# document

id: 4a9c962a307342e123c52ead81126504
created_at: 2023-03-16 14:00:46
updated_at: 2023-03-16 14:00:46
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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: 2023-03-16 14:00:46
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"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: 2023-03-16 14:00:46
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"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: 2023-03-16 14:00:46
updated_at: 2023-03-16 14:00:46
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"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: 2023-03-16 14:00:46
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"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: 2023-03-16 14:00:46
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"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: 2023-03-16 14:00:46
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"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: 2023-03-16 14:00:46
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"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: 2023-03-16 14:00:46
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"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: 2023-03-16 14:00:46
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"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: 2023-03-16 14:00:46
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"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: 2023-03-16 14:00:46
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"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: 2023-03-16 14:00:46
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"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
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"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
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"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: 2023-03-16 14:00:46
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"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: 2023-03-16 14:00:46
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"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: 2023-03-16 14:00:46
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"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: 2023-03-16 14:00:46
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"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: 2023-03-16 14:00:46
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"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: 2023-03-16 14:00:46
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"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
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"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
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"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: 2023-03-16 14:00:46
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"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: 2023-03-16 14:00:46
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"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
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"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 "


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"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: 2023-03-16 14:00:46
updated_at: 2023-03-16 14:00:46
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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: 2023-03-16 14:00:46
updated_at: 2023-03-16 14:00:46
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vector_id: 25153
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: 2023-03-16 14:00:46
updated_at: 2023-03-16 14:00:46
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vector_id: 17964
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: 2023-03-16 14:00:46
updated_at: 2023-03-16 14:00:46
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vector_id: 26876
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: 2023-03-16 14:00:46
updated_at: 2023-03-16 14:00:46
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vector_id: 33793
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: 2023-03-16 14:00:46
updated_at: 2023-03-16 14:00:46
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vector_id: 31317
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: 2023-03-16 14:00:46
updated_at: 2023-03-16 14:00:46
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vector_id: 23066
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: 2023-03-16 14:00:46
updated_at: 2023-03-16 14:00:46
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vector_id: 17660
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: 2023-03-16 14:00:46
updated_at: 2023-03-16 14:00:46
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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: 2023-03-16 14:00:46
updated_at: 2023-03-16 14:00:46
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vector_id: 23126
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: 2023-03-16 14:00:46
updated_at: 2023-03-16 14:00:46
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index: climate_gpt_only_giec
vector_id: 23503
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: 2023-03-16 14:00:46
updated_at: 2023-03-16 14:00:46
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vector_id: 34243
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: 2023-03-16 14:00:46
updated_at: 2023-03-16 14:00:46
content_type: text
index: climate_gpt_only_giec
vector_id: 1688
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: 2023-03-16 14:00:46
updated_at: 2023-03-16 14:00:46
content_type: text
index: climate_gpt_only_giec
vector_id: 24261
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: 2023-03-16 14:00:46
updated_at: 2023-03-16 14:00:46
content_type: text
index: climate_gpt_only_giec
vector_id: 3191
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: 2023-03-16 14:00:46
updated_at: 2023-03-16 14:00:46
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index: climate_gpt_only_giec
vector_id: 16422
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: 2023-03-16 14:00:46
updated_at: 2023-03-16 14:00:46
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index: climate_gpt_only_giec
vector_id: 29516
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: 2023-03-16 14:00:46
updated_at: 2023-03-16 14:00:46
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vector_id: 34744
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: 2023-03-16 14:00:46
updated_at: 2023-03-16 14:00:46
content_type: text
index: climate_gpt_only_giec
vector_id: 16869
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: 2023-03-16 14:00:46
updated_at: 2023-03-16 14:00:46
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vector_id: 32840
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: 2023-03-16 14:00:46
updated_at: 2023-03-16 14:00:46
content_type: text
index: climate_gpt_only_giec
vector_id: 27647
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: 2023-03-16 14:00:46
updated_at: 2023-03-16 14:00:46
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vector_id: 21120
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: 2023-03-16 14:00:46
updated_at: 2023-03-16 14:00:46
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index: climate_gpt_only_giec
vector_id: 35046
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: 2023-03-16 14:00:46
updated_at: 2023-03-16 14:00:46
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index: climate_gpt_only_giec
vector_id: 34210
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: 2023-03-16 14:00:46
updated_at: 2023-03-16 14:00:46
content_type: text
index: climate_gpt_only_giec
vector_id: 24508
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: 2023-03-16 14:00:46
updated_at: 2023-03-16 14:00:46
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index: climate_gpt_only_giec
vector_id: 29419
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: 2023-03-16 14:00:46
updated_at: 2023-03-16 14:00:46
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index: climate_gpt_only_giec
vector_id: 27118
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: 2023-03-16 14:00:46
updated_at: 2023-03-16 14:00:46
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index: climate_gpt_only_giec
vector_id: 36724
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: 2023-03-16 14:00:46
updated_at: 2023-03-16 14:00:46
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index: climate_gpt_only_giec
vector_id: 37661
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: 2023-03-16 14:00:46
updated_at: 2023-03-16 14:00:46
content_type: text
index: climate_gpt_only_giec
vector_id: 8963
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: 2023-03-16 14:00:46
updated_at: 2023-03-16 14:00:46
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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: 2023-03-16 14:00:46
updated_at: 2023-03-16 14:00:46
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index: climate_gpt_only_giec
vector_id: 1219
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: 2023-03-16 14:00:46
updated_at: 2023-03-16 14:00:46
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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: 2023-03-16 14:00:46
updated_at: 2023-03-16 14:00:46
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vector_id: 19415
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: 2023-03-16 14:00:46
updated_at: 2023-03-16 14:00:46
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vector_id: 22280
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: 2023-03-16 14:00:46
updated_at: 2023-03-16 14:00:46
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vector_id: 25068
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: 2023-03-16 14:00:46
updated_at: 2023-03-16 14:00:46
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vector_id: 15920
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: 2023-03-16 14:00:46
updated_at: 2023-03-16 14:00:46
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vector_id: 15186
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: 2023-03-16 14:00:46
updated_at: 2023-03-16 14:00:46
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vector_id: 21592
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: 2023-03-16 14:00:46
updated_at: 2023-03-16 14:00:46
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vector_id: 22451
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: 2023-03-16 14:00:46
updated_at: 2023-03-16 14:00:46
content_type: text
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vector_id: 5013
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: 2023-03-16 14:00:46
updated_at: 2023-03-16 14:00:46
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vector_id: 12653
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: 2023-03-16 14:00:46
updated_at: 2023-03-16 14:00:46
content_type: text
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vector_id: 26523
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: 2023-03-16 14:00:46
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vector_id: 14142
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: 2023-03-16 14:00:46
updated_at: 2023-03-16 14:00:46
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vector_id: 2119
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: 2023-03-16 14:00:46
updated_at: 2023-03-16 14:00:46
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vector_id: 14348
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: 2023-03-16 14:00:46
updated_at: 2023-03-16 14:00:46
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vector_id: 20752
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: 2023-03-16 14:00:46
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vector_id: 18966
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: 2023-03-16 14:00:46
updated_at: 2023-03-16 14:00:46
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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: 2023-03-16 14:00:46
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vector_id: 28436
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: 2023-03-16 14:00:46
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vector_id: 36565
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: 2023-03-16 14:00:46
updated_at: 2023-03-16 14:00:46
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vector_id: 32779
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: 2023-03-16 14:00:46
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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: 2023-03-16 14:00:46
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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: 2023-03-16 14:00:46
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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: 2023-03-16 14:00:46
updated_at: 2023-03-16 14:00:46
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vector_id: 13812
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: 2023-03-16 14:00:46
updated_at: 2023-03-16 14:00:46
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index: climate_gpt_only_giec
vector_id: 764
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: 2023-03-16 14:00:46
updated_at: 2023-03-16 14:00:46
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vector_id: 4897
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: 2023-03-16 14:00:46
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vector_id: 28023
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: 2023-03-16 14:00:46
updated_at: 2023-03-16 14:00:46
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vector_id: 23886
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: 2023-03-16 14:00:46
updated_at: 2023-03-16 14:00:46
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vector_id: 17774
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: 2023-03-16 14:00:46
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vector_id: 11675
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."


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vector_id: 33005
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: 2023-03-16 14:00:46
updated_at: 2023-03-16 14:00:46
content_type: text
index: climate_gpt_only_giec
vector_id: 8037
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: 2023-03-16 14:00:46
updated_at: 2023-03-16 14:00:46
content_type: text
index: climate_gpt_only_giec
vector_id: 14215
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: 2023-03-16 14:00:46
updated_at: 2023-03-16 14:00:46
content_type: text
index: climate_gpt_only_giec
vector_id: 10618
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: 2023-03-16 14:00:46
updated_at: 2023-03-16 14:00:46
content_type: text
index: climate_gpt_only_giec
vector_id: 7905
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: 2023-03-16 14:00:46
updated_at: 2023-03-16 14:00:46
content_type: text
index: climate_gpt_only_giec
vector_id: 21853
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: 2023-03-16 14:00:46
updated_at: 2023-03-16 14:00:46
content_type: text
index: climate_gpt_only_giec
vector_id: 14638
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: 2023-03-16 14:00:46
updated_at: 2023-03-16 14:00:46
content_type: text
index: climate_gpt_only_giec
vector_id: 34720
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: 2023-03-16 14:00:46
updated_at: 2023-03-16 14:00:46
content_type: text
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vector_id: 4873
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: 2023-03-16 14:00:46
updated_at: 2023-03-16 14:00:46
content_type: text
index: climate_gpt_only_giec
vector_id: 21851
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: 2023-03-16 14:00:46
updated_at: 2023-03-16 14:00:46
content_type: text
index: climate_gpt_only_giec
vector_id: 23707
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: 2023-03-16 14:00:46
updated_at: 2023-03-16 14:00:46
content_type: text
index: climate_gpt_only_giec
vector_id: 6918
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: 2023-03-16 14:00:46
updated_at: 2023-03-16 14:00:46
content_type: text
index: climate_gpt_only_giec
vector_id: 24621
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
content_type: text
index: climate_gpt_only_giec
vector_id: 2856
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
content_type: text
index: climate_gpt_only_giec
vector_id: 31370
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
content_type: text
index: climate_gpt_only_giec
vector_id: 29711
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
content_type: text
index: climate_gpt_only_giec
vector_id: 7138
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
content_type: text
index: climate_gpt_only_giec
vector_id: 26253
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
content_type: text
index: climate_gpt_only_giec
vector_id: 12834
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
content_type: text
index: climate_gpt_only_giec
vector_id: 18702
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
content_type: text
index: climate_gpt_only_giec
vector_id: 27240
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
content_type: text
index: climate_gpt_only_giec
vector_id: 30782
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
content_type: text
index: climate_gpt_only_giec
vector_id: 14924
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
content_type: text
index: climate_gpt_only_giec
vector_id: 35829
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
content_type: text
index: climate_gpt_only_giec
vector_id: 2923
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
content_type: text
index: climate_gpt_only_giec
vector_id: 16526
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
content_type: text
index: climate_gpt_only_giec
vector_id: 36134
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
content_type: text
index: climate_gpt_only_giec
vector_id: 26101
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
content_type: text
index: climate_gpt_only_giec
vector_id: 27594
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
content_type: text
index: climate_gpt_only_giec
vector_id: 35575
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
content_type: text
index: climate_gpt_only_giec
vector_id: 27503
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
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index: climate_gpt_only_giec
vector_id: 3049
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
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index: climate_gpt_only_giec
vector_id: 7494
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
content_type: text
index: climate_gpt_only_giec
vector_id: 12164
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
content_type: text
index: climate_gpt_only_giec
vector_id: 3692
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
content_type: text
index: climate_gpt_only_giec
vector_id: 36165
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
content_type: text
index: climate_gpt_only_giec
vector_id: 20257
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
content_type: text
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vector_id: 32624
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
content_type: text
index: climate_gpt_only_giec
vector_id: 26155
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
content_type: text
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vector_id: 35418
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
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vector_id: 32101
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
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vector_id: 12017
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
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vector_id: 27096
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
content_type: text
index: climate_gpt_only_giec
vector_id: 26337
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
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vector_id: 15838
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
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vector_id: 10482
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
content_type: text
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vector_id: 15859
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
content_type: text
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vector_id: 32499
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: 2023-03-16 14:00:47
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vector_id: 15500
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
content_type: text
index: climate_gpt_only_giec
vector_id: 28926
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
content_type: text
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vector_id: 28094
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
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index: climate_gpt_only_giec
vector_id: 21659
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: 2023-03-16 14:00:47
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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: 2023-03-16 14:00:47
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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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
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vector_id: 32582
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
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index: climate_gpt_only_giec
vector_id: 4923
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
content_type: text
index: climate_gpt_only_giec
vector_id: 37654
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
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index: climate_gpt_only_giec
vector_id: 8938
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
content_type: text
index: climate_gpt_only_giec
vector_id: 32808
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
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vector_id: 4222
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
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index: climate_gpt_only_giec
vector_id: 5998
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
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vector_id: 11630
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
content_type: text
index: climate_gpt_only_giec
vector_id: 8100
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
content_type: text
index: climate_gpt_only_giec
vector_id: 13906
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
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"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: 2023-03-16 14:00:47
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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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
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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: 2023-03-16 14:00:47
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content:
"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: 2023-03-16 14:00:47
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content:
"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 "


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created_at: 2023-03-16 14:00:47
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content:
"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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content:
"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
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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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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: 2023-03-16 14:00:47
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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)."


id: 7341b50cd064a65b4c2abf19e030dff3
created_at: 2023-03-16 14:00:47
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content:
"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: 2023-03-16 14:00:47
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"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: 2023-03-16 14:00:47
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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: 2023-03-16 14:00:47
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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 "


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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
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"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: 2023-03-16 14:00:47
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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: 2023-03-16 14:00:47
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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 "


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created_at: 2023-03-16 14:00:47
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"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
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"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: 2023-03-16 14:00:47
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"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: 2023-03-16 14:00:47
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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
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"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."


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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: 2023-03-16 14:00:47
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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
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"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
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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)."


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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)."


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"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: 2023-03-16 14:00:47
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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
created_at: 2023-03-16 14:00:47
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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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
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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: 2023-03-16 14:00:47
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vector_id: 4375
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
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content:
"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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
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content:
"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: 2023-03-16 14:00:47
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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: 2023-03-16 14:00:47
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content:
"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: 2023-03-16 14:00:47
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content:
"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 "


id: 4165bd6bfd5388c4d534013f6fc28c93
created_at: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
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vector_id: 7686
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: 2023-03-16 14:00:47
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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: 2023-03-16 14:00:47
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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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
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index: climate_gpt_only_giec
vector_id: 3630
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: 2023-03-16 14:00:47
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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: 2023-03-16 14:00:47
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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: 2023-03-16 14:00:47
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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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
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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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
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vector_id: 16463
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
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index: climate_gpt_only_giec
vector_id: 25161
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
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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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
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index: climate_gpt_only_giec
vector_id: 23597
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
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index: climate_gpt_only_giec
vector_id: 6774
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
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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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
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index: climate_gpt_only_giec
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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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
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index: climate_gpt_only_giec
vector_id: 34295
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
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index: climate_gpt_only_giec
vector_id: 34170
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
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index: climate_gpt_only_giec
vector_id: 12782
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
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index: climate_gpt_only_giec
vector_id: 12040
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
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index: climate_gpt_only_giec
vector_id: 15503
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
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index: climate_gpt_only_giec
vector_id: 19612
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: 2023-03-16 14:00:47
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vector_id: 13177
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
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vector_id: 11482
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
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vector_id: 36523
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
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index: climate_gpt_only_giec
vector_id: 33145
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
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index: climate_gpt_only_giec
vector_id: 8937
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
content_type: text
index: climate_gpt_only_giec
vector_id: 23059
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
content_type: text
index: climate_gpt_only_giec
vector_id: 26411
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
content_type: text
index: climate_gpt_only_giec
vector_id: 28279
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
content_type: text
index: climate_gpt_only_giec
vector_id: 20193
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
content_type: text
index: climate_gpt_only_giec
vector_id: 24554
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
content_type: text
index: climate_gpt_only_giec
vector_id: 20063
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
content_type: text
index: climate_gpt_only_giec
vector_id: 34377
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
content_type: text
index: climate_gpt_only_giec
vector_id: 19599
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
content_type: text
index: climate_gpt_only_giec
vector_id: 24572
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
content_type: text
index: climate_gpt_only_giec
vector_id: 7926
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
content_type: text
index: climate_gpt_only_giec
vector_id: 6130
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
content_type: text
index: climate_gpt_only_giec
vector_id: 31325
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
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vector_id: 6343
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
content_type: text
index: climate_gpt_only_giec
vector_id: 10216
content:
"Review Editors:\r\nGregory M. Flato (Canada), Fredolin Tangang (Malaysia), Muhammad Irfan Tariq (Pakistan)"


id: 7a04a53d8a530245953d92bc84f4539c
created_at: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
content_type: text
index: climate_gpt_only_giec
vector_id: 16771
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
content_type: text
index: climate_gpt_only_giec
vector_id: 11769
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
content_type: text
index: climate_gpt_only_giec
vector_id: 13685
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
content_type: text
index: climate_gpt_only_giec
vector_id: 31552
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
content_type: text
index: climate_gpt_only_giec
vector_id: 34834
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
content_type: text
index: climate_gpt_only_giec
vector_id: 35465
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
content_type: text
index: climate_gpt_only_giec
vector_id: 18130
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
content_type: text
index: climate_gpt_only_giec
vector_id: 10055
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
content_type: text
index: climate_gpt_only_giec
vector_id: 24970
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
content_type: text
index: climate_gpt_only_giec
vector_id: 12593
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
content_type: text
index: climate_gpt_only_giec
vector_id: 12207
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
content_type: text
index: climate_gpt_only_giec
vector_id: 14975
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
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index: climate_gpt_only_giec
vector_id: 36542
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
content_type: text
index: climate_gpt_only_giec
vector_id: 36662
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
content_type: text
index: climate_gpt_only_giec
vector_id: 17616
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} "


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vector_id: 5919
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
content_type: text
index: climate_gpt_only_giec
vector_id: 1156
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: 7792b441e55748da74bc086e87277d1c
created_at: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
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vector_id: 16365
content:
"Figure 10.1 | Diagram of the processes leading to the construction of regional climate information (blue) and user-relevant regional climate information \r\n(brown). 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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
content_type: text
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vector_id: 32857
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
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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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
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vector_id: 14320
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
created_at: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
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index: climate_gpt_only_giec
vector_id: 29437
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
created_at: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
content_type: text
index: climate_gpt_only_giec
vector_id: 14706
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
content_type: text
index: climate_gpt_only_giec
vector_id: 28470
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
content_type: text
index: climate_gpt_only_giec
vector_id: 14669
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
content_type: text
index: climate_gpt_only_giec
vector_id: 3051
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
created_at: 2023-03-16 14:00:47
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index: climate_gpt_only_giec
vector_id: 15771
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: 12cf78ab70828e76d0493a1f79c33836
created_at: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
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vector_id: 460
content:
"Figure  10.3 | Schematic diagram to display interacting spatial and temporal scales relevant to regional climate change \r\ninformation. Figure adapted from Orlanski (1975). The processes included in the different models and model components considered in \r\nChapter  10 are indicated as a  function of these scales. The various types of models (including global and regional climate models) for \r\nconstructing regional climate information are assessed in Section 10.3.1 and Box 10.3."


id: 7bcecf78494b1a59203e9d16317be16
created_at: 2023-03-16 14:00:47
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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."


id: a9727573b94750d3619c01f18af1ec56
created_at: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
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vector_id: 24195
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
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vector_id: 8658
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
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vector_id: 35137
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
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vector_id: 16623
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: 2023-03-16 14:00:47
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vector_id: 17246
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
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vector_id: 32338
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: 2023-03-16 14:00:47
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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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
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vector_id: 25845
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
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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: 2023-03-16 14:00:47
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vector_id: 37216
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: 2023-03-16 14:00:47
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content_type: text
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vector_id: 16182
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: 2023-03-16 14:00:47
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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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
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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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
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vector_id: 14786
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: 2023-03-16 14:00:47
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vector_id: 26311
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: 2023-03-16 14:00:47
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vector_id: 34994
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
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vector_id: 9595
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
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vector_id: 23788
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
content_type: text
index: climate_gpt_only_giec
vector_id: 3210
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: 2023-03-16 14:00:47
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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)."


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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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
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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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
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vector_id: 24710
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
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vector_id: 13260
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
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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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
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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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
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vector_id: 8262
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
content_type: text
index: climate_gpt_only_giec
vector_id: 34770
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
content_type: text
index: climate_gpt_only_giec
vector_id: 2998
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
content_type: text
index: climate_gpt_only_giec
vector_id: 891
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
content_type: text
index: climate_gpt_only_giec
vector_id: 34100
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
content_type: text
index: climate_gpt_only_giec
vector_id: 12460
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
content_type: text
index: climate_gpt_only_giec
vector_id: 6634
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
created_at: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
content_type: text
index: climate_gpt_only_giec
vector_id: 1173
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
content_type: text
index: climate_gpt_only_giec
vector_id: 3641
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
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vector_id: 30408
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
content_type: text
index: climate_gpt_only_giec
vector_id: 26660
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
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index: climate_gpt_only_giec
vector_id: 35594
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
content_type: text
index: climate_gpt_only_giec
vector_id: 34221
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
content_type: text
index: climate_gpt_only_giec
vector_id: 17143
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
content_type: text
index: climate_gpt_only_giec
vector_id: 3422
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
content_type: text
index: climate_gpt_only_giec
vector_id: 14962
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
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vector_id: 21720
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
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index: climate_gpt_only_giec
vector_id: 25536
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
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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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
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vector_id: 12290
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
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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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
content_type: text
index: climate_gpt_only_giec
vector_id: 25226
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
content_type: text
index: climate_gpt_only_giec
vector_id: 14474
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
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index: climate_gpt_only_giec
vector_id: 36004
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
content_type: text
index: climate_gpt_only_giec
vector_id: 9199
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
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index: climate_gpt_only_giec
vector_id: 16272
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
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index: climate_gpt_only_giec
vector_id: 36942
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
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index: climate_gpt_only_giec
vector_id: 23332
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
content_type: text
index: climate_gpt_only_giec
vector_id: 28944
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
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index: climate_gpt_only_giec
vector_id: 24964
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
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updated_at: 2023-03-16 14:00:47
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vector_id: 32516
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
content_type: text
index: climate_gpt_only_giec
vector_id: 27188
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
content_type: text
index: climate_gpt_only_giec
vector_id: 1275
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
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index: climate_gpt_only_giec
vector_id: 8174
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
content_type: text
index: climate_gpt_only_giec
vector_id: 12743
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
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vector_id: 10385
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
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vector_id: 9291
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: 2023-03-16 14:00:47
updated_at: 2023-03-16 14:00:47
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vector_id: 15583
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 "


id: 2e5a3b8a3e13e19aa07bd32d71a99832
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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vector_id: 4737
content:
"in data acquisition and not paucity of data (GCOS, 2015). This is \r\nbecause GPCC is the result of a  single time scale, single Essential \r\nClimate Variable (ECV) and single data collection centre. There is \r\nno similar drop-off of the rainfall reports in the Global Historical \r\nClimatology Network Daily database (GHCNd, Menne et al., 2012) \r\nor the Integrated Surface Database (ISD) at the sub-daily time scale."


id: 9ecaab4de8c5246481998dd3fd673dfd
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 22524
content:
"homogenization stems from the homogenization of temperature \r\nobservations from dense networks. Recent studies suggest that \r\nour ability to remove biases quickly diminishes for sparse networks \r\n(Gubler et al., 2017; Lindau and Venema, 2018a). This affects early \r\ninstrumental data and observations that are not strongly correlated \r\nbetween stations, such as wind and humidity (Chimani et al., 2018)."


id: 84e9085a6ca97622b5045f01c8e4000d
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 18428
content:
"It is virtually certain that statistical homogenization methods reduce the \r\nuncertainties of long-term estimates. Considering a decomposition of \r\nthe long-term warming error into a bias and a noise uncertainty around \r\nthe bias, the (trend) bias especially will be reduced, but also most of \r\nthe noise uncertainty. This conclusion is based on our understanding \r\nof the causes of inhomogeneities and their statistical nature combined \r\nwith the design principles of statistical homogenization methods, as \r\nwell as on analytical (Lindau and Venema, 2018b), numerical (Venema \r\net  al., 2012; Williams et  al., 2012) and empirical validation studies \r\n(Hausfather et al., 2016; Gubler et al., 2017; Killick et al., 2020)."


id: d20b20537c64e806b43cf0ab49ccab79
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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index: climate_gpt_only_giec
vector_id: 30575
content:
"The above section is about the homogenization of land stations. \r\nSatellite data has its own issues and methods for homogenization \r\n(Brinckmann et al., 2013; Huang et al., 2015; Brogniez et al., 2016). \r\nThe homogenization of radiosonde data and land station data use \r\nsimilar methods (Haimberger et al., 2012; Jovanovic et al., 2017)."


id: ae09a84d6c76e541e2ce2a598a653ef5
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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vector_id: 24920
content:
"Data scarcity arises largely due to the lack of maintenance of \r\nobserving stations, inaccessibility of the data held in national \r\nnetworks, and uneven spatial distribution of stations that lead to \r\na low density in many regions. This is particularly problematic when \r\ntrying to assess regional climate change, for which a  high density \r\nof observational data is desirable. Although in several regions \r\nnumerous stations provide (monthly) data covering more than \r\n100 years for both temperature and precipitation (GCOS, 2015), large \r\nareas of the world remain sparsely covered. The post-1990 decline in \r\nthe total number of stations contributing to the Global Precipitation \r\nClimatology Centre (GPCC) monthly product may be related to delays "


id: 11319d6f0624ed76b494e165f006dacf
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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index: climate_gpt_only_giec
vector_id: 182
content:
"Data scarcity arising from changing coverage in observation station \r\nnetworks results in substantial problems for climate monitoring \r\n(e.g.,  trend analysis of extreme events requires high temporal and \r\nspatial resolutions) or model evaluation (Section 10.3.3.1). It is virtually \r\ncertain that the scarcity and decline of observational availability in "


id: ab08289f7525151dcdb4b30e836c6678
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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vector_id: 24430
content:
"some regions (but not necessarily globally), increase the uncertainty of \r\nthe long-term global temperature and precipitation estimates. As an \r\nexample, Lin and Huybers (2019) found that changes in the number \r\nof rain gauges after 1975 resulted in spurious trends in extremes of \r\nIndian rainfall in a 0.25deg gridded dataset spanning the 20th century. \r\nIn fact, the number of stations used to construct the gridded dataset \r\ndropped by half after 1990, leading to inhomogeneity and spurious \r\ntrends (Section 10.6.3). Over the southern part of the Mediterranean, \r\nwhich is an area sparsely covered by meteorological stations, data \r\nscarcity can lead to large uncertainties in the different gridded datasets \r\nand strongly affect model evaluation (Section  10.6.4). Satellite \r\nobservations can compensate the ground-based precipitation radar \r\ndata sparsity to prevent an oversight of significant climate change \r\nsignals (Yokoyama et al., 2019)."


id: 7202d78eaa403bed9ca0a887118f3141
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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index: climate_gpt_only_giec
vector_id: 15562
content:
"There are techniques for estimating and reconstructing missing \r\ndata. The methods depend on the variable of interest, the temporal \r\nresolution (e.g., daily or monthly), and the type of climate (wet or dry), \r\namong others. There has been very little evaluation of the performance \r\nof classical and data mining methods (e.g., Sattari et al., 2017). The \r\nclassical methods include the arithmetic mean, inverse distance \r\nweighting method, multiple regression analysis, multiple imputation, \r\nand single best estimator, while the data-mining methods include \r\nmultilayer perceptron artificial neural network, support vector machine, \r\nadaptive neuro-fuzzy inference system, gene expression programming \r\nmethod, and K-nearest neighbour. Crowd-sourced data (individuals \r\ncontribute their own data points to create a dataset for others to use) \r\ncould play a role in minimizing data scarcity (Section 10.2.4)."


id: 3a821289751960a929ebc7ed68f034e1
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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content:
"Derived gridded datasets require merging data from different sources of \r\nobservations and/or reanalysis data on a regular grid (Section 10.2.1.2; \r\ne.g., Xie and Arkin, 1997). However, in situ observations are distributed \r\nirregularly, especially over sparsely populated areas. This leads to \r\nan interpolation challenge. Gridded products of climate variables, \r\nincluding temperature and precipitation, are strongly affected (high \r\nconfidence) by the interpolation method over complex orography and \r\ndata scarce regions (Hofstra et al., 2008; Herrera et al., 2016)."


id: 6dba3e41437fe85431abb19908c23fee
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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index: climate_gpt_only_giec
vector_id: 14872
content:
"There are two main approaches to produce gridded datasets: (i) based \r\non in situ observations only, and (ii) combining in situ observations \r\nwith remote-sensing data and/or reanalysis data. The first approach \r\nhas been widely employed in regions with high station density using \r\ninterpolation techniques, such as inverse-distance weighting, optimal \r\ninterpolation, and kriging (Chen et al., 2008; Haylock et al., 2008; Frei, \r\n2014; Isotta et al., 2014; Masson and Frei, 2014; Hiebl and Frei, 2016; \r\nNguyen-Xuan et  al., 2016). The second approach has been mainly \r\napplied in data-sparse regions with low station density, using simple \r\nbias adjustment, quantile mapping, and kriging techniques with in \r\nsitu observations, remote-sensing and reanalysis data (Cheema and \r\nBastiaanssen, 2012; Erdin et al., 2012; Dinku et al., 2014; Abera et al., \r\n2016; Krahenmann et al., 2018)."


id: 536aa98959bc6d1c66d8a75cf5b1f1fd
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 10594
content:
"direction and temperature as the minimum standard ancillary data \r\nfor solid precipitation monitoring (Nitu et al., 2018)."


id: a1b02d3eb5f99476d774624b63492121
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 22999
content:
"All the limitations and challenges of observational data discussed \r\nin Section 10.2.2 also apply to its use for post-processing of climate \r\nmodel data. High quality and long observational data series are \r\nparticularly relevant to quantify uncertainties. Different reanalyses \r\npresent significant discrepancies when used as key predictor variables \r\nat the daily scale and may even affect the downscaled climate change \r\nsignal (Brands et  al., 2012; Dayon et  al., 2015; Manzanas et  al., \r\n2015; Horton and Bronnimann, 2019). There is high confidence that \r\nreanalysis uncertainties limit the quality of statistical downscaling in \r\nsome regions, although no assessment has been made for the most \r\nrecent reanalysis products."


id: 8f776313a927d82ac630a0fa4a2dc5c4
created_at: 2023-03-16 14:00:48
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content:
"An important issue for bias adjustment is the correct representation \r\nof the required spatial scale. Ideally, bias adjustment is calibrated \r\nagainst area-averaged data of the same spatial scale as the climate \r\nmodel output. Hence, high-quality observed gridded datasets with an \r\neffective resolution close to the nominal model resolution are required. \r\nDriven by the need to also generate regional-scale information in \r\nstation-sparse regions, researchers have considered derived datasets \r\nthat blend in situ and remote-sensing data to produce high-resolution \r\nobservations to be used as predictands (Sections 10.2.1.2 and 10.2.2.4; \r\nHaiden et al., 2011; Wilby and Yu, 2013)."


id: 44b7bed2b8938d356d5eb7fd5792ca6a
created_at: 2023-03-16 14:00:48
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index: climate_gpt_only_giec
vector_id: 8237
content:
"data to reconstruct past climate changes for analysis of a  specific \r\nprocess or case (Widmann et  al., 2010). Recently, assimilation of \r\nmultiple series from various data sources, including tree rings, ice \r\ncores, lake cores, corals, and bivalves, has allowed production of \r\nreconstructions that can be widely shared and applied to multiple \r\npurposes, as with modern reanalyses (Hakim et  al., 2016; Franke \r\net al., 2017; Steiger et al., 2018; Tardif et al., 2019). Most of these \r\npaleo-reanalyses are global but there are products using regional \r\nmodels or targeted at specific regions such as Europe, East Africa and \r\nthe Indian Ocean (Fallah et al., 2018; Klein and Goosse, 2018)."


id: e75bc8d1909251786985f408a1baebf8
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 33893
content:
"Delobbe, 2016; Humphrey et al., 2017; Yang and Ng, 2019). While \r\nthey are far less reliable and accurate than professional observations, \r\ncrowdsourced data are abundantly available and can give spatial \r\nrepresentations at very high resolution. This technological trend \r\ncould prove very useful (high confidence), and the regional climate \r\ncommunity is making efforts to understand the extent to which these \r\ndata sources can be exploited, at least as a complement to traditional \r\ndatasets (Overeem et al., 2013; Meier et al., 2017; Uijlenhoet et al., \r\n2018; de Vos et al., 2019; Langendijk et al., 2019b)."


id: b720cd9d08da38b9f221e6b0fe5daf72
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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index: climate_gpt_only_giec
vector_id: 26347
content:
"Much of the information available on future regional climate arises \r\nfrom studies based on climate model simulations (Chapters 3, 4 \r\nand 8). In this section, different types of models (Section  10.3.1) \r\nand model experiments (Section  10.3.2) for generating regional \r\nclimate information are discussed, followed by an assessment of \r\nthe performance, added value, and fitness-for-purpose of different \r\nmodel types (Section 10.3.3). The focus is on the representation of \r\nlarge- to local-scale phenomena and processes relevant for regional \r\nclimate. Finally, uncertainties of regional climate projections and \r\nmethodologies to manage these are assessed (Section 10.3.4)."


id: f0d94700b21fe3c20ca75032d035e38
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 35388
content:
"Regional climate change information may be derived from a hierarchy \r\nof different model types covering a  wide range of spatial scales \r\nand processes (Figure  10.5). The application of any model relies \r\non assumptions, depending on the specific model as well as the \r\napplication. Table 10.1 gives an overview of the generic assumptions \r\nof the different model types discussed here for generating regional \r\nclimate information. The violation of these assumptions will affect \r\nthe model performance, which is discussed in Section 10.3.3."


id: 443925e6277dab76d1b81554b56d8344
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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index: climate_gpt_only_giec
vector_id: 8157
content:
"An encouraging development for understanding climate variations \r\nover the past 250 years or so at the global and regional scale lies \r\nin the field of data rescue, in which hitherto hidden archives of \r\nmeteorological data are brought to the forefront (Sections 1.5.1.1 \r\nand 2.5). Surface observations from data rescue projects may then \r\nbe assimilated to derive long-term high-resolution gridded surface \r\nregional reanalysis (Devers et al., 2020). Global extended reanalyses \r\nsuch as 20CR (Compo et al., 2011), ERA-20C (Poli et al., 2016a, b) \r\nor CERA-20C (Laloyaux et  al., 2018) may be further downscaled \r\nto quantify the variability of past climate at the regional scale \r\n(Caillouet et al., 2016, 2019)."


id: c1e0bc69bcc9f90f7a486d49646e5cef
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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content:
"One of the main scientific challenges related to high-resolution \r\nregional climate modelling is dealing with the representation of \r\nfine-scale processes (e.g., Yano et al., 2018) in observational datasets. \r\nAdditionally, reliable observation networks following WMO standards \r\nhave a  very sparse geographical representation. Hence, regional \r\nclimate models have started to use high-resolution data combined \r\nwith crowdsourced observations (Zheng et al., 2018). Recent efforts \r\nhave led to the production of homogeneously processed long-term \r\ndatasets for regional climate model evaluation (Goudenhoofdt and "


id: 5dbd78d1028118b9ab6ed73cc5eba81b
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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index: climate_gpt_only_giec
vector_id: 12273
content:
"Table 10.1 | Assumptions underlying different model types in simulating regional climate and climate change. Violating these assumptions will affect model \r\nperformance (see links to different subsections for details). All assumptions regarding future climate are in addition to those regarding present climate and predicated on \r\nthe driving global model simulating a plausible global climate sensitivity (Section 1.3.5, Chapters 4 and 7). The assumptions listed for future climate applications of perfect prognosis \r\nstatistical downscaling and bias adjustment are often called the 'stationarity assumption'. Numbers in curly brackets refer to chapters and sections assessing these assumptions."


id: 495fb00e5968389c5be0a9f6cc1d6336
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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index: climate_gpt_only_giec
vector_id: 8927
content:
"State-of-the-art global models are generally used to derive climate \r\ninformation at continental to global scales both for past and future \r\nclimates (e.g., Chapters 3 and 4). The nominal horizontal resolution \r\nin CMIP5 global models is typically 100-200 km. The effective \r\nresolution, for which the shape of the kinetic energy spectrum is \r\nsimulated correctly, is about three to five times larger (Klaver et al., \r\n2020), and a similar relationship also applies to RCMs (Skamarock, \r\n2004). This strongly limits their ability to resolve local details. Since "


id: eb1aed90b58fc55e6bd86389deb5217a
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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index: climate_gpt_only_giec
vector_id: 34490
content:
"Model-based regional climate projections are all based upon \r\nsome type of global model, including state-of-the-art Earth system \r\nmodels (ESMs), coupled atmosphere-ocean general circulation models \r\n(GCMs) or atmosphere-only general circulation models (AGCMs) \r\n(see Section 1.5.3.1). They are collectively referred to as global models."


id: b47246eba2fd2551babe88a3a441534
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 25913
content:
"AR5 the progress in reducing biases and providing more credible \r\nregional projections by global models has been moderate in spite \r\nof the more realistic representation of a number of processes and \r\nthe increase in resolution of some models. For AR6, several of the \r\nnew CMIP6 (Eyring et  al., 2016a) model intercomparison projects \r\n(MIPs) address some of these limitations. The list of MIPs is provided \r\nin Chapter 1 (Table 1.3). High-Resolution MIP (HighResMIP; Haarsma \r\net al., 2016) and Global Monsoons MIP (GMMIP; Zhou et al., 2016) \r\nspecifically address the regional climate challenge using global \r\nmodels. HighResMIP focuses on producing global climate projections \r\nat a horizontal resolution of around 50 km grid spacing or finer while \r\nGMMIP aims at better understanding and predicting the monsoons."


id: 60e7778b349d0f5939ba81e6c342a9e6
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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index: climate_gpt_only_giec
vector_id: 12816
content:
"An alternative to increasing resolution everywhere is offered by \r\nvariable resolution global models, that is, with regionally finer \r\nresolution. They have been developed since the 1970s (Li, 1999), \r\nresulting in a first coordinated effort (SGMIP) by Fox-Rabinovitz et al. \r\n(2006, 2008). They are expected to offer the finest resolution possible \r\nin the region of interest, while still resolving the climate processes at \r\nthe global scale (although at lower resolution). An overview of recent \r\ndevelopments is in McGregor (2015). This is a rapidly developing field \r\n(Krinner et al., 2014; Ferguson et al., 2016; Huang et al., 2016) that \r\nwill possibly contribute to improved future regional projections."


id: ccb1162ed7be4d280bfeddf26f39d768
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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index: climate_gpt_only_giec
vector_id: 29742
content:
"The Coordinated Regional Climate Downscaling Experiment \r\n(CORDEX) initiative (Giorgi et al., 2009; Giorgi and Gutowski, 2015; "


id: 6bac28ad57b2c35760112a7d5036dd6e
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 14566
content:
"Perfect-prognosis models are statistical models calibrated between \r\nobservation-based large-scale predictors (e.g., from reanalysis) and \r\nobserved local-scale predictands (Maraun and Widmann, 2018b). \r\nRegional climate projections are then generated by replacing the \r\nquasi-observed predictors by those from climate model (typically \r\nglobal model) projections. Predictor patterns that are common to \r\nobservations and climate model data can be defined by common \r\nempirical orthogonal functions (Benestad, 2011). The perfect prognosis \r\napproach can either be used to generate daily (or even sub-daily) \r\ntime series, or local weather statistics (e.g., Benestad et al., 2018)."


id: 5d1260718efeedead3565286e82f25b8
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 12158
content:
"Regression-like models (Maraun and Widmann, 2018b) rely on a transfer \r\nfunction linking an observed local statistic (such as the temperature at \r\na given day) to some set of large-scale predictors. Recent developments \r\ninclude stochastic regression models to explicitly  simulate local \r\nvariability (San-Martin et al., 2017; those explicitly modelling temporal \r\ndependence are assessed in Section 10.3.1.3.4). The use of machine \r\nlearning techniques has been reinvigorated, including genetic \r\nprogramming to construct a  data-driven model structure (Zerenner \r\net al., 2016) and deep and convolutional neural networks (Reichstein \r\net al., 2019; Bano-Medina et al., 2020)."


id: 282570751afe585b2700cf9860ed56fc
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 3723
content:
"In the delta change approach, selected observations are modified \r\naccording to corresponding changes derived from dynamical model \r\nsimulations. Traditionally, only long-term means have been adjusted, \r\nbut recently approaches to modify temporal dependence (Webber \r\net  al., 2018) have been developed, as well as quantile mapping \r\napproaches that individually adjust quantiles of the observed \r\ndistribution (Willems and Vrac, 2011). By construction, the approach \r\ncannot modify the spatial and temporal dependence structure of the \r\ninput observations (Maraun, 2016)."


id: c7e1884f325f3ca09cbc49a7242ee706
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 29029
content:
"Analogue methods (Martin et  al., 1996; Maraun and Widmann, \r\n2018b) compare a simulated large-scale atmospheric field with an \r\narchive of observations and select, using some distance metric, the \r\nclosest observed field in the archive. The downscaled atmospheric \r\nfield is then chosen as the local atmospheric field observed on the \r\ninstant the analogue occurred. New analogue methods have been \r\ndeveloped to simulate unobserved values including a  rescaling of \r\nthe analogue (Pierce et  al., 2014) or by combining analogues and \r\nregression models (Chardon et al., 2018)."


id: 357f0f8eb8d289954f0e903d4e10c60f
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 5825
content:
"Weather generators are statistical models that simulate weather time \r\nseries of arbitrary length. They are calibrated to represent observed \r\nweather statistics, in particular daily or even sub-daily variability. One \r\nvariant of these models are advanced stochastic perfect-prognosis \r\nmethods, conditioned on large-scale atmospheric predictors on a daily \r\nbasis, for instance multisite generalized linear models (Chandler, \r\n2020). Another widely used variant is change-factor weather \r\ngenerators: the weather generator parameters are calibrated against \r\npresent and future climate model simulations, and the climate change \r\nsignals are then applied to the parameters calibrated to observations. \r\nRecent research has mainly focussed on multi-site Richardson type \r\n(Markov-chain) weather generators (Keller et  al., 2015; Dubrovsky \r\net  al., 2019), some explicitly modelling extremes and their spatial \r\ndependence (Evin et al., 2018)."


id: a833c59e3ca0fdff46fc8e4bbd73acdf
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 23983
content:
"Bias adjustment is a  statistical post-processing technique used to \r\npragmatically reduce the mismatch between the statistics of climate \r\nmodel output and observations. The approach estimates the bias or \r\nrelative error between a chosen simulated statistical property (such \r\nas the long-term mean or specific quantiles of the climatological \r\ndistribution) and that observed over a  calibration period; the \r\nsimulated statistic is then adjusted taking into account the simulated \r\ndeviation. Bias adjustment methods are regularly applied on a spatial \r\nscale similar to that of the simulation being adjusted, but they are \r\noften used as a simple statistical downscaling method by calibrating \r\nthem between coarse resolution (e.g., global) model output and finer \r\nobservations (Maraun and Widmann, 2018b)."


id: 937bec9015c431bcdb94c484fa6f3978
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 20721
content:
"Typical implementations of bias adjustment are (i) additive \r\nadjustments, where the model data is adjusted by adding a constant, \r\n(ii) rescaling, where the model data is adjusted by a  factor, and \r\n(iii) more flexible quantile mapping approaches that adjust different \r\nranges of a  distribution individually. Hempel et  al. (2013), Pierce \r\net  al. (2015), Switanek et  al. (2017), and Lange (2019) developed \r\nvariants of quantile mapping that preserve trends in the mean or \r\neven further distributional statistics. Multivariate bias adjustment "


id: 5b57300f7cdc0ad8dfd6945598accfee
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 11875
content:
"A wide variety of approaches has been proposed to combine the \r\nadvantages of different statistical approaches. For instance, to \r\novercome the scale mismatch between climate model output and \r\nobservations, bias adjustment has been combined with stochastic \r\ndownscaling (Volosciuk et  al., 2017; Lange, 2019) or rescaled \r\nanalogues (Pierce et al., 2014). Other approaches known as emulators \r\nhave been developed to emulate an RCM using a statistical model "


id: 9d61097b6f24349b6d7eafea38707b10
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 22289
content:
"and also applied to a range of driving global models (Deque et al., \r\n2012; Haas and Pinto, 2012; Walton et  al., 2015, 2017; Beusch \r\net al., 2020; Erlandsen et al., 2020)."


id: 4b577674ab7d5d2d108a7904a384b803
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 9267
content:
"PGW experiments, boundary conditions for the downscaling are taken \r\nfrom reanalysis data, but modified according to the thermodynamic \r\nsignals of climate change. The boundary conditions thus represent \r\nthe sequence of observed weather, but with adjusted temperatures, \r\nhumidity and atmospheric stability. Recent applications of PGW \r\nexperiments include assessments of climate change in Japan (Adachi \r\net  al., 2012; Kawase et  al., 2012, 2013), the Los Angeles area \r\n(Walton et al., 2015), Hawaii (C. Zhang et al., 2016), and the Alps \r\n(Keller et  al., 2018). Recently, PGW studies have been generalized \r\nto modify global model simulations with the objective of separating \r\nthe drivers of regional climate change, such as the Mediterranean \r\namplification (e.g., Brogli et al., 2019b; Section 10.3.2.3)."


id: 8f8ff4cd51344102c9d90ef778c52931
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 20134
content:
"The most commonly used model experiments to generate regional \r\nclimate information are transient simulations. Alternative experiment \r\ntypes serve specific purposes. The role of these experiment types for \r\ngenerating regional climate information is assessed in this subsection."


id: ae718a6f6274563a121f5fb892045c7d
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 24974
content:
"Transient simulations intend to represent the evolving climate \r\nstate of the Earth system (Chapter  4). They are typically based on \r\ncoupled global model simulations, such as those in the Diagnostic, \r\nEvaluation and Characterization of Klima (DECK) and ScenarioMIP \r\npart of CMIP6 covering the period 1850-2100 (Eyring et al., 2016a), \r\nand HighResMIP (1950-2050; Haarsma et al., 2016). Global transient \r\nclimate simulations may be further downscaled by either dynamical or \r\nstatistical downscaling. Currently available CORDEX RCM simulations \r\n(1950-2100) are based on CMIP5 (Gutowski Jr. et al., 2016)."


id: 9449a77aa972c890659fc796fe615a8c
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 20840
content:
"Sensitivity studies are used to identify the impact of a specific forcing, \r\ndriver or process on regional climate phenomena and changes and \r\nimprove the process understanding. The influence of a single external \r\nforcing can be assessed with transient historical simulations within \r\ntwo different frameworks (Bindoff et al., 2013; Gillett et al., 2016). The \r\nfirst entails simulations taking prescribed (often observed) changes \r\nonly in the external forcing of interest, the others being fixed at \r\na constant value (often pre-industrial). The second framework is based \r\non simulations in which all external forcings are applied other than \r\nthe one of interest. Both approaches may not give the same results \r\nsince the climate response to a  range of forcings is not necessarily \r\nequal to the sum of climate responses to individual forcings (Ming and \r\nRamaswamy, 2011; Jones et al., 2013; Schaller et al., 2013; Shiogama \r\net al., 2013; Marvel et al., 2015; Deng et al., 2020)."


id: c1ffe11981da4e08c69ba91687ac5e62
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 28101
content:
"Results from downscaling experiments often suffer from large-scale \r\ncirculation biases in the driving global models such as misplaced storm \r\ntracks (Section 10.3.3.4), while changes in atmospheric circulation \r\nare often uncertain owing to both climate response uncertainty \r\n(Section 10.3.4.2) and internal variability (Section 10.3.4.3). In a given \r\napplication, if one can assume that changes in the regional climate \r\nare dominated by thermodynamic rather than by circulation changes, \r\nso-called pseudo-global warming (PGW) experiments (Schar et  al., \r\n1996) may be helpful in mitigating the effects of circulation biases, \r\nand to fix the large-scale circulation to present climate. In classical "


id: 37ca929a2c7e900defc60851f5d41284
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 6152
content:
"To study the influence of internal variability, new approaches such \r\nas partial coupling simulations are now routinely used since AR5. \r\nThese are coupled ocean-atmosphere simulations in which the \r\ninteraction between atmosphere and ocean is only one-way over \r\na specified ocean basin or sub-basin and two-way everywhere else. \r\nDifferent implementations have been used such as SST anomaly \r\nNewtonian relaxation at the air-sea interface or prescription of \r\nwind-stress anomalies from reanalysis (Kosaka and Xie, 2013, 2016; \r\nEngland et  al., 2014; McGregor et  al., 2014; Douville et  al., 2015; \r\nDeser et al., 2017a). Such simulations have been applied to identify "


id: 5fc636601ba98be14b54d978397a0e9c
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 12645
content:
"Zubler et al., 2011; Nabat et al., 2014; Gutierrez et al., 2018; Druge et al., \r\n2019; Bozkurt et al., 2020) and the added value of downscaling compared \r\nto the reanalysis fields (Section 10.3.3.2). Although the reanalysis model \r\nitself can introduce biases especially for non-assimilated variables (such \r\nas precipitation) it is assumed that in such a  setting, discrepancies \r\nbetween the modelled and observed climate arise mostly from errors \r\nin the downscaling method (Laprise et  al., 2013) or internal climate \r\nvariability generated by the downscaling method (Bohnisch et  al., \r\n2020; Ehmele et  al., 2020). Since AR5, reanalysis-driven RCMs have \r\nbeen extensively evaluated for many regions, especially in the CORDEX \r\nframework (see region specific examples in the Atlas)."


id: bea615009e9c4a6a8c06e946f168b1b1
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 27550
content:
"the regional impacts of the Pacific Decadal Variability (PDV) and \r\nAtlantic Multi-decadal Variability (AMV) (Kosaka and Xie, 2013; \r\nWatanabe et  al., 2014; Delworth et  al., 2015; Boer et  al., 2016; \r\nRuprich-Robert et al., 2017, 2018)."


id: f4c01c26eaf0489aed216ac8ff04c2b2
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 36013
content:
"Nudging experiments have been used to identify the relative roles of \r\ndynamic and thermodynamic processes in climate model biases and \r\nspecific extreme events (Wehrli et al., 2018, 2019). Another related \r\nframework is used to evaluate the impact land conditions have on \r\na climate phenomenon in a pair of experiments with one simulation \r\nserving as control run, and a perturbed simulation with prescribed \r\nland conditions (i.e., soil moisture, leaf area index, or surface albedo) \r\ncharacterizing a specific state of the land surface (i.e., afforestation \r\nor deforestation). The difference between the perturbed and control \r\nsimulations enables a robust assessment of the possible impact of \r\nland conditions on events like droughts and heatwaves (Seneviratne \r\net al., 2013; Stegehuis et al., 2015; Hauser et al., 2016, 2017; van den \r\nHurk et al., 2016; Vogel et al., 2017; Rasmijn et al., 2018; Strandberg \r\nand Kjellstrom, 2019)."


id: 59930acf7d00024c4b84d13ca6e876c9
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 11591
content:
"Over Europe, the VALUE initiative assessed statistical downscaling \r\nfor marginal, temporal, and spatial aspects of temperature and \r\nprecipitation including extremes, and performed a  process-based \r\nevaluation of specific climatic phenomena (Gutierrez et  al., 2019; \r\nMaraun et al., 2019a). Alternatively, statistical downscaling can be \r\nevaluated in so-called perfect model or pseudo-reality simulations \r\n(Charles et  al., 1999), where a  high-resolution climate model \r\nsimulation is used as a proxy for a hypothetical present and future \r\nrealities. A statistical downscaling model is first calibrated with this \r\npseudo present-day climate and, subsequently, assessed whether it \r\ncorrectly reproduces the pseudo-future conditions (Dixon et al., 2016)."


id: da2544aee95bd2d840cae0d21b2778b5
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 31843
content:
"RCM sensitivity simulations have been used in a  similar way to \r\nassess the contribution of external forcings and large-scale drivers \r\nto projected regional climate change (Nabat et  al., 2014; Brogli \r\net al., 2019a, b) and the influence of selected drivers on observed \r\nextreme events (Meredith et  al., 2015b; J. Wang et  al., 2017; \r\nArdilouze et al., 2019)."


id: c7980b0e8444577009b1ae0641413b75
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 28987
content:
"In summary, there is robust evidence that sensitivity experiments are \r\nkey to assessing the influence of different forcings and drivers on \r\nregional climate change."


id: 77a88190504bf8eecee8f4b8875f8a6b
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 16382
content:
"Assessing model performance is a prerequisite for building confidence in \r\nregional climate projections. This subsection assesses the performance \r\nof different model types at simulating regional climate and climate \r\nchange. The subsection builds on the assessment of global model \r\nperformance in Chapter 3, and complements the model assessment in \r\nChapter 8, which focuses on the water cycle, and the Atlas."


id: d1a3daa261f23f022f1d04d791a84f46
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 30513
content:
"Experiments driven by quasi-perfect boundary conditions or predictors \r\n(observations or reanalysis) can be useful to evaluate downscaling \r\nperformance (Frei et  al., 2003; Laprise et  al., 2013), including \r\nthe simulation of observed past trends (Lorenz and Jacob, 2010; "


id: 48946f0636c63cc43f7c04b9e054692d
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 8795
content:
"Figure 10.6 shows the mean summer temperature and precipitation \r\nbiases of several state-of-the-art climate model ensembles for \r\nthe western Mediterranean. It additionally illustrates the role of "


id: abf802831cbf806aa66847013c3f4471
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 24577
content:
"observational uncertainty for model evaluation (Section 10.2), where \r\nobservations display differences that can be substantial. Model \r\nperformance varies strongly from model to model, but also between \r\nensembles. These biases are an expression of model error that leads \r\nto misrepresented phenomena and processes, and thus limit the \r\nconfidence in future projections of regional climate. The focus of this \r\nsubsection is therefore to evaluate the representation of relevant \r\nregional-scale phenomena for representing regional climate."


id: 2ab78a9f97e6caa28f10da064b05d746
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 4146
content:
"usually synoptic-scale weather patterns. The regime-based approach \r\nhas been used with both global models (e.g.,  Barton et  al., 2012; \r\nCatto et al., 2015; Taylor et al., 2019) and RCMs (Endris et al., 2016; \r\nBukovsky et al., 2017; Whan and Zwiers, 2017; Pinto et al., 2018), but \r\nalso with perfect prognosis and bias adjustment methods (Marteau \r\net al., 2015; Addor et al., 2016; Beranova and Kysely, 2016; Soares and \r\nCardoso, 2018; Soares et al., 2019b)."


id: d715e9a1aa40d7e845824cb3ad28b17f
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 31352
content:
"There is high confidence that to assess whether a  climate model \r\nrealistically simulates required aspects of present-day regional \r\nclimate, and to increase confidence of future projections of these \r\naspects, evaluation needs to be based on diagnostics taking into \r\naccount multiple variables and process understanding."


id: b55dde96b97d8fd3fb42c708f727df32
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 26072
content:
"New diagnostics for multivariate dependencies are needed to \r\ncharacterize compound events (Section  11.8; Hobaek Haff et  al., \r\n2015; Wahl et al., 2015; Sippel et al., 2016, 2017; Tencer et al., 2016; \r\nBevacqua et al., 2017; Careto et al., 2018; Zscheischler et al., 2018). \r\nHowever, their success depends on the availability of adequate \r\nobservational data (Section  10.2.2). Multivariate dependencies \r\ndiscovered in compound events can also be used for designing and \r\nevaluating multivariate bias adjustment and statistical downscaling. \r\nProcess-based diagnostics are useful for identifying the cause of model \r\nerrors, although it is not always possible to associate a  systematic \r\nerror with a  specific cause (Eyring et  al., 2019). AR5 discussed \r\ntwo approaches of process-based evaluation: (i) the isolation of \r\nphysical components or parametrizations by dedicated experiments \r\n(Section 10.3.2.4) and (ii) diagnostics conditioned on relevant regimes, "


id: 5a17442975f56c86c7fb9c0e33411a29
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 11686
content:
"Obtaining regional information from global simulations may involve \r\na  range of different methods (Section  10.3.1). An approach with \r\nhigher complexity or resolution is useful if it adds further, useful \r\ninformation to that of a reference model. Section 10.5 discusses the \r\nset of considerations that determine if the information is useful. This \r\nfurther useful information is often referred to as added value and \r\nis a function of variables, processes, and the temporal and spatial \r\nscales targeted taking into account the needs of specific users \r\n(Di Luca et al., 2012; Ekstrom et al., 2015; Giorgi and Gutowski, 2015; \r\nTorma et  al., 2015; Rummukainen, 2016; Falco et  al., 2019). There \r\nis no common definition of added value, but here it is considered \r\na  characteristic that arises when one methodology gives further \r\nvalue to what another methodology yields."


id: feda394cda1226acb044996874a97470
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 37643
content:
"a specific regional climate and its change can be highly uncertain \r\n(Hall, 2014). RCMs have difficulties improving especially large-scale \r\ncirculation biases, although some successful examples exist. But \r\ndue to their enhanced representation of complex topography and \r\ncoastlines, RCMs may add value to simulating the regional expression \r\nof teleconnections. Bias adjustment cannot mitigate fundamental \r\nmisrepresentations of the large-scale atmospheric circulation \r\n(Maraun et  al., 2017, Cross-Chapter Box  10.2). This subsection \r\nillustrates the relevance of large-scale circulation biases for regional \r\nclimate assessments with selected examples from the mid- to high \r\nlatitudes and tropics."


id: 1fe94b6e3459ff6493769ddc45f1b316
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 2456
content:
"10.3.3.3.1 Mid- to high-latitude atmospheric variability \r\nphenomena: Blocking and extratropical cyclones"


id: f0daddecd6931c30f453d223ddb29206
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 35389
content:
"A first step in determining added value in downscaling is to analyse \r\nwhether the downscaling procedure gives detail on spatial or \r\ntemporal scales not well-resolved by a global model, thus potentially \r\nrepresenting climatic features missing in the GCM. This added detail, \r\nreferred to as potential added value (PAV; Di Luca et al., 2012), is \r\ninsufficient for demonstrating added value in downscaling (Takayabu \r\net al., 2016), but lack of PAV indicates that the downscaling method \r\nlacks usefulness. Added value is not guaranteed simply by producing \r\nmodel output at finer resolution. It depends on several factors, such \r\nas the simulation setup and the specific climatic variables analysed \r\n(Di Luca et al., 2012; Hong and Kanamitsu, 2014; Xue et al., 2014). \r\nA variety of performance measures are needed to assess added value \r\n(Section  10.3.3.1; Di Luca et  al., 2016; Wilks, 2016; Ivanov et  al., \r\n2017, 2018; Soares and Cardoso, 2018)."


id: f6aa65adc5a620416bf4be724f09cc3
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 36322
content:
"A further challenge, especially at increasingly higher resolutions, is \r\nthat adequate observational data may not be available to assess \r\nadded value (Section 10.2, e.g., Di Luca et al., 2016; Zittis et al., 2017; \r\nBozkurt et  al., 2019). This implies a  need for additional efforts to \r\nobtain, catalogue and quality-control higher resolution observational \r\n(or observation-based) datasets (Thorne et al., 2017; Section 10.2). \r\nUnivariate demonstration of added value is necessary, but may be \r\ninsufficient, as better agreement with observations in the downscaled \r\nvariable may be a consequence of compensating errors that are not \r\nguaranteed to compensate similarly as climate changes. Multivariate \r\nanalysis of added value is better able to demonstrate physical \r\nconsistency between observed and simulated behaviour (Prein et al., \r\n2013a; Meredith et al., 2015a; Reboita et al., 2018)."


id: dae87709a8d07a57124680b0f22ac5a7
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 31960
content:
"10.3.3.3 Performance at Simulating Large-scale Phenomena \r\nand Teleconnections Relevant for Regional Climate"


id: c21f8ede7e117bb457345d2749bf8067
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 28124
content:
"Regional climate is often controlled by large-scale weather phenomena, \r\nmodes of variability and teleconnections (e.g., Sections 2.3 and 2.4, \r\nAnnex IV). In particular, extreme events are often caused by specific, \r\nin some cases persistent, circulation patterns (Sections 11.3-11.7). \r\nIt is therefore important for climate models to reasonably represent \r\nnot only continental, but also regional climate and its variability for \r\nsuch extremes. As explained in Section  3.3.3, standard resolution \r\nglobal models can suffer biases in the location, occurrence frequency \r\nor intensity of large-scale phenomena, such that statements about "


id: 9d5ed50d3fd6e5872a3e008f201d5c77
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 22287
content:
"RCMs have a  very limited ability to reduce large-scale circulation \r\nerrors of the driving GCM (Hall, 2014). In a study of five ERA-Interim\u0002driven RCMs, Jury et al. (2018) showed that RCMs typically simulate \r\nfewer blocking events over Europe than are present in the driving \r\ndata, irrespective of the RCM horizontal resolution. Based on a simple \r\nblocking bias-decomposition method, they suggest that blocking \r\nfrequency biases can contribute to the RCM mean surface biases. \r\nOver some large domains, reanalysis-driven RCMs can significantly \r\nimprove the representation of storm characteristics compared to the "


id: 7d1837474b9c9aacc733b688d0cbe961
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 17271
content:
"The robust quantification of the influence of atmospheric circulation \r\nerrors on regional climate remains a challenge as many parametrized \r\nprocesses such as cloud radiative effects and soil moisture or snow \r\nfeedbacks can also contribute and interact with the circulation errors. \r\nAtmospheric nudging experiments where the simulated circulation is \r\nconstrained to be close to that observed have been used to separate \r\nthe circulation effect from other contributions to regional climate \r\nbiases (Wehrli et al., 2018). The nudging approach requires detailed \r\nand careful implementation in order to limit detrimental effects due \r\nto the added tendency term in the model equations (Zhang et al., \r\n2014; Lin et al., 2016). Based on single-model experiments, Wehrli \r\net  al. (2018) show that the circulation induced biases are often \r\nnot the main contributors to mean and extreme temperature and \r\nprecipitation biases for many regions and seasons."


id: 2d3896f5aae60412bd72288b6544554c
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 4562
content:
"There is high confidence that atmospheric circulation biases can \r\ndeteriorate the model representation of regional land surface climate. \r\nAssessing the relative contributions of atmospheric circulation and \r\nother sources of bias remains a challenge due to the strong coupling \r\nbetween the atmosphere and other components of the climate \r\nsystem, including the land surface."


id: cf2c89aea7c503738a455437ec989fd7
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 30130
content:
"Model performance in simulating ENSO characteristics, including \r\nENSO spatial pattern, frequency, asymmetry between warm and cold \r\nevents, and diversity, is assessed in Chapter  3 (Section  3.7.3). The \r\nability of the recent generation of GCMs and RCMs to adequately \r\nsimulate ENSO-related teleconnections is reviewed here along with \r\nrelevant methodological issues (see also Annex IV2.3.2, Figure 3.38 \r\nand Section 3.7.3)."


id: 55eaf6e23d9ae272c7d7c8a3ad89d46d
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 11000
content:
"Langenbrunner and Neelin (2013) show that there is little \r\nimprovement in CMIP5 relative to CMIP3 in amplitude and spatial \r\npatterns of the ENSO influence on boreal winter precipitation \r\n(spatial  pattern correlations against observations are typically less \r\nthan 0.5). However, the CMIP5 ensemble accurately represents \r\nthe amplitude of the precipitation response in regions where \r\nobserved teleconnections are strong. Garcia-Villada et  al. (2020) \r\nfound a decline in performance of the representation of simulated "


id: 2179a296a61a57dff5bed4cb73e4891f
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 2688
content:
"Biases in the representation of large-scale atmospheric circulation can \r\nresult in biased representation of regional climate. While, in principle, "


id: 975c6799224ce19b1e940270cb651b21
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 21337
content:
"Regional climate is shaped by a wide range of weather phenomena \r\noccurring at scales from about 2000 km to 2 km (Figure 10.3). These \r\nmodulate the influence of large-scale atmospheric phenomena and \r\ncreate the characteristic and potentially severe weather conditions. \r\nThe climate in different regions will be affected by different mesoscale \r\nphenomena, of which several may be relevant. A skilful representation \r\nof these phenomena is a  necessary condition for providing credible \r\nand relevant climate information for a given region and application. \r\nTherefore, it is important to understand the strengths and weaknesses \r\nof different model types in simulating these phenomena. The \r\nperformance of different dynamical climate model types to simulate \r\na selection of relevant mesoscale weather phenomena is assessed here."


id: 3987cbf000114772cf1ec15766cbdbb9
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 6428
content:
"In RCMs, the effects of tropical large-scale modes and \r\nteleconnections are inherited through the boundary conditions \r\nand influenced by the size of the numerical domain. Done et  al. \r\n(2015) and Erfanian and Wang (2018) claim that large domains that \r\ninclude source oceanic regions are required to capture the remote \r\ninfluence of teleconnections, although, without spectral nudging, \r\nthis can lead to biased synoptic-scale patterns (Prein et al., 2019). \r\nRCMs generally reproduce the regional precipitation responses \r\nto ENSO, and can sometimes even improve the representation of \r\nthese teleconnections compared to the driving reanalysis (Endris \r\net  al., 2013; Fita et  al., 2017), but the overall performance may \r\ndepend both on the driving reanalysis or GCM (Endris et al., 2016; \r\nChandrasa and Montenegro, 2020) and on the chosen RCMs (Whan \r\nand Zwiers, 2017)."


id: c339158c2d371a8f4c8b68a100ed5418
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 28285
content:
"New studies since AR5 have shown that model performance \r\nassessment regarding ENSO teleconnections remains a  difficult \r\nchallenge due to the different types of ENSO and model errors in \r\nENSO spatial patterns, as well as the strong influence of atmospheric \r\ninternal variability at mid- to high latitudes (Coats et al., 2013; Polade \r\net  al., 2013; Capotondi et  al., 2015; Deser et  al., 2017c; Tedeschi \r\nand Collins, 2017; Garcia-Villada et  al., 2020). Another difficulty \r\ncomes from the non-stationary aspects of teleconnections in both \r\nobservations and models, raising methodological questions on how \r\nbest to compare a given model with another model or observations \r\n(Herein et al., 2017; Perry et al., 2017; O'Reilly, 2018; O'Reilly et al., \r\n2019; Abram et al., 2020)."


id: 777de1dde7931bab0d623d1040671faa
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 16353
content:
"There is robust evidence that an accurate representation of both \r\natmospheric circulation and sea surface temperature (SST) variability \r\nare key factors for the realistic representation of ENSO teleconnections \r\nin climate models. A  robust and thorough evaluation of model \r\nperformance regarding ENSO teleconnections is a challenging task \r\nwith many methodological issues related to asymmetry between the \r\nwarm and cold phases, non-stationarity and time-varying interaction \r\nbetween the Pacific and other ocean basins, signal-to-noise issues \r\nin the mid-latitudes and observational uncertainties, particularly for \r\nprecipitation (Section 10.2.2.3)."


id: c39241246a92b05a5c74ca8327c93a2f
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 28340
content:
"4 km resolution showed good skill in simulating the diurnal cycle \r\nof temperature and wind on days of weak synoptic forcing in the \r\nRocky Mountains (Letcher and Minder, 2017) as well as in simulating \r\nthe mountain-plain wind circulation over the Tianshan mountains in \r\ncentral Asia (Cai et al., 2019), while in the Alps, a 1 km resolution has \r\nbeen required (Zangl, 2004)."


id: 26b5b0b54e76958e93a65d0af659ced7
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 3487
content:
"Fohn winds are synoptically-driven winds across a mountain range \r\nthat are warm and dry due to adiabatic warming in the downwind \r\nside. In an RCM study for the Japanese Alps, Ishizaki and Takayabu \r\n(2009) found that at least 10 km resolution was required to \r\nrealistically simulate the basic characteristics of Fohn events."


id: e3876dd159a2a44bb1395a75f150785b
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 33276
content:
"There is high confidence that climate models with resolutions \r\nof around 10 km or finer are necessary for realistically simulating \r\nmountain wind systems such as slope and valley winds and the \r\nchannelling of winds in valleys."


id: eef80cd0afe2bad7c1d5c9309fa7d4b9
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 35072
content:
"Simulating coastal climates and the influence of big lakes are \r\na modelling challenge, due to the complex coastlines, the different \r\nheat capacities of land and water, the resulting wind system, and \r\ndifferential evaporation. The AR5 concluded that RCMs can add value \r\nto the simulation of coastal climates."


id: 9266a54c6b00a1c41dc5d28dd7af6e6c
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 20556
content:
"Initial studies with convection-permitting global models suggests \r\nthat improvements in representing convection, as described for \r\nRCMs above, have a positive impact on the tropical and extratropical \r\natmospheric circulation and, thus, regional climate (Satoh et  al., \r\n2019; Stevens et al., 2019; see also Section 8.5.1.2 and Chapter 7). \r\nComputational constraints currently limit these simulations to \r\na length of few months only, such that they cannot yet be used for \r\nroutine climate change studies."


id: 19c4913c02b1015f06415f499d918dab
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 1524
content:
"Summer coastal low-level jets off the mid-latitude western continental \r\ncoasts are forced by the semi-permanent subtropical anticyclones, \r\ninland thermal lows, strong across-shore temperature contrasts in \r\nupwelling regions, and high coastal topography. They are important \r\nfactors in shaping regional climate by, for instance, preventing \r\nonshore advection of humidity and thereby causing aridity in the \r\nIberian Peninsula (Soares et al., 2014), or by transporting moisture \r\ntowards precipitating regions as in the North American monsoon \r\n(Bukovsky et al., 2013)."


id: b8d85a7f7b26d209f0626b2e5d513359
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 26608
content:
"Mountain slope and valley winds are localized thermally generated \r\ndiurnal circulations that have a strong influence on temperature and \r\nprecipitation patterns in mountain regions. During the day, heating \r\nof mountain slopes induces upslope winds; during the night this \r\ncirculation reverses. This phenomenon is not realistically represented \r\nby global models and coarse-resolution RCMs. RCM simulations at "


id: b09704ae767f90e06d4cf197a56065ec
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 25302
content:
"Reanalyses and most global models do not well resolve the details \r\nof coastal low-level jets (Bukovsky et al., 2013; Soares et al., 2014), \r\nbut they are still able to represent annual and diurnal cycles and \r\ninterannual variability (Cardoso et  al., 2016; Lima et  al., 2019). \r\nBukovsky et al. (2013) found RCM simulations at a 50 km resolution "


id: 50ee45bfd35002976573108a5b52c14b
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 10183
content:
"to improve the representation of the coastal low-level jet in the Gulf \r\nof California and the associated precipitation pattern compared to \r\nthe driving global models. Lucas-Picher et  al. (2017) find indirect \r\nevidence via precipitation patterns that 12 km simulations further \r\nimprove the representation. Soares et al. (2014) demonstrated that \r\nan 8 km resolution RCM simulated a  realistic three-dimensional \r\nstructure of the Iberian coastal low-level jet, and the surface winds \r\ncompare well with observations. Lucas-Picher et al. (2017) showed \r\nthat a  0.44deg resolution RCM underestimated winds along the \r\nCanadian east coast, whereas a 0.11deg resolution version simulated \r\nmore realistic 10 metre wind speed. Also, the Etesian winds in the \r\nAegean Sea were realistically simulated by 12 km resolution RCMs \r\n(Dafka et al., 2018)."


id: c550652cff281ed4c113c92d030430b0
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 28625
content:
"in an RCM to realistically represent regional temperatures (Samuelsson \r\net al., 2010; Deng et al., 2013; Mallard et al., 2014; Thiery et al., 2015; \r\nPietikainen et al., 2018), as well as remote effects (Spero et al., 2016). \r\nThe most common approach in RCMs is the two-layer lake model, \r\nincluding a  lake-ice model, with parametrized vertical temperature \r\nprofiles (Mironov et al., 2010; Golosov et al., 2018). For the Caspian \r\nSea, it is found that a three-dimensional ocean model simulated the \r\nSST fields better than a one-dimensional lake model when coupled to \r\nthe same RCM (Turuncoglu et al., 2013)."


id: 9c7649d58c386fcc7fa0a9731f1c29df
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 22171
content:
"There is medium evidence and high agreement that it is important to \r\ninclude interactive lake models in RCMs to improve the simulation \r\nof regional temperature, in particular in seasonally ice-covered areas \r\nwith large fractions of lakes. There is medium evidence of the local \r\ninfluence of lakes on snow and rainfall as well as the importance of \r\nincluding lakes in regional climate simulations."


id: 5e8a9bcefc6c9937477a9ad5b7124b65
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 12410
content:
"Big lakes modify the downwind climate. In particular during winter \r\nthey are relatively warm compared to the surrounding land, provide \r\nmoisture, destabilize the passing air column and produce convective \r\nsystems. The increase in friction when moving air reaches land causes \r\nconvergence and uplift, and may trigger precipitation. Gula and \r\nPeltier (2012) found that a state-of-the-art GCM does not realistically \r\nsimulate these effects over the North American Great Lakes, but \r\na 10 km RCM better represents them and thereby simulates realistic \r\ndownwind precipitation patterns, in particular enhanced snowfall \r\nduring the winter season. Similar results were found by Wright \r\net  al. (2013), Notaro et  al. (2015) and Lucas-Picher et  al. (2017). \r\nIn a  convection permitting simulation of the Lake Victoria region, \r\na  too strong nocturnal land breeze resulted in unrealistically high \r\nprecipitation (Finney et al., 2019)."


id: 6b921caaa7d55ad61fdcd609dad100e5
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 14547
content:
"There is high confidence that climate models with sufficiently high \r\nresolution are necessary for realistically simulating lake and coastal \r\nweather including coastal low-level jets, lake and sea breezes, as well \r\nas lake effects on rainfall and snow."


id: 75c7de44af5f6907a71688d518217109
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 16125
content:
"Both SRCCL (Jia et al., 2019) and SROCC (Hock et al., 2019) highlight \r\nthe weaknesses of climate models at simulating atmosphere-surface \r\nfeedbacks. The performance at simulating some of these feedbacks \r\nis assessed below (climate feedbacks in urban areas are discussed in \r\nBox 10.3)."


id: 12ecc39f839293a4fb3c75bc00699b40
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 483
content:
"In regions like Fenno-Scandinavia or central-eastern Canada, very large \r\nfractions of land are covered by small and medium sized lakes. Other \r\nregions have fewer but larger lakes, such as central-eastern Africa, \r\nthe eastern border between the USA and Canada, and central Asia. In \r\nthese regions it has been considered essential to include a lake model "


id: fbf321df42efba7084ce0a06f48a78d1
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 37133
content:
"The influence of ice-sheet mass balance on regional climate, explored \r\nwith global and regional models by (Noel et al., 2018; Fettweis et al., \r\n2020), is discussed in Section 9.4."


id: dfcad4ceea0c2f43559516112402f8e
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 32705
content:
"Volcanoes also provide forcings with a  marked regional impact \r\n(Cross-Chapter Box  4.1). This implies that models are expected to \r\ncapture these effects (Bethke et al., 2017). Both proxy analyses and \r\nsimulations have demonstrated reduced Asian monsoon precipitation \r\nafter tropical and Northern Hemisphere (NH) volcanic eruptions due \r\nto reduced humidity and divergent circulation (Man and Zhou, 2014; \r\nZhuo et al., 2014; F. Liu et al., 2016; Stevenson et al., 2016). Global \r\nmodel experiments (Zanchettin et al., 2013; Ortega et al., 2015; Sjolte \r\net al., 2018; Michel et al., 2020) have suggested that tropical volcanic "


id: c02505fe20afc063ce9144afcc2b3cbd
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 27784
content:
"daytime temperature extremes, few compare modelled results with \r\nobservations. Global model studies have found improvements in \r\nsimulated surface temperature when including irrigation (Thiery \r\net  al., 2017), in particular in areas where the model used has \r\na  strong land-atmosphere coupling (Chen and Dirmeyer, 2019). \r\nAn RCM study over the North China Plain showed that the inclusion \r\nof irrigation led to a better representation of the observed nighttime \r\nwarming (Chen and Jeong, 2018)."


id: 48a80a671a64d8356281a8dac3982882
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 8808
content:
"There is medium confidence that representing irrigation is important \r\nfor a realistic simulation of South Asian monsoon precipitation. There \r\nis limited evidence that including irrigation in climate models improves \r\nthe simulation of maximum and minimum daily temperatures as well \r\nas precipitation for other regions."


id: 610b905739a1f243327765e49f400a2
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 12840
content:
"Regional land-radiation management, including modifying the \r\nalbedo through, for instance, no-tillage practices, has been suggested \r\nas a  measure to decrease regional maximum daily temperatures \r\n(see  review in Seneviratne et  al., 2018), but although modelled \r\nresults and theoretical understanding are coherent, few studies \r\nhave verified the results with observations. Hirsch et al. (2018) is an \r\nexception, showing that implementing minimal tillage, crop residue \r\nmanagement and crop rotation in a global model over regions where \r\nit is practiced, improves the simulation of surface heat fluxes."


id: 2ea1001ff3133878e4f47c64e7e3978f
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 4781
content:
"The performance of statistical downscaling models, bias adjustment \r\nand weather generators is determined by the chosen model structure \r\n(e.g.,  to represent variability and extremes or spatial dependence) \r\nand, if applicable, the predictors selected (Maraun et  al., 2019a). \r\nThe VALUE initiative has assessed a  range of such methods in \r\na perfect-predictor experiment where the predictors are taken from \r\nreanalysis data (Maraun et al., 2015, 2019a; Gutierrez et al., 2019). \r\nTable 10.2 shows an overview comprising performance results from \r\nVALUE and other studies. These results isolate the performance of the \r\nstatistical method in the present climate. The overall performance in \r\na climate change application also depends on the performance of the \r\ndriving climate model (Sections 10.3.3.3-10.3.3.6) and the fitness of \r\nboth the driving model and the statistical method for projecting the \r\nclimatic aspects of interest (Section 10.3.3.9)."


id: 6566a5f4ad60b9e9052d4c0cb6d82b40
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 13530
content:
"The inclusion of irrigation in global and regional models over the \r\nSouth Asian monsoon region (Section  10.6.3) has been found to \r\nbe important to represent the monsoon circulation and rainfall \r\ncorrectly (Lucas-Picher et al., 2011; Guimberteau et al., 2012; Shukla \r\net al., 2014; Tuinenburg et al., 2014; Cook et al., 2015a; Devanand \r\net al., 2019). Similarly, the inclusion of irrigation over northern India \r\nand western Pakistan could be important for the correct simulation \r\nof precipitation over the Upper Indus Basin in northern Pakistan \r\n(Saeed et  al., 2013). Irrigation in the East African Sahel inhibits \r\nrainfall over the irrigated region and instead enhances rainfall to the \r\neast, coherent with both observations and theoretical understanding \r\nof the local circulation anomalies induced by the lower surface \r\nair temperatures over the irrigated region (Alter et  al., 2015). \r\nAlthough several studies show how modelled irrigation reduces "


id: ee61cb96116d1fe11856fdc47b80e018
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 34974
content:
"Perfect prognosis methods can perform well when the synoptic \r\nforcing (i.e., the explanatory power of large-scale predictors) is strong \r\n(Schoof, 2013). Using this approach, downscaling of precipitation is \r\nparticularly skilful in the presence of strong orographic forcing. The \r\nrepresentation of daily variability and extremes requires analogue \r\nmethods or stochastic regression models, although the former \r\ntypically do not extrapolate to unobserved values (Gutierrez et al., \r\n2019; Hertig et al., 2019). Temporal precipitation variability is well\u0002represented by analogue methods and stochastic regression, but \r\nanalogue methods typically underestimate temporal dependence \r\nof temperature (Maraun et al., 2019b). Spatial dependence of both \r\ntemperature and precipitation is only well-represented by analogue "


id: 8b1db4833abbdcd3a6ed1d6a01474684
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 19412
content:
"Table 10.2 | Performance of different statistical method types in representing local weather at daily resolution. Individual state-of-the-art implementations \r\nmay perform better. '+': should work reasonably well based on empirical evidence and/or expert judgement; 'o': problems may arise depending on the specific context; '-': weak \r\nperformance either by construction or inferred from empirical evidence; '?': not studied. The categorisation assumes that predictors are provided by a well-performing dynamical \r\nmodel. Statements about extremes refer to moderate events occurring at least once every 20 years. Adopted and extended from Maraun and Widmann (2018b)."


id: 77cc83981fb3f96377508c7992fc5716
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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vector_id: 16408
content:
"References: (1) Casanueva et al. (2020); (2) Dubrovsky et al. (2019); (3) Evin et al. (2018); (4) Frost et al. (2011); (5) Gutierrez et al. (2013); (6) Gutierrez et al. (2019); \r\n(7) Gutmann et al. (2014); (8) Hertig et al. (2019); (9) Hu et al. (2013a); (10) Huth et al. (2015); (11) Keller et al. (2015); (12) Maraun et al. (2019b); (13) San-Martin et al. (2017); \r\n(14) Widmann et al. (2019)."


id: e8c7969865ca6bf90bb6af4320409671
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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vector_id: 34138
content:
"methods, for which analogues are defined jointly across locations, \r\nand by stochastic regression methods explicitly representing spatial \r\ndependence (Widmann et al., 2019). Overall, there is high confidence\r\nthat analogue methods and stochastic regression are able to \r\nrepresent many aspects of daily temperature and variability, but \r\nthe analogue method is inherently limited in representing climate \r\nchange (Gutierrez et al., 2013)."


id: 2b025f7529f37061ec2e0cb133467774
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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vector_id: 4196
content:
"This subsection assesses the performance of bias adjustment in \r\na perfect predictor context. In practice, climate model imperfections \r\nmay cause substantial additional issues in the application of \r\nbias adjustment. These are assessed separately in Cross-Chapter \r\nBox 10.2."


id: 38149a12efaafd0e2273764d5cae5533
created_at: 2023-03-16 14:00:48
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vector_id: 6195
content:
"underestimate precipitation trends (CMIP5) (Sections 3.3.1 and 3.3.2). \r\nFor regional trends, AR5 concluded that the CMIP5 ensemble cannot \r\nbe taken as a  reliable representation of reality and that the true \r\nuncertainty can be larger than the simulated model spread (Kirtman \r\net  al., 2014). Case studies of regional trend simulations by global \r\nmodels can be found in Sections 10.4.1 and 10.6, and region-by\u0002region assessments in the Atlas. A  key limitation for assessing the \r\nrepresentation of regional observed trends by single transient \r\nsimulations of global models (or downscaled versions thereof) is the \r\nstrong amplitude of internal variability compared to the forced signal \r\nat the regional scale (Section 10.3.4.3). Even on multi-decadal time \r\nscales, an agreement between observed and individual simulated \r\ntrends would be expected to occur only by chance (Laprise, 2014)."


id: 27502bdfb67215e51b31b1219135e102
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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vector_id: 3586
content:
"In the context of downscaling, the ability of downscaling methods to \r\nreproduce observed trends when driven with boundary conditions or \r\npredictors taken from reanalysis data (which reproduce the observed \r\ninternal variability on long time scales) can be assessed. For temperature \r\nin the continental USA, reanalysis-driven RCMs skilfully simulated recent \r\nspring and winter trends, but did not reproduce summer and autumn \r\ntrends, (Bukovsky, 2012). Over Central America, observed warming \r\ntrends were reproduced (Cavazos et al., 2020). In contrast, a reanalysis\u0002driven coupled atmosphere-ocean RCM covering the Mediterranean \r\ncould not reproduce the observed SST trend (Sevault et al., 2014)."


id: 421a31ec0ea79f8da249505a231d489c
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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index: climate_gpt_only_giec
vector_id: 7798
content:
"Several studies show improved simulations of present-day impacts, \r\nwhen the impact model is fed with bias-adjusted climate model \r\noutput, including the assessment of river discharge (Rojas et al., 2011; \r\nMuerth et al., 2013; Montroull et al., 2018), forest fires (Migliavacca \r\net al., 2013), crop production (Ruiz-Ramos et al., 2016), and regional \r\nocean modelling (Macias et al., 2018)."


id: 928757391283fe1082e2d5c24aee5e6e
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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index: climate_gpt_only_giec
vector_id: 20577
content:
"Similar studies have been carried out for statistical downscaling \r\nand bias adjustment using predictors from reanalyses (or in case of \r\nbias adjustment, dynamically downscaled reanalyses). For a  range \r\nof different perfect prognosis methods, Huth et  al. (2015) found \r\nthat simulated temperature trends were too strong for winter and \r\ntoo weak for summer. The performance was similar for the different \r\nmethods, indicating the importance of choosing informative predictors. \r\nSimilarly, Maraun et al. (2019b) found that the performance of perfect \r\nprognosis methods depends mostly on the predictor and domain \r\nchoice (for instance, temperature trends were only captured by those \r\nmethods including surface temperature as predictor). Bias adjustment \r\nmethods reproduced the trends of the driving reanalysis, apart from \r\nquantile mapping methods, which deteriorated these trends."


id: 31ac5986c0af36437a18d84eaac0aec0
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 5224
content:
"There is high confidence that bias adjustment can improve the \r\nmarginal distribution of simulated climate variables, if applied to \r\na climate model that adequately represents the processes relevant \r\nfor a given application (Cross-Chapter Box 10.2)."


id: 9e455933cc1bb0e33a2acb1b6fefbc9
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 22425
content:
"This section assesses how well climate models perform at realistically \r\nsimulating historical regional climatic trends. Current global model \r\nensembles reproduce global to continental-scale surface temperature \r\ntrends at multi-decadal to centennial time scales (CMIP5, CMIP6), but "


id: 90c41062e26cd20322f55a2e866ac560
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 20320
content:
"Overall, there is high confidence that including all relevant forcings is \r\na prerequisite for reproducing historical trends."


id: 9e6ff45b110ff4bcd935d69220b2aab0
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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index: climate_gpt_only_giec
vector_id: 22458
content:
"Including additional components, feedbacks and drivers can \r\nsubstantially modify the simulated future climate. For example, \r\nKjellstrom et  al. (2005) and Somot et  al. (2008) have shown that \r\na  regional ESM can significantly modify the SST response to \r\nclimate change of its driving global model with implications for \r\nthe climate change signal over both the sea and land. In particular, \r\ncoupled ocean-atmosphere RCMs may increase the credibility of \r\nprojections in regions of strong air-sea coupling such as the East \r\nAsia-western North Pacific domain (Zou and Zhou, 2016b, 2017). \r\nRecent studies demonstrate the importance of including regional \r\npatterns of evolving aerosols in RCMs for simulating regional climate \r\nchange (Boe et al., 2020a; Gutierrez et al., 2020). RCMs not including \r\nthe plant physiological response to increasing CO2 concentrations \r\nhave been shown to substantially underestimate projected increases "


id: 1f07a5faffb07f9d54732afdc47dd4e7
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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index: climate_gpt_only_giec
vector_id: 2322
content:
"The evaluation of model performance at historical variability and long\u0002term changes provides further relevant information (Flato et al., 2014). \r\nTrend evaluation may provide very useful insight, but has limitations, \r\nin particular at the regional scale, mainly due to multi-decadal internal \r\nclimate variability (Section  10.3.3.8), observational uncertainty \r\n(in  both driving reanalysis and local trends; Section  10.2), and the \r\nfact that often not all regional forcings are known, and that past \r\ntrends  may be driven by forcings other than those driving future \r\ntrends (Sections 10.4.1 and 10.6.3)."


id: 7c5cda4327931a579df44518313b5267
created_at: 2023-03-16 14:00:48
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content:
"in extreme temperatures across Europe compared to global models \r\nthat explicitly model this effect (Schwingshackl et al., 2019)."


id: ca6400135bc1664d278797f5efb34558
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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index: climate_gpt_only_giec
vector_id: 29406
content:
"Overall, there is high confidence that an assessment of model fitness \r\nfor projections applying process-based evaluation, process-based \r\nplausibility checks of projections and a comparison of different model \r\ntypes, increases the confidence in climate projections. There is high \r\nconfidence that increasing model resolution, dynamical downscaling, \r\nstatistical downscaling with well-simulated predictors controlling \r\nregional climate change, and adding relevant model components can \r\nincrease the fitness for projecting some aspects of regional climate \r\nwhen accompanied by a process-understanding analysis."


id: 2e97adb53f18d8e75453f88b690dc780
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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vector_id: 4774
content:
"A difference between the climate changes simulated by two models \r\ndoes not automatically imply the more complex or higher resolution \r\nmodel is superior (e.g.,  Dosio et  al., 2019). Studies comparing \r\nconvection-permitting RCM simulations to simulations of climate \r\nmodels with parametrized convection find, depending on the \r\nconsidered models, regions and seasons, either similar or qualitatively \r\ndifferent projected changes in short duration extreme precipitation \r\n(Chan et  al., 2014a, b, 2020; Ban et  al., 2015; Tabari et  al., 2016; \r\nFosser et al., 2017; Kendon et al., 2017, 2019; Vanden Broucke et al., \r\n2018). Process studies provide evidence that convection-permitting \r\nsimulations better represent crucial local and mesoscale features of \r\nconvective storms and thus simulate more plausible changes (Meredith \r\net al., 2015a; Prein et al., 2017; Fitzpatrick et al., 2020), but further \r\nresearch is required to confirm and reconcile the different findings."


id: 84e2fffa967d83119a0d20d5c904c046
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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index: climate_gpt_only_giec
vector_id: 18427
content:
"Studies assessing the fitness of statistical approaches for regional \r\nclimate projections are still very limited in number. For statistical \r\ndownscaling, a key issue is to include predictors that control long-term \r\nchanges in regional climate. Models differing only in the choice \r\nof predictors may perform similarly in the present climate, but may \r\nproject opposite precipitation changes (Fu et  al., 2018; Manzanas \r\net al., 2020). In addition to trend-evaluation studies (Section 10.3.3.8), \r\nperfect-model experiments (Section 10.3.2.5) have been used to assess \r\nwhether a  given model structure with a  chosen set of predictors is \r\ncapable of reproducing the simulated future climates (Gutierrez et al., \r\n2013; Raty et al., 2014; Dayon et al., 2015; Dixon et al., 2016; San-Martin \r\net al., 2017). Importantly, it is found that standard analogue methods \r\ninherently underestimate future warming trends because of missing \r\nanalogues for a warmer climate (Gutierrez et al., 2013). "


id: 42f9cca273074cb0e8bd1ab6527b47ae
created_at: 2023-03-16 14:00:48
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content:
"The special class of high-resolution global models (Sections 1.5.3.1 \r\nand 10.3.3.1, Chapter  3; Haarsma et  al., 2016; Prodhomme et  al., \r\n2016) is expected to improve some of the regional processes \r\nthat are not appropriately represented in standard global models \r\n(Roberts et  al., 2018). There is general consensus that increasing \r\nglobal model resolution improves some long-standing biases \r\n(Chapter  3, Section  10.3.3.3, and Figures 10.6 and 10.7; Demory \r\net al., 2014, 2020; Schiemann et al., 2014; Dawson and Palmer, 2015; "


id: 7868a219cf5c31ed170a42bd8635f091
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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index: climate_gpt_only_giec
vector_id: 16506
content:
"In any case, the fitness of regional climate projections based on \r\ndynamical downscaling or statistical approaches depends on the fitness \r\nof the driving models in projecting boundary conditions, predictors and \r\nchange factors (Hall, 2014; Maraun and Widmann, 2018b)."


id: e7d3e05913ec7302a6a4a9cbecf47718
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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vector_id: 33968
content:
"Despite their known errors that affect model performance, there \r\nis high confidence that global models provide useful information \r\nfor the production of regional climate information. There is robust \r\nevidence and high agreement that the increase of global model \r\nresolution helps in reducing the biases limiting performance at the \r\nregional scale, although resolution per se does not automatically \r\nsolve all performance limitations shown by global models. There is \r\nrobust evidence that stochastic parametrizations can help to improve \r\nsome aspects of the global model performance that are relevant to \r\nregional climate information."


id: 6cfcfdaa88c75b5cbe25d709302ef692
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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index: climate_gpt_only_giec
vector_id: 14773
content:
"Global models tend to have difficulties in simulating climate over \r\nregions where unresolved local scale processes, feedbacks and \r\nnon-linear scale interactions result in a  degradation of the model \r\nperformance compared to models with higher resolution. In this case, \r\nRCMs and variable resolution global models can resolve part of these \r\nprocesses in the regions of interest at an acceptable computational \r\ncost (Rummukainen, 2016; Giorgi, 2019; Gutowski Jr. et al., 2020)."


id: 1c6eb7226f13b40399563ecb3a40211c
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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index: climate_gpt_only_giec
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content:
"Overall, 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, Section  10.3.3.7). \r\nBias adjustment has proven beneficial as an interface between \r\nclimate model projections and impact modelling in many different \r\ncontexts (high confidence) (Section  10.3.3.7). Weather generators \r\nrealistically simulate many statistical aspects of present-day daily \r\ntemperature and precipitation (high confidence) (Section 10.3.3.7). \r\nThe performance of these approaches and their fitness for future \r\nprojections also depends on predictors and change factors taken from \r\nthe driving dynamical models (high confidence) (Section 10.3.3.9)."


id: 50ee00d31dfaecebf281f242f2fed762
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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content:
"The assessment of RCM performance needs to focus not only on \r\nmean climatology (Atlas), but also trends (Section  10.3.3.8) and \r\nextremes (Chapter 11), and the RCM's ability at correctly reproducing \r\nrelevant processes, forcings and feedbacks including aerosols, plant \r\nresponses to increasing CO2, and so on, (Schwingshackl et al., 2019; \r\nBoe et al., 2020a; Sections 11.2. and 10.3.3.3 to 10.3.3.8) to be fit for \r\nfuture projections (Section 10.3.3.9)."


id: 271c6da33dd023f013280879be360c7b
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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index: climate_gpt_only_giec
vector_id: 3547
content:
"When RCMs are driven by global models, part of the uncertainty \r\nin the RCM simulation is introduced by the global model biases \r\n(Kjellstrom et  al., 2018; Sorland et  al., 2018; Christensen and \r\nKjellstrom, 2020). As RCMs are typically not able to mitigate global \r\nmodel biases in large-scale dynamical processes, if such biases \r\nare substantial, and if the corresponding large-scale processes are \r\nimportant drivers of regional climate, downscaling is questionable \r\n(Section  10.3.3.3). However, when global models have weak \r\ncirculation biases and regional climate change is controlled mainly \r\nby regional-scale processes and feedbacks, dynamical downscaling \r\nhas the potential to add substantial value to global model \r\nsimulations (Section 10.3.3.4 and Atlas; Hall, 2014; Rummukainen, \r\n2016; Giorgi, 2019; Schwingshackl et al., 2019; Boe et al., 2020a; \r\nLloyd et al., 2021)."


id: 86a827f37ec6622fb701c3c75fcbe0dc
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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index: climate_gpt_only_giec
vector_id: 18724
content:
"Regional climate projections are affected by three main sources \r\nof uncertainty (Sections 10.2.2, 1.4.3 and 4.2.5): unknown future \r\nexternal forcings, imperfect knowledge and implementation of the \r\nresponse of the climate system to external forcings, and internal \r\nvariability (Lehner et al., 2020). In a regional downscaling context, \r\nuncertainties arise in every step of the modelling chain. Here the \r\npropagation of uncertainties (Section 10.3.4.1), the management of "


id: c5d7dfa90b85fe20e06ec415bf01a77d
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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index: climate_gpt_only_giec
vector_id: 28701
content:
"uncertainties (Section  10.3.4.2), the role of the internal variability \r\nfor regional projections (Section 10.3.4.3), and the design and use \r\nof ensembles to account for uncertainties (Section 10.3.4.4) will be \r\nassessed. Observational uncertainty, in particular for the calibration of \r\nstatistical downscaling methods (Section 10.2.3.1), also contributes \r\nto projection uncertainty."


id: 70210490c081b5615c8aca73aa2efc3f
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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index: climate_gpt_only_giec
vector_id: 15256
content:
"some regional future climate changes are not well-characterized by \r\nmulti-model mean and spread."


id: 6f7ade5a0b04f57caa5ae679fadf82ff
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 15169
content:
"These physical climate storylines help to physically explain contradicting \r\nregional projections and thus make the conveyed information a better \r\nrepresentation of the true uncertainty (Hewitson et al., 2014a). \r\nAdditionally, the attribution of regional uncertainties to drivers may in \r\nprinciple help reduce uncertainty in the case where some storylines can \r\nbe ruled out because the projected changes in the driving processes \r\nappear to be physically implausible (Zappa and Shepherd, 2017). There \r\nis thus high confidence that storylines attributing uncertainties in \r\nregional projections to uncertainties in changes of remote drivers aid \r\nthe interpretation of uncertainties in climate projections."


id: c0ddbf04aa4815795bb6a3b55c63a8dc
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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index: climate_gpt_only_giec
vector_id: 27914
content:
"Uncertainty propagation often increases the spread in regional climate \r\nprojections when comparing global model and downscaled results, \r\nwhich has been used as an argument against top-down approaches \r\nto climate information (Prudhomme et al., 2010). Increased spread \r\nin the modelling chain may also arise from a more comprehensive \r\nrepresentation of previously unknown or underrepresented \r\nuncertainties (Maraun and Widmann, 2018b). The increased spread \r\nin this case goes together with a better representation of processes \r\nand thus an increased model fitness-for-purpose (Section 10.3.3.9)."


id: 1e8bfeaf768b2b418d506923c693fc33
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 2250
content:
"Another approach that has continued to develop for characterising \r\nand reducing projection uncertainties is the use of emergent \r\nconstraints (Chapters 1, 4, 5 and 7; Hall et  al., 2019). The idea is \r\nto link the spread in climate model projections via regression to \r\nthe spread in present climate model biases for relevant driving \r\nprocesses. Models with lower biases are assigned higher weight in \r\nthe projections, which in turn reduces the spread of the projections \r\nin a physical way and may additionally reduce projection uncertainty. \r\nFor instance, Simpson et  al. (2016) have reduced the spread in \r\nprojections of North American winter hydroclimate by linking this \r\nspread to model biases in the representation of relevant stationary \r\nwave patterns. Other examples of using emergent constraints in \r\na regional context are Brown et al. (2016), G. Li et al. (2017), Giannini \r\nand Kaplan (2019), Ose (2019) and Zhou et al. (2019)."


id: 3de14ac74c3b743f60a5b24d2e37e7f
created_at: 2023-03-16 14:00:48
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index: climate_gpt_only_giec
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content:
"A regional climate projection based on a  single simulation from \r\na single global model or driving a single RCM alone will inevitably \r\nbe affected by not considering the internal variability (Figure 10.10). \r\nThis is mainly due to the dominant influence of the chaotic atmospheric "


id: 2828cdd43a487c8a25788c116510f86b
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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index: climate_gpt_only_giec
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content:
"external forcing estimates (Chapters 2, 6 and 7) and the limitations \r\nof assumptions underlying the statistical methods used to derive \r\nobservational large ensembles (McKinnon et al., 2017; McKinnon and \r\nDeser, 2018; Castruccio et al., 2019). Calibration methods inspired by "


id: be4d6fb7767c9d288fbda407d5ee9201
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 27491
content:
"circulation on regional climate variability, in particular at mid- to high \r\nlatitudes. Internal variability is an irreducible source of uncertainty \r\nfor mid- to long-term projections with an amplitude that typically \r\ndecreases with increasing spatial scale and lead time (Sections 1.4.3 \r\nand 4.2.1). However, regional-scale studies show that both large\u0002and local-scale internal variability together can still represent \r\na substantial fraction of the total uncertainty related to hydrological \r\ncycle variables, even at the end of the 21st century (Lafaysse et al., \r\n2014; Vidal et al., 2016; Aalbers et al., 2018; Gu et al., 2018)."


id: 5477225b78cfc0ad7213636602d09b1e
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 10744
content:
"The appropriate ensemble size for a robust use of SMILEs depends on \r\nthe model and physical variable being investigated, the spatial and \r\ntime aggregation being performed, the magnitude of the acceptable \r\nerror and the type of questions one seeks to answer (Deser et al., 2012, \r\n2017b; Kang et al., 2013; Wettstein and Deser, 2014; Dai and Bloecker, \r\n2019; Maher et al., 2019). It is noteworthy that the recent development \r\nof ensembles with a very large ensemble size (greater than 100) have \r\nled to new insights and methodologies to robustly assess the required \r\nensemble size for questions such as the estimation of the forced response \r\nto external forcing or a forced change in modes of internal variability, \r\nsuch as ENSO, and its associated teleconnections (Herein et al., 2017; \r\nMaher et al., 2018; Haszpra et al., 2020; Milinski et al., 2020)."


id: a4ff727937ae88709d581f4b62f66782
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content:
"The use of SMILEs assumes that they have a realistic representation \r\nof internal variability and its evolution under anthropogenic climate \r\nchange (Eade et  al., 2014; McKinnon et  al., 2017; McKinnon and \r\nDeser, 2018; Chen and Brissette, 2019). Assessing the realism of \r\nsimulated internal variability for past and current climates remains \r\nan active research field with a  number of issues such as the \r\nshortness and uncertainties of the observed record, in particular in \r\ndata-scarce regions (Section  10.2.2.3), the signal-to-noise paradox \r\n(Section 4.4.3.1; Scaife and Smith, 2018), uncertainty in past observed "


id: f45714b49c07a91844aef3bea4174477
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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content:
"weather and seasonal forecasts can be used to improve the reliability \r\nof regional-scale climate projections from large ensembles (Brunner \r\net al., 2019; O'Reilly et al., 2020). Interestingly, reliability is improved \r\nwhen the calibration is performed separately for the dynamical and \r\nresidual components of the ensemble resulting from dynamical \r\nadjustment (Section 10.4.1; O'Reilly et al., 2020)."


id: c920778e7ebbc0f9ccea38d9a6418bd
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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vector_id: 29207
content:
"Importantly, accurately partitioning uncertainty in regional climate \r\nprojections can provide an incentive for immediate action, accepting \r\na  large range of possible outcomes due to internal variability, \r\nwhile confounding model uncertainty with internal variability may \r\nbe understood as a lack of knowledge and lead to delayed action \r\nin adaptation decision-making (Section  10.5.3; Maraun, 2013b; \r\nMankin et al., 2020)."


id: 57748b08af1ca49e6211647b7c7484e1
created_at: 2023-03-16 14:00:48
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content:
"There is high confidence that the availability of SMILEs allows a robust \r\nassessment of the relative contributions of model uncertainty and \r\ninternal variability in regional-scale projection uncertainty. There is \r\nhigh confidence that the use of SMILEs with appropriate ensemble \r\nsize leads to an improved estimate of regional-scale forced response \r\nto an external forcing as well as of the full spectrum of possible \r\nchanges associated with internal variability. There is high confidence \r\nthat these improved estimates are beneficial for characterizing the \r\nfull distribution of outcomes that is a  key ingredient of climate \r\ninformation for robust decision-making and risk-analysis frameworks."


id: 55c0ecf44ddaaa3cf86ae26bddeb3a24
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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content:
"10.3.4.4 Designing and Using Ensembles for Regional Climate \r\nChange Assessments to Take Uncertainty Into Account"


id: 1da17a4de2da92c5a93bba4f7b535b32
created_at: 2023-03-16 14:00:48
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content:
"An important consideration is which set of global models should be \r\nused for global model-RCM combinations. If adequate resources \r\nexist, then large numbers of global model-RCM combinations are \r\npossible (Deque et  al., 2012; Coppola et  al., 2021; Vautard et  al., \r\n2021). However, coordinated downscaling programmes can be limited \r\nby the human and computational resources available, for producing \r\nensembles of downscaled output, which limits the number of feasible \r\nglobal model-RCM combinations. With this limitation in mind, a small \r\nset of GCMs may be chosen that span the range of equilibrium climate \r\nsensitivity in available global models (e.g., Mearns et al., 2012, 2013; \r\nInatsu et al., 2015), though this range may be inconsistent with the "


id: e7fc43dbdb26fffa4745ec6a1c239b4e
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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content:
"There is high confidence that ensembles for regional climate projections \r\nshould be selected such that models unrealistically simulating processes \r\nrelevant for a given application are discarded, but at the same time, the \r\nchosen ensemble spans an appropriate range of projection uncertainties."


id: 2509bdc85732289cd6375f72df67b60e
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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vector_id: 3271
content:
"Bias adjustment is an approach to post-process climate model output and has become widely used in climate hazard and impact studies \r\n(Gangopadhyay et  al., 2011; Hagemann et  al., 2013; Warszawski et  al., 2014) and national assessment reports (Cayan et  al., 2013; \r\nGeorgakakos et al., 2014). Despite its wide use, bias adjustment was not assessed in AR5 (Flato et al., 2014). Several problems have been \r\nidentified that may arise from an uncritical use of bias adjustment, and that may result in misleading impact assessments. The rationale of \r\nthis Cross-Chapter Box is to provide an overview of the use of bias adjustment in this Report, and to assess key limitations of the approach."


id: a9913a8f5b9cb21d5d3bc0ed613e8ca6
created_at: 2023-03-16 14:00:48
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content:
"Bias-adjusted climate model output is used extensively throughout this Report. Several results from Chapter 8, and many of the \r\nclimatic impact-drivers in Chapter 12 (Section 12.2) are based on bias adjustment. The Atlas presents many results both as raw and \r\nbias-adjusted data (Atlas.1.4.5). The application of bias adjustment in the WGI report was informed by the assessment in Chapter 10 \r\nand this Cross-Chapter Box. Finally, bias adjustment is crucial for many studies assessed in the WGII report. An overview of bias \r\nadjustment can be found in Section 10.3.1.3, a general performance assessment of individual method classes in Section 10.3.3.7. \r\nThe fitness of bias adjustment for climate change applications is assessed in Section 10.3.3.9."


id: ef56e63096db7931593dd3764d78106d
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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content:
"An argument made for the use of bias adjustment is the fact that impact models are commonly very sensitive, often non-linearly, to \r\nthe input climatic variables and their biases, in particular when threshold-based climate indices are required (Dosio, 2016). There are, \r\nhowever, cases where bias adjustment may not be necessary or useful, such as: when only qualitative statements are required; when \r\nonly changes in mean climate are considered (instead of absolute values); when percentile-based indices are used."


id: d4e3806b1a6e491172652d083f822f75
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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vector_id: 31020
content:
"There is medium confidence that the selection of climate models with low biases in the frequency, persistence and location of \r\nlarge-scale atmospheric circulation can reduce negative impacts of bias adjustment."


id: 7cb27c19bbdc5d1e3e272fdb635857a
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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content:
"Using bias adjustment for statistical downscaling\r\nBias adjustment is often used to downscale climate model results from grid box data to finer resolution or point scale. It is sometimes \r\neven directly applied to coarse-resolution global model output to avoid an intermediate dynamical downscaling step (Johnson and\r\nSharma, 2012; Stoner et al., 2013). But bias adjustment does not add any information about the processes acting on unresolved scales \r\nand is therefore by construction not capable of bridging substantial scale gaps (Maraun, 2013a; Maraun et al., 2017). Using bias"


id: 107ad8c2160fb1a2acbcccc3693f0524
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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vector_id: 76
content:
"Overall, there is high confidence that the use of bias adjustment for statistical downscaling, in particular to downscale coarse resolution \r\nglobal models, has severe limitations."


id: 60a1502ec7ef4763cc1ef926d60bff2
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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content:
"Bias adjustment in the presence of observational uncertainty and internal variability\r\nObservational uncertainties and internal variability introduce uncertainty in the estimation of biases and thus in the calibration of \r\nbias-adjustment methods. Dobor and Hlasny (2019) found a considerable influence of the choice of the observational dataset and \r\ncalibration period on the adjustment for some regions. RCM biases are typically larger than observational uncertainties, but in some \r\nregions, and in particular for wet-day frequencies, spatial patterns and the intensity distribution of daily precipitation, the situation \r\nmay reverse (Kotlarski et al., 2019). Switanek et al. (2017) found a strong influence of internal variability and thus of the choice of \r\ncalibration period on the calibration of quantile mapping and on the modification of the climate change signal."


id: 71f7b97f769482bc6d5e831547cebedd
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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vector_id: 15558
content:
"Bias adjustment is typically evaluated using cross-validation, that is, by calibrating the adjustment function to one period of the observational \r\nrecord, and by evaluating it on a different one. Maraun et al. (2017) and Maraun and Widmann (2018a) demonstrated that, in the presence \r\nof multi-decadal internal variability, cross-validation may lead to a rejection of a valid bias adjustment or even lead to a positive evaluation \r\nof an invalid adjustment. The authors therefore argued that, in the presence of substantial internal variability, the evaluation of bias \r\nadjustment requires to consider aspects that have not been adjusted, such as temporal, spatial, or multivariable dependence."


id: c28333ee754fccc3568e808ef3d6ba89
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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content:
"There is high confidence that observational uncertainty and internal variability adversely affect bias adjustment and introduce \r\nuncertainties in bias-adjusted future projections."


id: ba6b29efa87720be0c7c47dca1d73715
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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vector_id: 26867
content:
"In this chapter, regional-scale attribution is defined as the process \r\nof evaluating the relative contributions of multiple causal factors \r\n(or drivers) to regional climate change (Cross-Working Group Box: \r\nAttribution in Chapter 1; Rosenzweig and Neofotis, 2013; Shepherd, \r\n2019). Attribution at regional scale builds upon the usual definition of \r\nattribution used in the AR5 (Cross-Working Group Box: Attribution in \r\nChapter 1; Hegerl et al., 2010). However, in contrast with global-scale \r\nattribution methods where internal variability might be considered as \r\na noise problem (Section 3.2), the preliminary detection step is not \r\nalways required to perform regional-scale attribution since causal "


id: fff9f126d2847c79c5bd86170afc8c6a
created_at: 2023-03-16 14:00:48
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content:
"This section focuses on the assessment of the methodologies used to \r\nidentify the physical causes of past and future regional climate change \r\nin the context of the ongoing anthropogenic influence on the global \r\nclimate. The main foci are the attribution of past regional-scale changes \r\n(Sections 10.4.1-2) and the robustness and future emergence of the \r\nregional-scale response to anthropogenic forcing (Section 10.4.3)."


id: 26c3612585a6742945ebc20ffc9a22f7
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content:
"factors of regional climate change may also include internal modes of \r\nvariability in addition to external natural and anthropogenic forcing. \r\nImportantly, regional-scale (or process-based) attribution also seeks \r\nto determine the physical processes and uncertainties involved in the \r\ncausal factor's influence (Cross-Working Group Box: Attribution in \r\nChapter 1)."


id: 8ee31d31106c71f2f0921950b4d5432
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content:
"anthropogenic-induced warming while the aerosol forcing leads to \r\na cooling offsetting a fraction of the GHG change (C. Li et al., 2016, \r\n2017). While the influence of external natural forcing can often be \r\ndetected as well, its contribution to observed changes is usually much \r\nsmaller (C. Li et al., 2017; Wan et al., 2019). Temperature detection \r\nresults are found to be robust to the use of different observational \r\ndatasets and detection methodologies (Dileepkumar et al., 2018)."


id: e849233aa16caf04f5cc5c266e6c8c14
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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vector_id: 34053
content:
"Section  10.4.1 describes regional-scale attribution methodologies \r\nand assesses their application to regional changes of temperature \r\nand precipitation. Section 10.4.2 presents three illustrative attribution \r\nexamples that illustrate a number of specific regional-scale challenges \r\nand methodological aspects. Section 10.4.3 focuses on methodologies \r\nused to assess the robustness and emergence of the regional climate \r\nresponse to anthropogenic forcing. A  basic description of future \r\nregional climate change for all regions considered in the report \r\n(as defined in Section 1.4.5) appears in the Atlas."


id: f9e837c1d652f87e643a2e8351bcc254
created_at: 2023-03-16 14:00:48
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content:
"It is noteworthy that these methods require a very significant reduction \r\nof spatial and temporal dimensions in order to reliably estimate the \r\nco-variance matrix of internal variability (an entire region is thus often \r\nconsidered as being only one or a few spatial points that represent \r\nthe spatial average of the whole region or a few sub-regions; time \r\nsamples are often 5- or 10-year averages). Finally, model bias is rarely \r\nconsidered in statistical models used in detection and attribution \r\nregional studies, while it has been shown to have a strong impact \r\non the stability of detection results and their associated confidence \r\nintervals when increasing the spatial dimension (Ribes and Terray, \r\n2013). New statistical methods are emerging to provide some \r\nalternative to standard optimal fingerprinting but they have not yet \r\nbeen evaluated and applied at regional scales (Section 3.2.2)."


id: 550c407bcd1feb928a8139891df6b818
created_at: 2023-03-16 14:00:48
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content:
"Optimal fingerprint regression-based methods have been applied to \r\ndetection and attribution of mean temperature anthropogenic signal \r\nin several regions of the world such as Canada, India, central Asia, \r\nnorthern and western China, Australia, and North Africa (Xu et al., \r\n2015; C. Li et al., 2017; Dileepkumar et al., 2018; Y. Wang et al., 2018; \r\nPeng et al., 2019; Wan et al., 2019). The influence of anthropogenic \r\nforcing, and in particular that of greenhouse gases (GHGs), is \r\nrobustly detected in annual and seasonal mean temperatures for \r\nall considered regions. Most of the observed regional temperature \r\nchanges since the mid-twentieth century can only be explained by \r\nexternal forcings, with anthropogenic influence being the dominant \r\nfactor. GHG increase is found to be the primary factor of the "


id: 95996ebdd7679c620390166c6b0a2bb5
created_at: 2023-03-16 14:00:48
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content:
"10.4.1.2 Other Spatiotemporal Statistical Methods for Isolating \r\nRegional Climate Responses to External Forcing"


id: afce4db3137c56674acb6602c40647f1
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content:
"The primary objective of any attribution method is to optimally \r\nseparate the influences of external forcing and internal variability \r\non a global or regional climate record. In a multi-model ensemble \r\ncontext, the estimation of the externally-forced climate response has \r\nbeen typically performed by ensemble averaging of linear trends or "


id: ada3b8dbca4834d0946912c713fc5ee3
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content:
"regional domain spatial average, thus not taking into account the \r\navailable and complete space and time co-variance information. \r\nSince AR5, methods using spatiotemporal information have been \r\nfurther developed and used to improve the separation between \r\nexternal and internal drivers in multiple or single historical climate \r\nrealizations performed by a given global model."


id: 590dba02cbdadd26f5cc13d9d483ad05
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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index: climate_gpt_only_giec
vector_id: 11506
content:
"The ensemble empirical mode decomposition method (Wu and \r\nHuang, 2009; Wilcox et  al., 2013; Ji et  al., 2014; Qian and Zhou, \r\n2014) decomposes data, such as time series of historical temperature \r\nand precipitation, into independent oscillatory modes of decreasing \r\nfrequency. The last step of the method leaves behind a  smooth \r\nand low-frequency residual time series. Typically, the non-linear \r\nanthropogenic trend (e.g.,  of 20th-century temperature) can be \r\nreconstructed by summing the long-term mean, the residual, and \r\neventually the lowest-frequency mode to account for a multi-decadal \r\nforced signal, for instance associated with anthropogenic aerosol \r\nforcing. The ensemble empirical mode decomposition method is an \r\nexample of a data-driven, non-parametric approach that can be used \r\nto directly provide an estimate of the forced response without the \r\nneed for model data (Qian, 2016)."


id: 2f95c57d15075ec067a3b265534069fa
created_at: 2023-03-16 14:00:48
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vector_id: 4909
content:
"Dynamical adjustment methods have been used by, for instance, \r\nDeser et al. (2016), Saffioti et al. (2016), O'Reilly et al. (2017), Gong \r\net al. (2019), and R. Guo et al. (2019). Deser et al. (2016) focused on \r\nthe causes of observed and simulated multi-decadal trends in North \r\nAmerican temperature. They demonstrated that the main advantage \r\nof this technique is to narrow the spread of temperature trends \r\nfound by the model ensemble and to bring the dynamically-adjusted \r\nobservational trend much closer to the forced response estimated \r\nby the model ensemble mean. Similar results were obtained by \r\nSaffioti et al. (2016) regarding recent observed winter temperature "


id: 4412785715ae52b1122f304f6a9e0cdf
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updated_at: 2023-03-16 14:00:48
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index: climate_gpt_only_giec
vector_id: 8124
content:
"The univariate detection method does not use spatial pattern \r\ninformation, but compares observed trends in gridded datasets \r\nwith distributions of trends from ensembles of simulations during \r\nthe historical period (Knutson et al., 2013; Knutson and Zeng, 2018). \r\nThe trends arising from simulations constrained by natural forcing-only "


id: b6a8eaca910eb6ddbadca3d919f8e887
created_at: 2023-03-16 14:00:48
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content:
"simulate internal variability modes that are known to be important \r\ndrivers of regional climate change (Sections 3.7 and 10.3.3.3 \r\nand Annexes IV.2 and IV.3). Models are likely to have different \r\nperformance in different regions and therefore their evaluation \r\nneeds to be performed in terms of key physical processes and \r\nmechanisms relevant to the climate of the region under consideration \r\n(Section 10.3.3)."


id: 8d621d30cd993fe3cfa97e7504f1e60b
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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index: climate_gpt_only_giec
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content:
"To conclude, there is very high confidence (robust evidence and high \r\nagreement) that the use of diverse and independent attribution \r\nmethods, multiple model ensemble types and observed datasets \r\nstrengthens the robustness of results of regional-scale attribution \r\nstudies. Since AR5, multiple SMILEs have provided an adequate \r\ntestbed for new attribution methodologies aimed at separating \r\nforced signals from internal variability in observational records as \r\nwell as small-size single-model ensembles."


id: bfced59f72e7a7ee166e46e781082027
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content:
"This section focuses on three illustrative examples that span \r\ndifferent regions, time scales, and attribution methods, without \r\naiming at being comprehensive. These examples illustrate attribution \r\nstatements that are based upon multiple lines of evidence, combining \r\nmultiple observational datasets, different generations and types of \r\nmodels, process understanding and assessment of various sources \r\nof uncertainty. Detection and attribution assessments for all AR6 \r\nregions and specific variables can be found in the Atlas."


id: dc072a990f372a5791346384acfc3e5
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content:
"New methods aiming to remove underlying model biases before \r\nperforming detection and attribution, for instance related to \r\nprecipitation changes, are emerging based on image transformation \r\ntechniques such as warping (Levy et al., 2014a). By correcting location \r\nand seasonal precipitation biases in CMIP5 models, Levy et al. (2014b) \r\nshowed that the agreement between observed and fingerprint \r\npatterns can be improved, further enhancing the ability to attribute \r\nobserved precipitation changes to external forcings. The improvement \r\nmainly relies on the assumption that precipitation changes are tied to \r\nthe underlying climatology, which has been shown to be a reasonable \r\nassumption in regions of the world where intensification of the \r\nhydrological cycle is expected (Held and Soden, 2006)."


id: e1268b719c36b0be6f0521665103776a
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content:
"The role of SST forcing in the rainfall decline is assessed first. Competing \r\nmechanisms from equatorial Atlantic SSTs and inter-hemispheric \r\nSST gradients regulate decadal variability in the Sahel (Nicholson, \r\n2013), alternatively explained by tropical warming leading to "


id: c97bb26826a218ac6b3aaa49592c4f58
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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content:
"Importantly, evidence that the models employed in regional-scale \r\nattribution are fit for purpose is essential in order to estimate the \r\ndegree of confidence in the attribution results (Section 10.3.3). For \r\nexample, models need to be evaluated and assessed in their ability to "


id: 83e918316df9f1a5642bfa788359ada2
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content:
"which they are not, suggests that the underlying ocean surface might \r\nbe essential in driving the decline (Figure 10.11e)."


id: bbaba184e98ea927eda624635aab0366
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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content:
"period in order to sample the uncertainty in CMIP5 aerosol radiative \r\nforcing, Shonk et al. (2020) found differences of 0.5 mm day-1 for Gulf \r\nof Guinea rainfall between strong and weak aerosol experiments \r\nas illustrated in Figure 10.11c, although the drying appears further \r\nsouth than observed due to model bias."


id: 75229e3544629df62f3c4abd0c9d3c40
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content:
"mechanisms governing such a change have been shown to be largely \r\ndynamical in nature (D'Agostino et  al., 2019), suggesting that the \r\nmid-Holocene cannot be used to inform the credibility of changes \r\ndue to greenhouse warming."


id: cfd9000cffb04735f35081378c183981
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updated_at: 2023-03-16 14:00:48
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content:
"There is very high confidence (robust evidence and high agreement) \r\nthat patterns of 20th-century ocean and land surface temperature \r\nvariability have caused the Sahel drought and subsequent recovery \r\nby adjusting meridional gradients. There is high confidence (robust \r\nevidence and medium agreement) that the changing temperature \r\ngradients that perturb the West African monsoon and Sahel rainfall \r\nare themselves driven by anthropogenic emissions: warming by \r\nGHG emissions was initially restricted to the tropics but suppressed \r\nin the North Atlantic due to nearby emissions of sulphate aerosols, \r\nleading to a  reduction in rainfall. The North Atlantic subsequently \r\nwarmed following the reduction of aerosol emissions, leading to \r\nrainfall recovery."


id: d8db2c23655f433117e9ad3cf6b95e43
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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content:
"The influence of SST anomalies on south-eastern South America \r\nprecipitation have been studied extensively on interannual to \r\nmulti-decadal time scales (Paegle and Mo, 2002). The positive phase \r\nof El Nino-Southern Oscillation (ENSO; Annex IV.2.3) is related \r\nto stronger mean and extreme rainfall over south-eastern South \r\nAmerica (Ropelewski and Halpert, 1987; Grimm and Tedeschi, 2009; \r\nRobledo et al., 2016). The ENSO influence may be modulated by the \r\nPDV (Kayano and Andreoli, 2007; Fernandes and Rodrigues, 2018) \r\nand the AMV (Kayano and Capistrano, 2014). PDV and AMV also \r\ninfluence the south-eastern South American climate independently \r\nof ENSO (Barreiro et al., 2014; Grimm and Saboia, 2015; Robledo \r\net  al., 2020). While Pacific SSTs dominate the overall influence of \r\noceanic variability in the region, the Atlantic variability seems \r\nto dominate on multi-decadal time scales and has been proposed \r\nas a  driver for the long-term positive trend (Seager et  al., 2010; "


id: b84d570d9a98168bd94e6c72e0a9fbd
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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vector_id: 26526
content:
"Barreiro et al., 2014). Based on experiments designed to test how \r\nsouth-eastern South America precipitation is modulated by tropical \r\nAtlantic SSTs, Seager et  al. (2010) showed that cold anomalies \r\nin the tropical Atlantic favour wetter conditions by inducing an \r\nupper-tropospheric flow towards the equator, which, via advection \r\nof vorticity, leads to ascending motion over south-eastern South \r\nAmerica (Figure  10.12a). Monerie et  al. (2019) supported this \r\nargument showing a negative relationship between south-eastern \r\nSouth America precipitation and the AMV index (Huang et  al., \r\n2015) using an AGCM coupled to an ocean mixed-layer model with \r\nnudged SSTs."


id: 3c151a085e28efa03f686ed80b4cb127
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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content:
"There is high confidence that South-Eastern South America summer \r\nprecipitation has increased since the beginning of the 20th century. \r\nSince AR5, science has advanced in the identification of the drivers of \r\nthe precipitation increase in South-Eastern South America since 1950, \r\nincluding GHG through various mechanisms, stratospheric ozone \r\ndepletion and Pacific and Atlantic variability. There is high confidence\r\nthat anthropogenic forcing has contributed to the South-Eastern \r\nSouth America summer precipitation increase since 1950, but very \r\nlow confidence on the relative contribution of each driver to the \r\nprecipitation increase."


id: 73ef931ac8ae81dab22bae295b362a09
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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content:
"the south-west (Delworth et al., 2015; Seager and Ting, 2017). The \r\nmulti-year drought of 2012-2016 has been linked to the multi-year \r\npersistence of anomalously high atmospheric pressure over the \r\nnorth-eastern Pacific Ocean, which deflected the Pacific storm track \r\nnorthward and suppressed regional precipitation during California's \r\nrainy season (Swain et al., 2017). Going into more detail, Prein et al. \r\n(2016a) used an assessment of changing occurrence of weather \r\nregimes to judge that changes in the frequency of certain regimes \r\nduring 1979-2014 have led to a decline in precipitation by about \r\n25%, chiefly related to the prevalence of anticyclonic circulation \r\npatterns in  the north-east Pacific. Finally, the moderate model \r\nperformance in representing Pacific SST decadal variability and \r\nits remote influence (Section  3.7.6) as well as its change under \r\nwarming may affect attribution results of observed and future \r\nprecipitation changes (Seager et al., 2019)."


id: 5a5464f51ecf58f9d21508f300bc0425
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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vector_id: 11720
content:
"Importantly, as the AR6 assessment views the PDV as being mostly \r\ndriven by internal variability (Section 3.7.6), the lines of evidence cited \r\nabove suggest that the contribution of natural and anthropogenic \r\nforcings to the precipitation decline has a  small amplitude. Unlike \r\nthe precipitation deficit, the accompanying south-western North \r\nAmerica warming is driven primarily by anthropogenic forcing from \r\nGHGs rather than atmospheric circulation variability and may help to \r\nenhance the drought through increased evapotranspiration (Knutson \r\net  al., 2013; Diffenbaugh et  al., 2015; Williams et  al., 2015, 2020; \r\nLehner et al., 2018, 2020)."


id: 12cb9b8e238614043ec583b6c58a12c3
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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vector_id: 457
content:
"A second possible causal factor is the role for ocean-forced or \r\ninternal atmospheric circulation change. Analysis of observed \r\nand CMIP5-simulated precipitation indicates that the drought \r\nprevalence since 1980 is linked to natural, internal variability in the \r\nclimate system (Knutson and Zeng, 2018). Based on observations \r\nand ensembles of SST-driven atmospheric simulations, Seager \r\nand Hoerling (2014) suggested that robust tropical Pacific and \r\ntropical North Atlantic forcing drove an important fraction of \r\nannual mean precipitation and soil moisture changes and that \r\nearly 21st  century multi-year droughts could be attributed to \r\nnatural decadal swings in tropical Pacific and North Atlantic SSTs. \r\nA cold state of the tropical Pacific would lead by well-established \r\natmospheric teleconnections to anomalous high pressure across \r\nthe North Pacific and southern North America, favouring a weaker \r\njet stream and a  diversion of the Pacific storm track away from "


id: 65460385dc2e25066836abb2a0b71137
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
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vector_id: 13512
content:
"To conclude, there is high confidence (robust evidence and medium \r\nagreement) that most (>50%) of the anomalous atmospheric \r\ncirculation that caused the south-western North America negative \r\nprecipitation trend can be attributed to teleconnections arising from \r\ntropical Pacific SST variations related to PDV. There is high confidence\r\n(robust evidence and medium agreement) that anthropogenic \r\nforcing has made a  substantial contribution (about 50%) to the \r\nsouth-western North America warming since 1980."


id: b6b028f7a49121649d686b0c9d9f719
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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vector_id: 26260
content:
"change has not yet fully occurred for many land regions \r\n(Section 10.4.3). Although the contribution of anthropogenic forcing \r\nto long-term regional precipitation change has been detected in \r\nsome regions, a robust quantification of the contributions of different \r\ndrivers remains elusive. The delayed emergence of the anthropogenic \r\nprecipitation fingerprint with respect to temperature is likely due \r\nto the opposing sign of the fast and slow land precipitation forced "


id: 3452894587a638a9ad7e7a5a78efd59c
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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content:
"The robustness of regional-scale attribution differs strongly between \r\ntemperature and precipitation changes. While the influence of \r\nanthropogenic forcing on regional temperature long-term change \r\nhas been detected and attributed in almost all land regions, a robust \r\ndetection and attribution of human influence on regional precipitation "


id: 17e26de85550ba735eb157a95d287342
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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content:
"responses and time-dependent SST change patterns (Sections 8.2.1 \r\nand Section 10.4.3), stronger internal variability (Section 10.3.4.3) as \r\nwell as larger observational uncertainty (Section 10.2) and impact of \r\nmodel biases. The contribution of internal variability to the observed \r\nchanges can also be very sensitive to the period length and level \r\nof spatial aggregation for the region under scrutiny (Section 4.4.1 \r\nand Cross-Chapter Box 3.1; Kumar et al., 2016). Finally, even in the \r\ncase of temperature changes at multi-decadal time scale, internal \r\nvariability can still be a substantial driver of regional changes due to \r\ncancellation between different external forcings (Nath et al., 2018)."


id: 4cda22f5f5b568bdf23748f1ae1140fb
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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index: climate_gpt_only_giec
vector_id: 9519
content:
"To conclude, it is virtually certain (robust evidence and high agreement) \r\nthat anthropogenic forcing has been a major driver of temperature \r\nchange since 1950 in many sub-continental regions of the world. \r\nThere is high confidence (robust evidence and medium agreement) \r\nthat anthropogenic forcing has contributed to multi-decadal mean \r\nprecipitation changes in several regions, for example western Africa, \r\nsouth-east South America, south-western Australia, northern central \r\nEurasia, and South and East Asia. However, at regional scale, the role \r\nof internal variability is stronger while uncertainties in observations, \r\nmodels and external forcing are all larger than at the global scale, \r\nprecluding a  robust assessment of the magnitude of the relative \r\ncontributions of greenhouse gases, including stratospheric ozone, \r\nand different aerosol species."


id: ac922d457797dc6d7299dd747bc24960
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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vector_id: 24696
content:
"Regional climate projections are one key element of the multiple lines \r\nof evidence that are used for climate risk assessments as well as for \r\nadaptation and policy decisions at regional scales (Sections 10.3.3.9 \r\nand 10.5). Regional climate projections can be separated into \r\ntwo components: the regional-scale forced response or regional-scale \r\nclimate sensitivity when normalized by the global mean temperature \r\nchange (Seneviratne and Hauser, 2020) and the climate internal \r\nvariability characterizing the future period or global warming level \r\nunder scrutiny. This section assesses a few methodological aspects \r\nrelated to robustness and emergence properties of the regional\u0002scale forced response as well as the possible influence of internal \r\nvariability on the emergence of the anthropogenic signal."


id: 735bf6db8b751cd135dd108ac9430219
created_at: 2023-03-16 14:00:48
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content:
"Figure  10.14 illustrates a  similar comparison based on the CMIP6 \r\nmulti-model ensemble forced with the scenario SSP5-8.5 and \r\napplied to two large-scale continental areas. The forced response \r\nto anthropogenic forcing is simply taken as the CMIP6 multi-model \r\nmean of future regional climate change relative to the 1850-1900 \r\nreference period. Robustness of the forced response is based on \r\nboth significance of the change and model agreement about the \r\nsign (direction) of change (Cross-Chapter Box Atlas.1; Figure 10.14). \r\nCaution has to be exercised against a  too literal interpretation of \r\nlack of robust change given that significance and sign agreement \r\ncan be sensitive to spatial and temporal aggregation (Cross-Chapter \r\nBox Atlas.1, Figure  2) and lack of a  robust change does not \r\nnecessarily translate to lack of regional-scale climate change impacts \r\n(McSweeney and Jones, 2013; Hibino and Takayabu, 2016)."


id: fdc044ac83758d2b800102fbbca930d5
created_at: 2023-03-16 14:00:48
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content:
"Standard methodologies to derive the regional forced response include \r\npattern-scaling and the time-shift or epoch approach (Section 4.2.4; \r\nTebaldi and Arblaster, 2014; Vautard et al., 2014; Herger et al., 2015; \r\nTebaldi and Knutti, 2018; Christensen et  al., 2019). Pattern-scaling \r\nassumes that the spatial patterns of regional change, often based on \r\na time-averaged 20- or 30-year period at the end of the 21st century, \r\nare roughly constant in time, and simply scale linearly with global \r\nmean warming. The time-shift approach defines a target in terms of \r\nglobal warming level (GWL) and locates the time segment, usually \r\n20 or 30 years, in historical or scenario simulations in which global \r\nmean warming matches the required GWL (Section  10.1.2 and \r\nCross-Chapter Box  11.1). Physical consistency between multiple "


id: 7f2d32fd62d23bc071cd2127daeab3d6
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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vector_id: 17559
content:
"If projected regional mean temperature (Figure  10.14a) and \r\nprecipitation (Figure 10.14b) changes were to scale linearly with global \r\nmean warming, the adjusted spatial patterns would be congruent "


id: 82d5638f67c09afc70a84fddeec36eab
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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index: climate_gpt_only_giec
vector_id: 18108
content:
"spatial pattern amplitude with increasing GWL suggests that the \r\ninitial transient regional response overshoots the long-term change \r\nin regions such as northern Africa for summer temperature and south\u0002western South America for precipitation (Zappa et al., 2020). In the \r\nlatter region, long simulations with stabilized GHG concentrations \r\neven suggest a  change of sign when near-equilibrium is reached \r\n(Sniderman et  al., 2019). The reverse behaviour, increasing pattern \r\namplitude with increasing GWL, is seen for summer temperature in \r\nsouthern and eastern Europe and for precipitation in south-western \r\nNorth America (Sniderman et al., 2019; Zappa et al., 2020), suggesting \r\nthat, in these regions, the initial transient response is lagging global \r\nmean warming and final regional climate change will be reached once \r\nGHG concentrations are stabilized."


id: ab1f1d9b7c0a067c622ae988d736a81c
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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vector_id: 24442
content:
"with each other at different GWLs. While pattern scaling seems to \r\nbe a reasonable first-order approximation for both temperature and \r\nprecipitation changes in tropical and high latitude regions (high pattern \r\ncorrelation values), there are a number of regions exhibiting substantial \r\namplitude differences at different GWLs (northern Africa and Middle \r\nEast, southern and eastern Europe for temperature; south-western \r\nNorth America, Chile and north-eastern Brazil for precipitation). These \r\ndifferences hint at the possible influence of non-linear mechanisms \r\n(Good et  al., 2015), including soil-moisture feedbacks (Seneviratne \r\net al., 2010; Vogel et al., 2017), a time-dependent balance between the \r\ndifferent contributions of fast and slow response to greenhouse gas \r\nforcing as well as changing SST response patterns (Long et al., 2014; \r\nGood et al., 2016; Ceppi et al., 2018; Zappa et al., 2020). Decreasing "


id: f47e467b15815104ee8cfa28c63e363f
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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content:
"period (Section 1.4.1). The reference period can be the pre-industrial, \r\nthe very recent past or even a  time-evolving baseline, depending \r\non both the framing and assumption that adaptation to the current \r\nclimate has already occurred (King et al., 2015; Zhang and Delworth, \r\n2018; Brouillet and Joussaume, 2020). These choices will then \r\ndetermine the type of simulations and periods that will be used to \r\nconstruct the noise distribution. Finally, the permanence of future \r\nemergence cannot be taken for granted when emergence occurs in \r\nthe late-21st century based on simulations ending in 2100 (Hawkins \r\net al., 2014; King et al., 2015; Lehner et al., 2017a)."


id: cd1757c6f64f85934c3ae95f62deab25
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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vector_id: 29819
content:
"There is high confidence that the time-evolving contribution of \r\ndifferent mechanisms operating at different time scales can modify \r\nthe amplitude of the regional-scale response of temperature, and \r\nboth the amplitude and sign of the regional-scale response of \r\nprecipitation, to anthropogenic forcing. These mechanisms include \r\nnon-linear temperature, precipitation and soil-moisture feedbacks, \r\nand slow and fast response of SST patterns and atmospheric \r\ncirculation changes to increasing GHGs."


