections could underestimate \r\nvulnerability in these regions. The sea level rise contribution of the \r\nAIS therefore crucially depends on the behaviour of individual ice \r\nshelves and outlet glacier systems and whether they enter MISI for \r\na  given level of warming (Box  9.4; Pattyn and Morlighem, 2020). \r\nAs for Antarctic simulations generally (Sections 9.4.2.2 and 9.4.2.3), \r\nthere is medium confidence in simulating MISI but low confidence \r\nin projecting the sub-shelf melting and ice-shelf disintegration that \r\ndrive it."


id: 17a82097213e6db10146ca016a4176f0
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 1371
content:
"in this assessment. The study of DeConto and Pollard (2016) projected \r\nthe appearance of extensive surface meltwater several decades \r\nearlier than Trusel et al. (2015) and was therefore assessed to be too \r\nuncertain to include in SROCC projections of the AIS."


id: 4570ea82a25fb8807aba378b37338ee1
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 9436
content:
"A major uncertainty in future Antarctic mass losses is the possibility \r\nof rapid and/or irreversible ice losses through instability of marine \r\nparts of the ice sheet, via the proposed mechanisms of marine ice \r\nsheet instability (MISI) and marine ice cliff instability (MICI), and \r\nwhether these processes will lead to a collapse of the West Antarctic \r\nIce Sheet (WAIS)."


id: 8b2233e43bd4b800c0a7e35eb485fe92
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 21640
content:
"The SROCC assessed that ice-sheet interactions with the solid Earth \r\nare not expected to substantially slow sea level rise from marine-based \r\nice in Antarctica over the 21st  century (medium confidence), but \r\nthat these processes could become important on multi-century and \r\nlonger time scales. More recent modelling of deglaciation of the Ross \r\nEmbayment by Lowry et al. (2020) is consistent with this assessment. \r\nHowever, new projections for Pine Island Glacier (Kachuck et  al., \r\n2020) support previous work (Barletta et al., 2018) suggesting that \r\nlower mantle viscosity in this region leads to a negative feedback on \r\ndecadal time scales. Grounding line stabilization by the solid Earth \r\nresponse may therefore occur over the 21st century in the Amundsen \r\nSea Embayment, where most mass loss is occurring (Section 9.4.2.1), \r\nbut more generally occurs over multi-centennial to millennial time \r\nscales (medium confidence)."


id: 9d552c4a57f27716ac16d824ab745587
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 24912
content:
"The MICI hypothesis describes rapid, unmitigated calving triggered \r\nby ice-shelf collapse (Pollard et  al., 2015). The SROCC noted that \r\nthe MICI mechanism led one model (DeConto and Pollard, 2016) \r\nto lose mass far more rapidly, but excluded the mechanism from \r\nits projections due to uncertainty in the timing of the ice-shelf \r\ndisintegration (Section  9.4.2.3). They stated that MICI could lead \r\nto sea level contributions beyond 2100 considerably higher than \r\nthe likely range projected by other models. However, given the low \r\nagreement on the exact MICI mechanism and limited evidence of its "


id: aefecf5ab74fe45fafa71a3ae4b3a736
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 28031
content:
"extensive exploration of uncertainties, and inclusion of a  positive \r\nfeedback of meltwater on climate (Golledge et al., 2019). However, \r\ntwo of the projections did not include SMB changes that would \r\noffset dynamic losses (Levermann et  al., 2014; Ritz et  al., 2015), \r\nand the scenario dependence may have been further amplified by \r\nhighly sensitive sub-shelf melt parametrizations and use of simplified \r\nSMB schemes (Golledge et  al., 2015, 2019; Bulthuis et  al., 2019; \r\nOppenheimer et al., 2019)."


id: 74770f17c1f307e25591eae797e91bf3
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 17725
content:
"In summary, poorly understood processes of instabilities, characterized \r\nby deep uncertainty, have the potential to strongly increase Antarctic \r\nmass loss under high greenhouse gas emissions on century-to\u0002multicentury time scales (Box  9.4). These instabilities are therefore \r\nconsidered separately in assessments of the future contribution \r\nto global mean sea level (GMSL; Sections 9.4.2.5, 9.4.2.6, 9.6.3.2 \r\nand 9.6.3.5)."


id: 6f9b3275b1eb698fc51e4ca5ba834d1c
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 16894
content:
"The relationship between emissions scenario and AIS response \r\nvaries across the studies, with emulated ISMIP6 projections showing \r\na slight negative scenario dependence in the median (-0.01 m) from \r\nSSP1-2.6 to SSP5-8.5, and LARMIP-2-based projections showing \r\na  slight positive scenario-dependence in the median (0.02 m; \r\nTable 9.3). A lack of clear scenario dependence in the median masks \r\nlarge individual variations across climate and ice-sheet models, \r\nwhereby the net AIS contribution response to emissions scenario \r\ndepends on the relative magnitudes of the atmosphere, ocean \r\nand ice-sheet responses (Barthel et al., 2020; Seroussi et al., 2020; \r\nEdwards et al., 2021). Climate and ice-sheet models do not project \r\nthat the AIS response will be the same under high or low greenhouse \r\ngas emissions in 2100; rather, there is no consensus on the sign of \r\nthe change. In contrast, strong scenario dependence is seen from "


id: 9906fc56073490d88649057d2b07ef8a
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 24141
content:
"and 9.4.2.3) in the ISMIP6 emulator gives a median Antarctic projection \r\nof about 0.08 m in 2100 in all scenarios before historical correction, \r\ncompared with around 0 m using only the mean Antarctic distribution; \r\nthe published projections use a  joint distribution (Edwards et  al., \r\n2021). Reese et al. (2020) find that using the basal melt sensitivities \r\nof LARMIP-2 yields an order of magnitude greater mass loss under \r\nRCP8.5 than with the ISMIP6 mean Antarctic values. Halving the basal \r\nmelt sensitivity parameter range (i.e., in line with a continental mean \r\nestimate: Section 9.4.2.3) would lead to a halving of the LARMIP-2 \r\ndynamic contribution. This would reconcile the LARMIP-2 and ISMIP6 \r\nemulator median and 95th percentile projections using the common \r\nsubset of models within about 0.02-0.05 m. There is therefore limited \r\nevidence that the ISMIP6 and LARMIP-2 projections could be reconciled \r\nby using common ice-sheet models and basal melt sensitivity values."


id: 9e67dfb99598f5a2e9a2b710cca136cf
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 25094
content:
"In summary, it is likely that the AIS will continue to lose mass \r\nthroughout this century under all emissions scenarios  - that is, \r\ndynamic losses driven by ocean warming and ice-shelf disintegration \r\nwill likely continue to outpace increasing snowfall (medium \r\nconfidence). The upper end of projections is not well constrained, \r\ndue to different assumptions about the future sensitivity of sub-shelf \r\nbasal melting to ocean warming and the proposed marine ice cliff \r\ninstability triggered by ice-shelf disintegration (Sections 9.4.2.3 \r\nand 9.4.2.4; Box 9.4)."


id: 30339aeac79fd2264296a02c0c7753ac
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 5738
content:
"The West and East Antarctic ice sheets are considered to be tipping \r\nelements  - that is, susceptible to critical thresholds.  The SR1.5 \r\n(Hoegh-Guldberg et  al., 2018) assessed that a  threshold for WAIS \r\ninstability may be close to 1.5degC-2degC (medium confidence), as only \r\nRCP2.6 led to long-term projections of less than 1 m (Golledge \r\net al., 2015; DeConto and Pollard, 2016). Based on the agreement "


id: 5fd08b08a9bb32852e0643ca9f68e790
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 14165
content:
"The SROCC assessed the median and likely range of Antarctic SLE \r\ncontributions at 2300 as 0.16 (0.07-0.37) m under RCP2.6 and \r\n1.46  (0.60-2.89) m under RCP8.5, based on three studies. It was "


id: b8431a37b1f6720de1688c288cd9720f
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 29631
content:
"sheet reorganizes to a new state, leading to large losses from East \r\nAntarctica (including the Aurora Subglacial Basin) and leading to \r\na further 10 m sea level contribution per degree of warming; other \r\nstudies also show much higher mass loss per degC at higher levels of \r\nwarming (Section 9.6.3.5 and Figure 9.30; Van Breedam et al., 2020; \r\nDeConto et al., 2021)."


id: 692d536f95e36d8452d191ef47f7ab
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 15771
content:
"A major advance since SROCC is the availability of high-accuracy \r\nmass loss estimates for individual glaciers (Hugonnet et al., 2021). \r\nThese results show that, during the last 20 years, the highest regional \r\nmass loss rates (>720 kg m-2 yr -1) were observed in the Southern \r\nAndes, New Zealand, Alaska, Central Europe, and Iceland. Meanwhile, \r\nthe lowest regional mass loss rates (<250 kg m-2 yr-1) were observed \r\nin High Mountain Asia, the Russian Arctic, and the periphery of \r\nAntarctica. Glacier mass loss in Alaska (25% of 2000-2019 total \r\nmass loss), the periphery of Greenland (13%), Arctic Canada North \r\n(11%), Arctic Canada South (10%), the periphery of Antarctica (8%), \r\nthe Southern Andes (8%) and High Mountain Asia (8%), represent \r\nthe majority (83%) of the total glacier mass loss during the last \r\n20 years (2000-2019)."


id: 1dc2601c1d906f4e080eba51f4e6c574
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 2419
content:
"last glacial period and the Holocene (e.g., Solomina et al., 2015, 2016; \r\nEaves et al., 2019; Hall et al., 2019; Marcott et al., 2019; Bohleber et al., \r\n2020; Davies et al., 2020; Palacios et al., 2020) confirm the dominant \r\nrole of orbital forcing for millennial-scale glacier fluctuations, but \r\nemphasize the role of other forcings - solar and volcanic activity, ocean \r\ncirculation, sea ice and internal climate variability - in explaining the \r\nregional variability of glacier fluctuations at shorter time scales. Shakun \r\net  al. (2015) demonstrated that, during the last deglacial transition \r\n(18-11 ka), the mid-to-low-latitude glacier retreat was driven by an \r\nincrease in atmospheric CO2 and global temperature."


id: 6a6b3cc9e92d81005f845205e15284e3
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 16003
content:
"Section  3.4.3.1 assesses new attribution studies for glaciers and \r\nfinds that human influence is very likely the main driver of the global, \r\nnear-universal retreat of glaciers since the 1990s. The SROCC assessed \r\nthat it is very likely that atmospheric warming is the primary driver \r\nfor the global glacier recession. Since SROCC, a  study of glaciers \r\nin New Zealand used event attribution to confirm a  connection \r\nbetween extreme glacier mass loss years and anthropogenic warming \r\n(Vargo et al., 2020)."


id: 93b766ffe55c9f3cb5aecd5b370c4da1
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 23195
content:
"The SROCC stated with high confidence that, besides temperature, \r\nother factors, such as precipitation changes or internal glacier \r\ndynamics, have modified the temperature-induced glacier response \r\nin some regions. Deposition of a thin layer (<2 cm) of light-absorbing \r\nparticles (e.g.,  black carbon, brown carbon, algae, mineral dust \r\nor volcanic ash) can exert an important control on glacier mass \r\nbalance, by decreasing surface albedo and thus increasing absorbed \r\nshortwave radiation and melt (see also Section 7.3.4.3). The SROCC \r\nfound limited evidence and low agreement that this process has "


id: 884332ad563a2e36da3861bf374ae805
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 21130
content:
"The AR5 (Masson-Delmotte et  al., 2013) noted that early-to-mid\u0002Holocene glacier minima could be attributed to high summer insolation \r\n(high confidence), unlike the current situation. Since AR5, new and \r\nimproved chronologies of glacier size variations from the end of the "


id: 81abe816b8a4ab8b104b431ae31faaab
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 19941
content:
"Figure 9.21 (continued): Reconstructed glacier mass change through the 20th century (Marzeion et al., 2015) and observed during 1961-2016 (Zemp et al., 2019). Projected \r\n(2015-2100) glacier mass evolution is based on the median of three RCP emissions scenarios (Marzeion et al., 2020). In all cases, uncertainties are the 90% confidence interval. \r\nFor a better comparison between regions, the maximum relative mass change was set to 200%, although for three regions, the volume changes between 1901 and 2015 \r\nexceeded that value. For the Low Latitude, New Zealand, and High Mountain Asia glaciers, the changes were larger than 1000%, 350%, and 250%, respectively. See Figure 9.2 \r\nfor the location of each region. Further details on data sources and processing are available in the chapter data table (Table 9.SM.9)."


id: c4361d377809ef9371016663a867ab5
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 31806
content:
"Kraaijenbrink et al., 2017; Maussion et al., 2019; Zekollari et al., 2019; \r\nRounce et al., 2020) and simplified energy balance calculations (Sakai \r\nand Fujita, 2017; Shannon et al., 2019). Compared to simpler, empirical \r\nparametrizations, full energy-balance models are not necessarily the \r\nmost appropriate choice for simulating future glacier response to \r\nclimate change, even at the local scale (Reveillet et al., 2017, 2018), \r\nbecause of parameter and forcing uncertainties. All models account \r\nfor glacier retreat and advance, but only two models (Anderson and \r\nMackintosh, 2012; Huss and Hock, 2015) include frontal ablation."


id: f836e10e6b4600600f24bbcbff403d30
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
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vector_id: 41044
content:
"Since AR5, glacier mass projections have been coordinated by the \r\nGlacier Model Intercomparison Project (GlacierMIP; Hock et  al., \r\n2019a; Marzeion et al., 2020). The SROCC (Hock et al., 2019b) relied \r\non six global-scale glacier models based on previously published \r\nglacier model projections (Hock et  al., 2019a). It found with high \r\nconfidence that glaciers will lose substantial mass by the end of \r\nthe century, but assigned medium confidence to the magnitude and \r\ntiming of the projected glacier mass loss, because of the simplicity \r\nof the models, the limited observations in some regions to calibrate \r\nthem, and the diverging initial glacier volumes."


id: e04652003a8a2c31bd502f4a88c7ea2e
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 36708
content:
"Since SROCC, Marzeion et al. (2020) projected 21st century global\u0002scale glacier mass changes based on seven global-scale and four \r\nregional-scale glacier models (Annex II). All models used the same \r\ninitial and boundary conditions, forming a more coherent ensemble \r\nof projections compared to SROCC. Nevertheless, challenges remain \r\nbecause of scarcity of glacier thickness, surface mass balance (SMB) \r\nand frontal ablation data for model calibration, but also due to \r\nuncertainties in glacier outlines, surface elevations and ice velocities. \r\nThe global SMB models are of varying complexity, including mass \r\nbalance sensitivity approaches (van de Wal and Wild, 2001), \r\ntemperature-index methods (Anderson and Mackintosh, 2012; \r\nMarzeion et  al., 2012; Radic et  al., 2014; Huss and Hock, 2015; "


id: cc9e3b20056cac3b8fbd74197730e929
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 33324
content:
"Luthi et  al., 2010; Zekollari et  al., 2020). The models indicate that \r\nthe disequilibrium between the glaciers and present atmospheric \r\nconditions (1995 to 2014) reduces and then disappears at around \r\nyear 2070 (Marzeion et  al., 2020). There is therefore very high \r\nconfidence that the disequilibrium of glaciers will persist as warming \r\ncontinues, and that glaciers will continue to lose mass for at least \r\nseveral decades because of their lagged response, even if global \r\ntemperature is stabilized."


id: 31fff0a24fe3b243a7071df73cfaf1bd
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 6051
content:
"The mass loss rates vary between regions and there are distinctively \r\ndifferent patterns between scenarios (Marzeion et  al., 2020). \r\nThe  global models agree that regions characterized by relatively \r\nlittle glacier-covered area (Low Latitude, Central Europe, Caucasus, \r\nWestern Canada and USA, North Asia, Scandinavia and New Zealand) \r\nwill lose nearly all (>80%) glacier mass by 2100 in the RCP8.5 \r\nscenario, but their corresponding contribution to sea level rise will \r\nbe small. A study using detailed ice dynamics for the largest glacier \r\nof the European Alps, Great Aletsch Glacier, projects 60% of present \r\nice volume will be lost by 2100 in RCP2.6 and an almost complete \r\nwastage of the ice in RCP8.5 (Jouvet and Huss, 2019). Due to their \r\nlarger mass, the largest contribution to sea level rise comes from \r\nglaciers in the Arctic and Antarctic regions (Antarctic, Arctic Canada, \r\nAlaska, Greenland, Svalbard and Russian Arctic), in spite of having "


id: 8ba5a349677cebc2debb66b7c7c1fdef
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 21740
content:
"Although the GlacierMIP projections (Hock et al., 2019a; Marzeion \r\net al., 2020) were forced by RCP scenarios, two global glacier models \r\n(Huss and Hock, 2015; Maussion et al., 2019) were also run with \r\n13 GCMs and SSP scenarios (Table 9.4). These results show increased "


id: 6718e1a38e65be42ba1f9ee4678f3461
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 15410
content:
"the smallest relative mass loss, and it is expected that they will \r\ncontinue to contribute to sea level rise beyond 2100. The regions \r\nwith intermediate glacier mass (Southern Andes, High Mountain \r\nAsia and Iceland) show decreasing mass loss rates for RCP2.6 \r\nthroughout the 21st  century, and increasing rates for  RCP8.5 that \r\npeak in the mid-to-late 21st century (Figure 9.21). The peak in mass \r\nloss rate followed by reduction is due to decreasing glacier volume \r\nand stabilizing mass balance (Marzeion et  al., 2020). Vatnajokull, \r\nthe largest glacier in Iceland, is projected to lose about 50% of \r\nits mass by 2300 in extended RCP4.5 and 80-100% in extended \r\nRCP8.5 scenarios (Schmidt et  al., 2019). In summary, both global \r\nand regional studies agree that glacier mass loss will continue in \r\nall regions, with larger mass loss for high-emissions scenarios (high \r\nconfidence) (see also Section 8.4.1.7.1)."


id: 14b367324826d508303a7a59b92dbe13
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 868
content:
"reported medium confidence in the estimation that Earth's total \r\nperennial ground ice volume is equivalent to 2-10  cm of global \r\nsea level (Zhang et  al., 2000). There is no evidence suggesting \r\nthat a  large part of this volume, if melted, would run off and \r\ncontribute to global sea level. Therefore, and because of the modest \r\ntotal volume of mobilizable water, the contribution of permafrost \r\nthaw to past and future sea level budgets is usually neglected \r\n(see Section 9.6.3.2)."


id: 2f8909c0260977efd9c88f2def0878a2
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 5626
content:
"This section focuses on the physical aspects of permafrost (perennially \r\nfrozen ground) as an element of the climate system, drawing on \r\nthe assessment of observed global permafrost changes provided \r\nin Section  2.3.2.5, and more specifically model evaluation and \r\nprojections. The permafrost carbon feedback is assessed in Box 5.1. \r\nSection 12.4 of this Report provides permafrost information relevant \r\nto impacts and risk on regional scales."


id: c1744f8008388ee668f7b682a8052fe
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 31341
content:
"Closer to the surface, the active layer undergoes annual cycles of \r\nfreeze and thaw. The SROCC reported medium confidence in ALT \r\nincrease as a  pan-Arctic phenomenon. Recent evidence presented \r\nin Section  2.3.2.5 shows pervasive ALT increase in the European \r\nand Russian Arctic in the 21st century, and in high elevation areas \r\nin Europe and Asia since the mid-1990s. Emergence of a  clearer \r\nglobal picture is hampered by: (i) uneven distribution of observing \r\nsites; (ii)  substantial variability among the existing sites, strongly \r\ninfluenced by local conditions (soil constituents and moisture, \r\nsnow cover, vegetation); (iii) interannual variability; and (iv) thaw \r\nsettlement in ice-rich terrain (Streletskiy et al., 2017; O'Neill et al., \r\n2019). In summary, in agreement with SROCC and recent evidence \r\npresented in Section  2.3.2.5, there is medium confidence that ALT \r\nincrease is a pan-Arctic phenomenon."


id: a7aa7640b59142d1456b3ae759985f72
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 26711
content:
"The current extent of the global permafrost region is about \r\n22 +- 3x106 km2 (Gruber, 2012). Permafrost underlies about 15% of \r\nNorthern Hemisphere land and more than 50% of the unglacierized \r\nland north of 60degN (Zhang et al., 1999; Gruber, 2012; Obu et al., \r\n2019). It is also found in high-altitude areas of mountain ranges \r\nin both hemispheres - estimated in SROCC (Hock et al., 2019b) as \r\nrepresenting about 27-29% of the global permafrost area (medium \r\nconfidence) and most unglacierized areas in Antarctica (Vieira et al., \r\n2010; Obu et al., 2020). Ground ice volume in permafrost is variable, \r\nreaching up to 90% in syngenetic permafrost deposits (Kanevskiy \r\net al., 2013; Gilbert et al., 2016). The SROCC (Meredith et al., 2019) "


id: 80ba6ff249641e7260398c8353921ae3
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 19780
content:
"There is medium confidence that the observed acceleration and \r\ndestabilization of rock glaciers is related to warming temperatures \r\nand increase in water content at the permafrost table in recent \r\ndecades (Deline et al., 2015; Cicoira et al., 2019; Marcer et al., 2019; \r\nPERMOS, 2019; Kenner et al., 2020). There is also medium confidence\r\nthat observed increases in size and frequency of rock avalanches \r\nare linked to permafrost degradation in rock walls (Ravanel et al., \r\n2017; Patton et al., 2019; Tapia Baldis and Trombotto Liaudat, 2019). \r\nIn summary, there is medium confidence that mountain permafrost \r\ndegradation at high altitude has increased the instability of mountain \r\nslopes in the past decade."


id: 5a030699c4d94df2293e58ecff4d93ba
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
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vector_id: 13083
content:
"The SROCC assessed with high confidence that the extent of subsea \r\npermafrost, formed before submersion on Arctic continental shelves \r\nduring the last deglaciation, is much reduced compared to older \r\nstudies that had estimated the entire formerly exposed Arctic shelf \r\narea to be underlain by permafrost. This is supported by observations \r\n(Shakhova et al., 2017) that show rapid thaw of recently submerged \r\npermafrost on the East Siberian Shelf. A modelling study (Overduin \r\net al., 2019) estimates that 97% of permafrost under Arctic shelves \r\nis currently thinning."


id: e1b6de73be18ec45503bf8bedc5048d0
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
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vector_id: 36984
content:
"There is a  lack of formal studies attributing observed permafrost \r\nchanges (thaw depth, thermal state) or associated landscape changes \r\nto anthropogenic forcing. However, the observed Arctic warming has \r\nbeen attributed to anthropogenic forcing (e.g., Najafi et al., 2015) and \r\nan obvious physical link exists between ground temperatures (and thus \r\npermafrost) and surface air temperatures. Therefore, physically \r\nconsistent and convergent lines of evidence lead to medium confidence\r\nin anthropogenic forcing being the dominant cause of the observed \r\npan-Arctic permafrost changes. Added to this, local permafrost change \r\nby soil and ecosystem disturbance is induced by increasing human \r\nindustrial activities in the Arctic (e.g., Raynolds et al., 2014)."


id: 438b41016129ad999d899c201520728a
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
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vector_id: 9108
content:
"A total soil column depth of at least about 10 m is required to \r\nadequately represent the dampening effect of seasonal-scale heat \r\nexchanges between shallow and deeper ground, and thus to correctly \r\nsimulate ALT (Lawrence et al., 2008; Ekici et al., 2015). However, many \r\nCMIP6 models still have shallower total soil columns (Burke et al., \r\n2020) and the proportion of models with deeper total soil columns \r\nhas not increased since CMIP5 (Koven et al., 2013). Another recently \r\nidentified process, usually not represented in the current (CMIP6) \r\ngeneration of climate models (Zhu et al., 2019), is warming-driven "


id: 699dc463f3ea17eec87a8d3d2ac21298
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 15848
content:
"decomposition and burning of organic material that provides strong \r\nthermal insulation of underlying ground. Decay of the insulating \r\norganic material can lead to increased permafrost thaw, creating \r\na positive feedback loop."


id: 539273fe7a0f6edc20045c85edcf0087
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 11978
content:
"This further points to deficiencies in the land modules as the main reason \r\nfor biases, consistent with conclusions drawn from the analysis of CMIP5 \r\noutput (Koven et al., 2013), as reported in SROCC and AR5."


id: 42d8d6f14b044f8db0ffe7520894231b
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 8950
content:
"In spite of more realistic description of permafrost-related processes in \r\nmany coupled climate models, the CMIP6 models still produce a very \r\nscattered ensemble of estimates of current permafrost extent, and \r\nthere is high confidence that this is strongly linked to deficiencies of \r\nthe representation of soil processes. Furthermore, current-generation \r\nclimate models tend to neglect several physical disturbances that \r\ncan lead to faster permafrost thaw. Because of large uncertainties \r\nin the future evolution of these drivers (see SROCC), there is limited \r\nevidence that these shortcomings lead to an underestimate of \r\npermafrost degradation rates in response to climate change in the \r\nCMIP6 ensemble. In summary, there is high confidence that coupled \r\nmodels correctly simulate the sign of future permafrost changes \r\nlinked to surface climate changes, but only medium confidence in the \r\namplitude and timing of the transient response."


id: 9a9531f3fe617bde008f51bda5ae8f7a
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 24438
content:
"The AR5 (Collins et  al., 2013) and SROCC (Meredith et  al., 2019) \r\n(based on available CMIP5 output) both expressed high confidence\r\nthat future pan-Arctic thaw depth will increase and near-surface \r\npermafrost extent will decrease under future global warming, and \r\nmedium confidence in the magnitude of the simulated changes \r\nbecause of model deficiencies and the large spread of the results."


id: 6c1b69c5f1f349c2f9034842702cbb8b
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 16320
content:
"Figure  9.22a also shows that the corresponding land-atmosphere \r\nsimulations with prescribed observed sea surface temperatures and \r\nsea ice concentrations, and the land-only simulations with prescribed \r\nreanalysis-based meteorological forcing, do not provide an improved \r\nsimulation of the current permafrost extent, although, by construction, \r\nthey can be expected to exhibit lower land surface climate biases. "


id: ecb86b9f1a48b43047336bce246259fe
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 39008
content:
"Terrestrial snow cover is characterized via three variables: (i) areal snow \r\ncover extent (SCE); (ii) the time period of continuous snow cover  - \r\nsnow cover duration (SCD) that reflects snow-on and snow-off dates \r\n(i.e., the first and last days of observed snow cover); and (iii)  snow \r\naccumulation - expressed either as snow depth (SD) or snow water \r\nequivalent (SWE), the depth of water stored by the snowpack."


id: 2f6081ab6c4326d4d7ca62b98108951f
created_at: 2023-04-19 13:21:38
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vector_id: 5599
content:
"with diagnosed relationships between the observed present-day \r\nspatial distribution of permafrost and air temperature) to be about \r\n4.0x106  km2 degC-1 (Chadburn et  al., 2017) for global surface air \r\ntemperature (GSAT) changes with respect to the present below \r\nabout +3degC. This equilibrium permafrost sensitivity, relevant for \r\nassessing long-term permafrost changes at a  stabilized warming \r\nlevel, is about 20% higher than the transient centennial-scale \r\nnear-surface permafrost extent sensitivity (diagnosed from seasonal \r\nthaw down to 3 m depth) suggested by direct analysis of CMIP5 \r\noutput (Slater and Lawrence, 2013). Compared to these and other \r\nstudies reported in AR5 and SROCC (Koven et al., 2013), the recently \r\nsuggested equilibrium extent sensitivity to GSAT changes of about \r\n1.5x106 km2 degC-1 based on idealized ground temperature modelling \r\n(Liu et al., 2021) appears unrealistically low."


id: 9a916f139e531fcabe38b2fd7dba5743
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 24436
content:
"Observed large-scale snow cover changes, their attribution to \r\nhuman activity, and their effects on the hydrological cycle are also \r\ndiscussed in Chapter 2 (Section 2.3.2.2), Chapter 3 (Section 3.4.2) \r\nand Chapter 8 (Section 8.2.3.1) of this Report. The role of snow in \r\nthe global surface albedo feedback is assessed in Section  7.4.2.3. \r\nThe  effect of aerosol deposition on snow albedo and associated \r\nclimate forcing is assessed in Section 7.3.4.3."


id: a6601ffa80b6d63294d683b02751e662
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 26491
content:
"Due to the role of air temperature as a major driver of permafrost \r\nchange, SROCC (Hock et al., 2019b) expressed very high confidence\r\nthat permafrost in high mountain regions is expected to undergo \r\nincreasing thaw and degradation during the 21st  century, with \r\nstronger consequences expected for higher greenhouse gas emissions \r\nscenarios. Recently published studies (e.g., Zhao et al., 2019) support \r\nthis SROCC assessment."


id: 3cb8de4bf445ec1697ed8206d2211614
created_at: 2023-04-19 13:21:38
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vector_id: 7866
content:
"Since SROCC, progress has been made in characterizing seasonal \r\nNH snow cover changes through the combined analysis of datasets \r\nfrom multiple sources (surface observations, remote sensing, land \r\nsurface models and reanalysis products). A recent combined dataset \r\n(Mudryk et al., 2020) identified negative NH SCE trends in all months \r\nbetween 1981 and 2018, exceeding -50 x 103 km2 yr-1 in November, \r\nDecember, March and May (Figure 9.23a,b). The loss of spring SCE \r\nis also reflected in earlier spring snow melt, derived from surface \r\nobservations (Bulygina et  al., 2011; Brown et  al., 2017), satellite \r\nobservations (Wang et al., 2013; Estilow et al., 2015; Anttila et al., \r\n2018), and model-based analyses (Liston and Hiemstra, 2011). \r\nThere is considerable inter-dataset and regional variability, but the \r\ncontinental-scale trends of snow-off dates from these datasets are \r\nconsistently negative (Brown et al., 2017; Kouki et al., 2019)."


id: ac2c89950b6971f785132e3e68047478
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 27538
content:
"In summary, based on high agreement across CMIP6 and older model \r\nprojections, fundamental process understanding, and paleoclimate \r\nevidence, it is virtually certain that permafrost extent and volume \r\nwill shrink as global climate warms."


id: 225e405ce9302fb20d0c9a05c698caa3
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 3267
content:
"Mean snow cover extent in January and February, the usual months \r\nof maximum extent, covers about 45% of the Northern Hemisphere \r\n(NH) land surface - more than 45 million km2 over the 1967-2014 \r\nperiod (Estilow et al., 2015). In contrast, maximum seasonal snow \r\ncover in South America, the dominant ice-free land mass in the \r\nSouthern Hemisphere in terms of seasonal snow cover extent, \r\nremains well below 1 million km2 (Foster et al., 2009) or less than 2% \r\nof the Southern Hemisphere land surface."


id: d72f9eca0a9ef52717fdc47a3dc3869f
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 35167
content:
"(Estilow et al., 2015). Continental trends from these coarse resolution \r\nestimates (about 200 km) show declining snow cover during \r\nthe spring period, consistent with surface warming (Hernandez\u0002Henriquez et al., 2015; Mudryk et al., 2017). Therefore, as assessed in \r\nSection 2.3.2.2, there is very high confidence that the NH spring SCE \r\nhas been decreasing since 1978."


id: cc7f470dbbefeede3c771ca93ffa46eb
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 33301
content:
"Mudryk et al., 2017). The positive trends from the NOAA CDR are also \r\ninconsistent with later autumn snow-on dates since 1980 (-0.6 to \r\n-1.4 days per decade), based on historical surface observations, \r\nmodel-derived analyses and independent satellite datasets (updated \r\nfrom Derksen et  al., 2017). The SCE trend sensitivity to surface \r\ntemperature forcing in the NOAA CDR is anomalous compared to \r\nother datasets during October and November (Mudryk et al., 2017). \r\nThere is therefore medium confidence that the NH SCE trend for \r\nthe 1981-2016 period was also negative during these two months \r\n(Mudryk et al., 2020)."


id: 3e1e4b4eaef1f1de84e4ba0698e6a0d9
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 8101
content:
"In the low-to-mid latitude (18degS-40degS) South American Andes, \r\na  dry-season snow cover decrease of about 12% per decade has \r\nbeen reported for the 1986-2018 period (Cordero et al., 2019), linked \r\nto El Nino-Southern Oscillation (ENSO) changes dominant in the \r\nnorthern part, and an additional influence of poleward migration of \r\nthe westerly wind zone in the southern part of the study area. Further \r\nsouth, long-term warming has been identified as the dominant cause \r\nof observed winter snow cover reduction over the 1972-2016 period \r\nat about 53degS in Brunswick Peninsula (Aguirre et al., 2018)."


id: b6a40644632f85dfe3b4fe0a31fe21c2
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 29335
content:
"The AR5 (Hock et  al., 2019b) reported on SWE and SD in situ \r\nobservations mostly from mountain areas, the majority of which \r\nshowed negative trends over their respective observational periods. \r\nHowever, AR5 did not provide an assessment of large-scale snow \r\nmass changes across the Northern Hemisphere. The SROCC attributed \r\nmedium confidence to reports of negative SWE trends in the Russian \r\nArctic between 1966 and 2014, and stated that seasonal maximum \r\nSD trends in the North American Arctic were mostly insignificant \r\nand inconsistently positive or negative. It further attributed medium \r\nconfidence to gridded products that suggest negative pan-Arctic SWE \r\ntrends between 1981 and 2016, and high confidence in a general \r\ndecline of mountain snow mass at lower elevations, albeit with \r\nregional variations."


id: 6106af855e4466704fbdce987decbd0f
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 14363
content:
"Recent remote sensing global-scale studies (Hammond et al., 2018; \r\nNotarnicola, 2020) report that, since 2000, snow cover area and/or \r\nduration decreased in 78% of global mountain areas (Notarnicola, \r\n2020). Due to the shortness of these records and high spatial \r\nvariability, they only provide limited evidence in medium agreement\r\nthat snow cover area and duration changes over that recent period \r\nare more consistently negative at higher (>4000 m) than at lower \r\nelevations, and do not alter the high confidence in longer-term \r\nmountain snow cover decrease at lower elevations since the middle \r\nof the 20th century that was already reported in SROCC."


id: 867bedd497b72014fe4ff1f4eee6d124
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 20828
content:
"As assessed in detail in Section 3.4.2, it is very likely that anthropogenic \r\ninfluence contributed to the observed reductions in Northern \r\nHemisphere spring snow cover since the mid-20th century. The reasons \r\nfor this assessment are: (i) physical consistency of the observed \r\nspring snowpack and surface temperature changes in observations \r\nand models; (ii) the strong observed hemispheric and regional spring \r\nSCE and SWE trends; and (iii) the general attribution of hemispheric \r\ntemperature changes to human influence. Consistent between multiple \r\nobservational products and historical climate model simulations, the \r\nobserved NH SCE sensitivity to NH land (>30degN) warming (Mudryk \r\net  al., 2017) is approximately -1.9x106  km2  degC-1 (95% confidence \r\nrange of +-0.9x106 km2 degC-1) throughout the snow season."


id: bc87987d49f92c4d27d5e8eacd50f77a
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 30415
content:
"Compared to numerous studies on spring SCE changes, less attention \r\nhas been paid to changes in NH snow cover during the onset period \r\nin the autumn, a  challenging period to retrieve snow information \r\nfrom optical satellite imagery due to persistent clouds and decreased \r\nsolar illumination at higher latitudes. Positive trends in October and \r\nNovember SCE in the NOAA CDR (Hernandez-Henriquez et al., 2015) \r\nare not replicated in other surface, satellite, and model datasets \r\n(Brown and Derksen, 2013; Peng et  al., 2013; Hori et  al., 2017; "


id: d365b3d84472a81cf6811d657fa6cb6e
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 34536
content:
"significant during transitional seasons and at transitional (from \r\nno snow to snow) altitudes, and exhibit strong regional variations, \r\nconsistent with earlier reports for the Swiss and Austrian Alps \r\n(Schoner et al., 2019) and the Pyrenees (Lopez-Moreno et al., 2020)."


id: 6e061f4c7f3b6c03b860001b95f5d1d1
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 16620
content:
"from site measurements confirm mostly negative trends in North \r\nAmerica (Kunkel et al., 2016) between 1960-1961 and 2014-2015, \r\nand strong spatial variability in Eurasia (Zhong et al., 2018) between \r\n1966 and 2012, with spatial patterns bearing some resemblance to \r\nthe shorter satellite-based trends reported by Pulliainen et al. (2020). \r\nHowever, over this longer period, the Eurasian measurements (Zhong \r\net al., 2018) exhibit, on average, a positive trend. On the Qinghai-Tibet \r\nPlateau, site measurements between 1961 and 2010 (Xu et al., 2017) \r\nsuggest a shift from an initial increase of spring SD until about 1980 \r\nto a decreasing trend afterwards."


id: f38c4cf69728f22d017aa772ce22765b
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 40227
content:
"In summary, since AR5, intercomparison, dataset blending of gridded \r\nproducts, and bias correction using snow course measurements \r\ncontributed to an improved estimate of the average 1980-2018 \r\nMarch SWE over NH non-alpine land north of 40degN of 2938 [likely\r\nrange 2846-3062] Gt, with medium confidence in the magnitudes \r\nof continental-scale trends over that period. However, there is \r\nhigh confidence in a general decline of NH spring SWE since 1981 \r\n(Section  2.3.2.2). In mountain areas, in situ observations tend to \r\nsuggest that annual maximum SWE reductions are generally stronger \r\nat elevation bands where shifts from solid to liquid precipitation \r\naffected the snow mass."


id: 22c4adc85869482f4a4b40a72f1ffab3
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 3328
content:
"Concerning the assessment of SWE trends in mountainous regions, \r\nSROCC noted a  need for observations spanning several decades \r\nbecause of very strong temporal variability. Moreover, determining \r\nSWE trends in mountain regions is challenging because the coarse \r\nresolution (typically 25 to 50 km) of gridded SWE products is \r\ninadequate in areas of mountainous terrain (Snauffer et al., 2016). \r\nBased on a compilation of a large number of studies of SWE trends \r\nin mountain regions, SROCC noted strong regional variations, but \r\na general consistency in greater reductions in SWE at lower elevations \r\nassociated with shifts from solid to liquid precipitation. A  recent \r\nsynthesis of snow observations in the European Alps (Matiu et al., \r\n2021) shows a 1971-2019 seasonal (November to May) SD trend of \r\n-8.4% per decade, along with negative maximum SD and seasonal \r\nsnow cover duration trends. The trends are stronger and  more "


id: 788ae7c04476d752fa7b5a108f0c9470
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 18408
content:
"Building on AR5 (Flato et al., 2013) and subsequent published work, \r\nSROCC (Meredith et  al., 2019) stated that CMIP5 models tended \r\nto underestimate the observed decrease of Northern Hemisphere \r\nspring SCE due to inappropriate parametrization of snow processes, \r\nmisrepresentation of the snow-albedo feedback, underestimated \r\ntemperature sensitivity, and biased climatological spring snow cover. "


id: f56db334c3f3a6181229ad71c39b2622
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 40546
content:
"Analysis of the available CMIP6 historical simulations for the \r\n1981-2014 period shows that, on average, CMIP6 models simulate \r\nwell the observed SCE (Mudryk et al., 2020), except for outliers and \r\na median low bias during the winter months (Figure 9.24a). This is \r\nan improvement over CMIP5 (Mudryk et  al., 2020), where many \r\nsnow-related biases were linked to inadequacies of the vegetation \r\nmasking of snow cover over the boreal forests (Thackeray et  al., \r\n2015). A  comparison between CMIP5 and CMIP6 results (Mudryk \r\net al., 2020) shows that there is no notable progress in the quality \r\nof the representation of the observed 1981-2014 monthly snow \r\ncover trends."


id: b595b1f39ad5150a3a2eb487bdc73199
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 29177
content:
"a transition from seasonal to persistent snow cover due to a strong \r\nsnow-albedo feedback, are a  typical feature of glacial inceptions \r\n(e.g., Baum and Crowley, 2003; Calov et al., 2005), and these can \r\nbe irreversible on centennial or longer time scales because of this \r\nfeedback. In summary, based on physical understanding and the \r\nabsence of occurrence of such events in climate model projections, \r\nabrupt future changes of seasonal snow cover on large scales in \r\nthe absence of concomitant abrupt atmospheric change as a driver \r\nappear very unlikely in the context of current and projected warming."


id: beee424c81562a4a389fa23bf3c1d0ef
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 30856
content:
"The AR5 (Collins et al., 2013) stated that substantial NH spring snow \r\ncover reductions at the end of the 21st century were very likely under \r\nstrong emissions scenarios, and expressed medium confidence in the \r\nprojected geographic patterns of annual maximum SWE changes. \r\nBased on studies using downscaled CMIP5 or regional climate model \r\noutput, either directly or via snowpack models driven by such output, \r\nSROCC (Hock et al., 2019b) reported likely SD or mass decreases at \r\nlower elevations in many mountain ranges over the 21st century and \r\nhigh confidence in smaller future changes at higher elevations."


id: f1e3189eb5b5996a90234a39d0cd8054
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 39942
content:
"Since AR5, one study (Brown et  al., 2017), applying a  method \r\ndeveloped by de Elia et  al. (2013) to a  CMIP5 sub-ensemble, \r\nsuggested that over most of the Northern Hemisphere, the projected \r\ndecrease of SCD will exceed natural variability before this will be the \r\ncase for annual maximum SWE. The same study reports that, over \r\nlarge parts of Eastern and Western North America and Europe, forced \r\nSCD changes are projected to exceed natural variability in the 2020s \r\nin spring and autumn, while the signals tend to emerge later in the \r\nArctic regions and particularly late, after 2060, in Eastern Siberia \r\nunder the RCP8.5 scenario. Thackeray and Fletcher (2016) have \r\nshown that inter-model spread in projected spring SCE trends could \r\nbe reduced through improved simulation of spring season warming \r\nbecause of the tight coupling between temperature and SCE linked \r\nto the snow-albedo feedback (Qu and Hall, 2014; Thackeray and \r\nFletcher, 2016)."


id: be666599261766581a0298b913dcf1d4
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 30760
content:
"Across all emissions scenarios, and with negligible scenario \r\ndependence (Figure 9.24b), CMIP6 models consistently (all models \r\nand all months) simulate Northern Hemisphere snow cover decrease \r\nin response to future GSAT change over the 21st century (Mudryk \r\net al., 2020). The simulated SCE decrease is close to a linear function \r\nof global temperature change for all months (shown in Figure 9.24b \r\nfor spring, with medium confidence in an average sensitivity of about \r\n-8% per degC of GSAT increase), except when snow cover vanishes. \r\nThis occurs at about +2degC of GSAT change above the 1995-2014 \r\nlevel (that is, about +3degC above the pre-industrial level) for the \r\nmonths of July and August, and at about +3degC above the 1995-2014 \r\nlevel for June and September. Possible effects of such changes on \r\nthe hydrological cycle are assessed in Section 8.2.3.1."


id: 2399bdf46b03427273557c9ec89978a1
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 3461
content:
"The SROCC found that four of the five available tide gauge \r\nreconstructions that extend back to at least 1902 showed a robust \r\nacceleration (high confidence) of GMSL rise over the 20th century, \r\nwith estimates for the period 1902-2010 (-0.002 to +0.019 mm yr-2) \r\nthat were consistent with AR5. New tide gauge reconstructions \r\npublished since SROCC (Dangendorf et  al., 2019; Frederikse et  al., \r\n2020b) support this assessment and suggest that increased ocean \r\nheat uptake related to changes in Southern Hemisphere winds \r\nand increased mass loss from Greenland are the primary physical \r\nmechanisms for the acceleration (Section  2.3.3.3). Therefore, \r\nthe  SROCC assessment on the acceleration of GMSL rise over the \r\n20th century is maintained."


id: 404c49dfc37c516ce16d0e59df0952eb
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 8502
content:
"In summary, consistent projections from all generations of global \r\nclimate models, elementary process understanding and strong \r\ncovariance between snow cover and temperature on several time \r\nscales make it virtually certain that future Northern Hemisphere snow \r\ncover extent and duration will continue to decrease as global climate \r\ncontinues to warm, and process understanding strongly suggests \r\nthat this also applies to Southern Hemisphere seasonal snow cover \r\n(high confidence)."


id: 5d173eaa0542d9a2057f32a946ca7977
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 13649
content:
"The evaluation of the sea level budget presented here, and in \r\nSection 9.6.1.2, draws on assessments of the individual components \r\n(Sections 2.3.3.1 and 9.2.4.1 for global-mean thermosteric and \r\nSections 9.5.1.1, 9.4.1.1 and 9.4.2.1 for ice mass loss contributions \r\nto GMSL change from glaciers and ice sheets). Following SROCC \r\napproach, the mass loss from ice sheet peripheral glaciers is "


id: 27effd419fe2bc90172c8d7212adb576
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 4219
content:
"Seasonal snow cover, by definition, has a  clear annual cycle \r\nwith usually complete disappearance in spring and summer and \r\nre-formation in autumn or winter. Therefore, there is very high \r\nconfidence that the current and projected changes to seasonal snow \r\ncover are reversible (Verfaillie et  al., 2018). In the case of global \r\nor regional cooling, abrupt large-scale snow-cover changes, with "


id: f59cd5ed47ff1d399e8c85c54f7b1272
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 40580
content:
"included in the ice-sheet contributions to GMSL change (glacier mass \r\nloss from regions 5 and 19 of the Randolph Glacier Inventory  6.0 \r\n(RGI Consortium, 2017) are added to ice-sheet mass loss where \r\napplicable, with uncertainties added in quadrature). The total change \r\nin GMSL for each component, and their sum, is summarized in \r\nTable 9.5 (uncertainties added in quadrature). For consistency across \r\nthe report, and to simplify the treatment of uncertainties, all budget \r\ncalculations are based on the difference between the first and last \r\nyear in each period (Palmer et al., 2021), rather than a linear fit to the \r\nunderlying time series as used in SROCC and AR5."


id: 906bfc9773251ad2c425ec7893a5d395
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 22612
content:
"Since SROCC, two new estimates of the LWS contribution have \r\nbeen published (Section  9.6.1.1; Caceres et  al., 2020; Frederikse \r\net  al., 2020b). For the early 21st  century (the periods 1993-2018 \r\nand 2006-2018) both publications find a positive LWS contribution \r\n(Table  9.5), based on the most recent GRACE-derived estimates. \r\nThis contrasts with the negative LWS contribution presented for the \r\nsame periods in SROCC based on World Climate Research Programme \r\n(WCRP) Global Sea Level Budget Group (2018), and reinforces the \r\nlow confidence assessment of the LWS contribution."


id: df3f8940608f61db5b6f10991c26f845
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 36531
content:
"The SROCC (Oppenheimer et  al., 2019) attributed anthropogenic \r\nforcing to be the dominant cause of GMSL rise since 1970 (see also \r\nSection  3.5.3.2), but detection and attribution (Cross-Working \r\nGroup Box: Attribution in Chapter  1) of 20th  century externally \r\nforced regional sea level changes is more challenging, as regional \r\nvariability is larger (Section  9.6.1.3), and therefore the signal-to\u0002noise ratio is smaller (Richter and Marzeion, 2014; Monselesan \r\net al., 2015; Palanisamy et al., 2015). Whereas SROCC assessed with \r\nhigh confidence that GMSL rise is attributable to anthropogenic \r\ngreenhouse gas emissions, they assessed with medium confidence\r\nthat the regional anomalies in ocean basins are a  combination of \r\nthe response to anthropogenic greenhouse gas emissions and \r\ninternal variability."


id: b5f63f108e2d62466e560ca9a61b5a09
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 29234
content:
"The Arctic ocean is typically excluded from global sea level studies, \r\nowing to the uncertainties associated with resolving sea level in \r\nice-covered regions, strong variations in gravitational, rotational, \r\nand deformational (GRD) effects, and uncertain glacial isostatic \r\nadjustment (GIA) estimates (Box 9.1). Spanning 1991-2018, a very \r\nlikely sea level rise of 1.16-1.81 mm yr-1 is observed (Rose et al., \r\n2019). Since SROCC, the forced response in regional sea level \r\nvaries in time with the relative influence of different forcing agents \r\n(Fasullo et al., 2020)."


id: f1065eb1a6bfbf1cc62c41ff3dfeae41
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 39778
content:
"A region that has been studied intensely in the context of sea \r\nlevel detection and attribution is the tropical Pacific. Observed \r\nsea level trends in the tropical Pacific show a PDO-like (Annex IV) \r\neast-west dipole (with a  greater rate of rise in the west, see \r\nSection  9.6.1.3). This dipole does not occur in CMIP5 simulations \r\nwith the magnitude and duration that was observed in the 1990s \r\nand 2000s, neither in response to historical forcing, nor as internal \r\nvariability after removing the variability associated with the PDO \r\n(Bilbao et al., 2015). Hamlington et al. (2014) did obtain a residual \r\ntrend pattern for 1993-2010 in the tropical Pacific that may link to \r\nanthropogenic warming of the tropical Indian Ocean. Allowing for \r\nPDO and ENSO variations, (Royston et  al., 2018) describe patches \r\nof the Pacific Ocean where the sea level trend for 1993-2015 is "


id: dbd2a92acb75c99af330dcb324e600f9
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 35959
content:
"will not emerge before late in the century. Adding the projected sea \r\nlevel change from land ice mass loss and groundwater extraction \r\nstrengthens and modifies the forced signal, leading to times of \r\nemergence 10 to 20 years earlier in most parts of the ocean, except \r\nin regions close to sources of mass loss, with emergence over 50% of \r\nthe ocean area by 2020, and nearly everywhere by 2100 (medium \r\nconfidence) (Lyu et al., 2014; Richter et al., 2017)."


id: 5c2dff0401e405a79eaf8b9bd0cef660
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 13474
content:
"distinguishable from temporally correlated noise. The acceleration in \r\neastern Pacific sea level rise is largely accounted for by variations \r\nresembling PDO and ENSO (Hamlington et al., 2020a)."


id: bbc19e531d6714920d44dd5612b08583
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 30261
content:
"In the future, the anthropogenic signal in regional sea level change \r\nfrom ocean density and dynamics is projected to emerge first in \r\nregions with relatively small internal variability, such as the tropical \r\nAtlantic Ocean and the tropical Indian Ocean (Jorda, 2014; Lyu et al., \r\n2014; Richter and Marzeion, 2014; Bilbao et  al., 2015). The  signal \r\nis projected to emerge over 50% of the ocean area by the 2040s \r\n(Lyu  et  al., 2014), but in regions where variability is large and \r\nprojected changes are small, such as the Southern Ocean, the signal "


id: b717f6ed8d1bce6e699c154531ea560
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 29423
content:
"In summary, detection of forced regional changes for some ocean \r\nareas in recent decades is possible (medium confidence), but \r\nattribution of regional sea level change to forcings over longer \r\nperiods (20th century) and for all ocean basins is not yet possible."


id: 73d4da959b90b007adb7a04fe5647e91
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 17631
content:
"Contributors: Gudfinna Adalgeirsdottir (Iceland), Fabio Boeira Dias (Finland/Brazil), Catia M. Domingues (Australia, United Kingdom/\r\nBrazil), Gerhard Krinner (France/Germany, France), Johannes Quaas (Germany), Lucas Ruiz (Argentina)"


id: 52ccf38a9ee67c2b33b77344dc75e07d
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 11837
content:
"Increased atmospheric greenhouse gas emissions since the 19th century have led to a net positive radiative forcing of Earth's climate \r\n(Sections 2.2 and 7.3) and a corresponding accumulation of energy in the Earth system. Quantification of this energy gain is essential \r\nto our understanding of observed climate change, and for estimates of climate sensitivity (Section 7.5). The global energy inventory is \r\nclosely linked to our understanding of observed global sea level change, through the energy associated with loss of land-based ice and \r\nthe effect of thermal expansion associated with ocean warming (Box 9.1, Sections 2.3.3.1 and 9.6.1; Table 9.5). "


id: 740d3451e6687aa6d006a63b54f459de
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 17657
content:
"The Earth system gained substantial energy over the period 1971-2018 (high confidence), with an assessed very likely range of \r\n325-546 ZJ or 0.43-0.72 W m-2 expressed per unit area of the Earth's surface (Cross-Chapter Box 9.1, Figure 1a; Section 7.2, Box 7.2). \r\nOcean warming dominates the energy inventory change (high confidence), accounting for 91% of the observed energy increase for the \r\nperiod 1971-2018, with upper-ocean warming (0-700 m) accounting for 56% (Section 7.2). Much smaller amounts went into melting \r\nof ice (3%) and heating of the land (5%) and atmosphere (1%). Overall, the percentage contributions are similar to those reported in \r\nIPCC's Fifth Assessment Report (AR5) for the period 1971-2010 (Rhein et al., 2013)."


id: de7454f82fda2f682c0888714a218790
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 36404
content:
"The observed global mean sea level (GMSL) budget is assessed through comparison of the sum of individual components of GMSL \r\nchange with independent observations of total GMSL change from tide gauge and satellite altimeter observations (Cross-Chapter \r\nBox 9.1, Figure 1b; Sections 2.3.3 and 9.6.1 and Table 9.5). The assessed sum of the observed components indicates that GMSL very \r\nlikely increased by 72 mm to 117 mm over the period 1971-2018 (Table 9.5), with the largest contributions from ocean thermal \r\nexpansion (50%) and melting of ice sheets and glaciers (42%). The assessed total GMSL change (Section  2.3.3) for the period \r\n1971-2018 has a very likely range of 73-146 mm and, as a result, the sea level budget is closed for this period (Cross-Chapter Box 9.1, \r\nFigure 1b; Section 9.6.1, Table 9.5)."


id: 718d19279dae773b2f51ec046f040180
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
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index: climate_gpt_v2
vector_id: 17226
content:
"The sea level budget closure demonstrates improved quantification of the processes of observed GMSL change for this period relative to \r\nprevious IPCC assessments (Church et al., 2013b; Oppenheimer et al., 2019). A related assessment presented in Chapter 7 demonstrates \r\nclosure of the global energy budget (high confidence) (Box 7.2) and strengthens the confidence in scientific understanding of both of \r\nthese key aspects of climate change."


id: c72f29f76814821f50899bf9d285bc2c
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 32363
content:
"Proxy constraints on GMSL and global ice volume are assessed in \r\nSections 2.3.2.4. and 2.3.3.3 (see also FAQ 9.1). This section updates \r\nprior assessments of drivers of past GMSL changes and climatically \r\ncoherent areas of relative sea level (RSL) variability. GMSL changes are \r\nframed in terms of global mean surface temperature (GMST) but noting \r\nthat amplified high-latitude warming is a robust equilibrium response \r\nto elevated CO2 (Masson-Delmotte et al., 2013): polar air temperatures \r\nduring past warm periods were up to twice the GMST changes shown \r\nin Table  9.6. The SROCC assessment that past multi-metre sea level \r\nchanges have resulted from significant ice-sheet changes beyond those \r\npresently observed is confirmed (very high confidence)."


id: c1f6ba22ce62fb24a03c52e60bb790ae
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 31422
content:
"As SROCC (Oppenheimer et al., 2019) noted, paleo sea level records \r\nprovide information on past ice-sheet changes, and process-based \r\nice-sheet models of past warm periods inform equilibrium responses. \r\nHowever, given uncertainties in paleo sea level and polar paleoclimate, \r\nand limited temporal resolution of paleo sea level records, there is low \r\nconfidence in the utility of paleo sea level records for quantitatively \r\ninforming near-term GMSL change. Nonetheless, the paleorecord does \r\ncontextualize sea level and can test projection models (see also FAQ 1.3). "


id: 996c5a78ff8be58446a549d82a11fec5
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 24216
content:
"Table 9.6 | Reference ranges of age, global mean surface temperature, atmospheric carbon dioxide (CO2) concentration, and global mean sea level \r\n(GMSL) for the paleo periods discussed in this chapter."


id: 897d76c55da763acf9c918bed286af5e
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 21365
content:
"During the mid-Pliocene Warm Period (MPWP), GMST was \r\n2.5degC-4degC warmer than 1850-1900 (medium confidence) and GMSL \r\nwas between 5 and 25 m higher than today (medium confidence) \r\n(Table  9.6 and Section  2.3.3.3). The AR5 (Masson-Delmotte et  al., \r\n2013) concluded that ice-sheet models consistently produce \r\nnear-complete deglaciation of the Greenland and West Antarctic \r\nice sheets, and multi-meter loss of the East Antarctic Ice Sheet \r\n(EAIS) in response to MPWP climate conditions. Studies since AR5 \r\nhave yielded a consistent but broader range, due in part to larger \r\nensembles exploring more parameters (DeConto and Pollard, 2016; \r\nYan et al., 2016; DeConto et al., 2021). Partly on the basis of these \r\nstudies, SROCC proposed a 'plausible' upper bound on GMSL of 25 m \r\n(low confidence) with evidence suggesting an Antarctic contribution \r\nof anywhere between 5.4 and 17.8 m."


id: 3424e500d5d2c6b2a96a6dfeab998ca2
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
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index: climate_gpt_v2
vector_id: 6410
content:
"The AR5 found that the Last Interglacial (LIG) GMSL was >5 m \r\n(very high confidence) but <10 m (high confidence). Their best \r\nestimate of 6 m was based on two studies (Kopp et  al., 2009; \r\nDutton and Lambeck, 2012). The SROCC concluded that, during the \r\nLIG, Greenland's contribution to the GMSL highstand (the highest \r\nsea levels during the LIG) of 6-9 m increased gradually, whereas \r\nthe Antarctic contribution occurred early, from about 129 ka. Due \r\nto widely varying reconstructions from model studies (Greenland) \r\nand the paucity of direct evidence of ice-sheet change (Antarctic), \r\nthe magnitude of sea level contributions from both ice sheets was \r\nassigned low confidence."


id: df02424afb18684a4fa2472aae486978
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
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index: climate_gpt_v2
vector_id: 36489
content:
"The SROCC (Meredith et  al., 2019) noted that Greenland may \r\nhave been ice-free for extensive periods during Pleistocene \r\ninterglaciations, implying a  high sensitivity of the Greenland Ice \r\nSheet to warming levels close to present day. The AR5 (Church et al., \r\n2013b) assigned medium confidence to a  Marine Isotope Stage \r\n11 (MIS 11) GMSL of 6-15 m above present, requiring a  loss of \r\nmuch of the Greenland and West Antarctic ice sheets, and a possible \r\ncontribution from East Antarctica. High-resolution multi-proxy sea \r\nsurface temperature reconstructions and climate model simulations "


id: 2faf14d3a1adb08a7343dafecfbbc178
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
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index: climate_gpt_v2
vector_id: 5648
content:
"Recent LIG ice-sheet simulations agree that peak loss from the \r\nGreenland Ice Sheet occurred late (125-120 ka; Goelzer et al., 2016; \r\nTabone et al., 2018; Plach et al., 2019) when Northern Hemisphere \r\ninsolation was greater than at present (medium confidence) (Capron \r\net al., 2017), consistent with inferences from marine sediment records \r\n(Hatfield et al., 2016; Irvali et al., 2020) and far-field GMSL indicators \r\n(Rohling et al., 2019). Best estimates of the GMSL contribution from \r\nGreenland (Figure 9.17) differ between models: <=1 m (Albrecht et al., \r\n2020; Clark et  al., 2020), 1-2 m (Calov et  al., 2015; Goelzer \r\net al., 2016; Bradley et al., 2018), up to 3 m (Tabone et al., 2018; Plach \r\net al., 2019), and >5 m (Yau et al., 2016). There is high confidence\r\nthat the response time of the Greenland Ice Sheet to LIG warming \r\nwas multi-millennial, and high confidence that it contributed to LIG \r\nGMSL change, but low agreement in the contribution magnitude."


id: feedb3ccc334c3eaede99be36d940645
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
content_type: text
index: climate_gpt_v2
vector_id: 42247
content:
"In summary, paleo-environmental and modelling studies indicate \r\nthat, under past warming of the level achieved during the LIG \r\n(ca. 0.5degC-1.5degC), it is likely that both the Greenland and Antarctic ice \r\nsheets responded dynamically over multiple millennia (high confidence)."


id: 4969ad0d97f84a0f8fdd4607bf5fc0df
created_at: 2023-04-19 13:21:38
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vector_id: 10139
content:
"At the Last Glacial Maximum (LGM) geological proxies and GIA models \r\nindicate that GMSL was 125-134 m below present (Section 2.3.3.3 \r\nand Figures 9.17 and 9.18). New studies have not changed AR5's \r\nconclusions regarding the size or timing of the LGM and last glacial \r\ntermination, but have further examined the LGM sea level budget. \r\nBased on a synthesis of multiple prior studies, (Simms et al., 2019) \r\nestimated central 67% probability contributions to the LGM lowstand \r\n(i.e., lowest levels during the LGM) of 76 +-  7  m from the North \r\nAmerican Laurentide Ice Sheet, 18 +- 5 m from the Eurasian Ice Sheet, \r\n10 +- 2 m from Antarctica, 4 +- 1 m from Greenland, 5.5 +- 0.5 m from \r\nglaciers, and 2.4 +- 0.3 m due to an increase in ocean density. Of the \r\nresidual, up to about 1.4 m may be ascribed to groundwater, leaving \r\na shortfall of 16 +- 10 m yet to be allocated among land ice reservoirs \r\nor lakes."


id: e06c183524f1d965e9ba3563e309865f
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
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index: climate_gpt_v2
vector_id: 36734
content:
"In the middle Holocene, GMST peaked at 0.2degC-1.0degC higher than \r\n1850-1900 temperature between 7 and 6 ka (Section  2.3.1.1.2). \r\nGMSL rise slowed coincidently with final melting of the Laurentide ice \r\nsheet by 6.7 +- 0.4 ka (Ullman et al., 2016), after which only Greenland \r\nand Antarctic ice sheets could have contributed significantly. At 6 ka, \r\nGMSL was -3.5 to +0.5 m (medium confidence) (Section  2.3.3.3). \r\nSimulations of the Holocene Thermal Maximum give a Greenland Ice \r\nSheet broadly consistent with geological reconstructions so, despite \r\nuncertainties regarding the timing of minimum ice-sheet volume "


id: 2b226c5714ea8ae0ea1532fcb14cb68
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
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content:
"This section first assesses sea level projections since AR5 (Church \r\net al., 2013b) and including SROCC (Oppenheimer et al., 2019) based \r\non Representative Concentration Pathways (RCPs; Section 9.6.3.1). \r\nProcess-level assessments in sections 9.2.4, 9.4.1.3, 9.4.1.4, 9.4.2.5, \r\n9.4.2.6 and 9.5.1.3 are synthesized (Section 9.6.3.2) to produce new \r\nglobal mean and regional sea level projections based on the Shared \r\nSocio-economic Pathways up to 2150 (Section 9.6.3.3) and on global \r\nwarming levels up to 2100 (Section 9.6.3.4). Long-term global mean \r\nsea level (GMSL) projections, both at 2300 and on multimillennial \r\ntime scales, are also assessed (Section 9.6.3.5)."


id: 36c03e44d7c412235b55e94a918d0baf
created_at: 2023-04-19 13:21:38
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index: climate_gpt_v2
vector_id: 6861
content:
"In summary, both proxies and model simulations indicate that GMSL \r\nchanges during the early to mid-Holocene were the result of episodic \r\npulses, due to drainage of meltwater lakes, superimposed on a trend \r\nof steady rise due to continued ice-sheet retreat (high confidence)."


id: 59c873a2b14ac931130cff73c89c2252
created_at: 2023-04-19 13:21:38
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vector_id: 13037
content:
"Since AR5, a number of projections of GMSL rise have been published \r\nbased on the RCPs (Kopp et al., 2014, 2017; Slangen et al., 2014b; "


id: 43ea36d4a1ac7680219fb5c6477ae044
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
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vector_id: 9183
content:
"Grinsted et al., 2015; Jackson et al., 2016; Mengel et al., 2016; Bakker \r\net  al., 2017; Bittermann et  al., 2017; Le Bars et  al., 2017; Nauels \r\net al., 2017; Wong et al., 2017; Goodwin et al., 2018; Nicholls et al., \r\n2018; Le  Cozannet et  al., 2019; Palmer et  al., 2020). See Garner \r\net  al. (2018)  or a  database (Tables 9.SM.5, 9.SM.6). Some studies \r\nalso produced associated global sets of regional projections (Kopp \r\net al., 2014, 2017; Slangen et al., 2014b; Le Cozannet et al., 2019; \r\nPalmer et al., 2020). Since SROCC (Le Cozannet et al., 2019) focused \r\non the low end of the probability distribution of GMSL rise, Palmer \r\net  al. (2020) extended projections beyond 2100 using a  climate \r\nmodel emulator (Cross-Chapter Box  7.1), and Horton et  al. (2020) \r\nconducted a  survey of 106 sea level experts, providing additional \r\ncontext for interpreting sea level rise projections for 2100 and 2300."


id: 67b3d4564ec887789f0aa3257d2fb61
created_at: 2023-04-19 13:21:38
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content:
"MICI because there is low confidence in the current ability to quantify \r\nMICI (Section 9.4.2.4). Low confidence is also ascribed to projections \r\nbased on SEJ, because individual experts participating in the SEJ study \r\nmay have incorporated processes in whose quantification there is \r\nlow confidence, and the experts' reasoning has not been examined in \r\ndetail. In general, the range of GMSL projections based on ice-sheet \r\nmodels not incorporating MICI overlaps with, but is lower than, \r\nprojections incorporating MICI or employing SEJ (Figure 9.25)."


id: 38717de9972e3de499b755e208c16b3e
created_at: 2023-04-19 13:21:38
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content:
"There is high agreement across published GMSL projections for \r\n2050, and there is little sensitivity to emissions scenario (Figure 9.25, \r\nleft panel). Up to 2050, projections are broadly consistent \r\nwith extrapolation of the observed acceleration of GMSL rise \r\n(Sections 2.3.3.3, 9.6.1.1 and 9.6.1.2). Considering only projections \r\nincorporating ice-sheet processes in whose quantification there is at \r\nleast medium confidence, the GMSL projections for 2050, across all \r\nemissions scenarios, fall between 0.1 and 0.4 m (5th-95th percentile \r\nrange). Projections incorporating MICI or SEJ do not extend this range \r\nunder RCP2.6 or RCP4.5 but do extend the upper part of the range to \r\n0.6 m under RCP8.5. On the basis of these studies, we therefore \r\nhave high confidence that GMSL in 2050 will be between 0.1 and \r\n0.4 m higher than in 1995-2014 under low- and moderate-emissions \r\nscenarios, and between 0.1 and 0.6 m under high-emissions scenarios."


id: bc758ade003d2ce4c65c864e2625ed5d
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
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index: climate_gpt_v2
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content:
"As noted by SROCC, the largest differences between projections of \r\nGMSL in 2100 are due to the ice-sheet projection method, which \r\ngenerally fall into one of three categories: (i) projections from \r\nice-sheet models that represent processes where there is at least \r\nmedium confidence (Sections 9.4.1.2 and 9.4.2.2); (ii) projections \r\nfrom an Antarctic ice-sheet model that incorporates the marine \r\nice cliff instability (MICI; Section  9.4.2.4; DeConto and Pollard, \r\n2016); or (iii) projections based on SEJ (Sections 9.4.1.3, 9.4.1.4, \r\n9.4.2.5 and 9.4.2.6; Bamber and Aspinall, 2013; Bamber et  al., \r\n2019). Low  confidence is ascribed to projections incorporating "


id: 4cbe071ec6d06eace49bb856f3a528ed
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
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content:
"RCP4.5, and 0.4 and 2.4 m under RCP8.5. In summary, RCP-based \r\nprojections published since AR5 show high agreement for 2050, \r\nbut exhibit broad ranges and low agreement for 2100, particularly \r\nunder RCP8.5."


id: 6514f70feae24f0ab56612935032c27e
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
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"Conversely, there is low agreement across published GMSL projections \r\nfor 2100, particularly for higher-emissions scenarios, as well as a higher \r\ndegree of sensitivity to the choice of emissions scenario (Figure 9.25, \r\nright panel). Considering only projections representing processes in \r\nwhose quantification there is at least medium confidence, the GMSL \r\nprojections for 2100 fall between 0.2 and 1.0 m (5th-95th percentile \r\nrange) under RCP2.6 and RCP4.5, and between 0.3 and 1.6 m under \r\nRCP8.5. Considering also projections incorporating MICI or SEJ \r\n(low confidence), the projections for 2100 fall between 0.2 and 1.0 m \r\n(5th-95th percentile  range) under RCP2.6,  0.2, and 1.6 m under "


id: 10fd6704270b8c482630298ff2770a16
created_at: 2023-04-19 13:21:38
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"This section describes the choices made for the contributions \r\nto the updated global mean and regional sea level projections \r\n(Section 9.6.3.3) based on assessments in this Report and compares "


id: 51b1e497904f6c6a568588549a78a31e
created_at: 2023-04-19 13:21:38
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"Table 9.7 | Methods used to project the drivers of global mean sea level (GMSL) and relative sea level (RSL) change in the Shared Socio-economic Pathway \r\n(SSP) and warming-level-based projections of GMSL, RSL and extreme sea level (ESL) change. Section numbers indicate location of primary assessment text."


id: 81e9abfbeaa4c14741a31c15ce0369da
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"a\r\n Ice-sheet models include some of the larger islands in the Antarctic periphery, so there is some overlap in the projected glacier contribution and the projected Antarctic \r\ncontribution, but the effect is estimated to be on the order of 0.5-1 cm or less (Edwards et al., 2021)."


id: 6d73998a02d92ac847dac48d9642c74
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"changes on mass loss), and the dynamic contribution was estimated \r\nbased on a  multi-model assessment interpolated as a  quadratic \r\nfunction of time."


id: b4031032f24ef2bde7ebb8e0d227a23b
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
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vector_id: 28913
content:
"models (Wada  et  al.,  2012). Together, these yielded a  range of \r\nabout 0.02-0.09 m of GMSL rise by 2080-2099. The rate of water \r\nimpoundment in reservoirs was likewise based on two approaches: \r\none assuming the continuation of the average rate over 1971-2010 \r\n(and thus -0.01 to -0.03 m by 2080-2099; Chao et al., 2008); and \r\nthe other assuming no net impoundment after 2010 (Lettenmaier \r\nand Milly, 2009). Together, these yield a  GMSL contribution from \r\ngroundwater impoundment of -0.03 to 0 m. Combining groundwater \r\ndepletion and water impoundment led AR5 and SROCC to infer \r\na projected range of -0.01 to +0.11 m by 2100."


id: 73241adcca91bdce1dcc058ea6bce565
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updated_at: 2023-04-19 13:21:38
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index: climate_gpt_v2
vector_id: 17506
content:
"ocean dynamic sea level change along the coast (Section 9.2.3.6) and \r\nin semi-enclosed basins, such as the Mediterranean, where coarse \r\nmodels can misrepresent key dynamic processes. Regional high\u0002resolution models can improve projections of coastal ocean dynamic \r\nsea level change (Section 12.4; Hermans et al., 2020), but have not \r\nbeen implemented at a global scale."


id: b5d81a8a412340ace31640cf08883122
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
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content:
"Figure 9.26 | Median global mean and regional relative sea level projections (m) by contribution for the SSP1-2.6 and SSP5-8.5 scenarios. Upper time \r\nseries: Global mean contributions to sea level change as a function of time, relative to 1995-2014. Lower maps: Regional projections of the sea level contributions in \r\n2100 relative to 1995-2014 for SSP5-8.5 and SSP1-2.6. Vertical land motion is common to both Shared Socio-economic Pathways (SSPs). Further details on data sources and \r\nprocessing are available in the chapter data table (Table 9.SM.9)."


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"Figure 9.27 | Projected global mean sea level rise under different Shared Socio-economic Pathway (SSP) scenarios.Likely global mean sea level (GMSL) change \r\nfor SSP scenarios resulting from processes in whose projection there is medium confidence. Projections and likely ranges at 2150 are shown on right. Lightly shaded ranges and \r\nthinner lightly shaded ranges on the right show the 17th-83rd and 5th-95th percentile ranges for projections including low confidence processes for SSP1-2.6 and SSP5-8.5 \r\nonly, derived from a p-box including structured expert judgement and marine ice-cliff instability projections. Black lines show historical GMSL change, and thick solid and \r\ndash-dotted black lines show the mean and likely range extrapolating the 1993-2018 satellite altimeter trend and acceleration. Further details on data sources and processing \r\nare available in the chapter data table (Table 9.SM.9)."


id: 29d050119a85c7ce3ef307a37725520f
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
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content:
"While prior assessment reports, starting with the First Assessment \r\nReport (Warrick et al., 1990), have focused on projecting GMSL up \r\nto the year 2100, time has progressed, and the year 2100 is now \r\nwithin the time frame of some long-term infrastructure decisions. \r\nFor this reason, projections up to the year 2150 are also highlighted \r\n(Table  9.9). Over this time period, assuming no acceleration in \r\nice-sheet mass fluxes after 2100, processes in which there is medium \r\nconfidence lead to GMSL rise of 0.5-1.0 m under SSP1-2.6 and "


id: bef26719b80dfd3deffe72d656b9d90
created_at: 2023-04-19 13:21:38
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content:
"1.0-1.9 m under SSP5-8.5. Processes in which there is low confidence\r\ncould drive GMSL rise under SSP5-8.5 to 1.0-4.8 m (17th-83rd \r\npercentile) or even 0.9-5.4 m (5th-95th percentile)."


id: d3749f390f481a2243474a574e9a7cf9
created_at: 2023-04-19 13:21:38
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"Median projected RSL changes are shown in Figure 9.28, with driving \r\nfactors highlighted in Figure  9.26. Approximately 60% (SSP1-1.9) \r\nto 70% (SSP5-8.5) of the global coastline has a projected median \r\n21st  century regional RSL rise within +-20% of the global mean "


id: 9b3fa26eafe51b6b76b18e00f274a700
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
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content:
"increase (medium confidence). Consistent with AR5, loss of land \r\nice mass will be an important contributor to spatial patterns in \r\nRSL change (high confidence), with ocean dynamic sea level being \r\nparticularly important as a  dipolar contributor in the north-west \r\nAtlantic, a positive contributor in the Arctic Ocean, and a negative \r\ncontributor in the Southern Ocean south of the Antarctic Circumpolar \r\nCurrent (medium confidence) (Section  9.2.4.2). As today, VLM \r\nwill remain a  major driver of RSL change (high confidence). \r\nUncertainty in RSL projections is greatest in tectonically active \r\nareas in which VLM varies over short distances (e.g., Alaska) and in \r\nareas potentially subject to large ocean dynamic sea level change \r\n(e.g., the north-western Atlantic) (high confidence)."


id: 517ab7f9f48e802eca78d7a02eb6ce78
created_at: 2023-04-19 13:21:38
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content:
"do so between about 2160 and 2300 under SSP5-8.5. However, \r\nprocesses in whose projections there is low confidence could lead to \r\nsubstantially earlier exceedances under higher emissions scenarios: \r\nunder SSP5-8.5, 1.0 m could be exceeded by about 2080 and \r\n2.0 m could be exceeded by about 2110 (17th percentile of p-box, \r\nincorporating projections based on SEJ and MICI), with 5th percentile \r\nprojections as early as about 2070 for 1.0 m and 2090 for 2.0 m."


id: c3dd1ad113c362a753d7e09af11d58cf
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
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content:
"Global warming levels represent a  new dimension of integration \r\nin the AR6 cycle (Section 1.6.2, Cross-Chapter Box 11.1). The SR1.5 \r\n(Hoegh-Guldberg et al., 2018) concluded that, based on an assessment \r\nof GMSL projections published for 1.5degC and 2.0degC scenarios, there is \r\nmedium agreement that GMSL in 2100 would be 0.04-0.16 m higher \r\nin a 2degC warmer world, compared to a 1.5degC warmer world based \r\non 17-84% confidence interval projections (0.00-0.24  m based \r\non 5-95% confidence interval projections) with a  central value of \r\naround 0.1 m. The SR1.5 did not attempt to standardize the definition \r\nof warming-level scenarios, or to examine additional warming levels. \r\nNo new integrated GMSL projections for 1.5degC or 2.0degC scenarios \r\nhave been published since SR1.5."


id: cd8d01a686c23f1d71da6acec0b75fdc
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
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content:
"An alternative perspective on uncertainty in future sea level rise is \r\nprovided by looking at uncertainty in time rather than elevation; \r\nthat is, looking at the range of dates when specific thresholds of \r\nsea level rise are projected to be crossed (Figure 9.29). Considering \r\nonly medium confidence processes, GMSL rise is likely to exceed \r\n0.5 m between about 2080 and 2170 under SSP1-2.6 and between \r\nabout 2070 and 2090 under SSP5-8.5. It is likely to exceed 1.0 m \r\nbetween about 2150 and some point after 2300 under SSP1-2.6, \r\nand between  about 2100 and 2150 under SSP5-8.5. It is unlikely \r\nto exceed 2.0 m until after 2300 under SSP1-2.6, while it is likely to "


id: 8c575119b5eb8e0da8ef5c7c9bb0d46e
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
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vector_id: 21867
content:
"Taking into account all these approaches, including published \r\nprojections for RCP2.6, under SSP1-2.6 GMSL will rise between \r\n0.3  and 3.1 m by 2300 (low confidence). This projection range \r\nindicates that, while SROCC projections under low emissions to 2300 \r\nare consistent with no ice-sheet acceleration after 2100, there is the \r\npossibility of a  much broader range of outcomes at the high end, \r\nreflected in the range of published GMSL projections. Under SSP5-8.5, \r\nGMSL will rise between 1.7 and 6.8 m by 2300 in the absence of \r\nMICI and by up to 16 m considering MICI, a wider range than AR5 \r\nor SROCC assessments, but consistent with published projections \r\n(low confidence)."


id: e44f3e2c887c0b604f61092cade4f67f
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
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vector_id: 37465
content:
"suggests a  2000-year commitment at 1.5degC of about 2.3-3.1 m, \r\nwith approximately an additional 1.4-2.3 m commitment between \r\n1.5degC and 2.0degC (i.e.,  about 3 to 5 m degC-1). Taken together, both \r\nstudies show a 2000-year GMSL commitment of about 2-6 m for \r\npeak warming of about 2degC, 4-10 m for 3degC, 12-16 m for 4degC, and \r\n19-22 m for 5degC (medium agreement, limited evidence) (Table 9.10). \r\nGMSL rise continues after 2000 years, leading to a  10,000-year \r\ncommitment of about 6-7 m for 1.5degC of peak warming (based on \r\nClark et al., 2016), and based on both studies of about 8-13 m for \r\n2.0degC, 10-24 m for 3.0degC, 19-33 m for 4.0degC, and 28-37 m for 5degC \r\n(medium agreement, limited evidence) (Table 9.10)."


id: 8e43e7cdd443f02899883f1dfd84c2a3
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
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content:
"On still longer time scales, AR5 concluded with low confidence that \r\nthe multi-millennial GMSL commitment sensitivity to warming was \r\nabout 1-3 m degC-1 GSAT increase. Two process-model studies since \r\nAR5 (Clark et al., 2016; Van Breedam et al., 2020) indicate higher \r\ncommitments (Figure 9.30). Ice sheets dominate the multi-millennial \r\nsea level commitment (Sections 9.4.1.4 and 9.4.2.6), but the two \r\nstudies disagree on the relative contribution of the Greenland and \r\nAntarctic ice sheets. Notably, processes such as MICI (Section 9.4.2.4) \r\nthat are a major factor behind the deep uncertainty in century-scale \r\nAIS response do not appear to have a  substantial effect on the \r\nmulti-millennial magnitude (DeConto and Pollard, 2016). Only one of \r\nthe studies of multimillennial GMSL commitments includes scenarios \r\nconsistent with 1.5degC of peak warming (Clark et al., 2016); this study "


id: 2c31bf7e9006fe7dcf313852f3322769
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
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vector_id: 5036
content:
"An indicative metric for the equilibrium sea level response can \r\nbe provided by comparing paleo GSAT and GMSL during past \r\nmultimillennial warm periods (Sections 2.3.1.1, 2.3.3.3 and 9.6.2; \r\nFigure 9.9). However, caution is needed as the present and past warm \r\nperiods differ in astronomical and other forcings (Cross-chapter \r\nBox 2.1) and in terms of polar amplification. The Last Interglacial \r\n(likely 5-10 m higher GMSL than today and 0.5degC-1.5degC warmer \r\nthan 1850-1900; Section  9.6.2; Table  9.6) is consistent with the \r\nClark et  al. (2016) projections for the 10,000-year commitment \r\nassociated with 1.5degC of warming. Similarly, the Mid-Pliocene Warm \r\nPeriod (very likely 5-25 m higher GMSL than today and very likely \r\n2.5degC-4degC warmer) (Section  9.6.2; Table  9.6) is consistent with \r\nthe range of 10,000-year commitments associated with 2.5-4degC "


id: a81ac661add38e8fab02595cbb2d95b5
created_at: 2023-04-19 13:21:38
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vector_id: 26787
content:
"Table 9.11 | Global mean sea level (GMSL) projections between 1995-2014 and 2300 for total change and individual contributions. Low emissions \r\nprojections from: AR5 (Church et al., 2013b); RCP2.6 from SROCC (Oppenheimer et al., 2019) and published projections (Table 9.SM.8); and SSP1-2.6 \r\n(from this Report). High emissions projections from: AR5 (Church et al., 2013b); RCP8.5 from SROCC (Oppenheimer et al., 2019) and published projections \r\n(Table 9.SM.8); and SSP5-8.5 (this Report). Values for AR5 (Church et al., 2013b) and SROCC (Oppenheimer et al., 2019) are adjusted from the 1986-2005 baseline used \r\nin past reports. Only total values are shown for published ranges. Only the Antarctic contribution changed between AR5 (Church et al., 2013b) and SROCC (Oppenheimer et al., \r\n2019). If a range is given, it is the 17th-83rd percentile range."


id: 468cad5ffee9b3ebb852f9b72becf622
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"of warming, but GMSL reconstructions provide only a  weak, \r\nbroad constraint on model-based projections. An additional paleo \r\nconstraint comes from the Early Eocene Climatic Optimum, which \r\nindicates that 10-18degC of warming is associated with ice-free \r\nconditions and a likely GMSL rise of 70-76 m (Sections 2.3.3 and \r\n9.6.2). Together with model-based projections (Clark et  al., 2016; \r\nVan Breedam et al., 2020), this period suggests that commitment \r\nto ice-free conditions would occur for peak warming of about \r\n7degC-13degC (medium agreement, limited evidence)."


id: ba6071228c17dc768afbb75bc3aa57eb
created_at: 2023-04-19 13:21:38
updated_at: 2023-04-19 13:21:38
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content:
"On the basis of modelling studies, paleo constraints, single-ice-sheet \r\nstudies finding multimillennial nonlinear responses from both the \r\nGreenland and Antarctic ice sheets (Sections 9.4.1.4 and 9.4.2.6), \r\nand the underlying physics, we conclude that GMSL commitment \r\nis nonlinear in peak warming on time scales of both 2,000 and \r\n10,000 years (medium confidence) and exceeds the AR5 assessment \r\nof 1-3 m degC-1 (medium agreement, limited evidence) (Table  9.9). \r\nAlthough thermosteric sea level will start to decline slowly about \r\n2,000 years after emissions cease, the slower responses from the \r\nGreenland and Antarctic ice sheets mean that GMSL will continue \r\nto rise for 10,000 years under most scenarios (medium confidence). "


id: e68fbacc25f8c50f33f28c7381eab24b
created_at: 2023-04-19 13:21:38
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content:
"In this box, we outline a storyline (Glossary, Box 10.2; Shepherd et al., 2018) for high-end sea level projections for 2100. This storyline \r\nconsiders processes whose quantification is highly uncertain regarding the timing of their possible onset and/or their potential to \r\naccelerate sea level rise. These processes are therefore not considered for the assessed upper bound of likely sea level rise by 2100 in \r\nsection 9.6.3.3, as the likely range includes only processes that can be projected skilfully with at least medium confidence (based on \r\nagreement and evidence)."


id: dd7291c89f3cc2087450cffdecf3f131
created_at: 2023-04-19 13:21:38
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"As noted by SROCC, stakeholders with a low risk tolerance (e.g., those planning for coastal safety in cities and long-term investment in \r\ncritical infrastructure) may wish to consider global-mean sea level rise above the assessed likely range by the year 2100, because 'likely' \r\nimplies an assessed likelihood of up to 16% that sea level rise by 2100 will be higher (see also Siegert et al., 2020). Because of our \r\nlimited understanding of the rate at which some of the governing processes contribute to long-term sea level rise, we cannot currently \r\nrobustly quantify the likelihood with which they can cause higher sea level rise before 2100 (Stammer et al., 2019)."


id: 98b428555542d9842534267f224037fe
created_at: 2023-04-19 13:21:38
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"In light of such deep uncertainty, we employ a  storyline approach in examining the potential for, and early warning signals of \r\na high-end sea level scenario unfolding within this century. In doing so, we note upfront that the main uncertainty related to high-end \r\nsea level rise is 'when' rather than 'if' it arises: the upper limit of 1.01 m of likely sea level range by 2100 for the SSP5-8.5 scenario will \r\nbe exceeded in any future warming scenario on time scales of centuries to millennia (high confidence), but it is uncertain how quickly \r\nthe long-term committed sea level will be reached (Section 9.6.3.5). Hence, global mean sea level might rise well above the likely\r\nrange before 2100, which is reflected by assessments of ice-sheet contributions based on structured expert judgement (Bamber et al., \r\n2019) leading to a 95th percentile of projected future sea level rise as high as 2.3 m in 2100 (Section 9.6.3.3)."


id: e793e649e50783ecae146ebc5eff7a7a
created_at: 2023-04-19 13:21:38
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content:
"A plausible storyline for such high-end sea level rise in 2100 assumes a strong warming scenario (Section 4.8). The storyline considers \r\nfaster-than-projected disintegration of marine ice shelves and the abrupt, widespread onset of marine ice cliff instability (MICI) and \r\nmarine ice sheet instability (MISI) in Antarctica (Section 9.4.2.4), and faster-than-projected changes in both the surface mass balance \r\nand dynamical ice loss in Greenland. While conceptual studies provide medium evidence of these processes, substantial uncertainties \r\nand low agreement in quantifying their future evolution arise from limited process understanding, limited availability of evaluation \r\ndata, missing or crude representation in model simulations, their high sensitivity to uncertain boundary conditions and parameters, \r\nand/or uncertain atmosphere and ocean forcing (Sections 9.4.1.2; 9.4.2.2)."


id: 50d5fff21dbe1537cecf711352546440
created_at: 2023-04-19 13:21:38
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"In Greenland, stronger mass loss than currently projected might also occur (Aschwanden et al., 2019; Khan et al., 2020; T. Slater \r\net  al., 2020). For example, warming-induced dynamical changes in atmospheric circulation could enhance summer blocking and \r\nproduce more frequent extreme melt events over Greenland similar to the record mass loss of more than 500 Gt in summer 2019 \r\n(Section 9.4.1.1; Delhasse et al., 2018; Sasgen et al., 2020). Cloud processes in polar areas that are not well represented in models \r\ncould further enhance surface melt (Hofer et al., 2019), as could feedbacks between surface melt and the increasing albedo from \r\nmeltwater, detritus and pigmented algae (Section 9.4.1.1; Cook et al., 2020). The same ice dynamical processes associated with basal \r\nmelt and MISI discussed for Antarctica could also occur in Greenland, as long as the ice sheet is in contact with the ocean."


id: c9b8b41ca2a51eec3ca3b256fcd071d
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"The strength of all these processes is currently understood to depend strongly on global mean temperature and polar amplification, \r\nwith additional linkages through feedback from global mean sea level (Gomez et al., 2020). These dependencies on a joint forcing \r\nimply that processes are strongly correlated. Hence, both their uncertainties and their possible cascading contribution to high-end sea \r\nlevel rise are expected to combine. Therefore, high-end sea level rise can occur if one or two processes related to ice-sheet collapse"


id: 771a459841d8b409cc994936abae5417
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
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"in Antarctica result in an additional sea level rise at the maximum of their plausible ranges (Sections 9.4.2.5 and 9.6.3.3; Table 9.7) or \r\nif several of the processes described in this box result in individual contributions to additional sea level rise at moderate levels. In both \r\ncases, global-mean sea level rise by 2100 would be substantially higher than the assessed likely range, as indicated by the projections \r\nincluding low confidence processes reaching in 2100 as high as 1.6 m at the 83rd percentile and 2.3 m at the 95th  percentile \r\n(Section 9.6.3.3)."


id: 2c297b46a75f97832ba837bc692b1141
created_at: 2023-04-19 13:21:39
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content:
"can result from changes (either positive or negative) in the surge or \r\ntidal components, and can include non-linear interactions between \r\ntide, surge, and RSL (Arns et al., 2015; Schindelegger et al., 2018). \r\nThe positive phase of the 18.6-year nodal cycle of the astronomical \r\ntide  is a  further consideration, contributing to an increased flood \r\nhazard relative to the long-term average (Talke et al., 2018; Peng \r\net al., 2019; Baranes et al., 2020). Failing to consider the non-linear \r\ninteractions between tide, surge and RSL may overestimate trends in \r\nESWL (low confidence) (Arns et al., 2020). In some regions, changes \r\nin ESWL depend more on changes in surge or tide than on sea \r\nlevel trends."


id: 2689aa3be6e4cdb9ee2048aa05989eeb
created_at: 2023-04-19 13:21:39
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"An extreme sea level (ESL) refers to an occurrence of exceptionally \r\nhigh or low local sea surface height (Box 9.1). This section focuses on \r\noceanographic-driven changes in ESL (Box 9.1)."


id: e81a37947c019ef424d5869573012dc0
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
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"The AR5 (Church et al., 2013b) concluded that changes in extreme still \r\nwater levels (ESWL), combining RSL, tide and surge as observed by tide \r\ngauges (Box 9.1) are very likely to be caused by observed increases in \r\nRSL, but noted low confidence in region-specific results owing to the \r\nlimited number of studies considering localized contributions from \r\nstorm surge, tide or wave effects. Influences from dominant modes \r\nof climate variability, particularly ENSO and NAO (Annex IV), were \r\nalso noted. Climate modes affect sea level extremes in many regions, \r\nas a result of both sea level anomalies (Sections 9.2.4.2 and 9.6.1.3) \r\nand changes in storminess (Section 11.7). The SROCC (Oppenheimer \r\net al., 2019) concluded with high confidence that inclusion of local \r\nprocesses (wave effects, storm surges, tides plus other regional \r\nmorphology changes due to erosion, sedimentation and compaction) \r\nis essential for estimation of changes in ESL events."


id: 2183aaceaa2d11b5857185c791c298e6
created_at: 2023-04-19 13:21:39
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content:
"Ongoing development of the Global Extreme Sea Level Analysis \r\n(GESLA) tide gauge database (Woodworth et al., 2016) along with \r\ndata archaeology (Talke and Jay, 2013) extends availability of tide \r\ngauge records back to the mid 19th century (or earlier). Dynamical \r\ndatasets used to assess trends in ESL at global or regional scales - \r\nfor example, tide and surge contributions from the Global Tide and \r\nSurge Reanalysis (GTSR; Muis et  al., 2016, 2020), or wave setup/\r\nswash contributions from available wave hindcasts/reanalyses \r\n(Melet et al., 2018) - have model biases introduced with resolution \r\nand parametrization limitations, incomplete atmospheric data, \r\nand currently span only a few decades, so they are not yet long or \r\naccurate enough to assess long-term trends in ESLs. Therefore, there \r\nis medium confidence in observed trends in ESWL, but only low \r\nconfidence in modelled ESL trends."


id: ce899e14ec76a8d5f89b65b508f95e28
created_at: 2023-04-19 13:21:39
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"As in AR5 and SROCC, tide gauge observations show that RSL rise \r\n(Section 9.6.1.3) is the primary driver of changes in ESWL at most \r\nlocations and, across tide gauges, has led to a median 165% increase \r\nin high-tide flooding over 1995-2014 relative to those over \r\n1960-1980 (high confidence) (Figure 9.31). Some locations exhibit \r\nsubstantial differences between long-term RSL trends and ESWL \r\n(high confidence), particularly given decadal to multi-decadal \r\nvariations of other ESWL contributors (Rashid and Wahl, 2020). Since \r\nSROCC, RSL rise has been shown to be the dominant contributor \r\nto ESWL rise at most gauge sites along the Chinese coast, but, at \r\nsome locations, the surge contribution dominates (Feng et  al., \r\n2019). Trends in the difference between ESWL and mean RSL rise "


id: 18e15e06b2d318b363ea873c4ab02c37
created_at: 2023-04-19 13:21:39
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"The AR5 indicated that the amplitude and phase of major tidal \r\nconstituents have exhibited long-term change, but that their effects \r\non ESL were not well understood. The SROCC (Bindoff et al., 2019) \r\nreported changes in tides (amplification and dampening) at some \r\nlocations to be of comparable importance to changes in mean sea \r\nlevel for explaining changes in high water levels, with the sign of \r\nchange being dependent on stability of shoreline position. RSL rise \r\ncauses water depth-based alterations to the resonant characteristics "


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content:
"of the basin, changes the bottom friction and increases the wave \r\nspeed (Pickering et  al., 2012) and remains the primary hypothesis \r\nfor observed tidal changes. Other contributing processes include \r\nstrong localized anthropogenic drivers (e.g.,  port development, \r\ndredging, flood defences, land reclamation), changes in stratification \r\nassociated with ocean warming (Section  9.2.1.3), and changes in \r\nseabed roughness associated with ecological change (e.g.,  Haigh \r\net  al., 2019). Tide gauge data show that, although principal tidal \r\ncomponents have varied in amplitude on the order of 2% to \r\n10% per century (Jay, 2009; Ray, 2009), identifying direct causality \r\nremains challenging (Haigh et  al., 2019). Combined, observations \r\nand models indicate RSL rise and direct anthropogenic factors are \r\nthe primary drivers of observed tidal changes at tide gauge stations \r\n(medium confidence)."


id: c0141e99bce606bd509ec72621c9ecb8
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 31084
content:
"Waves contribute to ESL via wave setup, infra-gravity waves and \r\nswash processes (Dodet et  al., 2019), with Extreme Total Water \r\nLevel (ETWL; Box  9.1) used to represent ESWL with addition of \r\nwave setup, and Extreme Coastal Water Level (ECWL; Box 9.1) also \r\nincluding contributions from swash. The SROCC (Oppenheimer et al., \r\n2019) reported the dependency of these processes on nearshore \r\ngeomorphology and deep-water wave climate, and thus sensitivity \r\nto internal climate variability and climate change. Few long-term "


id: 9e9aaa1a9e5c5eb537324574a658ba70
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 25133
content:
"at 19-31% of the 634 stations by 2050, consistent with SROCC. \r\nBy 2100, the median frequency amplification factor is projected to \r\nbe 163 for SSP1-2.6, 325 for SSP2-4.5, and 532 for SSP5-8.5, with \r\nrespectively 60%, 71%, and 82% of the stations experiencing \r\na  currently 1% annual probability event at least yearly (medium \r\nconfidence) (Figure 9.32)."


id: 278fdbd3166bd13f4c1a0af5ceee7e1c
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 4139
content:
"Assessment of dynamic ETWL changes for regions is presented in \r\nChapter  12, following the methods of Vousdoukas et  al. (2018) \r\nand Kirezci et  al. (2020). Consistent with studies using the static \r\napproach, Vousdoukas et al. (2018) finds that by 2050 the historical \r\n1% average annual probability ETWL will have increased to a 2-50% \r\naverage annual probability for most high latitude regions, and more \r\noften (up to multiple times a  year, >100% annual probability) in \r\nthe tropics, under both RCP4.5 and RCP8.5. For 2100, present-day \r\n1% average annual probability extreme sea levels will be exceeded \r\nmultiple times each year almost everywhere. In summary, despite \r\nwaves and surges being non-negligible contributors to projected \r\nETWL and ECWL changes (Vousdoukas et  al., 2018; Melet et  al., \r\n2020), RSL change is expected to be the main driver in changes in \r\nfuture ESL return periods in most areas (medium confidence)."


id: 5f449ea13938c78bdaa134c07a7e6d6c
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 14069
content:
"The SROCC (Bindoff et  al., 2019) concluded that the majority of \r\ncoastal regions will experience statistically significant changes \r\nin tidal amplitudes through the 21st  century. Comprehensive \r\nhigh-resolution (of the order 10 km) numerical modelling studies \r\nprovide evidence for spatially coherent changes in tidal amplitudes in \r\nshelf seas as a result of RSL rise (Haigh et al., 2019, and references \r\ntherein). There is high confidence that GMSL rise will be the primary \r\ndriver of global tidal amplitude increases and decreases over the next \r\n100-200 years, changing the baseline tide that ESLs are imposed \r\non. At local and regional scales, anthropogenic factors such as major \r\nland reclamation efforts, as in the East China Sea (Song et al., 2013) \r\nor differing national coastal management strategies (maintaining the \r\npresent coastline position or managed retreat) will locally modulate \r\nthe influence of GMSL rise on tidal amplitude (medium confidence)."


id: feea2b1c948f5b58b94461e48712dfcd
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 42244
content:
"Employing a similar static approach - fitting a Gumbel distribution \r\nbetween Mean Higher High Water (average of higher high water \r\nheight of each tidal day) and a  threshold following Buchanan \r\net al. (2016) - this Report updates SROCC projections of ESL with \r\nthe RSL projections from Section  9.6.3.3 (see also Supplementary \r\nMaterial  9.SM.4). By 2050, the median increase in frequency \r\namplification factor at 634 tide gauge stations is 19 for SSP1-2.6, \r\n22 for SSP2-4.5, and 30 for SSP5-8.5 (Figure 9.32). This means that, \r\nby 2050, a  historical (1995-2014) 1% annual probability ESL will \r\nhave increased to an 19-30% annual probability. The 1% historical \r\nannual probability event is expected to become an annual event "


id: fdaf7c0e0c1fb4f77387fb23fa38fd7
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 41992
content:
"different locations by effects of changes in tracks (Section  11.7.1; \r\nGarner et al., 2017). There is low confidence in projected changes in \r\nESL driven by changes in tropical cyclone climatology."


id: 9b1422832035805ec4f581c448c908d0
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 24536
content:
"The SROCC (Oppenheimer et al., 2019) concluded that the intensity \r\nof severe tropical cyclones will increase in a  warmer climate \r\n(Section 11.7.1), but low confidence remains in the future frequency \r\nof tropical cyclones. Changes in tropical cyclone climatology will \r\ncontribute to variations in frequency and magnitude of future ESL \r\nsurge events, although estimates of this contribution range widely \r\n(Lin et al., 2012; McInnes et al., 2014, 2016; Little et al., 2015; Garner \r\net al., 2017; Mori et al., 2019; Muis et al., 2020). In the Gulf of Mexico, \r\nchanges in ESL due to tropical cyclone activity may be as important \r\nas SLR in enhancing future flood hazards (Marsooli et  al., 2019). \r\nFor  the Korean Peninsula, a  maximum change in 100-year return \r\nheight associated with typhoon-induced storm surges of 10% under \r\n4degC warming is found (Yang et al., 2018). The effects of projected \r\nchanges in tropical cyclone intensity may be enhanced or offset in "


id: 781f1ee219c5d051b9e2cc340e501c9a
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 18345
content:
"Changes in surface wave conditions occur in response to changes in \r\nfrequency; intensity and position of forcing winds and storms (Morim \r\net  al., 2018, 2019); reduction in sea ice and associated changes \r\nin fetch conditions (Thomson and Rogers, 2014; Casas-Prat and \r\nWang, 2020); and changes in coastal morphology associated with \r\nRSL rise (Wandres et al., 2017; Storlazzi et al., 2018). A few studies \r\nconsidering the contribution of a  non-stationary wave climate on \r\nfuture changes in ESL infer a small but non-negligible contribution \r\n(Vousdoukas et al., 2018; Melet et al., 2020). The SROCC presented \r\nqualitative assessments of projected changes in wave conditions. "


id: afbafd1f97f8a06286ddef86c175311b
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 28149
content:
"The process-based assessment of observed and projected change in \r\nthe ocean, cryosphere and sea level undertaken here reveals advances \r\nand gaps in reconstructions, observations, models and process \r\nunderstanding. Revisiting the updated assessments since AR5 and \r\nSROCC helps to gauge the robustness of understanding and quantitative \r\nassessments. The CMIP6 family of models builds on the experience \r\nof the CMIP5 models, and the projections of ISMIP6, LARMIP-2 and \r\nGlacierMIP strengthen understanding. Taken together with emulators \r\nof these simulations (Box 9.3) and transparent statistical approaches \r\n(Section 9.6.3), this chapter provides projections that are consistent \r\nwith the assessment of equilibrium climate sensitivity in this Report \r\nand that have improved estimates of uncertainty."


id: 5de5ccefe10691b42239a77cbe7c8e5a
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 13821
content:
"The largest uncertainties in future sea level and cryosphere change are \r\nrelated to the Greenland and Antarctic ice sheets (Sections 9.4.1.3, \r\n9.4.1.4, 9.4.2.5 and 9.4.2.6). While the ISMIP6 and LARMIP-2 \r\nprotocols provide simulations permitting uncertainty estimation \r\nand probabilistic inferences, remaining deep uncertainty  relates to \r\nice-sheet processes and the atmospheric and oceanic conditions \r\nsimulated by CMIP models in polar regions (Sections 9.4.2.3 and \r\n9.4.2.4). ISMIP6 and LARMIP-2 have not been simulated beyond \r\n2100, which greatly reduces the amount and variety of state-of-the\u0002art projections available to make ice-sheet and sea level projections \r\nbeyond 2150. After 2150, limited agreement causes us to consider all \r\nprojections as low confidence. Critically, the uncertainty in ice-sheet \r\nprojections is the leading uncertainty in projections of future global \r\nsea level for the second half of this century and beyond (Section 9.6.3)."


id: a5725f6e5a04806eebb3fbe277faa5f7
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 26338
content:
"The SROCC (Collins et  al., 2019) highlighted compound events, or \r\ncoincident occurrence of multiple hazards, as an example of deep \r\nuncertainty, and noted that failing to account for multiple factors \r\ncontributing to extreme events will lead to underestimation of the \r\nprobabilities of occurrence (high confidence). Statistical studies \r\nhave shown that high rain or streamflow often co-occurs with storm \r\nsurge as examples of 'compound' surge-rain or surge-discharge \r\nevents (Sections 11.8.1 and 12.4.5.6; Wahl and Chambers, 2015; \r\nMoftakhari et al., 2017; Ward et al., 2018; Wu et al., 2018; Couasnon \r\net al., 2020). Dynamical modelling studies show that co-occurrence \r\nof flood drivers raises ESLs at some locations in estuaries, such \r\nas the Rhine Delta (Zhong et al., 2013), the Netherlands (van den \r\nHurk et  al., 2015), Taiwan, China (Chen and Liu, 2016), and the \r\nHudson River, USA (Orton et al., 2020), particularly when hydrologic "


id: 3bd42da3863f1e873244a7cde15f309a
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 7715
content:
"Glacier inventory and projection uncertainty has been a significant \r\nsource of past sea level budget uncertainty and remains a dominant \r\nuncertainty until mid-century. Emissions scenario becomes the largest "


id: cc6421d5aec8b67bb1ee660cae5760ad
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 33275
content:
"source of glacier change uncertainty by 2100, just as the relative \r\nimportance of glacier loss is projected to decrease (Section 9.5.1). "


id: 48961ad058b746694049225a4cea27f8
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 9987
content:
"We acknowledge the contribution of invited expert reviewers and \r\nthe Ice Sheet Mass Balance Intercomparison Exercise (IMBIE) Team. \r\nTheir  valuable input and advice have significantly improved the \r\nchapter. We thank colleagues, institutions and, in particular, our \r\nfamilies for their support. Thanks to the Technical Support Unit and \r\nespecially Sophie Berger for her support."


id: e9e069934cd93a38ee37ee7be10684bb
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 38497
content:
"New high-resolution climate models show that sea surface \r\ntemperature, overturning circulation, ocean heat content change \r\nand sea ice cover are considerably improved in most models when \r\ncompared to the coarser resolution models. Change in the Southern \r\nOcean and adjacent shelves (Section 9.2.3.2) is intimately linked to \r\nthe future of the Antarctic Ice Sheet (Section 9.4.2.3), and projection \r\nof the Southern Ocean depends on oceanic and atmospheric drivers \r\naffecting heat (and carbon) uptake and sea ice. However, resolution \r\nremains a factor, as most CMIP6 models are far from resolutions that \r\ndirectly represent coastal and regional shallow-water processes, such \r\nas those beneath Antarctic ice shelves, in Greenland fjords and the \r\neddying convection found by the Overturning in the Subpolar North \r\nAtlantic Program."


id: e8f6a47fe5802639462f00c71d22e67e
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 38338
content:
"Processes that change on long time scales  - particularly Atlantic \r\nMeridional Overturning Circulation, ocean heat content, and ice \r\nsheets - require additional projections beyond the CMIP scenarios to \r\nexplore longer-term commitment, post-forcing recovery measured in \r\ncenturies rather than years or decades, and potential tipping points \r\nand thresholds. Only a few new studies focused on longer time scales, \r\nand none based on CMIP6 models."


id: 60acde9a473a20c57f4cb90933c28c03
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 14306
content:
"FAQ 9.1 | Can Continued Melting of the Greenland and Antarctic Ice Sheets Be Reversed? How Long \r\nWould It Take for Them to Grow Back?"


id: 5c57fea1323503cfa2edaf43a80979c9
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 13499
content:
"Evidence from the distant past shows that some parts of the Earth system might take hundreds to thousands of \r\nyears to fully adjust to changes in climate. This means that some of the consequences of human-induced climate \r\nchange will continue for a very long time, even if atmospheric heat-trapping gas levels and global temperatures \r\nare stabilized or reduced in the future. This is especially true for the Greenland and Antarctic ice sheets, which \r\ngrow much more slowly than they retreat. If the current melting of these ice sheets continues for long enough, \r\nit becomes effectively irreversible on human time scales, as does the sea level rise caused by that melting."


id: 7197fe7c30fc8d2f79018e7cf0967db6
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 17233
content:
"These (and other) self-reinforcing processes explain why relatively small increases in temperature in the past led \r\nto very substantial sea level rise over centuries to millennia, compared to the many tens of thousands of years it \r\ntakes to grow the ice sheets that lowered the sea level in the first place. These insights from the past imply that, \r\nif human-induced changes to the Greenland and Antarctic ice sheets continue for the rest of this century, it will \r\ntake thousands of years to reverse that melting, even if global air temperatures decrease within this or the next \r\ncentury. In this sense, these changes are therefore irreversible, since the ice sheets would take much longer to \r\nregrow than the decades or centuries for which modern society is able to plan."


id: 8c6fbd7434d8c6764622a09a67da8c8b
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 21895
content:
"Once ice sheets are destabilised, it takes them tens of thousands of years to re-grow. \r\nThese changes strongly affect sea level. "


id: 633bb3b613d8d3658568208a7c7a59c2
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 14750
content:
"FAQ 9.1, Figure 1 | Ice sheets growth and decay. (Top) Changes in ice-sheet volume modulate sea level variations. The grey line depicts data from a range \r\nof physical environmental sea level recorders such as coral reefs while the blue line is a smoothed version of it. (Bottom left) Example of destabilization \r\nmechanism in Antarctica. (Bottom right) Example of destabilization mechanism in Greenland."


id: 58e7e3f0079990694b512705dfc6df8
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 12883
content:
"As of 2018, global average sea level was about 15-25 cm higher than in 1900, and 7-15 cm higher than in 1971. \r\nSea level will continue to rise by an additional 10-25 cm by 2050. The major reasons for this ongoing rise in sea \r\nlevel are the thermal expansion of seawater as its temperature increases, and the melting of glaciers and ice \r\nsheets. Local sea level changes can be larger or smaller than the global average, with the smallest changes in \r\nformerly glaciated areas, and the largest changes in low-lying river delta regions."


id: f7f023bb73c885778fc786488f9c1c25
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 40995
content:
"Across the globe, sea level is rising, and the rate of increase has accelerated. Sea level increased by about 4 mm \r\nper year from 2006 to 2018, which was more than double the average rate over the 20th century. Rise during \r\nthe early 1900s was due to natural factors, such as glaciers catching up to warming that occurred in the Northern \r\nHemisphere during the 1800s. However, since at least 1970, human activities have been the dominant cause of \r\nglobal average sea level rise, and they will continue to be for centuries into the future."


id: e2861f64f134b4ec636e1bb02c364d73
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 37152
content:
"Sea level rises either through warming of ocean waters or the addition of water from melting ice and bodies of \r\nwater on land. Expansion due to warming caused about 50% of the rise observed from 1971 to 2018. Melting \r\nglaciers contributed about 22% over the same period. Melting of the two large ice sheets in Greenland and \r\nAntarctica has contributed about 13% and 7%, respectively, during 1971 to 2018, but melting has accelerated \r\nin the recent decades, increasing their contribution to 22% and 14% since 2016. Another source is changes in \r\nland-water storage: reservoirs and aquifers on land have reduced, which contributed about an 8% increase \r\nin sea level."


id: 4969856207a5405005b1845ed865597d
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
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index: climate_gpt_v2
vector_id: 10138
content:
"By 2050, sea level is expected to rise an additional 10-25 cm whether or not greenhouse gas emissions are reduced \r\n(FAQ 9.2, Figure 1). Beyond 2050, the amount by which sea level will rise is more uncertain. The accumulated \r\ntotal emissions of greenhouse gases over the upcoming decades will play a big role beyond 2050, especially in \r\ndetermining where sea level rise and ice-sheet changes eventually level off."


id: a2098f841edebb37a33843c511504ec6
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 25746
content:
"Even if net zero emissions are reached, sea level rise will continue because the deep ocean will continue to warm \r\nand ice sheets will take time to catch up to the warming caused by past and present emissions: ocean and ice \r\nsheets are slow to respond to environmental changes (see FAQ 5.3). Some projections under low emissions show \r\nsea level rise continuing as net zero is approached at a rate comparable to today (3-8 mm per year by 2100 versus \r\n3-4 mm per year in 2015), while others show substantial acceleration to more than five times the present rate by \r\n2100, especially if emissions continue to be high and processes that accelerate retreat of the Antarctic Ice Sheet \r\noccur widely (FAQ 9.1)."


id: b79b50468f6581c6c93d5d1c5ade738c
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
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index: climate_gpt_v2
vector_id: 29512
content:
"Sea level rise will increase the frequency and severity of extreme sea level events at coasts (see FAQ 8.2), such as \r\nstorm surges, wave inundation and tidal floods: risk can be increased by even small changes in global average \r\nsea level. Scientists project that, in some regions, extreme sea level events that were recently expected once in \r\n100 years will occur annually at 20-25% of locations by 2050 regardless of emissions, but by 2100 emissions choice \r\nwill matter: annually at 60% of locations for low emissions, and at 80% of locations under strong emissions."


id: c328250feb391cf5c4b3cf6d668139fa
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 31609
content:
"The Gulf Stream is part of two circulation patterns in the North Atlantic: the Atlantic Meridional Overturning \r\nCirculation (AMOC) and the North Atlantic subtropical gyre. Based on models and theory, scientific studies \r\nindicate that, while the AMOC is expected to slow in a warming climate, the Gulf Stream will not change much \r\nand would not shut down totally, even if the AMOC did. Most climate models project that the AMOC slows in the \r\nlater 21st century under most emissions scenarios, with some models showing it slowing even sooner. The Gulf \r\nStream affects the weather and sea level, so if it slows, North America will see higher sea levels and Europe's \r\nweather and rate of relative warming will be affected."


id: 900b62ce91fd75f732907e7f28efe4fb
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
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index: climate_gpt_v2
vector_id: 22548
content:
"The Gulf Stream is the biggest current in the North Atlantic Ocean. It transports about 30 billion kilograms \r\nof water per second northward past points on the east coast of North America. It is a  warm current, with \r\ntemperatures 5degC to 15degC warmer than surrounding waters, so it carries warmer water (thermal energy) from its \r\nsouthern origins and releases warmth to the atmosphere and surrounding water."


id: abcc6a760ca191d8dcf46a9396014bb1
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 27467
content:
"The Gulf Stream is part of two major circulation patterns, the Atlantic Meridional Overturning Circulation (AMOC) \r\nand the North Atlantic Subtropical Gyre (FAQ 9.3, Figure 1). The rotation of the Earth causes the big currents in \r\nboth circulations to stay on the western side of their basin, which in the Atlantic means the circulations combine \r\nto form the Gulf Stream. Other large currents contribute to gyres, such as the Kuroshio in the North Pacific \r\nand the East Australian Current in the South Pacific, but the Gulf Stream is special in its dual role. There is no \r\ncomparable deep overturning circulation in the North Pacific to the AMOC, so the Kuroshio plays only one role \r\nas part of a gyre."


id: 8f2284e0200bb4155eb1ef45fd90c963
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 22383
content:
"The gyres circulate surface waters and result primarily from winds driving the circulation. These winds are not \r\nexpected to change much and so neither will the gyres, which means the gyre portion of the Gulf Stream and the \r\nKuroshio will continue to transport thermal energy poleward from the equator much as they do now. The gyre \r\ncontribution to the Gulf Stream is 2 to 10 times larger than the AMOC contribution."


id: ea5a8931738ed59a7b1f44b7215ee1a7
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
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index: climate_gpt_v2
vector_id: 38586
content:
"The Gulf Stream's role in the AMOC is supplying surface source water that cools, becomes denser and sinks to \r\nform cold, deep waters that travel back equatorward, spilling over features on the ocean floor and mixing with \r\nother deep Atlantic waters to form a southward current at a depth of about 1500 metres beneath the Gulf \r\nStream. This overturning flow is the AMOC, with the Gulf Stream in the upper kilometre flowing northward, \r\nand the colder deep water flowing southward."


id: 8c62d1947c3afe286caea95269485aa0
created_at: 2023-04-19 13:21:39
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vector_id: 21876
content:
"The AMOC is expected to slow over the coming centuries. One reason why is freshening of the ocean waters: \r\nby meltwater from Greenland, changing Arctic sea ice, and increased precipitation over warmer northern seas. \r\nAn array of moorings across the Atlantic has been monitoring the AMOC since 2004, with recently expanded \r\ncapabilities. The monitoring of the AMOC has not been long enough for a trend to emerge from variability \r\nand detect long-term changes that may be underway (see FAQ 1.2). Other indirect signs may indicate slowing \r\noverturning - for example, slower warming where the Gulf Stream's surface waters sink. Climate models show \r\nthat this 'cold spot' of slower-than-average warming occurs as the AMOC weakens, and they project that this \r\nwill continue. Paleoclimate evidence indicates that the AMOC changed significantly in the past, especially during \r\ntransitions from colder climates to warmer ones, but that it has been stable for 8000 years."


id: abf3e1ac9389a689aa9cac8157d67fb4
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 27488
content:
"What happens if the AMOC slows in a warming world? The atmosphere adjusts somewhat by carrying more heat, \r\ncompensating partly for the decreases in heat carried by AMOC. But the 'cold spot' makes parts of Europe warm \r\nmore slowly. Models indicate that weather patterns in Greenland and around the Atlantic will be affected, with \r\nreduced precipitation in the mid-latitudes, changing strong precipitation patterns in the tropics and Europe, and \r\nstronger storms in the North Atlantic storm track. The slowing of this current combined with the rotation of the \r\nEarth means that sea level along North America rises as the AMOC contribution to the Gulf Stream slows."


id: 42f16c8817f36043b246876f692e4b9a
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 8974
content:
"The North Atlantic is not the only site of sensitive meridional overturning. Around Antarctica, the world's densest \r\nseawater is formed by freezing into sea ice, leaving behind salty, cold water that sinks to the bottom and spreads \r\nnorthward. Recent studies show that melting of the Antarctic Ice Sheet and changing winds over the Southern \r\nOcean can affect this southern meridional overturning, affecting regional weather."


id: 37a14a55ba78d5191bd1b1c7275663f3
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 6996
content:
"The Gulf Stream, a warm current, is expected to weaken but not cease. This slowdown will affect regional weather \r\nand sea level. "


id: d6f9ea635d7b255d9470c8279c4080f5
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 35126
content:
"Mansour Almazroui (Saudi Arabia), Alessandro Dosio (Italy), William J. Gutowski (United States of \r\nAmerica), Rein Haarsma (The Netherlands), Rafiq Hamdi (Belgium), Bruce Hewitson (South Africa), \r\nWon-Tae Kwon (Republic of Korea), Benjamin L. Lamptey (Niger, Ghana/Ghana), Douglas Maraun \r\n(Austria/Germany), Tannecia S. Stephenson (Jamaica), Izuru Takayabu (Japan), Laurent Terray \r\n(France), Andrew Turner (United Kingdom), Zhiyan Zuo (China)"


id: 3906e17e7d79b471afb1b2a34cf81221
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 7238
content:
"(United  Kingdom/Canada), Jonathan  K.P.  Shonk (United Kingdom), Jana  Sillmann (Norway/\r\nGermany), Didier Swingedouw (France), Bart van den Hurk (The Netherlands), Robert Vautard \r\n(France), Victor Venema (Germany/The Netherlands), Sergio  M. Vicente-Serrano (Spain), Piotr \r\nWolski (South Africa/Poland), Cunde Xiao (China), Jakob Zscheischler (Germany)"


id: 512137fb8835fecd5a343eadb8e9cc16
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 11557
content:
"Review Editors:\r\nGregory M. Flato (Canada), Fredolin Tangang (Malaysia), Muhammad Irfan Tariq (Pakistan)"


id: 7a04a53d8a530245953d92bc84f4539c
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 18664
content:
"Doblas-Reyes, F.J., A.A. Sorensson, M. Almazroui, A. Dosio, W.J. Gutowski, R. Haarsma, R. Hamdi, B. Hewitson, \r\nW.-T. Kwon, B.L. Lamptey, D. Maraun, T.S. Stephenson, I. Takayabu, L. Terray, A. Turner, and Z. Zuo, 2021: Linking \r\nGlobal to Regional Climate Change. In Climate Change 2021: The Physical Science Basis. Contribution of Working \r\nGroup I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. \r\nZhai, A.  Pirani, S.L.  Connors, C. Pean, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. \r\nLonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekci, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, \r\nCambridge, United Kingdom and New York, NY, USA, pp. 1363-1512, doi:10.1017/9781009157896.012."


id: 5a949881d1f3e2f96116175f0e9e905e
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 13194
content:
"The use of multiple sources of observations and tailored \r\ndiagnostics to evaluate climate model performance increases \r\ntrust in future projections of regional climate (high confidence).\r\nThe availability of multiple observational records, including reanalyses, \r\nthat are fit for evaluating the phenomena of interest and account for \r\nobservational uncertainty, are fundamental for both understanding \r\npast regional climate change and assessing climate model \r\nperformance at regional scales (high confidence). Employing tailored, \r\nprocess-oriented and potentially multivariate diagnostics to evaluate \r\nwhether a  climate model realistically simulates relevant aspects of \r\npresent-day regional climate increases trust in future projections \r\nof these aspects (high confidence). {10.2.2, 10.3.3}"


id: 664bd7236d5c7c8cd3d941fc62e18651
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 15262
content:
"Currently, scarcity and reduced availability of adequate \r\nobservations increase the uncertainty of long-term \r\ntemperature and precipitation estimates (virtually certain).\r\nPrecipitation measurements in mountainous areas, especially of solid \r\nprecipitation, are strongly affected by gauge location and setup (very \r\nhigh confidence). Over data-scarce regions or over complex orography, \r\ngridded temperature and precipitation products are strongly affected \r\nby interpolation methods. Lack of access to the raw station data used \r\nto create gridded products compromises the trustworthiness of these \r\nproducts since the influence of the gridding process on the product \r\ncannot be assessed. The use of statistical homogenization methods \r\nreduces uncertainties related to long-term warming estimates at regional \r\nscales (virtually certain). {10.2.2, 10.6.2, 10.6.3, 10.6.4, Box 10.3} "


id: d8454a4239e8df820e00f8c0a14dd54e
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 35353
content:
"Chapter  10 is the first of four chapters that assess regional-scale \r\ninformation in this Report. The region-by-region assessment \r\nof past and future changes in extremes (Chapter  11), climatic \r\nimpact-drivers (Chapter  12) and mean climate (Atlas) relies on \r\nthe sources and methodologies used for constructing regional \r\nclimate change information assessed in Chapter  10. Building on \r\nthe assessment of observations and modelling tools of Chapter 10, \r\nChapter  11  assesses  the observation and modelling of extremes. \r\nChapter  10 assesses methodologies to attribute multi-decadal \r\nregional trends to the interplay between external forcing and internal \r\nvariability, while Chapter  11 assesses the attribution of extreme \r\nevents. The assessment of climate services in Chapter 12 builds on \r\nthe assessment of distillation of regional climate information from \r\nmultiple lines of evidence in Chapter 10."


id: ed86687d237b122f646a9503be1f1a9e
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 39148
content:
"Regional reanalyses provide surrogates of observed climate \r\nvariables that are highly relevant in areas with scarce surface \r\nobservations. Regional reanalyses represent the distributions of \r\nprecipitation, surface air temperature, and surface wind, including the \r\nfrequency of extremes, better than global reanalyses (high confidence). \r\nHowever, their usefulness is limited by their short length, the typical \r\nregional model errors, and the relatively simple data assimilation \r\nalgorithms. {Section 10.2.1} "


id: f148952e420f510e792e711295190d7b
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 39834
content:
"Distilling regional climate information from multiple lines of \r\nevidence and taking the user context into account will increase \r\nthe fitness, usefulness and relevance for decision-making \r\nand enhances the trust users will have in applying it \r\n(high confidence). This distillation process can draw upon multiple \r\nobservational datasets, ensembles of different model types, process \r\nunderstanding, expert judgement and indigenous knowledge. \r\nImportant elements of distillation include attribution studies, the \r\ncharacterization of possible outcomes associated with internal \r\nvariability and a comprehensive assessment of observational, model \r\nand forcing uncertainties and possible contradictions using different \r\nanalysis methods. Taking the values of the relevant actors into \r\naccount when co-producing climate information, and translating this \r\ninformation into the broader user context, improves the usefulness \r\nand uptake of this information (high confidence). {10.5}  "


id: 82f396eaf88a2f3629069005ebfc99f6
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 20168
content:
"Statistical downscaling, bias adjustment and weather \r\ngenerators are useful approaches for improving the \r\nrepresentation of regional climate from dynamical climate \r\nmodels. Statistical downscaling methods with carefully chosen \r\npredictors and an appropriate model structure for a given application \r\nrealistically represent many statistical aspects of present-day daily \r\ntemperature and precipitation (high confidence). Bias adjustment \r\nhas proven beneficial as an interface between climate model \r\nprojections and impact modelling in many different contexts \r\n(high confidence). Weather generators realistically simulate many \r\nstatistical characteristics of present-day daily temperature and \r\nprecipitation, such as extreme temperatures and wet- and dry-day \r\ntransition probabilities (high confidence). {10.3.3} "


id: 502fd1f2ff3c0a76ab165f910dad67ee
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 11396
content:
"Human influence has been a  major driver of regional mean \r\ntemperature change since 1950 in many sub-continental \r\nregions of the world (virtually certain). Regional-scale detection \r\nand attribution studies as well as observed emergence analysis \r\nprovide robust evidence supporting the dominant contribution of \r\nhuman influence to regional temperature changes over multi-decadal \r\nperiods. {10.4.1, 10.4.3}"


id: ae6c9343658c0c49e021bc7ff53fb279
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 27918
content:
"Various mechanisms operating at different time scales can \r\nmodify the amplitude of the regional-scale response of \r\ntemperature, and both the amplitude and sign of the response \r\nof precipitation, to human influence (high confidence). These \r\nmechanisms include non-linear temperature, precipitation and soil \r\nmoisture feedbacks, slow and fast responses of sea surface temperature \r\npatterns and atmospheric circulation changes to increasing greenhouse \r\ngases. {10.4.3}"


id: 5f7a71fb36a1b2eb2775b1a4addd974
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 14106
content:
"Confidence in the distilled regional climate information is \r\nenhanced when there is agreement across multiple lines of \r\nevidence. For example, the apparent contradiction between the \r\nobserved decrease in Indian monsoon rainfall over the second half of \r\nthe 20th century and the projected long-term increase is explained by \r\nattribution of the trends to different forcings, with aerosols dominating \r\nrecently and greenhouse gases in the future (high confidence). For \r\nthe Mediterranean region, the agreement between different lines of \r\nevidence, such as observations, projections by regional and global \r\nmodels, and understanding of the underlying mechanisms, provides \r\nhigh confidence in summer warming that exceeds the global average. \r\n{10.5.3, 10.6, 10.6.3, 10.6.4, Cross-Chapter Box 10.3}"


id: 5d579801655a8d67f2edcbbb1c27575e
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 13711
content:
"Many types of urban parametrizations simulate radiation and \r\nenergy exchanges in a  realistic way (very high confidence).\r\nFor urban climate studies focusing on the interplay between the \r\nurban heat island and regional climate change, a simple single-layer \r\nparametrization is fit for purpose (medium confidence). New networks \r\nof monitoring stations in urban areas provide key information to \r\nenhance the understanding of urban microclimates and improve \r\nurban parametrizations. {Box 10.3}"


id: 6e4adc8fa4ba94884127470bbd67d436
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 16673
content:
"The outcome of distilling regional climate information can \r\nbe limited by inconsistent or contradictory information. Initial \r\nobservational analyses of the Cape Town drying showed a  strong, \r\npost-1979 association between increasing greenhouse gases, changes \r\nin a key mode of variability (the Southern Annular Mode) and drought \r\nin the Cape Town region. However, not all global models show this \r\nassociation, and subsequent analysis extending farther back in time, \r\nwhen human influence was weaker, showed no strong association in \r\nobservations between the Southern Annular Mode and Cape Town \r\ndrought. Thus, despite the consistency among global-model future \r\nprojections, there is medium confidence in a projected future drier \r\nclimate for Cape Town. Likewise, the distillation process results in \r\nlow confidence in the influence of Arctic warming on mid-latitude \r\nclimate because of contrasting lines of evidence. {10.5.3, 10.6.2, \r\nCross-Chapter Box 10.1, Cross-Chapter Box 10.3}"


id: f80ce22116836ff4b63ef54fc52f424c
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 41012
content:
"The difference in observed warming trends between cities and \r\ntheir surroundings can partly be attributed to urbanization \r\n(very high confidence). Annual mean daily minimum temperature \r\nis more affected by urbanization than annual mean daily maximum \r\ntemperature (very high confidence). The global annual mean surface \r\nair temperature response to urbanization is, however, negligible (very \r\nhigh confidence). {Box 10.3}"


id: f8d87cf522de89238afeeccf28f70d7a
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 41143
content:
"Future urbanization will amplify the projected air temperature \r\nchange in cities regardless of the characteristics of the \r\nbackground climate, resulting in a warming signal on minimum \r\ntemperatures that could be as large as the global warming \r\nsignal (very high confidence). A  large effect is expected from \r\nthe combination of future urban development and more frequent \r\noccurrence of extreme climatic events, such as heatwaves (very high \r\nconfidence). {Box 10.3}"


id: 7f8ce4179ae3c70441fd4a4059cf7329
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 19585
content:
"The process of distilling regional climate information from \r\nmultiple lines of evidence can vary substantially from one \r\ncase to another. Although methodologies for distillation have been \r\nestablished, in practice the process is conditioned by the sources \r\navailable, the actors involved and the context, which depend heavily \r\non the regions considered, and is framed by the question being \r\naddressed. To make the most appropriate decisions and responses to \r\nchanging climate, it is necessary to consider all physically plausible \r\noutcomes from multiple lines of evidence, especially in the case \r\nwhen they are contrasting. {10.5, 10.6, Cross-Chapter Box  10.1, \r\nCross-Chapter Box 10.3} "


id: 362197c6eb1d7350569a5f3802fb44a4
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 6773
content:
"regional climate information that is useful and relevant to the decision \r\nscale (Box 10.1). To respond to this need, the AR6 WGI Report includes \r\nfour regional chapters of which this is the first. Chapter 10 assesses the \r\nsources and methodologies used by the Chapters 11, 12 and Atlas to \r\nconstruct regional information. Chapter 10 builds on the assessment \r\nof methodologies considered to construct global climate change \r\ninformation in Chapters 2 to 4 and on the processes assessed in \r\nChapters 5 to 9. Additionally, this chapter assesses the methodologies "


id: 172895b0653d63b9332dc07a81e5f49b
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 1290
content:
"This chapter assesses the foundations for the distillation of regional \r\nclimate change information from multiple lines of evidence. The \r\nAR5, SR1.5 and SRCCL reports underlined the relevance of assessing "


id: ad4a8264cf02eecb0274a64a4e4e408c
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
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index: climate_gpt_v2
vector_id: 27732
content:
"Figure 10.1 | Diagram of the processes leading to the construction of regional climate information (blue) and user-relevant regional climate information \r\nbrown). The chapter sections and the other chapters of the Report involved in each step are indicated in rectangles. WGII stands for Working Group II. Literature refers to \r\nscientific and technical literature, and climate experts refers to climate scientists, practitioners and local communities, as defined in Section 10.5."


id: e0b4504b804ef9836cabf41d40ec213d
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 36791
content:
"for the co-production of regional climate information, the role of the \r\ndifferent actors involved in the process and the relevance of the user \r\ncontext and values."


id: 80d96ab6cc6002d0b1349e5510073268
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 19803
content:
"a variety of methodologies and approaches have been developed to \r\nconstruct regional climate change information. The sources include \r\nglobal and regional climate model simulations, statistical downscaling \r\nand bias adjustment methods. A commonly used source is long-term \r\n(end-of-century) model projections of regional climate change, as well \r\nas near-term (next 10 years) climate predictions (Kushnir et al., 2019; \r\nRossler et al., 2019a). Regional observations, with their associated \r\nchallenges, are a  key source for the regional climate information \r\nconstruction process (Q. Li et  al., 2020). High-quality observations \r\nthat enable monitoring of the regional aspects of climate are used to \r\nadjust inherent model biases and are the basis for assessing model \r\nperformance. Process understanding and attribution of observed \r\nchanges to large- and regional-scale anthropogenic and natural \r\ndrivers and forcings are also important sources."


id: 6a47952a18bcc2d2698675cb16b6016d
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 15979
content:
"All these sources are used, when available, to distil regional climate \r\ninformation from multiple lines of evidence (Figure 10.1). The resulting \r\nclimate information can then be integrated, following a co-production \r\nprocess involving both the user and the producer, into a user context \r\nthat often is already taken into account when constructing the regional \r\nclimate information. In fact, the distillation process leading to the \r\nclimate information can consider the specific context of the question at \r\nstake, the values of both the user and the producer, and the challenge \r\nof communicating across different communities (Section 10.5)."


id: ca91ee8c3c4bccd334a7edd34bb57a1f
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index: climate_gpt_v2
vector_id: 32982
content:
"The chapter (Figure 10.2) starts with an introduction of the concepts \r\nused in the distillation of regional climate information (Section 10.1). \r\nSection  10.2 addresses the aspects associated with the access to \r\nand use of observations, while different modelling approaches "


id: 6c954760a37ff9fb85d59d508960e7e6
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index: climate_gpt_v2
vector_id: 16391
content:
"Given the many types of regional climates, the broad range of spatial \r\nand temporal scales (Section 10.1.2), and the diversity of user needs, "


id: c464a1315b3ba86bc995083f34d6d535
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 31850
content:
"discusses concepts and definitions of what can be considered \r\na region, the relevant temporal scales and region-specific aspects of \r\nthe baselines used."


id: 6c54fe46ddd8ebc00ab38ab9d164ad69
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 16351
content:
"are introduced and assessed in Section  10.3. Section  10.3 also \r\naddresses the performance of models in simulating relevant climate \r\ncharacteristics as needed to estimate the credibility of future \r\nprojections. Section 10.4 assesses the interplay between anthropogenic \r\ncauses and internal variability at regional scales, and its relevance for \r\nthe attribution of regional climate changes and the emergence of \r\nregional climate change signals. Section 10.5 tackles the issue of how \r\nregional climate information is distilled from different sources taking \r\ninto account the context and the  values of both the producer and \r\nthe user. Section 10.6 illustrates the distillation approach using three \r\ncomprehensive examples. Finally, Section 10.7 lists some limitations to \r\nthe assessment of regional climate information."


id: 239a2b256ffb7c07079ab5b2a8dbd814
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 3463
content:
"Large-scale climate and the associated phenomena have been defined \r\nin Chapter 2 (e.g., Cross-Chapter Box 2.2) as ranging from global and \r\nhemispheric, to ocean basin and continental scales. The definition of \r\nthe regional scale is case specific in the AR6 WGI Report. Section 1.4.5\r\nprovides definitions of the different regional types adopted by the \r\ndifferent chapters. In this chapter, regional scales are defined as \r\nranging from the size of sub-continental areas (e.g., the Mediterranean \r\nbasin) to local scales (e.g.,  coastlines, mountain ranges and cities) \r\nwithout prescribing any formal regional boundaries. These spatial \r\nlength scales range from a few thousand down to a few kilometres \r\nand the relevant driving modes and processes at regional scales are \r\nsummarized in Figure 10.3. In contrast to Chapters 11, 12 and Atlas, \r\nwhich make a  region-by-region assessment of climate change, this \r\nchapter does not necessarily restrict itself to the use of the AR6 WGI "


id: 735a935ba842bed7d8be4deccc2ecf58
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content_type: text
index: climate_gpt_v2
vector_id: 17545
content:
"The global coupled atmosphere-ocean-land-cryosphere system, \r\nincluding its feedbacks, shows variability over a  wide spectrum \r\nof spatial and temporal scales (Hurrell et  al., 2009). This section "


id: 7bcecf78494b1a59203e9d16317be16
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index: climate_gpt_v2
vector_id: 18992
content:
"Reference Regions (Section 1.4.5 and Atlas.1.3). Different regional \r\ndefinitions have been used in sections 10.4 and 10.6, selected for their \r\nadequacy to illustrate methodological aspects (e.g., for the attribution \r\nof long-term regional trends, regions that display such trends have \r\nbeen selected). Typological regions (Section 1.4.5 and Atlas.1.3) are \r\nused in Box 10.3 and Cross-Chapter Box 10.4."


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vector_id: 27039
content:
"The concept of a unified and seamless framework for weather and \r\nclimate prediction (A. Brown et  al., 2012; Hoskins, 2013) provides \r\nthe context for understanding and simulating regional climate across \r\nmultiple spatial and temporal scales. This concept is embodied in the \r\nsubseasonal-to-seasonal (Vitart et  al., 2017) and the seasonal-to\u0002multi-annual (Smith et al., 2020) prediction activities that generate \r\nregional climate information across temporal scales. The seamless \r\nframework benefits from the convergence of methods traditionally \r\nused in weather forecasting and climate projections, in particular \r\nthe role of the initialization in climate models and the strategies for the \r\nevaluation of physical processes relevant at different temporal scales."


id: 47a04d47b35ee55f8119e203bfc6073c
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 9823
content:
"The relatively short observational record (Section 10.2) is a primary \r\nchallenge to estimate the forced signal and to isolate low-frequency, \r\nmulti-decadal and longer-term internal variability (Frankcombe \r\net al., 2015; Overland et al., 2016; Bathiany et al., 2018). Because \r\nonly one realization of the actual climate exists, it is non-trivial to \r\nextract estimates of internal and forced variability from the available \r\ndata (Frankcombe et al., 2015). As an alternative, approaches that \r\nuse large observational ensembles can be applied (Section  10.4; \r\nMcKinnon and Deser, 2018)."


id: ef5c206f2e8ab3857c2d469b58c420dd
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 39481
content:
"There is a  close relationship between spatial and temporal scales \r\n(Figure 10.3). For example, an individual convective storm may exhibit \r\nscales of variability ranging from metres and seconds to kilometres \r\nand hours, while for El Nino-Southern Oscillation (ENSO) the scales \r\nof variability are regional to hemispheric in extent and multi-year \r\nin length. These scales interact and the interactions are represented \r\nin climate models, although the ability of current models to simulate \r\nregional phenomena and even large-scale climate drivers still leaves \r\nroom for improvement (Section 10.3) and limits their capability to \r\nrepresent the interactions across spatial and temporal scales."


id: 79290c223ad5bd5652e6bc31f289383e
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 18504
content:
"It is important to note that in this chapter and subsequent regional \r\nchapters, including the Interactive Atlas, the baselines and reference \r\nperiods used for climate change estimates from regional models may \r\nvary from those used in Chapters 1 to 9. In these chapters three main \r\ntime baselines are defined for the past, for example, pre-industrial \r\n(before 1750), early industrial (1850-1900) and recent (1995-2014), \r\nwhile the future reference periods are 2021-2040 (near term), \r\n2041-2060 (mid-term) and 2081-2100 (long term) (Section  1.4.1 \r\nand Cross-Chapter Box  1.2). Regional climate simulations used in \r\nthe recent literature have been performed with different baselines. \r\nThe differences are often due to the availability of the boundary \r\nconditions from global simulations, leading to periods chosen \r\nfor those simulations like 1950-2005, in line with the CMIP5 \r\nhistorical simulations followed by projections from 2005 onwards "


id: 7cdfa86e5bab033724f562561caab466
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 19164
content:
"refers to them as sources in specific examples where regional climate \r\ninformation is built. Section  8.2 offers a  companion discussion \r\nfocussing on changes in the water cycle. An example of how changes \r\nin one region could act as a source for changes in a neighbouring one \r\nis assessed in the Cross-Chapter Box 10.1 for the linkages between \r\npolar and mid-latitude regions, an interaction that has led to \r\nsubstantial recent research. This section also introduces the sources \r\nof uncertainty in model-derived regional climate information and \r\nhow the quantification of the uncertainties influences the confidence \r\nof the regional climate information."


id: dd6e46a3fe52983079a5bf0f25768d76
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 36224
content:
"radiative effects of up to two orders of magnitude larger than the global \r\naverage (B. Li et al., 2016; K. Li et al., 2016; Mallet et al., 2016). Black \r\ncarbon, instead, is known to absorb solar radiation, leading to regional \r\natmospheric warming patterns due to its inhomogeneous spatial \r\ndistribution (Gustafsson and Ramanathan, 2016). Patterns of forcing \r\ngenerally follow those of aerosol burden. However, temperature and \r\nprecipitation responses are both local and remote (Z. Li et al., 2016; \r\nKasoar et al., 2018; L. Liu et al., 2018; Samset et al., 2018; Thornhill \r\net al., 2018; Westervelt et al., 2018). For instance, changes in aerosol \r\nconcentrations in the NH have been reported to modulate monsoon \r\nprecipitation in West Africa and the Sahel (Undorf et  al., 2018; \r\nSection 10.4.2.1) and in Asia (H. Zhang et al., 2018; Section 10.6.3)."


id: 2bf7d0c708a6866b228e43a73448f549
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 4986
content:
"There are important differences in the processes affected by \r\ngreenhouse gases (GHGs) over land and ocean. Notably, this leads \r\nto preferential warming of the land regions, which are themselves \r\nskewed towards the Northern Hemisphere (NH)."


id: b412f708e33cd0b58ed306e2112ffe46
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 28924
content:
"An updated assessment of past changes in stratospheric ozone can \r\nbe found in Section 2.2.5.2. The AR6 assesses that both GHG and \r\nstratospheric ozone depletion have contributed to the expansion of \r\nthe zonal mean Hadley cell in the Southern Hemisphere (SH) for the \r\nperiod 1981-2000 with medium confidence (Section 3.3.3; Garfinkel \r\net al., 2015; Waugh et al., 2015; Grise et al., 2019). There is medium \r\nconfidence that stratospheric ozone depletion contributed to the \r\nstrengthening trend of the summer Southern Annular Mode (SAM) \r\nfor the period 1970-1990, but this influence has been weaker since \r\n2000 (Section  3.7.2). The poleward shift of the SH westerlies has \r\nalso been explained by stratospheric ozone depletion (Solman and \r\nOrlanski, 2016). Section 10.4 assesses its role in the multi-decadal \r\nincrease of rainfall in south-eastern South America and Section 10.6.2 \r\ndoes so for the occurrence of the Cape Town drought."


id: c40efb99da5438d3673c6259380d462d
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 31774
content:
"Both natural and anthropogenic aerosols are often emitted at \r\na regional scale, have a short atmospheric lifetime (from a few hours \r\nto several days; Section  6.1), are dispersed regionally and affect \r\nclimate at a regional scale through radiative cooling/heating and cloud \r\nmicrophysical effects (Chapter 8; Rotstayn et al., 2015; Sherwood et al., \r\n2015). The majority of aerosols scatter solar radiation, but with strong \r\nregional variations (Shindell and Faluvegi, 2009) that lead to regional "


id: fc66737b905d614580a8568adb6603bc
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 41770
content:
"Reconstructed modes of climate variability based on proxy records \r\nallow evaluation of the influence on those modes (Zanchettin et al., \r\n2013; Ortega et al., 2015; Sjolte et al., 2018; Michel et al., 2020)."


id: 761681e1f7db446f8d6797e2907f1ac5
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 18003
content:
"are assessed in Chapter 4. The specificities of their regional influence \r\nare briefly discussed here. More details of their typical temporal \r\nscales and regional influences can be found in Annex IV."


id: 1531669d9759a6d3d8ca6c93f926cf0f
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 942
content:
"Anthropogenic aerosols play a key role in climate change (Chapter 6). \r\nAlthough the global mean optical depth caused by anthropogenic \r\naerosols did not change from 1975 to 2005 (Chapter 6), the regional \r\npattern changed dramatically between Europe and eastern Asia \r\n(Fiedler et  al., 2017, 2019; Stevens et  al., 2017). Large regional \r\ndifferences in present-day aerosol forcing exist with consequences \r\nfor regional temperature, hydrological cycle and modes of variability \r\n(Chapter 8, Section 10.6). Examples of regions with a notable role \r\nfor anthropogenic aerosol forcing are the Indian monsoon region \r\n(Section  10.6.3) and the Mediterranean basin (Section  10.6.4). \r\nAnthropogenic aerosols are also very relevant in many urban areas \r\n(Box 10.3; Gao et al., 2016; Kajino et al., 2017)."


id: 98af3cd9df3c755dbcb9a42359be039e
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 24079
content:
"Atmospheric modes of variability may have seasonally-dependent \r\nregional effects like the North Atlantic Oscillation (NAO) in European \r\nwinter (Tsanis and Tapoglou, 2019) and summer (Blade et al., 2012; \r\nDong et al., 2013). Even though these modes are internal to the climate \r\nsystem, their variability can be affected by anthropogenic forcings. \r\nFor instance, the SAM (Hendon et al., 2014) is both internally driven \r\n(Smith and Polvani, 2017), but also affected by recent stratospheric \r\nozone changes (Bandoro et al., 2014). The teleconnections between \r\nthese modes of variability and surface weather often exhibit \r\nconsiderable non-stationarity (Hertig et al., 2015)."


id: 6d15df3d757004be6a24169284c6cff3
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 16469
content:
"The SRCCL assessed that nearly three-quarters of the land surface is \r\nunder some form of land use, particularly in agriculture and forest \r\nmanagement (Jia et  al., 2019). The effects of land management \r\non climate are much less studied than land cover effects although \r\nnet cropland has changed little over the past 50 years, while \r\nland management has continuously changed (Jia et  al., 2019). \r\nSection 7.3.4.1 assesses the global influence of both land use and \r\nirrigation on the effective radiative forcings. Land cover changes \r\nand land management can influence climate locally, such as the \r\nurban heat island and non-locally as in the case of increased rainfall \r\ndownwind of a  city (Jia et  al., 2019; Box  10.3) or the monsoon \r\ncirculation affected by irrigation (Section  10.6.3). The influence \r\nof land cover changes and land management on regional climate \r\nextremes is assessed in Section 11.1.6."


id: b6f22d3dc2b5f099363328b2565cca8a
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 29395
content:
"It is very likely that the global land surface air temperature \r\nresponse to urbanization is negligible (Section  2.3.1.1.3). However, \r\nthere is evidence that urbanization may regionally amplify the air \r\ntemperature response to climate change in different climatic zones \r\n(Mahmood et  al., 2014), either under present (Doan et  al., 2016; \r\nKaplan et  al.,  2017;  X.  Li et  al., 2018) or future climate conditions \r\n(Argueso et al., 2014; Kim et al., 2016; Kusaka et al., 2016; Grossman\u0002Clarke et al., 2017; Krayenhoff et al., 2018). For instance, in northern \r\nBelgium, Berckmans et al. (2019) found that including urbanization \r\nscenarios for the near future (up to 2035) have a  comparable \r\ninfluence on minimum temperature (increasing it by 0.6degC) to that of \r\nthe GHG-induced warming under RCP8.5."


id: ee834271bee0fb5b8bedaeb8e0fd0095
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 39339
content:
"Uncertainty and confidence are treated in the same way in regional \r\nclimate change information as in larger-scale (continental and \r\nglobal) climate problems (Chapter 1 and Section 10.3.4). The degree \r\nof confidence in climate simulations and in the resulting climate \r\ninformation typically depends on the identification of the role of the \r\nuncertainties (Section 10.3.4). Since the direct verification of simulations \r\nof future climate changes is not possible, model performance and \r\nreliable (i.e., trustworthy) uncertainty estimates need to be assessed \r\nindirectly through process understanding and a systematic comparison \r\nwith observations of past and current climate (Section 10.3.3; Knutti \r\net al., 2010; Eyring et al., 2019). The observational uncertainty, which is \r\nparticularly large at regional scales, also has to be taken into account \r\n(Section 10.2). These uncertainty estimates are then propagated in the \r\ndistillation process to generate climate information."


id: 4d53b4efa9e45dbca97a6f1b3a6a8286
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 10884
content:
"Internal climate variability on seasonal to multi-decadal temporal \r\nscales is substantial at regional scales. This variability arises from \r\ninternal modes of atmospheric and oceanic variability, intrinsically \r\ncoupled climate modes, and may additionally be driven by processes \r\nother than those originating the modes. It also interacts with the \r\nresponse of the climate system to external forcing. The teleconnections \r\nassociated with the modes are useful to understand the relationship \r\nbetween large and regional scales (Annex IV: Modes of Variability). \r\nA description of various large-scale modes of variability can be found \r\nin Chapters 2, 3 and 8, and in Annex IV, while their future projections "


id: a704ca1d9d9c2f87162aabf0aabbdba3
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 26590
content:
"Uncertainties in model-based future regional climate information arise \r\nfrom different sources and are introduced at various stages in the \r\nprocess (Lehner et al., 2020): (i) forcing uncertainties associated with "


id: 24a740ec7ed37efb606217579109f8e9
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 3639
content:
"The information may be provided in the form of summarized raw \r\ndata, a set of user-oriented indicators, a set of figures and maps with \r\neither a brief description, in the form of a storyline, or formulated as \r\nrich and complex climate adaptation plans. The information typically \r\nincludes a description of the sources and assumptions, estimates of \r\nthe associated uncertainty and its sources, and guidance to prevent \r\npossible misunderstandings in its communication."


id: c3bb74fb0fe0cc00b96e55083488db1d
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
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index: climate_gpt_v2
vector_id: 31710
content:
"the future scenario or pathway that is assumed; (ii) internal variability; \r\nand (iii) uncertainties related to imperfections in climate models, also \r\nreferred to as structural or model uncertainty. However, the relative \r\nrole of each of these sources of uncertainty differs between the global \r\nand the regional scales as well as between variables and also between \r\ndifferent regions (Lehner et al., 2020). One way to address the internal \r\nvariability and model uncertainties is to consider results from both \r\nmultiple models and multiple realizations of the same model (Eyring \r\net al., 2016a; Lehner et al., 2020; Diaz et al., 2021). These models are \r\nat times also combined with different weights that are a function of \r\ntheir performance and independence to increase the confidence of the \r\nmulti-model ensemble (Abramowitz et al., 2019; Brunner et al., 2019)."


id: 3c309004617f6f5f3f85176d0c7b194a
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 7777
content:
"The choices made for the distillation have typically been part of \r\na linear supply chain, starting from the access to climate data that \r\nare transformed into maps or derived climate data products, and \r\nfinally formulating statements that are communicated and delivered \r\nto a broad range of users (Hewitt et al., 2012; Hewitson et al., 2017). \r\nThis methodology has proven to be valuable in many cases, but it \r\nis equally fraught with dangers of not communicating important \r\nassumptions, not estimating the impact of relevant uncertainties, \r\nand possibly causing misunderstandings in the handover to the \r\nuser community. This has led to the emergence of new pathways \r\nto generate user-oriented climate information, many in the context \r\nof emerging climate services (Buontempo et al., 2018; Hewitt et al., \r\n2020), which are assessed in Section 10.5 and in Chapter 12."


id: 9c27d677e1d175a0acdb040a272752ef
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
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index: climate_gpt_v2
vector_id: 24730
content:
"Other elements that play a  role are the inconsistency between \r\nthe global and regional models in dynamical downscaling or the \r\nobservational and methodological uncertainty in bias-adjustment \r\nmethods (Sorland et al., 2018). These elements, in addition to those \r\ntypical of the uncertainty in global and large-scale phenomena \r\n(Chapters 1-9), affect the overall confidence of regional climate \r\ninformation. This complex scene with different sources of uncertainty \r\nmakes the collection of results available from multi-model, \r\nmulti-member simulations most useful when synthesized through \r\na distillation process (Section 10.5.3)."


id: aca3a8d74626ce99dd495238a30a7b62
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 27618
content:
"This chapter is part of a  cluster devoted to regional climate \r\n(Chapters 10, 11, 12 and Atlas). It introduces many of the aspects \r\nrelevant to the generation of regional climate information that are \r\ndealt with in detail elsewhere. Figure  10.4 summarizes how these \r\nchapters relate to one another and to the rest of the report."


id: 63bb59ed1ee6494dc8f9dde4b1ee751c
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 14834
content:
"The approaches adopted in the distillation of regional climate \r\ninformation are diverse and range from the simple delivery of data \r\nas information to co-production with the user using as many lines \r\nof evidence as possible (Lourenco et  al., 2016). The availability \r\nand selection of the sources and the approach followed has \r\nimplications for the usefulness of the information. For instance, it is \r\nwell-established that it is invalid to take a time series from a gridcell \r\nof a  model simulation as equivalent to an observational estimate \r\nof a  point within the cell, due to the lack of representativeness \r\n(Section  10.3), and consequently the information building solely \r\non this type of data source is of limited use. Relevant decisions are \r\nmade during the distillation process, such as what method is most \r\nsuitable to a specific user context and the question being addressed. "


id: 21cb63ba6b1d74e270244facd221d571
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 3160
content:
"Other chapters also include a strong regional component and provide \r\ncontext for the assessment of regional climate. Chapter 1 introduces \r\nthe different types of climatic regions used in the AR6 WGI Report "


id: 41d0d82e744bb61b95f898a4a7d1ad95
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 8775
content:
"information for the sub-continental scale. Chapter  5 assesses the \r\nknowledge about the carbon and biogoechemical cycles, whose \r\nfluxes and responses show variability that is strongly regional in \r\nnature. Chapter  6 assesses the regional evolution of short-lived \r\nclimate forcers as well as their influence on regional climate and air \r\nquality. Chapter 8 assesses observed and projected changes in the \r\nvariability of the regional water cycle, including monsoons, while \r\nchanges of the regional oceans, changes in cryosphere and regional \r\nsea level change are assessed in Chapter 9."


id: 44e1018e8b0c5cfad8975612d9f0c53
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 9349
content:
"and the main types of climatic models. Chapter  2 describes the \r\nrecent and current state of the climate from observations, most of \r\nwhich are key for the production of regional information. Chapter 3 \r\nassesses  human influence on the climate system and Chapter  4 \r\nassesses climate change projections, with a  global focus. These \r\nthree chapters include phenomena that are important for shaping \r\nregional climate such as general circulation, jets, storm tracks, \r\nblocking and modes of variability. At the same time, the visualization \r\nof information in global maps in these chapters provides valuable "


id: ed09d83958b6fd455b20c8b19ce10bd8
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
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index: climate_gpt_v2
vector_id: 39071
content:
"This box summarizes the information on linking global and regional climate change information in the Fifth Assessment Report (AR5) \r\nand the three Special Reports of the IPCC Sixth Assessment Cycle. This information frames the treatment of the production of regional \r\nclimate information in previous reports and identifies some of the gaps that the AR6 WGI Report needs to address."


id: 2343af4ccf692125915222cc91621079
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 3408
content:
"In WGI Chapter 9 (Flato et al., 2014), regional downscaling methods were addressed as tools to provide climate information at the \r\nscales needed for many climate impact studies. The assessment found high confidence that downscaling adds value both in regions with \r\nhighly variable topography and for various small-scale phenomena. Regional models necessarily inherit biases from the global models \r\nused to provide boundary conditions. Furthermore, the ability of AR5 to systematically evaluate regional climate models (RCMs), and \r\nstatistical downscaling schemes, were hampered because coordinated intercomparison studies were still emerging. However, several \r\nstudies demonstrated that added value arises from higher resolution in regions where stationary small-scale features like topography \r\nand complex coastlines are present, and from improved representation of small-scale processes like convective precipitation."


id: 1574e33cd305ebb504d7ce7fb8312dd5
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 991
content:
"WGI Chapter 14 (Christensen et al., 2013) stressed that credibility in regional climate change projections increases when key drivers \r\nof the change are known to be well-simulated and well-projected by climate models."


id: e897622e49c1bd702e8534a9dd1abf6b
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 38260
content:
"Chapter 21 noted that the production of downscaled datasets (by both dynamical and statistical methods) remains weakly coordinated, \r\nand that results indicate that high-resolution downscaled reconstructions of the current climate can have significant errors. Key in this \r\nwas that the increase in downscaled datasets has not narrowed the uncertainty range, and that integrating these data with historical \r\nchange and process-based understanding remains an important challenge."


id: 5ed14ec5261b1cb16a1862410b3070ca
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 13976
content:
"The chapter identified the common perception that higher resolution (i.e., more spatial detail) equates to more usable and robust \r\ninformation, which is not necessarily true. Instead, it is through the integration of multiple sources of information that robust understanding \r\nof change is developed."


id: 3ae6c8ed20c9f2b4f1f924203aae3a7d
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
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index: climate_gpt_v2
vector_id: 7559
content:
"WGII Chapter 21 highlighted that the different contexts of an impact study are defining features for how climate risk is perceived. \r\nPerspectives were characterized as top-down (physical vulnerability) and bottom-up perspectives (social vulnerability). The top-down \r\nperspective uses climate change impacts as the starting point of how people and/or ecosystems are vulnerable to climate change, and \r\ncommonly applies global-scale scenario information or refines this to the region of interest through downscaling procedures. Conversely, \r\nin the 'bottom-up' approach the development context is the starting point, focusing on local scales, and layers climate change on \r\ntop of this. An impact focus tends to look to the future to see how to adjust to expected changes, whereas a vulnerability-focused \r\napproach is centred on addressing the drivers of current vulnerability."


id: 17479852591c7a83e1b93f33190d1f14
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updated_at: 2023-04-19 13:21:39
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vector_id: 1317
content:
"The SRCCL (Jia et al., 2019) assessed that there is robust evidence and high agreement that land cover and land use or management \r\nexert significant influence on atmospheric states (e.g., temperature, rainfall, wind intensity) and phenomena (e.g., monsoons), at various \r\nspatial and temporal scales, through their biophysical influences on climate. There is robust evidence that dry soil moisture anomalies \r\nfavour summer heatwaves. Part of the projected increase in heatwaves and droughts can be attributed to soil moisture feedbacks \r\nin regions where evapotranspiration is limited by moisture availability (medium confidence). Vegetation changes can also amplify \r\nor dampen extreme events through changes in albedo and evapotranspiration, which will influence future trends in extreme events \r\n(medium confidence)."


id: 279d6efcabde8c4e831c2a934db2d948
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 4148
content:
"The influence of different changes in land use (e.g., afforestation, urbanization), on the local climate depends on the background \r\nclimate (robust evidence, high agreement). There is high confidence that regional climate change can be dampened or enhanced by \r\nchanges in local land cover and land use, with sign and magnitude depending on region and season."


id: d0f742adc7ffe88dc8d2f7e72a3dd93
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 34110
content:
"Water management and irrigation were generally not accounted for by CMIP5 global models available at the time of SRCCL. \r\nAdditional water can modify regional energy and moisture balance particularly in areas with highly productive agricultural crops \r\nwith high rate of evapotranspiration. Urbanization increases the risks associated with extreme events (high confidence). Urbanization \r\nsuppresses evaporative cooling and amplifies heatwave intensity (high confidence) with a strong influence on minimum temperatures \r\n(high confidence)."


id: b921efc0935c0fb1879a0c2ac52db3e8
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 29773
content:
"Special Report on the Ocean and Cryosphere in a Changing Climate (SROCC; IPCC, 2019b)\r\nThe SROCC (IPCC, 2019b) stated that observations and models for assessing changes in the ocean and the cryosphere have \r\nbeen developed considerably during the past century but observations in some key regions remain under-sampled and were very \r\nshort relative to the time scales of natural variability and anthropogenic changes. Retreat of mountain glaciers and thawing of \r\nmountain permafrost continues and will continue due to significant warming in those regions, where it is likely to exceed global \r\ntemperature increase."


id: f1f82500b6a205ab804fde59198d865
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 39959
content:
"The SROCC assessed that it is virtually certain that Antarctica and Greenland have lost mass over the past decade and observed glacier \r\nmass loss over the last decades is attributable to anthropogenic climate change (high confidence). It is virtually certain that projected \r\nwarming will result in continued loss in Arctic sea ice in summer, but there is low confidence in climate model projections of Antarctic \r\nsea ice change because of model biases and disagreement with observed trends. Knowledge and observations of the polar regions \r\nwere sparse compared to many other regions, due to remoteness and challenges of operating in them."


id: e949aa52e216ac362bb02e7a0288089d
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 38403
content:
"The sensitivity of small islands and coastal areas to increased sea levels differs between emissions scenarios and regionally, \r\nand a consideration of local processes is critical for projections of sea level influences at local scales."


id: 7c361a40108602ab4d107b7887748d61
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 19064
content:
"Special Report on Global Warming of 1.5degC (SR1.5; IPCC, 2018b)\r\nThe SR1.5 (Hoegh-Guldberg et al., 2018) assessed that most land regions were experiencing greater warming than the global average, \r\nwith annual average warming already exceeding 1.5degC in many regions. Over one quarter of the global population live in regions that \r\nhave already experienced more than 1.5degC of warming in at least one season. Land regions will warm more than ocean regions over \r\nthe coming decades (transient climate conditions)."


id: 26295cde9a8590abe4ef4a8166d205b7
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 3882
content:
"Transient climate projections reveal observable differences between 1.5degC and 2degC global warming in terms of mean temperature and \r\nextremes, both at a global scale and for most land regions. Such studies also reveal detectable differences between 1.5degC and 2degC \r\nprecipitation extremes in many land regions. For mean precipitation and various drought measures there is substantially lower risk for \r\nhuman systems and ecosystems in the Mediterranean region at 1.5degC compared to 2degC."


id: 6e3c0d1d7a8b5bab1ef0e6c0e2e5a745
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 16661
content:
"The different pathways to a 1.5degC warmer world may involve a transition through 1.5degC, with both short- and long-term stabilization \r\n(without overshoot), or a temporary rise and fall over decades and centuries (overshoot). The influence of these pathways is small for \r\nsome climate variables at the regional scale (e.g., regional temperature and precipitation extremes) but can be very large for others \r\n(e.g., sea level)."


id: 99cb0f140aaca02b73a1a37a675699ba
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 24287
content:
"Contributors: Francisco J. Doblas-Reyes (Spain), Herve Douville (France), Nathan P. Gillett (Canada), Gerhard Krinner (France/Germany, \r\nFrance), Dirk Notz (Germany), Krishnan Raghavan (India), Alex C. Ruane (United States of America), Sonia I. Seneviratne (Switzerland), \r\nLaurent Terray (France), Cunde Xiao (China)"


id: b22f14c3ea1a6be9d1f39f85016f9f3e
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 28577
content:
"The Arctic has very likely warmed more than twice the global rate over the past 50 years with the greatest increase during the cold \r\nseason (Atlas.11.2). Several mechanisms are responsible for the enhanced lower troposphere warming of the Arctic, including ice \r\nalbedo, lapse rate, Planck and cloud feedbacks (Section 7.4.4.1). The rapid Arctic warming strongly affects the ocean, atmosphere, \r\nand cryosphere in that region (Section 2.3.2.1 and Atlas.11.2). Averaged over the decade 2010-2019, monthly average sea ice area \r\nin August, September and October has been about 25% smaller than during 1979-1988 (high confidence) (Section 9.3.1.1). It is very \r\nlikely that anthropogenic forcings mainly due to greenhouse gas increases have contributed substantially to Arctic sea ice loss since \r\n1979, explaining at least half of the observed long-term decrease in summer sea ice extent (Section 3.4.1.1)."


id: 29b5171fbc4474d7ac27ceb1652907ad
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 4562
content:
"The above proposed hypotheses are based on concepts of geophysical fluid dynamics and surface coupling and can, in principle, \r\nhelp explain the existence of a link between the Arctic changes and the mid-latitudes with the potential to affect many impact \r\nsectors (Barnes and Screen, 2015). However, the validity of some dynamical underlying mechanisms, such as a reduced meridional \r\ntemperature contrast inducing enhanced wave amplitude, has been questioned (Hassanzadeh et al., 2014; Hoskins and Woollings, \r\n2015). On the contrary, the reduced meridional temperature contrast has been related to reduced meridional temperature advection \r\nand thereby reduced winter temperature variability (Collow et al., 2019)."


id: 5dce607bf5c8fa6499b7887a2f52d469
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 13796
content:
"Studies that support the Arctic influence are mostly based on observational relationships between the Arctic temperature or sea ice \r\nextent and mid-latitude anomalies or extremes (Cohen et al., 2012; Francis and Vavrus, 2012, 2015; Budikova et al., 2017). They are "


id: a0454a4c7e8b9dd667346859b7270e07
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 25408
content:
"An additional argument in the criticism is the inability of climate models to simulate a significant response to Arctic sea ice loss, larger \r\nthan the natural variability (Screen et al., 2014; Walsh, 2014; H.W. Chen et al., 2016; Peings et al., 2017; Dai and Song, 2020), or that \r\na very large multi-model ensemble is needed (Liang et al., 2020), although some studies find a significant response in summer, because \r\nthen the internal variability is weaker (Petrie et al., 2015)."


id: b009b5b6fe442937f6f0097d3172ad4b
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 28213
content:
"Finally, a  warmer Arctic climate can, without any additional changes in atmospheric dynamics, reduce cold extremes in winter \r\ndue to advection of increasingly warmer air from the Arctic into the mid-latitudes (Screen, 2014; Ayarzaguena and Screen, 2016; \r\nAyarzaguena et al., 2018)."


id: 6b20c3ca548ab61c2b4de269405719d1
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 16141
content:
"In conclusion, there is low confidence in the relative contribution of Arctic warming to mid-latitude atmospheric changes compared to \r\nother drivers. Future climate change could affect mid-latitude variability in a number of ways that are still to be clarified, and which \r\nmay also include the influence of Arctic warming. The linkages between the Arctic warming and the mid-latitude circulation are an \r\nexample of contrasting lines of evidence that cannot yet be reconciled (Section 10.5)."


id: f4ad2f278f244a28b607011a2503ee64
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 40424
content:
"observations are valuable (high confidence) for regional applications \r\nsince they provide multi-channel images at very high spatiotemporal \r\nresolutions, typically 16 channels, 1-2 km, every 10 to 15 minutes. \r\nThe advanced geostationary satellites are: Himawari-8 and 9 (Kurihara \r\net  al., 2016), GOES-East and GOES-17 (Goodman et  al., 2018), \r\nMeteosat-10 and 11 (Schmetz et al., 2002) and FY-4 (Cao et al., 2014). \r\nGeostationary satellite networks or constellations form an essential \r\ncomponent of the Global Observation System (https://www.wmo.int/\r\npages/prog/www/OSY/GOS.html), providing measurements not only \r\nfor various cloud properties and moisture but also for air quality, land \r\nand ocean surface conditions, and lightning."


id: 4ae2509714bfe04944269724e78f9e4a
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 10416
content:
"Considerable challenges (and opportunities) remain in using \r\nobservations for climate monitoring, for evaluating and improving \r\nclimate models (Section  10.3.1), for constructing reanalyses and \r\npost-processing model outputs, and therefore, ultimately, for increasing \r\nour confidence in the attribution of past climate changes and in future \r\nclimate projections at the regional scale. While an assessment of \r\nlarge-scale observations can be found in Chapter  2 (Cross-Chapter \r\nBox 2.2 and Section 2.3), this section discusses the specific aspects \r\nof the observations at regional scale and over the typological \r\nregions considered in the regional chapters (Section  10.1.5). This \r\nsection focuses on land regions and does not consider the specific \r\nrequirements of ocean observations (see Chapter 9 and SROCC (IPCC, \r\n2019b) for more information on this aspect)."


id: 76d719463b671319f408aed519b7cf9e
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 18120
content:
"Low Earth orbit (LEO) satellites, with orbits typically at 400-700 km, \r\nprovide advanced measurements of the Earth's surface. \r\nSun-synchronous polar orbiters can also cover the polar regions, which \r\ncannot be observed with GEO satellites. Examples of LEO observations \r\nfor land surface monitoring are NASA's Landsat (Wulder et al., 2016), \r\nESA's Soil Moisture Ocean Salinity Earth Explorer (SMOS) mission (Kerr \r\net al., 2012), the Sentinel missions of the Copernicus programme, and \r\nJAXA's ALOS-2 (Ohki et al., 2019), providing high spatial resolution \r\nland surface images. Many kinds of data are accumulated for land use \r\nand land cover studies, targeting aspects like urban footprint (Florczyk \r\net al., 2019), land-cover data (Global Land 30; CCI-LC: ESA, 2021; Chen \r\nand Chen, 2018), land surface temperature data (Landsat, Parastatidis \r\net al., 2017), and surface albedo (Chrysoulakis et al., 2019)."


id: fa8fc74a725a3ad7ab238033872964d3
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 41456
content:
"Satellite products provide a  valuable complement to in situ \r\nmeasurements, particularly over regions where in situ measurements \r\nare unavailable. They have been discussed in earlier chapters \r\n(e.g.,  Chapters 2 and 8) for large-scale assessment. Currently \r\n54 essential climate variables (ECVs; Bojinski et al., 2014) are defined \r\nby the Global Climate Observing System (GCOS) program, and passed \r\non, for example, to NASA programmes through the Decadal Survey, to \r\nthe Copernicus Climate Change Service of the European Union, to the \r\nESA Climate Change Initiative ESA-CCI, as well as to the international \r\ncollaborations with geostationary Earth orbit  (GEO) satellites. Their "


id: a3ca3394a1b516c3e65cbb680752a06a
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 26013
content:
"of which have been released to the community (Dinku et al., 2014; \r\nOyler et  al., 2015; Manz et  al., 2016; Dietzsch et  al., 2017; Yang \r\net al., 2017; Bliznak et al., 2018; Krahenmann et al., 2018; Panziera \r\net al., 2018; Shen et al., 2018). However, some of these datasets are \r\nlimited by their short record, varying between one (Shen et al., 2018) \r\nand 64 years (Oyler et al., 2015)."


id: c74a54915739aa55230f83c877f93686
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 32378
content:
"Reanalysis products are numerical climate simulations that use \r\ndata assimilation to incorporate as many irregular observations as \r\npossible. These products encompass many physical and dynamical \r\nprocesses. They generate a  coherent estimate of the state of the \r\nclimate system on uniform grids either at global (Chaudhuri et al., \r\n2013; Balsamo et al., 2015), regional (Chaney et al., 2014; Maidment \r\net al., 2014; Dahlgren et al., 2016; Langodan et al., 2017; Attada et al., \r\n2018; Mahmood et  al., 2018) or country scales (Rostkier-Edelstein \r\net al., 2014; Krahenmann et al., 2018; Mahmood et al., 2018)."


id: ae6798091fbb9cca1f1add7fcfeacde
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 27912
content:
"Derived observational products are created from raw datasets \r\ncollected from surface stations, remote-sensing instruments, or \r\nresearch vessels, which are converted into meaningful physical \r\nquantities by applying a suitable measurement theory, using either \r\nstatistical interpolation techniques (Section  10.2.2.4) or numerical \r\natmospheric and land surface models (Bosilovich et al., 2015)."


id: de8bf707c90aa2601ff8437593b3c714
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 36418
content:
"The data assimilation schemes used in regional reanalyses are often \r\nrelatively simple methods, specifically nudging (Kaspar et al., 2020) \r\nand 3DVAR (Mesinger et al., 2006; Bromwich et al., 2016; Dahlgren \r\net al., 2016), rather than the more complex schemes implemented \r\nin state-of-the-art global reanalysis systems. This is partly due \r\nto limitations of computational resources. Recently, a  number of \r\nregional reanalyses using more sophisticated methods, such as "


id: bc337d1fb0592c316d1c77c80e66d812
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 30356
content:
"Most global observational datasets are available at coarse temporal \r\nand spatial resolution, and do not include all available station data \r\nfrom a particular region, due to data availability problems. Therefore, \r\nefforts have been made to develop regional or country-scale datasets \r\n(Annex I). Radar and satellite remote sensing are resources that can \r\nprovide a valuable complement to direct measurements at regional \r\nscale. Examples for precipitation have been described already, some "


id: 18091f50a626fa2d92da662fa8a4fc20
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 1437
content:
"4DVAR and Ensemble Kalman filter, have been published (Jermey and \r\nRenshaw, 2016; Fukui et al., 2018; Mahmood et al., 2018; Su et al., \r\n2019). The regional reanalyses also incorporate uncertainties \r\ndue to deficiencies of the models, data assimilation schemes and \r\nobservations. To estimate uncertainties, some regional reanalyses \r\napply data assimilation using ensemble forecasts (Bach et al., 2016). \r\nAnother approach compares multiple regional reanalyses produced \r\nwith different systems covering the same domain, which represents \r\nthe uncertainties better than single reanalysis systems with ensemble \r\ndata assimilation schemes (Kaiser-Weiss et al., 2019)."


id: 4456d378836b1ebfe0c7bccdb5e7d856
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 9261
content:
"(e.g., Heaney et al., 2016). QC principles also apply to model data \r\n(Tapiador et al., 2017). An important piece of information provided \r\nis the representativeness error (Section  10.2.1.1; Gervais et  al., \r\n2014). When problems in the data representativeness are identified, \r\nobservational datasets are provided with a quality mask (Contractor \r\net al., 2020), or the problematic data are either removed or corrected \r\n(Ashcroft et al., 2018). These are factors often taken into account in \r\nconstructing regional climate information (Kotlarski et al., 2019)."


id: 606b764cdfc4c692bf5c9af1ca2b2ddd
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 14271
content:
"Quality-controlled data are now produced widely at the regional level, \r\nas in the case of sub-daily precipitation records in the United Kingdom \r\n(Blenkinsop et al., 2017) and the USA (Nelson et al., 2016). However, \r\nmany more datasets and variables lack the same level of scrutiny \r\n(Alexander, 2016). Quality-controlled, high-resolution observational \r\ndatasets are especially needed at regional and local scales to assess \r\nmodels as their resolution increases (Di Luca et al., 2016; Zittis and \r\nHadjinicolaou, 2017), although the awareness and appropriate use \r\nof the QC information is challenging (Tapiador et  al., 2017) when \r\ngenerating regional climate information (high confidence)."


id: 52356d1b63569e56ca516a053c4dbd2e
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 11735
content:
"The above examples have been selected as they are present in \r\nmany stations and without going through homogenization they \r\ncould potentially have influenced global land warming estimates \r\n(Section 1.5.1). Single-break inhomogeneities tend to have a magnitude \r\ncomparable to global climate change (Tuomenvirta, 2001; Venema \r\net al., 2012) and are thus important for analyses of small regions. Also \r\nstation records in national networks often have similar changes, making \r\nthem important for national climate change estimates, but many of \r\nthese influences are averaged out at the global scale (Jones, 2016)."


id: 4b788a15e0d8f1d9daf1a32c12e9addd
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 10529
content:
"The main approach to reduce the influence of inhomogeneities in \r\nstation observations is statistical homogenization by comparing the \r\ndata from a candidate station with those of neighbouring reference \r\nstations in conjunction with the use of metadata (Trewin, 2010). \r\nThis is a  challenging task because both reference and candidate \r\nrecords normally have multiple inhomogeneities. Three challenges \r\nshould be considered. First, most of our understanding of statistical "


id: 7220cd2f33ea0c1b679c4302955b21a4
created_at: 2023-04-19 13:21:39
updated_at: 2023-04-19 13:21:39
content_type: text
index: climate_gpt_v2
vector_id: 17324
content:
"The QC procedure depends strongly on the specific nature of the \r\ndataset. It focuses on aspects such as the correct identification of \r\nsensor, time and location, detection of unfeasible or inconsistent \r\ndata, error estimation, assessment of the adequacy of the \r\nuncertainty information and the adequacy of the documentation "


