# document

id: df799d88e24b10a3411e64fe07ae4d31
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
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"Snow avalanche: There is limited evidence of directional changes \r\nin snow avalanches over North America. Mock and Birkeland \r\n(2000) identified a  1969-1995 decrease in snow avalanches over \r\nthe western United States, although they note the heavy influence \r\nof natural variability. A similar decline was observed over western \r\nCanada (Bellaire et al., 2016; Sinickas et al., 2016), but clear trends \r\nare difficult to discern given sparse observations and shifts in \r\navalanche management. We concur with the SROCC assessment \r\nof medium confidence and high agreement that snow avalanche \r\nhazards generally decrease at low elevations given lower snowpack, \r\neven as high elevations are increasingly susceptible to wet-snow \r\navalanches (Hock et al., 2019; see also Lazar and Williams, 2008)."


id: ad13a8061baae62f968f17dada283e64
created_at: 2023-04-19 13:21:41
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"Seasonal sea ice coverage along the majority of Canadian and Alaskan \r\ncoastlines is declining (robust evidence, high agreement) and there is \r\nhigh confidence that sea ice loss continues under climate change, as \r\nfurther assessed in Section 12.4.9."


id: 6c05c1bf89595ea8da3af46566822d8a
created_at: 2023-04-19 13:21:41
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"Observations and projections agree that snow and ice CIDs \r\nover North America are characterized by reduction in glaciers \r\nand the seasonality of snow and ice formation, loss of shallow \r\npermafrost, and shifts in the rain/snow transition line that \r\nalters the seasonal and geographic range of snow and ice \r\nconditions in the coming decades (very high confidence)."


id: f93b28014d8cbc238cef2b249d4f80d7
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
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"Ice storm: There is limited evidence in the literature of unique \r\nchanges in ice storms observed or projected over North America. \r\nGroisman et al. (2016) examined 40 years of observations and found \r\nweak decreases in freezing rain events over the south-eastern USA \r\nin the most recent decade. Ning and Bradley (2015) project that the \r\naverage snow-rain transition line, which is associated with mixed \r\nprecipitation, moves 2deg latitude northward over Eastern North \r\nAmerica by the end of the 21st  century under RCP4.5 (4deg under \r\nRCP8.5; see also Klima and Morgan, 2015)."


id: 5d29901490c752fc4900a29d14ff4bba
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
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"Relative sea level rise is virtually certain to continue in the oceans \r\naround North America, except in the northern part of north-eastern \r\nCanada and portions of southern Alaska. Regional mean RSLR \r\nprojections for the oceans around North America range from 0.4-1.0 \r\nm under SSP1-2.6 to 0.7-1.4 m under SSP5-8.5 for 2081-2100 relative \r\nto 1995-2014 (median values), which means that there are locally \r\nlarge deviations from the projected GMSL change (Section 9.6.3.3), \r\nincluding decreases in RSL in northern north-eastern Canada \r\nfrom land uplift (see also Sweet et al., 2017; Greenan et al., 2018; \r\nOppenheimer et al., 2019). The RSLR projections here may however \r\nbe underestimated due to potential partial representation of land \r\nsubsidence in their assessment (Section 9.6.3.2)."


id: bea3ca9d045cc68f823a80b28caa676
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
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"Hail: There is limited evidence and low agreement for observed \r\nchanges in the frequency or intensity of North American hail \r\nstorms. J.T. Allen et al. (2015) and Allen (2018) found that temporal \r\ninconsistencies in the US and Canadian hail records made long-term \r\nclimate analysis difficult, although B.H. Tang et al. (2019) identified \r\nan increasing frequency of environmental conditions conducive \r\nfor large hail (diameter >= 5 cm) over the central and eastern USA. \r\nThere is limited evidence and medium agreement in projections of \r\nincreased hail damage potential over North America. Some regional \r\nand convective-permitting model projections indicate a longer hail \r\nseason with fewer events and larger hail sizes that result in higher \r\nhail damage potential (Brimelow et al., 2017; Trapp et al., 2019)."


id: f507f07650a7d0a37c1f924d705a50f
created_at: 2023-04-19 13:21:41
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"with particularly high retreats in NWN (160 m) and CNA (330 m). The \r\ntotal length of sandy coasts in North America that are projected to \r\nretreat by more than a median of 100 m by 2100 under RCP4.5 and \r\nRCP8.5 is about 15,000 km and 25,000 km respectively, an increase \r\nof approximately 70%."


id: 9a4d544ea46a2521357f05520544816f
created_at: 2023-04-19 13:21:41
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"Marine heatwave: There is high confidence in observed increases \r\nin marine heatwave (MHW) frequency and future increases in marine \r\nheatwaves are very likely around North America (Box 9.2). The total \r\nnumber of MHW days per decade increased in the North American \r\ncoastal zone, albeit somewhat more in the Pacific (Oliver et al., 2018; \r\nSmale et  al., 2019). Projected increases in degree heating weeks \r\n(Heron et al., 2016) and degree heating months (Frieler et al., 2013) \r\nindicate increasing bleaching-level and mortality-level heating stress \r\nthreshold events for reefs in Florida and Mexico."


id: 3554ba9fb263adce90ec99972fdf7ac1
created_at: 2023-04-19 13:21:41
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"Mean SST is projected to increase by 1degC (3degC) around North America by \r\n2100, with a  hotspot of around 4degC (5degC) off the North American \r\nAtlantic coastline under RCP4.5 (RCP8.5) conditions (see Interactive \r\nAtlas). Frolicher et al. (2018) projected increasing MHW frequency and \r\nspatial extent at a 2degC global warming level with the largest increases \r\nin the Gulf of Mexico and off the southern USA East Coast (>20x) as \r\nwell as off the coast of the Pacific Northwest (>15x). Projections for \r\nSSP1-2.6 and SSP5-8.5 both show an increase in MHWs around North \r\nAmerica by 2081-2100, relative to 1985-2014 (Box 9.2, Figure 1)."


id: b2f364a80c8b4e811c40e423a14aa17b
created_at: 2023-04-19 13:21:41
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"There is high confidence that most coastal CIDs in North America \r\nwill continue to increase in the future with climate change. An \r\nobserved increase in relative sea level rise is virtually certain to \r\ncontinue in North America (other than around the Hudson Bay \r\nand southern Alaska) contributing to more frequent and severe \r\ncoastal flooding in low-lying areas (high confidence) and \r\nshoreline retreat along most sandy coasts (high confidence). \r\nMarine heatwaves are also expected to increase all around the \r\nregion over the 21st century (high confidence)."


id: 764ce71d881e7d88808e7ddf8b536eda
created_at: 2023-04-19 13:21:41
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"Coastal erosion: There is limited evidence of changes in North \r\nAmerican episodic storm erosion caused by waves and storm surges. \r\nObservations show increased extreme wave energy on the Pacific \r\ncoast, but no clear trend on other USA coasts given substantial \r\nnatural variability (Bromirski et al., 2013; Vose et al., 2014). In terms of \r\nlong-term coastal erosion, shoreline retreat rates of around 1 m yr-1\r\nhave been observed during 1984-2015 along the sandy coasts of \r\nNWN and NCA while portions of the US Gulf Coast have seen a retreat \r\nrate approaching 2.5 m yr-1 (Luijendijk et al., 2018; Mentaschi et al., \r\n2018). Sandy shorelines along ENA and WNA have remained more or \r\nless stable during 1984-2014, but a shoreline progradation rate of \r\naround 0.5 m yr-1 has been observed in NEN. Mentaschi et al. (2018) \r\nreport 1984-2015 coastal area land losses of 630 km2 and 1260 km2 \r\nalong the Pacific and Atlantic coasts of the USA, respectively."


id: b8f8007befe2a35f24eaae310cfc9635
created_at: 2023-04-19 13:21:41
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"The assessed direction of change in climatic impact-drivers for North \r\nAmerica and associated confidence levels are illustrated in Table 12.8."


id: a82701cf22f7184629e98984bc64d0bb
created_at: 2023-04-19 13:21:41
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"Projections indicate that sandy coasts in most of the region \r\nwill experience shoreline retreat through the 21st  century (high \r\nconfidence). Median shoreline change projections presented by \r\nVousdoukas et  al. (2020b) show that sandy shorelines in NWN, \r\nENA, and NCA will retreat by between 40 and 80 m by mid-century \r\n(relative to 2010) under both RCP4.5 and RCP8.5. Projections for NEN \r\nand WNA are lower at 20-30 m under the same RCPs. The highest \r\nmedian mid-century projection in the region is for CNA at around \r\n125 m under both RCPs. RCP4.5 projections for 2100 show shoreline \r\nretreats of 100 m or more along the sandy coasts of NWN, CNA, \r\nand NCA, while retreats of between 40 and 80 m are projected in \r\nother regions. Under RCP8.5, retreats exceeding 100 m are projected \r\nin all regions except NEN and WNA (approximately 80 m) by 2100, "


id: 8c4d9b350ad1e6d7d11d77c677105a50
created_at: 2023-04-19 13:21:41
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"et al., 2018; Taylor et al., 2018), with livestock temperature-humidity \r\ntolerance thresholds increasingly surpassed (Lallo et  al., 2018). \r\nIn  CAR, a  median increase of more than a month per year where \r\ntemperatures exceed 35degC is projected by end of the 21st century \r\nunder SSP5-8.5 (Figure  12.4a-c and Figure  12.SM.1). Heatwaves \r\nare projected to increase in CAR by the mid- and end-century under \r\nRCP8.5 (Sections 11.3.5 and 11.9, and Table 11.13). Figure 12.4d-f \r\nand Figure 12.SM.2 also show an increase of about 30-60 days in \r\nwhich HI exceeds 41degC by 2041-2060 under SSP5-8.5 relative to \r\n1995-2014 in CAR, with an additional increase of about 50-100 \r\ndays by end of the 21st  century for RCP8.5/SSP5-8.5, but this \r\nincrease remains below 50 days for RCP2.6/SSP1-2.6. The Pacific \r\nIslands region is also among those projected to have an increase in \r\nWBGT by end-century under RCP8.5, increasing the risk of heat stress \r\nin the region (Newth and Gunasekera, 2018)."


id: 27f28078140e9c4f231f4101fb502009
created_at: 2023-04-19 13:21:41
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"It is very likely that the significant recent warming trends \r\nobserved in the small islands will continue in the 21st century, \r\nwhich will likely further increase heat stress in these regions."


id: 6301cbf5bf6b911b7f6c4f1aa62cdb2e
created_at: 2023-04-19 13:21:41
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"The AR5 identified the key climate and ocean-related hazards affecting \r\nsmall islands, which occur at different time scales and have diverse \r\nimpacts on multiple sectors (Christensen et al., 2013; Nurse et al., 2014). \r\nRecent findings from SR1.5 and SROCC emphasize that the multiple \r\ninterrelated climate hazards currently faced by low-lying islands and \r\ncoastal areas will be amplified in the future, especially at higher global \r\nwarming levels (Hoegh-Guldberg et al., 2018; IPCC, 2019b)."


id: d3a3c08a081e6fef8aef8f884019bd6a
created_at: 2023-04-19 13:21:41
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"Mean precipitation: Observational datasets have generally \r\nrevealed no significant long-term trends in rainfall in the Caribbean \r\nover the 20th century when analysed at seasonal and inter-decadal \r\ntimescales, except for some areas where there is evidence for \r\ndecreasing trends for the period 1901-2010 but not for the period \r\n1951-2010 (Cross-Chapter Box Atlas.2, Table  1, and Atlas.10.2; \r\nKnutson and Zeng, 2018). Although there are spatial variations, \r\nannual rainfall trends in the western Indian Ocean are mostly \r\ndecreasing, with generally non-significant trends in the western \r\ntropical Pacific since the 1950s (low confidence). Significant drying \r\ntrends are noted in the southern Pacific subtropics and south-western \r\nFrench Polynesia during the 1951-2015 period (McGree et al., 2019), \r\nand in some areas of Hawaii during the 1920-2012 period (medium \r\nconfidence) (Cross-Chapter Box Atlas.2, Table 1, and Atlas.10.2)."


id: efd337961fdb20821e24dfc05f5f903e
created_at: 2023-04-19 13:21:41
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"Mean air temperature: Significant warming trends are clearly \r\nevident in the small islands, such as those in the Pacific, CAR, \r\nand western Indian Ocean, particularly over the latter half of the \r\n20th  century (see Figure Atlas.11; Atlas.10.2; Cross-Chapter Box \r\nAtlas.2, Table  1). This observed warming signal in the tropical \r\nwestern Pacific has been attributed to anthropogenic forcing (Wang \r\net al., 2016). There is high confidence of warming over small islands \r\neven at 1.5degC GWL (Atlas.10.4 and Figure Atlas.28; Hoegh-Guldberg \r\net al., 2018). Mean temperature is very likely to increase by 1degC-2degC \r\n(2degC-4degC) by 2041-2060 (2081-2100) under RCP8.5 (BOM and \r\nCSIRO, 2014) and SSP3-7.0 (Atlas.10.4, Figure  4.19 and Figure \r\nAtlas.12; Almazroui et al., 2021)."


id: 53db677a4ee213472e1d1e9eb24050ef
created_at: 2023-04-19 13:21:41
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"Atlas.10.4 projects precipitation reduction over the Caribbean \r\n(high confidence) (Almazroui et al., 2021) and parts of the Atlantic and \r\nIndian oceans, particularly in June to August, by end of 21st century \r\nunder SSP5-8.5. Precipitation is generally projected to increase under \r\nSSP5-8.5 and for higher GWLs in the small islands in parts of the \r\nwestern and equatorial Pacific, but there is low confidence in broad \r\nchanges given drier conditions projected for the southern subtropical \r\nand eastern Pacific Ocean (limited agreement given spatial and \r\nseasonal variability) (Atlas.10.4 and Figure Atlas.28)."


id: 944b7fb997dcce08c8198d13a856e88d
created_at: 2023-04-19 13:21:41
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"Extreme heat: Observational records indicate warming trends in \r\nthe temperature extremes since the 1950s in CAR and the Pacific \r\nsmall islands (high confidence) (Sections 11.3.2 and 11.9, and \r\nTable 11.13). A detectable anthropogenic increase in summer heat \r\nstress has been identified over a number of island regions in CAR, \r\nwestern tropical Pacific, and tropical Indian Ocean, based on wet bulb \r\nglobe temperature (WBGT) index trends for 1973-2012 (medium \r\nconfidence) (Knutson and Ploshay, 2016). An increasing trend in \r\nthe maximum daytime heat index is also noted in CAR during the \r\n1980-2014 period, as well as more extreme heat events since 1991 \r\n(Ramirez-Beltran et al., 2017)."


id: 81e5288253666ed8b61ee1fb1f635c46
created_at: 2023-04-19 13:21:41
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"River flood: There is limited evidence on observed changes in river \r\nflooding in the small islands. Long-term records in Hawaii indicate \r\nno clear trends in peak flow, except for the significant decrease \r\nin peak streamflow in Hawaii Island over the period 1967-2016 \r\n(Bassiouni and Oki, 2013; Clilverd et  al., 2019). Similarly, there is \r\nno significant trend in the frequency and height (after adjusting for \r\naverage sea level rise) of river flood in Fiji over the period 1892-2013 \r\n(McAneney et al., 2017). There is low confidence on the direction of \r\nfuture change of river flooding in the small islands due to the limited \r\nliterature. In Oahu, Hawaii, extreme peak flow events with high "


id: ffff430ca185906f2f8cef6f2c5190e0
created_at: 2023-04-19 13:21:41
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"Compared with the recent past, it is likely that the intensity and \r\nfrequency of hot (cold) temperature extremes will increase (decrease) \r\nin the small islands (Section 11.9 and Table 11.13; BOM and CSIRO, \r\n2014). Warm spell conditions will occur up to half the year in CAR \r\nat 1.5degC GWL with an additional 70 days at 2degC (Hoegh-Guldberg "


id: 9d318f89757953b5d8beacf0bd92ca9
created_at: 2023-04-19 13:21:41
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"return periods are projected to increase by end of the 21st century \r\nunder RCP8.5, but there is also high uncertainty in these projections \r\n(Leta et al., 2018)."


id: eb0c691171f8e2ad283fdbd2b236ed5f
created_at: 2023-04-19 13:21:41
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"Hydrological drought: There is low confidence of widespread \r\nchanges to hydrological drought in CAR or Pacific small islands in \r\nrecent decades, although an increasing number of studies document \r\nlocal changes. Records in Hawaii indicate downward trends in low \r\nstreamflow and base flow from 1913 to 2008 (Bassiouni and Oki, \r\n2013). Decadal variability of Hawaiian streamflow coincides with \r\nrainfall fluctuations associated with the Pacific Decadal Variability \r\nalthough significant average declines in surface and baseflow runoff \r\nof about 8% and 11% per decade, respectively, have been noted \r\nduring the 1987-2016 period (Clilverd et al., 2019)."


id: 2979df9aae719fdc29e094b0b2706016
created_at: 2023-04-19 13:21:41
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"There is low confidence in hydrological drought change projections, \r\ngiven low signal-to-noise ratios and the challenge in representing \r\nisland scales in global analyses. Prudhomme et al. (2014) recognized \r\nCAR as one of the regions with the highest increase in regional \r\ndeficit index (RDI; a measure of the fraction of area in hydrological \r\ndrought conditions) by the end of the 21st  century under RCP8.5. \r\nDaily streamflow and extreme low flows in two watersheds in Oahu, \r\nHawaii are projected to decline by mid- and end of the 21st century \r\nunder RCP4.5 and RCP8.5, which would result in more frequent \r\nhydrological droughts in this area (Leta et al., 2018)."


id: bfc22b635ca91b77f77fadddf42872e
created_at: 2023-04-19 13:21:41
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"Landslide: Heavy rainfall, such as from tropical cyclones, can trigger \r\nlandslides over steep terrain in the small islands (Bessette-Kirton \r\net al., 2019). There is limited evidence to determine long-term trends \r\nin rainfall-induced landslides in the small islands (Kirschbaum et al., \r\n2015; Sepulveda and Petley, 2015; Froude and Petley, 2018; Bessette\u0002Kirton et  al., 2019). There is low confidence in future changes in \r\nlandslides in the small islands. The direction of change may depend \r\non future changes in precipitation, tropical cyclones, climate modes \r\n(e.g.,  El Nino-Southern Oscillation, ENSO), as well as human \r\ndisturbance, but more data and understanding of the complexity of \r\nthese relationships are needed, especially in these vulnerable areas \r\n(Sepulveda and Petley, 2015; Gariano and Guzzetti, 2016; Froude and \r\nPetley, 2018)."


id: b4a0dbcfa95f7f8d2164a9d09d5a243a
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"Fire weather: There is limited evidence on trends in wildfire in CAR \r\nand the Pacific. Records of wildfire in Hawaii from 2005 to 2011 \r\nindicate a peak in area burned during the hot and dry summer months, \r\nbut Trauernicht et al. (2015) note the difficulty in establishing the link \r\nbetween past climate and wildfire trends due to human activities and \r\nvegetation changes. Availability of literature limits assessment on \r\nfuture fire weather in the small islands. Drying and warming trends \r\ntend to increase fire probability aside from the climate impact on fuel \r\nloading, for example, grassland fires in Hawaii (Trauernicht, 2019), and \r\nwildfires in Puerto Rico (Van Beusekom et al., 2018)."


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created_at: 2023-04-19 13:21:41
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"Observed and projected rainfall trends vary spatially across \r\nthe small islands. Higher evapotranspiration under a warming \r\nclimate are projected to partially offset future increases \r\nor amplify future reductions in rainfall, resulting in drier \r\nconditions and increased water stress in the small islands \r\n(medium confidence)."


id: 6d85ddd0f59dc690c462fbf54db12d79
created_at: 2023-04-19 13:21:41
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"Projections indicate that small islands will generally face \r\nfewer but more intense tropical cyclones (medium confidence) \r\nalthough there is substantial variability across small island \r\nregions given projected regional shifts in storm tracks."


id: 5d9b3dfa259c2856253938ad4723bca4
created_at: 2023-04-19 13:21:41
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"Relative sea level: Relative sea level rise (RSLR) continues to be \r\na major threat to small islands and atolls, since it can exacerbate the \r\nimpacts of other climate hazards on low-lying coastal communities \r\nand infrastructures, ecosystems, and freshwater resources (Nurse \r\net al., 2014; Hoegh-Guldberg et al., 2018). In the Indian Ocean-South \r\nPacific region, a new tide gauge-based reconstruction finds a regional \r\nmean RSL change of 1.33 [0.80 to 1.86] mm yr-1 over 1900-2018 \r\n(Frederikse et  al., 2020) compared to a  GMSL change of around \r\n1.7  mm yr-1 (Section  2.3.3.3 and Table  9.5). RSLR rates based on \r\nsatellite altimetry for the period 1993-2018 in the region increased \r\nto 3.65 [3.23 to 4.08] mm yr -1 (Frederikse et al., 2020), compared to \r\na GMSL change of 3.25 mm yr-1 (Section 2.3.3.3 and Table 9.5). "


id: 8169c692bcc1b696cf95a8985bf8ed8d
created_at: 2023-04-19 13:21:41
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"Tropical cyclone: Tropical cyclones have devastating impacts on the \r\nsmall islands due to intense winds, storm surge and rainfall, although the \r\nassociated rainfall can also be beneficial for freshwater resources. It is \r\nlikely that tropical cyclone intensity and intensification rates at a global \r\nscale have increased in the past 40 years but it is not clear if regional\u0002scale changes are basin-wide or due to shifts in tropical cyclone tracks \r\n(Section 11.7.1.2). Other, less data-sensitive tropical cyclone features, \r\nsuch as the poleward migration of where tropical cyclones reach \r\npeak intensity in the western North Pacific since the 1940s (medium \r\nconfidence) and the slowdown in tropical cyclone translational speed \r\nover contiguous USA since 1900 (medium confidence), can affect rainfall \r\nand flooding over small islands in CAR and the Pacific (Section 11.7.1.2)."


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"Relative sea-level rise is very likely to continue surrounding the \r\noceans in the Small Island States. Around the small islands, regional \r\nmean RSLR projections vary widely, from 0.4-0.6 m under SSP1-2.6 \r\nto 0.7-1.6 m under SSP5-8.5 for 2081-2100 relative to 1995-2014 \r\n(median values), but in general they are situated in areas with RSL \r\nchanges ranging from the mean projected GMSL change to above\u0002average values (Section  9.6.3.3). These RSLR projections may \r\nhowever be underestimated due to potential partial representation \r\nof land subsidence in their assessment (Section 9.6.3.2)."


id: a4730f4d4fe3cb7f55f785f4778af066
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
content_type: text
index: climate_gpt_v2
vector_id: 26143
content:
"Coastal flood: Relative sea level rise, storm surges and swells \r\ncontribute to coastal inundation in the small islands, where studies \r\non historical trends in coastal flooding are currently limited. For \r\nexample, a  swell event due to distant extratropical cyclones in \r\nDecember 2008 raised extreme water levels leading to flooding \r\naffecting five Pacific island nations: Marshall Islands, Micronesia, \r\nPapua New Guinea, Kiribati and Solomon Islands (Hoeke et al., 2013; \r\nMerrifield et al., 2014). Over low-lying atoll islands in the north-west \r\ntropical Pacific, potential increases in the frequency and areal extent \r\nof coastal flooding, especially at higher SLR scenarios, are expected \r\nto have negative consequences for freshwater resources and island \r\nhabitability (Storlazzi et al., 2015, 2018). Select tide gauges across \r\nthe Pacific also indicate increasing trends in the frequency of minor \r\nflooding since the 1960s (Marra and Kruk, 2017)."


id: 49e4569c54550cd27e01888fe3369745
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
content_type: text
index: climate_gpt_v2
vector_id: 10236
content:
"As relative sea levels increase, the potential for coastal flooding \r\nincreases in the small islands (high confidence). Across the Pacific and \r\nCAR small islands, the 5-95th percentile range of the 1-in-100-year \r\nETWL is projected to increase (relative to 1980-2014) by 10-35 cm \r\nand by 14-41 cm by 2050 under RCP4.5 and RCP8.5, respectively \r\n(Figure 12.4q). By 2100, this range is projected to be 27-81 cm and \r\n44-188 cm under RCP4.5 and RCP8.5, respectively (Figure 12.4p,r; \r\nVousdoukas et al., 2018; Kirezci et al., 2020). Furthermore, by 2050, \r\nthe present-day 1-in-100-year ETWL is projected to have median \r\nreturn periods of between 1-in-1-year and 1-in-50-year in both the \r\nPacific and CAR small islands, with some Pacific islands projected \r\nto experience the present-day 1-in-100-year ETWL more than once "


id: 152670a5305d546d3e53c0e0af5e8f37
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
content_type: text
index: climate_gpt_v2
vector_id: 934
content:
"d chang\r\ned in C\r\nd). The t\r\n and Co\r\nEtht\r\nCold spell\r\n confiden\r\nease in p\r\nin weste\r\nh Pacific\r\n/sources \r\ne)\r\no high c\r\n5-8.5 (m"


id: 8bc5c13acf70abc36b49217048428af8
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
content_type: text
index: climate_gpt_v2
vector_id: 21760
content:
"a decrease in frequency (two annual events) over the eastern Pacific \r\nOcean (Box 9.2; Oliver et al., 2018). The intensity of MHWs has also \r\nincreased between 0.2degC and 0.5degC over the equatorial portions of \r\nthe North Atlantic and the South Pacific. Over the eastern tropical \r\nPacific, the decrease in intensity and duration of MHW is between \r\n0.5degC and 1.0degC and between 30 and 75 days, respectively (Box 9.2; \r\nOliver et al., 2018). There is high confidence that MHWs will increase \r\naround all small island nations. Marine heatwaves are projected to \r\nbe more intense and prolonged where the largest changes are noted \r\nin the equatorial region with maximum annual intensities up to 1.2degC \r\n(1.8degC) and annual mean duration reaching 100 days (200 days) at \r\n1.5degC (2.0degC) warming levels (Frolicher et al., 2018). Projections for \r\nSSP1-2.6 and SSP5-8.5 both show an increase in MHWs around all \r\nsmall island nations by 2081-2100, relative to 1985-2014 (Box 9.2, \r\nFigure 1)."


id: 8e71376138bc880435c0adf2746e5f66
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
content_type: text
index: climate_gpt_v2
vector_id: 22249
content:
"a year (Vousdoukas et al., 2018; Oppenheimer et al., 2019). By 2100, \r\nthe present-day 1-in-50-year ETWL is projected to occur around \r\nthree times a year by 2100 with an SLR of 1 m at Pacific and CAR \r\nsmall islands (Vitousek et al., 2017). In the western tropical Pacific, \r\nthe magnitude and frequency of coastal flooding due to SLR can be \r\nmodulated by changes in the wave climate (Shope et al., 2016)."


id: 521db7356a9efbf5c74b254f5cfa3b23
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
content_type: text
index: climate_gpt_v2
vector_id: 11718
content:
"In summary, relative sea level rise is very likely in the oceans \r\naround small islands, and along with storm surges and waves \r\nwill exacerbate coastal inundation in small islands. Shoreline \r\nretreat is projected along sandy coasts of most small islands \r\n(high confidence). There is high confidence that MHWs will \r\nincrease around all small island nations."


id: 52e89928e333f4405cda9aa5f4ae11bb
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
content_type: text
index: climate_gpt_v2
vector_id: 11852
content:
"The assessed direction of change in climatic impact-drivers for \r\nCAR and Pacific small islands and associated confidence levels are \r\nillustrated in Table 12.9. Cold, snow, and ice-related climatic impact\u0002drivers, and sand and dust storms are not broadly relevant in the small \r\nislands that were assessed."


id: 64a25e055a44d3e1637604c62d2e3347
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
content_type: text
index: climate_gpt_v2
vector_id: 14987
content:
"Mean ocean temperature: It is very likely that global mean sea \r\nsurface temperature (SST) has increased by 0.88 [0.68 to 1.01] degC "


id: cc2595ebf6325018f14e7a5533b6b40c
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
content_type: text
index: climate_gpt_v2
vector_id: 33236
content:
"and polar zones (Sections 2.3.3.5 and 5.3.3.2). Over the period \r\n1870-1899 to 2080-2099, ocean surface pH is projected to decline \r\nby -0.16  +-  0.002 under SSP1-2.6, and by -0.44 +- 0.005 under \r\nSSP5-8.5 (Sections 4.3.2.5 and 5.3.4.1). Declining ocean pH will \r\nexacerbate negative impacts on marine species (medium confidence) \r\n(Albright et al., 2016; Kwiatkowski et al., 2016; Watson et al., 2017)."


id: 8e7b8aa704574a22719dd1d82b98ba0a
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
content_type: text
index: climate_gpt_v2
vector_id: 22257
content:
"Ocean salinity: Salinity contrasts have increased since the 1950s, \r\nnear the ocean surface (virtually certain) and in the subsurface \r\n(very likely), with high-salinity regions becoming more saline and \r\nlow-salinity regions becoming fresher (Section  2.3.3.2). At the \r\nbasin scale, it is very likely that the Pacific and the Southern Oceans \r\nhave freshened and that the Atlantic has become more saline \r\n(Section 2.3.3.2). The SROCC (IPCC 2019b) assessment that a general \r\npattern of fresh ocean regions getting fresher and salty ocean \r\nregions getting saltier will continue in the 21st century is confirmed \r\nin Section 9.2.2.2."


id: 3ae6bed6651fd6fa744771e1eead218e
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
content_type: text
index: climate_gpt_v2
vector_id: 7558
content:
"Ocean acidity: With the increasing CO2 concentration, the global \r\nmean ocean surface pH is decreasing and is now the lowest it \r\nhas been for at least a  thousand years (very high confidence) \r\n(Section  2.3.3.5). It is very likely that, since the 1980s, ocean \r\nsurface pH has changed at a rate of -0.016 to -0.019 per decade in \r\nthe subtropical open oceans, at -0.010 to -0.026 per decade in the \r\ntropical Pacific, and at -0.003 to -0.026 per decade in open subpolar "


id: a242e119c072abe310145ce4248ec5ed
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
content_type: text
index: climate_gpt_v2
vector_id: 25778
content:
"there is medium confidence in specific regional declines that are \r\nexpected to expand both anoxic and hypoxic zones, with such \r\nreductions of oxygen expected to persist for thousands of years \r\n(Yamamoto et al., 2015; Frolicher et al., 2020)."


id: c7c6c78fb576aec5d79503764e0462c8
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
content_type: text
index: climate_gpt_v2
vector_id: 32451
content:
"25 days per decade for later autumn advance, with consequences for \r\nArctic marine mammals (AMMs) due to sea ice habitat loss (Laidre \r\net al., 2015). The Arctic is projected to be ice-free more often during \r\nsummer under 2degC global warming compared to 1.5degC global warming \r\n(Section 9.3.1.1; see also Sections 12.4.9 and 4.4.2.1), opening new \r\nshipping lanes for international commerce (Valsson and Ulfarsson, \r\n2011) and lengthening the season for offshore resource extraction \r\n(Schaeffer et al., 2012). Iceberg numbers are expected to increase as \r\na result of global warming, forming an elevated hazard to shipping and \r\noffshore facilities (Bigg et al., 2018)."


id: 740da5c03d8e212fbed5c700865f431f
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
content_type: text
index: climate_gpt_v2
vector_id: 17659
content:
"Sea ice: The Arctic sea ice area for September has decreased from 6.23 \r\nto 3.76 million km2 and for March from 14.52 to 13.42 million km2\r\nbetween 1979-1988 and 2010-19 (Section 2.3.2.1.1). There is high \r\nconfidence that sea ice in the Arctic will further decrease in the future \r\nunder all emissions scenarios (Section 9.3.1.1). In contrast, there is no \r\nclear observed trend in the Antarctic sea ice area over the past few \r\ndecades and there is low confidence of future changes (Section 9.3.1.1). \r\nThe duration of the summer ice season in the Arctic has increased by 5 \r\nto 20 weeks between 1979 and 2013, with a significant trend ranging \r\nfrom 5 to 17 days per decade for earlier spring retreat and from 5 to "


id: 1365da8df23c2c7c00d7dccd090b93aa
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
content_type: text
index: climate_gpt_v2
vector_id: 621
content:
"It is virtually certain that global mean SST will continue \r\nto increase throughout the 21st  century, resulting in the \r\nexceedance of numerous climatic impact-driver thresholds \r\nrelevant to marine ecosystems (medium confidence). Marine "


id: 1dfc4c30dbbb627a501ef2f046294cf6
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
content_type: text
index: climate_gpt_v2
vector_id: 2468
content:
"Table 12.10 | Summary of confidence in direction of projected change in climatic impact-drivers in open and deep ocean regions, representing their \r\naggregate characteristic changes for mid-century for scenarios RCP4.5, SSP2-4.5, SRES A1B, or above within each AR6 region (defined in Chapter 1), \r\napproximately corresponding (for CIDs that are independent of sea level rise) to global warming levels between 2degC and 2.4degC (see Section 12.4 for \r\nmore details of the assessment method). The table also includes the assessment of observed or projected time-of-emergence of the CID change signal from the natural \r\ninterannual variability if found with at least medium confidence in Section 12.5.2. Asterisks indicate regions that extend across both sides of the map."


id: aa55ddd709f2032eb2922bf3c2cc86df
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
content_type: text
index: climate_gpt_v2
vector_id: 27197
content:
" Emerging after 2050 and by 2100 at least in scenarios RCP8.5/SSP5-8.5 (medium to high confidence)"


id: ad67e707c8f54453e5a3a8fff4027ef4
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
content_type: text
index: climate_gpt_v2
vector_id: 27748
content:
"heatwave days are projected to increase in global oceans, with \r\na larger increase in the tropical ocean and Arctic Ocean (high \r\nconfidence). It is virtually certain that upper-ocean stratification \r\nwill continue to increase in the 21st  century. Future ocean \r\nwarming will very likely assist the development of both anoxic \r\nand hypoxic zones, with such reductions of oxygen expected to \r\npersist for thousands of years. Future projections also indicate \r\nfreshening of the Pacific, Southern and Indian oceans and \r\na saltier Atlantic Ocean (medium confidence)."


id: 33b5dbe6138f3f78c862adca9bc2f9c3
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
content_type: text
index: climate_gpt_v2
vector_id: 6342
content:
"on higher mean temperatures, with J.R. Lee et al. (2017) projecting \r\nmore than 50 additional degree days above freezing (2098 RCP8.5 \r\ncompared with 2014) over parts of the Antarctic Peninsula but \r\nsmaller changes over mainland Antarctica."


id: d6a18fe955243569fc7f17cea971534c
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
content_type: text
index: climate_gpt_v2
vector_id: 35065
content:
"Mean precipitation: Atlas.11.2 indicated medium confidence in \r\nobserved increases in Arctic precipitation, with the largest rises in the \r\ncold season. Antarctic precipitation showed no significant overall \r\ntrend since the 1970s, with a positive trend over the 20th century \r\n(Sections 9.4.2.1 and Atlas.11.1). Increases in Arctic and Antarctic \r\nprecipitation during the 21st century are very likely, with projected \r\npercentage increases that are much higher than most subpolar \r\nregions of the world (Figure Atlas.29)."


id: 44c47cf65dba82605ac82a75f7ee81c8
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
content_type: text
index: climate_gpt_v2
vector_id: 9324
content:
"The assessed direction of change in climatic impact-drivers for \r\nopen and deep ocean regions and associated confidence levels are \r\nillustrated in Table  12.10, following the AR6 WGI ocean reference \r\nregions (Figure Atlas.2b)."


id: 5f10970cc1d9aeeddd28629de95e749e
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
content_type: text
index: climate_gpt_v2
vector_id: 14024
content:
"Floods and heavy precipitation: Observations and model \r\nprojections indicate high confidence in increasing Arctic river runoff \r\nin response to increasing total precipitation (Box et  al., 2019; \r\nDurocher et  al., 2019; Meredith et  al., 2019) with a  shift towards \r\nearlier meltwater flooding (AMAP, 2017). Higher Arctic precipitable \r\nwater totals are also connected with observed increases in heavy \r\nprecipitation and convective activity (high confidence) (Ye et  al., \r\n2015; Kharin et al., 2018; Chernokulsky et al., 2019). Higher flood \r\nmagnitudes are also driven by future increases in rain-on-snow event \r\ndays, amounts, and runoff, which are more significant in the Arctic \r\nthan in mid-latitudes (where seasonal snow cover is often further \r\nreduced; AMAP, 2017; Jeong and Sushama, 2018b)."


id: cb9f91c4863054f8634952b4d73fae1e
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
content_type: text
index: climate_gpt_v2
vector_id: 33150
content:
"Several recent climate assessments of polar regions describe robust \r\npatterns of recent and future climatic changes driving impacts and \r\nrisk for polar environmental, societal, and economic assets. These \r\nhave included the IPCC SROCC (Meredith et al., 2019), the Report \r\non Snow, Water, Ice and Permafrost in the Arctic (AMAP, 2017), and \r\nnational assessments for the USA (Markon et al., 2018) and Canada \r\n(Derksen et al., 2018). This section examines Greenland and Iceland, \r\nthe Russian Arctic, Antarctica, and the Arctic portions of Northern \r\nEurope and North America (Figure 1.18c)."


id: 7b39e7b0dbc868c8fe98dc681e8f7655
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
content_type: text
index: climate_gpt_v2
vector_id: 18878
content:
"Landslide and snow avalanche: There is a growing number of studies \r\non mass movements in polar regions. Although there is low confidence\r\nin widespread observational trends for landslides or snow avalanches, \r\na rise in the number of future landslides is supported by strong links to \r\nincreases in heavy precipitation, glacier retreat, and thawing of ice-rich \r\npermafrost that can lead to retrogressive thaw slumps in Arctic regions \r\n(Section 2.3.2.5; Kokelj et al., 2015; Derksen et al., 2018; Lewkowicz \r\nand Way, 2019; Patton et al., 2019; Ward Jones et al., 2019)."


id: bb94069d09f8a34d93f05490f7f6b264
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
content_type: text
index: climate_gpt_v2
vector_id: 30221
content:
"Mean air temperature: Atlas.11.2 shows high confidence in\r\nwarming of the Arctic in observations and projections, measuring \r\namong the fastest-warming places at more than twice the global \r\nmean, with substantially higher temperature increases in the cold \r\nseason (see also AMAP, 2017; Meredith et  al., 2019). Atlas.11.1 \r\nassessed very likely warming in observations of West Antarctica from \r\n1957 to 2016, but limited evidence of mean air temperature change \r\nacross East Antarctica even as there is high confidence in future \r\nwarming across the continent (Figure Atlas.29; Meredith et al., 2019)."


id: 863642982885b86c3c79778c5f2b59d8
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
content_type: text
index: climate_gpt_v2
vector_id: 20764
content:
"Aridity and drought: Recent decades have seen a general decrease \r\nin Arctic aridity, with projections indicating a  continuing trend \r\ntowards reduced aridity (high confidence) as increased moisture \r\ntransport leads to higher precipitation, humidity and streamflow \r\n(Meredith et  al., 2019) and a  corresponding decrease in dry days \r\n(Khlebnikova et al., 2019a). There is low confidence overall of recent \r\nor projected drought changes in polar regions (Section 11.9) even \r\nas increasing evidence shows that drainage from permafrost thaw, \r\nhigher potential evapotranspiration, and changing seasonal patterns \r\nof melt have caused lake reduction and soil moisture deficits in \r\nseveral areas that match with projections of future drought increase \r\ndespite overall precipitation increases (Andresen and Lougheed, \r\n2015; Bring et al., 2016; Spinoni et al., 2018a; Feng et al., 2019; Finger \r\nHiggens et al., 2019)."


id: 46213ac38dd997cff5e46b5712b662ba
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
content_type: text
index: climate_gpt_v2
vector_id: 9556
content:
"Fire weather: Fire season lengthened from 1979 to 2015 over Arctic \r\nportions of North America (Jain et al., 2017), corresponding also to \r\na 1975-2015 increase in lightning-ignited fires in Arctic North-Western \r\nNorth America (Girardin et  al., 2013; Veraverbeke  et  al.,  2017). "


id: c7c825a6ee32dce591acee44aa75d521
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
content_type: text
index: climate_gpt_v2
vector_id: 32453
content:
"Abatzoglou et al. (2019) climate model simulations project significant \r\nfire weather index increases in boreal forests of Arctic Europe, \r\nArctic Russia and Arctic North-Eastern North America (medium \r\nconfidence). Trends towards more frequent fires in tundra regions \r\nare expected to continue, driven in particular by increasing potential \r\nevapotranspiration and changes in vegetation (high  confidence) \r\n(Hu et al., 2015; AMAP, 2017; Young et al., 2017)."


id: 1c5d604ce126bcc62ee9472df0b37aad
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
content_type: text
index: climate_gpt_v2
vector_id: 2180
content:
"Antarctic Periphery: 7 +- 12%, 13 +- 10%, and 16 +- 19%. Areas \r\nwith receding glaciers are also potentially vulnerable to glacial lake \r\noutburst floods (Harrison et al., 2018)."


id: 37f903b6e51a97f5d2b9772e6f8d61bb
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
content_type: text
index: climate_gpt_v2
vector_id: 7053
content:
"Mean wind speed and severe storm: There is medium confidence \r\nof mean wind decrease over the Russian Arctic, Greenland and \r\nIceland, and Arctic North-Eastern North America (Karnauskas et al., \r\n2018a; Jung and Schindler, 2019), but low confidence of changes \r\nin the other Arctic regions and Antarctica. Bintanja et  al. (2014) \r\nprojected that a  strengthening of the Southern Annular Mode \r\nwould decrease easterlies along Antarctica's coasts with only small \r\nchanges in katabatic winds (although this effect may diminish \r\nwith stratospheric ozone recovery). In contrast, Gorter et al. (2014) \r\nregional climate model projections indicated a  reduction in mean \r\nwinds over the interior of Greenland by RCP4.5 2100 while coastal \r\nwinds increase. Reanalysis data and climate models indicate few \r\ncoherent regional trends of polar cyclone frequency or relationships \r\nwith cyclone depth and size (Akperov et  al., 2018, 2019; Day and \r\nHodges, 2018; Zahn et al., 2018)."


id: 65b2aa62edf0c4c9b6209ff149597a30
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
content_type: text
index: climate_gpt_v2
vector_id: 15155
content:
"Snow: Atlas.11.1 identified likely increases in surface mass balance \r\n(driven by snowfall) across Antarctica in the 20th  century (medium \r\nconfidence). In the Arctic, overall snow extent and seasonal duration \r\nare projected to continue recent declines (high confidence), although \r\nmid-winter snowpack increases in some of the coldest and high\u0002elevation locations given higher precipitation totals (medium \r\nconfidence) (Section 9.5.3, Atlas.9 and Atlas.11.2; Bring et al., 2016; \r\nDanco et  al., 2016; AMAP, 2017; Meredith et  al., 2019). Higher \r\ntemperatures result in a  higher percentage of Arctic precipitation \r\nfalling as rain (particularly in autumn and spring) (high confidence), \r\nwith most land regions (outside of Greenland and Antarctica) becoming \r\ndominated by rainfall (more than 50% of total precipitation) by RCP8.5 \r\n2100 (Bintanja and Andry, 2017; Irannezhad et al., 2017)."


id: 792ff6dad53c331671305926f6d3d9df
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
content_type: text
index: climate_gpt_v2
vector_id: 18515
content:
"Future declines in Arctic sea ice are virtually certain, although there \r\nis low confidence in declines of Antarctic sea ice given dynamical \r\nprocesses in the Southern Ocean and the recovery of stratospheric \r\nozone (Section 9.3; Meredith et al., 2019). Projections of an 'ice-free' \r\nArctic vary, depending on definitions representing transportation \r\nneeds, however Laliberte et  al. (2016) noted that the median of \r\n42 CMIP5 models projected <5% sea ice for the month of September \r\nby 2050, with equivalent conditions for the entirety of the August-\r\nOctober period by 2090. Section 9.3.1 assessed high confidence that \r\npractically ice-free conditions (<1 million km2 in the September mean) \r\nwould likely first appear before 2050 even under strong mitigation \r\nscenarios (Sigmond et al., 2018; Stroeve and Notz, 2018; Notz and \r\nSIMIP Community, 2020)."


id: 6368ff6638fdecd728d713cfac0e4809
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vector_id: 14784
content:
"once every five years (or more frequently) by 2100. Arctic coastal \r\nerosion is also expected to increase with climate change (medium \r\nconfidence; high agreement but limited evidence of projections), \r\naccelerated in some regions by subsurface permafrost thaw and \r\nincreased wave energy (Gibbs and Richmond, 2015; Fritz et al., 2017; \r\nOppenheimer et al., 2019; Casas-Prat and Wang, 2020). A longer ice\u0002free season for the RCP8.5 2080s is projected to help drive more \r\nthan 100 m of shoreline retreat in North-Western North America \r\nArctic coastal communities (Melvin et al., 2017; Greenan et al., 2018; \r\nMagnan et  al., 2019). Assessment of coastal flooding and erosion \r\nchanges in Antarctica are limited by a lack of studies."


id: 8ece6c57bb9f575a0a5a394895be9d56
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
content_type: text
index: climate_gpt_v2
vector_id: 22309
content:
"Lake and river ice: There is high confidence in observations of \r\nsignificant declines in seasonal ice cover thickness and duration over \r\nmost Arctic lakes, with many lakes projected to lose more than one \r\nmonth of ice cover by mid-century (medium confidence) (Meredith \r\net al., 2019; Sharma et al., 2019). Some lakes that previously froze to \r\nthe bottom ('bedfast') now maintain liquid bottom water year round, \r\nand others shift from perennial to seasonal ice cover (Surdu et al., \r\n2016; Engram et al., 2018). Yang et al. (2020a) identified a decline in \r\nArctic cold-season river ice extent in satellite observations (particularly \r\nin Alaska) and projected reductions in average Northern Hemisphere \r\nseasonal river ice duration of 6.10 +- 0.08 days per  degree global \r\nsurface air temperature."


id: 240c671d3f82a86c2b54266b0d3d682f
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
content_type: text
index: climate_gpt_v2
vector_id: 3522
content:
"Marine heatwave: Recent years have seen marine heatwaves \r\n(MHWs) and increasing extreme coastal SSTs in Arctic systems (Lima \r\nand Wethey, 2012; Collins et al., 2019; Frolicher, 2019). Projections \r\nshow increases in MHW intensity, frequency and duration will be \r\nlarger over the Arctic Ocean than mid-latitude oceans due in part to \r\nlow interannual variability under current sea ice (high confidence). \r\nFrolicher et  al. (2018) used 12 CMIP5 models to project median \r\nMHW days increasing about 25-fold and 50-fold at the 2degC and \r\n3.5degC GWLs, respectively, in response to mean ocean warming \r\nand sea ice loss, and the smallest global changes still leading to \r\nincreases in the Southern Ocean around Antarctica (see also Cross\u0002chapter Box 9.1)."


id: afe4b137fd3e330a31c3ded8f3b469c7
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
content_type: text
index: climate_gpt_v2
vector_id: 28183
content:
"Heavy snowfall and ice storm: There is limited evidence of changes \r\nin heavy snowfall due to competing influences of shortened snowfall \r\nseasonality with more intense (and larger overall) precipitation in \r\nthe Arctic. Episodic heavy snowfall trends in Antarctica are difficult \r\nto separate from large interannual variability (limited evidence) \r\n(Gorodetskaya et al., 2014, Turner et al., 2020). Limited evidence also \r\nhinders clear signals in ice storms, although warming shifts the \r\nfreezing line (around which ice storms occur) poleward and upslope \r\n(Bintanja and Andry, 2017). Groisman et  al. (2016) used 40 years \r\nof observations to identify an increase of freezing rain events in \r\nNorway, North America, and eastern and western Russia. Increases \r\nin winter rainfall have led to more frequent development of difficult \r\nwildlife and livestock grazing conditions as basal ice conditions coat \r\nthe ground below snowpack (Peeters et al., 2019)."


id: 395218ee80ccd906578a1ddf98e2bb2
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
content_type: text
index: climate_gpt_v2
vector_id: 7297
content:
"Relative sea level: Satellite altimetry and tide data show that \r\nrelative sea levels (with glacial isostatic adjustment) are rising \r\nin Arctic Europe and Arctic North-Western North America, declining in \r\nportions of southern Alaska and Arctic North-Eastern North America \r\nand no clear trend in Greenland and the Russian Arctic (Sweet et al., \r\n2018; Rose et al., 2019), which is broadly consistent with findings in \r\nOppenheimer et al. (2019). Areas with low or negative change have \r\nsubstantial land uplift counteracting the global mean sea level trend \r\n(Greenan et al., 2018; Sweet et al., 2018; Madsen et al., 2019). SROCC \r\nprojections indicate high confidence in future rises in relative sea \r\nlevel for all Arctic regions other than areas of substantial land uplift \r\nin north-eastern Canada, the west coast of Greenland, and narrow \r\nportions of West Antarctica (Oppenheimer et al., 2019)."


id: fe8888a9b26be40a2d689377ba61f65d
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
content_type: text
index: climate_gpt_v2
vector_id: 42156
content:
"Coastal flooding and erosion: Higher sea levels and reduced \r\ncoastal sea ice protection will increase future extreme sea levels in \r\nthe Arctic (high confidence for Arctic Northern Europe, the Russian \r\nArctic, and Arctic North-Western North America (medium confidence) \r\nfor Greenland and Iceland and Arctic North-Eastern North America \r\ngiven glacial isostatic adjustment). Vousdoukas et al. (2018) project \r\nthat the current 1-in-100-year extreme total water level would have \r\nmedian return periods of 1-in-20-years to 1-in-50-years by 2050, \r\nincreasing to 1-in-5-years to 1-in-20-years by 2100 under RCP4.5 \r\nalong nearly the entire Arctic coastline by 2100 (excluding GIC for \r\nwhich projections are not available). Projections for RCP8.5 indicate \r\nthat the present-day 1-in-100-year ETWL would have median return \r\nperiods of 1-in-10-years to 1-in-50-years by 2050 and would occur "


id: 3fdef8e79e784e4c0922b5037985a2f5
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
content_type: text
index: climate_gpt_v2
vector_id: 8431
content:
"This section focuses on CIDs affecting specific zones with heightened \r\nvulnerability and coherent characteristics that cut across traditional \r\ncontinental regions (see also Section 12.3). It is designed to match the \r\nstructure of the Cross-Chapter Papers in the WGII Report, although "


id: 65e24c6a136e806a3dd6989652654d82
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
content_type: text
index: climate_gpt_v2
vector_id: 15188
content:
"polar regions were addressed in more extensive detail in Sections \r\n12.4.8 and 12.4.9 of this Report and the Mediterranean Region will \r\nnot be handled separately given that its climatic impact-drivers are \r\ndiscussed in Sections 12.4.1 and 12.4.5 as well as in Cross-Chapter \r\nBox 10.3."


id: 26ec34a16ac633836b90486d2875685a
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
content_type: text
index: climate_gpt_v2
vector_id: 4006
content:
"In conclusion, biodiversity hotspots around the world will \r\neach face unique challenges as climatic impact-drivers change. \r\nHowever, heat, drought and length of dry season, fire weather, \r\nsea surface temperature and deoxygenation are relevant \r\ndrivers to terrestrial, freshwater and marine ecosystems, and \r\nhave marked increasing trends."


id: c4e6ee64b4faa88eebce9de41cd011c4
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
content_type: text
index: climate_gpt_v2
vector_id: 31949
content:
"Hotspots of biodiversity are defined by the AR6 WGII as 'geographic \r\nareas with exceptionally high richness of species, including rare \r\n(endemic) species' (WGII Cross-Chapter Paper 1). The AR6 assessment \r\nis based on 238 distinctive regions often called the 'Global 200 \r\necoregions' (Olson and Dinerstein, 2002)."


id: bfa43596273c605c4007ab19415966c5
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
content_type: text
index: climate_gpt_v2
vector_id: 30985
content:
"Cities and settlements by the sea are exposed to specific climate \r\nand climate change patterns and to compound coastal hazard risks \r\n(AR6  WGII Cross-Chapter Paper 2). The AR5 WGII found that, in \r\ngeneral, 'urban climate change-related risks are increasing (including \r\nrising sea levels and storm surges, heat stress, extreme precipitation, \r\ninland and coastal flooding, landslides, drought, increased aridity, \r\nwater scarcity, and air pollution)' (Revi et al., 2014). Since AR5 \r\na  number of studies have been carried out to understand urban \r\nclimate and its change. Box 10.3 identified a continuing strong role of \r\nthe urban heat island in amplifying heat extremes in cities, although \r\nchanges in the urban heat island are an order of magnitude smaller \r\nthan projected localized warming trends (very high confidence)."


id: 6ea8fe58851f4525a0b34307be2d3b94
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
content_type: text
index: climate_gpt_v2
vector_id: 16737
content:
"Coastal cities' proximity to the sea somewhat mitigates the effect \r\nof urban heat islands (high confidence) (Salvati et  al., 2017; \r\nSantamouris et  al., 2017; Y. Wang et  al., 2018; Martinelli et  al., \r\n2020). Cities and settlements by the sea typically experience \r\nhigher humidity levels than inland regions, combining with heat to \r\nenhance heat stress and induce exceedance of critical heat stress \r\nthresholds for outdoor activities, with potential enhanced exposure \r\nto heat for informal settlements (J. Wang et al., 2019). Such threshold \r\nexceedances are projected to increase for many coastal areas (high \r\nconfidence), including the Persian Gulf where heat stress is projected \r\nto be extreme (Pal and Eltahir, 2016; Ahmadalipour and Moradkhani, \r\n2018), and some low-lying areas in Europe such as the Po Valley and \r\ncoastal Mediterranean areas (Coppola et al., 2021a; Schwingshackl \r\net al., 2021; see also the heat stress index shown in Figure 12.4d-f)."


id: 5370263bb5a18c838a3d27a61b74c37
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
content_type: text
index: climate_gpt_v2
vector_id: 11954
content:
"There is low confidence in broad patterns of future drying or wet \r\ntrends across the land and freshwater biodiversity hotspots in the \r\nhumid tropics, although drying trends have been detected and \r\npredicted in parts of the Amazon (Fu et al., 2013; Boisier et al., 2015). \r\nThere is medium confidence (limited evidence, high agreement) \r\nthat in several regions the length of the dry season has already \r\nincreased and is projected to further increase in some parts of the \r\nMediterranean, Amazonia and sub-Saharan Africa (Debortoli et al., \r\n2015; Dunning et al., 2018; Hochman et al., 2018; Saeed et al., 2018). \r\nLonger dry seasons also extend the seasonal length and geographical \r\nextent of fire weather in all future scenarios (medium confidence) \r\n(Jolly et al., 2015; Abatzoglou et al., 2019)."


id: 7878602e3dccf3432bff90b72c5051ac
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
content_type: text
index: climate_gpt_v2
vector_id: 18399
content:
"Barlow  et  al.,  2016; Otto et  al., 2018). Over the period 1961-2013, \r\nthe annual area of drylands in drought has increased, on average \r\nby slightly more than 1% per year, with large interannual variability \r\n(Olsson et al., 2019). In general, droughts have increased in several arid \r\nand semi-arid areas over the last decades (medium confidence), and \r\nare likely to increase in the future as indicated by a number of indices \r\ncalculated from climate (Liu et al., 2018b; Zkhiri et al., 2019; Coppola \r\net al., 2021b; Driouech et al., 2021; see also Figure 12.4j-l)."


id: e7b58a46ac7c6d5ae1f6243fd02af046
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
content_type: text
index: climate_gpt_v2
vector_id: 38072
content:
"cyclones are projected to become more intense and severe tropical \r\ncyclones more frequent (high confidence) (Section 11.7)."


id: c636e7079a87ec888c6269f5f99639a8
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
content_type: text
index: climate_gpt_v2
vector_id: 32184
content:
"The SROCC highlighted coastal settlements in the Arctic as being \r\nparticularly exposed to several CID changes (Magnan et al., 2019). \r\nEnhanced waves, due to extended season of sea ice retreat, are \r\nprojected to foster coastal flooding and erosion (Section  12.4.9; \r\nGudmestad, 2018; Casas-Prat and Wang, 2020). Climate change is \r\nalso affecting sea ice quality and season length along coasts of the \r\nArctic Ocean where populations depend on sea ice for hunting or \r\ntransportation (Section 12.4.9; Pearce et al., 2015)."


id: dc66b4d7f1719433c9a9e4d6c7011dd2
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
content_type: text
index: climate_gpt_v2
vector_id: 36043
content:
"In summary, coastal cities and settlements are particularly \r\naffected by a  number of climatic impact-drivers that have \r\nalready changed and will continue to change whatever \r\nthe emissions scenario. These include increases in extreme \r\nheat, pluvial floods, coastal erosion and coastal flood (high \r\nconfidence). Increasing relative sea level, compounding with \r\nincreasing tropical cyclone storm surge and rainfall intensity, \r\nwill increase the probability of coastal city flooding (high \r\nconfidence). Arctic coastal settlements are particularly exposed \r\nto climate change due to sea ice retreat (high confidence)."


id: fc20037403a9cccdb7677ad35dcebcf
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
content_type: text
index: climate_gpt_v2
vector_id: 41724
content:
"Drylands, which include hyper-arid, arid, semi-arid and dry sub-humid \r\nareas (IPCC, 2019c), lie on all continents and cover 46% of the global \r\nland area and host more than one-third of the current population \r\n(Olsson et al., 2019). Huang et al. (2016b) found that aridity changes \r\nhave helped expand dryland area by about 4% from 1948 to 2004, \r\nwith the largest expansion of drylands occurring in semi-arid regions \r\nsince the early 1960s. Section  4.5.1 assessed high confidence of \r\na future poleward expansion of the Hadley cell, leading to a poleward \r\nshift of dryland areas in all scenarios considered. There is no evidence \r\nof a future global trend in aridification of drylands (IPCC, 2019a), but \r\nhigh confidence of aridification in some areas (e.g., Mediterranean, \r\nCentral America, Southern Africa; IPCC, 2019a; see also Figure 12.4j-l). \r\nHowever, drivers of desertification largely include land-cover changes \r\nand land-use management, along with climate change (IPCC, 2019a)."


id: 74208940795da1745053afa31b5ee822
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
content_type: text
index: climate_gpt_v2
vector_id: 17668
content:
"In conclusion, desert and semi-arid areas are strongly affected \r\nby climatic impact-drivers such as extreme heat, drought and \r\ndust storms. Heat hazards are very likely increasing in all \r\nfuture climate scenarios, but uncertainty remains regarding \r\nany broadly consistent future changes in other climatic \r\nimpact-drivers for deserts and semi-arid regions."


id: 1804028f0c91f72a28b2fae544866e37
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
content_type: text
index: climate_gpt_v2
vector_id: 1429
content:
"Mountains cover about 30% of the land areas on Earth (not counting \r\nAntarctica) and deliver a  number of vital services to humanity \r\n(WGII Cross-Chapter Paper 5; IPCC, 2019b). Climate change in high \r\nmountains was addressed in SROCC, which emphasized changes in \r\nseveral climatic impact-drivers. These included an observed general \r\ndecline in low-elevation snow cover, glaciers and permafrost (high \r\nconfidence), which induced changes in natural hazards such as \r\ndecrease in slope stability (high confidence), changes to the frequency \r\nof glacial lake outbursts (limited evidence), and climate effects on \r\nother climatic impact-drivers (avalanche, rain-on-snow floods) with \r\nvarious degrees of confidence (Hock et al., 2019)."


id: 733332ee828701f5e13db9d38f9baeb2
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
content_type: text
index: climate_gpt_v2
vector_id: 17517
content:
"Warming temperatures and extreme heat are major climatic impact\u0002drivers with multiple potential impacts on societies, health, and \r\nhabitability in semi-arid and arid regions that are already near \r\nphysiological limits for outdoor activities. Semi-arid regions will very \r\nlikely undergo a warming in all future scenarios (Chapter 4 and Atlas) \r\nand likely undergo an increase in duration, magnitude and frequency \r\nof heatwaves (Chapter 11) (Figure 12.4a-c). It is likely that heat stress \r\nwill be much more intense by the end of the century in many areas \r\nunder all scenarios, such as deserts and semi-arid zones in Asia (Murari \r\net al., 2015; Mishra et al., 2017), Australia and Africa (Zhao et al., 2015; \r\nXia et al., 2016; Guo et al., 2017; Dosio et al., 2018; Schwingshackl \r\net al., 2021), with consequences for labour productivity with respect \r\nto high heat-humidity conditions (Figure 12.4d-f)."


id: f53c744be1055aa28493d93bc8cd8bad
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
content_type: text
index: climate_gpt_v2
vector_id: 40513
content:
"There is a growing body of literature indicating elevation-dependent \r\nwarming (EDW; different rates of warming by altitude although not \r\nnecessarily increasing with altitude) in several mountain regions but \r\nnot globally (Hock et al., 2019; Pepin et al., 2019; Ahmed et al., 2020; \r\nB. Li et al., 2020; Williamson et al., 2020; You et al., 2020; Micu et al., \r\n2021). Statistically significant elevational enhancement to long\u0002term trends in maximum near-surface air temperatures and diurnal \r\ntemperature range were observed in southern central Himalaya \r\nand in the Swiss Alps (Rottler et  al., 2019; Thakuri et  al.,  2019). "


id: b7cf07576b83d4a066c4412930245296
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
content_type: text
index: climate_gpt_v2
vector_id: 29557
content:
"Aguilar-Lome et al. (2019) reported that winter daytime land surface \r\ntemperatures in the Andean region between 7degS and 20degS show \r\nthe strongest trends at higher elevations: +1.7degC per decade above \r\n5000m above sea level. Palazzi et  al. (2019) identified changes in \r\nalbedo and downward thermal radiation as key drivers of EDW \r\naccording to the simulation outputs of a  high-spatial-resolution \r\nmodel in three important mountainous areas: the Colorado Rocky \r\nMountains, the Greater Alpine Region and the Himalayas-Tibetan \r\nPlateau, but mechanisms for EDW remain complex (Hock et  al., \r\n2019). Warming is also affecting mountain lake surface temperatures, \r\nincreasing probabilities of ice-free winters and the frequency and \r\nduration of 'lake heatwaves' (high confidence) (O'Reilly et al., 2015; \r\nWoolway et al., 2020, 2021) with a high variability from lake to lake."


id: e6a8599b45fc08ffc442d451feccc818
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
content_type: text
index: climate_gpt_v2
vector_id: 37876
content:
"river ice, changes in the amount and seasonality of streamflows and \r\nhydrologic droughts in snow-dominated and glacier-fed river basins \r\n(e.g., in Central Asia; Sorg et al., 2014; Reyer et al., 2017b) (medium \r\nconfidence), and decreases in the stability of mountain slopes and \r\nsnowfields. Glacier recession could lead to the creation of new glacial \r\nlakes in places like the Himalaya-Karakoram region (Linsbauer et al., \r\n2016) and in Alaska and Canada (Carrivick and Tweed, 2016; Harrison \r\net  al., 2018) (medium confidence). With increasing temperature \r\nand precipitation these can increase the occurrence of glacier lake \r\noutburst floods and landslides over moraine-dammed lakes (high \r\nconfidence) (Carey et al., 2012; Rojas et al., 2014; Iribarren Anacona \r\net al., 2015; Cook et al., 2016; Haeberli et al., 2017; Kapitsa et al., \r\n2017; Narama et al., 2018; Wilson et al., 2018; Drenkhan et al., 2019; \r\nS. Wang et al., 2020)."


id: 83956701a04afaa36b36edaaf642e37f
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
content_type: text
index: climate_gpt_v2
vector_id: 20294
content:
"In conclusion, mountains face complex challenges from \r\nspecific climatic impact-drivers drastically influenced by \r\nclimate change: regional elevation-dependent warming (high \r\nconfidence), low-to-mid-altitude snow cover and snow-season \r\ndecrease even as some high elevations see more snow \r\n(high  confidence), glacier mass reduction and permafrost \r\nthawing (high confidence), and increases in extreme \r\nprecipitation and floods in most parts of major mountain \r\nranges (medium confidence)."


id: cd08e6d5606be351db2bf0c0874ec09d
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
content_type: text
index: climate_gpt_v2
vector_id: 33413
content:
"Tropical forests, which are among the world's most biologically diverse \r\necosystems, are essentially located in Central and South America, \r\nAfrica and South East Asia (AR6 WGII Cross-Chapter Paper 7). The AR5 \r\nand SR1.5 indicated several specific climatic impact-driver changes \r\nthat are particularly important to tropical forests: mean temperature \r\nincrease, long-term drying trends (including shifts in the length of the \r\ndry season), prolonged drought, wildfires and surface CO2 increase for \r\ninland forests (IPCC, 2013, 2018). The SRCCL assessed an enhanced \r\nrisk and severity of wildfires in tropical rainforests (high confidence), \r\nbut fires are not only a  natural process but are also affected by \r\ndeforestation and other human influences (IPCC, 2019a)."


id: d22839de0ae15d4b716c5733a758fa56
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
content_type: text
index: climate_gpt_v2
vector_id: 34328
content:
"Temperature is rising in all tropical regions covered with forests and \r\nwill very likely continue to rise, reaching levels unprecendented in \r\nrecent decades as the temperature trends rapidly emerge from weak \r\nhistorical interannual variability (Sections 12.4.1-12.4.4 and 12.5.2; \r\nsee also Chapter 4 and Atlas)."


id: 14d621514b8b232e0cc0554854711de1
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
content_type: text
index: climate_gpt_v2
vector_id: 892
content:
"Regional patterns of increasing drought or unusual wet and dry \r\nperiods are predicted with agreement over many climate models \r\nsuch as over the Amazon basin (Boisier et  al., 2015; Duffy et  al., \r\n2015; Zulkafli et al., 2016; Coppola et al., 2021b). There is medium \r\nconfidence (limited evidence, high agreement) that in several \r\ntropical-forest regions (e.g., Amazonia, West Africa) the dry season \r\nlength has increased (Fu et al., 2013; Debortoli et al., 2015; Saeed \r\net al., 2017; Dunning et al., 2018; Wadsworth et al., 2019), and there \r\nis low confidence (limited evidence) that deforestation influences the \r\nshift in the onset of the wet season in south Amazonia (Leite-Filho \r\net  al., 2019). In contrast, the wet season is increasing in northern \r\nAustralia tropical forests (Catto et al., 2012)."


id: 4d0fd29493c52ad6b31a797815d90147
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
content_type: text
index: climate_gpt_v2
vector_id: 10829
content:
"Declines in low-elevation snow depth and seasonal extent are \r\nprojected for all SSP-RCPs (see Sections 12.4.1-12.4.6), along with \r\nreductions in mountain glacier surface area, increases in permafrost \r\ntemperature, decreases in permafrost thickness, changes in lake and "


id: 54a0f4229ee98a7515cc2601878c36d2
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
content_type: text
index: climate_gpt_v2
vector_id: 12164
content:
"In conclusion, most tropical forests are challenged by a mix \r\nof emerging warming trends that are particularly large in \r\ncomparison to historical variability (medium confidence). Water \r\ncycle changes bring prolonged drought, longer dry seasons, \r\nand increased fire weather to many tropical forests, with \r\nplants also responding to CO2 increases (medium confidence)."


id: 9b21f197d7325b1777665e72a8d49a44
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
content_type: text
index: climate_gpt_v2
vector_id: 24543
content:
"Section 12.4 assessed changes in climatic impact-drivers by region, \r\nprimarily based on a large number of local- and regional-scale studies \r\n(even though global studies are also used). This section presents an \r\nassessment of changes in CIDs at the global scale. It is based on both \r\na bottom-up synthesis of the results in Section 12.4, and a top-down \r\nassessment from global-scale studies undertaken here. Cross-Chapter \r\nBox 12.1 summarizes global-scale CIDs with levels of warming."


id: bb4122eebe0a7a662630e4dc10583d2d
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
content_type: text
index: climate_gpt_v2
vector_id: 30162
content:
"Global-scale studies use similar indices of climatic impact-drivers \r\nacross space, although these indices may not always be those used \r\nat the local or regional scale. Most published global-scale studies \r\nconcentrate on single sectors or climatic impact-drivers, but some \r\ntake a multi-sectoral perspective (e.g., Warszawski et al., 2014; Arnell \r\net al., 2016, 2019; Schleussner et al., 2016; Mitchell et al., 2017; Betts \r\net al., 2018; Byers et al., 2018; Mora et al., 2018; O'Neill et al., 2018; \r\nZscheischler et al., 2018). Only a few published global-scale studies \r\n(e.g., Coppola et al., 2021; Schwingshackl et al., 2021) have used \r\nCMIP6 scenarios to date. "


id: 10fb80697f2c6eec2de979af80fec6ae
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
content_type: text
index: climate_gpt_v2
vector_id: 180
content:
"All regions will experience, before 2050, increased warming, an \r\nincrease of extreme heat and a decrease in cold spells, regardless of \r\nthe emissions trajectory (high confidence). Tropical regions, but also \r\nmid-latitude regions to a lesser extent, will experience an increasing \r\nnumber of days with heat indices crossing dangerous thresholds \r\nused to characterize heat stress, such as HI > 41degC (Figure 12.4). \r\nThe increase, by the end of century, exceeds 100 days per year in \r\nmost tropical areas under SSP5-8.5 but remains much more limited "


id: 96cdc9ebabae5b3d4fa167227a75d9a0
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
content_type: text
index: climate_gpt_v2
vector_id: 23732
content:
"experiencing simultaneous severe water stress events (based on SPEI) \r\nin a year increased from 15% under current conditions to up to 60% at \r\nthe end of the 21st century under high emissions."


id: 9257a4538e83b874cb1e06beeb8a1c38
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
content_type: text
index: climate_gpt_v2
vector_id: 22969
content:
"Integration of the results of Section  12.4 shows that the total \r\nnumber of tropical cyclones is projected to decrease through the \r\n21st  century, particularly with high emissions, but the number of \r\nvery intense tropical cyclones is projected to increase in most areas \r\n(at least medium confidence) (e.g.,  Bacmeister et  al., 2018; see \r\nSection 11.7.1). Furthermore, regions with glaciers will lose glacier \r\nmass and regions concerned with snow cover will see a reduction in \r\nsnow depth, the duration, or extent of cover (medium confidence in \r\npolar regions, high confidence elsewhere)."


id: bf1c0574614867a70fbb0f846f9d2aa6
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
content_type: text
index: climate_gpt_v2
vector_id: 30895
content:
"The regional assessment in Section  12.4 shows that fire weather \r\nis projected to increase with medium or high confidence in every \r\ncontinent of the world, including Arctic polar regions. Globally, fire \r\nweather is projected to increase in future, primarily due to higher \r\ntemperatures and exacerbated where precipitation reduces. By 2050, \r\n60% of the global land area would see a  significant increase in \r\nfire weather under RCP8.5 (Abatzoglou et  al., 2019). There is less \r\nconfidence in the projected distribution of change in fire weather \r\nacross regions in global-scale studies. For example, Moritz et  al., \r\n(2012) projected an increase in fire weather in mid- and high latitudes \r\nbut a reduction in the tropics, whilst Yu et al. (2019) and Bedia et al. \r\n(2015) projected an increase in the tropics. These differences reflect \r\ndifferences in methodologies and fire weather indices adopted in \r\ndifferent studies."


id: f64c332b3fa0ed6c2131ceb9891e56f7
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
content_type: text
index: climate_gpt_v2
vector_id: 40699
content:
"Relative sea level rise (RSLR) is projected in all regions (except for \r\na few Arctic polar regions) with likelihoods varying from very likely\r\nto virtually certain depending on the region. This will increase the \r\nfrequency of extreme sea levels and, depending on the level of \r\ncoastal flood protection, coastal flooding (Vousdoukas et al., 2018; \r\nKirezci et al., 2020). In terms of globally averaged extreme total water \r\nlevel (ETWL) frequency changes, the present-day 1-in-100-year event \r\nis projected to become 1-in-30-year and 1-in-20-year events by 2050 "


id: c5f34b2b80d40da48f8c00e664139482
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
content_type: text
index: climate_gpt_v2
vector_id: 32134
content:
"under RCP4.5 and RCP8.5, respectively. The present day 1-in-100-\r\nyear ETWL is projected to become a 1-in-5-year event by 2100 under \r\nRCP4.5, while under RCP8.5, such events are projected to occur more \r\nthan once a year (Vousdoukas et al., 2018)."


id: 3eea273b5973aba5bc4e7c477aa2ac1d
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
content_type: text
index: climate_gpt_v2
vector_id: 8253
content:
"There is high confidence that most of the world's sandy coasts will \r\nexperience shoreline retreat, in the absence of terrestrial or offshore \r\nsediment sources. Median projections presented by (Vousdoukas et al., \r\n2020b) indicate that 13.6% (36,097 km) and 15.2% (40,511 km) of \r\nthe world's sandy beaches could retreat by more than 100 m by 2050 \r\n(relative to 2010) under RCP4.5 and RCP8.5 respectively, implying \r\na 12% increase in severely threatened shoreline length under RCP8.5, \r\nrelative to RCP4.5. These median projections increase to 35.7%-49.5% \r\n(RCP4.5 and RCP8.5, respectively; or 95,061 km-131,745 km) by the \r\nend of the century, implying a 38% increase in severely threatened \r\nshoreline length under RCP8.5, relative to RCP4.5."


id: 41feb4ff0fd2235524961a8d40968682
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
content_type: text
index: climate_gpt_v2
vector_id: 8805
content:
"Changes in climatic impact-drivers may remain within the range of \r\nnatural variability or have a time of emergence that varies by region \r\nand scenario. This section assesses the evidence for the effects of \r\nanthropogenic climate change on the emergence of changes in \r\nCID index, past, present and future, as evidenced by the literature \r\nassessed in other chapters, as well as additional literature assessed \r\nhere, at both global and regional scales. In many cases, however, \r\nsufficient literature for a robust region-by-region assessment of ToE is \r\nlacking. The assessment herein is made by CID. Regional emergence \r\nassessment is reported in Tables 12.3-12.11 but is undertaken in this \r\nsection."


id: dc6d55256a94eaf287d4ff96da6f027c
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
content_type: text
index: climate_gpt_v2
vector_id: 36047
content:
"In a few other regions, only a small number of CIDs are projected to \r\nchange with high confidence (e.g., Sahara, Central Africa, Western \r\nAfrica, Madagascar, Arabian Peninsula, South-Eeastern South \r\nAmerica, New Zealand, Small Islands). The lower confidence levels \r\nassociated with changes in CIDs in these regions can be due either \r\nto weaker change signals compared to natural variability, or due to \r\nlimited evidence and model uncertainties leading to low agreement, \r\nand does not mean that climate change will affect these regions any \r\nless than in other regions."


id: 9d51a5f666dc5084324afdc6993f55f8
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
content_type: text
index: climate_gpt_v2
vector_id: 24910
content:
"In summary, there is high confidence that all regions of the \r\nworld will experience changes in several climatic impact\u0002drivers by mid-century, albeit at region-specific rates of \r\nchange and confidence levels for each CID. Consequently, \r\nchanging CIDs have the potential to affect climate-related \r\nrisks in all regions of the world."


id: 9f2639c65499cc0b77a7a2afbf48a21b
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
content_type: text
index: climate_gpt_v2
vector_id: 25218
content:
"Droughts, aridity and fire weather: There is low confidence in \r\nthe emergence of drought frequency in observations, for any type \r\nof drought, in all regions. Even though significant drought trends \r\nare observed in several regions with at least medium confidence\r\n(Sections  11.6 and 12.4), agricultural and ecological drought \r\nindices have interannual variability that dominates trends, as can \r\nbe seen from their time series (medium confidence) (H. Guo et al., \r\n2018; Spinoni et  al., 2019; Haile et  al., 2020; M. Wu et  al., 2020). \r\nStudies of the emergence of drought with systematic comparisons \r\nbetween trends and variability of indices are lacking, precluding "


id: 3e19d634f4d266c9a63c6815f182c3c1
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
content_type: text
index: climate_gpt_v2
vector_id: 8095
content:
"(Section 9.6.1.4; Lyu et al., 2014) (medium confidence). Emergence \r\nstudies for ETWL and coastal erosion are lacking and hence it is not \r\ncurrently possible to robustly assess emergence in these CIDs."


id: fefcb41247f17dc8fa7733972c326c2c
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
content_type: text
index: climate_gpt_v2
vector_id: 42260
content:
"a comprehensive assessment of future drought emergence. Historical \r\nclimate simulations indicate that fire weather indices have already \r\nemerged in several regions (the Amazon basin, Mediterranean, \r\nCentral America, West and Southern Africa) (low confidence, limited \r\nevidence) (Abatzoglou et al., 2019), and emergence is projected with \r\nlow confidence by the middle of the century in several other regions \r\n(Southern Australia, Siberia, most of North America and Europe) \r\nwhen considering several indices together."


id: 85150be86fa7d5124b0483a8e03efab7
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
content_type: text
index: climate_gpt_v2
vector_id: 20556
content:
"Mean ocean temperature and marine heatwave: The emergence \r\nof the sea surface temperature increase signal has been observed in \r\nglobal oceans over the last century, and the largest S/N values are \r\nfound in the tropical Atlantic and tropical Indian oceans (Hawkins \r\net al., 2020). There is high confidence in the widespread occurrence \r\nof marine heatwaves in all basins and marginal seas over the last \r\ndecades (Chapter  9), but the emergence of this signal above the \r\nnatural variability has not yet been addressed in detail."


id: 79059156d79a31d24759051f98f4bd82
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
content_type: text
index: climate_gpt_v2
vector_id: 18480
content:
"Wind: Observed mean surface wind speed trends are present in many \r\nareas (Section  12.4), but the emergence of these trends from the \r\ninterannual natural variability and their attribution to human-induced \r\nclimate change remains of low confidence due to various factors \r\nsuch as changes in the type and exposure of recording instruments, \r\nand their relation to climate change is not established. For future \r\nconditions, there is limited evidence of the emergence of trends in \r\nmean wind speeds due to the lack of studies quantifying wind speed \r\nchanges and their interannual variability. The same limitation also \r\nholds for wind extremes (severe storms, tropical cyclones, sand and \r\ndust storms)."


id: 357d152018536b6ea482ab7599265a0d
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
content_type: text
index: climate_gpt_v2
vector_id: 6660
content:
"Sea ice: Sea ice area decrease in the Arctic in all seasons has \r\nalready emerged from the interannual variability (high confidence) \r\n(Chapter  9). By contrast, the Antarctic sea ice area shows no \r\nsignificant trend, and therefore no emergence."


id: 918d1dd646e9493d7567a1bb8dc3bb5d
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
content_type: text
index: climate_gpt_v2
vector_id: 22829
content:
"For other snow and ice CIDs (heavy snowfall and ice storm, hail, snow \r\navalanche), there is limited evidence of emerging signals."


id: f66df2e64974fcb590d9cb7f6a41d89b
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
content_type: text
index: climate_gpt_v2
vector_id: 40725
content:
"Relative sea level, coastal flood and coastal erosion: Near\u0002coast RSLR will emerge before 2050 for RCP4.5 along the coasts of \r\nall AR6 regions (with coasts) except East Asia, the Russian Far East, \r\nMadagascar, the southern part of Eastern North America and the \r\nAntarctic regions (medium confidence) (Section 9.6.1.4; Bilbao et al., \r\n2015). Under RCP8.5, emergence of near-coast RSLR is projected \r\nby mid-century along the coasts of all AR6 regions (with coasts), \r\nexcept WAN where emergence is projected to occur before 2100 "


id: ce4bd1ecb0fe96da7a45a0666d440fc4
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
content_type: text
index: climate_gpt_v2
vector_id: 33630
content:
"Table 12.12 | Emergence of CIDs in different time periods, as assessed in this section. The colour corresponds to the confidence of the region with the highest \r\nconfidence: white cells indicate where evidence is lacking or the signal is not present, leading to overall low confidence of an emerging signal."


id: 6bdaf2e3b767ed6574024e45c302690f
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
content_type: text
index: climate_gpt_v2
vector_id: 16277
content:
"1. High confidence except over a few regions (CNA and NWS) where there is low agreement across observation datasets."


id: d99171f1a8110715f4d3a9c33b22f8a8
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
content_type: text
index: climate_gpt_v2
vector_id: 35576
content:
"Cross-Chapter Box 12.1 | Projections by Warming Levels of Hazards Relevant to the \r\nAssessment of Representative Key Risks and Reasons for Concern"


id: b842923ecb209ccf909ceed5eb3fe8bd
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
content_type: text
index: climate_gpt_v2
vector_id: 29630
content:
"Contributors: Claudia Tebaldi (United States of America), Guofinna Aoalgeirsdottir (Iceland), Sybren Drijfhout (United Kingdom), \r\nJohn  Dunne (United States of America), Tamsin Edwards (United Kingdom), Erich Fischer (Switzerland), John Fyfe (Canada), \r\nRichard Jones (United Kingdom), Robert Kopp (United States of America), Charles Koven (United States of America), Gerhard Krinner \r\n(France), Friederike Otto (United Kingdom/Germany), Alex C. Ruane (United States of America), Sonia I. Seneviratne (Switzerland), \r\nJana Sillmann (Norway/Germany), Sophie Szopa (France), Prodromos Zanis (Greece)"


id: 7db978195f0d3e842aa6d83bb2197998
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
content_type: text
index: climate_gpt_v2
vector_id: 19293
content:
"RFCs further synthesize the landscape of risks from climatic changes into five categories (from the IPCC Third Assessment Report \r\nonward; Smith et al., 2001): "


id: 8679209a5d02bdd879ac9697cce50992
created_at: 2023-04-19 13:21:41
updated_at: 2023-04-19 13:21:41
content_type: text
index: climate_gpt_v2
vector_id: 20825
content:
"Importantly, the assessment of risk in WGII considers hazards as only one component of an integrated assessment that involves their \r\ncomplex interaction with exposure and vulnerability of the systems at risk (Reisinger et al., 2020)."


id: e240943ab5b267778c25a9813629e64d
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 37088
content:
"Hazards relevant to RKRs and RFCs are identified among aspects of the climate system that have an episodic, short-term nature, like \r\nextreme events (particularly relevant to RFC2 but contributing to many other risk categories). Increasing GWLs translate into changing \r\ncharacteristics of frequency, duration, intensity, seasonality and spatial extent for many of these hazards that are also apparent in \r\nscenario-based results (Chapters 11 and 12, and Sections 12.4 and 12.5.1). Other relevant hazards coincide with long-term trends \r\nembodying a gradual change that may result in unfavourable environmental conditions. Also, increasing GWLs increase the likelihood \r\nof compound temporal or spatial occurrence of similar or different hazards (Section 11.8). Furthermore, RFC5's focus on singular \r\nevents includes concern surrounding potential tipping points and irreversible behaviour in the physical climate system."


id: 33b3e86267fccb53bd3ecb61069f5c2d
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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index: climate_gpt_v2
vector_id: 6341
content:
"Cross-Chapter Box 12.1, Table 1 organizes information by hazard and presents current state and future change assessments with \r\nincreasing GWLs (defined by increasing global surface air temperature, GSAT; see Cross-Chapter Box 2.3). We draw on individual \r\nchapters across the WGI report for the assessment of how these hazards vary with GWL. Hazards for which a relation to GWLs has not \r\nbeen assessed are not reported in the table."


id: 6554568733fc09f29d3545e0f7b1dc8f
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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index: climate_gpt_v2
vector_id: 15101
content:
" most cases co\r\nfound in more \r\n Tipping Points \r\nur as a Funct\r\nagnitude and in\r\nas 1.5degC; chang\r\n(in high-latitude \r\nceedance may s\r\nred to today, ch\r\nand four times \r\nhose associated \r\nare (e.g., 1-in-10 \r\n over all contin\r\nat lower GWLs a\r\non generally foll\r\n). Increase in fre\r\nents (high confi\r\n50-year events, \r\nnfidence).\r\nded area extent\r\nfrequency and m\r\nn areas where h\r\nfidence).\r\nwith GSAT; aver\r\needs of most int\r\nck of change in \r\nmes more freque\r\na GWL of 2.0 [1\r\nnding to a GWL \r\nl oceans and Ar"


id: 3d1b7eba9deb6808f9795b111eb1b9bc
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 7931
content:
"Climate services are a significantly evolving source of climate change \r\ninformation to support adaptation, mitigation and risk management \r\ndecisions. As an evolving field, there are multiple definitions of \r\nclimate services (Brasseur and Gallardo, 2016). The Global Framework \r\nfor Climate Services defines a  climate service as the provision of \r\nclimate information to assist decision-making. The service includes \r\nappropriate engagement from users and providers, is based on \r\nscientifically credible information and expertise, has an effective \r\naccess mechanism, and responds to user needs (Hewitt et al., 2012)."


id: 746e4bbf917927fc114dca8e0456b069
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 17717
content:
"This section introduces the current climate services landscape, \r\nassesses climate service practices and products related to climate \r\nchange information and associated challenges. Cross-Chapter \r\nBox 12.2 provides concrete examples of climate services. The section \r\nbuilds on the introduction to climate services in Section  1.2.3.3 \r\nand the assessment of regional climate information construction - \r\nincluding storylines  - discussed in Sections 10.3.4.2, 10.5.3 and \r\nCross-Chapter Box 10.3. The Atlas supports the provision of climate \r\ninformation across WGs by providing interactive maps and further \r\ndetails to the material made publicly accessible for use in climate \r\nservices. WGII (Chapter 17) further elaborates on climate services as \r\nenablers for climate risk management."


id: 17446e3c154b013ac13bf3702ffe1639
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 1316
content:
"The idea of climate services is not new and has its roots in meteorology \r\nand climatology (Larosa and Mysiak, 2019). It can be traced back to \r\nthe late 1970s and the US National Climate Program Act of 1978 \r\n(Henderson, 2016). The development of the Global Framework for \r\nClimate Services (GFCS) after the World Climate Conference-3 in \r\nGeneva brought international attention and renewed impetus to the \r\nclimate services field (Hewitt et al., 2012). As a result, large investments \r\nhave been made globally and regionally in the development of "


id: 6095ada0456e406d86d7263a48e46ed4
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 14292
content:
"climate services shift incentives for climate science away from the \r\npublic interest towards profit-seeking, this will result in less publicly \r\naccessible and transparent climate information and more private \r\nknowledge (Keele, 2019; Tart et al., 2020)."


id: d631d1d47e6fadae664ff57a8cdd1e36
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 35001
content:
"activities related to basic knowledge in climate change sciences and \r\nclimate-related risks (De Bruin et al., 2020; Sultan et al., 2020)."


id: 71aa5b6052aac223d0d197ca02df158c
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 17244
content:
"Since AR5 and SROCC (Chapter  2) there has been considerable \r\nprogress in understanding climate information user needs (Baztan \r\net  al., 2017; Golding et  al., 2017a, b, 2019; Bruno Soares et  al., \r\n2018a; Hewitt and Golding, 2018; Singh et  al., 2018; Sivakumar \r\nand Lucio, 2018; Bessembinder et  al., 2019; Hewitt et  al., 2020b; \r\nSultan et al., 2020; Y. Wang et al., 2020), better facilitation of user \r\nengagement (Buontempo et al., 2014, 2018; Buontempo and Hewitt, \r\n2018) and an appreciation from climate scientists of the need to \r\ninvolve communication specialists and social scientists to support \r\nthe co-design and co-development process that is fundamental to \r\na successful climate service (Buontempo et al., 2014; Gregow et al., \r\n2016; Damm et al., 2020)."


id: 8adc65d9228ce4d8a2782ab6622565da
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 21601
content:
"Information moves from 'useful' to 'usable' only when users \r\neffectively incorporate this information into a  decision process \r\n(Lemos et al., 2012; Bruno Soares and Dessai, 2016; Prokopy et al., \r\n2017; see also WGII, Chapter 17). Climate services include a range \r\nof knowledge brokerage activities such as: identifying knowledge \r\nneeds; dissemination of knowledge; coordinating and networking; \r\ncompiling and translating; building capacity through informed \r\ndecision-making; analysing, evaluating and developing policy; and \r\npersonal consultation (e.g., De Bruin et al., 2020). When analysing \r\nfour European climate services, Reinecke (2015) found that different \r\nclimate services emphasized different knowledge brokerage activities."


id: cbea65bf2b3459ea7707610fb68123e6
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 33202
content:
"There are various types of climate service providers and products related \r\nto key sectors and regions, such as those described in Sections 12.3 \r\nand 12.4 (Hewitt et al., 2017b). For instance, studies have described \r\nsectoral climate services in support of agriculture (Falloon et al., 2018; \r\nHansen et al., 2019), health (Jancloes et al., 2014; Lowe et al., 2017), \r\ntourism (Morin et al., 2018; Damm et al., 2020; Matthews et al., 2021), \r\nenergy (Troccoli, 2018b; Goodess  et  al., 2019; Soret et  al.,  2019), \r\ndisaster risk reduction (Golding et al., 2019; Street et al., 2019), water "


id: e8674c12357b248d330227e01e88e629
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updated_at: 2023-04-19 13:21:42
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content:
"Figure 12.12 | Illustration of different types of climate services. Products, for instance, can focus only on climate-related information or can be designed to integrate \r\nclimate information with other decision-relevant context (vertical axis) and they can be very generic in terms of relevance to a wide range of sectors or stakeholders or \r\ncustomized to fit the needs of a specific sector or stakeholder (horizontal axis). Figure adapted from Visscher et al. (2020)."


id: e78a5fdaa2c9d8c1b0b4e8e59e81a6bd
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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"Climate services set new scientific challenges to physical climate \r\nresearch (high confidence). Over at least the last decade, for instance, \r\nmany questions have appeared in terms of optimal estimation of \r\nchanges and uncertainties from projections of model ensembles, \r\nensemble optimization, or adjustment of model biases while preserving \r\nessential information on trends and cross-variable, time and space \r\nconsistencies, downscaling information at the local scale (Benestad \r\net al., 2017; Hewitt et al., 2017b; Marotzke et al., 2017; Hewitt and \r\nLowe, 2018; Knutti, 2019; see also Section  10.5). Other challenges \r\nrelated to climate services are the inter-operability of data (Giuliani \r\net al., 2017), access to data (open/FAIR Guiding principles; Wilkinson \r\net al., 2016; Georgeson et al., 2017), format of data (including moving \r\naway from percentile-based probabilistic forecasts (e.g., Haines, 2019)) \r\nand funding mechanisms (Bruno Soares and Buontempo, 2019)."


id: c15e47fa2d5bc5cb677c1571db92f712
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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index: climate_gpt_v2
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content:
"Understanding and modelling of weather and climate extremes is of \r\ngreat relevance for climate services and is continuing to set challenges \r\nfor research, such as modelling changes in impact-relevant threshold \r\nexceedance and return periods for a  variety of extremes (Maraun \r\net al., 2015; Sillmann et al., 2017; Hewitt et al., 2021; Schwingshackl \r\net al., 2021; see also Chapter 11). Extreme event attribution has also \r\nbeen used in context of climate services (Philip et al., 2020) as it is \r\nof interest to some stakeholder groups (Sippel et al., 2015; Marjanac \r\nand Patton, 2018; Jezequel et  al., 2019, 2020). The usefulness \r\nor applicability of available extreme event attribution methods \r\n(Section  11.2.4 and Cross-Working Group Box on Attribution in \r\nChapter 1) for assessing climate-related risks remains subject to \r\ndebate (Shepherd, 2016; Mann et al., 2017; Lloyd and Oreskes, 2018)."


id: f1c6f961695f438c9349a4266e75355e
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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index: climate_gpt_v2
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content:
"Figure 12.12 maps a general categorization of practices and products \r\nthat have emerged from reviewing climate service literature and \r\nuser interviews (Visscher et  al., 2020). The categories range from \r\nvery generic products or expert analysis focused particularly on \r\nclimate information (climate-centric approaches) to more integrated \r\nproducts that include shared open-source products and capacity \r\nbuilding as well as tailored products that treat climate information \r\nas part of a  larger decision-making context (climate-inclusive \r\napproaches). Three specific examples that elaborate in more detail \r\non specific practices and products related to those general categories \r\nare provided in Cross-Chapter Box 12.2."


id: 81222cec93ca6d083be3fbeabd8a9fd4
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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content:
"van Huysen et al., 2018); (vii) the lack of trained scientists skilled at \r\nconducting societally relevant research (Rozance et al., 2020)."


id: d3546004a960e0aac24e1527793a2070
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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index: climate_gpt_v2
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content:
"Other challenges related to governance and dealing with complex \r\nsystems are sometimes acknowledged but less well described in the \r\nclimate services domain (Hewitt et al., 2020a). Importantly, decision \r\ncontexts are strongly rooted in past practice (which often does not \r\neven make optimal use of past climate information), stakeholder \r\nexperience, and history. Even important emerging concepts of \r\nco-production, entry points, and champions do not always fall \r\nnaturally into these realities without significant effort. The social \r\nsciences have an important role in helping understand and tackle \r\nthese challenges (Bruno Soares and Buontempo, 2019)."


id: 6623ffa028adda777f323919c2b2090c
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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content:
"Contributing Authors: Suraje Dessai (United Kingdom/Portugal), Jana Sillmann (Norway/Germany), Carlo Buontempo (United \r\nKingdom/Italy), Cecilia Conde (Mexico), Aida Diongue-Niang (Senegal), Francisco J. Doblas-Reyes (Spain), Christopher Jack (South \r\nAfrica), Richard Jones (United Kingdom), Benjamin Lamptey (Niger/Ghana), Xianfu Lu (United Kingdom/China), Douglas Maraun \r\n(Austria/Germany), Ben Orlove (United States of America), Roshanka Ranasinghe (Netherlands/Sri Lanka/Australia), Alex C. Ruane \r\n(United States of America), Anna Steynor (South Africa), Bart van den Hurk (Netherlands), Robert Vautard (France)"


id: 3da77b1b20bc82f7858f61f4e09f8f8
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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index: climate_gpt_v2
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content:
"The diversity of climate services practices and products is illustrated here using three case studies each representing one of the \r\nbroad categories of user -provider engagement (Cross-Chapter Box 12.2, Figure 1)."


id: 169ac2949f8d90b32f69b0e0d06e018d
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 1189
content:
"The Copernicus Climate Change Service (C3S) provides free and open access to climate data, tools and information through a website. \r\nIt also includes demonstration projects that show how C3S data can be used in practice through case studies, training sessions and \r\nworkshops (Thepaut et al., 2018). A large audience of the service is composed of intermediate users, loosely defined as the community \r\nof operators in one of the intermediate steps between the primary producers of climate data and the ultimate beneficiaries."


id: f51692e641f46282680a8f1f903961eb
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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"C3S also facilitates the tailoring process, by providing a series of working open-source, cloud-based demonstrators which show how \r\nclimate data can be transformed into actionable information to meet specific user requirements. This tailoring process covers the chain \r\nbetween the definition of key indicators all the way to the user interface. The definition and production of C3S products involve scientists \r\nthat produced and assessed the data. A variety of potential users are involved in the definition of indicators and other products."


id: f949182e2c9ef05d50e5d4456505e8b3
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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content:
"Through its quality assurance process and demonstrators, C3S provides a basic evaluation of all climate data; it provides access, and it \r\nencourages the users to develop their own case-specific analysis within the C3S infrastructure. Trustworthiness and relevance of such \r\nan analysis are substantially strengthened through a distillation process, co-designed by the user and data provider, and drawing upon \r\nmultiple lines of evidence and process-based evaluation of model fitness (Section 10.5)."


id: 12b121e53e061d95614aeff371113e50
created_at: 2023-04-19 13:21:42
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"Science-application engagement is extremely challenging, especially in critically important but complex contexts such as rapidly \r\ngrowing cities in developing nations (Culwick and Patel, 2017). The publicly funded Future Resilience for African CiTies And Lands \r\n(FRACTAL) project was conceived and designed in response to extensive and strong evidence and experience that useful and useable \r\nclimate services require strong mutual relationships across the science-application interface that can be built using supportive \r\nprocesses and structures (A. Taylor et al., 2017)."


id: e07beeb9daf3cf4498810ac245de7afd
created_at: 2023-04-19 13:21:42
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"Informed by this understanding, FRACTAL was grounded in a very reflexive and context-guided approach with city decision-making \r\nat its core (A. Taylor et al., 2017). Representatives from selected southern African cities were included in the proposal design and, \r\nthroughout the project, a core principle was to allow the city partners to lead and guide the process."


id: 629d208a90d3cabeba298634a8a6515b
created_at: 2023-04-19 13:21:42
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content:
"This case study emphasizes the positive contributions of the fit, tailoring and contextualization of climate information with respect \r\nto the specific decision-making needs of particular users (Section 10.5; AR6 WGII Section 17.4.4.2.2), the importance of participatory \r\nplanning for risk management in urban areas (AR6 WGII Sections 6.3.3.3 and 6.4.2) and the importance of networks and organizations \r\nwhich link researchers, policymakers and end-users to promote adaptation in African cities (AR6 WGII Box 9.4). Upscaling this type \r\nof interactive activity to cater for the large number of user demands remains a challenge."


id: 511e648d60c23cfe1ddebf3023d36245
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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content:
"Cross-Chapter Box 12.2, Figure 2 | Climate Risk Narrative infographic developed through the FRACTAL Windhoek Learning Lab process. Figure \r\nadapted from Jack et al. (2020)."


id: 69988c468b5bcb5d02de24e112a357b4
created_at: 2023-04-19 13:21:42
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content:
"This broad category involves one-to-one engagement between a provider and a user with very specific needs. One such user is the \r\nAsian Development Bank (ADB), which has committed to making all its investments climate resilient by implementing a climate \r\nrisk management framework (ADB, 2014, 2018; Lu, 2019). The climate risk management framework mandates all climate-sensitive \r\ninvestment projects undertake a climate risk and adaptation assessment, to identify material risks of a changing climate to the proposed \r\nproject and potential adaptive measures to be incorporated into project design, implementation, maintenance and/or monitoring. \r\nTypically, loan project processing teams procure consulting services for a bespoke climate risk and adaptation assessment (CRA) "


id: aa11cc2fb87808c44240f5fa38c371eb
created_at: 2023-04-19 13:21:42
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content:
"for a  specific project. The user-provider engagement is highly targeted and goal-oriented. An example of such a  focused user-\r\nprovider engagement is the CRA carried out as part of an investment project in Vietnam, the Water Efficiency Improvement in Drought\u0002Affected Provinces (WEIDAP) project."


id: f6c4f96ac8bc1356e1b5fb08e50b1649
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updated_at: 2023-04-19 13:21:42
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content:
"This case study illustrates that climate information distillation including a sustained iterative engagement between climate information \r\nusers, producers and translators can improve the quality of the information and the decision-making (Section 10.5; WGII Section 17.4.4.2.2)."


id: 920aec77ed60cd9c35adeeca837fefa2
created_at: 2023-04-19 13:21:42
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content:
"Cross-Chapter Box 12.2, Table 1 | Summary of annual province-level changes in temperature, precipitation and evapotranspiration under the \r\nthree broad scenarios in southern Vietnam. Scenario 1: warm-and-wet; Scenario 2: hot-and-wet; Scenario 3: hotter. Source: Table 3 in ADB (2020)."


id: 3e29265b5f3449aaaca3e8ab0b859af0
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"science community and practitioners/stakeholders on the ground. \r\nCo-development of CID index definitions with impact scientists \r\nor stakeholders helps ensure their salience and utility (Sections \r\n12.1, 12.2, 12.3 and 12.6)."


id: 38026403cc9c57f1881e141910ca7f5
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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content:
"The assessment in this chapter is based on a rapidly growing body of \r\nnew evidence from the peer-reviewed literature, direct calculations \r\nof  climate projections from several new model ensembles, and \r\nresults from other AR6 WGI chapters. Although a large amount a new \r\ninformation on CID changes and their uptake in climate services \r\nhas become available since AR5, some challenges still remain. This \r\nsection summarizes some of these main challenges, with a view to \r\nfacilitating improved assessments in future. The section is organized \r\nfollowing the order of chapter sections and consolidated according \r\nto key assessment components."


id: d0a572f6beeb555c91dd74f1fed07866
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content:
"projections challenge a uniformly robust assessment across all \r\nCIDs and regions of the world (Sections 12.4 and 12.5)."


id: 1886ac7f33594d1e887fecb1f12ae7ba
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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content:
"A climatic impact-driver is a physical climate condition that directly affects society or ecosystems. Climatic impact\u0002drivers may represent a long-term average condition (such as the average winter temperatures that affect indoor \r\nheating requirements), a common event (such as a frost that kills off warm-season plants), or an extreme event \r\n(such as a coastal flood that destroys homes). A single climatic impact-driver may lead to detrimental effects \r\nfor one part of society while benefiting another, while others are not affected at all. A climatic impact-driver \r\n(or its change caused by climate change) is therefore not universally hazardous or beneficial, but we refer to it \r\nas a 'hazard' when experts determine it is detrimental to a specific system."


id: 73cae4b0bc18db6bd97c156bf602099c
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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"Many aspects of our daily lives, businesses and natural systems depend on weather and climate, and there is \r\ngreat interest in anticipating the impacts of climate change on the things we care about. To meet these needs, \r\nscientists engage with companies and authorities to provide climate services - meaningful and possibly actionable \r\nclimate information designed to assist decision-making. Climate science and services can focus on CIDs that \r\nsubstantially disrupt systems to support broader risk management approaches. A single CID change can have \r\ndramatically different implications for different sectors or even elements of the same sector, so engagement \r\nbetween climate scientists and stakeholders is important to contextualize the climate changes that will come. \r\nClimate services responding to planning and optimization of an activity can focus on more gradual changes in \r\noperating climate conditions."


id: 4cf4b439aad76f5f8c0b825f3b019fa3
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content:
"A climatic impact-driver (CID) is a climate condition that directly affects elements of society or ecosystems.\r\nClimatic impact-drivers and their changes can lead to positive, negative, or inconsequential outcomes (or a mixture)."


id: d82be2869a0bc89d3e4cafca62dde803
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"Climatic thresholds tell us about the tolerance of society and ecosystems so that we can better scrutinize the types \r\nof climate changes that are expected to impact things we care about. Many systems have natural or structural \r\nthresholds. If conditions exceed those thresholds, the result can be sudden changes or even collapses in health, \r\nproductivity, utility or behaviour. Adaptation and risk management efforts can change these thresholds, altering \r\nthe profile of climate conditions that would be problematic and increasing overall system resilience."


id: f465f8d942497fd5ed8709eb80f6d327
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content:
"Decision makers have long observed that certain weather and climate conditions can be problematic, or \r\nhazardous, for things they care about (i.e., things with socio-economic, cultural or intrinsic value). Many elements \r\nof society and ecosystems operate in a suitable climate zone selected naturally or by stakeholders considering \r\nthe expected climate conditions. However, as climate change moves conditions beyond expected ranges, they \r\nmay cross a climatic 'threshold' - a level beyond which there are either gradual changes in system behaviour or \r\nabrupt, non-linear and potentially irreversible impacts."


id: 4044f911f655a93a821be8d980e25563
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content:
"Climatic thresholds can be associated with either natural or structural tolerance levels. Natural thresholds, for \r\ninstance, include heat and humidity conditions above which humans cannot regulate their internal temperatures \r\nthrough sweat, drought durations that heighten competition between species, and winter temperatures that \r\nare lethal for pests or disease-carrying vector species. Structural thresholds include engineered limits of drainage \r\nsystems, extreme wind speeds that limit wind turbine operation, the height of coastal protection infrastructure, \r\nand the locations of irrigation infrastructure or tropical cyclone sheltering facilities."


id: d017cfac2d08af97e6216e2aa7a55113
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"Thresholds may be defined according to raw values (such as maximum temperature exceeding 35degC) or percentiles \r\n(such as the local 99th percentile daily rainfall total). They also often have strong seasonal dependence \r\n(see FAQ 12.3). For example, the amount of snowfall that a deciduous tree can withstand depends on whether \r\nthe snowfall occurs before or after the tree sheds its leaves. Most systems respond to changes in complex ways, \r\nand those responses are not determined solely or precisely by specific thresholds of a single climate variable. \r\nNonetheless, thresholds can be useful indicators of system behaviours, and an understanding of these thresholds \r\ncan help inform risk management decisions."


id: bd53648ec824081bc34104c27637ba12
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"Adaptation and other changes in societies and environment can shift climatic thresholds by modifying vulnerability \r\nand exposure. For example, adaptation efforts may include breeding new crops with higher heat tolerance levels \r\nso that corresponding dangerous thresholds occur less frequently. Likewise, increasing the height of a flood \r\nembankment protecting a given community can increase the level of river flow that may be tolerated without \r\nflooding, reducing the frequency of damaging floods. Stakeholders therefore benefit from climate services that \r\nare based on a co-development process, with scientists identifying system-relevant thresholds and developing \r\ntailored climatic impact-driver indices that represent these thresholds (FAQ 12.1). These thresholds help focus the \r\nprovision of action-relevant climate information for adaptation and risk management. "


id: f5e41ac0c8926ef59bc703775c1115bc
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content:
"Many systems have thresholds that can lead to sudden changes, if climate conditions exceed them. \r\nAdaptation and risk management efforts can increase overall system resilience by identifying and \r\nchanging tolerance thresholds."


id: bac2974f43ff1b9ecb77a1ec204221db
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"FAQ 12.2, Figure 1 | Crop response to maximum temperature thresholds. Crop growth rate responds to daily maximum temperature increases, \r\nleading to reduced growth and crop failure as temperatures exceed critical and limiting temperature thresholds, respectively. Note that changes in other \r\nenvironmental factors (such as carbon dioxide and water) may increase the tolerance of plants to increasing temperatures."


id: a820f4b89eca3f81d2bbc2601b1057e5
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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index: climate_gpt_v2
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content:
"Human-driven climate change can alter the regional characteristics of a climate hazard by changing the magnitude \r\nor intensity of the climate hazard, the frequency with which it occurs, the duration that hazardous conditions \r\npersist, the timing when the hazard occurs, or the spatial extent threatened by the hazard. By examining each \r\nof these aspects of a hazard's profile change, climate services may provide climate risk information that allows \r\ndecision makers to better tailor adaptation, mitigation and risk management strategies."


id: 9889e47471c397b89115f5e070abcf4c
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content:
"A climate hazard is a climate condition with the potential to harm natural systems or society. Examples include \r\nheatwaves, droughts, heavy snowfall events and sea level rise. Climate scientists look for patterns in climatic \r\nimpact-drivers to detect the signature of changing hazards that may influence stakeholder planning (FAQ 12.1). \r\nClimate service providers work with stakeholders and impacts experts to identify key system responses and \r\ntolerance thresholds (FAQ 12.2) and then examine historical observations and future climate projections to \r\nidentify associated changes to the characteristics of a regional hazard's profile. Climate change can alter at least \r\nfive different characteristics of the hazard profile of a region (FAQ 12.3, Figure 1):"


id: 1b9710e9ef795cefb34787ac850700eb
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updated_at: 2023-04-19 13:21:42
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index: climate_gpt_v2
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content:
"Magnitude or intensity is the raw value of a climate hazard, such as an increase in the maximum yearly temperature \r\nor in the height of flooding that results from a coastal storm with a 1% change of occurring each year."


id: 5f89efc47ce8f065be018805bcb1e617
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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index: climate_gpt_v2
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content:
"Frequency is the number of times that a climate hazard reaches or surpasses a threshold over a given period. \r\nFor example, increases to the number of heavy snowfall events, tornadoes, or floods experienced in a year or in \r\na decade."


id: 744a5c05990c7094b28876d004efad3
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updated_at: 2023-04-19 13:21:42
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"Duration is the length of time over which hazardous conditions persist beyond a threshold, such as an increase \r\nin the number of consecutive days where maximum air temperature exceeds 35degC, the number of consecutive \r\nmonths of drought conditions, or the number of days that a tropical cyclone affects a location."


id: 85b52b6fca6e7fa8f7a7cde9c41ce744
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"Timing captures the occurrence of a hazardous event in relation to the course of a day, season, year, or other \r\nperiod in which sectoral elements are evolving or co-dependent (such as the time of year when migrating animals \r\nexpect to find a seasonal food supply). Examples include a shift towards an earlier day of the year when the last \r\nspring frost occurs or a delay in the typical arrival date for the first seasonal rains, the length of the winter period \r\nwhen the ground is typically covered by snow, or a reduction in the typical time needed for soil moisture to move \r\nfrom normal to drought conditions."


id: 8315bd32c9abe7e97b2e01ca9ff070cd
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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index: climate_gpt_v2
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content:
"Spatial extent is the region in which a hazardous condition is expected, such as the area currently threatened by \r\ntropical cyclones, geographical areas where the coldest day of the year restricts a particular pest or pathogen, \r\nterrain where permafrost is present, the area that would flood following a common storm, zones where climate \r\nconditions are conducive to outdoor labour, or the size of a marine heatwave."


id: d5dc68797ad0d34185ff914e7bc7c165
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 34941
content:
"Hazard profile changes are often intertwined or stem from related physical changes to the climate system. For \r\nexample, changes in the frequency and magnitude of extreme events are often directly related to each other \r\nas a result of atmospheric dynamics and chemical processes. In many cases, one aspect of hazard change is more \r\napparent than others, which may provide a first emergent signal indicating a larger set of changes to come \r\n(FAQ 1.2)."


id: a8cfd5eb92abc449161af87c8b0db40
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 26910
content:
"Information about how a hazard has changed or will change helps stakeholders prioritize more robust adaptation, \r\nmitigation and risk management strategies. For example, allocation of limited disaster relief resources may be \r\ndesigned to recognize that tropical cyclones are projected to become more intense even as the frequency of \r\nthose storms may not change. Planning may also factor in the fact that even heatwaves that are not record\u0002breaking in their intensity can still be problematic for vulnerable populations when they persist over a long \r\nperiod. Likewise, firefighters recognize new logistical challenges in the lengthening of the fire weather season \r\nand an expansion of fire conditions into parts of the world where fires were not previously a great concern. \r\nStrong engagement between climate scientists and stakeholders therefore helps climate services tailor and \r\ncommunicate clear information about the types of changing climate hazards to be addressed in resilience efforts."


id: f00eef9f12150904856c012a756de61f
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 39612
content:
"Climate change can alter the intensity and magnitude, frequency, duration, timing and spatial extent of a region's \r\nclimate hazards. "


id: 9018b80598c5de356f2dddb35aa113e8
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 22560
content:
"FAQ 12.3, Figure 1 | Types of changes to a region's hazard profile. The first five panels illustrate how climate changes can alter a hazard's intensity \r\n(or magnitude), frequency, duration, and timing (by seasonality and speed of onset) in relation to a hazard threshold (horizontal grey line, marked 'H'). The \r\ndifference between the historical climate (blue) and future climate (red) shows the changing aspects of climate change that stakeholders will have to manage. \r\nThe bottom right-hand panel shows how a given climate hazard (such as a current once-in-100-year river flood, geographic extent in blue) may reach new \r\ngeographical areas under a future climate change (extended area in red)."


id: 5f34ad0fe8b090c2eeb3b77e7e5d065c
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 14059
content:
"Jose Manuel Gutierrez (Spain), Richard G. Jones (United Kingdom), Gemma Teresa Narisma \r\n(Philippines)"


id: 3145c41286d76b0f37ab6c56c421237a
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 5921
content:
"Lincoln M. Alves (Brazil), Muhammad Amjad (Pakistan), Irina V. Gorodetskaya (Portugal/\r\nBelgium, The Russian Federation), Michael Grose (Australia), Nana Ama Browne Klutse (Ghana), \r\nSvitlana  Krakovska (Ukraine), Jian Li (China), Daniel Martinez-Castro (Cuba, Peru/Cuba), \r\nLinda O. Mearns (United States of America), Sebastian H. Mernild (Denmark, Norway/Denmark), \r\nThanh Ngo-Duc (Vietnam), Bart van den Hurk (The Netherlands), Jin-Ho Yoon (Republic of Korea)"


id: df8f89536675c6856e31c77d9c6a661c
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 36578
content:
"(Tanzania), Tugba  Ozturk (Turkey), Mohammad  Rahimi (Iran), Mehwish  Ramzan (Pakistan), \r\nRosh  Ranasinghe (The Netherlands/Sri Lanka, Australia), Johan  Reyns (The Netherlands/\r\nBelgium), Annette Rinke (Germany), Daniela  Schmidt (United  Kingdom), Stephane Senesi \r\n(France), Sonia  I.  Seneviratne (Switzerland), Chris Shaw (United Kingdom), Stefan Sobolowski \r\n(Norway/United States of America), Samuel  Somot (France), Anna A. Sorensson (Argentina), \r\nTannecia S. Stephenson (Jamaica), Mouhamadou Bamba Sylla (Rwanda/Senegal), Fredolin Tangang \r\n(Malaysia), Claas Teichmann (Germany), Peter W. Thorne (Ireland/United Kingdom), Blair Trewin \r\n(Australia), Geert-Jan  van  Oldenborgh (The Netherlands), Jan Melchior van Wessem (The \r\nNetherlands), Robert Vautard (France), Sergio M. Vicente-Serrano (Spain), Alejandro Vichot-Llano \r\n(Cuba), Etienne Vignon (France), Yu Xiaoyong (China, Germany), Xuebin Zhang (Canada)"


id: a7b31f0e789fe5bf46b21975053d014d
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 26720
content:
"Maialen Iturbide (Spain), Jorge Bano-Medina (Spain), Joaquin Bedia (Spain), Ana Casanueva \r\n(Spain), Ezequiel Cimadevilla (Spain), Antonio S. Cofino (Spain), Javier Diez Sierra (Spain), \r\nJesus  Fernandez (Spain), Markel Garcia (Spain), Sixto Herrera (Spain), Rodrigo Manzanas \r\n(Spain), Josipa Milovac (Spain/Croatia), Juan Jose Saenz de la Torre (Spain), Daniel San Martin \r\n(Spain), Ivan Sanchez (Spain), Elena Suarez (Spain), Max Tuni (Spain)"


id: ff8b5656491a9ac02deb739626fa2adb
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 42360
content:
"Maialen Iturbide (Spain), Ma. Laurice Preciado Jamero (Philippines), Emilie Vanvyve (United \r\nKingdom/Belgium)"


id: e2229b59f254d7bc78f1f5accb1bd5db
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 37064
content:
"Gutierrez, J.M., R.G. Jones, G.T. Narisma, L.M. Alves, M. Amjad, I.V. Gorodetskaya, M. Grose, N.A.B. Klutse, S. \r\nKrakovska, J. Li, D. Martinez-Castro, L.O. Mearns, S.H. Mernild, T. Ngo-Duc, B. van den Hurk, and J.-H. Yoon, \r\n2021: Atlas. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth \r\nAssessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, \r\nS.L. Connors, C. Pean, S. Berger, N.  Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, \r\nJ.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. 1927-2058, doi:10.1017/9781009157896.021."


id: f84dab50df18f9ba6274cbfb65b3d5c0
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 41058
content:
"The Atlas of the Working Group I Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change \r\n(IPCC), is dedicated to the memory of Gemma Teresa Narisma, one of the Atlas Coordinating Lead Authors."


id: 996e3246b732a4f8badb99011faf2439
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 24220
content:
"Gemma was an internationally renowned scientist, Executive Director of the Manila Observatory (MO) and Professor of physics at \r\nthe Ateneo de Manila University in the Philippines. She undertook and coordinated research into land-atmosphere interactions, \r\nthe implications of land-use/biosphere changes on local and regional climate and aerosols and monsoons. She also worked in \r\nAustralia and the United States and was a key figure in regional climate research in South East Asia. In the Philippines, she undertook \r\nmultidisciplinary research involving local stakeholders and government on climate impacts and risks to support climate change policy, \r\nrisk assessment and development planning. "


id: 95f95b29b3e69b363c5ffff164d39392
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 23579
content:
"Gemma was also an inspirational teacher, mentor and colleague. She supported and encouraged the young scientists she taught and \r\nworked with and focused on ensuring her research would help and empower those most at risk. And with her kindness and generosity, \r\nher soft, strong and positive energy, her sweet smile and personality she was an exceptional Coordinating Lead Author, building \r\nconsensus, motivating and supporting the team whilst also linking to other chapters and Working Group II. Her loss is felt deeply, and \r\nshe will always be remembered with great affection."


id: 3e7de9a6bab1305c9807746ce9864684
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 8176
content:
"It is very likely that the Arctic has warmed at more than twice \r\nthe global rate over the past 50 years and that the Antarctic \r\nPeninsula experienced a  strong warming trend starting in \r\n1950s. It is likely that Arctic annual precipitation has increased, \r\nwith the highest increases during the cold season. Antarctic \r\nprecipitation and surface mass balance showed a significant \r\npositive trend over the 20th century, while strong interannual \r\nvariability masks any existing trend over recent decades1\r\n(medium confidence). Significant warming trends are observed \r\nin other West Antarctic regions and at selected stations in East \r\nAntarctica since the 1950s (medium confidence). Under all assessed \r\nemissions scenarios, both polar regions are very likely to have higher \r\nannual mean surface air temperatures and more precipitation, with \r\ntemperature increases higher than the global mean, most prominently \r\nin the Arctic. {Atlas.11.1.2, Atlas.11.1.4, Atlas.11.2.2, Atlas.11.2.4}"


id: 4b0934a672c8932d11e3dcf7050a62d9
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 10443
content:
"Significant improvements in technical infrastructure, open \r\ntools and methodologies for accessing and analysing observed \r\nand simulated climate data, and the progressive adoption of \r\nFAIR (findability, accessibility, interoperability and reusability) \r\ndata principles have very likely broadened the ability to \r\ninteract with these data for a  wide range of activities, \r\nincluding fundamental climate research, providing inputs into \r\nassessments of impacts, building resilience and developing \r\nadaptations. Tools to analyse and assess climate information have \r\nimproved to allow development of information that goes beyond \r\naverages (e.g., on future climate thresholds and extremes) and that \r\nis relevant for regional climate risk assessments. {Atlas.2.2, Atlas.2.3}"


id: 967110a81d6679972a8d58ab72cafbbe
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 23671
content:
"The Interactive Atlas is a new WGI product developed to take \r\nadvantage of the interactivity offered by web applications \r\nby allowing flexible and expanded exploration of some key \r\nproducts underpinning the assessment (including extreme \r\nindices and climatic impact-drivers). This provides a transparent \r\ninterface for access to authoritative IPCC results, facilitating their use \r\nin applications and climate services. The Interactive Atlas implements \r\nFAIR principles and builds on open tools and, therefore, is an \r\nimportant step towards making IPCC results more reproducible and \r\nreusable. {Atlas.2, Interactive Atlas}"


id: fbce9ac93239f3aa3fbe9815eb7a7547
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 41669
content:
"Information on global and regional climate change in the form of \r\nmaps, tables, graphs and infographics has always been a key output \r\nof IPCC reports. With the consensus that climate has changed and will \r\ncontinue to do so, policymakers are focusing more on understanding \r\nits implications, which often requires an increase in regional and \r\ntemporal details of observed and future climate. The WGI contribution \r\nto AR5 included globally comprehensive coverage of land regions \r\nand some oceanic regions in the Atlas of Global and Regional \r\nClimate Projections (IPCC, 2013a), focusing on projected changes in \r\ntemperature and precipitation. In the WGII contribution, Chapter 21, "


id: cf8fe13534553872a95ff41495924e71
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 33877
content:
"The Interactive Atlas is a  novel product of this Report that allows \r\nfor a flexible spatial and temporal analysis of the results presented \r\nin the Atlas and other chapters, the Technical Summary (TS) and the \r\nSummary for Policymakers (SPM), supporting and expanding on their \r\nassessments. The Interactive Atlas includes two components. The first "


id: da4051a7ec3c8fc20e5d8f7302d44e2c
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 35698
content:
"Regional Context (Hewitson et al., 2014) included continental-scale \r\nmaps of observed and future temperature and precipitation changes, \r\nsub-continental changes in high percentiles of daily temperature \r\nand precipitation, and a  table of changes in extremes over sub\u0002continental regions (updating an assessment in the Special Report \r\non Managing the Risks of Extreme Events and Disasters to Advance \r\nClimate Change Adaptation; SREX). However, there was only limited \r\ncoordination between these two contributions despite the largely \r\ncommon data sources and their relevance across the two working \r\ngroups and to wider communities of climate change-related policy \r\nand practice. This resulted in inefficiencies and the potential for \r\nconfusing or inconsistent information. The Atlas, with its links with \r\nother WGI/II/III chapters, has been designed to help address this."


id: c6cc37ac4c9187149ded4a3a7bef2fec
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 32281
content:
"the displayed products (describing both the underlying datasets \r\nand the applied post-processing) and most of the figures included \r\nin the Atlas chapter can be reproduced using the scripts and data \r\nprovided in the WGI-Atlas repository (see Iturbide et al., 2021 and \r\nhttps://github.com/IPCC-WG1/Atlas)."


id: e1deab749990c0b834f929ba5968ea27
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 37020
content:
"Over the past decades scientists have engaged in a wide array of \r\ninvestigations aimed at quantifying and understanding the state \r\nof the components of the land surface-ocean-atmosphere system, \r\nthe complex nature of their interactions and impacts over different \r\ntemporal and spatial scales. As a result, a great deal has been learned \r\nabout the importance of an appropriate choice of these scales \r\nwhen estimating changes due to internal climate variability, trends, \r\ncharacterization of the spatio-temporal variability, and quantifying \r\nthe range of and establishing confidence in climate projections. It is \r\ntherefore important to be able to explore a whole range of spatial \r\nand temporal scales and this section presents the basic definitions of \r\nthose, and the domains of analysis, used by the Atlas accounting for \r\npotential synergies between WGI and WGII."


id: 5a01a4673990fb45f602ceb7aa86e2be
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 13081
content:
"Given the aims of the Atlas, there are several important factors to \r\nconsider. There is a clear requirement for climate change information \r\nover a  wide range of 'regions', and classes thereof, and temporal \r\nscales. There is also often the need for integrated information \r\nrelevant for policy, practice and awareness raising. However, most \r\nother chapters in WGI are disciplinary, focusing on specific processes \r\nin the climate system or on its past or future behaviour, and have \r\nlimited space to be spatially and temporally comprehensive. The Atlas \r\nprovides an opportunity to facilitate this integration and exploration \r\nof information."


id: 5a542fd3c148a439ddaa41fdfaf9a515
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 13144
content:
"Developing this information often requires a  broad range of data \r\nsources (various observations, global and regionally downscaled \r\nbaselines and projections) to be analysed and combined and, \r\nwhere appropriate, reconciled. This is a topic which is assessed from \r\na  methodological perspective in Chapter  10 using a  limited set of \r\nexamples (see also Cross-Chapter Box 10.3). The Atlas then builds on \r\nthis work with a more comprehensive treatment of the available results, \r\nlargely (but not exclusively) based on CMIP5, CMIP6 and CORDEX, to \r\nprovide wider coverage and to further demonstrate techniques and \r\nissues. These multiple lines of evidence are integrated in the Interactive \r\nAtlas, a new AR6 WGI product described in Atlas.2 allowing for flexible \r\nspatial and temporal analysis of this information with a  predefined \r\ngranularity (e.g.,  flexible seasons, regions and baselines, and future \r\nperiods of analysis including time slices and warming levels)."


id: 3c25bc6538d14364542c0d524ed1eb72
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 7772
content:
"Chapter 1 has extensively explored this topic in Section 1.4.1 and \r\nCross-Chapter Box 1.2. A summary of the main points relevant to the \r\nAtlas chapter and the Interactive Atlas are provided here."


id: 4fbff7d3c0ef5ea56e8f3c377a820d31
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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index: climate_gpt_v2
vector_id: 11323
content:
"There is no standard baseline in the literature although the World \r\nMeteorological Organization (WMO) recommends a 30-year baseline \r\napproach such as the climate-normal period 1981-2010. However, it \r\nretains the 1961-1990 period as the historical baseline for the sake \r\nof supporting long-term climate change assessments (WMO, 2017). \r\nUsing the WMO standards also provides sample sizes relevant to \r\ncalculating changes in statistics other than the mean. The AR6 WGI \r\nhas established the 1995-2014 period as the recent-past baseline \r\nperiod - for similar reasons to the 1986-2005 period used in AR5 WGI \r\n(IPCC, 2013b) - since 2014 (2005) is the final year of the historical \r\nsimulations of the models (more details in Cross-Chapter Box 1.2)."


id: 6b68dc498a4f770520277b958d237bbd
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 16197
content:
"Generating information relevant to policy or practice requires \r\nunderstanding the context of the systems that they focus on. In addition \r\nto the hazards these systems face, their vulnerability and exposure, \r\nand the related socio-economic and other physical drivers, also need \r\nto be understood. To ensure this relevance, the Atlas is informed by the \r\nassessments in Chapter 12 and the regional and thematic chapters \r\nand cross-chapter papers of WGII. Therefore, it focuses on generating \r\ninformation on climatic impact-drivers and hazards applicable to \r\nassessing impacts on and risks to human and ecological systems \r\nwhilst noting the potential relevance of these to related contexts such \r\nas the United Nations (UN) Sustainable Development Goals and the \r\nUN Sendai Framework for Disaster Risk Reduction."


id: c29ed7e0dae838c0098d412e5bb560eb
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 31525
content:
"The choice of a  baseline can significantly influence the analysis \r\nresults for future changes in mean climate (Cross-Chapter Box 1.2; \r\nHawkins and Sutton, 2016) as well as its variability and extremes. \r\nThus, assessing the sensitivity of results to the baseline period is \r\nimportant. The Interactive Atlas (Atlas.2) allows users to explore and \r\ninvestigate a wide range of different baseline periods when analysing \r\nchanges for future time slices or global warming levels:"


id: 6e7f02f0d428da1fd5161bc88ba996b8
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 16706
content:
"Transparency and reproducibility are promoted in the Atlas \r\nchapter implementing FAIR principles for Findability, Accessibility, \r\nInteroperability, and Reusability of data (Wilkinson et  al., 2016). \r\nMore specifically, the Interactive Atlas provides full metadata of "


id: dab351db8ee8746ee91f2dfd22763932
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 35770
content:
"GCM are used, as in Nikulin et  al. (2018). Detailed reproducible \r\ninformation on the GWLs used in the Atlas is provided in the Atlas \r\nrepository (Iturbide et al., 2021)."


id: 4cd43e14825a2bb758b6cc9684bbb5fd
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 10786
content:
"This promotes cross-Working Group consistency and facilitates \r\ncomparability with previous reports and across datasets. For instance, \r\nthe AR5 and long-term WMO baselines facilitate the intercomparison \r\nof CMIP5, CORDEX and CMIP6 projections since all have historical \r\nsimulations in these periods. Using more recent baselines introduces \r\ndiscontinuity for the CMIP5 and CORDEX models, since historical \r\nsimulations end in 2005. A  pragmatic approximation to deal with \r\nthis issue is to use scenario data to fill the missing segment, for \r\nexample for 2006-2014 use the first years of RCP8.5-driven transient \r\nprojections in which the emissions are close to those observed. This \r\napproach is used in the Atlas chapter and Chapter 12."


id: f3d174afe207fa0c23bdd903e230ac31
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 40273
content:
"Climate information at many temporal scales and over a wide range of \r\ntemporal averaging periods is required for the assessment of climate \r\nchange and its implications. These range from annual to multi-decadal \r\naverages required to characterize low-frequency variability and trends \r\nin climate to hourly or instantaneous maximum or minimum values of \r\nimpactful climate variables. In between, information on, for example, \r\nseasonal rainfall is important and implies the need to include averaging \r\nperiods whose relevance are geographically dependent. As a result, \r\nthe Atlas chapter presents results over a wide range of time scales, \r\nfrom daily to decadal, and averaging periods with the Interactive Atlas \r\nallowing a choice of user-defined seasons and a range of predefined \r\ndaily to multi-day climate indices."


id: 79c59c13c20e3b08da4a2473ccbf48d5
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 18631
content:
"When assessing changes over the recent past, many studies analyse \r\ndatasets using a  range of climatologically significant periods \r\n(i.e., 30 years or more) with precise start and end dates depending \r\non data availability and the year of the study. To account for this, \r\nwhen generating assessments from this literature the term 'recent \r\ndecades' is used to refer to a period of approximately 30 to 40 years \r\nwhich ends within the period 2010-2020. An equivalent approximate \r\ndescription using specific years would be 'since the 1980s'."


id: 1cf45e9103fa6a9411178ba66e565a5c
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 2274
content:
"Many factors influence the spatial scales and regions over which \r\nclimate information is required and can be reliably generated. Despite \r\nall efforts in researching, analysing and understanding climate and \r\nclimate change, a key factor in determining spatial scales at which \r\nanalysis can be undertaken is the availability and reliability of \r\ndata, both observational and from model simulations. In addition, \r\ninformation is required over a wide range of spatial domains defined \r\neither from a climatological or geographical perspective (e.g., a region \r\naffected by monsoon rainfall or a river basin) or from a socio-economic \r\nor political perspective (e.g.,  least-developed countries or nation \r\nstates). Chapter 1 provides an overview of these topics (Section 1.5.2). \r\nThis subsection discusses some relevant issues, summarizes recent \r\nadvances in defining domains and spatial scales used by AR6 analyses \r\nand how these can be explored with the Interactive Atlas."


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content:
"Regarding the future reference periods, the Interactive Atlas \r\npresents projected global and regional climate changes at near-, \r\nmid- and long-term periods, respectively 2021-2040, 2041-2060 \r\nand 2081-2100, for a range of emissions scenarios (Atlas.1.4.3 and \r\nCross-Chapter Box 1.4)."


id: 726e8946f9b3ce3890eddd3a483342ed
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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content:
"Noting the approach taken in the recent IPCC Special Report on Global \r\nWarming of 1.5degC (SR1.5) above 1850-1900 levels (IPCC, 2018b), the \r\nAtlas also presents global and regional climate change information at \r\ndifferent global warming levels (GWLs, see Cross-Chapter Box 11.1). \r\nIn particular, to provide policy-relevant climate information and \r\nrepresent the range of outcomes from the emissions scenario and time \r\nperiods considered, GWLs of 1.5degC, 2degC, 3degC and 4degC are considered. \r\nThe information is computed from all available scenarios (e.g., only \r\n1.5degC and 2degC GWL information can be computed from projections \r\nunder the SSP1-2.6 scenario). The Interactive Atlas allows comparison \r\nof timings for global warming across the different scenarios and of \r\nspatial patterns of change, for example information at 2degC GWL is \r\ncalculated from SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 projections \r\n(Section 4.2.4)."


id: 5e64ead4181b49afaab4850b42452bc4
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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content:
"Recent IPCC reports - AR5 Chapter 14 (Christensen et al., 2013) and \r\nSR1.5 Chapter 3 (Hoegh-Guldberg et al., 2018) - have summarized \r\ninformation on projected future climate changes over sub-continental \r\nregions defined in the SREX report (Seneviratne et  al., 2012) and \r\nlater extended in AR5 from the 26 regions in SREX to include the \r\npolar, Caribbean, two Indian Ocean, and three Pacific Ocean regions \r\n(hereafter known as AR5 WGI reference regions; Figure Atlas.2a). In \r\nrecent literature, new sub-regions have been used, for example for \r\nNorth and South America, Africa and Central America, together with \r\nthe new definition of reference oceanic regions. Iturbide et al. (2020) \r\ndescribes an updated version of the reference regions which is used \r\nin this report (hereafter known as AR6 WGI reference regions) and \r\nis shown in Figure Atlas.2b. The goal of these subsequent revisions \r\nwas to ensure that they represented sub-continental areas of greater \r\nclimatic coherency."


id: 52732cd0c3c2d52820b842b4d8dfc184
created_at: 2023-04-19 13:21:42
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content:
"To calculate GWL information for the datasets used in the Atlas \r\n(CMIP6 and CMIP5; see Atlas.1.4), this chapter adopted the \r\nprocedure used in Cross-Chapter Box 11.1. A model future climate \r\nsimulation reaches the defined GWL of 1.5degC, 2degC, 3degC or 4degC \r\nwhen its global near-surface air temperature change averaged over \r\nsuccessive 20-year periods first attains that level of warming relative \r\nto its simulation of the 1851-1900 climate (1851-1900 defines the \r\npre-industrial baseline period for calculating the required global \r\nsurface temperature baseline, Cross-Chapter Box 1.2). Note that this \r\nprocess is different from the one used in the SR1.5 report which used \r\n30-year future periods. If a projection stabilizes before reaching the \r\nrequired threshold it is unable to simulate climate at that GWL and \r\nis thus discarded. For CORDEX simulations, the periods of the driving "


id: b366c6e4e3f2d9944bbac561316a02ca
created_at: 2023-04-19 13:21:42
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content:
"The rationale followed for the definition of the reference regions was \r\nguided by two basic principles: 1) climatic consistency and better \r\nrepresentation of regional climate features, and 2) representativeness \r\nof model results (i.e., sufficient number of model grid boxes). \r\nThe finer resolution of CMIP6 models (as compared, on average, \r\nto CMIP5) yields better model representation of the reference \r\nregions allowing them to be revised for better climatic consistency "


id: 81aef87a07fb3ce38a6ae5f19c0d4535
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updated_at: 2023-04-19 13:21:42
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content:
"Figure Atlas.2 | WGI reference regions used in the (a) AR5 and (b) AR6 reports (Iturbide et al., 2020). Asterisks indicate regions that extend across both sides \r\nof the map."


id: cbfa2aab1d61df5fecf6b5dc9795598f
created_at: 2023-04-19 13:21:42
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"is an important determinant or influencer. In these cases the domains \r\nare subject to particular physical processes that are important for \r\nits climatology or that involve systems affected by the climate \r\nin a  way that observations and climate model simulations can be \r\nused to understand. Many of these are the basis of the chapters and \r\ncross-chapter papers of the AR6 WGII report, namely river basins, \r\nbiodiversity hotspots, tropical forests, cities, coastal settlements, \r\ndeserts and semi-arid areas, the Mediterranean, mountains and polar \r\nregions. It is therefore important to generate climate information \r\nrelevant to these typological domains and some examples of these \r\nused in the Interactive Atlas are shown in Figure Atlas.4."


id: 1d5186717c2b84c7580a48a2d7efb325
created_at: 2023-04-19 13:21:42
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vector_id: 2336
content:
"(e.g.,  dividing  heterogeneous  regions) while preserving the model \r\nrepresentation. Figure Atlas.3 illustrates this issue, displaying the \r\nnumber of grid boxes (over land for land regions) in the AR6 reference \r\nregions for two Interactive Atlas reference grids of horizontal \r\nresolutions of 1deg and 2deg, representative of the typical resolution of \r\nCMIP6 and CMIP5 models respectively. This figure shows that the \r\nnew reference regions are well suited for the assessment of model \r\nresults, with poorest model coverage for the New Zealand (NZ), \r\nCaribbean (CAR) and Madagascar (MAD) regions."


id: 6e82ce159b1a7aa26178b18b7aa7de7a
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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vector_id: 16708
content:
"The AR6 WGI (land and open ocean) reference regions are used in \r\nthe Interactive Atlas as the default regionalization for atmospheric \r\nvariables. However, these regions are not optimum for the analysis \r\nof oceanic variables since, for instance, the five upwelling regions \r\n(Canary, California, Peru, Benguela and Somali) are mostly included \r\nin 'land' regions. Therefore, the alternative set of oceanic regions \r\ndefined by their biological activity (Figure Atlas.4) is used in the \r\nInteractive Atlas for the regional analysis of oceanic variables (see \r\nFay and McKinley, 2014; Gregor et  al., 2019). Due to the many \r\npotential definitions of the regions relevant for WGI and WGII, some \r\nadditional typological and socio-economic regions have also been \r\nincluded in the Interactive Atlas."


id: 9906669b795924ceb2d3e8d89d992e50
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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"This section introduces the observational data sources and \r\nreanalyses that are used in the assessment of regional climate \r\nchange and for evaluating and bias adjusting the results of models \r\n(more information on observational reference datasets is available \r\nin Annex  I). It also introduces the different global and regional \r\nclimate model outputs that are used for regional climate assessment \r\nconsidering both historical and future climate projections (Annex \r\nII). Many of these models are run as part of coordinated Model \r\nIntercomparison Projects (MIPs), including CMIP5, CMIP6 and \r\nCORDEX, described below. Combining information from these \r\nmultiple data sources is a significant challenge (see Section 10.5 for \r\nan in-depth treatment of the problem) though if they can be used "


id: 6dc34e8e3d4a2298f5009d1c801377ce
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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content:
"to generate robust information on regional climate change it can \r\nguide policy and  support decisions responding to these changes. \r\nAn important and necessary part of this process is to check for \r\nconsistency amongst the data sources."


id: 2d916a08ce7cb46a313fcd290e710c18
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
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vector_id: 5309
content:
"Chapter 10 by providing the opportunity to visualize and expand on \r\nits assessment. This includes displaying maps of density of stations' \r\nobservations (including those that are used in the different datasets) and \r\nassessing observational uncertainty by using multiple datasets."


id: 732a7038977b0a55cef5ee244479f7af
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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"Two of the most commonly used variables in climate studies are \r\ngridded surface air temperature and precipitation. There are many \r\ndatasets available (Annex I) and Chapter 2 provides an assessment \r\nof key global datasets, including blended land-air and sea surface \r\ntemperature datasets to assess global mean surface temperature \r\n(GMST). The Atlas separately analyses atmospheric and oceanic \r\nvariables, and for the former a number of common global datasets \r\nsupporting the assessment done in other chapters is used, including "


id: dbb393da49693880d8e0608002871ca9
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 35945
content:
"There are various sources of observational information available for \r\nglobal and regional analysis. Observational uncertainty is a key factor \r\nwhen assessing and attributing historical trends, so assessment \r\nshould build on integrated analyses from different datasets (disparity, \r\ninadequacy and contradictions in existing datasets are assessed in \r\nSection  10.2). The Atlas chapter can supplement and complement "


id: 156f8c83d2fd9caca4affd502491ae40
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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"discussions on discrepancies/conflicts in observations presented \r\nin Chapter  10 and expanding on and replicating their results for \r\nother regions. In particular, the Interactive Atlas includes the global \r\nand regional observational products described here to assess \r\nobservational uncertainty over the different regions analysed."


id: e09f7332f43b893487821fb4fa99825d
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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vector_id: 36777
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"Figure Atlas.5 shows the spatial coverage of the total number of \r\nobservation stations for different periods (1901-1910, 1971-1980, \r\nand 2001-2010) for two illustrative datasets: the CRU TS4 dataset \r\nfor precipitation and the SST data in HadSST4. The former illustrates \r\nspatially the declining trend of station observation data used in the \r\nprecipitation datasets for certain regions (South America and Africa) \r\nafter the 1990s. This demonstrates the regional inhomogeneity and \r\ntemporal change in station density, which is in part a consequence \r\nof many stations not reporting to the WMO networks and their data \r\nbeing held domestically or regionally. During early years (before 1950) \r\na  limited number of observations are available. This information is \r\nused in the Interactive Atlas to blank out regions not constrained with \r\nobservations in those datasets providing station density information."


id: c06a66e3a90f50469bc4b287a2daaa3
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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index: climate_gpt_v2
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content:
"The Atlas and Interactive Atlas use information from ERA5 and \r\nfrom the bias-adjusted version WFDE5 (Cucchi et  al., 2020) which \r\nis combined with ERA5 information over the ocean and used as \r\nthe Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) \r\nobservational reference dataset W5E5 (Lange, 2019b). This reference \r\nis also used in the Atlas for model evaluation (Atlas.1.4.4) and for \r\nbias-adjusting model outputs (Atlas.1.4.5)."


id: 26b90a6b7c434ce0a758c77ca4110168
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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"In addition to surface observations, satellites have been widely \r\nused to produce rainfall estimates. The advantage of satellite-based \r\nrainfall products is their global coverage including remote areas but \r\nthere is significant uncertainty in these products over complex terrain \r\n(Rahmawati and Lubczynski, 2018; Satge et al., 2019). Another recent \r\ndevelopment has been on gridded datasets for climate extremes \r\nbased on surface stations, such as HadEX3 (Dunn et al., 2020), as \r\ndescribed in Section 11.2.2."


id: 7f9a97418e7d7cfc6cda44403297fe6d
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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vector_id: 19589
content:
"There are some studies assessing observational datasets globally \r\n(Beck et  al., 2017; Q. Sun et  al., 2018) and regionally (Manzanas \r\net  al., 2014; Salio et  al., 2015; Prakash, 2019), reporting large \r\ndifferences among them and stressing the importance of considering \r\nobservational uncertainty in regional climate assessment studies. \r\nUncertainty in observations is also a key limitation for the evaluation \r\nof climate models, particularly over regions with low station density \r\n(Kalognomou et  al., 2013; Kotlarski et  al., 2019). More detailed \r\ninformation on these issues is provided in Section 10.2."


id: 797919cb35c0951e22bdfcba701bff0e
created_at: 2023-04-19 13:21:42
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content:
"For regional studies, observational datasets with global coverage are \r\ncomplemented by a  range of regional observational analyses and \r\ngridded products, such as E-OBS (Cornes et al., 2018) over Europe, \r\nDaymet (Thornton et al., 2016) over North America, or APHRODITE \r\n(Yatagai et al., 2012) over Asia. These are highlighted in various other \r\nchapters and the Atlas expands on their treatment, complementing "


id: 42f3ddca85673d25cc5e4de3df4f97c3
created_at: 2023-04-19 13:21:42
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"analysis based on time slices for particular scenarios (Atlas.1.3.1). \r\nThis dimension allows for enhanced comparability of CMIP5 and \r\nCMIP6, since it constrains the regional patterns to the same global \r\nwarming level for both datasets."


id: bf612aa5ea69c78346cd4204c167892e
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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content:
"change signals compared to global models and adds any value, \r\nespecially in terms of high-resolution features and extremes."


id: 8c0a2ef4215f9b3de8176db381f30bd7
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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"A lot of progress has been made by the regional climate modelling \r\ncommunity since AR5 (Table AII.1) to produce and make available \r\nevaluation (reanalysis-driven) simulations over the different \r\nCORDEX domains along with downscaled CMIP5 historical and \r\nfuture climate projection information under a  range of emissions \r\nscenarios, mainly RCP2.6, RCP4.5 and RCP8.5 (Tables AII.3 and AII.4). \r\nHowever, these ensembles cover only a fraction of the uncertainty \r\nrange spanned by the full CMIP5 ensemble in the different domains \r\n(e.g., Figures Atlas.16, Atlas.17, Atlas.21, Atlas.22, Atlas.24, Atlas.26, "


id: 86d304f114998caad7fabd1b55659143
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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index: climate_gpt_v2
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content:
"Chapter 3 and Flato et al. (2013) describe the evaluation of CMIP6 \r\nand CMIP5 models, respectively, assessing surface variables and \r\nlarge-scale indicators. Section 10.3.3 assesses the general capability \r\nof GCMs to produce climate output for regions."


id: ec96803758b4aaf39bfd159d6aae028f
created_at: 2023-04-19 13:21:42
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"Information from the existing CMIP5 and CMIP6 datasets is \r\nsupplemented with downscaled regional climate simulations from \r\nCORDEX. This facilitates an assessment of the effects from higher \r\nresolution, including whether this modifies the projected climate "


id: c4f042eeb903653c9b9989c6c9c3113c
created_at: 2023-04-19 13:21:42
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"Atlas.28 and Atlas.29; Ito et  al., 2020b). Therefore, comparison of \r\nCMIP5 and CORDEX results should be performed carefully, providing \r\nresults not only for the full CMIP5 ensemble but also for the \r\nsub-ensemble formed by the driving models since results can diverge \r\n(Fernandez et al., 2019; Iles et al., 2020)."


id: 8496720261b0eec1e1af6dc0ca3da58c
created_at: 2023-04-19 13:21:42
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"and also the reanalysis-driven simulations for evaluation purposes. \r\nIn the case of the European domain, the dataset considered is the \r\n0.11deg simulations (CORDEX EUR-11, the same dataset as used in \r\nChapter 12), which has been interpolated to a regular 0.25deg resolution \r\ngrid (the same used for the regional observations). In the case of the \r\nMediterranean domain, oceanic information (sea surface temperature, \r\nSST) is interpolated to a regular 0.11deg grid. In all cases, the indices are \r\ncomputed on the original grids and the interpolation process is applied \r\nto the resulting indices. Moreover, for the sake of comparability with \r\nCMIP6 results (in particular when using baseline periods beyond 2005), \r\nthe historical period of the CORDEX datasets has been extended to \r\n2006-2014 using the first years of RCP8.5-driven transient projections \r\nin which the emissions are close to those observed (see Atlas.1.3.1); \r\nnote that this procedure is also applied to CMIP5 simulations."


id: d8c3da789901f177721c370989c83f59
created_at: 2023-04-19 13:21:42
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"The Atlas chapter and the Interactive Atlas use CORDEX information for \r\nthe following 11 individual CORDEX domains (out of the 14 domains \r\nshown in Figure Atlas.6): North, Central and South America; Europe; \r\nAfrica; South, East and South East Asia; Australasia; Arctic and \r\nAntarctica; in addition, oceanic information has been used from the \r\nMediterranean domain, which provides simulations from coupled \r\natmosphere-ocean regional climate models (RCMs). In order to \r\nharmonize the information across domains and to maximize the \r\nsize of the resulting ensembles, all the available simulations for each \r\nindividual CORDEX domain (including the standard 0.44deg CORDEX \r\nand the 0.22deg CORDEX-CORE) have been interpolated to a common \r\nregular 0.5deg-resolution grid to provide a grand ensemble covering the \r\nhistorical and future emissions scenarios RCP2.6, RCP4.5 and RCP8.5, "


id: b991a26e803e1d8698dd928c64f66d6
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content:
"For the different CORDEX domains, the full ensembles of models (GCM\u0002RCM matrix) used in the Atlas for the different scenarios and variables \r\nare described in the Supplementary Material (Tables  Atlas.SM.3-\r\nAtlas.SM.14) and in the Atlas repository (Iturbide  et  al., 2021), "


id: 996112ecadb6c3aa2742e42fb725ff1c
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content:
"Figure Atlas.7 | Evaluation of annual and seasonal air temperature and precipitation for the six North America sub-regions, NWN, NEN, WNA, CNA, ENA \r\nand NCA (land only) for CORDEX-NAM RCM simulations driven by reanalysis or historical GCMs. Seasons are June-July-August (JJA) and December-January-\r\nFebruary (DJF). Rows represent sub-regions and columns correspond to the models. Magenta text indicates the driving historical CMIP5 GCMs (including ERA-Interim in the first \r\nset of slightly separated columns) and the black text to the right of the magenta text represents the driven RCMs. The colour matrices show the mean spatial biases; all biases \r\nhave been computed for the period 1985-2005 relative to the observational reference (E5W5, see Atlas.1.4.2). Further details on data sources and processing are available in \r\nthe chapter data table (Table Atlas.SM.15)."


id: 9ef30e6d2d40796e42598bb54afb2e2
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"including full metadata relative to ESGF versions used and the periods \r\nwith data available for the different simulations. In particular, the \r\nhistorical scenario information is only available from 1970 onwards \r\nfor some models and therefore the common period 1970-2005 is \r\nused for historical CORDEX data in the Atlas. As a result, the WMO \r\nbaseline period 1961-1990 is not available in the Interactive Atlas for \r\nCORDEX data."


id: 1765133df4bd6da6aafe8685bf30ecc
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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index: climate_gpt_v2
vector_id: 1335
content:
"biases for mean temperature and precipitation have been calculated \r\nfor the 11 CORDEX domains analysed."


id: e03104646985409d36093d4a8c5e783d
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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vector_id: 36692
content:
"Sections Atlas.4 to Atlas.11 assess research on CORDEX simulations \r\nover different regions, analysing past and present climate as well \r\nas future climate projections. They also focus on regional model \r\nevaluation in order to extend and complement the validation \r\nof global models in Chapter  3, considering the specific regional \r\nclimate and relevant large-scale and regional phenomena, drivers \r\nand feedbacks (Section  10.3.3). Besides the literature assessment, \r\nsome simple evaluation diagnostics have been computed for \r\nthe simulations used in the Atlas chapter to provide some basic \r\ninformation on model performance across regions. In particular, "


id: f4fd158f5e76d202d086a5bcd4ab7331
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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content:
"CORDEX-CORE spans the spread of the CMIP5 climate change signals for interquartile ranges of annual mean temperature and \r\nprecipitation for most of the reference regions covered (Box Atlas.1, Figure 1; Teichmann et al., 2021). However, it is still a small \r\nensemble and for other variables like extremes or climatic impact-drivers it has only been partially investigated in Coppola et al. (2021b) \r\nand needs further analysis."


id: 6e79f4246754f6f308bb792621cb2ca7
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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content:
"extreme indices and climatic impact-drivers (CIDs) included in the \r\nInteractive Atlas are bias adjusted (in particular TX35 and TX40 in \r\ncoordination with Chapter 12). To facilitate integration with WGII, the \r\nAtlas uses the same bias-adjustment method as in ISIMIP3 (Lange, \r\n2019a) and the same observational reference (W5E5, see Atlas.1.4.2), \r\nupscaled to the same resolution as the model to avoid downscaling \r\nartefacts (Cross-Chapter Box  10.2). The ISIMIP3 bias-adjustment \r\nmethod is a  trend-preserving approach that is recommended for \r\ngeneral applications, as it reduces biases while preserving the \r\noriginal climate change signal (Casanueva et  al., 2020). Following \r\nthe recommendations given in Chapter 10, results in the Interactive \r\nAtlas are displayed for both the adjusted and the raw model output."


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"Bias adjustment is often applied to data from climate model \r\nsimulations to improve their applicability for assessing climate impacts \r\nand risk (e.g.,  in the Inter-Sectoral Impact Model Intercomparison \r\nProject, ISIMIP; Rosenzweig et al., 2017). Bias-adjustment approaches \r\n(Section  10.3.1.3) are particularly beneficial when threshold-based \r\nindices are used, but they can introduce other biases, in particular \r\nwhen applied directly to coarse-resolution GCMs (Cross-Chapter \r\nBox 10.2). Bias-adjustment techniques should be chosen carefully for \r\na  specific application. In the Atlas, bias adjustment is not applied \r\nsystematically (in particular, it is not applied for the variables \r\nassessed in the Atlas chapter), and only some threshold-dependent "


id: 2b231b055d07aecb6ef5e3febf6f7635
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"Contributors: Alessandro Dosio (Italy), Melissa I. Gomis (France/Switzerland), Richard G. Jones (UK), Maialen Iturbide (Spain), \r\nMegan Kirchmeier-Young (Canada/USA), June-Yi Lee (Republic of Korea), Stephane Senesi (France), Sonia I. Seneviratne (Switzerland), \r\nPeter W. Thorne (Ireland/UK), Xuebin Zhang (Canada)"


id: eab57f69044cd9475fb558d4ed61fbe1
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"with more or less complexity (Retchless and Brewer, 2016), the most common implementation for maps in the climate science \r\ncommunity remains the overlay of symbols and/or masking of the primary variable. This Cross-Chapter Box reviews the approaches \r\nfollowed in previous IPCC reports and describes the methods used across this WGI report, presenting the rationale and discussing its \r\nrelative merits and limitations."


id: ea5be543b2f31917b1d704c9f3c120e
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"The AR5 WGII (Hewitson et al., 2014) used hatching to represent non-significant trends in observations. For future projections, an \r\nelaborated approach with four mutually exclusive and exhaustive categories was proposed (to avoid some of the limitations of the \r\nAR5 WGI approach): very strong agreement (same as in WGI); strong agreement; divergent change; and little or no change. These \r\ndepended on the percentage of models showing change greater than the baseline variability and/or agreeing on sign of change (using \r\na 66% agreement threshold). Leaving the robust regions uncovered minimized any interference with the perception of underlying \r\ncolours that encoded the primary information of the figure."


id: 93d65c3ddac8443ced2cc8928074beb1
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"The two special reports IPCC SR1.5 (Hoegh-Guldberg et al., 2018) and SROCC (IPCC, 2019a, c) adopted a simplified approach, using \r\nonly model agreement (>=66% of models agree on sign of change) to characterize robustness. However, cross-hatching was used in \r\nSR1.5 to highlight robust areas where models agree, whereas the SROCC used hatching/shading to represent regions where models \r\ndisagree. Similarly, stippling was used in SR1.5 to indicate regions with significant trends, whereas it was used in SROCC to represent \r\nregions where the trends were not significant."


id: eae21e47ee93746cd711705081ecbf39
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"present when aggregated at larger scale (Sections 11.2.4 and 11.2.5). Consequently, changes averaged over regions or a number of \r\ngrid boxes emerge from internal variability at a lower level of warming than at the grid-box level (e.g., Cross-Chapter Box Atlas.1, \r\nFigure 2). Hence, focus on grid-box significance enhances the risk of Type II errors for overlooking signals significant at the level of \r\nAR6 regions. The significance of signals is also affected by the interdependence of single simulations considered in a given ensemble, \r\nfor example when several come from the same modelling group and share parametrizations or model components (Knutti et al., \r\n2013; Maher et al., 2021). The risk of Type II errors increases when a model ensemble includes several related simulations showing \r\nno signal."


id: 1d8a6c333d73630053c675ff013c2882
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"The AR6 WGI adapts the approaches applied in previous IPCC reports into a comprehensive framework based on the two general \r\nprinciples followed by AR5 WGII: 1) not obscuring (with stippling or hatching) the areas where relevant/robust information needs to \r\nbe highlighted (since stippling and hatching obstruct the visualization of the colours, which can affect the perception/interpretation \r\nof the underlying data); 2) using mutually exclusive and exhaustive categories to avoid leaving areas undefined. The three adopted \r\napproaches (one for observations and two for model projections) are described in Cross-Chapter Box Atlas.1, Table 1. This framework \r\nintegrates as much as possible the specificities of each WGI Chapter, proposing in some cases alternative thresholds."


id: 7acb02dd182f94da2816ea6e15c6098c
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"Approach A is intended for observations and consists of two categories, one for areas with significant trends (colour, no overlay) and \r\none for non-significant ones (coloured areas overlaid with 'x'), typically using a two-sided test for a significance level of 0.1; Chapter 2 \r\nand Atlas trends have been calculated using ordinary least squares regression accounting for serial correlation (Santer et al., 2008)."


id: 8eaeca2b0ef28348161feaf27441e828
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updated_at: 2023-04-19 13:21:42
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content:
"Cross-Chapter Box Atlas.1, Figure 1 illustrates the application of this method considering the effect of the baseline period (1850-1900 \r\nversus 1995-2014) and shows that it provides similar results to related approaches proposed in the literature (Zappa et al., 2021)."


id: d55e6422c4a720972444ad8a6ed32c03
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"The two alternative approaches discussed above allow visualization of different levels of detail of information on the projected change \r\nand are intended for different communication purposes. Approach B just informs on the consistency of the sign of change independent"


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"of its significance relative to internal variability, whereas approach C puts the projected changes into the context of internal variability \r\nand allows the highlighting of areas of conflicting signals. It is important to note that different approaches can be applied to the same \r\nvariable between different chapters for different communication purposes. For example, in maps showing multi-model mean changes \r\nof precipitation, Chapter 4 adopts approach C but Chapter 8 applies approach B."


id: aa4298593f3e64853da764831ecb989d
created_at: 2023-04-19 13:21:42
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"In terms of visual implementation, the approach follows recommendations resulting from conversations with IPCC national delegations: \r\n1) having a consistent approach across WGs would aid consistency and reduce the risk of confusion; 2) defining 'hatching' as 'diagonal \r\nlines' in the caption would aid accessibility for non-expert audiences; 3) a clear and concise legend that explains what these patterns \r\nrepresent should be included directly in the figure; 4) information about model uncertainty should be overlaid such that it does not \r\ndetract from the data underneath."


id: 15a2404e0913c598367def68864dad78
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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"Since stippling is commonly used to represent statistical significance, diagonal lines were chosen to 'obscure' the problematic \r\ncategories in the above approaches; it also facilitates the visualization of uncertainty in the Interactive Atlas when zooming in. To \r\navoid confusion, methods or thresholds that were unrelated to the three approaches hereby presented were visualized with a different \r\npattern (i.e., model improvement between low- and high-resolution simulations in Chapter 3; agreement between observation-based \r\nproducts in Chapter 5; correlation between two variables in Chapter 6)."


id: 8c8db85cf1cb8584f5aea2cce53798af
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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content:
"Cross-Chapter Box Atlas.1, Table 1 | Approaches for representing robustness (uncertainty) in maps of observed (approach A) and projected \r\n(approaches B and C) climate changes."


id: 5a833bea8952e37d6b5a681547aad4a3
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"Cross-Chapter Box Atlas.1, Figure 1 | Illustration of the simple, (a) and (b), and advanced, (c-f), approaches (B and C in Cross-Chapter Box Atlas.1, \r\nTable 1) for uncertainty representation in maps of future projections. Annual multi-model mean projected precipitation change (%) from CMIP6 for the \r\nperiod 2040-2060 (left) and 2080-2100 (right) relative to the baseline periods 1995-2014 (a-d) and 1850-1900 (e and f) under a high-emissions (SSP3-7.0) future. \r\nDiagonal and crossed lines follow the indications in Cross-Chapter Box Atlas.1, Table 1. Further details on data sources and processing are available in the chapter \r\ndata table (Table Atlas.SM.15)."


id: dc6dae5fe752a71110c03da62b322dc8
created_at: 2023-04-19 13:21:42
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"The Interactive Atlas includes two components. The first component \r\n(Regional Information) allows for flexible spatial and temporal analysis \r\n(Atlas.1.3) with a  predefined granularity (predefined climatological \r\nand typological regions, and user-defined seasons) through a  wide \r\nrange of maps, graphs and tables generated in an interactive manner \r\nbuilding on a collection of global and regional observational datasets \r\nand climate projections (including CMIP5, CMIP6 and CORDEX; \r\nAtlas.1.4). In particular, the Interactive Atlas provides trends and \r\nchanges for observations and projections in the form of interactive \r\nmaps for predefined historical and future periods of analysis, the former \r\nincluding the recent past and paleoclimate (Cross-Chapter Box 2.1) \r\nand the latter including future time slices (near, medium and long \r\nterm) across scenarios (RCPs and SSPs; see Cross-Chapter Box 1.4) \r\nand GWLs (1.5degC, 2degC, 3degC and 4degC; see Cross-Chapter  Box  11.1). "


id: 404d610404718b61fe404a57a51e63be
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"The WGI Interactive Atlas is a new AR6 product developed as part \r\nof the Atlas in consultation with other chapters to facilitate flexible \r\nsynthesis information for regions, and to support the Technical \r\nSummary (TS) and the Summary for Policymakers (SPM), as well as \r\nthe handshake with WGII. It includes multiple lines of evidence to \r\nsupport the assessment of observed and projected climate change \r\nby offering information for regions using both time slices across \r\nscenarios and GWLs. Coordination has been established with \r\nother chapters (particularly the regional chapters), adopting their \r\nmethodological recommendations (Chapter 10) and using common \r\ndatasets and agreed extreme indices and climatic impact-drivers \r\n(CIDs) to support and expand their assessment (Chapters 11 and 12)."


id: e430b6cf4217e3bf181d95d66f0a806e
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"Dimensions of analysis include time periods for \r\nscenarios and global warming levels (1.5oC, 2oC, 3oC \r\nand 4oC)."


id: 9acea70159072131ebea05b97243b04e
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"8 | Screenshots from the Interactive Atlas (regional information). (a) The main interface includes a global map and controls to define a particular choice \r\niable, period (reference and baseline), and season (in this example, annual temperature change from CMIP6 for a global warming level of 2degC under SSP3-7.0 \r\n0-1900). (b-e) Various visuals for the regionally averaged information for the selected reference regions."


id: 3e830f44c6897faf113badd83d685d02
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"It also provides regional information (aggregated spatial values) for \r\na number of predefined (reference and typological) regions in the form \r\nof time series, annual cycle plots, scatter plots (e.g., temperature versus \r\nprecipitation), table summaries, and ensemble and seasonal stripe \r\nplots. This allows for a comprehensive analysis (and intercomparison, \r\nparticularly using GWLs as a dimension of integration) of the different \r\ndatasets at a global and regional scale."


id: ab29583a9b47345f3f9e862e6ffe5c1d
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"The limited number of variables which can be treated on a printed \r\nAtlas also prevents the inclusion of relevant extreme indices and CIDs. \r\nThe development of an online Interactive Atlas for AR6 was proposed \r\nas a  solution to overcome these obstacles, facilitating the flexible \r\nexploration of key variables/indices and datasets assessed in all \r\nchapters through a wide range of maps, graphs and tables generated \r\nin an interactive manner, and thus also providing support to the TS and \r\nSPM. One of the main concerns raised by this new online interactive \r\nproduct was the potential danger of having an unmanageable number \r\nof final products impossible to assess following the IPCC review \r\nprocess. This was addressed by designing the Interactive Atlas with \r\nlimited and predefined functionality and granularity, thus facilitating \r\nthe review process and including use of open-source tools and code \r\nfor traceability and reproducibility of results."


id: 88f79f3b0e0ad60c1969b80521aa9710
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"The second component of the Interactive Atlas (Regional Synthesis) \r\nprovides synthesis information about changes in CIDs in several \r\ncategories such as heat and cold, wet and dry, or coastal and \r\noceanic, supporting exploration of the regional assessment findings \r\nsummarised in the TS and the SPM."


id: ad024e02c8f1e02f6263b957ed58136d
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"The Interactive Atlas can be consulted online at http://interactive\u0002atlas.ipcc.ch. Figure Atlas.8 illustrates the main functionalities \r\navailable: the controls at the top of the window allow the interactive \r\nselection of the dataset, variable, period (reference and baseline) and \r\nseason which define a  particular product of interest (e.g.,  annual \r\ntemperature change from CMIP6 for a  global warming level of \r\n2degC under SSP3-7.0 relative to 1850-1900 in this illustrative case). \r\nRegionally aggregated information can be obtained interactively by \r\nclicking on one or several sub-regions on the map and by selecting \r\none of the several options available for visuals (time series, annual \r\ncycle plots, scatter and stripe plots) and tables."


id: 45d0c8260a1d36e8f4ad9b2d905cbcee
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"The Interactive Atlas builds on the work done in the context of \r\nthe Spanish National Adaptation Plan (PNACC  - AdapteCCa; \r\nhttp://escenarios.adaptecca.es) to develop an interactive online \r\napplication centralizing and providing key regional climate change \r\ninformation to assist the Spanish climate change impact and \r\nadaptation community. The functionalities included in the AR6 WGI \r\nInteractive Atlas are an evolution of those implemented in AdapteCCa \r\nand have been adapted and extended to cope with the particular \r\nrequirements of the datasets and functionalities it includes. In \r\nparticular, the Interactive Atlas allows analysis of global and regional \r\ninformation on past trends and future climate changes through \r\na wide range of maps, graphs and tables generated in an interactive \r\nmanner, and building on six basic products (Figure Atlas.8): "


id: d18e597398eef5bab11e78a95687b75a
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"A major goal during the development of the Interactive Atlas has \r\nbeen ensuring transparency and reproducibility of results, and \r\npromoting open science and Findability, Accessibility, Interoperability, \r\nand Reuse (FAIR) principles (Wilkinson et  al., 2016) described in \r\nAtlas.2.3. As a result, full metadata are provided in the Interactive \r\nAtlas for each of the products, and the scripts used to generate the \r\nintermediated products (e.g., extreme indices and CIDs) and figures \r\nare available online in a  public repository (Iturbide et  al., 2021), \r\nwhich also includes simple notebooks illustrating key parts of the \r\ncode suitable for reusability. These scripts are based on the climate4R \r\nopen-source framework (Iturbide et al., 2019) and full metadata have \r\nbeen generated for all final products using the METACLIP framework \r\n(Bedia et  al., 2019), which builds on standards and describes \r\nprovenance of the datasets as well as the post-processing workflow."


id: 54d22295c41bfca307a376d48d2a7674
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"The idea of an online interactive Atlas was first discussed in the IPCC \r\nExpert Meeting on Assessing Climate Information for the  Regions \r\n(IPCC, 2018a). The meeting stressed the need for the AR6 regional \r\nAtlas to go beyond the AR5 experience in supporting and expanding \r\nthe assessment of key variables/indices and datasets conducted in \r\nall chapters, ensuring traceability, and facilitating the 'handshake' \r\nbetween WGI and WGII. One of the main limitations of previous \r\nproducts, including the AR5 WGI Atlas (IPCC, 2013a), is their static \r\nnature with inherent limited options and flexibility to provide \r\ncomprehensive regional climate information for different regions \r\nand applications. For instance, the use of standard seasons limits the \r\nassessment in many cases, such as regions affected by monsoons or \r\nseasonal rainband migrations or other phenomena-driven seasons. "


id: 2cc79d2ca35995da2cd39cd2c4d81198
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"The essential variables are computed for observations and reanalysis \r\ndatasets as described in Atlas.1.4.1 and Atlas.1.4.2 (note that the Atlas \r\ndoes not include observational datasets for extremes). Trend analyses \r\nare available for two alternative baseline periods (1961-2015 and \r\n1980-2015, selected according to data availability). This expands the \r\ninformation available in Chapter 2 for global observational datasets, \r\nincluding new periods of analysis and new regional observational \r\ndatasets which provide further insight into observational uncertainty. \r\nThe Interactive Atlas also includes paleoclimate information from the \r\nPaleoclimate Model Intercomparison Projects PMIP3/4 for temperature \r\nand precipitation for the Last Glacial Maximum, Last Interglacial, \r\nmid-Holocene and mid-Pliocene periods (see Cross-Chapter Box 2.1)."


id: bbb8eacc6c8f6cb555a9e26dcda25248
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"information of the multi-model ensemble for particular region(s) selected \r\nby the user from a number of predefined alternatives (see Atlas.1.3.3 \r\nand Atlas.1.3.4 for reference and typological regions, respectively)."


id: 792f01ad38fe5a7cc3bcba15ac5aa2dd
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content:
"Both essential variables and indices/CIDs are computed for CMIP5, \r\nCMIP6 and CORDEX model projections (Atlas.1.4.3 and Atlas.1.4.4). \r\nThe calculations are performed on the original model grids and results \r\nare interpolated to the reference regular grids at horizontal resolutions \r\nof 2deg (CMIP5), 1deg (CMIP6) and 0.5deg (CORDEX) (Iturbide et al., 2021). \r\nInformation is available for the historical, SSP1-2.6, SSP2-4.5, SSP3-7.0 \r\nand SSP5-8.5 scenarios for CMIP6, and historical, RCP2.6, RCP4.5 and \r\nRCP8.5 for CMIP5 and CORDEX, as documented in the supplementary \r\nmaterial Tables Atlas.SM.1-2 (for CMIP5/CMIP6) and Tables Atlas.\r\nSM.3-14 (for the different CORDEX domains). All products (maps, \r\ngraphs and tables) are available for different reference periods of \r\nanalysis, either time slices (2021-2040, 2041-2060 and 2081-"


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"2100 for near-, mid- and long-term future periods, respectively; see \r\nAtlas.1.3.1), or GWLs (1.5degC, 2degC, 3degC or 4degC; see Atlas.1.3.2), with \r\nchanges relative to a number of alternative baselines (including 1850-\r\n1900 pre-industrial, and 1995-2014 recent past; see Atlas.1.3.1). Note \r\nthat instead of blending the information from the different scenarios, \r\nthe Interactive Atlas allows comparison of the GWL spatial patterns \r\nand timings across the different scenarios (Cross-Chapter Box 11.1)."


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"(e.g.,  regridding, aggregation, index calculation, bias adjustment), \r\nevaluation and quality control (when applicable). Full metadata are \r\ngenerated for all final products using the METACLIP framework (Bedia \r\net  al., 2019), based on the Resource Description Framework (RDF) \r\nstandard to describe the datasets and data-processing workflow."


id: 534ff27f43881d038fa51176fcbb5b7e
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updated_at: 2023-04-19 13:21:42
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"Some of the above indices (in particular, TX35 and TX40) are highly \r\nsensitive to model biases and the application of bias-adjustment \r\ntechniques is recommended to alleviate this problem (see Cross-Chapter \r\nBox 10.2). Bias adjustment is performed as explained in Atlas.1.4.5."


id: 74f0ab870031dda1a6b009e2c7a3a81a
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"The advanced approach for representing robustness includes \r\na  new category for identifying conflicting signals, where models \r\nare projecting significant changes but of opposite signs. This is \r\ndemonstrated in Figure Atlas.9 which shows a  region of central \r\nAfrica where models have significant changes in surface winds \r\nwith some projecting increases and others decreases. This is clearly \r\ndemonstrated in the time series below the map which shows these \r\nwind-speed changes aggregated over the CAF reference region for \r\neach of the CMIP6 models and the opposing signals in many of these."


id: 871e560c9f03a74d86ad1eb198a7dd5d
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index: climate_gpt_v2
vector_id: 20937
content:
"Provenance is defined as a  'record that describes the people, \r\ninstitutions, entities, and activities involved in producing, influencing, \r\nor delivering a piece of data or a thing'. This information can be used to \r\nform assessments about their quality, reliability or trustworthiness. In \r\nthe context of the outcomes of the Interactive Atlas, having an effective \r\nway of dealing with data provenance is a necessary condition to ensure \r\nnot only the reproducibility of results, but also to build trust on the \r\ninformation provided. However, the relative complexity of the data \r\nand the post-processing workflows involved may prevent a  proper \r\ncommunication of data provenance with full details for reproducibility. \r\nTherefore, a special effort was made in order to build a comprehensive \r\nprovenance metadata model for the Interactive Atlas products."


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"The accessibility and reproducibility of scientific results have become \r\na major concern in all scientific disciplines (Baker, 2016). During the \r\ndesign and development of the Interactive Atlas, special attention \r\nwas paid to these issues in order to ensure the transparency of the \r\nproducts feeding into the Interactive Atlas (which are all publicly \r\navailable). Accessibility is implemented in collaboration with the IPCC \r\nData Distribution Centre (DDC), since all products underpinning the \r\nInteractive Atlas, including the intermediate products required for \r\nthe indices and CIDs (monthly aggregated data), are curated and \r\ndistributed by the IPCC-DDC and include full provenance information \r\nas part of their metadata. Atlas products are generated using the open\u0002source climate4R framework (Iturbide et al., 2019) for data processing "


id: f4d0f4fe7860e5c7f3c3f3df20665745
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"(Candan et al., 2001). It is an abstract model that has become a general \r\nmethod for conceptual description of information for the Web, using \r\na  variety of syntax notations and serialization formats. METACLIP \r\n(Bedia et  al., 2019) exploits RDF through specific vocabularies, \r\nwritten in the Web Ontology Language (OWL), describing different \r\naspects involved in climate product generation, from the data source \r\nto the post-processing workflow, extending international standard \r\nvocabularies such as PROV-O (Moreau et al., 2015). The METACLIP \r\nvocabularies are publicly available in the METACLIP repository (Bedia \r\nand Martin, 2021)."


id: d5ff97e15995e5c20daeb4aaef451020
created_at: 2023-04-19 13:21:42
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"The Interactive Atlas gives access to a  specific set of climate \r\nvariables from a  large number of climate model simulations, \r\nparticularly the (global) CMIP5, CMIP6 and (regional) CORDEX \r\narchives. The global model outputs generally give a relatively coarse \r\npicture of climate change, which is an important line of evidence \r\nfor the detection and attribution of climate change, but is rarely \r\ndirectly applicable for local climate change assessment or support \r\nof policy design (van den Hurk et al., 2018). To provide additional \r\ndetail, downscaling global projections with regional climate models \r\n(RCMs) or statistical downscaling can be undertaken but also \r\nadds a  source of uncertainty as it involves additional modelling \r\n(Section 10.3)."


id: f901cd2c6e94b104a34a841515b59f70
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"The primary purpose of the IPCC is to provide a  policy-relevant, \r\nnon-prescriptive assessment of the state of knowledge on climate \r\nchange and its impacts. This purpose is different from the provision \r\nof information targeted to implement climate policies, which is the \r\nfocus of climate services and national climate change assessment \r\ncommunities. IPCC assessments are based on quantitative \r\nobservational and model-generated data that are also used in many \r\nactivities supporting the development of climate policies. However, \r\nthe functionality of the Interactive Atlas is primarily aimed at \r\nsupporting the knowledge assessment."


id: bdd881956c8e488e53b51ecc2b47ba26
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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"The information displayed in the Interactive Atlas allows a number of \r\nsources of uncertainty to be quantified. 'Observational uncertainty' \r\nis represented by the use of multiple (albeit often not completely \r\nindependent) observational datasets. 'Uncertainty due to internal \r\nvariability' cannot be quantified directly since multiple realizations \r\nfrom historic and future projections are not accessible (the Interactive \r\nAtlas uses a  single realization of each model). The use of a  large \r\ncollection of model systems allows for an elaborate quantification of \r\n'model uncertainty'. In addition, a comparison of CMIP5 and CMIP6 \r\nsupports evidence of progress in model quality since AR5, while the \r\nevaluation of the added value of RCMs reveals model uncertainty \r\nrelated to spatial resolution (Section 10.3). Finally, the assessment \r\nof 'scenario uncertainty' is supported by the inclusion of multiple \r\nemissions scenarios for both CMIP5, CORDEX and CMIP6."


id: fc6eef0617a37108efd7cc6a80b6cd3
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"Much of the assessment in this report is based on multiple lines of \r\nevidence (Cross-Chapter Box 10.3). The Interactive Atlas facilitates \r\ncombining multiple observational and model-generated datasets and \r\nspatial and temporal analyses that combine to support statements \r\non the characteristics of the climate system. The use of predefined \r\nspatial and temporal aggregations imposes constraints on the ability \r\nto make specific or tailored assessments but does provide essential \r\nbackground and uncertainty information to generate broad findings \r\nand provide confidence statements on these. Also, the inclusion of \r\na selection of extremes and climatic impact-drivers (CIDs) is a new \r\nelement in the Interactive Atlas and facilitates broader application, \r\nincluding the handshake with WGII. Below, some guidelines on the \r\nuse, interpretation and limitations of the Interactive Atlas are given."


id: fb3bb6b6414b1aac961d9d47d0f17003
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content:
"The communication of uncertainty has a profound influence on the \r\nperception of information that is exchanged during the communication \r\nprocess. An assessment of uncertainty communication and the \r\nbarriers to climate information construction is given in Section 10.5.4."


id: 8dc003e41fdd57c323c7c01d411202a4
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content:
"tabular and graphical representation alternatives included as options \r\nin the Interactive Atlas (Figure Atlas.8) facilitate exploring the \r\ninformation interactively from different perspectives and in different \r\nlevels of detail, thus favouring communication with the large and \r\ndiverse audience of IPCC products."


id: aa0db409c8e5dc9ed15f90c0c0bc0fa6
created_at: 2023-04-19 13:21:42
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"To support the use of visuals provided in the Interactive Atlas for \r\napplication to different audiences, new insights since AR5 have \r\nemerged from a  range of scientific disciplines, including the \r\ncognitive and psychological sciences (Harold et  al., 2016). Studies \r\nhave used interviews and online surveys to assess interpretations of \r\nvisuals used to communicate climate information and uncertainties \r\n(Daron et  al., 2015; Lorenz et  al., 2015; McMahon et  al., 2015; \r\nRetchless and Brewer, 2016). They commonly find wide-ranging \r\ninterpretations and  varied understandings of climate information \r\namongst respondents due to the choice of visuals. In addition, Taylor \r\net  al. (2015) found that preferences for a  particular visualization \r\napproach do not always align with the approaches that achieve \r\ngreatest accuracy in interpretation. Choosing appropriate visuals for \r\na particular purpose and audience can be informed by testing and \r\nevaluation with target groups."


id: 523a5d137db123ecd7eb0e3c2eb3fa2
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content:
"Communication aimed at informing the general public about assessed \r\nscientific findings on climate change have a different purpose and \r\nformat than if intended to inform a specific target audience to support \r\nadaptation or mitigation policies (Whetton et al., 2016). The growing \r\nsocietal engagement with climate change means IPCC reports are \r\nincreasingly used directly by businesses, the financial sector, health \r\npractitioners, civil society, the media, and educators at all levels. The \r\nIPCC reports could effectively be considered a tiered set of products \r\nwith information relevant to a range of audiences."


id: afea7120c76af8e7ce1e36d73878b439
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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content:
"The Interactive Atlas does provide access to a  collection of \r\nobservational and modelling datasets, presented in a  form that \r\nsupports the distillation of information on observed and projected \r\nclimate trends at the regional scale. Access to the repository of \r\nunderlying datasets enables further processing for particular purposes. \r\nAs noted above, it is not the intention nor the ambition of this IPCC \r\nassessment and the Interactive Atlas component to provide a climate \r\nservice for supporting targeted policies. For this an increasing number \r\nof dedicated climate change assessment programmes have been \r\ncarried out, aiming at mapping climate change information relevant \r\nfor adaptation and mitigation decision support."


id: 669aeb4212f750f5dce709e4e66c2b6
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"For instance, EEA (2018) provides an overview of European national \r\nclimate change scenario programmes. Most of these use CMIP5 (or \r\nearlier) global climate change ensembles driven by an agreed set of \r\ngreenhouse gas (GHG) emissions scenarios, followed by downscaling \r\nusing RCMs and/or statistical methods, in order to generate regionally \r\nrepresentative hydro-meteorological indicators of climate change. \r\nIn some cases, output of selected downscaled global and regional \r\nmodels is provided to users (Whetton et al., 2012; Daron et al., 2018). \r\nUptake by users is strongly dependent on providing justification of \r\nthe selection or for the downscaling procedure and if further steps are \r\nneeded to tailor the information to local scales (Lemos et al., 2012). "


id: 12d341469a5bcbcff41504de012ef4c7
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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index: climate_gpt_v2
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content:
"The visual communication of climate information can take many \r\nforms. Besides the standard visual products typically used for \r\ncommunicating global and regional climate information to \r\npractitioners (e.g., maps, time series or scatter plots), the Interactive \r\nAtlas incorporates new visuals, for example, 'stripes' (RMetS, 2019), \r\nfacilitating the communication of key messages (e.g., warming and \r\nconsistency across models) to a less technical audience. The various "


id: 1504cb50c91af593ddc74a50cfe69481
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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content:
"More comprehensive programmes provide probabilistic climate \r\ninformation by careful analysis and interpretation of ensembles \r\nof model outputs (Lowe et  al., 2018). The information is generally \r\ntailored to professional practitioners with expertise to interpret \r\nand process this probabilistic data. This top-down probabilistic \r\ninformation chain is not always able to highlight the essential climate \r\nchange information for users, and alternative bottom-up approaches \r\nare encouraged (Frigg et al., 2013). Section 12.6.2 assesses climate \r\nservices including the national climate assessments and user uptake."


id: 6bd97693bc53d0ed40d69bdbee39c78d
created_at: 2023-04-19 13:21:42
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content:
"regions. In the latter, emergence times are only calculated for those \r\nregions which have data available in more than 50% of the land \r\narea (unlike Berkeley Earth, CRUTEM does not include spatial \r\ninterpolation, see Section 2.3.1.1.3) and these are similar for all but \r\none of the regions indicating that observational uncertainty does not \r\nchange the main conclusion of widespread emergence of surface \r\ntemperature signals over land regions."


id: 8cc9b8da9faa217034c1ac53ba9ab072
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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content:
"As described earlier, information on projected future changes is \r\nrequired both at different time periods in the future under a range \r\nof emissions scenarios but also for different global warming levels. \r\nFigure Atlas.12 shows the global surface air temperature (GSAT) \r\nchange projection calculated from the CMIP6 ensemble mean for the \r\nmiddle of the century under the SSP1-2.6 and SSP3-7.0 emissions \r\nscenarios compared to the end-of-century warming under SSP3-7.0 \r\nand for a  global warming level of 2degC. The patterns of changes \r\nare similar to the observed warming and there is a  high level of \r\nconsistency with CMIP5 in terms of both patterns and magnitude \r\nof change (Interactive Atlas). However, for the long-term future, \r\nwarming in the CMIP6 ensemble is generally higher, reflecting the \r\nincrease in the top end of the range of climate sensitivities amongst \r\nthe CMIP6 GCMs (Figure Atlas.13)."


id: 75f6bae6e8848c3b82fc9252e8d97963
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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content:
"Most other chapters in WGI assess past or future behaviour of specific \r\naspects of the global climate system and this section introduces \r\nsome of the key results, specifically from Chapters 2, 4 and 9. This \r\nprovides a global overview on observations and information from the \r\nCMIP5 and CMIP6 ensembles to underpin the regional assessments \r\nin the rest of the Atlas Chapter and the results displayed in the \r\nInteractive Atlas. Thus, its aim is not to generate an assessment of \r\nregional climate change directly but to provide the global context \r\nfor this information derived later in the Atlas. Atlas.3.1 considers \r\nglobal atmospheric and land surface information with global ocean \r\ninformation in Atlas.3.2."


id: 74a9494c49fee46c5e10644969bd701e
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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content:
"The principal atmospheric quantities of interest for understanding how \r\nclimate change may impact human and ecological systems, as well as \r\nbeing key global indicators of change, are surface air temperature and \r\nprecipitation. They are therefore a significant focus of the regional \r\nclimate assessments in the following regional sections of the chapter \r\n(Atlas.4 to Atlas.11) and of the Interactive Atlas. Changes in these \r\nvariables over land during the recent past (1961-2015) are shown \r\nin Figure Atlas.11 using results from two global datasets (assessed \r\nin Chapter 2) to illustrate both where there is robust information on \r\nobserved trends and observational uncertainty."


id: 9e3a9af3c9c44bac5e93dce2dbb40fb4
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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index: climate_gpt_v2
vector_id: 25065
content:
"For temperature, a clear signal of warming is seen over most land \r\nareas with an amplification at high latitudes, though all continents \r\napart from Africa also have regions where trends are not significant. \r\nSignificant changes in annual mean precipitation are seen over \r\nmuch more limited areas though with consistent increasing trends \r\nover some northern high-latitude regions and decreasing trends over \r\nsmaller regions in tropical Africa, the Americas and South West Asia. \r\nThe information conveyed in Figure Atlas.11 on both consensus in \r\nthe signal of change and on observational uncertainty is used in this \r\nchapter as a line of evidence to assess historical observed trends."


id: e906d135d8c6f3334899fd5767528afb
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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index: climate_gpt_v2
vector_id: 38352
content:
"Changes in annual mean precipitation present a  more complex \r\npicture with regions of decrease as well as increase, and areas where \r\nthere is model disagreement on the sign of the change, even when \r\nthe signal is strong in the long-term future period as shown in Cross\u0002Chapter Box Atlas.1, Figure  1. However, as with the temperature \r\nchanges, there is a  high level of consistency in the patterns and \r\nmagnitude of the precipitation changes, with changes in some areas \r\nbeing larger in the long-term future period. Considering changes \r\nover land, Cross-Chapter Box Atlas.1, Figure 1 also shows that at \r\nlower warming levels there are many regions, especially in the \r\nSouthern Hemisphere, where there is no robust signal of change \r\nfrom the models."


id: 9db61ab0fcbd527f0a28f0dad57de51e
created_at: 2023-04-19 13:21:42
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content:
"As an alternative way of viewing and summarizing information in \r\nthe observational data, the panels (c) and (d) in Figure Atlas.11 show \r\nthe time at which any significant temperature trends from the Berkeley \r\nEarth and CRUTEM5 datasets, averaged over the reference regions, \r\nemerged from interannual variability - with a signal-to-noise ratio \r\ngreater than two (Hawkins et al., 2020). In the former, a regionally \r\naveraged warming signal has emerged over all of the land reference "


id: 62d67e708e9924dea9b0c8b3881e5a31
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updated_at: 2023-04-19 13:21:42
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content:
"Hemisphere higher latitudes in winter. Finally, they demonstrate the \r\ngreater warming projected by the CMIP6 ensemble, as an average \r\nover the ensemble and the upper end of the range. See Chapter 4 for \r\nan in-depth assessment of these results."


id: be1a8531080b05ac7d48c330d46877a2
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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content:
"Global warming leads to systematic changes in regional climate \r\nvariability via various mechanisms such as thermodynamic responses \r\nvia altered lapse rates (Kroner et al., 2017; Brogli et al., 2019) and \r\nland-atmosphere feedbacks (Boe and Terray, 2014). These can modify \r\ntemporal and spatial variability of temperature and precipitation, \r\nincluding an altered seasonal and diurnal cycle and return frequency \r\nof extremes. Regional influences from and feedbacks with sea \r\nsurface, clouds, radiation and other processes also modulate the \r\nregional response to enhanced warming, both locally and, via \r\nteleconnections, remotely."


id: b92678d59e7bfae1b19f26705b8f6e5
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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content:
"Given their potential to influence extremes in temperature, \r\nprecipitation and other climatic impact-drivers and hazards, and \r\nthus risks to human and ecological systems, it is important to \r\nunderstand these links for developing adaptations in response \r\nto clear anthropogenic influences on individual hazards. This "


id: e1dae3c248f6c4a61f62eb49352f47a
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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content:
"will also support the related fields of disaster risk reduction and \r\nglobal sustainable development efforts (Steptoe et al., 2018). They \r\ndemonstrated that 15 regional hazards shared connections via \r\nthe El Nino-Southern Oscillation (ENSO), with the Indian Ocean \r\nDipole, North Atlantic Oscillation and the Southern Annular Mode \r\n(see Annex  IV) being secondary sources of significant regional \r\ninterconnectivity (Figure Atlas.14). Understanding these connections \r\nand quantifying the concurrence of resulting hazards can support \r\nadaptation planning as well as multi-hazard resilience and disaster \r\nrisk reduction goals."


id: 18d8ad0097773e1c59b842be1d093aa6
created_at: 2023-04-19 13:21:42
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"of the teleconnections for selected seasons for the interannual to \r\ndecadal modes and at an annual scale for the multi-decadal modes. \r\nThe columns related to the interannual to decadal modes focus on \r\nthe seasons where these connections are strongest but each mode \r\nof variability will often have influences in other seasons (for more \r\ndetails see Annex IV). The table shows that for many regions, seasonal \r\ntemperature and precipitation is substantially modulated by these \r\nmodes of variability - all regions feel some influence, and variability \r\nin ocean basins often has influence in multiple remote regions."


id: 90a43cbd766b3797cc57c4cbb92ac619
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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index: climate_gpt_v2
vector_id: 22653
content:
"The main modes of variability influencing global and regional \r\nclimate are comprehensively described in Annex IV. In the context \r\nof the assessment in the Atlas chapter, they are important because \r\nof their influence on the variability of temperature (Part A) and \r\nprecipitation (Part B) in regions around the world. This is quantified \r\nin Table Atlas.1, which lists the fraction of interannual variance in \r\nseasonal mean temperature and precipitation explained by variability \r\nin these modes. The table provides information on the influence "


id: c324573e79ef5b41439aa8c54843f078
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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content:
"Figure Atlas.14 | Influence of major modes of variability (see Annex IV) on regional extreme events relevant to assessing multi-hazard resilience. Ribbon \r\ncolours define the driver from which they originate and their width is proportional to the correlation. Crossed lines represent where there is conflicting evidence for a correlation \r\nor where the driver is not directly related to the hazard; dots represent drivers that have both a positive and negative correlation with the hazard. Figure is copied from Steptoe \r\net al. (2018)/CCBY4.0."


id: 42bec25a7ce22f5fc49ced7c63d543de
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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vector_id: 8937
content:
"emissions, demonstrating the much larger changes seen with the \r\nhigher-emissions scenario. The projected changes in sea level show \r\nthe significantly greater increases, of up to 1 m locally, under a high\u0002emissions future. Regional details of these projected changes under \r\na range of emissions scenarios and time periods can be explored in \r\nthe Interactive Atlas. An in-depth assessment of these changes is \r\npresented in Section 5.3 and Chapter 9."


id: 39c0bb34db9bd868e967999165ec799f
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 7374
content:
"As with the atmosphere, there are several key ocean-related \r\nquantities which are relevant for understanding how climate change \r\nmay impact human and ecological systems and/or key global \r\nindicators of change. These include ocean surface temperature \r\nand heat content, sea surface height, sea ice cover and thickness, \r\nand certain chemical properties such as ocean acidity and oxygen \r\nconcentration. For example, there is growing awareness of the \r\nthreat presented by ocean acidification to ecosystem services and its \r\nsocio-economic consequences are becoming increasingly apparent \r\nand quantifiable (Hurd et al., 2018), and SR1.5 (IPCC, 2018c) noted \r\na  significant impact of low levels of global warming on the state \r\nof the global oceanic ecosystems and food security. For instance, \r\n70-90% of coral reefs are projected to decline at a warming level of \r\n1.5degC, with larger losses at 2degC. "


id: 27afe21c4256b0ff10a41c8b421484f5
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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content:
"Thus, because of their importance to coastal populations and \r\ninfrastructure and ocean ecosystems, the Interactive Atlas focuses on \r\nchange in sea-surface temperature, sea level and pH. Figure Atlas.15 \r\nshows projected changes to sea surface temperature (SST) and \r\nsea level at the end of the century under SSP1-2.6 and SSP5-8.5 "


id: aedee1d829b9926cd681f521498d7840
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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index: climate_gpt_v2
vector_id: 28010
content:
"southern African region including the Namib and Kalahari deserts; \r\neach climate region has its local variations resulting in very high \r\nspatial and temporal variations (Peel et al., 2007). Based on the varied \r\nclimates, nine sub-regions are defined for Africa (Figure Atlas.16): \r\nthe Mediterranean region (MED) including North Africa, Sahara \r\nincluding parts of the Sahel (SAH), West Africa (WAF), Central Africa \r\n(CAF), North Eastern Africa (NEAF), South Eastern Africa (SEAF), \r\nWest Southern Africa (WSAF), East Southern Africa (ESAF) and \r\nMadagascar (MDG)."


id: 460a3e012ef9fd4f6b5b9e4978c84d38
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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index: climate_gpt_v2
vector_id: 9540
content:
"The assessment in this section focuses on changes in average \r\ntemperature and precipitation (rainfall and snow), including the \r\nmost recent years of observations, updates to observed datasets, \r\nthe consideration of recent studies using CMIP5 and those using \r\nCMIP6 and CORDEX simulations. Assessment of changes in extremes \r\nis in Chapter  11 (Tables 11.4-11.6) and climatic impact-drivers in \r\nChapter 12 (Tables 12.1-12.12)."


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vector_id: 1225
content:
"The climatic features that characterize the intra-seasonal and \r\ninterannual variability of Africa are mainly the Madden-Julian \r\nOscillation (MJO), which is confined to the deep tropics during \r\nboreal winter, Pacific Decadal Variability (PDV), and the shift of the \r\nAtlantic Inter-tropical Convergence Zone in response to changes \r\nin the meridional SST gradient. A  positive phase of PDV weakens \r\nAfrican monsoons (Figure AIV.8d; Meehl and Hu, 2006), and MJO \r\nphase 4 suppresses convection over equatorial Africa (Figure AIV.10a; \r\nsee Annex IV). Other features influence specific sub-regions. For \r\ninstance, El Nino events increase precipitation in eastern Africa and "


id: 885bc0178f7f684153fee314b614c8ee
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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index: climate_gpt_v2
vector_id: 21154
content:
"decrease precipitation in southern Africa. Over southern Africa there \r\nis a strong link between ENSO and droughts (Meque and Abiodun, \r\n2015). The positive phase of the Indian Ocean Dipole (IOD) increases \r\nrainfall in eastern tropical Africa in boreal autumn to early winter \r\n(Figure AIV.5d), while the negative phase induces the reduction in \r\nrainfall. The West African Monsoon is influenced by Atlantic Zonal \r\nMode (AZM) with decreased rainfall over the Sahel and increased \r\nrainfall over Guinea (Losada et  al., 2010). Positive Atlantic Multi\u0002decadal Variability (AMV) influences positive anomalies all year \r\nround over a broad Mediterranean region, including North Africa."


id: b4784f03840b137c875a68ba0036cc4c
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 28996
content:
"West Africa has also experienced an overall reduction of rainfall \r\nover the 20th century, with a recovery towards the last 20 years \r\nof the century (Christensen et  al., 2013). Over the last three \r\ndecades rainfall has decreased over East Africa, especially between \r\nMarch and May/June. Projected rainfall changes over Africa in \r\nthe mid- and late 21st century is uncertain. In regions of high or \r\ncomplex topography such as the Ethiopian Highlands, downscaled \r\nprojections indicate likely increases in rainfall and extreme rainfall \r\nby the end of the 21st  century. However, North Africa and the \r\nsouth-western parts of South Africa are likely to have a reduction \r\nin precipitation."


id: 4772079f38649f3ce30b83ea671c67cf
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 9783
content:
"The consequence of increased temperature and evapotranspiration, \r\nand decreased precipitation amount, in interaction with climate \r\nvariability and human activities, have contributed to desertification in \r\ndryland areas in sub-Saharan Africa (medium confidence) as reported \r\nin SRCCL (Mirzabaev et al., 2019)."


id: f97c05426b8f7e131cf205df222f9cb6
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 41259
content:
"Gaetani et al., 2017; Taylor et al., 2017; Biasutti, 2019) during the \r\nperiod 1980-2010. Southern African rainfall shows a  significant \r\ndowntrend of -0.013  mm day-1 year-1 in recent decades and \r\n-0.003 mm day-1 year -1 for longer periods during 1900-2010 (low \r\nconfidence) (Jury, 2013)."


id: de3fc264f3966743eac9d93cda4be6ff
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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index: climate_gpt_v2
vector_id: 36361
content:
"there are growing efforts to boost African climate science by running \r\nand evaluating climate models over Africa (Endris et  al., 2013; \r\nKalognomou et al., 2013; Gbobaniyi et al., 2014; Engelbrecht et al., \r\n2015; Klutse et al., 2016; Gibba et al., 2019)."


id: 7d2998fe990cf9147137e6a7efd69c1f
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 19201
content:
"The CMIP project previously did not result in improved performance \r\nfor Africa (Flato et al., 2013; Rowell, 2013; Whittleston et al., 2017) \r\nand culling ensembles based on existing metrics for Africa fails to \r\nreduce the range of uncertainty in precipitation projections (Roehrig \r\net al., 2013; Yang et al., 2015; Rowell et al., 2016), but biases over \r\nAfrica are lower in CMIP6 compared to CMIP5 (Almazroui et  al., \r\n2020c). Nonetheless, the CMIP5 ensemble has been evaluated over \r\nAfrica to advance its application for climate research (Biasutti, 2013; \r\nRowell, 2013; Dike et al., 2015; McSweeney and Jones, 2016; Onyutha \r\net al., 2016; Wainwright et al., 2019) as has, more recently, the CMIP6 \r\nensemble (Almazroui et al., 2020c)."


id: 8217e423e9c09be155720732fed2a22f
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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index: climate_gpt_v2
vector_id: 20008
content:
"Temperature increases over Africa in the 20th  century can be \r\nattributed to the strong evidence of a continent-wide anthropogenic \r\nsignal in the warming (Figure  3.9; Hoerling et  al., 2006; Min and \r\nHense, 2007; Stott et  al., 2010, 2011; Niang et  al., 2014). More \r\nspecifically over West Africa, the clear emergence of temperature \r\nchange (Figure Atlas.11) is due to the relatively small natural climate \r\nvariability in the region which generates narrow climate bounds \r\nthat can be easily surpassed by relatively small climate changes \r\n(Niang et  al., 2014). Warming over North Africa is largely due to \r\nanthropogenic climate forcing (Knippertz et al., 2003; Barkhordarian \r\net al., 2012; Diffenbaugh et al., 2017)."


id: fd1d7f0383900de6561bef4020a9871b
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 41884
content:
"Model development has advanced in the world, but Africa still lags \r\nas a focus and in its contribution (James et al., 2018). None of the \r\ncurrent generation of global climate models (GCMs) was developed \r\nin Africa (Watterson et al., 2014), and the relevant processes in the \r\ncontinent have not been the priority for model development but \r\ntreated in a  one-size-fit-all approach (James et  al., 2018) except \r\nfor a  few studies that focused on convective-permitting climate \r\nprojections (Stratton et  al., 2018; Kendon et  al., 2019). However, "


id: 3172ffb527e188e22d9fe345c2774cd1
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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index: climate_gpt_v2
vector_id: 5941
content:
"and 2degC GWLs (Klutse et al., 2018; Lennard et al., 2018; Maure et al., \r\n2018; Mba et  al., 2018; Nikulin et  al., 2018; Osima et  al., 2018). \r\nWhile a few studies addressed the whole African continent (Lennard \r\net al., 2018; Nikulin et al., 2018), some focused on specific regions of \r\nAfrica (Diedhiou et al., 2018; Klutse et al., 2018; Kumi and Abiodun, \r\n2018; Maure et  al., 2018; Mba et  al., 2018). CORDEX simulations \r\nproject robust warming over Africa in excess of the global mean "


id: 94b4d220df9e9bb64c5108d28664b1c8
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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index: climate_gpt_v2
vector_id: 23367
content:
"(Lennard et  al., 2018; Nikulin et  al., 2018), and over West Africa \r\nthe magnitude of regional warming reaches the 2080-2100 global \r\nwarming level one to two decades earlier (Mora et al., 2013; Niang \r\net  al., 2014; Sylla et  al., 2016; Klutse et  al., 2018). Temperature \r\nincreases projected under RCP8.5 over Sudan and northern Ethiopia \r\nimply that the Greater Horn of Africa would warm faster than the \r\nglobal mean relative to 1971-2000 (Osima et al., 2018). Over North "


id: a91d97aef945870d4d54084d5e389121
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 26971
content:
"The Horn of Africa has experienced significantly decreased rainfall \r\nduring the long rains season from March to May (high confidence) \r\nand drying trends in this and other parts of Africa are attributable \r\nto oceanic influences (high confidence), resulting from both internal \r\nvariability and anthropogenic causes. Drying over the Sahel in the \r\nlast  century was attributed to an increase in the South Atlantic \r\nSST and more recently over southern African as a  response to \r\nanthropogenic-forced Indian Ocean warming. Drying over East Africa \r\nis associated with decadal natural variability in SSTs over the Pacific \r\nOcean. The enhanced rainfall intensity over the Sahel in the last two \r\ndecades is associated with increased greenhouse gases indicating an \r\nanthropogenic influence (medium confidence)."


id: 9beae803a095bc11914a34455c742be4
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 24681
content:
"Relative to the late 20th  century, annual mean temperature over \r\nAfrica is projected to rise faster than the global average (very high \r\nconfidence) with the increase likely to exceed 4degC by the end of the \r\ncentury under RCP8.5 emissions. The central interiors of southern and \r\nnorthern Africa are likely to warm faster than equatorial and tropical \r\nregions (high confidence)."


id: 75ff5581e3c8f430be448eb6debf38d
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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index: climate_gpt_v2
vector_id: 17988
content:
"The rate of surface temperature increase has generally been more \r\nrapid in Africa than the global average and by at least 0.1degC-0.2degC \r\nduring 1961-2015 (high confidence). Minimum temperatures have \r\nincreased more rapidly than maximum temperatures over inland \r\nsouthern Africa (medium confidence). Since 1970, mean temperature \r\nover East Africa has shown an increasing trend but showed \r\na decreasing trend in the previous 40 years (medium confidence)."


id: 2cd12d4ea587ba2084f28f088845d7b8
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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index: climate_gpt_v2
vector_id: 5151
content:
"South East Asia (SEA) and South West Asia (ARP and WCA) with the \r\nTibetan Plateau (TIB) being relevant and thus referred to in both \r\nthe East and South Asia assessments. Note also TIB forms a major \r\npart of the Hindu Kush Himalaya region, which is assessed in \r\nCross-Chapter Box 10.4, and relevant findings are summarized and \r\ncross-referenced in the East and South Asia sections below."


id: 217c2c4e15de7443c3bb145574e65118
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 3102
content:
"The CMIP5 model projections indicate an increase of temperature in \r\nboth boreal winter and summer over East Asia for RCP4.5. Based on \r\nCMIP5 model projections, there is medium confidence in an intensified \r\nEASM and increased summer precipitation over East Asia. More than \r\n85% of CMIP5 models show an increase in mean precipitation of the \r\nEASM, while more than 95% of models project an increase in heavy \r\nprecipitation events (Christensen et al., 2013).The SROCC states that \r\nfuture projections of annual precipitation indicate increases of the \r\norder of 5-20% over the 21st  century in many mountain regions, \r\nincluding the Himalaya and East Asia (Hock et al., 2019b). The SR1.5 \r\nreports that statistically significant changes in heavy precipitation \r\nbetween 1.5degC and 2degC of global warming are found in East Asia \r\n(Hoegh-Guldberg et al., 2018)."


id: 77fe37e797438e3de5cfd0ca49355e17
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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index: climate_gpt_v2
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content:
"Observational studies indicated significant decadal variations in the \r\nEAWM (Wang and Lu, 2016; He et al., 2017). It weakened significantly \r\naround the late 1980s, being relatively strong during 1976-1987 and \r\nweaker during 1988-2001. The EAWM has recovered in intensity \r\nafter 2004 and caused frequent and prevalent severe cold spells, as \r\nwell as a number of unusually harsh cold winters in many parts of \r\nEast Asia during the period 2004-2012 (Wang and Chen, 2014; Kug \r\net al., 2015; Ge et al., 2016; Gong et al., 2018). Negative zonal mean \r\nwinter SAT anomalies were observed over the whole of East Asia \r\nfrom 1980 to 1988, with positive anomalies observed over high and \r\nlow latitudes from 1988 to 2010 (Miao and Wang, 2020)."


id: bf7f22a87cb84ee55601283171c5dea
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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index: climate_gpt_v2
vector_id: 30954
content:
"Precipitation trends over East Asia show considerable regional \r\ndifferences (medium confidence). Mean precipitation has shown \r\nnegligible sensitivity to the warming trend with consequently limited \r\noverall trends in China though summer rainfall daily frequency and \r\nintensity show respectively decreasing and increasing trends from "


id: 1848935f9c8595f7f0d8f2278483dfd8
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 1475
content:
"The IPCC AR5 (Christensen et al., 2013) reports a significant increase in \r\nmean temperatures in south-eastern China, associated with a decrease \r\nin the number of frost days under the SRES A2 emissions scenario. "


id: ed56802ad3592a1fc3b48093d89648bb
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 39128
content:
"T. Wu et al., 2019). The performance of models is sensitive to cumulus \r\nconvection schemes and horizontal resolution (Haarsma et al., 2016; \r\nWu et  al., 2017; Kusunoki, 2018b). High-resolution atmospheric \r\nglobal climate models (AGCM) successfully reproduce the intensity \r\nand the spatial pattern of the EASM rainfall (Li et al., 2015; Yao et al., \r\n2017; Ito et al., 2020a) and improve the simulation of the diurnal \r\ncycle of precipitation rates and the probability density distributions \r\nof daily precipitation over Korea, Japan and northern China (Lin et al., \r\n2019), but increasing horizontal resolution (at the typical scales \r\nused in GCMs) is not always a  panacea for solving model biases \r\n(Roberts et al., 2018)."


id: 73977bc5b5dba9eff28f5306d747c8ec
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 17590
content:
"Agriculture intensification through oasis expansion in Xinjiang region \r\nhas increased summer precipitation in the Tian Shan mountains (high \r\nconfidence from medium evidence with high agreement) (Zhang \r\net al., 2009, 2019b; Deng et al., 2015; Guo and Li, 2015; Yao et al., \r\n2016; Xu et al., 2018; Cai et al., 2019). However, there is very low \r\nconfidence of the effect of oasis expansion on the temperature \r\nwarming trend (Han and Yang, 2013; Li et al., 2013; Yuan et al., 2017)."


id: a153623b4494eb34e744cc85c5baeb5d
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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index: climate_gpt_v2
vector_id: 25601
content:
"In the context of climate warming, intense snowfalls have hit China \r\nfrequently in recent winters and have caused severe damages to the \r\nsustainability of society (Sun et  al., 2019). Observations generally \r\nshow a  decrease in the frequency and an increase in the mean \r\nintensity of snowfalls in north-western, north-eastern and south\u0002eastern China and the eastern Tibetan Plateau since the 1960s (Zhou \r\net al., 2018), but the results may depend on the objective criteria for \r\nidentifying winter snowfall (J. Luo et al., 2020)."


id: d6af19039253526a6082c1ae39e9d749
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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index: climate_gpt_v2
vector_id: 35074
content:
"Projected annual precipitation changes in the CMIP5 and CMIP6 \r\nensembles are positive for all warming levels in ECA and TIB and \r\nfor the higher warming levels in EAS. Changes in precipitation per \r\ndegree Celsius global warming are larger in DJF than in JJA in ECA \r\nbut show smaller seasonal difference in EAS (Figure Atlas.17). The \r\nEASM precipitation is projected to increase but with a  complex \r\nspatial structure (Kitoh, 2017; Moon and Ha, 2017). Simulations "


id: eb3b61cf7f0da27581ea206e6205bbb1
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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index: climate_gpt_v2
vector_id: 38741
content:
"and varying seasonally and spatially (Luo et al., 2018). A decrease \r\nof precipitation was projected in the western region of Xinjiang \r\nduring summer. More extreme rainfall events were projected to occur \r\nduring summer and autumn."


id: 3e6f711c84ed6c9b6f52c5fdad395bdf
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 8166
content:
"In East Asia annual mean temperature has been increasing since the \r\n1950s (high confidence). The linear trend of annual mean surface \r\nair temperature likely exceeded 0.1degC per decade over most of \r\nEast Asia from 1961 to 2015. Trends of annual precipitation show \r\nconsiderable regional differences with areas of both increases and \r\ndecreases (medium confidence), and with increases over north-west \r\nChina and South Korea (high confidence). Agricultural intensification \r\nthrough oasis expansion in Xinjiang region has increased summer \r\nprecipitation in the Tian Shan mountains (high confidence)."


id: 3d139e7906af215a02073320191e40b1
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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index: climate_gpt_v2
vector_id: 7928
content:
"GCMs still show poor performance in simulating the mean rainfall and \r\nits variability over East Asia, especially over regions characterized by \r\ncomplex topography. The CMIP6 models have improved from CMIP5 \r\nfor climatological temperature and winter monsoon but show little \r\nimprovements for the summer monsoon. The RCMs produce relatively \r\nmore detailed regional features, but do not always produce superior \r\nsimulations compared with the driving GCMs."


id: c5607d2f40cb8d06010c93c7f7ef9e9e
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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index: climate_gpt_v2
vector_id: 32033
content:
"The annual mean surface temperature over East Asia and the Tibetan \r\nPlateau will very likely increase under all emissions scenarios and \r\nGWLs. Larger warming magnitudes will likely occur in the northern \r\npart of EAS and in ECA and TIB. Precipitation is likely to increase over \r\nland in most of EAS at the end of the 21st  century under higher\u0002emissions scenarios (SSP3-7.0, RCP8.5 and SSP5-8.5) and global \r\nwarming levels, and in ECA and TIB under all emissions scenarios \r\nand global warming levels. Summer precipitation increase is likely\r\nto occur in East Asia, corresponding to the strengthened summer \r\nmonsoon circulation."


id: c1d2c5acd826250a9ff77195e9eaeb6d
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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index: climate_gpt_v2
vector_id: 31401
content:
"The increasing temperature trends under RCP scenarios were \r\nconsistently reproduced in projections using CORDEX-EA models \r\n(Y. Kim et al., 2016) as reported in AR5 using GCMs. However, changes \r\nin annual and seasonal mean precipitation exhibit significant inter\u0002RCM differences with larger magnitudes and variability than in the \r\nGCMs (Ham et  al., 2016; Ozturk et  al., 2017; H. Sun et  al., 2018; \r\nD. Zhang et al., 2018). RCM simulations project that the Meiyu-Baiu\u0002Changma heavy rainfall will significantly increase in northern Japan \r\nat the end of the 21st century under the RCP8.5 scenario (Osakada \r\nand Nakakita, 2018), but projected precipitation amount and the \r\nnumber of precipitation days in summer around and over Japan differ \r\nas a result of RCM uncertainty (Suzuki-Parker et al., 2018). Annual \r\ntotal snowfall is projected to decrease in most parts of Japan except \r\nfor Japan's northern island under RCP2.6 (Kawase et al., 2021)."


id: 4e08fcee24be30ea18cbfcdbeff83235
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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index: climate_gpt_v2
vector_id: 10993
content:
"North Asia extends from the Ural Mountains in the west to the \r\nPacific Ocean in the east and from the Russian Arctic in the north \r\nto West and East Central Asia and East Asia in the south. Its most \r\nrecognizable features are boreal forests and permafrost. In AR6 \r\nNorth Asia is divided into three reference regions (Figure Atlas.17): \r\nWest Siberia (WSB) with a continental climate, warm summers and \r\ncold winters, many waterlogged areas and several natural zones due \r\nto a large extent from south to north and heterogeneity in regional \r\nclimates; East Siberia (ESB) which is mainly highland with extensive \r\npermafrost and a more severe continental climate characterized by \r\nharsh, long winters and short, hot summers, and by less precipitation \r\nand snow cover than in neighbouring regions; and the Russian Far \r\nEast (RFE) with a monsoon-influenced climate, cold winters and wet "


id: 68dfad7dc9f148492b4715309b440607
created_at: 2023-04-19 13:21:42
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index: climate_gpt_v2
vector_id: 15719
content:
"Projejctions based on statistical downscaling of 37 CMIP5 GCMs \r\nfor Xinjiang, China, show pronounced temperature increases of \r\n0.27degC to 0.51degC per decade from 2021 to 2060 while precipitation \r\nchanges were projected to be between -1.7% to 6.8% per decade "


id: 90055b0b8890bcbf7fc3092fca121f0
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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index: climate_gpt_v2
vector_id: 22543
content:
"summers in the south, and cold winters and cool summers almost \r\nwithout precipitation in the north. WSB and ESB are mainly influenced \r\nby NAO and NAM (Annex IV.2.1) and the Arctic Oscillation (AO) with \r\nassociated atmospheric blocking by the Siberian High (SH) that \r\nexhibits a pronounced decadal-to-multi-decadal variability (see also \r\nTable Atlas.1). RFE is under the influence of the ENSO (Annex IV.2.3) \r\nand the PDV (Annex IV.2.6) that mostly affect rainfall variability."


id: 2d4f763ef35a860f81da220f330f49a8
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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index: climate_gpt_v2
vector_id: 5256
content:
"In the previous IPCC assessment cycles, the three sub-regions \r\ncomprising North Asia in this section, along with Eastern Europe and \r\nthe Asian Arctic, were considered as either Northern Eurasia or Russia \r\nin AR4 and AR5. The AR5 WGI stated that for North and Central Asia \r\nCMIP5 models had difficulty in representing climatological means of \r\nboth temperature and precipitation, which is partly related to the \r\nscarceness of observational data in northern parts of the region and \r\nto issues related to the estimation of biases with coarse-resolution \r\nmodels (Christensen et  al., 2013). In CMIP5 projections under \r\ndifferent RCP scenarios, North Asian temperatures increase more \r\nin winter (DJF) than summer (JJA; Seneviratne et  al., 2012). With \r\nmost models projecting increased precipitation significantly above \r\nthe 20-year natural variability, it was concluded that precipitation in \r\nNorth Asia will very likely increase (Christensen et al., 2013)."


id: 724d6ef0453ee85b235242fe5187afee
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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index: climate_gpt_v2
vector_id: 17354
content:
"Annual precipitation totals very likely increased over North Asia in the \r\nlast half century along with more heavy and less light precipitation, \r\nmore freezing rain and less freezing drizzle (Figure Atlas.11 and the \r\nInteractive Atlas; Wen et al., 2014; Groisman et al., 2016; Ye et al., \r\n2017; Chernokulsky et al., 2019). The highest increase was observed \r\nover regions of Siberia and RFE with estimated trends of 10-25 mm \r\nper decade for the 1976-2014 period (Kokorev and Sherstiukov, 2015) \r\nor 5% per decade for the 1976-2018 period (Roshydromet, 2019). \r\nIncreases over southern RFE are the largest (over 50 mm per decade) "


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vector_id: 10293
content:
"Increases in surface air temperature (SAT) have been observed since \r\nthe mid-1970s over the whole of North Asia (Frolov et  al., 2014), \r\nand particularly over the north-eastern part (Figure Atlas.11; Gruza \r\net al., 2015). Trends of annual SAT in the northern part of the region \r\nduring the last decades were very likely twice as strong as the \r\nglobal average (Figure Atlas.11; Frolov et al., 2014; Mokhov, 2015; \r\nSherstyukov, 2016) with trends in RFE of 0.8degC-1.2degC per decade \r\nfor the 1976-2014 period and more intense warming strengthening \r\nfrom south to north observed in spring in ESB (Frolov et al., 2014; \r\nIppolitov et al., 2014; Kokorev and Sherstiukov, 2015)."


id: f9786501cde35dc369ed68f22f5f6df2
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 41255
content:
"Figure Atlas.18 | Linear trends for the 1980-2015 period based on \r\nstation data from the World Data Centre of the Russian Institute for \r\nHydrometeorological Information (RIHMI-WDC; Bulygina et  al., 2014). \r\n(a) Snow-season duration from 1 July to 31 December (days per decade); (b) snow\u0002season duration from 1 January to 30 June (days per decade); (c) maximum annual \r\nheight of snow cover (mm per decade). Trends have been calculated using ordinary \r\nleast squares regression and the crosses indicate non-significant trend values (at the \r\n0.1 level) following the method of Santer et al. (2008) to account for serial correlation. \r\nFurther details on data sources and processing are available in the chapter data table \r\n(Table Atlas.SM.15)."


id: 7adffbdef33abe9687bcdeae8f0a8b17
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 18818
content:
"Recent strong warming in polar regions (Section Atlas.11.2) was \r\naccompanied by cooling in winter in mid-latitude regions particularly "


id: 537c2375f81f0a69876fc5cf3c0b3d1e
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 11963
content:
"and are mostly due to positive changes in convective precipitation \r\nintensity in the region in the summer season (JJA) during 1966-2016 \r\n(medium confidence) (Chernokulsky et  al., 2019). A  decreasing \r\ntrend was observed in central WSB, northern ESB, the Baikal and \r\nTransbaikal regions, the Amur River region, and Primorie territories of \r\nRFE (the Kamchatka and Chukchi peninsulas) with up to -20 mm per \r\ndecade for the 1976-2014 period (Kokorev and Sherstiukov, 2015) or \r\n15-20% per decade for the 1976-2018 period (Roshydromet, 2019). \r\nOverall, solid precipitation predominantly decreased in North Asia \r\nand very likely caused both less snow cover extent (SCE) and snow \r\nwater equivalent (SWE), attributable to the anthropogenic influence \r\nwith high confidence (Sections 2.3.2.2 and 3.4.2)."


id: 8af2fff9712a419d70c57a584a27c74e
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 21615
content:
"seasons (Miao et al., 2014; Peng et al., 2019). Most GCMs capture \r\nthe main decadal SAT trend (Miao et  al., 2014), though CMIP5 \r\nGCMs fail to capture the decreasing temperature trend over East \r\nSiberia (Fei and Yong-Qi, 2015). Possible causes of GCMs' inability \r\nto represent the recent slowdown of warming is further discussed in \r\nCross-Chapter Box 3.1. For CMIP5, models with higher resolution do \r\nnot always perform better than those with lower resolutions (medium \r\nconfidence) (Miao et al., 2014)."


id: cabf37638cf9cfae05f80f5799f483a4
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 33011
content:
"Sixteen CMIP5 model simulations of SAT variability over Eurasia were \r\nevaluated against CRU observations for permafrost sub-regions (Peng \r\net al., 2019), showing a warm bias in north-west Eurasia, capturing \r\nthe climate warming over the 20th century and its acceleration during \r\nthe late 20th  century. CMIP5 GCMs generally underestimate daily \r\ntemperature range compared with observations over north-eastern \r\nRussia (Sillmann et al., 2013; Lindvall and Svensson, 2015). Currently \r\nthere is no literature on the CMIP6 ensemble over the region though \r\na  few single-model studies are available (Voldoire et  al., 2019; \r\nT. Wu et al., 2019)."


id: d31fbbc12105950d6e75603c5da5e566
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 34487
content:
"Snow characteristics depend on both temperature and precipitation, \r\nand observed trends over North Asia show large spatial heterogeneity \r\nand interannual variability (Figure Atlas.18) leading to medium \r\nconfidence that maximum snow depth has increased over Siberia, the \r\nOkhotsk Sea coast and in southern RFE since the 1960s (Callaghan \r\net al., 2011; Loginov et al., 2014), with trends during 1976-2016 of \r\n1.8 cm (in WBS), 1.1 cm (in ESB), and 4.6 cm (in RFE) per decade \r\n(Bulygina et  al., 2017). Snow cover duration increased in Yakutia, \r\nSakhalin Island and some other coastal areas of the Pacific Ocean \r\nin RFE during 1980-2009 (Callaghan et al., 2011), and decreased in \r\nWSB and ESB (Bulygina et al., 2017; Roshydromet, 2019). However, \r\nGorbatenko et al. (2019) reported that in south-eastern WSB maximal \r\nsnow depth has increased by 5-20 cm and duration of steady snow \r\ncover by between 4 and 10 days during 1989-2016 (Figure Atlas.18)."


id: 96ba82e87ac6442a5552891111fdbe85
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 23723
content:
"There is very limited use of RCMs for North Asia. CORDEX-CAS \r\ncovers North Asia, except parts of RFE, and ARCTIC-CORDEX \r\ncovers the northern regions (Figure Atlas.6). For CORDEX-CAS three \r\nRCMs (REMO, ALARO-0 and CLMcom) have been used and have \r\nwarm biases for maximum temperatures, cold biases for minimum \r\ntemperatures and a wet bias in the north during the winter (Top et al., \r\n2021). Rain gauges, however, are known to have problems in terms \r\nof measuring properly solid precipitation (e.g., due to drifting snow) \r\nwhich can greatly affect the accuracy of precipitation observations \r\nover North Asia (Harris et al., 2014)."


id: 797ead22b44c9e217c2cf15c9135bbcc
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 18577
content:
"Temperature trends and means derived from reanalysis datasets \r\n(JRA-25 and MERRA) correctly represented temperature variability \r\nshown in observational data over the Asian territory of Russia \r\nfor the 1976-2010 period (Loginov et  al., 2014). Assessment of \r\nCRU TS 3.22, CRUTEMP4, ERA-Interim and NCEP2 datasets against \r\nstation data over North Asia for annual and seasonal air temperature \r\nhas shown that the ERA-Interim reanalysis outperforms others for \r\nthe 1981-2005 period (Kokorev and Sherstiukov, 2015). The latter \r\nreanalysis also underestimates summer precipitation and shows large \r\nwet biases over north-east Asia during spring and underestimates \r\nmean seasonal temperature over north-east Asia in spring (MAM), \r\nautumn (SON), and winter (DJF), but overestimates it in summer (JJA) \r\ncompared with the CRU dataset (medium confidence) (Ozturk et al., \r\n2017; Top et al., 2021)."


id: 7993db853d6fa4a4ece4f69dbe3c7702
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 18594
content:
"CMIP5 and CMIP6 projections are consistent in the direction and \r\nranges of surface temperature change which are higher than the \r\nglobal average and with ensemble-mean warming of around 6degC \r\nfor the 4degC GWL. Projected precipitation changes are also consistent \r\nwith significant increases in winter, of up to 40% in the ensemble \r\nmean for the highest warming levels, and lower increases in summer \r\nexcept for WSB where changes are small and suggest drying at the \r\n4degC GWL (Figure Atlas.17 and the Interactive Atlas)."


id: 4d9ea821018a4f4a8838f06e745dde42
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 10934
content:
"The CMIP5 ensemble projects a warming of the annual mean SAT over \r\nnorthern Eurasia in the 21st century, likely in the range of 0.8degC-1.0degC \r\n(RCP2.6), 2.3degC-3.1degC (RCP4.5) and up to 7.2degC (RCP8.5) (Miao \r\net al., 2014; Peng et al., 2019). Mid-latitude permafrost sub-regions \r\nin Eurasia are projected to warm more than the global mean and \r\nnon-permafrost territories, with ensemble area-averaged changes \r\nof 1.7degC (RCP2.6), 3.2degC (RCP4.5) or 6.4degC (RCP8.5) in 2081-2100 \r\nrelative to 1986-2005 (Peng et al., 2019)."


id: e98dae289b69a72a14eddc9360fad4ff
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 38444
content:
"GCMs capture the main synoptic processes affecting North Asia \r\nand the CMIP5 ensemble simulates the temporal evolution of the \r\nmagnitude and position of the Siberian High (SH) over the period \r\n1872-2005 (Fei and Yong-Qi, 2015). CMIP5 models simulate \r\na  weakened intensity of the winter SH and a  strengthened \r\ninterannual variability compared to observations (Fei and Yong-Qi, \r\n2015). The characteristics of blocking events over the region (number, \r\nduration, intensity and frequency) were reasonably well reproduced \r\nby GCMs (Mokhov et al., 2014), and most overestimate the annual \r\nmean temperature over northern Eurasia (Interactive Atlas). Biases \r\nin simulated annual surface air temperature simulation primarily \r\ncome from the winter (DJF) season and are relatively smaller in other "


id: c06ee6cfe061acc929f6118a53d9dcd3
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 31155
content:
"Over the Central Asia CORDEX domain, RegCM4.3.5 simulations \r\ndriven by two different CMIP5 GCMs (HadGEM2-ES and MPI-ESM-MR) \r\nproject SAT warming for 2071-2100 relative to 1971-2000 of about \r\n3degC-4degC during the summer for RCP4.5 to over 7degC for all seasons for \r\nRCP8.5. Projected warming is most evident on the large continental \r\nSiberian Plateau with boreal and sub-boreal climates and biomes "


id: 231db3e8955b61093c67178c847509ca
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 3371
content:
"(i.e., taiga forests and tundra) during the winter season (Ozturk et al., \r\n2017). The Voeikov Main Geophysical Observatory (MGO) RCM, \r\ndriven by five CMIP5 GCMs for the RCP8.5 scenario, projects a faster \r\nincrease in annual minimum temperature as compared with maximum \r\ntemperature over the whole territory of Russia (Kattsov et al., 2017), \r\nand the smallest change in growing season lengths (i.e., periods with \r\ndaily temperatures over 5degC, 10degC and 15degC) in the area of northern \r\ntaiga in WSB and ESB comparable with other territories of Russia \r\nduring the 21st century (Torzhkov et al., 2019)."


id: cab0b3410db1850267935b0e9673ea78
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 33001
content:
"Surface air temperature and precipitation in North Asia are projected \r\nto increase further (high confidence) with warming higher than \r\nthe global average and around 6degC at the 4degC GWL. Temperature \r\nchange in 2080-2099 relative to 1981-2000 is likely in the range \r\nof 3degC in summer to 4.9degC in winter under the RCP4.5 scenario, and \r\n5.6degC in  summer to 9.7degC in winter under the RCP8.5 scenario. \r\nPrecipitation is projected to increase with ensemble-mean changes \r\nof 9% in summer under both RCP4.5 and RCP8.5, and of 22% and \r\n56% in winter respectively."


id: f5fc414bb85eeb51e7da0f01df9d3351
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 40635
content:
"For precipitation, MGO RCM projects for the Arctic-CORDEX domain \r\nunder the RCP8.5 scenario increases in annual totals for northern \r\nNorth Asia, a decrease in summer over ESB for 2006-2100 relative \r\nto 1951-2005 and significant increases in the upper limit of intense \r\nprecipitation over most of the region in winter (Kattsov et al., 2017; \r\nKhlebnikova et al., 2018). Other RCM projections show that in most \r\nseasons and for all future periods, precipitation in Siberia is not \r\nprojected to change with respect to the 1971-2000 period, except \r\nunder the RCP8.5 scenario for the winter and autumn (Ozturk et al., \r\n2017). This very limited and controversial evidence leads to low \r\nconfidence in RCM precipitation projections for North Asia and since \r\nthe projections of GCMs and ESMs are more physically consistent, \r\nassessment of future precipitation changes is based on CMIP5/\r\nCMIP6 presented in Figure Atlas.17 and the Interactive Atlas."


id: 87b73dea500715dcacd15e2b10fb4c28
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 21035
content:
"The countries in this region are mostly semi-arid to arid and therefore \r\ndepend heavily on the summer monsoon (June-September, JJAS) \r\nwhich is when most of the precipitation falls over the South Asia \r\nregion (SAS; Figure Atlas.17). The topographic mechanical effect of the \r\nTibetan Plateau (TIB) promotes moisture convergence downstream \r\nwhich triggers the early summer monsoon onset particularly over the \r\nBay of Bengal and south China. In winter, westerly disturbances (WD) \r\noriginating over the Atlantic Ocean bring moisture. The interaction \r\nbetween the WD and the Himalayas causes precipitation over \r\nnorthern and western parts of South Asia that is crucial to maintain \r\nthe glacier mass balance. The observed teleconnection patterns over \r\nSAS for temperature show cooling effects during NAM and warming \r\neffects when in positive phase with ENSO, IOB, AMM and AMV \r\n(Annex IV). IOD also influences South Asian precipitation (Annex IV)."


id: 7b96e27db0f00117d9830ab85b65ba4c
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 18952
content:
"Most of the CMIP5 and some CMIP6 GCMs overestimate the annual \r\nmean air temperature and precipitation over the North Asia region \r\n(medium confidence). GCMs generally represent the observed \r\ndecadal temperature trend (medium confidence) and biases primarily \r\ncome from the winter (DJF) season (high confidence). Results of \r\na very limited number of RCMs applied over the whole region show \r\nthat they have warmer biases for maximum and colder biases for \r\nminimum temperatures (limited evidence, medium agreement). \r\nSparsity of observational data particularly in the northern part of ESB \r\nand the whole of the RFE results in low confidence in the assessments \r\nof model performance in North Asia."


id: ea64bc4f7480e7b298adcba7e5b1a7b5
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 38599
content:
"1960-1990 as the baseline period. Similarly, SREX (Table  3-3 in \r\nSeneviratne et  al., 2012) reported low confidence (due to lack of \r\nliterature) in trends in climate indices related to extreme precipitation \r\nevents. The Indian summer monsoon circulation was found to have \r\nweakened, but this was compensated by increased local atmospheric \r\nmoisture content leading to more rainfall (medium confidence). It is \r\nlikely that the occurrence of snowfall events is decreasing in South Asia \r\nalong with other regions due to an increase in winter temperatures \r\n(Hock et  al., 2019b). Based on satellite- and surface-based remote \r\nsensing it is very likely that aerosol optical depth has increased over \r\nsouthern Asia since 2000."


id: 4ef1c746733e21b896134dbd79c79dbd
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 11173
content:
"meridional moisture transport over the Arabian Sea (Bay of Bengal) \r\n(low  confidence) (Latif et  al., 2017). Positive annual precipitation \r\ntrends are observed in global and regional datasets (Figure Atlas.11 \r\nand the Interactive Atlas) during 1961-2015 and over arid provinces \r\nof Pakistan (for rabi and kharif cropping seasons) during 1951-2015 \r\nof 2.8-34.8 mm per decade (Khan et al., 2020) imply high confidence\r\nfor increased precipitation in Pakistan. Observations located in \r\nthe monsoon-dominated strip in Pakistan indicate that the mean \r\nmonsoon onset became earlier during 1971-2010 (Ali et al., 2020)."


id: 915a620c4ffd42633c66ceb64c82c68f
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 22780
content:
"Snow and glaciers are major water resources of all countries in South \r\nAsia. Glacier melting is mainly controlled by natural phenomena \r\nbut anthropogenic emissions of black carbon (BC) are now making \r\na significant contributing to total glacial melting in the Hindu Kush \r\nHimalaya (HKH) region (Menon, 2002; Ramanathan et  al., 2007; \r\nRamanathan and Carmichael, 2008). BC concentration is seven to \r\n10 times higher in mid-altitudes (1000-4000 metres above sea \r\nlevel) than at high altitudes (>4000 metres above sea level). The \r\nconcentration of BC sampled from the surface of snow/ice samples \r\nas well as ice-core records shows decreasing ice albedo and an \r\nacceleration in glacier melting (Cross-Chapter Box  10.4; Wester \r\net al., 2019). Karakoram and western HKH snow cover is increasing, \r\na  phenomena known as the 'Karakoram anomaly', and partially \r\nattributed to an increase in the strength of westerly disturbances \r\n(Wester et al., 2019)."


id: 4d2d28ae9a5dcb1ed74e365db21f3ee3
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 10858
content:
"Recent studies show that annual mean land temperatures over India \r\nwarmed at a rate of around 0.6degC per century during 1901-2018, \r\nwhich was primarily contributed by a  significant increase in \r\nannual maximum temperature of 1.0degC per century, while the \r\nannual minimum temperature showed a lesser increasing trend of \r\n0.18degC per century during this period, with a  significant rise only \r\nin the recent few decades (1981-2010) at a  rate of 0.17degC per \r\ndecade (Srivastava et al., 2017, 2019). The annual average of daily \r\nmaximum and minimum temperatures has increased over almost all \r\nPakistan with a faster increasing trend in the south (high confidence). \r\nMinimum temperatures have increased faster (0.17degC-0.37degC per \r\ndecade) than maximum temperatures (0.17degC-0.29degC per decade) \r\nwith the diurnal temperature range reduced (-0.15degC to -0.08degC \r\nper decade) in some regions (Khan et al., 2019)."


id: 96209ac1dbad21c0841500849860900d
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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index: climate_gpt_v2
vector_id: 23604
content:
"Significant glacier retreat has been observed since 1960 in TIB \r\nwith lower rates in the interior of the region (Yao et  al., 2007). \r\nA  large inter-decadal variation in snow cover is also observed \r\nfrom 1960 to 2010. Observations and model simulations showed \r\nthat the increasing temperature of frozen grounds is leading to \r\nthawing and reduced depth of permafrost, with further significant \r\nreductions projected under future global warming scenarios (medium \r\nconfidence) (Yang et al., 2019)."


id: 67fa9ae672bc25a583a2c0e9e3bd98f8
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 15564
content:
"ranges, are projected to warm more (over 6degC under SSP5-8.5, with \r\nhigher warming in winters than in summer; Interactive Atlas) and this \r\nwill accelerate glacier melting in the region. The warming pattern of \r\nmaximum and minimum temperatures are projected to intensify in \r\nhigher latitudes compared with mid-latitudes of South Asia in CMIP5 \r\nsimulations for all RCP scenarios (Ullah et al., 2020)."


id: 347edeee0017f85bb7240119274ab026
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 6472
content:
"Seasonal precipitation projections show increased winter precipitation \r\nover the western Himalayas and decreased precipitation over the \r\neastern Himalayas. On the other hand, summer precipitation projections \r\nshow a  robust increase over most of South Asia, with the largest \r\nover the arid region of southern Pakistan and in adjacent areas of \r\nIndia, under SSP5-8.5 (Almazroui et al., 2020b). Daily bias-adjusted \r\nprojections from 13 CMIP6 GCMs using all emissions scenarios project \r\na warmer (3degC-5degC) and wetter (13-30%) climate in South Asia in the \r\n21st century (Mishra et al., 2020)."


id: 7608a2779ce6cccaa0486acbaa180ba3
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 17995
content:
"Summer precipitation changes in South Asia are consistent between \r\nCMIP3 and CMIP5 projections, but the model spread is large for winter \r\nprecipitation changes. Changes in summer monsoon rainfall will \r\ndominate annual changes over South Asia (Woo et al., 2019). CMIP3 \r\nGCMs project a gradual increase in annual precipitation over monsoon\u0002dominated areas of Pakistan throughout the 21st Century and increases \r\nin humid and semi-arid climate areas (Saeed and Athar, 2018)."


id: 218c37e683bee361bf4516fc46720b1b
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 3111
content:
"Warming of 2.5degC-5degC is projected over northern Pakistan and India \r\n(Syed et al., 2014). CORDEX-South Asia projections over north-east \r\nIndia under RCP4.5 for the period 2011-2060, show increasing \r\ntrends for both seasonal maximum and minimum temperature \r\nover north-east India (Interactive Atlas). The future projections of \r\nSouth Asian monsoon from the CORDEX-CORE exhibit a  spatially \r\nrobust delay in the monsoon onset, an increase in seasonality, and \r\na reduction in the rainy season length over parts of South Asia at \r\nhigher levels of radiative forcing (Ashfaq et al., 2021)."


id: 4af03650a7080a75a2e5706e41a8efc9
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 10424
content:
"CMIP5 and CMIP6 surface temperature projections for South Asia \r\nare consistent across the range of GWLs with increases greater than \r\nthe global average, more so over TIB (Figure Atlas.17). CMIP6 models \r\nshow higher sensitivity to greenhouse gas emissions, projecting higher \r\nwarming for a given emissions scenario. The north-western parts of \r\nSouth Asia, mainly covering the Karakorum and Himalayan mountain "


id: faa83259136c4a6d95fc5dbb9a5dad20
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 41475
content:
"With TIB continuing to warm, snow cover and snow water equivalent \r\nare projected to decrease but with regional differences due to synoptic \r\ninfluences (Cross-Chapter Box  10.4; Wester et  al., 2019). There is \r\nlimited evidence on whether the 'Karakoram Anomaly' will persist \r\nin coming decades, but its long-term persistence is unlikely with "


id: 38f9a2e35ca9304df7fae4a8bde89536
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 7228
content:
"continued projected warming (high confidence) (Section 9.5.1.1). It \r\nis projected that peak river flow at higher altitudes will commence \r\nearlier, due to warming influences on snow cover area and snow/\r\nglacier melt rates and with more precipitation falling as rain rather \r\nthan snow, and the magnitude and seasonality of flow will change \r\nover South Asia (Charles et al., 2016)."


id: 5769430e6fb6e991d8a5eac880cd0677
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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vector_id: 12641
content:
"interactions, including those that are part of the Maritime Continent \r\nand the Philippines. Its climate is mainly tropical (i.e., hot and humid \r\nwith abundant rainfall). Rainfall seasonal variability in the region \r\nis mainly affected by the synoptic-scale monsoon systems, the \r\nnorth-south migration of the Inter-tropical Convergence Zone (ITCZ) \r\nand tropical cyclones (mainly for the Philippines and Indochina), while \r\nintra-seasonal variability can be influenced by the MJO (Annex IV). \r\nTemperature and especially rainfall are also interannually affected by \r\nENSO and Indian Ocean basin and Dipole (IOB/IOD) modes (Annex IV \r\nand Table Atlas.1)."


id: 6a4e5107a9b0010715bf8e984ee129a9
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 15982
content:
"Mean, minimum and maximum daily temperatures in South Asia \r\nare increasing and winters are getting warmer faster than summers \r\n(high confidence). The South Asian monsoon has shown contrasting \r\nbehaviour over India and Pakistan. There is high confidence that there \r\nhas been a decrease in mean rainfall over most parts of the eastern \r\nand central north regions of India and an increase in precipitation \r\nin Pakistan."


id: 44476648fdb8cfb8cd57d24332352eca
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 9250
content:
"The AR5 WGI showed that the mean annual temperature of South \r\nEast Asia has been increasing at a rate of 0.14degC-0.20degC per decade \r\nsince the 1960s, along with an increasing number of warm days and \r\nnights, and a decreasing number of cold days and nights (Christensen \r\net al., 2013). The AR5 also reported the lack of sufficient observational \r\nrecords to allow for a full understanding of past precipitation trends \r\nin most of the Asian region, including South East Asia, and that \r\nprecipitation trends that were available differed considerably across \r\nthe region and between seasons (Christensen et al., 2013)."


id: 132b0fa89823d873824003572e21a695
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 585
content:
"Global model performance over the region has improved from CMIP3 \r\nto CMIP5 to CMIP6 in the multi-model ensemble-mean simulation \r\nof the amplitude and phase of the seasonal cycles of temperature \r\nand precipitation. However, there was no appreciable improvement \r\nin regions with steep orography, and there has remained substantial \r\ninter-model spread in seasonal and annual mean temperatures \r\nover South Asia with generally cold biases which are largest in the \r\ncomplex Karakorum and Himalayan mountain ranges. CMIP6 GCMs \r\nalso show a dry bias (15-20%) in mean annual precipitation in the \r\nmajority of the South Asia region with a wet bias in Nepal, Pakistan \r\nand northern India."


id: 18888985dd9c1df8e5755b861dfa382a
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 1526
content:
"On projected changes, findings from AR5 showed that warming is \r\nvery likely to continue with substantial sub-regional variations over \r\nSouth East Asia (Christensen et  al., 2013). The median increase in \r\ntemperature over land projected by the CMIP5 ensemble mean \r\nranges from 0.8degC in RCP2.6 to 3.2degC in RCP8.5 by the end of the \r\n21st  century. Moderate future increases in precipitation are very \r\nlikely, with projected ensemble mean increases of 1% in RCP2.6 \r\nto 8% in RCP8.5 by 2100. In SR1.5, there is a projected increase in \r\nflooding and runoff over South East Asia for a 1.5degC to 2degC global \r\nwarming, and these will increase even more for a greater than 2degC \r\nlevel of warming (Hoegh-Guldberg et al., 2018)."


id: caa97a63b8c506bbe0104c1672723fe6
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 32995
content:
"It is likely that surface temperatures over South Asia will increase \r\nmore than the global average and more so over TIB, with projected \r\nincreases of 4.6degC (3.4degC-6.0degC) during 2081-2100 compared with \r\n1995-2014 under SSP5-8.5 and 1.3degC (0.7degC- 2.0degC) under SSP1-2.6 \r\n(Interactive Atlas). Summer monsoon precipitation in South Asia is \r\nlikely to increase by the end of the 21st century while winter monsoons \r\nare projected to be drier. Over the same time periods CMIP6 models \r\nproject an increase in annual precipitation in the range 14-36% \r\nunder SSP5-8.5 and 0.4-16% under SSP1-2.6 (medium confidence)."


id: 58e59d04717546863af5d02345bcf39d
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 12881
content:
"With continued warming, TIB snow cover and snow water equivalent \r\nare likely to decrease and with more precipitation falling as rain \r\nrather than snow in SAS. It is projected that the peak river flow at \r\nhigher altitudes will commence earlier due to the effect of warming \r\non snow cover and snow/glacier melt rates, causing changes in \r\nmagnitude and seasonality of flow."


id: d975472ff5d4d7d7cdfa158fd729c76f
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 35554
content:
"Changes in mean precipitation are less spatially coherent over \r\nSouth East Asia. Over Thailand, the average number of rain days has "


id: 8f85e9374df3fafe8c7bd70e2048614e
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 22464
content:
"decreased by 1.3 to 5.9 days per decade while average daily rainfall \r\nintensity has increased by 0.24-0.73 mm day-1 per decade (Limsakul \r\nand Singhruck, 2016). Precipitation is also affected by ENSO events \r\n(Tangang et al., 2017; Supari et al., 2018). Over South East Asia, there \r\nhas been a  significant increase in the amount of precipitation and \r\nits extremes with La Nina episodes in the past decades, especially \r\nduring the winter monsoon period (high confidence) (Villafuerte and \r\nMatsumoto, 2015; Limsakul and Singhruck, 2016; Cheong et al., 2018)."


id: 4e504f179474ff9d3dba5297e6592855
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 11052
content:
"Figure Atlas.11 shows trends in mean temperature and precipitation \r\nduring 1961-2015 for two global datasets, indicating a significant \r\noverall warming over South East Asia (high confidence), with \r\nhigher rates of warming in Malaysia, Indonesia, and the southern \r\nareas of mainland South East Asia (low confidence). Annual mean \r\nprecipitation trends (Atlas.1.4.1 and the Interactive Atlas, which \r\nincludes the regional dataset Aphrodite) over the region are mostly \r\nnot significant except for increases over parts of Malaysia, Vietnam \r\nand the southern Philippines (medium confidence)."


id: c8dbffd96a5786b6fafd9b14fd154846
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 32665
content:
"It is important to note that the availability, quality, and temporal \r\nand spatial density of observation data may lead to uncertainties \r\nand varying results in South East Asia (Juneng et al., 2016). Some \r\nefforts have been made to produce better observationally-based \r\ngridded datasets for the region (e.g., Nguyen-Xuan et al., 2016; van \r\nden Besselaar et al., 2017; Yatagai et al., 2020)."


id: 87271238ce7121000068b6d4e1f8f095
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 20941
content:
"Performance in simulating rainfall over South East Asia varies among \r\nCMIP5 GCMs (high confidence). Only some are capable of reasonably \r\nsimulating the rainfall seasonal cycle and spatial pattern (Siew et al., \r\n2013; Raghavan et  al., 2018). Over mainland South East Asia, the \r\nperformance of CMIP5 GCMs in simulating rainfall during the wet \r\nseason was superior to that for annual and dry-season precipitation \r\n(J. Li et al., 2019)."


id: 41307e91b6c799061af569397b2983e7
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 8647
content:
"Some RCMs showed a systematic cold bias (Manomaiphiboon et al., \r\n2013; Kwan et al., 2014; Ngo-Duc et al., 2014; Loh et al., 2016; Cruz \r\nand Sasaki, 2017; Cruz et al., 2017) that was mainly due to model "


id: 94cb254091beb4e7b865f3fe5d450ec1
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 23386
content:
"Projections show continued warming over South East Asia, but \r\nlikely by a  slightly smaller amount than the global average. \r\nProjected changes in rainfall over South East Asia vary, depending \r\non model, sub-region and season (high confidence), with consistent \r\nprojections of increases in annual mean rainfall from CMIP5 and \r\nCMIP6 over most land areas (medium confidence) and decreases in \r\nsummer rainfall from CORDEX projections over much of Indonesia \r\n(medium confidence)."


id: b3d2340d52d6ba3bd1d7405ba98c920f
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 28881
content:
"Laos, and of 5-10% over the eastern Philippines and northern \r\nVietnam. During JJA, significantly drier conditions are projected \r\nover almost the entire South East Asia region except over Myanmar \r\nand northern Borneo. Over the Indonesian region, especially Java, \r\nSumatra and Kalimantan, as much as a 20-30% decrease in mean \r\nrainfall is projected during JJA by the end of the 21st century. The \r\nprojected drier condition over Indonesia from CORDEX is consistent \r\nwith that of Kusunoki (2017), Giorgi et al. (2019), Kang et al. (2019) \r\nand Supari et al. (2020) and is associated with enhanced subsidence \r\nover the region (Kang et al., 2019; Tangang et al., 2020)."


id: 78255def9f2ea27b90bba3aa8f95b97
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 18353
content:
"The IPCC AR5 established it is very likely that temperatures will \r\ncontinue to increase over WCA in all seasons whilst projections \r\nof decreased annual mean precipitation had medium confidence\r\ndue to medium agreement resulting from model-dependent \r\nsub-regional and seasonal changes (Christensen et  al., 2013). The \r\nAR5 also concluded that for a better understanding of the climate \r\nof the region, results of high-resolution regional climate models also \r\nneed to be assessed and CMIP5 models generally had difficulties \r\nsimulating the mean temperature and precipitation climatology for \r\nSouth West Asia. This is partly related to the poor spatial resolution \r\nof the models not resolving the complex mountainous terrain and \r\nthe influence of different drivers of the European, Asian and African "


id: cde74090a2f1853523407adfdf9b9fc9
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 33557
content:
"It is virtually certain that annual mean temperature has been \r\nincreasing in South East Asia in the past decades while changes in \r\nannual mean precipitation are less spatially coherent though with \r\nsome increasing trends over parts of Malaysia, Vietnam and the \r\nsouthern Philippines (medium confidence)."


id: f356eb1150875603f2e750cdbcb51f8d
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 40189
content:
"Although various biases still exist, there is high confidence that \r\nthe models can reproduce seasonal climate patterns well over the \r\ndifferent sub-regions of South East Asia. There is medium confidence\r\nthat the RCMs show added value compared to their host GCMs over \r\nthe region."


id: 433fb9579fb69ed140f48746a6e8ef6
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 9043
content:
"climates. However, observational data scarcity and issues related to \r\nthe comparison of observations with coarse-resolution models added \r\nto the uncertainty and remained poorly analysed in peer-reviewed \r\nliterature on climate model performance (Christensen et al., 2013)."


id: 419e813a4b6337c318cfede999968796
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 8732
content:
"trend in winter (Khaydarov and Gerlitz, 2019). There is very high \r\nconfidence (robust evidence, high agreement) that the shrinking \r\nof the Aral Sea has induced an increase in surface air temperature \r\naround the Aral Sea region in the range of 2degC-6degC (Baidya Roy \r\net al., 2014; McDermid and Winter, 2017; Sharma et al., 2018). The \r\nplateau of Iran has experienced significant increases in the average \r\nmonthly values of daily maximum and minimum temperatures \r\nwith spatially varying rates of 0.1degC-0.3degC up to 0.3degC-0.4degC per \r\ndecade and greater spatial variation in minimum temperatures (high \r\nconfidence) (Mahmoudi et al., 2019; Fathian et al., 2020; Sharafi and \r\nMir Karim, 2020)."


id: 2b3924597625088e51d49839eb50d0ef
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 4849
content:
"Observed warming over northern ARP is higher than over the south, \r\nwhere minimum temperatures are increasing faster than maximum \r\ntemperatures (Almazroui, 2020a). The rate of mean temperature \r\nincrease is estimated at 0.10degC per decade over 1901-2010 (Attada \r\net  al., 2019), while it has reached 0.63degC (likely in the range of \r\n0.24degC-0.81degC) per decade for the more recent period of 1978-2019 \r\n(Almazroui, 2020a)."


id: 2b07aa68256f5999d6ce8d0ebde05ef9
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 4806
content:
"Since AR5, there has been an increasing number of studies on past \r\nclimate change in South West Asia though meteorological stations are \r\nsparsely scattered in the region. They are mainly located in the plains \r\nbelow 2 km of altitude, very scarce in mountainous areas and have \r\ndeclined in number in WCA since the end of the Soviet Union in 1991. \r\nThis increases the uncertainty in both temperature and precipitation \r\ntrends, particularly for elevated areas (high confidence) (Christensen \r\net al., 2013; Huang et al., 2014). So researchers use other sources of \r\nclimate data in the region, particularly freely available gridded data \r\n(Annex I)."


id: e82ee6ac10de24ea42e8e0197c9b1c14
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 38178
content:
"Globally, drylands showed an enhanced warming over the past \r\ncentury of 1.2degC-1.3degC, significantly higher than the warming over \r\nhumid lands (0.8degC-1.0degC) (J. Huang et al., 2017). A strong increase \r\nin annual surface air temperature of 0.27degC-0.47degC per decade has \r\nbeen found over WCA between 1960 and 2013 (very high confidence) \r\n(Han and Yang, 2013; Li et al., 2013; Hu et al., 2014, 2017; Huang \r\net al., 2014; Deng and Chen, 2017; Zhang et al., 2017, 2019a; H. Guo \r\net  al., 2018; Haag et  al., 2019; Yu et  al., 2019). Warming is most \r\nprominent in the spring based on the CRU dataset with rates likely\r\nranging from 0.64degC-0.82degC per decade (Hu et al., 2014). Analysis of \r\nseasonal temperature trends based on high-resolution 1 km x 1 km \r\ndownscaled dataset CHELSA and 20 stations in Uzbekistan has \r\nconfirmed the maximum significant trend in temperature from 0.6degC \r\nup to 1degC per decade in spring from 1979 to 2013 and no significant "


id: 5a1c72c2d9c9475902e769970452bcfd
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 13104
content:
"A decreasing trend of precipitation is reported for ARP with the mean \r\nvalue of -6.3  mm per decade (range of -30  mm-16  mm) for the \r\nperiod 1978-2019 (low confidence) with large interannual variability \r\nover Saudi Arabia, which covers 80% of the region (AlSarmi and \r\nWashington, 2011; Almazroui et al., 2012; Donat et al., 2014). The \r\nsame decreasing trend in precipitation totals and an increasing \r\ntrend in the number of consecutive dry days are found for most of \r\nthe Iranian Plateau (medium confidence) (Rahimi and Fatemi, 2019; \r\nFathian et al., 2020; Sharafi and Mir Karim, 2020). January-to-March \r\nmean snow cover and depth over mountainous areas decreased \r\nbetween 2000 and 2019 (low to medium confidence due to limited \r\nevidence) (Safarianzengir et al., 2020)."


id: 1d8eabbf2659c7f02e4487f588f1720e
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 2384
content:
"and baselines are shown in Figure Atlas.17 and further details can be \r\nexplored in the Interactive Atlas."


id: a5e74ae76c9fd99d64c2d4fc02250198
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 26411
content:
"There is limited evidence about the performance of GCMs and RCMs \r\nin representing the current climate of South West Asia due to very \r\nfew studies evaluating models over this region, but literature is now \r\nemerging particularly on CMIP5/CMIP6 and CORDEX simulations."


id: ece9be6e5c584e206d989d85f535187b
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 39051
content:
"Over ARP, surface temperature biases for 18 of 30 CMIP5 models are \r\nwithin one standard deviation of the observed variability (Almazroui \r\net al., 2017). A warm bias in summer and a cold bias for other months \r\nalong with an underestimation of wet-season precipitation and an \r\noverestimation in the dry season have been reported in 26 CMIP5 \r\nmodels (Lelieveld et al., 2016). Thirty CMIP6 GCMs have limited skill \r\nin simulating annual precipitation patterns, annual cycle statistics \r\nand long-term precipitation trends over Central Asia partially due to \r\nconsiderable wet biases of up to 100% in the southern Xinjiang and \r\nHexi Corridor regions (Guo et al., 2021). Also, CMIP6 models display \r\na wide range of performance in reproducing ENSO teleconnections \r\nthat influence the region (Barlow et al., 2021)."


id: 2ac6ad117f0813fa189144df29c5c573
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 4752
content:
"In ARP, the projected change in ensemble mean annual temperature \r\nfrom 30 CMIP6 models is from 1.6degC (SSP1-2.6) to 5.3degC (SSP5-8.5) \r\nby 2070-2099 compared to 1981-2010 (Almazroui et al., 2020a). \r\nThe projected warming is the highest in the north, reaching 5.9degC \r\nand lowest in the south (4.7degC). COSMO-CLM projections over the \r\nCORDEX-MENA domain show for ARP and WCA a strong warming \r\nwith marked seasonality for the end of the 21st  century, ranging \r\nfrom 2.5degC in winter under RCP4.5 to 8degC in summer under RCP8.5 \r\nand with large increases found over high-altitude areas in winter \r\nand spring (Bucchignani et al., 2018; Ozturk et al., 2018). The CMIP5 \r\nmulti-model mean warming in boreal summer in 2070-2099, \r\ncompared with 1951-1980, is projected to be about 2.5degC and \r\n6.5degC at the 2degC and 4degC global warming levels respectively \r\n(Huang et al., 2014)."


id: 9b7d08a4e53244ab07e459e7846eb369
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 24606
content:
"Four RCMs (REMO, RegCM4.3.5, ALARO-0, and COSMO-CLM5.0) \r\ndriven by ERA-Interim, NCEP2 reanalyses and two different GCMs \r\nreproduced reasonably well the spatio-temporal patterns for \r\ntemperature and precipitation though underestimated diurnal \r\ntemperature range and had cold biases over mountainous and high \r\nplateau regions in all seasons. There is low confidence in this result \r\nbecause of low station density and a lack of high-elevation stations, \r\nand with biases dependent on the choice of the observational \r\ndataset. However, the performance of both GCMs and RCMs is better \r\nthan reanalyses when compared to available observations (Mannig \r\net al., 2013; Ozturk et al., 2017; Russo et al., 2019; Top et al., 2021)."


id: d7adef24e55bb00529b812353fe0db38
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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index: climate_gpt_v2
vector_id: 35256
content:
"by 26.5% in Central Asia (Yang et al., 2017). However, regardless of \r\nthe sign of the precipitation change in the high-mountain regions \r\nof Central Asia, the influence of the warming on the snowpack will \r\nvery likely cause important changes in the timing and amount of \r\nthe spring melt (Diffenbaugh et al., 2013)."


id: 949f0eaa6407b8589c6632bce975c146
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 23352
content:
"The assessment in this section focuses on changes in average \r\ntemperature and precipitation (rainfall and snow), including the \r\nmost recent years of observations, updates to observed datasets, \r\nthe consideration of recent studies using CMIP5 and those using \r\nCMIP6 and CORDEX simulations. Assessment of changes in extremes \r\nis in Chapter  11 (Tables 11.10-12) and climatic impact-drivers in \r\nChapter 12 (Table 12.5)."


id: a3c5b5581b5f02f5aa833b6a8566cb70
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 26010
content:
"In ARP, the projected change in ensemble mean annual precipitation \r\nfrom 30 CMIP6 models ranges from 3.8% (-2.6 to 28.8%) to 31.8% \r\n(12.0-106.5%) under SSP1-2.6 and SSP5-8.5 emissions for the \r\nperiod 2080-2100 compared with 1995-2014 (Almazroui et  al., \r\n2020a). North-west ARP precipitation is projected to decrease \r\nbetween -6 to -27% per decade and in the south precipitation \r\nto increase by up to 8.6% per decade. CMIP6 projections are in \r\nline with those from CMIP3 and CMIP5, however they are less \r\nvariable in the central area in CMIP6. The uncertainty associated \r\nwith precipitation over ARP is large because of very low annual \r\namounts and high variability."


id: 2d7daf9fb5f052344e319e81003412d9
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 5299
content:
"Increases in annual surface air temperature over South West Asia \r\nare very likely in the range of 0.24degC-0.81degC per decade over the \r\nlast 50-60 years. Annual precipitation change over ARP since 1970 is \r\nestimated at -6.3 mm per decade (and in the range of -30 to 16 mm \r\nper decade) and over WCA is generally not significant except over \r\nthe elevated part of eastern WCA where increases between 1.3 mm \r\nand 4.8 mm per decade during 1960-2013 have been observed (very \r\nhigh confidence). In mountainous areas, the scarcity and decline of \r\nthe number of observation sites since the end of the former Soviet \r\nUnion in 1991 increase the uncertainty of the long-term temperature \r\nand precipitation estimates (high confidence)."


id: 513e562ccbbc3f0944759c868f543f64
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 11574
content:
"Mean temperature biases in RCMs are within +-3degC in South West \r\nAsia, and annual precipitation biases are positive in almost all \r\nparts of the region, except over the ARP where they are negative \r\nin the wet season (November to April) and over WCA in winter and \r\nspring (from December to May) (medium confidence). Since regional \r\nmodel evaluation literature has only recently emerged there is \r\nmedium evidence about the performance of RCMs in South West \r\nAsia though with medium to high agreement on mean temperature \r\nand precipitation biases. RCMs simulate colder temperatures than \r\nobserved over mountainous and high plateau regions (limited \r\nevidence, high agreement)."


id: 731bdd3987fe5b98951696b65640a1b4
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 17502
content:
"Further warming over South West Asia is projected in the 21st century \r\nto be greater than the global average, with rates varying from 0.25degC \r\nto 0.8degC per decade depending on the season and scenario, and the \r\nmaximum rates found in the northern part of the region in summer \r\n(high confidence). The influence of the warming on the snowpack \r\nwill very likely cause changes in the timing and amount of the spring \r\nmelt. CMIP6 projected changes in annual precipitation totals are \r\nin the range of -3 to 29% (SSP1-2.6) and 12-107% (SSP5-8.5) in \r\nARP (medium confidence). Strong spatio-temporal differences with \r\noverall precipitation decreases are projected in the central and \r\nnorthern parts of WCA in summer (JJA) with increases in winter (DJF) \r\n(medium confidence)."


id: 69fc3062c880f3443918a35653f0f719
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 15911
content:
"period events) and of short duration (sub-daily) extremes (medium \r\nconfidence). Likewise, however, there is a projected increase in the \r\nfrequency of drought in southern Australia (medium confidence) \r\nand in many parts of New Zealand (medium confidence). Owing \r\nto hotter and drier conditions there is high confidence that the \r\noccurrence of fire weather will increase in most of southern \r\nAustralia, and medium confidence that the fire danger index will \r\nincrease in many parts of New Zealand."


id: 54098119acd37bf0dc6aba34f452cdf7
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 12058
content:
"regions of New Zealand during the shorter 1980-2015 period, \r\nthis is in fact the reverse of the longer-term trends in 1961-2015 \r\n(Interactive Atlas)."


id: da773c0007e2bd815aa65725fc97724b
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 35734
content:
"The SROCC (Hock et  al., 2019b) reports on the observed and \r\nprojected decline in snow cover in Australasia, as well as the retreat \r\nof New Zealand glaciers following an advance in 1983-2008 due \r\nto enhanced snowfall. It also reports on the vulnerability of some \r\nAustralian communities and ecosystems to sea level rise, increases \r\nin the intensity and duration of marine heatwaves driven by human \r\ninfluence (high confidence), the decrease in frequency of tropical \r\ncyclones' landfall on eastern Australia since the late 1800s (low \r\nconfidence in an anthropogenic signal), and presents a case study \r\non the multiple hazards, compound risk and cascading impacts \r\nfrom climate extremes in Tasmania in 2015-2016 (including an \r\nattributable human influence on some events). The SRCCL (Mirzabaev \r\net al., 2019) found widespread vegetation 'greening' has occurred in \r\nparts of Australia, and an increase in the desertification and drought \r\nrisk in future in southern Australia."


id: 36ecb8c764edc358c68494c7c8191666
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 6886
content:
"Reliable station observations are available from around 1900 in \r\nAustralasia, but in some regions the coverage was and remains \r\npoor. Australia and New Zealand have continued to warm, and \r\nmany rainfall trends have continued since AR5. Changes and trends \r\nin temperature and precipitation from 1961 to 2015 from three \r\ndifferent global datasets are displayed in Figure Atlas.11 and the \r\nInteractive Atlas, and show significant (at 0.1 significance level) \r\nwarming trends over southern and eastern Australia. Most of the \r\nobserved changes in precipitation over the region are not significant \r\nover this period. Although observed datasets (e.g.,  GPCC and \r\nGPCP) generally agree on a significant drying trend in the southern "


id: b44dbd53b7e0e663261cd89bc163d7e0
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 28956
content:
"In Australia, there has been a decrease in snow depth and area since \r\nthe late 1950s, especially in spring (BOM and CSIRO, 2018). Based on \r\na reconstructed snow cover record, the recent rapid decrease in the \r\npast five decades has been shown to be larger by more than an order \r\nof magnitude than the maximum loss for any five-decade period \r\nover the past 2000 years (McGowan et al., 2018). In New Zealand, \r\nfrom 1977 to 2018, glacier ice volume decreased from 26.6 km3 to \r\n17.9 km3 (a loss of 33%; Salinger et al., 2019)."


id: f08b54f083d659f706eb0fe1c5e92093
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 39695
content:
"overall showed added value in the simulation of the current climate \r\n(Di Virgilio et al., 2019; Evans et al., 2021)."


id: 192c7540f390b63ee00818c18342607c
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 1646
content:
"Most studies assessed in AR5 WGII were based on Coupled Model \r\nIntercomparison Project Phase 3 (CMIP3) models and Special \r\nReport on Emissions Scenarios (SRES) scenarios and CMIP5 models \r\nwhenever available. The AR5 WGI reported that model biases in \r\nannual temperature and rainfall are similar to or lower than other \r\ncontinental regions outside the tropics, with temperature biases \r\ngenerally <1degC in the multi-model mean and <2degC in most models \r\nover Australia compared to reanalysis, and with a wet bias over the \r\nAustralian inland region but a  dry bias near coasts and mountain \r\nregions of both Australia and New Zealand."


id: 8b438b5b90a9f4ff8534eeaaeea2c8a1
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 21663
content:
"Similar to the global average (Chapter  4), mean temperature in \r\nAustralasia is projected to continue to rise through the 21st century \r\nat a  magnitude proportional to the cumulative greenhouse gas \r\nemissions (virtually certain, very high confidence, robust evidence), \r\nCMIP5 and CMIP6 results are shown in Figure Atlas.21. A higher end \r\nto the range of temperature projections is found in CMIP6 compared \r\nto CMIP5 (Grose et al., 2020), produced by a group of models with \r\nhigh climate sensitivity (Forster et al., 2020), and this creates a higher \r\nmulti-model-mean change. For example, projections for Australasia \r\nincluding ocean between 1995-2014 and 2081-2100 are 1.4degC \r\n(1.1degC-1.8degC, 10th-90th percentile range) in CMIP5 under RCP4.5, \r\nbut 1.8degC (1.3degC-2.5degC) in CMIP6 under SSP2-4.5."


id: d2b72376739070814e1045e91247646f
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 34418
content:
"Early results from CMIP6 suggest incremental improvements \r\ncompared to CMIP5 in the simulation of the mean annual \r\nclimatology of temperature and precipitation of the Indo-Pacific \r\nregion surrounding Australasia, the teleconnection between ENSO \r\nand IOD and Australian rainfall and other relevant climate features \r\n(Grose et al., 2020). These assessments suggest that confidence in \r\nprojections is similar to AR5 or incrementally improved. The CORDEX \r\nAustralasia simulations are found to have cold biases in daily \r\nmaximum temperature and an overestimation of precipitation but "


id: 14419a0710745c2981fac2a17f3ab34a
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 781
content:
"Using warming levels, the results can be directly compared, \r\naccounting for the different distribution of climate sensitivities in the \r\ntwo ensembles. In this framework, Australasia (land only) is projected "


id: 3e5d92232c669632fe1fec4cb33e9374
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 8151
content:
"of change in JJA, but significant changes are projected by some \r\nmodels. Other seasonal and regional rainfall changes in Australia are \r\nreviewed in Dey et al. (2019)."


id: bef5ea007fa915b1dd6a6c4ede784d99
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 30868
content:
"to warm by a similar amount to the global average: 1.4degC-1.8degC for \r\nthe 1.5degC warming level, through to 3.9degC-4.8degC for the 4degC warming \r\nlevel from the 1850-1900 baseline in CMIP6 using SSP5-8.5 (results \r\nusing other SSPs and from CMIP5 are similar). Projected warming \r\nis greater over land than ocean, greater in Australia than in New \r\nZealand, and greater over inland Australia than in coastal regions. Due \r\nto historical warming, projected temperature change from the AR6 \r\nbaseline of 1995-2014 is lower: 0.3degC-1.0degC for the 1.5degC warming \r\nlevel, through to 2.9degC-4.0degC for the 4degC warming level. Changes \r\nfor other warming levels, sub-regions and emissions pathways are \r\nshown in Figure Atlas.21 and can be explored in the Interactive \r\nAtlas. Regional modelling suggests projected temperature increase is \r\nhigher in mountainous areas than surrounding low-elevation areas in \r\nNew Zealand and Australia (Olson et al., 2016; MfE, 2018)."


id: be2b379caf408ba96ece03fb315b5b90
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 30724
content:
"For the NZ reference region, precipitation is projected to increase \r\nin winter and annual rainfall, with some differences in magnitude \r\nbetween CMIP5, CMIP6 and CORDEX (Figure Atlas.21). This \r\nprojection of rainfall increase is a function of changes in the southern \r\nextent of the region, and notable regional differences are expected. \r\nRegional modelling suggests precipitation increases in the west and \r\nsouth of New Zealand and decreases in the north and east (MfE, \r\n2018), with medium confidence and notable differences by season. \r\nLiu et al. (2018) project that the North Island will be drier, while the \r\nSouth Island will be wetter under both 1.5degC and 2degC warming levels. \r\nThe projected increase in precipitation in the far future (2081-2100) \r\nfor the southern regions of NZ has high agreement (Interactive Atlas). \r\nOther seasonal and regional rainfall changes in Australia can be \r\nexplored in the Interactive Atlas."


id: 6a98d65bf792ea21e507d39bb9036a05
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 16037
content:
"Under future warming, the snowpack in Australia is projected \r\nto decrease by approximately 15% and 60% by 2030 and 2070 \r\nrespectively under the SRES A2 scenario (Di Luca et al., 2018), while \r\nin New Zealand the number of annual snow days is projected to \r\ndecrease by 30 days or more by 2090 under RCP8.5 (MfE, 2018). New \r\nZealand is also projected to lose up to 88 +- 5% of its glacier volume \r\nby the end of the 21st century (Chinn et al., 2012; Hock et al., 2019a)."


id: e74377b3e307d9c7d824d104010a98eb
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 37982
content:
"There is very high confidence that the climate of Australia warmed \r\nby around 1.4degC and New Zealand by around 1.1degC since reliable \r\nrecords began in 1910 and 1909 respectively, with human influence \r\nthe dominant driver. Warming is virtually certain to continue, with \r\na  magnitude roughly equal to the global average temperature. \r\nA  significant decrease in April to October rainfall in the south-west \r\nof the state of Western Australia observed from 1910 to 2019 is \r\nattributable to human influence with high confidence and is very \r\nlikely to continue in future, noting consistent projections in CMIP5 \r\nand CMIP6. Other observed and projected rainfall trends are less \r\nsignificant or less certain. Model representation of the climatology "


id: 6608a16d1264bb0573cc3218ed79eb67
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 15210
content:
"Projections for EAU vary by season, with moderate model agreement \r\non a  decrease in rainfall in winter and spring, but with lower \r\nagreement in CMIP6 compared to CMIP5, and low model agreement \r\non the direction of change in summer (Figure Atlas.21). CAU shows \r\na  similar range of change as EAU, with low model agreement on \r\nthe direction of change in DJF, moderate agreement on direction "


id: 9b62e2ffa08fe6a94d474c94dd5400eb
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 24591
content:
"of Australasian temperature and rainfall has improved since AR5, \r\nthrough an incremental improvement between CMIP5 and CMIP6, and \r\nthe development of coordinated regional modelling through CORDEX\u0002Australasia. Snow cover is likely to decrease throughout the region at \r\nhigh altitudes in both Australia and New Zealand (high confidence)."


id: 4500b33ed1cc979e880351807e7533ec
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 9368
content:
"The assessment in this section focuses on changes in average surface \r\ntemperature and precipitation (rainfall and snow), including the \r\nmost recent years of observations, updates to observed datasets, \r\nthe consideration of recent studies using CMIP5 and those using \r\nCMIP6 and CORDEX simulations. Assessment of changes in extremes \r\nis in Chapter  11 (Tables 11.13-15) and climatic impact-drivers in \r\nChapter 12 (Table 12.6). It considers climate change over the regions \r\nshown in Figure Atlas.22, extending to all territories from Mexico to \r\nSouth America, including the Caribbean islands. This figure supports \r\nthe assessment of regional mean changes over the region which, due \r\nto the high climatological and geographical heterogeneity, has been \r\nsplit into two sub-regions: Central America and the Caribbean, and \r\nSouth America."


id: d3bd07bef8177b0bb390a1df9b22b705
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 34597
content:
"One of the most prominent features of the regional climate is the \r\nincidence of tropical cyclones (TCs), which represent an important \r\nhazard for almost all the countries of the region between June and \r\nNovember. A detailed assessment is given in Chapter 11."


id: ce8257d136e63d126fa6f5aed826b092
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 33677
content:
"According to AR5 (Christensen et  al., 2013), significant positive \r\ntrends of temperature have been observed in Central America (high \r\nconfidence), while significant precipitation trends are regionally \r\ndependent, especially during the summer. In addition, changes in \r\nclimate variability and in extreme events have severely affected \r\nthe region (medium confidence). A decrease in mean precipitation \r\nis projected in SCA and NCA. El Nino and La Nina teleconnections \r\nare projected to move eastwards in the future (medium confidence), \r\nwhile changes in their effects on other regions, including Central \r\nAmerica and the Caribbean is uncertain (medium confidence). There \r\nis medium confidence in projections showing an increase in seasonal \r\nmean precipitation on the equatorial flank of the ITCZ affecting parts \r\nof Central America and the Caribbean."


id: 5add7943d9f826ad7d9306d19e208a6e
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 13235
content:
"The Central America and Caribbean region is assessed considering \r\nthree reference regions Southern Central America (SCA), including \r\nthe isthmus and the Yucatan Peninsula; Northern Central America \r\n(NCA), including Mexico (centre and north); and the Caribbean \r\n(CAR), including the Greater Antilles, the Lesser Antilles, the Bahamas \r\nand other small islands (see Figure Atlas.22); NCA is also covered in \r\nSection Atlas.9 North America."


id: bc815191c8befa3fe77485465d9137b9
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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index: climate_gpt_v2
vector_id: 30410
content:
"In relation to the 1986-2005 baseline period, temperatures are very \r\nlikely to increase by the end of the century, even for the RCP2.6 \r\nscenario, with changes of more than 5degC in some regions for the \r\nRCP8.5 scenario. Precipitation change is projected to vary between \r\n+10% and -25% (medium confidence) (Christensen et al., 2013). The \r\nSR1.5 (Hoegh-Guldberg et al., 2018) states there is a high agreement\r\nand robust evidence that at the 1.5degC global warming level the \r\nCaribbean region will experience a 0.5degC-1.5degC warming compared \r\nto the 1971-2000 baseline period, with greatest warming over larger \r\nland masses."


id: 7d38922d2d7b5c1e12f9eaaa6cdd460b
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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index: climate_gpt_v2
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content:
"Significant warming trends between 0.2degC and 0.3degC per decade \r\nhave been observed in the three reference regions of Central America \r\nin the last 30 years (Planos Gutierrez et al., 2012; P.D. Jones et al., \r\n2016a; Hidalgo et al., 2017), with the largest increases in the North \r\nAmerican Monsoon region (high confidence) (Figure Atlas.11 and the \r\nInteractive Atlas; Cavazos et al., 2020). There is high confidence of "


id: 82ff73565b82cc85755997663d611a6b
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 20174
content:
"increasing temperature over parts of NCA, reaching 0.5degC per decade \r\nin Mexico and southern Baja California, with a lower rate (0.2degC per \r\ndecade) in the Yucatan Peninsula and the Guatemala Pacific coastal \r\nregion (Cueto et al., 2010; Garcia Cueto et al., 2013; Martinez-Austria \r\net al., 2016; Martinez-Austria and Bandala, 2017; Navarro-Estupinan \r\net al., 2018; Cavazos et al., 2020) and CAR (McLean et al., 2015) over \r\nthe last 30 to 40 years. Cooling trends have been detected in limited \r\nareas of Honduras and northern Panama (Hidalgo et al., 2017)."


id: d1938f3183d80c166f9767faf0e31187
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 34220
content:
"Changes in mean precipitation rates are less consistent and long-term \r\ntrends are generally weak. Different databases show significant \r\ndifferences depending mainly on the type and resolution of data \r\n(Centella-Artola et  al., 2020). Small positive trends were observed \r\nin the total annual precipitation (Stephenson et  al., 2014). In SCA \r\nand CAR, trends in annual precipitation are generally non-significant, \r\nwith the exception of small significant positive trends for sub-regions \r\nor limited periods (Planos Gutierrez et al., 2012; Hidalgo et al., 2017), \r\nand the 1970-1999 trends in precipitation in SCA are generally \r\nnon-significant (J.M. Jones et al., 2016; Hidalgo et al., 2017). Positive \r\ntrends in the duration of the MSD have been found in this region over \r\nthe past four decades (low confidence) (Anderson et al., 2019). For \r\nCAR see also Atlas.10 Small Islands."


id: 96f8bab8f940980356b4f90cdd05753
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 23753
content:
"Global and regional models consistently project warming in the \r\nwhole region for the end of the century, under RCP4.5 and RCP8.5 for \r\nCMIP5 projections with greater warming for continental compared to \r\ninsular territories, likely reaching values between 2degC and 4degC (high \r\nconfidence) (Campbell et al., 2011; Karmalkar et al., 2011; Cavazos \r\nand Arriaga-Ramirez, 2012; Cantet et  al., 2014; Chou et  al.,  2014; \r\nCoppola et  al., 2014; Hidalgo et  al., 2017; Colorado-Ruiz et  al., \r\n2018; Imbach et al., 2018). The greatest warming of 5.8degC for the \r\nend of the century was projected for northern Mexico under RCP8.5 \r\n(Colorado-Ruiz et  al., 2018), using an ensemble of CMIP5 GCMs \r\n(Interactive Atlas)."


id: d6b5f37b328eef0b8cf24eedafac1c9e
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 35078
content:
"The ability of climate models to simulate the climate in this region \r\nhas improved in many key aspects (Karmalkar et al., 2013; Fuentes\u0002Franco et al., 2014, 2015, 2017; Vichot-Llano et al., 2014; Vichot-Llano \r\nand Martinez-Castro, 2017; Martinez-Castro et al., 2018). Particularly \r\nrelevant for this region are increased model resolution and a better \r\nrepresentation of the land surface processes (high confidence)."


id: 287c8731d300d78d86181f9ae2d66d3d
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 4329
content:
"Figure Atlas.22 and the Interactive Atlas synthesize regional mean \r\nchanges in annual mean surface air temperature and precipitation \r\nfor the Central American reference regions for CMIP6, CMIP5 and \r\nCORDEX for different warming levels and time periods. At the \r\n1.5degC GWL, it is very likely that average annual temperature in \r\nCentral America over land surpasses 1.3degC (CAR), 1.7degC (NCA) and "


id: 455a07d0e3865ae9236b4517cee6cfa2
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 9422
content:
"Colorado-Ruiz et al. (2018) assessed an ensemble of 14 GCMs from \r\nCMIP5 for a  1971-2000 baseline period, projecting precipitation \r\ndecreases of between 5% and 10% by the end of the century for the "


id: 51c817d4574084dfb7c03ae121bc781a
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 11674
content:
"Projected change in mean annual precipitation shows a large spatial \r\nvariability across Central America and the Caribbean. Under moderate \r\nfuture emissions overall negative but non-significant precipitation \r\ntrends are projected for the 21st  century (low confidence). Under \r\nhigher-emissions scenarios and at higher GWLs, average precipitation \r\nis likely to decrease in most of the region, particularly in the north\u0002western and central Caribbean and part of continental Central \r\nAmerica, especially in SCA."


id: 3d2e478d2120670a2f6ba1855c089bea
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 7946
content:
"Regional synthesis of observed and modelled climate in South \r\nAmerica is challenging due to the latitudinal extent of the continent, \r\nthe Andes Mountains, and local-to-regional climatic features, \r\nwhich are influenced by multiple drivers. The main large-scale \r\ndrivers include many modes of natural variability (Annex IV.2): the \r\ninter-decadal  modes, Atlantic Multi-decadal Variability (AMV) and \r\nPacific Decadal Variability (PDV); the interannual-to-annual modes, \r\nEl Nino-Southern Oscillation (ENSO), the Indian Ocean Dipole (IOD), \r\nthe Southern Annular Mode (SAM) and the North Atlantic Oscillation \r\n(NAO); seasonal variability driven by the meridional migration of the \r\nInter-tropical Convergence Zone (ITCZ) and the timing and intensity \r\nof the South American Monsoon System (SAmerM, Section 8.3.2.4.5), \r\nthe Madden-Julian Oscillation sub-seasonal mode of natural \r\nvariability (MJO) and the behaviour at finer scales of the tropical \r\neasterly waves."


id: 369f826af6594a4ee10e5267f8c59ed3
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 6843
content:
"There is high agreement and high confidence in the projected decrease \r\nof precipitation by the end of the century for most of the region, \r\nparticularly for annual and summer precipitation, but there is low \r\nconfidence on the magnitude of this decrease which varies between \r\n5% and 50% for different projections and different sub-regions \r\n(see extended information in the Interactive Atlas)."


id: 9efb75c56d7a78f9971960e891d6c268
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 25198
content:
"The status of climate extreme trends and projections for the \r\nregion has been assessed in Chapter  11 and the main findings \r\nare synthesized here. There is high confidence in the projections \r\nof significant heatwave events at the end of the century in SCA \r\n(Angeles-Malaspina et al., 2018) and an increase in warm days and \r\nwarm nights over this region and CAR (Stennett-Brown et al., 2017). \r\nFor CAR islands, using dynamically downscaled CMIP3 models, \r\nKarmalkar et  al. (2013) projected an increase in drought severity \r\nat the end of the century, mainly due to a  precipitation decrease \r\nduring the early wet season. In SCA, projections suggest an increase \r\nin the MSD (Imbach et al., 2018) and an increase in consecutive dry \r\ndays (Chou et al., 2014), consistent with the projections of Stennett\u0002Brown et al. (2017)."


id: d90388257af732393b9d42546ae91214
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 35477
content:
"Significant warming trends between 0.2degC and 0.3degC per decade \r\nhave been observed in the three reference regions of Central \r\nAmerica in the last 30 years, with the largest increases in the North \r\nAmerican  Monsoon region (high confidence). Changes in mean \r\nprecipitation rates are less consistent and long-term trends are \r\ngenerally weak. Small positive trends were observed in the total \r\nannual precipitation in part of the region."


id: 12231bb7166e949f2bb7847972b0ca74
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 380
content:
"Warming in the continental part of the region is projected to increase \r\nin the range of the mean global values for GWL of 1.5degC and 3degC, \r\nbut in the Caribbean regional warming will be lower. Precipitation \r\nis projected to decrease with increasing GWLs, especially for CAR \r\nand SCA."


id: c93367c909ea911e0d6240c9f26d6955
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 32722
content:
"maximum temperature generally increasing faster than the minimum \r\ntemperature, while a negative trend of up to -0.5degC per decade was \r\nobserved over the tropics."


id: ac2297a3c2ff14f9a142791e9f9ca3e4
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 27531
content:
"According to AR5 WGII Chapter 27 (Magrin et al., 2014), during the \r\nlast decades of the 20th  century, observational studies identified \r\nsignificant trends in precipitation and temperature in South America \r\n(high confidence). Increasing trends in annual rainfall in South\u0002Eastern South America contrast with decreasing trends in central \r\nsouthern Chile and some regions of Brazil. Warming has been \r\ndetected throughout South America (near 0.7degC-1degC in the 40 years \r\nsince the mid-1970s), except for a cooling off the Chilean coast of \r\nabout -1degC over the same period."


id: b3e9ee797a9cc4e9fdc72faa634e8b58
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 28898
content:
"The AR5 WGI (Flato et al., 2013) noted that climate simulations from \r\nCMIP3 and CMIP5 models were able to represent well the main \r\nclimatological features, such as seasonal mean and annual cycle \r\n(high confidence), although some biases remained over the Andes, \r\nthe Amazonian basin and for the South America Monsoon. On the \r\nother hand, climate models from CMIP5 showed better results when \r\ncompared to CMIP3."


id: d97d16b84584b5bfc3b93f5c61cf8734
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 35558
content:
"The SR1.5 (Hoegh-Guldberg et  al., 2018) assessed that a  further \r\nincrease of 0.5degC or 1degC is likely to have detectable effects on \r\nmean temperature and precipitation in South America, particularly \r\nin tropical regions (NWS, NAS, SAM and NES), as well as in SES, \r\ngiven that changes in mean temperatures and precipitation have \r\nalready been attributed in the last decades for global warming of \r\nless than 1degC."


id: c1faed6cb13f863c9af6d26f957a88e9
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 31426
content:
"Studies on climatic trends in South America indicate that mean \r\ntemperature and extremely warm maximum and minimum \r\ntemperatures have shown an increasing trend (high confidence), \r\nparticularly for a  large region in Northern South America and the \r\nsouth-western Andes (NSA, SAM, NES, SWS and the north of SES; \r\nSkansi et  al., 2013; de Barros Soares et  al., 2017). Also, the trend \r\nof the difference between the annual mean of the daily maximum \r\ntemperature and the annual mean of the daily minimum temperature \r\nwas positive - up to 1degC per decade - over the extratropics with the "


id: 25245e5e33e2671b4db584e841303e23
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 3728
content:
"Since AR5, there has been limited attribution literature in the South \r\nAmerica. Recent publications based on observational and modelling \r\nevidence assessed that anthropogenic forcing in CMIP5 models \r\nexplains the overall warming (high confidence) over the entire \r\nSouth American continent, including the increase in the frequency of \r\nextreme temperature events (Hannart et al., 2015). It has a detectable \r\ninfluence in explaining positive and negative precipitation trends \r\nobserved in regions such as SES and the southern Andes (Vera \r\nand Diaz, 2015; de Barros Soares et al., 2017; Boisier et al., 2018; \r\nde Abreu et al., 2019). Despite that, there is limited evidence that \r\nhuman-induced greenhouse gas emissions had an influence on the \r\n2014/2015 water shortage in south-east Brazil (Otto et  al., 2015). \r\nExtreme event attribution on sub-continental scales is assessed in \r\nChapter 11 and continental-scale attribution in Chapter 3."


id: a53cf44bebbc43cbd5b5483aa98c2ad7
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 26301
content:
"In summary, analyses of historical temperature time series point \r\nstrongly to an increased warming trend (high confidence) across \r\nmany South American regions, except for a cooling off the Chilean \r\ncoast. Annual rainfall has increased over South-Eastern South \r\nAmerica and decreased in most tropical land regions, particularly in \r\ncentral Chile (high confidence). The number and strength of extreme \r\nevents, such as extreme temperatures, droughts and floods, have \r\nalready increased (medium confidence) (Table 11.7)."


id: e808030f8fdac8d8e2296443f43d7427
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 38136
content:
"It is noted that the major barrier to the study of climate change in \r\nmany regions of South America is still the absence or insufficiency \r\nof long time series of observational data (Carvalho, 2020; Condom \r\net al., 2020). Most national datasets were created in the 1970s and \r\n1980s, preventing a more comprehensive long-term trend analysis. \r\nTo fulfil the users' demand for climatological and meteorological \r\ndata products covering the whole region, several interpolation \r\ntechniques have been used with reanalysis and gridded gauge\u0002analysis products to add the necessary spatial detail to the \r\nclimate analyses over land and for climate variability and trend \r\nstudies, but these are subject to uncertainties (Skansi et al., 2013; \r\nRozante et al., 2020)."


id: aa28f0e64c580215e74d1f5caa58a4b5
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 27159
content:
"Since AR5 the number of publications on climate model performance \r\nand their projections in South America has increased, particularly for \r\nregional climate modelling studies (Giorgi et  al., 2009; Boulanger \r\net  al., 2016; Ambrizzi et  al., 2019) and the understanding of their \r\nstrengths and weaknesses (high confidence)."


id: 7efdeb95d07dc0482d4020977e1e3bab
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 19493
content:
"Most global and regional climate models can simulate reasonably \r\nwell the current climatological features of South America, such as \r\nseasonal mean and annual cycles. However, significant biases persist \r\nmainly at regional scales (high confidence) (Blazquez and Nunez, \r\n2013b; Gulizia et al., 2013; Joetzjer et al., 2013; Jones and Carvalho, \r\n2013; Torres and Marengo, 2013; Gulizia and Camilloni, 2015; \r\nZazulie et  al., 2017; Abadi et  al., 2018; Barros and Doyle, 2018; \r\nSolman and Blazquez, 2019; Fan et  al., 2020; Rivera and Arnould, \r\n2020; Teichmann et al., 2021). During the dry season, precipitation \r\nis underestimated in most models over Amazonia (medium evidence, \r\nhigh agreement) (Torres and Marengo, 2013; Yin et al., 2013; Solman \r\nand Blazquez, 2019). Over regions with complex orography, such as "


id: 6efd932597b5cc86f8600fa293c3ff9e
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 16799
content:
"The evaluation of statistical downscaling models (ESD) in representing \r\nregional climate features in South America has increased since AR5, \r\nhowever there are still few ESD studies over the different sub-regions. \r\nPrecipitation simulations based on ESD models are able to reproduce \r\nmean precipitation over tropical and subtropical South American \r\nregions, especially over maximum precipitation areas in western \r\nColombia, south-eastern Peru, central Bolivia, Chile and the La Plata \r\nbasin (medium confidence) (Souvignet et  al., 2010; Mendes et  al., \r\n2014; Palomino-Lemus et al., 2015, 2017, 2018; Soares dos Santos \r\net al., 2016; Troin et al., 2016; Borges et al., 2017; Bettolli and Penalba, \r\n2018; Araya-Osses et  al., 2020; Bettolli et  al., 2021). Temperature \r\nsimulations are fewer but show added value to GCM simulations \r\n(medium evidence, high agreement) (Souvignet et al., 2010; Borges \r\net al., 2017; Bettolli and Penalba, 2018; Araya-Osses et al., 2020)."


id: 293a4c7473a220c9aaad995bb333eed4
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 4466
content:
"Overall, climate modelling has made some progress in the past \r\ndecade but there is no model that performs well in simulating all \r\naspects of the present climate over South America (high confidence). \r\nThe performance of the models varies according to the region, time \r\nscale and variables analysed (Abadi et al., 2018). There is also a fairly \r\nnarrow spread in the representation of temperature and precipitation \r\nover South America by the CMIP5 GCMs and also the RCMs, with \r\nbiases that can be associated with the parametrizations and schemes \r\nof surface, boundary layer, microphysics and radiation used by the \r\nmodels. Finally, observational reference datasets, such as reanalysis \r\nproducts, used in the calibration and validation of climate models can \r\nalso be quite uncertain and may explain part of the apparent biases \r\npresent in climate models (high confidence)."


id: c60d620261ba58ba305a44cbd474ff3
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 32154
content:
"Projected changes in seasonal precipitation and their uncertainties \r\ngenerally agree with the annual changes, particularly for the decreases \r\nin SWS (Figure Atlas.22). DJF precipitation changes in NSA and SAM \r\nare largely uncertain, with weak agreements in the projections, \r\nparticularly for CMIP5 and CMIP6 ensembles, which project almost \r\nno change, and decreasing precipitation for NSA and a narrow range \r\nfrom slight increases to no change respectively for SAM."


id: 4730fcab8257b092259854ca319ae9a
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 9722
content:
"It is very likely that annual mean temperature will increase over South \r\nAmerica, with a  wide range of projected changes of 1.0degC-6.0degC \r\nby the end of the 21st  century (from RCP2.6/SSP1-2.6 to RCP8.5/\r\nSSP5-8.5 emissions, Figure Atlas.22). Overall, GCMs project higher \r\ntemperature change than RCMs in austral summer and winter \r\nover all sub-regions and in winter mainly over the central part of \r\nthe continent (Interactive Atlas; Coppola et al., 2014; Llopart et al., \r\n2021; Teichmann et al., 2021). The largest warmings over the South \r\nAmerican continent are projected for the Amazon basin (SAM and \r\nNSA) and the central Andes range (southern SAM, northern SWS \r\nand south-eastern NWS; Figure Atlas.22), especially during the dry \r\nand dry-to-wet transition seasons (austral winter and spring) (high \r\nconfidence) (Blazquez and Nunez, 2013a; Coppola et  al., 2014; \r\nPabon-Caicedo et al., 2020; Teichmann et al., 2021)."


id: 30f6fa3d922738d22717c659a6e1ed04
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 5865
content:
"In summary, it is virtually certain that the climate of South America \r\nhas warmed. Studies on climate trends in South America indicate that \r\nmean temperature and maximum and minimum temperatures have \r\nincreased over the last 40 years. Long-term observed precipitation \r\ntrends show an increase over South-Eastern South America and \r\ndecreases in most tropical land regions (high confidence)."


id: ea43d4963a5c2a98bae39710d80c1891
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 38572
content:
"Evaluation of global and regional climate model simulations have \r\nincreased over South America in the past decade and shown \r\nimproved performance. However, the results reveal that no model \r\nperforms well in simulating all aspects of the present climate (very \r\nlikely). On the other hand, there is still a lack of high-quality and high\u0002resolution observational data that may explain part of the important \r\nbiases present in climate models (high confidence)."


id: b5fc6039cd2561552651a78181c0fc40
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 29240
content:
"Using warming levels (Figure Atlas.22), the temperature is projected \r\nto increase at or above the level of global warming in all regions \r\napart from SSA with additional warming (compared to a 1995-2014 \r\nbaseline) of over 4degC for the 4degC warming level in NSA and SAM. \r\nChanges for other warming levels, sub-regions and emissions \r\npathways are shown in Figure Atlas.22 and can be explored with the \r\nInteractive Atlas."


id: eea9d796170f160772b0abe85decda81
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 39368
content:
"Climate model projections show a general increase in annual mean \r\nsurface temperature over the coming century for all emissions \r\nscenarios (RCPs and SSPs) (high confidence), consistent with the \r\nobserved warming, and with all regions except SSA warming faster \r\nthan the global average. Unlike temperature, annual precipitation \r\nhas patterns of decrease in North-Eastern South America (NES) \r\nand South-Western South America (SWS), and increase in Southern \r\nSouth America (SES) and North-Western South America (NWS) "


id: bf24ab28f0b1d9370064a211038a4e74
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 30904
content:
"(high  confidence), with small changes projected under a  low\u0002emissions scenario. However, there is low confidence in the magnitude \r\nbecause of the large spread among models, both GCMs and RCMs."


id: 79bb7ef2a90c25f2052c787bf31b1f56
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 18624
content:
"warming being greatest in high latitudes in Northern Europe. Annual \r\nprecipitation has increased in Northern Europe and decreased in \r\nparts of Southern Europe. The SROCC (Hock et  al., 2019b) reports \r\nwith high confidence that a reduction in snow cover at low elevation \r\nand glacier extent is observed in recent decades, with consequent \r\nchanges in annual and seasonal runoff patterns. According to the \r\nSRCCL report (IPCC, 2019b) there is high agreement that observed \r\nvegetation greening and forestation in the last 30 years cools \r\nsummer surface temperature and warms winter temperature due \r\nto decreased snow cover and increased snow shading in forested \r\nareas. It is very likely that aerosol column amounts have declined \r\nover Europe since the mid-1980s."


id: 9af2edc5485ee2e597d93c95b1a00d9
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 24507
content:
"The assessment in this section focuses on changes in average \r\ntemperature and precipitation (rainfall and snow), including the most \r\nrecent years of observations, updates to observed datasets, the \r\nconsideration of recent studies using CMIP5 and those using CMIP6 \r\nand CORDEX simulations. Assessment of changes in extremes is \r\nin Chapter  11 (Tables 11.16-11.18) and climatic impact-drivers in \r\nChapter 12 (Table 12.7)."


id: a70de4edb14c4ddf82c80e80292452f9
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 26604
content:
"Westerly winds and the accompanying Atlantic storm track with \r\ncyclones and anticyclones travelling from the Atlantic towards \r\ninland Europe are the main climatic features that characterize daily \r\nto interannual variability in the European region. The Siberian High \r\nin winter determines cold weather in Eastern Europe and can affect \r\nother regions with cold outbreaks. Intra-seasonal and interannual \r\nvariations are driven by modes of climate variability such as the \r\nNorth Atlantic Oscillation (NAO; Table Atlas.1 and Annex IV.2). \r\nGlobal warming can lead to systematic changes in regional climate \r\nvariability via thermodynamic responses such as altered lapse \r\nrates (Kroner et al., 2017; Brogli et al., 2019) and land-atmosphere \r\nfeedbacks (Zampieri and Lionello, 2011; Boe and Terray, 2014). \r\nRegional feedbacks involving the land-sea contrast, sea surface, land \r\nsurface, clouds, aerosols, radiation and other processes modulate the \r\nregional response to enhanced warming."


id: 38a49fcdb57c9fb99d8425c0636d0c40
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 7169
content:
"Four climatic regions are defined for Europe (Figure Atlas.24). The \r\nMediterranean region (MED) in the south is characterized by mild winters \r\nand hot and dry summers (Mediterranean climate; Section 10.6.4.2). It \r\ncovers both Europe and Africa, and MED assessments in this section \r\ngenerally imply the entire MED domain unless stated otherwise. The \r\nWestern and Central Europe region (WCE) has distinct summer and \r\nwinter seasons with increasing continentality of climate eastwards. \r\nThe Northern Europe region (NEU), close to the Atlantic Ocean, is \r\ncharacterized by high humidity and relatively mild winters, and strong \r\nexposure to the Atlantic storm track. Eastern Europe (EEU) covers the \r\nwestern part of Russia and neighbouring territories and has continental \r\ncharacteristics. Many regional datasets and model projections assessed \r\nhere do not sufficiently cover the EEU region."


id: 7c36ec1093e7820018888655a0c31abc
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 19065
content:
"The AR5 WGII (Kovats et  al., 2014) reports with high confidence\r\nthat observed climate trends show regionally varying changes \r\nin temperature and rainfall in Europe. The average temperature \r\nin Europe has continued to increase, with seasonally different rates of "


id: 5d6984d99d1aaa02ea565d06360ad07c
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 13723
content:
"is shown to add accuracy and reliability in comparison to global \r\nreanalysis products, but also introduces additional uncertainties, \r\nespecially for threshold-based climate indices (Kaiser-Weiss et  al., \r\n2019). However, gridded European datasets are unreliable over data\u0002sparse regions. Also, many datasets employ different approaches \r\nto interpolation and gridding, which adds to their uncertainty and \r\ncomplicates comparative evaluations (Fantini et al., 2018; Kotlarski \r\net  al., 2019; Berthou et  al., 2020). For some sub-regions and \r\nperformance metrics, differences between datasets have been shown \r\nto be of the same magnitude as errors in regional climate models \r\n(Prein et al., 2016; Prein and Gobiet, 2017; Fantini et al., 2018), but \r\nobservational uncertainty is substantially reduced when datasets of \r\nsimilar nature and representativeness are used (Kotlarski et al., 2019)."


id: cdde100c43fcc7aff0872c302e94e18e
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 33548
content:
"more frequent and severe meteorological droughts have occurred \r\nbetween 1960 and 2000 (Spinoni et al., 2015a; Gudmundsson and \r\nSeneviratne, 2016), and in some regions cannot be explained without \r\nanthropogenic forcing (Section 10.4.1.2; Knutson and Zeng, 2018). \r\nOther studies suggest that this trend can be seen as an expression of \r\nmulti-decadal internal variability driven mainly by the North Atlantic \r\nOscillation (Table Atlas.1; Kelley et  al., 2012; Zittis, 2018). Global \r\ndimming and brightening also are reported to affect precipitation \r\ntrends in the Mediterranean region (Section 8.3.1.6 and Figure 8.7)."


id: e12d9b8a89a77c6b0870d0bb337e6e4d
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 36883
content:
"In addition to the global display of observed temperature and \r\nprecipitation trends in Figure Atlas.11, annual mean temperature \r\nand precipitation trends between 1980 and 2015 calculated from \r\nthe gridded ensemble E-OBS dataset (Cornes et al., 2018) are shown \r\nin Figure Atlas.23, together with time series of temperature and \r\nprecipitation anomalies relative to the 1980-2015 mean value from \r\nE-OBS, CRU, EWEMBI and Berkeley for temperature, and E-OBS, CRU, \r\nGPCC and GPCP for precipitation (see also Figure  2.11 for global \r\nmean values, and Atlas.1.4.1 for description of global datasets)."


id: e098688d76880f0b8dc9f225166052b7
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 36768
content:
"Multi-decadal trends in mean precipitation are generally small and \r\nnon-significant. Apart from difficulties related to observational \r\ncoverage (Prein and Gobiet, 2017), gauge undercatch (e.g., Murphy \r\net  al., 2020) and data inhomogeneity (e.g.,  Camuffo et  al., 2013), \r\nstrong interannual and multi-decadal variability is dominant over \r\nat least the last two centuries. However, significant precipitation \r\ntrends have been recorded for recent periods, for example in south\u0002western Europe between 1960 and 2000 (Pena-Angulo et al., 2020), \r\nand between 1961 and 2015 in NEU (Interactive Atlas). Also, some \r\nstudies suggest that in the MED precipitation has declined and "


id: 721a2f1232f103ea56093f235928a12b
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 17320
content:
"The increasing trend in surface shortwave radiation, documented in \r\nAR5 (Hartmann et al., 2013) to have occurred since the 1980s and \r\nreferred to as a  brightening effect, is substantiated over Europe \r\nand the Mediterranean region (Nabat et al., 2014; Sanchez-Lorenzo \r\net  al., 2015; Cherif et  al., 2020). This increasing trend has been \r\nattributed to the decrease in anthropogenic sulphate aerosols over \r\nthe 1980-2012 period (Nabat et  al., 2014). In model sensitivity \r\nexperiments, the aerosol trend has been quantified to explain \r\n81 +- 16% of the European surface shortwave trend and 23 +- 5% "


id: bb99562d2e4edef9fcb74d50bfe35a5c
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 30229
content:
"Assessments of observed European trends in meteorological extremes \r\nand CIDs are reported elsewhere in this report. Section  11.3.5 \r\ndocuments and attributes an increase in the frequency and extent \r\nof heatwaves and daily maximum temperatures, and Section 11.6.2 "


id: e328455d05fefca084fb064df40918db
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 37264
content:
"of the European surface temperature warming. It is likely that \r\ntrends in anthropogenic aerosols in Europe have generated positive \r\ntrends  in shortwave radiation and surface temperature since the \r\n1980s (Sections 6.3.3.1, 8.3.1.6 and 10.6.4)."


id: 8115101a041c278247a3e330244bd002
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 19848
content:
"discusses the uncertainty concerning the detection of trends in \r\nmeteorological droughts, and the role of increasing atmospheric \r\nevaporative demand on hydrological and ecological/agricultural \r\ndroughts. Section  8.3.1.8 reports on increasing aridity trends in \r\nthe Mediterranean related to soil moisture declines and increases \r\nin atmospheric water vapor demand. Section 11.4.2 reports on the \r\nincreased likelihood and intensity of daily precipitation extremes, \r\nwhile Sections 11.5.2 and 12.4.5.2 discuss implications for peak \r\nstreamflow. Section  12.4.5.5 discusses the increased likelihood of \r\nwildfires, while Section  12.4.5.3 discusses the substantial decadal \r\nvariability in mean wind speed and the trends in wind storms and \r\ngusts. The acceleration of sea level rise in the Atlantic and European \r\nseas has been discussed in Section 12.4.5.5."


id: 5566cc4540f03c46142e9b753c139364
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 12302
content:
"Nabat et al. (2014) argued that including realistic aerosol variations \r\nenables climate models to correctly reproduce the summer warming \r\ntrend (as is required for attributing continental annual temperature \r\ntrends, Section  3.3.1.1). However, other studies showed models to \r\nbe sensitive also to local effects, such as land surface processes, \r\nconvection, microphysics and snow albedo (Vautard et  al., 2013; \r\nDavin et al., 2016). In Euro-CORDEX the warm and dry summer bias \r\nover southern and south-eastern Europe is reduced compared to \r\nthe previous ENSEMBLES simulations (Katragkou et al., 2015; Giot \r\net al., 2016; Prein and Gobiet, 2017; Dell'Aquila et al., 2018). Natural \r\nvariability has strongly affected the historical warming and large \r\nensembles are necessary for a correct estimation of the forced signal \r\nversus natural variability (Aalbers et al., 2018; Lehner et al., 2020)."


id: 8a7c16e0bdf415a0159f7ea76ee185a6
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 21535
content:
"Specific assessments of convection-permitting RCMs (CPRCMs, \r\nrunning at a resolution of typically 1 to 3 km and designed for extreme \r\nprecipitation characteristics) is undertaken in Section  10.3.3.4.1. \r\nA  unique CPRCM ensemble has been applied over the great \r\nAlpine domain and improves representation of mean and extreme \r\nprecipitation compared to coarser resolution models (Ban et  al., \r\n2021; Pichelli et al., 2021). The role of aerosol forcing is increasingly \r\nanalysed as new and more realistic aerosol datasets become available \r\n(Nabat et  al., 2013; Pavlidis et  al., 2020), and as RCMs begin to \r\ninclude interactive aerosols (Nabat et  al., 2012, 2015, 2020; Druge \r\net al., 2019). Explicitly accounting for aerosol effects in RCMs leads to \r\nimproved representation of the surface shortwave radiation at various \r\nscales: long-term means (Gutierrez et al., 2018), day-to-day variability \r\n(Nabat et al., 2015), and long-term trends (Nabat et al., 2014)."


id: 45a442f1fa1062d9e92af44bc1535fd7
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 9468
content:
"A global evaluation of annual mean temperature and precipitation \r\nfrom the CMIP6 ensemble is presented in Sections 3.3.1 and \r\n3.3.2 respectively. In general, annual mean temperature is slightly \r\nunderestimated at high latitudes and overestimated in the MED area. \r\nTemporal evolution of decadal temperature oscillations in Europe \r\nsimulated by the CMIP6 historical simulations is well reproduced \r\n(Fan et al., 2020). Fernandez-Granja et al. (2021) report an overall \r\nimprovement of CMIP6 compared to CMIP5 to reproduce atmospheric \r\nweather patterns over Europe."


id: f279469372b4112e74745e61253a6db3
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 40051
content:
"Statistical downscaling methods are assessed in Section  10.3.3.7, \r\nincluding the intercomparison and evaluation activities performed in \r\nthe framework of VALUE and Euro-CORDEX over Europe."


id: d9dc138ee59a64e78d651225be13e776
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 35626
content:
"In line with findings reported in Section  10.3.3.8, several studies \r\nargue that both GCMs and RCMs underestimate the observed \r\ntrend in European summer temperature (Dosio, 2016; Boe et  al., \r\n2020b), indicating that essential processes are missing or that the \r\nnatural variability is not correctly sampled (Dell'Aquila et al., 2018). "


id: 689d950dc4189cf89a6778d5e7581ad0
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 15673
content:
"Simulations from CMIP5 and CMIP6 indicate pronounced \r\ngeographical patterns and scenario dependence of the projections of \r\nmean temperature and precipitation. Global warming projected under "


id: aec7d653df58c1aacdf34191b7fcd791
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 27989
content:
"low confidence in the CORDEX simulations. Precipitation in JJA in \r\nEEU is reduced in CMIP6, while little change is shown in CMIP5. \r\nQuantitative estimations of climate change features from regional \r\nclimate projections in Eastern Europe (Partasenok et  al., 2015; \r\nKattsov et al., 2017) have low confidence due to the use of relatively \r\nsmall ensembles of GCMs and/or RCMs, and limited evaluation of \r\nmodel performance in the region."


id: 4b6ac89b5cdd61eeb0969f64aaed9776
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 10519
content:
"Over specific geographic features such as high mountains, RCMs \r\nfurther modify the climate change signal of precipitation simulated \r\nby the low-resolution GCMs (Giorgi et al., 2016; Torma and Giorgi, \r\n2020). This is especially true for summer precipitation over the Alps \r\nwhere opposite signs of changes in mean and extreme precipitation \r\nare generated by the CMIP5 GCM ensemble and the 12-km \r\nMed-CORDEX and EURO-CORDEX RCM ensembles (Section 10.6.4.7; \r\nGiorgi et al., 2016)."


id: 79a37270372bd637545a047f02936386
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 18604
content:
"Regional ocean warming in projections with RCSMs for the Baltic and \r\nNorth seas (Groger et al., 2015) and for the Mediterranean (Darmaraki \r\net al., 2019) is associated with increased intensity and frequency of \r\nmarine heatwaves in the Mediterranean (Section  12.4.5.5), strong \r\nfreshening in the Baltic, and, for some simulations, changes in the \r\ncirculation in response to non-uniform changes in air-sea interaction \r\n(Dieterich et al., 2019). Med-CORDEX RCSM and CMIP5 GCM results \r\nagree well on the Mediterranean SST warming rate (Mariotti et al., \r\n2015; Darmaraki et al., 2019); see also the Interactive Atlas."


id: d19ba603011889c8390f727c21ca96b9
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 34226
content:
"Assessments of projected changes in meteorological extremes and \r\nCIDs are reported elsewhere in this report. Extreme precipitation \r\nand  temperature often exhibit a  different response to global \r\nwarming than mean values. Increased intensity and frequency of \r\nextreme temperatures and heatwaves is assessed in Sections 11.3.5 \r\nand 12.4.5.1. Changes in the hydrological cycle include enhanced \r\nsoil moisture decline in southern Europe, drying in summer and \r\nautumn in Central Europe, and spring drought due to early snowmelt \r\nin Northern Europe (Sections 8.4.1, 11.6.5 and 12.4.5.2). Changes in \r\nmean and extreme wind are very uncertain (Section 12.4.5.3), while \r\nsea level rise will increase the frequency of occurrence of extreme sea \r\nlevel at most European coasts (Section 12.4.5.5)."


id: 6237675dbf41e7ce3ed287bba5896c7a
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 14555
content:
"Changes in precipitation clearly show a  seasonal signature and \r\na  meridional gradient over Europe. Mean precipitation increases \r\nby 4-5% per degC of global warming in NEU, EEU and WCE in DJF, \r\nand decreases in summer in WCE and MED (Figure Atlas.24; Jacob \r\net al., 2018). CMIP5 projections of precipitation change in MED are \r\nstrongest in DJF in the south, while changes in JJA are dominant in \r\nthe northern (European) part of MED (Lionello and Scarascia, 2018). \r\nThe European north-south gradient in precipitation response is \r\nconfirmed by the EURO-CORDEX experiment (Coppola et al., 2021a), \r\nbut Figure Atlas.24 shows that the JJA precipitation reduction in \r\nWCE projected by CMIP5 and CMIP6 at higher warming levels has "


id: 3e7eb2a271332f0b71d0227c1f071144
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 8179
content:
"It is virtually certain that annual mean temperature continues to \r\nincrease in each European region. There is medium confidence that \r\nannual mean precipitation in NEU, WCE and EEU has increased since \r\nthe early 20th  century. In the European Mediterranean trends in \r\nannual mean precipitation contain substantial spatial and temporal \r\nvariability (medium confidence). It is very likely that since the early \r\n1980s in snow-dominated areas in NEU and EEU the length of the \r\nsnowfall season is reduced with regional warming, and the melt \r\nonset dates have advanced. It is likely that decreasing trends in \r\nanthropogenic aerosols in Europe have generated positive trends \r\nin shortwave radiation and surface temperature since the 1980s."


id: 4a4eb01342a92af9d52fb66d28135a6c
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 10312
content:
"An assessment of recent literature largely confirms the findings of \r\nprevious IPCC reports but with additional detail and (in some cases) \r\nhigher confidence due to improvements in observations, reanalyses \r\nand methods. Observational datasets with global coverage are \r\ncomplemented by the E-OBS gridded ensemble temperature and \r\nprecipitation dataset, a  range of regional observational analyses, \r\nand regional reanalysis products. New RCM experiments, including \r\nCPRCMs and regional coupled climate system models, mostly \r\ncoordinated under the umbrella of CORDEX, have generated many \r\nnew projections and process studies."


id: 770085c587a3f99d870dd533e3345cc0
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 18140
content:
"At increasing levels of global warming, there is very high confidence \r\nthat temperature will increase in all European areas at a  rate \r\nexceeding global mean temperature increases, while increased \r\nmean precipitation amounts at high latitudes in DJF and reduced JJA \r\nprecipitation in southern Europe will occur with medium confidence\r\nfor global warming levels below 2degC, and with high confidence for \r\nhigher warming levels. At high latitudes and low-altitude mountain \r\nareas in Europe strong declines in snow accumulation are virtually \r\ncertain to occur with further increasing regional temperatures (very \r\nhigh confidence)."


id: 2789da946b67ada61d10ae5c1142b6ba
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 4134
content:
"The representation of mean European climate features by GCMs and \r\nRCMs is improved compared to previous IPCC assessments (medium \r\nconfidence), in spite of persisting biases in annual mean and seasonal \r\ntemperature and precipitation characteristics. The added value of \r\nregional downscaling of GCMs by RCM projections for summer mean \r\ntemperature, precipitation and shortwave radiation is constrained \r\nby the representation of processes that lead to a  systematic \r\ndifference between RCM and driving GCM, such as aerosol forcing \r\n(medium confidence)."


id: e8914465160d84048f55aab275751ca9
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 38254
content:
"causes (high confidence), in particular with primarily increasing \r\nannual precipitation and annual temperature (very high confidence). \r\nAssessment of CMIP5 ensemble projections concluded that mean \r\nannual temperature over North America and annual precipitation \r\nnorth of 45degN will very likely continue to increase in the future. Also, \r\nCMIP5 projects increases in winter precipitation over Canada and \r\nAlaska and decreases in winter precipitation over the south-western \r\nUSA and much of Mexico."


id: 8cc72376c64cbb800854460028850c13
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 21955
content:
"The assessment in this section focuses on changes in average \r\ntemperature and precipitation (rainfall and snow) for North America, \r\nincluding the most recent years of observations, updates to observed \r\ndatasets, the consideration of recent studies using CMIP5 and those \r\nusing CMIP6 and CORDEX simulations. Assessment of changes in \r\nextremes is in Chapter  11 (Tables 11.19-21) and climatic impact\u0002drivers in Chapter 12 (Table 12.8)."


id: 8dbcbc020e5299b1359f8207c69ed3ce
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 22128
content:
"The CMIP5 multi-model ensemble generally reproduces the observed \r\nspatial patterns but somewhat underestimates the extent and \r\nintensity of the North American Monsoon, and also underestimates \r\nwetting over Central North America over the period of 1950-2012 \r\nduring the winter season according to AR5 (Flato et  al., 2013). In \r\nthe long term (2081-2100), the largest changes of precipitation over \r\nNorth America are projected to occur in the mid- and high latitudes \r\nand during winter (Kirtman et al., 2013)."


id: 5202f68c7830dbd4925600b98cf10c13
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 11702
content:
"The SR1.5 (Hoegh-Guldberg et al., 2018) reported a stronger warming \r\ncompared to the global mean over Central and Eastern North \r\nAmerica, and a weakening of storm activity over North America under \r\n1.5degC of global warming. The SROCC (Hock et al., 2019b) reported \r\nthat snow depth or mass is projected to decline by 25% mainly at \r\nlower elevations over the high mountains in Western North America. \r\nThe SRCCL (Mirzabaev et al., 2019) observed vegetation greening in \r\nCentral North America with high confidence."


id: 8922595d6a21077b11b19ec41ccdfa05
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 21294
content:
"Compared to temperature, trends in annual precipitation over \r\n1961-2015 are generally non-significant though there are consistent \r\npositive trends over parts of ENA and CNA (Figure Atlas.11 and Daymet, \r\nInteractive Atlas) (high confidence). The global and regional datasets \r\nin Figure Atlas.11 and the Interactive Atlas also indicate significant \r\ndecreases in precipitation in parts of south-western USA and north\u0002western Mexico (Figure 2.15) though these are not all spatially coherent \r\nso there is only medium confidence in a drying trend over this region."


id: 7712259c041ecc8283e1cfc7955976f0
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 18148
content:
"The IPCC AR5 (Bindoff et al., 2013; Hartmann et al., 2013) found that \r\nthe climate of North America has changed due to anthropogenic "


id: 734b5afb064aaf4e17e24741a2a8a35e
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 17536
content:
"spring snowmelt in northern Canada was driven by warming-induced \r\nhigh-latitude changes such as atmospheric moisture, cloud cover, and \r\nenergy advection (Mioduszewski et al., 2014). "


id: ca04c277bc540bda0ec678a069cde093
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 32871
content:
"Several factors account for the differences in temperature and \r\nprecipitation trend significance. Observed trends in precipitation are \r\nrelatively modest compared to the very large natural interannual \r\nvariability of precipitation. Furthermore, the precipitation observing \r\nnetwork is spatially inadequate (Section  10.2.2.3) and temporally \r\ninconsistent (Section 10.2.2.2) over some regions of North America, \r\nparticularly over the Arctic and mountainous areas. So detection of \r\nmulti-decadal trends is difficult, especially for regions with summer \r\nconvective precipitation maxima that may be spatially patchy \r\n(Easterling et  al., 2017). See Section  2.3 for further discussion of \r\nprecipitation trends."


id: db53e041a22473e281bdd9b2278d855
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 35856
content:
"In an analysis of drivers of the record low snow water equivalent \r\n(SWE) values of spring 2015 in the western USA, it was found that the \r\nrelative importance of greenhouse gases varies spatially (Mote et al., \r\n2016). See also Section 3.4.2 for further discussion of anthropogenic \r\ninfluences on snow extent."


id: 9c0d902ef81032ca7516f802bd2b92d6
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 24708
content:
"Rupp et al. (2013) applied a standard fingerprinting approach to CMIP5 \r\nmodels and determined that the decline in Northern Hemisphere \r\nspring snow cover extent could only be explained by simulations \r\nthat included natural and anthropogenic forcing. In an attribution \r\nstudy focusing on direct physical causes, it was found that increased "


id: 48feaf45bd95ee0d497fc203dd9271c3
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 10065
content:
"North America has been extensively used as a test bed for regional \r\nclimate model (RCM) experiments, such as the North American \r\nRegional Climate Change Assessment Program (NARCCAP; \r\nMearns et al., 2009), the MultiRCM Ensemble Downscaling (MRED; "


id: 40668ad3e931496c9baeb779aa52e428
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 8523
content:
"However, deficiencies have also been reported. For example, excessive \r\nstorm occurrence over the east coast of North America was found \r\n(Poan et  al., 2018), and amplitude in the simulated annual cycle \r\nwas generally excessive in NA-CORDEX simulations. RCMs tend to \r\nproduce more (less) precipitation over mountains (the coastal plains; \r\nCerezo-Mota et  al., 2016) and winter precipitation in the western \r\nUSA had large positive biases in all RegCM simulations, regardless of \r\nthe driving GCM (Mahoney et al., 2021)."


id: 5d30e31e8ae028f21b7f20b3cd99698e
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 13671
content:
"Recently, convective-permitting RCMs have been used to simulate \r\nNorth American climate features and generated better simulations \r\nof precipitation. For example, summer precipitation over the \r\nsouth-western USA was improved due to better representation \r\nof organized mesoscale convective systems at the sub-daily scale \r\n(Castro et al., 2012; Liu et al., 2017; Prein et al., 2017a; Pal et al., \r\n2019), the diurnal cycle of convection (Nesbitt et  al., 2008), and \r\nin terms of means (and extremes) for the north-eastern USA \r\n(Komurcu et al., 2018)."


id: b86bc81af3a6ae8683b704ae289a9aba
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 29656
content:
"projecting increases. Projections from the NA-CORDEX ensemble are \r\nconsistent with those from the GCMs whilst providing greater detail \r\nof precipitation changes over the mountains and along the coasts \r\n(Interactive Atlas; Bukovsky and Mearns, 2020). Similar results are \r\nfound in other analyses of RCM projections (Wang and Kotamarthi, \r\n2015; Ashfaq et  al., 2016; Teichmann et  al., 2021). Also, further \r\nanalysis of the NA-CORDEX projections showed substantial changes \r\nin weather types related to increased monsoonal flow frequency and \r\ndrying of the northern Great Plains in summer (Prein et al., 2019)."


id: b586648e11c1598010d960d95be92274
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 29161
content:
"There are striking contrasts in the seasonal results for precipitation \r\nfor the sub-regions (Figure Atlas.26). The northern regions and \r\nENA all show steady increases with the global warming levels \r\n(very high confidence). For example, the projected increases in the \r\nNEN region range from 7% in the near term to 40% at the end of \r\nthe 21st  century for the SSP5-8.5 scenario. In contrast, projected \r\nchanges for NCA are for significant decreases both on an annual \r\nbasis (Interactive Atlas) and in winter, and which become greater as \r\nwarming increases (Akinsanola et al., 2020b; Almazroui et al., 2021). \r\nThe other two regions (WNA and CNA) exhibit mainly increases in \r\nwinter. In summer, distributions are in general less uniform except \r\nfor NWN and NEN, which display steady increases with global \r\nwarming levels (but smaller than in winter). WNA and CNA mainly \r\nshow decreases (based on the median values) but with some models "


id: ddf3e9b1f3d774ce5bc6d442b91c4749
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 36310
content:
"In summary, NEN, NWN and most of ENA will very likely experience \r\nincreased annual mean precipitation, with greater increases at \r\nhigher levels of warming (very high confidence). In NCA decreases \r\npredominate on an annual basis and particularly in winter "


id: d47521b63cbe950767854d1aa5d0c05c
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 34701
content:
"for example in the western USA (Fyfe et al., 2017). It is, however, likely\r\nthat some high-latitude regions will rather experience an increase in \r\ncertain winter snow cover properties (Mudryk et al., 2018; McCrary \r\nand Mearns, 2019), due to snowfall increase (Krasting et al., 2013) \r\nprevailing over the warming effect. Discussion of changes in snow \r\nin the future is also covered in Section 9.5.3, but for larger regions."


id: d8661c8e967fdf326e797adee6cc6064
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 35371
content:
"Across North America it is very likely that positive surface temperature \r\ntrends are persistent. Across near-Arctic latitudes of North America, \r\nincreases are exceptionally pronounced, greater than 0.5degC per \r\ndecade (high confidence). In parts of Eastern and Central North \r\nAmerica it is likely that annual precipitation has increased over the \r\nperiod 1961-2015 but with no clear trends in other regions except \r\nfor parts of the south-western USA and north-western Mexico where \r\nthere is medium confidence in drying."


id: 15d0813cb482d27b813215d17edcf81e
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 1057
content:
"(high confidence). Projected changes in summer are highly uncertain \r\nthroughout other regions apart from the far northern parts of NEN \r\nand NWN which will likely experience increases (high confidence)."


id: f34f32e56a196b2613a4f4440c6c6f48
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 40182
content:
"Model representation of the climatology of mean temperature \r\nand precipitation has likely improved compared to AR5 over North \r\nAmerica. This is aided by continuous model development, and \r\nthe existence of new coordinated modelling initiatives such as \r\nNA-CORDEX. There is high confidence that downscaling a  subset \r\nof CMIP models that spans the range of climate sensitivities in the \r\nfull ensemble is critical for producing a  representative range of \r\ndynamically downscaled projections."


id: d2a2bdcd539af9b4a3f9572a7e3bb374
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 34405
content:
"It is virtually certain that annual and seasonal surface temperatures \r\nover all of North America will continue to increase at a rate greater \r\nthan the global average, with greater increases in the far north. It is \r\nvery likely, based on global and regional model future projections, \r\nthat on an annual time scale precipitation will increase over most of \r\nNorth America north of about 45degN and in Eastern North America, \r\nand it is likely that it will decrease in the south-western USA and \r\nnorthern Mexico, particularly in winter. Elsewhere the direction of \r\nchange of precipitation is uncertain. It is virtually certain that snow \r\ncover will experience a decline over most regions of North America \r\nduring the 21st  century, in terms of water equivalent, extent and "


id: 5c0d17039abf15a68ee87b808189ec4a
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 13454
content:
"It is virtually certain that snow cover will experience a general decline \r\nacross North America during the 21st  century, in terms of extent, \r\nannual duration and SWE, based on CMIP5 (Maloney et al., 2014), \r\nCMIP6 (Mudryk et al., 2020), NA-CORDEX (Mahoney et al., 2021) and \r\nNARCCAP (e.g., McCrary and Mearns, 2019) simulations. For some \r\nregions the decline could be discernible over the next few decades, "


id: e881238b39eac0785fc515e0d6e9b27f
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 38242
content:
"annual duration. It is, however, likely that some high-latitude regions \r\nwill rather experience an increase in winter SWE, due to the snowfall \r\nincrease prevailing over the warming effect."


id: 9540968d73de58fd5ca5cc022dc47c0c
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 23459
content:
"The assessment in this section focuses on changes in average \r\ntemperature and precipitation for the main Small Islands regions, \r\nincluding the most recent years of observations, updates to observed \r\ndatasets, the consideration of recent studies using CMIP5 and those \r\nusing CMIP6 and CORDEX simulations. Assessment of changes in \r\nextremes is in Chapter 11 (Sections 11.3.2, 11.4.2, 11.7.1.5 and, for \r\nthe Caribbean, Tables 11.13-15) and of changes in climatic impact\u0002drivers in Chapter 12 (Section, 12.4.7 and Table 12.9)."


id: af4cf4e04c36ff4415cee0d01f994ea2
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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index: climate_gpt_v2
vector_id: 28074
content:
"Significant positive trends in temperature ranging from 0.15degC per \r\ndecade (over the period 1953-2010) to 0.18degC per decade (over \r\nthe period 1961-2011) are noted in the tropical western Pacific, \r\nwhere the significant increasing and decreasing trends in warm and \r\ncool extremes, respectively, are also spatially homogeneous (Jones \r\net al., 2013; Whan et al., 2014; Wang et al., 2016). Similarly, much of \r\nthe Caribbean region showed statistically significant warming (at the \r\n95% level) over the period 1901-2010 (P.D. Jones et  al., 2016b). \r\nObservation records in the Caribbean region indicate a  significant \r\nwarming trend of 0.19degC per decade and 0.28degC per decade in daily \r\nmaximum and minimum temperatures, respectively, with statistically \r\nsignificant increases (at the 5% level) in the number of warm \r\ndays and warm nights during 1961-2010 (M.A. Taylor et al., 2012; \r\nStephenson et al., 2014; Beharry et al., 2015)."


id: 3cbefd8163acbd185a6bc9ed34481bc1
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 7870
content:
"A weather station-based annual precipitation trend analysis over \r\n1901-2010 in the Caribbean region indicated some locations with \r\ndetectable decreasing trends (Knutson and Zeng, 2018), which \r\nwere attributable in part to anthropogenic forcing. These include \r\nsouthern Cuba, the northern Bahamas, and the Windward Islands, \r\nalthough significant trends were not found over the shorter periods \r\nof 1951-2010 and 1981-2010. In the Caribbean islands, a dataset \r\nof the Palmer Drought Severity Index (PDSI) from 1950 to 2016 \r\nshowed a clear drying trend in the region (Herrera and Ault, 2017). \r\nThe 2013-2016 period showed the most severe drought during the "


id: 2b4f129dfb6a000634c7dec5e04a4292
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 4867
content:
"The AR5 noted observed temperature increases of 0.1degC-0.2degC \r\nper decade in the Pacific Islands and that warming was very likely\r\nto continue across all Small Islands regions (Christensen et  al., \r\n2013; IPCC, 2013a). It also reported decreased rainfall over the \r\nCaribbean, increases over the Seychelles, streamflow reductions over \r\nthe Hawaiian Islands and projections of reduced rainfall over the \r\nCaribbean and drier rainy season for many of the south-west Pacific \r\nIslands (Christensen et al., 2013; IPCC, 2013a; Nurse et al., 2014). \r\nThe remaining findings are derived from the SROCC (IPCC, 2019a). "


id: f59c8f6037f411c7c3bf3210dad304c8
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 40579
content:
"topography and land use may result in changes in climatic conditions \r\nover small distances (Foley, 2018), making the observational density \r\nparticularly relevant."


id: 97328b05077543d6d2038fc688a2a432
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 23794
content:
"Moreover, many stations have little metadata available, including \r\nthose in Vanuatu, the Solomon Islands and Papua New Guinea \r\n(Whan et al., 2014). Compared to earlier decades, few metadata are \r\ncurrently being documented in the western Pacific islands (McGree \r\net  al., 2014), which will challenge the homogenization of long\u0002term observational records. Challenges in the Caribbean include \r\nmaintaining continuous daily time series with metadata, converting \r\nclimatological data into digital formats and making them freely \r\navailable (Stephenson et  al., 2014; Beharry et  al., 2015; P.D. Jones \r\net al., 2016a). This is also an issue in the Pacific as many data are \r\nkept in national (local) databases, with only a fraction having been \r\nincorporated into global datasets (Whan et al., 2014)."


id: 7567c69288a8e24fc9f5e92ba9776ecb
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 17887
content:
"Because of the small number of stations used for interpolation \r\nand the complex mountainous topography, gridded product for \r\nthese small islands should be interpreted with caution (Frazier and \r\nGiambelluca, 2017). For the Antilles, the error in estimating CRU2.0 \r\nmonthly precipitation can stand locally between 20% and 40%. \r\nOver the Caribbean, Cavazos et  al. (2020) found a  discrepancy \r\nacross gridded observational datasets (CRU, CHIRPS and GPCP) \r\nin detecting orographic precipitation, especially during boreal \r\nsummer, making their use in climate model evaluation challenging \r\n(Herrera and Ault, 2017). Furthermore, some reanalysis products \r\nsuch as the 0.7deg x 0.7deg ERA-Interim reanalysis are not adequate as \r\nmany of the smaller Caribbean islands are not represented as land \r\n(P.D. Jones et al., 2016a)."


id: 40b133216b4a23536a5618c61ccad760
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 8564
content:
"In Hawaii, between 1920 and 2012, over 90% of the islands showed \r\nreduced rainfall and streamflow, an increase in the frequency of days \r\nwith zero flow (Strauch et al., 2015; Frazier and Giambelluca, 2017), \r\nand robust positive trends in drought frequency and severity (McGree \r\net  al., 2016). Over the western Pacific, interannual and decadal \r\nvariabilities also drive long-term trends in rainfall. Recent analysis \r\nof station data showed spatial variations in the mostly decreasing \r\nbut non-significant trends in annual and extreme rainfall over the \r\nwestern Pacific from 1961 to 2011 (low confidence) (McGree et al., \r\n2014). Over the southern subtropical Pacific, decreases in annual, JJA, \r\nSON and extreme rainfall, and increasing drought frequency in the \r\nwestern region, has been observed since 1951 (Jovanovic et al., 2013; \r\nMcGree et al., 2016, 2019)."


id: 2963eece678cb85ba5e20280916538fb
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 4499
content:
"An assessment of model performance for the Caribbean region is \r\ncontained in Atlas.7.1 on Central America. In summary, the ability of \r\nclimate models to simulate the climate over the region has improved \r\nin many key respects with the application of increased model \r\nresolution and a better representation of the land surface processes \r\nof particular importance in these advances (high confidence). \r\nRegional climate models (RCMs) simulate realistically seasonal \r\nsurface temperature and precipitation patterns including the bimodal \r\nrainfall in the precipitation annual cycle although with some timing \r\nbiases in some regions (high confidence). The important regional \r\ncirculation and precipitation features, the Caribbean low-level jet and \r\nthe midsummer drought (MSD), are well represented over a variety of \r\nRCM domains covering the region (high confidence)."


id: d79bef3a1ea67389437c49188b52d6a2
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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index: climate_gpt_v2
vector_id: 35238
content:
"Over the western Indian Ocean significant warming trends have been \r\nreported for Mauritius (1.2degC during 1951-2016; MESDDBM, 2016), \r\nLa Reunion (0.18degC per decade over 1968-2019; Meteo-France, 2020) \r\nand Maldives (MEE, 2016). Both Mauritius and La Reunion have \r\nexperienced rainfall decreases of 8% during 1951-2016 and 1.2% \r\nper decade during 1961-2019 with generally weak, non-significant \r\nrainfall trends during 1967-2012."


id: f28f9a30481b49a4f434bdeb6ad56481
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 40074
content:
"Assessing observed climate change for Small Islands is often \r\nconstrained by low station density (Ryu and Hayhoe, 2014; \r\nP.D.  Jones et  al., 2016a), digitization requirements or data-sharing \r\nlimitations (P.D. Jones et  al., 2016a). Station data typically have \r\nlonger temporal coverage relative to satellite products but are \r\nlimited in spatial coverage (Cavazos et al., 2020). For Small Island \r\nnations, spatial  gaps between observations can be very large due \r\nto the isolation of the islands (Wright et  al., 2016). Additionally, \r\nover past decades, the number of station observations has declined \r\nsubstantially in Mauritius (Dhurmea et al., 2019), Hawai'i (Bassiouni \r\nand Oki, 2013; Frazier and Giambelluca, 2017) and most Pacific \r\nIsland countries since the 1980s (Jones et al., 2013; McGree et al., \r\n2014, 2016). In Fiji, meteorological stations were located on or by \r\nthe coast and are sparse in the interior (Kumar et al., 2013). Notable "


id: 645f30177dde0f0cd70ad012a91fc88e
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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index: climate_gpt_v2
vector_id: 14938
content:
"Over the tropical Pacific, surface temperature biases in CMIP6 models \r\nremain similar to those in CMIP5, although are reduced in the higher\u0002resolution models in the HiResMIP ensemble. CMIP6 models generally \r\nrepresent trends in sea surface temperatures better than CMIP5 \r\n(see Section 9.2.1 for more details). For precipitation, the persistent \r\ntropical Pacific bias of the double ITCZ (erroneous bands of excessive \r\nrainfall both sides of the equatorial Pacific) is still present in CMIP6 \r\nmodels although is slightly improved compared to those in CMIP3 \r\nand CMIP5 models (Section  3.3.2.3). Application of downscaling "


id: 22c68a65d04e6b6a360f67383edf7384
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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vector_id: 3329
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"techniques (RCMs and stretched-grid GCMs) using resolutions finer \r\nthan 10  km over the Pacific can capture topographic influences \r\non wind and rainfall to generate realistic simulations of island \r\nclimates - for example over Fiji and New Caledonia (Chattopadhyay \r\nand Katzfey, 2015; Dutheil et  al., 2019). In both cases applying \r\nbias adjustment to the sea surface temperatures used as a  lower \r\nboundary condition for the downscaling models was important to \r\ngenerate realistic simulations."


id: 84d4579ef3c437a36da79cffa7c9c209
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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index: climate_gpt_v2
vector_id: 20510
content:
"Projected median temperature increases for Small Islands from the \r\nCMIP5 ensemble range from 1degC (RCP4.5) to 1.5degC (RCP8.5) in \r\nthe period 2046-2065, and from 1.3degC (RCP4.5) to 2.8degC (RCP8.5) \r\nby 2081-2100 relative to 1986-2005 (Harter et  al., 2015). Spatial \r\nvariations in the warming trend are projected to increase by the \r\nend of the 21st century, with relatively higher increases in the Arctic "


id: 2d9a5d6d3c52b6c38fb2dc45fdce9b09
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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index: climate_gpt_v2
vector_id: 5316
content:
"and sub-Arctic islands, and in the equatorial regions compared with \r\nislands in the Southern Ocean (Harter et al., 2015). In the western \r\nPacific, temperatures are projected to increase by 2.0degC-4.5degC by \r\nthe end of the 21st  century relative to 1961-1990 (Wang et  al., \r\n2016). The warming over land in the Lesser Antilles is estimated \r\nto be about 1.6degC (3.0degC) by 2071-2100 for the RCP4.5 (RCP8.5) \r\nscenario, relative to 1971-2000 (Cantet et  al., 2014). Projections \r\nfrom the CMIP6 ensemble support these findings (Figure Atlas.28) \r\nand across global warming levels from 1.5degC to 4degC CMIP5 and \r\nCMIP6 consistently project lower levels of warming for Small Islands \r\nthan the global average (Interactive Atlas)."


id: 68cf5ff1bff37d8b120e97ec2f776b53
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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index: climate_gpt_v2
vector_id: 15701
content:
"It is very likely that all Small Island regions have warmed with significant \r\ntrends recorded from at least the 1960s in all territories or nations. \r\nTrends include increases of 0.15degC-0.18degC per decade in the tropical \r\nwestern Pacific (1953-2011), significant warming over the Caribbean \r\n(1901-2010) with trends of 0.19degC (0.28degC) per decade in daily \r\nmaximum (minimum temperatures) (1961-2010) and in La Reunion \r\nof 0.18degC per decade (1968-2019). There are fewer significant trends \r\nin precipitation in these regions though several locations in the \r\nCaribbean have detectable decreasing trends (high confidence), in \r\npart attributable to anthropogenic forcing (limited evidence). Also, it \r\nis likely that drying has occurred since the mid-20th century in some \r\nparts of the western Indian Ocean, and in the Pacific poleward of 20deg \r\nlatitude in both the northern and southern hemispheres."


id: f5311c18f6ae6fa5b5af22d2d582574d
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
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index: climate_gpt_v2
vector_id: 40502
content:
"It is very likely that Small Island regions will continue to warm in the \r\ncoming decades at a level slightly lower than the global mean. Small \r\nIsland regions in the western and Equatorial Pacific, north Indian and \r\nSouthern oceans are likely to be wetter in the future; and those in \r\nthe Caribbean, parts of the Atlantic and west Indian oceans, and the \r\nsouthern subtropical and eastern Pacific Ocean drier."


id: d1fd2ed99a4faf255879a39052b5832d
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 34296
content:
"Contributors: Donovan Campbell (Jamaica), Subimal Ghosh (India), Rafiq Hamdi (Belgium), Mark Hemer (Australia), Richard G. Jones \r\n(United Kingdom), James Kossin (United States of America), Simon McGree (Australia/Fiji), Blair Trewin (Australia), Sergio M. Vicente\u0002Serrano (Spain)"


id: 4352546831eccb7172d3c3c8f8d4632e
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 9056
content:
"Constructing regional climate information for Small Islands involves synthesis from multiple sources. This cross-chapter box presents \r\ninformation relevant to water resources, drawing on several chapters in AR6 and Atlas.10. It introduces the context and current \r\nevidence base followed by an assessment of trends and projections in rainfall, temperature and sea levels across Small Islands and it \r\nhighlights key findings."


id: 8d59dfce084419299b74ba1bda8a5e5b
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 22049
content:
"Cross-Chapter Box Atlas.2: presents an overview of observed sub-regional trends relevant to water resources in some Small Islands \r\nand island regions largely from 1951. Some general observed climate trends include higher magnitude and frequency of temperatures \r\nincluding warm extremes (high confidence) (Section 12.4.7.1, Table 11.13 and Atlas.10.2), declines in high-intensity rainfall events (low\r\nto medium confidence) (Table 11.14), regional sea level rises with strong storm surges and waves resulting in increased coastal flood \r\nintensity (high confidence) (Section 12.4.7.4 and Atlas.10.2), and increased intensity and intensification rates of tropical cyclones at global \r\nscale (medium confidence) (Sections 11.7.1.2 and 12.4.7.3) and ocean acidification (virtually certain) (Chapters 2, 6 and 9, and Atlas.3.2)."


id: 14115278058d4b79d163b518d69c198c
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 749
content:
"No significant long-term trends are observed for annual Caribbean rainfall over the 20th century (low confidence) (Atlas.10.2). Over the \r\nwestern Pacific, generally decreasing but non-significant trends are noted in annual total rainfall from 1961 to 2011 (low confidence) \r\n(Atlas.10.2). June-July-August (JJA) rainfall over the Caribbean shows some drying tendencies that may be linked to the combined \r\neffect of warm ENSO events and a positive NAO phase (Giannini et al., 2000; Mendez-Lazaro et al., 2014; Fernandes et al., 2015), or to \r\nwarm ENSO events and a positive PDV (Maldonado et al., 2016). However, the work of Herrera et al. (2018) suggests that anthropogenic \r\ninfluences may also be possible, although mechanisms proposed to date have not decoupled the influence of anthropogenic trends \r\nfrom natural decadal variability (Vecchi et al., 2006; Vecchi and Soden, 2007; DiNezio et al., 2009)."


id: 781f3cdf8e338db7f85d24c0b49d5948
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 18346
content:
"Cross-Chapter Box Atlas.2, Table 1 | Summary of observed trends for Small Island regions. SLR = sea level rise; TC = tropical cyclone; SPCZ = South \r\nPacific Convergence Zone."


id: bb5f22fe9765281a63d6f8d5b20873bd
created_at: 2023-04-19 13:21:42
updated_at: 2023-04-19 13:21:42
content_type: text
index: climate_gpt_v2
vector_id: 30179
content:
"Southern Hemisphere subtropical Pacific June-November drying has been associated with intensification of the subtropical ridge and \r\nassociated declines in baroclinicity (Whan et al., 2014). Austral summer drying in the south-west French Polynesia sub-region has been \r\nlinked with increased greenhouse gas and ozone changes (Fyfe et al., 2012). The Southern Hemisphere jet stream has likely shifted \r\npolewards (Section 2.3.1.4.3) which is attributed largely to a trend in the Southern Annular Mode (Section 3.7.2)."


