# document

id: e3c1f4247c4630981f04a454f2235c91
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
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" In the \r\nAlps, recent simulations project a reduction in snow water equivalent \r\n(SWE) at 1500 m above sea level of 80-90% by 2100 under the A1B \r\nscenario and a snow season that would start 2-4 weeks later and end \r\n5-10 weeks earlier than the 1992-2012 average (Schmucki et  al., \r\n2015), which is equivalent to a shift in elevation of about 700 m (Marty \r\net al., 2017b). For elevations above 3000 m above sea level, a decline in \r\nSWE of at least 10% is expected by the end of the century even when \r\nassuming the largest projected precipitation increase. Similar trends \r\nare observed for the Pyrenees and Scandinavia (Lopez-Moreno et al., \r\n2009; Raisanen and Eklund, 2012). For the northern French Alps above \r\n1500 m and the Otztal locations in the Austrian alps SWE has a similar \r\ndecreasing trend altitudinally dependent for RCP2.6, RCP4.5 and \r\nRCP8"


id: 1ce430b15691a11924ab535bb1f45de3
created_at: 2023-03-16 14:02:10
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"5 until mid-century and with significant differentiation among \r\nthem in the second half of the century up to snow-free conditions \r\nunder RCP8.5 (Hanzer et al., 2018; Verfaillie et al., 2018)."


id: c534f5174cb2c2300b14f0737d98841c
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
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vector_id: 28604
content:
"Glacier: Observations and future projections of European glacier \r\nmass changes are assessed in Section  9.5.1 grouped in two main \r\nregions: Scandinavia and central Europe regions. It is virtually certain\r\nthat glaciers will shrink in the future and there is medium confidence\r\nin the timing and mass change rates (Section 9.5.1). Central Europe \r\nis one of the regions where glaciers are projected to lose substantial \r\nmass even under low-emissions scenarios (Section 9.5.1.3; MedECC, \r\n2020). GlacierMIP projections indicate that glaciers in the central \r\nEurope region will lose 63 +- 31%, 80 +- 22% and 93 +- 13% of their \r\n2015 mass by the end of the century under RCP2.6, RCP4.5 and \r\nRCP8.5 respectively (Marzeion et al., 2020). For the same scenarios, \r\nglaciers in Scandinavia are projected to lose 55 +- 33%, 66 +- 34% and \r\n82 +- 24% of their 2015 mass"


id: 9926dfcf2df5467aa04f61f9c32af37a
created_at: 2023-03-16 14:02:10
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" The virtually certain shrink in glaciers \r\nis bolstered by RCM simulations from the EURO-CORDEX ensemble, \r\nwith the Global Glacier Evolution Model (GloGEM) indicating \r\na substantial reduction of glacier ice volumes in the European Alps \r\nby 2050 (47-52% with respect to 2017 for RCP2.6, RCP4.5 and \r\nRCP8.5). Under RCP2.6, about two-thirds (63+-11%) of the present\u0002"


id: 63f9f951d1b4d96248f36316d3020823
created_at: 2023-03-16 14:02:10
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content:
"Projections indicate that sandy coasts throughout the continent (except \r\nthose bordering the northern Baltic Sea) will experience shoreline retreat \r\nthrough the 21st century (high confidence). Median shoreline change \r\nprojections (CMIP5) relative to 2010, show that, by mid-century, \r\nshorelines will retreat by between 25 m and 60 m along sandy coasts \r\nin WCE and MED under both RCP4.5 and RCP8.5 (Athanasiou et al., \r\n2020; Vousdoukas et al., 2020b). Mid-century median projections for \r\nNEU indicate virtually no shoreline retreat under RCP4.5, but a retreat \r\nof around 40 m under RCP8.5. By 2100, median shoreline retreats of \r\naround 50 m are projected in NEU and MED under RCP4.5, increasing \r\nto around 80 m under RCP8.5. End-century median projections for \r\nWCE are far higher at 100 m (RCP4.5) and 160 m (RCP8.5). The total \r\nlength of sandy coasts in Europe that is projected to retreat by more \r\nthan a median of 100 m by 2100 under RCP4.5 and RCP8"


id: a09afb1485e7ef03bc5452b6c40547ab
created_at: 2023-03-16 14:02:10
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"5 is about \r\n12,000 km and 18,000 km respectively, an increase of approximately \r\n54% (Vousdoukas et al., 2020b)."


id: 28c70ad8233a4c27d3e820eae8a2a4a5
created_at: 2023-03-16 14:02:10
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"There is high confidence that extreme total water level (ETWL) \r\nmagnitude and occurrence frequency will increase throughout Europe \r\n(see Figure  12.4p-r), except in the northern Baltic Sea. Across the \r\nregion, the 5-95th percentile range of the 1-in-100-year ETWL is \r\nprojected to increase (relative to 1980-2014) by 4-40 cm and by \r\n6-47 cm by 2050 under RCP4.5 and RCP8.5, respectively. By 2100, this \r\nrange is projected to be 6-88 cm and 25-186 cm under RCP4.5 and \r\nRCP8.5, respectively (Figure 12.SM.6; Vousdoukas et al., 2018; Kirezci \r\net al., 2020). Mass addition across the Gibraltar Strait may play a role, \r\nalthough the extent of this contribution is currently unclear (Lionello \r\net al., 2017). Furthermore, under RCP4"


id: 3bf0d14fc3864def6a7e9990e6b1a934
created_at: 2023-03-16 14:02:10
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"5, the present day 1-in-100-year \r\nETWL is projected to have median return periods of between 1-in-5 and \r\n1-in-20 years by 2050 and occur at least once per year by 2100 in the \r\nMediterranean and Black Sea, while in the rest of Europe it is mostly \r\nprojected to have median return periods of between 1-in-20-years and \r\n1-in-50-years by 2050 and between 1-in-5-years and 1-in-20-years \r\nby 2100 (Vousdoukas et al., 2018). Under RCP8.5, occurrence of the \r\npresent day 1-in-100-year ETWL is projected to increase further to \r\nmedian return periods of 1-in-1-year to 1-in-5-years by 2050 and occur \r\nmore than once per year by 2100 in the Mediterranean and Black Sea, \r\nwhile in the rest of Europe it is mostly projected to have median return \r\nperiods between 1-in-5-years and more than once per year by 2100."


id: eac6e3aa78eb4e8437de1e89256adc9b
created_at: 2023-03-16 14:02:10
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"Mean air temperature: Atlas.9.2 assessed very likely mean warming \r\nin observations across North America, with highest increases at \r\nhigher latitudes and in the winter season. Atlas.9.4 assessed very \r\nlikely mean warming in future decades in all North American \r\nregions, with CMIP and CORDEX models showing median increases \r\nexceeding 2degC in much of the continental interior under RCP8.5 \r\n(2041-2060 compared to 1995-2014) and higher increases towards \r\nthe north. Mean temperatures at the end of century show strong \r\nscenario dependence, rising between 1degC and 2.5degC in RCP2.6 and \r\nabout 4degC to 8degC in RCP8.5 (Figures Atlas.12, Atlas.26 and Atlas.27). \r\nWarming also raises stream temperatures across the continent \r\n(DOE, 2015; Trtanj et al., 2016; van Vliet et al., 2016; Chapra et al., \r\n2017), and Hill et al. (2014) projected US stream warming by 0.6degC \r\n(+-0.3degC) per 1degC increase in local air temperature"


id: 9e33c0f7e8f959fa23ae500a758b2320
created_at: 2023-03-16 14:02:10
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" Mean warming \r\ndrives shifts in the seasonal timing of temperature thresholds, \r\nincluding increasing growing degree days (Mu et al., 2017), longer \r\ngrowing seasons (Gowda et al., 2018; G. Li et al., 2018; L.A. Vincent \r\net al., 2018), reduced chill hours (Luedeling, 2012; Lee and Sumner, \r\n2015; Xie et  al., 2015; Parker and Abatzoglou, 2019), and longer \r\npollen and allergy seasons (Fann et al., 2016; Anenberg et al., 2017; \r\nSapkota et al., 2019). Warmer temperatures reduce heating degree \r\ndays and increase cooling degree days (high confidence) (Bartos et \r\nal., 2016; US EPA, 2016; Craig et al., 2018; X. Zhang et al., 2019; \r\nCoppola et al., 2021b) "


id: 560215f9e419a7687ce584e24ccce6c8
created_at: 2023-03-16 14:02:10
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content:
"Figure  12.4b shows over a  month of additional days at CMIP6 \r\nSSP5-8.5 mid-century where temperatures exceed 35degC across \r\nmuch of southern Mexico and regions near the US-Mexico border, \r\nand these extreme temperatures occur at least once per year up to \r\nsouthern Canada. Coppola et al. (2021b) found similar patterns in \r\nCMIP5 and CORDEX-Core. Using locally tailored heat thresholds, \r\nMaxwell et  al. (2018) found that 'very hot' days in five US cities \r\nwill occur a median of three to five times more often by 2036-2065 \r\nunder RCP8.5 (2 to 3.5 times more often in RCP4.5), Oleson et al. \r\n(2018) projected that annual heatwave duration will exceed \r\none month in Houston in RCP8.5 2061-2080, and Anderson et al. \r\n(2018) projected 7 to 12  times more exceedances of thresholds \r\nassociated with high-mortality by 2061-2080 under RCP8.5 (6 to \r\n7 times more exceedances in RCP4.5). Schwingshackl et al"


id: d1d3f726bf4c78c590061d3ba85edea4
created_at: 2023-03-16 14:02:10
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" (2021) \r\nfound that Central and Eastern North America are among the regions \r\nwith the strongest trend in heat stress indicators. Studies also project \r\nincreasingly surpassed heat extreme thresholds for North American \r\ncrops (Gourdji et al., 2013), airplane weight restrictions (Coffel et al., \r\n2017), and peak load energy systems (Auffhammer et al., 2017)."


id: 6185e71d65f515d59c785fbf7e8df07f
created_at: 2023-03-16 14:02:10
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"in the future (high confidence). Significant decreases in frost days, \r\nconsecutive frost days, and ice days were identified in 1948-2016 \r\nstation observations across Canada, along with a  resulting \r\nlengthening of the frost-free season by more than a month in many \r\nregions (L.A. Vincent et al., 2018). Frost days also declined in nearly \r\nall Mexican cities from 1980 to 2010 (Garcia-Cueto et al., 2019), and \r\na 1917-2016 decline of about three weeks in frigid winter conditions \r\nchallenges ecosystems in the north-east USA and south-east Canada \r\n(Contosta et  al., 2020). Studies connect projections of a  longer \r\nfrost-free season in North America to a longer outdoor construction \r\nseason, shifts in frost variance to orchard damages, and lower weight \r\ntolerances for runways (Daniel et al., 2018; DeGaetano, 2018; Jacobs \r\net al., 2018)"


id: c28f3cddd084537d848056c1b26762fe
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" Frosts are projected to persist as an episodic hazard in \r\nmany regions given natural variability and cold air outbreaks even as \r\nmean temperature rises (high confidence)."


id: 4078348e1ea87047079cdfc299292e71
created_at: 2023-03-16 14:02:10
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"Cold spells are projected to decrease over North America under climate \r\nchange, with the largest decreases most common in the winter season \r\n(high confidence) (Section 11.9). Minimum winter temperatures are \r\nprojected to rise faster than the mean winter temperature (Underwood \r\net  al., 2017) and alter cold-hardiness zones used to determine \r\nagricultural suitability (Parker and Abatzoglou, 2016). Wuebbles \r\net  al. (2014) projections for RCP8.5 end-of-century show that the \r\nfour-day cold spell that happens on average once every five years \r\nis projected to warm by more than 10degC and CMIP5 models do not \r\nproject current 1-in-20-year annual minimum temperature extremes \r\nto recur over much of the continent. Multiple studies have shown \r\nthat Arctic warming can alter large-scale variability and change the \r\nfrequency and duration of mid-latitude cold air outbreaks, potentially \r\nleading to increasing cold hazards in some regions (low agreement) \r\n(Barcikowska et al"


id: a84e46c5fd59ec8de469cbc84177237b
created_at: 2023-03-16 14:02:10
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"the USA (Dudley et al., 2017; Wehner et al., 2017, Neri et al., 2020). \r\nSome rivers show ice-jam floods occurring a week earlier, but changes \r\nare mixed, given localized positive and negative changes across the \r\ncontinent (Rokaya et  al., 2018). There is medium confidence that \r\nclimate change will increase river floods over the USA and Canada \r\nbut low confidence for changes in Mexico. Wobus et  al. (2017a) \r\nused a  regional hydrologic model for 57,000 streams to project \r\nmore than a doubling in the frequency of current 1-in-100-year flow \r\nevents in many portions of the USA for RCP8.5 2050 with additional \r\ncontributions from earlier snowmelt. CMIP6 projections for SSP5-8"


id: 564bdd810086337a8cfb6cd3d79ae194
created_at: 2023-03-16 14:02:10
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"5 \r\n2065-2099 show strongest peak USA runoff increases in the east \r\n(Villarini and Zhang, 2020); however, several studies applying global \r\nhydrological models disagree with regional streamflow projections, \r\nindicating a decrease in the magnitude or frequency of floods over \r\na large portion of North America (e.g., Hirabayashi et al., 2013; Arnell \r\nand Gosling, 2016; see Figure 12.10a,c)."


id: f7b6e2763a25cc5c0241af66eab8bf9f
created_at: 2023-03-16 14:02:10
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"Aridity: Chapter 8 showed that aridity in North America generally \r\nmoves opposite to mean precipitation change with an added \r\nevaporative demand from warmer temperatures (high confidence in \r\naridity increase for Northern Central America; medium confidence for \r\nan increase in Central North America; high confidence for a decrease \r\nin North-Eastern North America; medium confidence decreases in \r\nEastern and North-Western North America; see also Section  11.9). \r\nProjected soil moisture declines (Figure 12.4j-l) are most widespread \r\nacross North America during the summer, with the largest declines in \r\nMexico and the southern Great Plains but also extending into Canada \r\n(Section  8.4.1.6; Swain and Hayhoe, 2015; Easterling et  al., 2017; \r\nBonsal et al., 2019; J. Lu et al., 2019). Yoon et al. (2018) found net \r\nreduction in southern Great Plains groundwater storage in RCP8.5 \r\nmid-century projections despite increases in mean precipitation \r\nand both wet and dry extremes"


id: f8440cdd823b989ec5182e0e5fb32219
created_at: 2023-03-16 14:02:10
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" Soil moisture drying could reach \r\nunprecedented levels by the CMIP6 RCP8.5 end-of-century, even \r\nwhen evaluating deeper soil columns relevant for crop rooting depth \r\n(B.I. Cook et al., 2020). Projected changes in the aridity index portray \r\na shift the geographic range of temperate drylands northward and \r\neastward in Central and Western North America (Schlaepfer et al., \r\n2017; Seager et  al., 2018) which also diminishes aquifer recharge \r\nrates in the southern Great Plains and in some western regions \r\nwhere snowpack is reduced (Meixner et al., 2016)."


id: 8753132dbc55b7b62c17981eba57a1db
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
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"Section 11.4 found that high precipitation is projected to increase \r\nacross North America (high confidence) except for portions of Western \r\nNorth America where projections are mixed (medium confidence of \r\nincrease). Maxwell et al. (2018) identified regional 'heavy precipitation \r\nday' thresholds for five cities across the USA and projected that \r\na tripling (or more) of these events is possible by RCP8.5 mid-century. \r\nProjections indicate changes to intensity-duration-frequency (IDF) \r\ncurves typically used for construction design and automobile hazards, \r\nas well as increases in the 10-year recurrence level of 24-hour \r\nrainfall intensities that challenge storm water drainage systems \r\n(Hambly et al., 2013; Cheng and AghaKouchak, 2015; J.E. Neumann \r\net  al.,  2015; Prein et  al., 2017b; Hettiarachchi et  al., 2018; Ragno \r\net al., 2018)"


id: 862f994fa49a1e6e0cfc9cbdaf1b2ab6
created_at: 2023-03-16 14:02:10
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" Precise levels of regional IDF characteristics may still \r\ndepend substantially on the method and resolution of downscaling \r\napplied (DeGaetano and Castellano, 2017; L.M. Cook et al., 2020)."


id: aae6ba92e64a967bd0424119089cba1d
created_at: 2023-03-16 14:02:10
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"Agricultural and ecological drought: Section  11.9 assessed \r\nmedium confidence for an increase in agricultural and ecological \r\ndrought in Western North America but otherwise found limited \r\nevidence for broadly observed changes in North American agricultural \r\nand ecological drought, even as increasing evaporative demand \r\nintensified vegetation stress and soil moisture deficits in recent events \r\n(Sections 11.6, 11.9). Section 11.9 asssessed medium confidence for \r\nmore intense agricultural and ecological drought conditions over \r\nNorth Central America, Western North America and Central North \r\nAmerica in a  2degC global warming level (about mid-century), with \r\nmedium confidence extending to Eastern North America and high \r\nconfidence for Northern Central America and Central North America \r\nunder a 4degC global warming level associated with higher emissions \r\nscenarios past 2050. Figure  12"


id: ebe291562791c28c5d1dd825c4d951cb
created_at: 2023-03-16 14:02:10
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"4g-i shows that the frequency \r\nof meteorological droughts (which often initiate hydrological, \r\nagricultural and ecological droughts) is largely projected to increase \r\nin North American areas where total precipitation decreases (and \r\nvice versa; see Section 11.9 and Coppola et al., 2021b), and higher "


id: 36d8ff5a2d03005fec1db25beaac93fe
created_at: 2023-03-16 14:02:10
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"Landslide: There is growing yet limited evidence for unique climate\u0002driven changes in landslide and rockfall hazards in North America, \r\neven as theory suggests decreases in slope and rockface stability \r\ndue to more intense rainfall, rain-on-snow events, mean warming, \r\npermafrost thaw, glacier retreat, and coastal erosion (Cloutier \r\net al., 2017; Coe et al., 2018; Handwerger et al., 2019; Hock et al., \r\n2019; Patton et al., 2019) although dry trends can decelerate mass \r\nmovements (Bennett et al., 2016). Landslide frequency has increased \r\nin British Columbia (Canada; Geertsema et al., 2006) and is expected \r\nto increase in North-Western North America given the combination \r\nof these factors (medium confidence) (Gariano and Guzzetti, 2016). \r\nCloutier et al. (2017) projected an increase in landslides in western \r\nCanada due to wetter overall conditions and reduced return period \r\nfor extreme rainfall. Robinson et  al"


id: c0e8f3f8db695ee838787572bd92cd5f
created_at: 2023-03-16 14:02:10
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" (2017) used scenarios based \r\nupon projection of 50-year recurrence of 7-day precipitation periods \r\nto highlight the potential for increased landslide hazards near Seattle \r\n(USA). Broad projections for the USA are more uncertain given "


id: e054e5fd08313209fca3d9c5ace3b1d7
created_at: 2023-03-16 14:02:10
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"Fire weather: Climatic conditions conducive to wildfire have increased \r\nin Mexico, Western and North-Western North America, primarily due \r\nto warming (high confidence). Abatzoglou and Williams (2016) found \r\nclimate change led to higher values for eight fuel aridity indices over \r\nthe western USA in recent decades, with 2000-2015 changes exposing \r\n75% more forested area to high fuel aridity and adding nine more high \r\nfire-potential days each year, similar to 1979-2013 western USA and \r\nMexico fire season expansion reported in Jolly et al. (2015). Increases \r\nin lightning-initiated fires have been distinguished from trends in man\u0002made fire in western Canada and the USA (Balch et al., 2017; Hanes \r\net al., 2019). Jain et al"


id: be11c795ae47b166e7696f7e8602847a
created_at: 2023-03-16 14:02:10
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content:
" (2017) identified a 1979-2015 expansion in \r\nfire weather season in eastern Canada and the south-western USA \r\n(with a smaller reduction in the northern Mountain West) along with \r\nregional shifts in the 99th percentile Canadian Fire Weather Index (FWI) \r\nand potential fire-spread days. Girardin and Wotton (2009) noted that \r\n1951-2002 trends in the Monthly Drought Code fire index in eastern \r\nCanada could hardly be distinguished from decadal variability."


id: e1552e31b6d4d4e35612c03aa01bd339
created_at: 2023-03-16 14:02:10
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"Climate change drives future increases in North American fire \r\nweather, particularly in the south-west (high confidence), although \r\nfurther studies on shifts in exposure and vulnerability are needed \r\nto understand overall fire risks (see WGII Chapter  14). A  significant \r\nincrease of FWI is apparent before 2050 under RCP8.5 in much of \r\nNorth America, including the frequency of 95th-percentile FWI days, \r\npeak seasonal FWI average, fire weather season length, and maximum \r\nfire weather index (Abatzoglou et al., 2019), and fire season across \r\nNorth America expands dramatically beyond 2degC global warming \r\nlevels (Q. Sun et al., 2019; Jain et al., 2020). X. Wang et al. (2017b) \r\nsimulated fire-spread days across Canada and found increases across \r\nmost of the areas studied by 2071-2100, with median changes of \r\n-20 to +140% (RCP2.6), -20 to +250% (RCP4.5) and 40 to 360% \r\n(RCP8.5) compared to 1976-2015. Prestemon et al"


id: 1c10d180e33c2d4a4b36e80be284b9fe
created_at: 2023-03-16 14:02:10
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" (2016) found more \r\nconducive conditions for lightning-ignited fires in  the south-eastern \r\nUSA by mid-century, while warming conditions in Alaska increasingly \r\npush July temperatures above 13.4degC, a  threshold for fire danger \r\nacross Alaska's tundra and boreal forest (Partain et al., 2016; Young \r\net al., 2017). Longer and more intense fire seasons would also raise \r\nparticulate matter and black carbon concentrations in the western \r\nUSA, reducing visibility at many National Parks (Yue et al., 2013; Val \r\nMartin et al., 2015)."


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vector_id: 32791
content:
"There is medium confidence of a general decrease in the number of \r\nextratropical cyclones producing high wind speeds in North America, \r\nexcept over northernmost parts, for a global warming level of 2degC or \r\nby the end of the century under RCP4.5 and RCP8.5 (Kumar et al., \r\n2015; Jeong and Sushama, 2018a; C. Li et al., 2018). GCMs cannot \r\ndirectly resolve tornadoes and severe thunderstorms, however \r\nprojections of favourable environments for severe storms (based \r\non convective available potential energy and wind shear) indicate \r\nmedium confidence for more severe storms and a longer convective \r\nstorm season in the USA, weaker increases extending north and east \r\n(Seeley and Romps, 2015; Glazer et al., 2021), and a corresponding \r\nincrease in autumn and winter tornadic storms (H.E. Brooks, 2013; \r\nDiffenbaugh et al., 2013; Brooks et al., 2014; see also Section 11.7.2). \r\nPrein et al"


id: e178a76feabf5a7d61dacc54eadbb0bc
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
content_type: text
index: climate_gpt_only_giec
vector_id: 32966
content:
" (2017a) used a convection-permitting model to project \r\na tripling of mesoscale convective systems over the USA for end-of\u0002century RCP8.5."


id: ab850623971976b94f31b0a2b1a7d47a
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
content_type: text
index: climate_gpt_only_giec
vector_id: 24501
content:
"Sand and dust storm: Land-use change has increased dust \r\nemissions in the western USA in the past 200 years (Neff et  al., \r\n2008). However, there is medium confidence for observed increases \r\nin Western North American sand and dust storm activity since 1980. \r\nIn their study of Valley Fever spread, Tong et  al. (2017) identified \r\na rapid intensification of dust storm activity using PM10 and PM2.5\r\nobservations from 1980-2011 across 29 monitoring sites in the \r\nsouth-western USA, similar to contiguous USA observations by \r\nBrahney et al. (2013). Hand et al. (2016) attributed the earlier onset \r\nof spring dusts in the south-west in large part to the Pacific Decadal \r\nOscillation, however. The increasing trend in dust since the 1990s \r\nin the south-western USA can be explained by precipitation deficit \r\nand surface bareness (Pu and Ginoux, 2018)"


id: 819e17ed93a080a28b5b0ce67634871
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
content_type: text
index: climate_gpt_only_giec
vector_id: 17924
content:
" Projections of future \r\nsand and dust storms over North America are based on aridity as \r\na  primary proxy for conducive conditions which lends medium \r\nconfidence of an increase over Mexico and the south-western USA. \r\nPu and Ginoux (2017) project about five more dusty days in spring \r\nand summer in the southern Great Plains under RCP8.5 at the end \r\nof the century, while dusty days decrease in northern regions where \r\nmean precipitation tends towards wetter conditions."


id: ef419066c729811a67c608480fe4d774
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
content_type: text
index: climate_gpt_only_giec
vector_id: 35114
content:
"Glacier: Section  9.5.1 assessed that glaciers in Alaska, western \r\nCanada and the western USA are expected to continue to lose mass \r\nand areal extent (high confidence). Compared to their 2015 state, \r\nglaciers in the western Canada and the USA region will lose 62 +- 30%, \r\n75 +- 29% and 85 +- 23%, of their mass by the end of the century for \r\nRCP2.6, RCP4.5 and RCP8.5 scenarios, respectively (Marzeion et al., \r\n2020). Meanwhile glaciers in Alaska will lose 26 +- 21%, 31 +- 24% \r\nand 44 +- 27%, of their 2015 mass under the same scenarios. \r\nThe overall loss of glacial mass can act as a meltwater supply for \r\nfreshwater resources, although this is expected to peak in the middle \r\nof the century and then fade as glaciers disappear (Fyfe et al., 2017; \r\nDerksen et al., 2018)"


id: ba71750a5ca1b513583cf6bbaf65ca6f
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
content_type: text
index: climate_gpt_only_giec
vector_id: 26870
content:
" Continued shrinkage of glaciers is projected \r\nto create further glacial lakes (medium confidence) similar to those \r\nthat have led to outburst floods in Alaska and Canada (Carrivick and \r\nTweed, 2016; Harrison et al., 2018)."


id: 2e0d0e5eed7b50fc20b8f97faeae2039
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
content_type: text
index: climate_gpt_only_giec
vector_id: 4680
content:
"Heavy snowfall: There is low agreement (limited evidence) for \r\nobserved changes in heavy snowfall in North America. Kluver and \r\nLeathers (2015) noted a  1930-2008 frequency increase for all \r\nsnow intensities in the northern Great Plains but declines in heavier \r\nsnow  events in the Pacific Northwest and declines in the south\u0002eastern USA. Changnon (2018) found that most extreme 30-day \r\nhigh-snowfall periods in the 1900-2016 record over the eastern USA \r\noccurred in the 1959-1987 period, which lies between the 1930s \r\nDust Bowl and recent warming. There is low agreement and medium \r\nevidence for broad projected changes to heavy snowfall over North \r\nAmerica given increased heavy precipitation and warmer winter \r\ntemperatures"


id: 15aab1114a104aed4f34150c94391ce0
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
content_type: text
index: climate_gpt_only_giec
vector_id: 923
content:
" Several recent regional studies have projected that \r\nlow-intensity events decrease more rapidly than heavy snowfall \r\nevents, resulting in an increase in the snowfall proportion from \r\nheavy snowfall events even as the number of such events decreases \r\n(O'Gorman, 2014; Lute et  al., 2015; Zarzycki, 2016; Janoski et  al., \r\n2018; Ashley et al., 2020)."


id: 500fcf12a68c67564e40665e36037f1
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
content_type: text
index: climate_gpt_only_giec
vector_id: 10037
content:
"Relative sea level: Chapter  9 found that observations indicate \r\nincreasing sea levels along most North American coasts (robust \r\nevidence, high agreement), although there is substantial regional \r\nvariation in relative sea level rise (robust evidence, high agreement). \r\nAround North America, over 1900-2018, a  new tide gauge-based \r\nreconstruction finds a  regional mean RSL change of 1.08  [0.79  to \r\n1.38] mm yr-1 in the subpolar North Atlantic, 2.49 [1.89 to 3.06] mm yr-1\r\nin the subtropical North Atlantic, and 1.20 [0.76 to 1.62] in the East \r\nPacific (Frederikse et  al., 2020), compared to a  GMSL change of \r\naround 1.7 mm yr-1 (Section 2.3.3.3 and Table 9.5). For the period \r\n1993-2018, these RSLR rates, based on satellite altimetry, increased \r\nto 2.17 [1.66 to 2.66]  mm yr-1, 4.04  [2.77  to 5.24]  mm yr-1 and \r\n2.35  [0.70 to 4.06]  mm  yr-1,  respectively (Frederikse et  al., 2020), \r\ncompared to a GMSL change of 3.25 mm yr-1 (Section 2.3.3.3 and \r\nTable 9.5)"


id: 582debc1ff45668a6a5f2b1abae8bb9a
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
content_type: text
index: climate_gpt_only_giec
vector_id: 11380
content:
" Relative sea level (RSL) is falling in portions of southern \r\nAlaska (Sweet et al., 2018) and much of the northern part of north\u0002eastern Canada and around Hudson Bay (where land is rising by \r\n>10 mm/year; Greenan et al., 2018)."


id: 56e36305a76c80435caf259ac1c872ca
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
content_type: text
index: climate_gpt_only_giec
vector_id: 11145
content:
"many North American regions under future climate change (high \r\nconfidence) although land uplift from glacial isostatic adjustment in \r\nnorthern and Hudson Bay portions of North-Eastern North America \r\nleads to only medium confidence of coastal flood increases in that \r\nregion. Sweet et al. (2018) found 2000-2015 observed increases of \r\nabout 125% in high-tide flooding frequencies along the southern \r\nAtlantic USA coastline, with 75% increases along the USA Gulf \r\nCoast and USA northern Atlantic coastlines. That same study noted \r\nthat a GMSL of 0.5 m in 2100 would increase high tide ('nuisance') \r\nflooding from current rates of about once a month for most coastal \r\nregions to about once every other day along the USA Atlantic and Gulf \r\ncoasts and smaller increases in frequency along the Pacific coast, and \r\nDahl et al. (2017a) found similar trends on the USA East Coast prior to \r\nmid-century"


id: fd73ab019fe05dc95d08d82d6353860
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
content_type: text
index: climate_gpt_only_giec
vector_id: 37388
content:
" The present day 1-in-50-year ETWL is projected to occur \r\naround three times per year by 2100 with an SLR of 1 m all around \r\nNorth America, except in most of Eastern North America where \r\nit is expected to have return periods of 1-in-1-year to 1-in-2-years \r\n(Vitousek et al., 2017). Ghanbari et al. (2019) projected corresponding \r\nshifts towards higher frequencies of major flooding events for 20 US \r\ncities. Figure 12.4r and Figure 12.SM.6 show increases of 70 cm or \r\nmore in the 100-year return period extreme total water level (ETWL) \r\nover much of the USA East Coast, British Columbia, Alaska, and the \r\nHudson Bay under RCP8.5 by 2100 (relative to 1980-2014), with \r\nlower increases in northern Mexico, northern Canada, Labrador, and \r\nthe Pacific and Gulf coasts of the USA (Vousdoukas et  al., 2018). \r\nProjected increases in coastal flooding generally follow patterns of \r\nRSL change, although sea ice loss in the north also increases open \r\nwater storm surge (Greenan et al., 2018)."


id: 9f40dedcfafa6d225139c209e16bbac
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
content_type: text
index: climate_gpt_only_giec
vector_id: 22595
content:
"This section covers the climatic impact-drivers affecting small islands \r\naround the world (see definition of SIDS in the Glossary; Cross\u0002Chapter Box Atlas.2) with a particular focus on small islands in the \r\nCaribbean (CAR) Sea and the Pacific Ocean. Caribbean and Pacific \r\nsmall islands have mostly tropical climates and local conditions are \r\nalso influenced by diverse topography ranging from low-lying islands \r\nand atolls to volcanic and mountainous terrain. Climate variability in \r\nthese islands is influenced by the trade winds, easterly waves, tropical \r\ncyclones (TC), and the migrations of the Inter-tropical Convergence \r\nZone (ITCZ), the North Atlantic Subtropical High, and the South Pacific \r\nConvergence Zone (SPCZ), and other modes of climate variability as \r\ndiscussed in Cross-Chapter Box Atlas.2"


id: 9c5528669fc4ca1cd837e9630fd424ff
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
content_type: text
index: climate_gpt_only_giec
vector_id: 22149
content:
" Furthermore, changes in the \r\nocean temperature and chemistry, and relative sea level have strong \r\nimpacts on these small islands given their geographical location and \r\ndependence on coastal and marine ecosystem services."


id: 3ff46c8e3a96f8dadb5eed043c815d3c
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
content_type: text
index: climate_gpt_only_giec
vector_id: 7446
content:
"The AR5 recognized the heterogeneity in these small islands in terms \r\nof physical geography, socio-economic and cultural backgrounds, as \r\nwell as their vulnerability to the impacts of climate change. Similar \r\nto previous reports, these regions have been assessed together \r\nin this section, given the similarities in the challenges they face in \r\naddressing climate change impacts and risk, which were thought - \r\nuntil AR4  - to be dominated by sea level rise (Nurse et  al., 2014; \r\nBetzold, 2015). Since then there has been a  substantial increase \r\nin the number and complexity of the literature on the drivers and \r\nimpacts of climate change on small islands (BOM and CSIRO, 2011, \r\n2014; Nurse et al., 2014; Gould et al., 2018; Keener et al., 2018). There \r\nare also increasing efforts being made to produce higher resolution \r\nclimate projections for small islands through downscaling methods \r\n(Elison Timm et al., 2015; McLean et al., 2015; Khalyani et al., 2016; \r\nC. Zhang et al"


id: 926c986845f0b3934719b5ece7e4f058
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
content_type: text
index: climate_gpt_only_giec
vector_id: 20558
content:
"Heavy precipitation and pluvial flood: Heavy precipitation days \r\nin CAR have increased in magnitude, and have been more frequent \r\nin the northern part during the latter part of the 20th century (low \r\nconfidence) (Section 11.4.2 and Table 11.14). The direction of change \r\nin extreme precipitation varies across the Pacific and depends on \r\nthe season (low confidence) (Section  11.4.2 and Cross-Chapter \r\nBox Atlas.2, Table  1). Although pluvial flooding events have been \r\nobserved in some islands, there is limited evidence for an assessment \r\non past changes in pluvial flooding, unlike in other regions. There is \r\nlow confidence in the projected change in magnitude of very heavy \r\nprecipitation days in CAR across different GWLs (Table 11.14). On the \r\nother hand, there is high confidence in the increase in frequency and \r\nintensity of extreme rainfall events (i.e., 1-in-20-year rainfall events) \r\nin the western tropical Pacific in the 21st century, even for RCP2"


id: 2dd9fbbbc3fbb0864e4c83f77bbf577c
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
content_type: text
index: climate_gpt_only_giec
vector_id: 4648
content:
"6 \r\nscenario, based on model agreement and mechanistic understanding \r\nbut low confidence in the magnitude of change in extreme rainfall \r\ndue to model bias (BOM and CSIRO, 2014)."


id: b01dda8a23b7d2e7fc3eee1ea9fc87e3
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
content_type: text
index: climate_gpt_only_giec
vector_id: 25233
content:
"Agricultural and ecological drought: Recent trends toward more \r\nfrequent and severe droughts have been noted in the small islands but \r\nonly with low confidence in broad trend patterns, given high spatial \r\nvariability including heightened drought on the leeward side of islands \r\n(e.g., Frazier and Giambelluca, 2017; Herrera and Ault, 2017; McGree \r\net  al., 2019; see Table  11.15, Cross-Chapter Box  Atlas.2, Table  1). \r\nAgricultural and ecological droughts are projected to increase in \r\nfrequency, duration, magnitude, and extent in small islands, such as \r\nin CAR (medium confidence) and parts of the Pacific (low confidence), \r\nparticularly where future declines in precipitation are compounded \r\nby higher evapotranspiration, under increasing levels of warming \r\n(Naumann et al., 2018; Taylor et al., 2018; Vichot-Llano et al., 2021)"


id: f700557e3fa1daf6c73fa1a33b8cec0c
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
content_type: text
index: climate_gpt_only_giec
vector_id: 36368
content:
" \r\nRelative to the period 1985-2014, decreases in annual surface and \r\ntotal column soil moisture become more robust in more areas in CAR \r\nby 2071-2100 under SSP3-7.0 and SSP5-8.5 scenarios (B.I. Cook \r\net al., 2020), but reliably representing drought features in small island \r\ndomains with global simulations is challenging (see also Section 11.9)."


id: 9037cffe2418791735c4c58a429736ce
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
content_type: text
index: climate_gpt_only_giec
vector_id: 20224
content:
"Aridity: Current estimates identify many small islands as being under \r\nwater stress and thus particularly sensitive to variations in rainfall and \r\ngroundwater, population growth and demand, and land-use change, \r\namong others (Cross-Chapter Box Atlas.2; Holding et al., 2016). From \r\n1950 to 2016, a heterogeneous but prevalent drying trend is found \r\nin CAR (low confidence), where drought variability is modulated \r\nby the tropical Pacific and North Atlantic oceans (Table 11.15 and \r\nCross-Chapter Box Atlas.2, Table 1; Herrera and Ault, 2017). In the \r\nfuture, increased aridity and decreased freshwater availability are \r\nprojected in many small islands due to higher evapotranspiration \r\nin a warmer climate that partially offsets increases or exacerbates \r\nreductions in precipitation (Karnauskas et al., 2016, 2018b; Hoegh\u0002Guldberg et al., 2018)"


id: fcc06cb1fe4a163cccf354f4488a15fc
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
content_type: text
index: climate_gpt_only_giec
vector_id: 37277
content:
" Increased aridity is projected for the majority \r\nof the small islands, such as in CAR, southern Pacific and western \r\nIndian Ocean, by 2041-2059 relative to 1981-1999 under RCP8.5 or \r\nat 1.5degC and 2degC GWLs, which will further intensify by 2081-2099 \r\n(medium confidence) (Karnauskas et al., 2016, 2018b). Groundwater \r\nrecharge is projected to increase in Maui, Hawaii except on the \r\nleeward side of the island, which underscores the importance of \r\ntopography and elevation on freshwater availability in different \r\nisland microclimates (Brewington et al., 2019; Mair et al., 2019)."


id: 68f81e1baaa3355b848eac2b6613e798
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
content_type: text
index: climate_gpt_only_giec
vector_id: 14105
content:
"Mean wind speed: Scarcity of observations limits assessment of \r\nlong-term changes in winds over the small islands in the Pacific and \r\nCAR. Records indicate that average daily wind speeds have slowly \r\ndeclined in Hawaii, but have remained constant across western and \r\nsouthern Pacific sites since the mid-20th century (Marra and Kruk, \r\n2017). Recent studies of reanalyses and hindcast simulations indicate \r\nan intensification of the Pacific trade winds during the 1992-2011 \r\nperiod, which contributed to the ocean cooling in the tropical central \r\nand eastern Pacific (England et al., 2014; Takahashi and Watanabe, \r\n2016). Projections estimate up to 0.4 m s-1 (8%) increase in annual \r\nwinds in CAR under RCP8.5, which is associated with changes in \r\nthe extension of the North Atlantic Subtropical High that enhances \r\nthe Caribbean low-level jet during the wet season, and stronger local \r\neasterlies due to enhanced land-ocean temperature differences in \r\nthe dry season (Costoya et al"


id: 42e0dc1d3d8f7b4089043843bc531ce1
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
content_type: text
index: climate_gpt_only_giec
vector_id: 7912
content:
"Projections of global changes in tropical cyclones indicate more \r\nfrequent Category  4-5 storms (high confidence) and increased rain \r\nrates (high confidence) (Knutson et al., 2020), with relative sea level rise \r\nexacerbating storm surge potential, but with large regional differences \r\n(see Section 11.7.1.5). By the late 21st century, tropical cyclones are \r\nprojected to be less frequent in the basins of the western and eastern \r\nNorth Pacific, Bay of Bengal, Caribbean Sea and in the Southern \r\nHemisphere, but will be more frequent in the subtropical central \r\nPacific (Murakami et al., 2014; Yoshida et al., 2017; Bell et al., 2019; \r\nKnutson et al., 2020). Over CAR, tropical cyclone intensity is expected \r\nto increase by the end of the century under RCP8.5 due to higher sea \r\nsurface temperatures but can be inhibited by increases in vertical wind \r\nshear in the region (medium confidence) (Kossin, 2017; Ting et  al., \r\n2019)"


id: a1c3bff57700776fe08bfaca52f29230
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
content_type: text
index: climate_gpt_only_giec
vector_id: 23012
content:
" The poleward movement of the area in which tropical cyclones \r\nreach peak intensity in the western North Pacific is likely to continue, \r\nwhich affects the tropical cyclone frequency over the small islands in \r\nthe area (Section 11.7.1.5; Kossin et al., 2016). Projections also indicate \r\nan increase (decrease) in the tropical cyclone frequency during El Nino \r\n(La Nina) events in the Pacific at the end of the 21st century (Chand \r\net al., 2017). RCP8.5 2080-2099 projections indicate a 2% increase in \r\nthe number of tropical cyclones in the north-central Pacific relative to \r\n1980-1999, with tracks shifting northward towards Hawaii (N. Li et al., \r\n2018). Given projected reductions to the overall number of tropical \r\ncyclones but increases in storm intensity, total rainfall and storm surge \r\npotential, we assess medium confidence of overall changes to tropical \r\ncyclones affecting the Caribbean and Pacific small islands."


id: 578b24c3f5b6a221daf20fe86aeaf154
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
content_type: text
index: climate_gpt_only_giec
vector_id: 11268
content:
"Coastal erosion: Recent studies have indicated variable and dynamic \r\nchanges in shorelines of reef islands (medium confidence), including \r\nboth erosion and accretion, which suggest factors other than SLR \r\naffecting shoreline changes, such as in the central and western Pacific \r\nwithin the past 50-to-60-year timeframe (Webb and Kench, 2010; \r\nLe  Cozannet et  al., 2014; Ford and Kench, 2015; Duvat and Pillet, \r\n2017). For example, islands on atolls in the central and western Pacific \r\nhave not substantially eroded or reduced in size in the past decades \r\nwhile sea level has been rising, but their position and morphology have \r\nchanged due to anthropogenic factors (e.g.,  seawalls, reclamation) \r\nand climate-ocean processes (Biribo and Woodroffe, 2013; McLean \r\nand  Kench, 2015)"


id: be0982ef1ef5aa342fda0dab59bd81cb
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
content_type: text
index: climate_gpt_only_giec
vector_id: 27455
content:
" Analysis of aerial and satellite imagery revealed \r\nsevere shoreline retreat in six islands and the disappearance of five \r\nvegetated reef islands in Solomon Islands in the western Pacific \r\nbetween 1947 and 2014, which may be due to the interaction between \r\nSLR and waves (Albert et al., 2016). In French Polynesia, changes in \r\nshoreline and island area have been observed since the 1960s, partly \r\ndue to the effect of TCs on sediment changes and human activities \r\n(Duvat and Pillet, 2017; Duvat et al., 2017). Coastal erosion has also \r\nbeen noted over the small, low-lying, sandy islands, such as in French \r\nPolynesia and Solomon Islands, among others (Luijendijk et al., 2018; \r\nMentaschi et  al., 2018). Average shoreline retreat rates between 1 \r\nand 2 m yr-1 are estimated for the islands in the equatorial Pacific \r\nand in CAR, while a retreat rate of 0.5 m yr-1 is estimated for islands \r\nin the South Pacific, based on satellite observations from 1984-2016 \r\n(Luijendijk et al"


id: 5b46f73b8b3097f55b4945289b11db88
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
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index: climate_gpt_only_giec
vector_id: 11867
content:
", 2018; Mentaschi et al., 2018). There was also a loss of \r\n610 km2 compared with a gain of 520 km2 in coastal area in Oceania \r\nduring the 1984-2015 period (Mentaschi et al., 2018)."


id: 7f330f9fbe12b6a313a938109c623072
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
content_type: text
index: climate_gpt_only_giec
vector_id: 17564
content:
"Oceans face challenges from anthropogenic perturbations to the global \r\nEarth system, which cause increasing ocean warming, carbon dioxide\u0002induced acidification and oxygen loss (Bindoff et al., 2019). Climate \r\nchange will affect the major oceanic CIDs described in Section 12.2: \r\nmean ocean temperature, marine heatwave, ocean acidity, ocean \r\nsalinity, and dissolved oxygen (O2), as well as severe wind storm and \r\nsea ice. These changes result in a shifting profile of hazards relevant \r\nto impact and risk assessments (Section  12.3). New evidence, the \r\nSROCC (IPCC 2019b) assessments and advances in the new CMIP6 \r\nclimate simulations reinforce confidence in projected changes in \r\nclimatic impact-drivers in the global oceans. As the ocean has taken \r\nup about 90% of the global warming for the period 1971-2018 \r\n(Section  7.2.2.2), the emergence of the sea surface temperature \r\nincrease signal has already been observed in global oceans over the \r\nlast century (Hawkins et al., 2020)"


id: c7a8f54f2b5fe64fbbf3da15fa5e74cc
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
content_type: text
index: climate_gpt_only_giec
vector_id: 28993
content:
" The signal in sea ice extent decrease \r\nhas already emerged in the Arctic Ocean (Landrum and Holland, 2020), \r\nwhile ocean acidification and low oxygen have also already emerged \r\nin many oceanic regions and will emerge in all global oceans by 2050 \r\nunder RCP8.5 (Section 12.5.2 and Table 12.10). This section assesses \r\nkey climatic impact-drivers that can be linked with sectoral and \r\nregional vulnerability and exposure in open and deep oceans, drawing \r\nfrom previous Chapters (Chapters 2, 3, 4, 5 and 9)."


id: 291100f3362b86b698b46acf3e551214
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
content_type: text
index: climate_gpt_only_giec
vector_id: 3884
content:
"Projections indicate that shoreline retreat will occur over most of \r\nthe small islands in the Pacific and CAR throughout the 21st century \r\nwith spatial variability (high confidence). Median shoreline change \r\nprojections (CMIP5) relative to 2010, presented by Vousdoukas et al. \r\n(2020b), show that, by mid-century, shorelines in the islands in the \r\nequatorial Pacific and South Pacific will retreat by around 40 m, \r\nunder both RCP4.5 and RCP8.5. In CAR islands, sandy shorelines are \r\nprojected to retreat by about 80 m by mid-century under both RCPs. \r\nBy 2100, more than 100 m of median shoreline retreat is projected \r\nfor all small islands under both RCPs; notably in CAR where retreats \r\napproaching 200 m (relative to 2010) are projected under both RCPs. \r\nThe total length of sandy coasts in CAR and Pacific small islands that \r\nis projected to retreat by more than a median of 100 m by 2100 under \r\nRCP4.5 and RCP8"


id: 13e2569553ffa9e210bbda7716bf2d34
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
content_type: text
index: climate_gpt_only_giec
vector_id: 636
content:
"from 1850-1900 to 2011-2020, and 0.60 [0.44 to 0.74] degC from \r\n1980 to 2020 (Section  2.3.1.1.6 and Table  2.4). There is very high \r\nconfidence that the Indian Ocean, the western equatorial Pacific \r\nOcean, and western boundary currents have warmed faster than the \r\nglobal average, while the Southern Ocean, the eastern equatorial \r\nPacific, and the North Atlantic Ocean have warmed more slowly or \r\nslightly cooled (Section 9.2.1.1). It is virtually certain that global mean \r\nSST will continue to increase in the 21st century at a rate depending \r\non future emissions scenario, with CMIP6 projections indicating an \r\nincrease of 0.86degC (likely range 0.43degC-1.47degC) under SSP1-2.6 \r\nand 2.89degC (2.01degC-4.07degC) under SSP5-8.5, by 2081-2100, \r\nrelative to 1995-2014 (Section  9.2.1.1)"


id: 56a175462f5103f4fd3861ecc7fb8ea5
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
content_type: text
index: climate_gpt_only_giec
vector_id: 11107
content:
" Global warming of 2degC \r\nabove pre-industrial levels is projected to increase SST, resulting \r\nin the exceedance of numerous hazard thresholds for pathogens, \r\nseagrasses, mangroves, kelp forests, rocky shores, coral reefs \r\nand other marine ecosystems (medium confidence) (Poloczanska \r\net al., 2013a, b, 2016; Liu et al., 2014; Portner et al., 2014; Graham \r\net al., 2015; Schoepf et al., 2015; Gobler et al., 2017; Henson et al., \r\n2017; Hoegh-Guldberg et  al., 2017; Krueger et  al., 2017; Hughes \r\net al., 2018a, b; Perry et al., 2018). It is virtually certain that upper\u0002ocean stratification has increased at a  rate of 4.9 +- 1.5% during \r\n1970-2018 and that this will continue to increase in the 21st century \r\n(Section 9.2.1.3), potentially leading to reduced nutrient supply and \r\ntotal productivity (low confidence) (Moore et al., 2018)."


id: 5973c94e7b338d01f2eed7d3900569bb
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
content_type: text
index: climate_gpt_only_giec
vector_id: 11567
content:
"At the regional scale, by 2100 the average Arctic surface salinity \r\nis projected to decrease by 1.5 +- 1.1 psu (practical salinity units), \r\nand the liquid freshwater column in the Arctic Ocean is projected \r\nto increase by 5.4 +- 3.8 m under RCP8.5, (Shu et al., 2018). In the \r\nIndian Ocean, sea surface salinity is projected to decrease by \r\nbetween 0.49  and 0.75 psu by 2080, compared to 2015, under \r\nRCP2.6 and RCP2.6, respectively (Akhiljith et al., 2019). Projections \r\nfor the North and South Atlantic oceans indicate increasing salinity \r\nin the upper layer (0-500 m) under both RCP4.5 and RCP8.5, due to \r\nthe decreasing freshwater input from the equator and increasing net \r\nevaporation (Skliris et  al., 2020). There is medium confidence that \r\nfresh ocean regions (Pacific, Southern and Indian oceans) will get \r\nfresher and salty ocean regions (Atlantic Ocean) will get saltier over \r\nthe 21st century (Section 9.2.2.2; IPCC, 2019b)"


id: ac2b304ace72401963292c06e57ea78
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
content_type: text
index: climate_gpt_only_giec
vector_id: 24620
content:
" Ocean warming and \r\nhigh-latitude surface freshening is projected to continue to increase \r\nupper-ocean stratification in the 21st century (Section 9.2.1.3)."


id: 89484f640987d8d9e37e067bed89d7b2
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
content_type: text
index: climate_gpt_only_giec
vector_id: 19136
content:
"Marine heatwave: Marine heatwaves (MHWs) have increased \r\nin frequency over the 20th century, with an approximate doubling \r\nsince the 1980s (high confidence), and their intensity and duration \r\nhave also increased (medium confidence) (Box  9.2). Projections \r\nshow that this increasing trend likely continues with 2-9 times more \r\nfrequent MHWs (at global scale) projected by 2081-2100, relative to \r\n1995-2014 under SSP1-2.6, and 4-18 times more frequent MWHs \r\nunder SSP5-8.5. The largest changes in MHW frequency are likely to \r\noccur in the tropical ocean and the Arctic, while there is medium \r\nconfidence of moderate increases in the mid-latitudes, and of small \r\nincreases in the Southern Ocean (Box 9.2). Permanent MHWs (more \r\nthan 360 days per year, relative to the historical climate conditions) \r\nare projected to occur in the 21st  century in parts of the tropical \r\nocean, in the Arctic Ocean, and around latitude 45degS, under SSP5-8.5 \r\n(Box 9.2)"


id: 3290aff7cf95287a91cc584583e8bcf9
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
content_type: text
index: climate_gpt_only_giec
vector_id: 5373
content:
" The occurrence of such permanent MHWs can be largely \r\navoided under the SSP1-2.6 scenario (Box  9.2). MHWs can have \r\ndevastating and long-term impacts on ecosystems (Oliver et al., 2018), \r\nmaking them an emerging hazard for marine ecosystems (Frolicher \r\nand Laufkotter, 2018; Smale et  al., 2019). A  series of MHWs that \r\noccurred in 2010-2011 had consequences for seagrass in western \r\nAustralia (Wernberg et al., 2013; Arias-Ortiz et al., 2018), and for the \r\nlobster fishery in the Gulf of Maine (Pershing et al., 2018). The MHWs \r\nthat occured western Australia in 2015/2016 led to the third-highest \r\nmass coral bleaching globally (Le Nohaic et al., 2017)."


id: cb283c829bfc9e0f4a21178dcce0d545
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
content_type: text
index: climate_gpt_only_giec
vector_id: 29518
content:
"Dissolved oxygen: Since the middle of the 20th  century, oxygen \r\nconcentrations of open and coastal waters have been declining, \r\nand such deoxygenation affects biological and biogeochemical \r\nprocesses in the ocean (Schmidtko et al., 2017). In recent decades, \r\nlow-oxygen zones in ocean ecosystems have expanded, and \r\nprojections indicate an acceleration with global warming (medium \r\nconfidence) (Diaz and Rosenberg, 2008; Gilly et  al., 2013; Gobler \r\net  al., 2014). A  2%  loss (4.8  +- 2.1 pmoles O2) in total dissolved \r\noxygen in the upper ocean layer (100-600 m) has been observed \r\nduring 1970-2010 (Section 2.3.4.2), with the highest oxygen loss of \r\nup to 30 mol  m-2 per decade in the equatorial and North Pacific, \r\nthe Southern Ocean and the South Atlantic Ocean (Section 5.3.3.2). \r\nGlobal mean ocean oxygen concentration is projected to decrease by \r\n6.36 +- 2.92 mmol m-3 under SSP1-2.6 and by 13.27 +- 5.28 mmol m-3\r\nunder SSP5-8"


id: d105d71ec06e92428734cc3cf66f3cbb
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
content_type: text
index: climate_gpt_only_giec
vector_id: 30423
content:
"5 in the subsurface (100-600 m) by 2080-2099, \r\ncompared to 1870-1899, which is respectively 71% and 40% greater \r\nthan previous estimates based on CMIP5 models (Section 5.3.3.2). \r\nIn the benthic ocean, projected future losses of dissolved oxygen \r\nconcentration by 2080-2099, compared to 1870-1899, are \r\n-5.14 +- 2.04 mmol m-3 under SSP1-2.6 and -6.04 +- 2.19 mmol m-3\r\nunder SSP5-8.5 (Kwiatkowski et al., 2020). Section 5.3.3.2 assessed \r\nvery likely global decreases in ocean oxygen concentrations although "


id: 68591ece601d1729f5f66fcddb7bc554
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
content_type: text
index: climate_gpt_only_giec
vector_id: 14000
content:
"Extreme heat, cold spell and frost: Ecosystem and societal \r\ntemperature thresholds in polar regions often reflect lower tolerance \r\nto heat and higher tolerance to cold. Extreme heat events have \r\nincreased around the Arctic and Iceland since 1979, including \r\nincreases in cold season warm days and nights, melt days, and Arctic \r\nwinter warm events (T > -10degC) as well as decreases in cold days and \r\nnights (Mernild et al., 2014; Matthes et al., 2015; Vikhamar-Schuler \r\net al., 2016; Graham et al., 2017; Sui et al., 2017; Dobricic et al., 2020; \r\nPena-Angulo et  al., 2020). Heatwaves causing high temperature \r\nrecords have been recently documented in West and East Antarctica \r\n(Wille et al., 2019; Robinson et al., 2020). There is high confidence\r\nthat polar amplification will drive increases in Arctic heat extremes \r\nas well as continuing declines in the magnitude and frequency of \r\ncold extremes (Matthes et al., 2015; Kharin et al"


id: 61c19bce6e91fef68586100881fc83c4
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
content_type: text
index: climate_gpt_only_giec
vector_id: 12950
content:
", 2018), although \r\ndynamical effects will still bring substantial cold air anomalies over \r\nthe Arctic (Wu and Francis, 2019). There is medium confidence for \r\nequivalent changes in extreme heat in Antarctica based primarily "


id: 572cebf1bf8a20e456bfa603e49989f0
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
content_type: text
index: climate_gpt_only_giec
vector_id: 11201
content:
"Permafrost: Observations from recent decades (assessed in \r\nSection  9.5.2 and Section  2.3.2.5) show increases in permafrost \r\ntemperature (very high confidence) and active layer thickness (medium \r\nconfidence) across the Arctic (AMAP, 2017; Derksen et  al.,  2018; \r\nMarkon et  al., 2018; Biskaborn et  al., 2019; Farquharson et  al., \r\n2019; Meredith et al., 2019; Romanovsky et al., 2020). Section 9.5.2 \r\nnoted that observations of active layer thickness in Antarctica are \r\ntoo limited to assess long-term trends (see also Hrbacek et  al., \r\n2018; Biskaborn et al., 2019). Future projections indicate continuing \r\nincreases in permafrost temperature and active layer thickness \r\nwith loss of permafrost across the Arctic (Section 9.5.2). Streletskiy \r\net al. (2019) noted that changes to Russian permafrost temperature \r\nand active layer thickness are most pronounced in areas where \r\npermafrost is continuous (underlying >90% of landmass)"


id: d978fc229256f5d057b326c5272abf07
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
content_type: text
index: climate_gpt_only_giec
vector_id: 31730
content:
" CMIP5 \r\nanalyses by Slater and Lawrence (2013) projected that, by RCP8.5 \r\n2100, shallow (<3 m) permafrost would be most probable only in \r\nportions of the Canadian Arctic Archipelago and the Russian Arctic \r\ncoastal and eastern upland regions."


id: dbc54351ad4cd8c7cffdbe6cc52abca6
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
content_type: text
index: climate_gpt_only_giec
vector_id: 32091
content:
"Sea ice: Consistent with SROCC (Meredith et al., 2019), Section 9.3.1 \r\nand Section 2.3.2.1.1 assess very high confidence that Arctic sea ice \r\nthickness, extent, and average age have significantly decreased over \r\nthe past four decades, with largest declines in September (when \r\nsea ice is at an annual minimum). Declines in landfast ice are most \r\nrapid in the Laptev Sea (Selyuzhenok et  al., 2015), with warming \r\nalso breaking perennial landfast ice blocking ocean channels ('ice \r\nplugs') in the Canadian Archipelago (Pope et al., 2017), and landfast \r\nice declining in the cold season by 7% per decade across the Arctic \r\n(1976-2007; Yu et  al., 2014). Observed trends and projections \r\nsuggest that perennial sea ice is being replaced by thin, seasonal ice, \r\nalthough multi-year ice will persist above the Canadian Archipelago \r\nand drift into sea transportation lanes (Howell et al., 2016; Derksen \r\net al., 2018)"


id: 5b4fd9cacb05f760b29e2d8082746bb5
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
content_type: text
index: climate_gpt_only_giec
vector_id: 11872
content:
" Trends from 1979 to 2013 show slightly earlier spring \r\nmelt for Arctic sea ice, but substantially delayed autumn freeze-up \r\nand a melt season lengthened by more than 3 days per decade off \r\nnorthern Alaska and Canada with the exception of portions of the \r\nBering Sea (Parkinson, 2014; Stroeve et  al., 2014). Section  9.3.2 \r\nassessed low confidence in long-term trends in sea ice extent or \r\nthickness near Antarctica."


id: ce042e0f5f81fc15a04d4542893f6af
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
content_type: text
index: climate_gpt_only_giec
vector_id: 29931
content:
"Glacier and ice sheet: Section  9.5.1 and Section  2.3.2.3 found \r\nthat glaciers have lost mass in all polar regions since 2000 (high \r\nconfidence), and Section 9.4 assessed high confidence in Greenland \r\nIce Sheet mass losses since 1980 and Antarctic Ice Sheet losses \r\nsince 1992 (dominated by West Antarctica, with losses in parts of \r\nEast Antarctica in the past two decades). New simulations from \r\nGlacierMIP (Marzeion et al., 2020) indicate glaciers in Iceland will \r\nlose 31 +- 35%, 41 +- 46% and 53 +- 45% of their mass in 2015 \r\nby the end of the century for RCP2.6, RCP4.5 and RCP8.5 scenarios, \r\nrespectively. Marzeion et  al"


id: a502dfc9017e6b5571cc0424fc0a5b32
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
content_type: text
index: climate_gpt_only_giec
vector_id: 23528
content:
"Climate change has caused and will continue to induce an \r\nenhanced warming trend, increasing heat-related extremes \r\nand decreasing cold spells and frosts in the Arctic (high \r\nconfidence), with similar changes in Antarctica but medium \r\nconfidence for extreme heat increases and West Antarctic \r\nfrost change decreases and low confidence for cold spell \r\nchanges and East Antarctica frost. The water cycle is projected \r\nto intensify in polar regions, leading to more rainfall, \r\nhigher river flood potential and more intense precipitation \r\n(high confidence). Projections indicate reductions in glaciers \r\nat both poles, with sea ice loss, enhanced permafrost \r\nwarming, decreasing permafrost extent, and decreasing \r\nseasonal duration and extent of snow cover in the Arctic \r\n(high confidence) even as some of the coldest regions will see \r\nhigher total snowfall given increased precipitation (medium \r\nconfidence)"


id: 51703d0b9f4abdfb563589561ff57ad3
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
content_type: text
index: climate_gpt_only_giec
vector_id: 10270
content:
" Projections indicate relative sea level rises in \r\npolar regions (high confidence), with the exception of regions \r\nwith substantial land uplift including North-Eastern North \r\nAmerica (high confidence), western Greenland, the northern \r\nBaltic Sea, and portions of West Antarctica. Higher sea levels \r\nalso contribute to high confidence for projected increases of \r\nArctic coastal flooding and higher coastal erosion (aided by \r\nsea ice loss) (medium confidence) with lower confidence for \r\nthose CIDs in regions with substantial land uplift."


id: f7f3315ce9e369cc119fed0b74c12258
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
content_type: text
index: climate_gpt_only_giec
vector_id: 36525
content:
"Mean temperature increase is a  major climatic impact-driver \r\nfor biodiversity hotspots, and it is very likely that it will affect all \r\nhotspot areas identified in the literature, at various rates in all \r\nclimate scenarios, except those located in the North Atlantic where \r\nwarming is uncertain (see Chapter  4). Terrestrial ecosystems will \r\nexperience an enhanced warming compared to ocean ecosystems, \r\nbecause land temperatures are warming faster than ocean \r\ntemperatures (Chapter 4). Marine ecoregions will experience ocean \r\nacidification and temperatures that increase faster in high latitudes \r\n(high confidence), but critical temperature and oxygen thresholds \r\nare projected to be crossed earlier (by mid-century RCP8.5) in \r\ntropical areas (Hughes et al., 2017a; Bruno et al., 2018)"


id: 8f67dc59187e6b2691a97179b57d44b0
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
content_type: text
index: climate_gpt_only_giec
vector_id: 20112
content:
" A warming \r\ntrend is also expected for freshwater ecosystems, with different \r\nlocal magnitudes due to combined effects of groundwater system \r\ninertia as well as hydrology changes (Knouft and Ficklin, 2017). \r\nIn tropical land areas, because interannual temperature variability \r\nis weak compared to mean changes, the temperature distribution \r\nrange is likely to be shifted to a very different range in all projection \r\nscenarios, with unprecedented values relative to pre-industrial \r\nconditions. High climate velocities are particularly noteworthy for \r\nbiodiversity hotspots given complex ecosystem dynamics and niche \r\nclimates not easily replicated under shifted geographies (Burrows \r\net  al., 2014; Halpern et  al., 2015; Dobrowski and Parks, 2016). In \r\nsome regions (e.g"


id: f65a15b933de291c45a9a26c1ee46b9e
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
content_type: text
index: climate_gpt_only_giec
vector_id: 36272
content:
", Central Africa, Amazon, South East Asia) the \r\nmean temperature change is already beyond the normal range of \r\nvariations as it has reached levels higher than three (and up to \r\nsix) times larger than the standard deviation of the interannual \r\nvariations (Hawkins et  al., 2020). Together with global warming, \r\nland and marine heatwaves are very likely to increase in the future \r\nclimate in biodiversity hotspots (Sections 12.4.1-12.4.7)."


id: b231533d7c2627f53cec87d99e2b2236
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
content_type: text
index: climate_gpt_only_giec
vector_id: 25538
content:
"Climate change-related variations in oceanic drivers (e.g., relative sea \r\nlevel, storm surge, ocean waves), combined with tropical cyclones, \r\nextreme precipitation and river flooding, are expected to lead to \r\nmore frequent and more intense coastal flooding and erosion (very \r\nhigh confidence) impacting cities and settlements located especially \r\nin low-elevation coastal zones and mega-deltas (Chan et  al., 2012, \r\n2018; Karymbalis et al., 2012; Hemer et al., 2013; Aerts et al., 2014; B. \r\nNeumann et al., 2015; Hauer et al., 2016; Ranasinghe, 2016; Hinkel et al., \r\n2018; Mavromatidi et al., 2018; Marcos et al., 2019; see also Sections \r\n12.3, 12.4.1-12.4.7 and 12.4.9). Coastal erosion and flooding also \r\npose challenges to critical infrastructure such as roads, subway tunnels, \r\nelectricity and phone networks, wastewater management plants and \r\nbuildings (Grahn and Nyberg, 2017; Pregnolato et al., 2017)"


id: 30526ad71e266d9073076f216c27869a
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
content_type: text
index: climate_gpt_only_giec
vector_id: 5030
content:
" Compound \r\nflooding due to simultaneous storm surges and high river flows have \r\nbeen found to be increasingly frequent in several cities and/or low\u0002lying areas in Europe and the USA (high confidence) (Wahl et al., 2015; \r\nBevacqua et al., 2019; Ganguli and Merz, 2019). Chapter 11 found that \r\nthe frequency of such compound flood events is projected to increase \r\n(high confidence). In addition to changes induced by sea level change, \r\nmany cities and settlements by the sea are in regions where tropical "


id: 3a6e6f49b4fe94ded9d1800333884938
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
content_type: text
index: climate_gpt_only_giec
vector_id: 6557
content:
"Deserts and semi-arid areas are prone to dust storms, which can \r\ndrive impacts on health and several other sectors (X. Zhang et al., \r\n2016; Tong et al., 2017). The SRCCL indicated that the evolution of \r\ndust under climate change is uncertain (Mirzabaev et  al., 2019), \r\nand there is a lack of evidence and agreement of a change in their \r\nfrequency or intensity so far in most regions (Sections 12.4.1-12.4.9). \r\nModel projections of future changes in dust are hindered by the \r\nuncertainties in future regional wind and precipitation as the climate \r\nwarms (Evan et al., 2016); in the effect of CO2 fertilization on source \r\nextent (Huang et al., 2017); and in the impact of human activities \r\nupon the land surface (Ginoux et al., 2012; see Chapter 10). Projected \r\ntrends in dust storms and dust loads in deserts and semi-arid areas \r\nvary from region to region. Dust loadings are expected to decrease \r\nover most of the Sahara and Sahel (low confidence) (Section 12.4"


id: ee392bd261978a5542ef7af50eb0cb6e
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
content_type: text
index: climate_gpt_only_giec
vector_id: 34952
content:
"1), \r\nincrease over Mexico and the south-west USA (medium confidence) \r\n(Section 12.4.6), and there is low confidence of a future trend due to \r\nclimate change in other continents (Sections 12.4.2-12.4.5)."


id: a69c457448520ce56ef13a7337416e40
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
content_type: text
index: climate_gpt_only_giec
vector_id: 23737
content:
"Elevation-dependent warming could speed up the observed, rapid \r\nupward shifts of the freezing level height (FLH) in several mountainous \r\nregions of the world and lead to faster changes in the snowline, the \r\nglacier equilibrium-line altitude and the snow/rain transition height \r\n(high confidence). In the Indus, Ganges and Brahmaputra basins in \r\nAsia, the FLH is projected to rise at a rate of 4.4 to 10.0 m yr-1 under \r\nRCP8.5 (Viste and Sorteberg, 2015). In the Argentinian Andes, FLH \r\nis projected under RCP8.5 to move up more than twice as much by \r\n2070 as during the entire Holocene under the worst case scenario \r\n(Drewes et al., 2018). On the western slope of the subtropical Andes \r\n(30degS-38degS) in central Chile, the mean value of the free tropospheric \r\nheight of the 0degC isotherm under wet conditions is projected to be \r\nclose to or higher than the upper quartile of the distribution in the \r\ncurrent climate, towards the end of the century and under RCP8"


id: f1afbb6eb4af2f2160fbd142036a4df1
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
content_type: text
index: climate_gpt_only_giec
vector_id: 35532
content:
"5 \r\n(Mardones and Garreaud, 2020). In the Alps and the Pyrenees, Spandre \r\net al. (2019) projected a rise in the natural snow elevation of between \r\n200-300 m and 400-600 m by mid-century under RCP2.6 and RCP8.5, \r\nrespectively. In the same region, the environmental equilibrium-line \r\naltitude is projected to exceed the maximum elevation of 69%, 81% \r\nand 92% of the glaciers by the end of the century under RCPs 2.6, \r\n4.5 and 8.5, respectively (Zebre et al., 2021)."


id: 70f74cc9c24505c6ad51e4e3455cfea9
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
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vector_id: 15406
content:
"Orographic effects enhance convection and stratiform heavy \r\nprecipitation (due to uplift) and make mountains prone to extreme \r\nprecipitation events. These events are projected to increase in major \r\nmountainous regions (Alps, parts of the Andes, British Columbia, North\u0002Western North America, Calabria, Carpathian, Hindu-Kush-Himalaya, \r\nRocky Mountains, Umbria; medium to high confidence depending on \r\nlocation), with potential cascading consequences of floods, landslides \r\nand lake outbursts in mountainous areas in all scenarios (medium \r\nconfidence) (Chapter  11 and Sections 12.4.1-12.4.9; Geertsema \r\net al., 2006; Gaire et al., 2015; Kim et al., 2015; Ciabatta et al., 2016; \r\nGariano and Guzzetti, 2016; Kharuk et al., 2016; Syed and Al Amin, \r\n2016; Cloutier et al., 2017; Gadek et al., 2017; Jurchescu et al., 2017; \r\nRajczak and Schar, 2017; Alvioli et al., 2018; Coe et al., 2018; Schlogl \r\nand Matulla, 2018; C.-W. Chen et al., 2019; Handwerger et al., 2019; \r\nHock et al"


id: 31777787bb09ec935e7755b09aaafabf
created_at: 2023-03-16 14:02:10
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", 2019; Patton et al., 2019; Vaidya et al., 2019; Kirschbaum \r\net al., 2020; Coppola et al., 2021b)."


id: 3efdb423b11108d54abc1b18585255df
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
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index: climate_gpt_only_giec
vector_id: 7296
content:
"(almost half) under SSP1-2.6. Several global-scale studies have \r\nshown that high temperature extremes will increase everywhere \r\n(high confidence) (Gourdji et  al., 2013; Perkins-Kirkpatrick and \r\nGibson, 2017; Harrington et  al., 2018; Jones et  al., 2018; Lehner \r\net al., 2018; Shi et al., 2018; Tebaldi and Wehner, 2018; Arnell et al., \r\n2019; Russo et  al., 2019; Schwingshackl et  al., 2021), although \r\nthe change depends on the indicator (see also Chapter  11). For \r\nexample, by 2080, at least 80% of the land surface is expected to \r\nexperience average summer temperatures greater than the historical \r\n(1920-2014) maximum with high (RCP8.5) emissions (Lehner et al., \r\n2018). The areas of rice and maize cropland with damaging extreme \r\ntemperatures during the reproductive season will increase by \r\na factor of three under RCP8.5 (Gourdji et al., 2013)"


id: 21aa5e9d1ac0acde537fd0a7e8874273
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
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index: climate_gpt_only_giec
vector_id: 2725
content:
" Under a high \r\nemissions scenario, heatwaves that are currently considered rare \r\nare projected to become the norm almost everywhere by the end of \r\nthe century (Russo et al., 2014). Heat stress as a combined function \r\nof temperature and humidity also increases at the global scale, \r\nespecially with high emissions (e.g., Matthews et al., 2017). Growing \r\ndegree-days and cooling degree-days also increase everywhere \r\n(Arnell et  al., 2019) with the absolute and proportional changes \r\ndepending on temperature threshold. Increases in temperatures \r\nwill result in reductions in heating degree days (Arnell et al., 2019; \r\nCoppola et al., 2021b) and a widespread reduced frequency of cold \r\nextremes (high confidence)."


id: 600ce1b801f81870a00c585c5562aabb
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
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vector_id: 12688
content:
"Tropical forests typically reach peak fire weather conditions in the dry \r\nseason (Taufik et al., 2017), in particular during long-lived droughts \r\n(Brando et al., 2014; Marengo et al., 2018), with consequences for \r\ntree mortality, forest and carbon sink loss (Brando et al., 2019), and \r\non the hydrological cycle in South America (Martinez and Dominguez, \r\n2014; Espinoza et al., 2020). Observations and reanalyses over the \r\npast three to four decades, combined into fire risk indices, show that \r\nthe fire weather season length has been increasing by about 20% \r\nglobally (Jolly et al., 2015), and this index exhibits particularly high \r\ntrend values over tropical forest areas of South and Central America \r\nand Africa. There is generally low confidence in future projections of \r\ngeneral fire weather risk evolution in tropical forests and evolutions \r\ndepend on the region (Abatzoglou et al., 2019)"


id: 9961af572fa05c3175e9ee5d0504ca4
created_at: 2023-03-16 14:02:10
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" Over the Amazon \r\nbasin the fire risk increase is projected to emerge well before 2050 \r\nwhile for other equatorial forests no significant evolution is found. \r\nIn Savanna areas the risk increase is found to be more general."


id: d6853e84324bc93a28f90ad7cf515fdd
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
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vector_id: 31263
content:
"Integrating the results of the regional assessments in Section  12.4 \r\nshows that changes in CIDs linked with the water cycle or atmospheric \r\ndynamics (e.g., storms) vary more among regions, largely due to the \r\nspatial pattern of changes in atmospheric circulation and changes \r\nin precipitation and evaporation (Chapters 8 and 11). There is high \r\nconfidence that heavy precipitation and pluvial floods will be increasing \r\nin a  majority of land regions, primarily due to the well-understood \r\nClausius-Clapeyron relationship describing the  increase in moisture \r\ncontent with air temperature (Chapters 8 and 11), but there is \r\na  large spatial variability in fluvial flood hazards"


id: aae72175a6e4a87f8b88f759fc7b95a5
created_at: 2023-03-16 14:02:10
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vector_id: 24405
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" Top-down global\u0002scale studies show that although fluvial flood hazards are projected \r\nto decrease in regions where there are large reductions in seasonal \r\nrainfall totals or where warmer temperatures mean less accumulated \r\nsnow, at the global scale, fluvial flood hazard (characterized as the \r\narea affected, size of peak or likelihood of an event) is projected to \r\nincrease substantially through the century (Giuntoli et al., 2015; Arnell \r\nand Gosling, 2016; Winsemius et al., 2016; Alfieri et al., 2017; Dottori \r\net al., 2018; Arnell et al., 2019). Projected changes in agricultural and \r\nhydrological drought characteristics are dependent on the indicator \r\nused to define drought (Sections 11.6 and Section 12.3.2), but there \r\nis at least medium confidence of an increase in the drought hazard in \r\nmany parts of the world. This is also reflected in global-scale studies, \r\nwith Naumann et  al"


id: cca83a8b23599f3d1ba6e5a9adf4abc3
created_at: 2023-03-16 14:02:10
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vector_id: 29736
content:
" (2018), for example, showing that the global \r\nmean average drought duration (based on the SPEI index which is \r\ncalculated from the difference between precipitation and potential \r\nevaporation) increased from 7  months with the current climate to \r\n18.5 months for a global warming level of 3degC. The apparent global \r\nincrease in drought occurrence is greater when evaporation is captured \r\nin the drought indicator (e.g., SPEI) than when the indicator is based on \r\nprecipitation alone (as in SPI; Carrao et al., 2018). There is evidence that \r\nthe likelihood of simultaneous events in several locations will increase: \r\nTrnka et al. (2019) found that the proportion of wheat-growing areas "


id: 254dcd9396ecdd2823d1328ef75afd27
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
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vector_id: 3297
content:
"Figure 12.11 | Synthesis of the climatic impact-driver (CID) changes projected by 2050 (2041-2060) with high confidence, relative to reference period \r\n(1995-2014), together with the sign (direction) of change. Information is taken from the CID tables in Section 12.4. Some CIDs are grouped in order to streamline the \r\ninformation in order to fit in all information in the figure. Mean temperature, extreme heat, cold spells and frost are grouped under a single 'heat' icon, as they are projected to \r\nchange simultaneously, albeit heat and cold are changing in opposite directions. Coastal CIDs (relative sea level, coastal flooding and coastal erosion at sandy beaches) are also \r\ngrouped. In the figure, the 'coastal' icon indicates regions where at least two of the three individual coastal CIDs are projected to change with high confidence"


id: 99ab9f5369eb5b1c975765a890d88ec5
created_at: 2023-03-16 14:02:10
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content:
" Cases where \r\nonly two of the three CIDs increase with high confidence are in Arctic Northern Europe, Russian Arctic and Arctic North-Western North America. A single icon is used for aridity, \r\nhydrological drought, and agricultural and ecological drought, and only the number of drought types that change is indicated. For the 'Snow, ice' icon, information is taken from \r\nthe evolution of the 'Snow, glacier and ice sheet' CID; most regions also have similar changes for 'permafrost' and 'lake, river and sea ice'. Exceptions are for North-Eastern \r\nNorth America, Russian Arctic and Arctic North-Western North America where snow is decreasing with medium confidence (thus not appearing in the figure), while permafrost \r\nand lake, river and sea ice is decreasing with high confidence. The location of the icons within the regions is arbitrary. Icon sources: https://www.flaticon.com/authors/freepik."


id: 2729fbe339b72094d3a76f8e07725500
created_at: 2023-03-16 14:02:10
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vector_id: 3556
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"The emergence of a climate change signal occurs when that signal \r\nexceeds some critical threshold (usually taken to be a  measure of \r\nnatural variability; see for example, Hawkins and Sutton, 2012) \r\nor when the probability distribution of an indicator becomes \r\nsignificantly different to that over a reference period (e.g., Chadwick \r\net al., 2019; see also Chapter 10 and Section 1.4.2), in which case \r\nexternal anthropogenic forcings can be detected as causal factors. \r\nThe 'time of emergence' (ToE) or 'temperature of emergence' is \r\nthe time or global warming level thresholds associated with this \r\nexceedance. Emergence is particularly relevant to impacts, risk \r\nassessment and adaptation because human and natural systems \r\nare largely adapted to natural variability but may be vulnerable if \r\nexposed to changes that go beyond this variability range; this is not \r\nto say that changes within natural variability have no impact, as \r\noccurrence of damaging extremes proves"


id: 38129edcd284ae81aeff1683c5bee6af
created_at: 2023-03-16 14:02:10
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" Emergence also informs \r\nthe timing of adaptation measures. The emergence of a change is \r\nalways relative to a reference period (e.g., the pre-industrial period \r\nor a recent past), depending on the framing question. In the former \r\ncase, the goal is to estimate the amplitude of an anthropogenically \r\ndriven change while in the latter, it is to estimate the amplitude of \r\nchange relative to a baseline that is familiar to stakeholders. Both \r\nquestions are important for risk assessment, but the former may be \r\nmore directly interpretable in a  mitigation context. The variability \r\nalso refers to a time scale, generally interannual to inter-decadal. See \r\nSection 1.4.2 and Chapter 10 for more details about how emergence \r\nis defined and used in the literature."


id: 2ff4e8f119712bded31927d661cb0501
created_at: 2023-03-16 14:02:10
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vector_id: 4973
content:
"Figure 12.11 highlights that each region will, with high confidence, \r\nexperience changes in multiple CIDs, challenging the vulnerability of \r\nthe region and its adaptation and mitigation capacity. All non-polar \r\nregions with a  coastline will see an increase in relative sea level, \r\nextreme sea level and coastal erosion, and will also see an increase in \r\nhot extremes, a decrease in cold extremes, and many will experience \r\nan increase in heavy precipitation. One cluster of regions  - East \r\nSouthern and West Southern Africa regions, the Mediterranean, \r\nNorthern Central America, Western North America, several regions \r\nin South America and Australia - will experience, in addition to the \r\naforementioned globally changing CIDs, increases in either drought/\r\naridity or fire weather (high confidence). This will impact upon \r\nagricultural resources, infrastructure and health and ecosystems"


id: a887aa201ef9a106d1f708b925d5a638
created_at: 2023-03-16 14:02:10
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vector_id: 24043
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" \r\nA second cluster of regions including mountainous areas or regions \r\nwith seasonal snow cover will experience (in addition to increases \r\nin heat extremes, more intense short-duration rainfall, and increases in \r\ncoastal hazards where coasts exist) reductions in snow and ice cover \r\nand/or increases in river flooding in many cases (Western, North\u0002Western, Central and Eastern North America, Arctic regions, Andes \r\nregions, Europe, Siberia, Central and East Asia, Southern Australia \r\nand New Zealand) (high confidence). These are places where energy \r\nproduction, ski tourism, river transportation, and infrastructure could \r\nin particular face increased risks."


id: fd85675a9f2393889420398d6237fdea
created_at: 2023-03-16 14:02:10
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vector_id: 37404
content:
"Estimations of ToE must be done with caution given the many sources of \r\ninherent uncertainties, such as observations representing only a single \r\nrealization of climate history, internal variability (whose frequency - \r\ne.g., annual or decadal - needs to be precisely defined), model biases, \r\nand potential low-frequency changes in variability (Chapter  10; \r\nLehner et  al., 2017). In addition, a  homogeneous interpretation of \r\nmultiple studies is hampered by heterogeneous methodologies used \r\nto calculate emergence. In this section, we assess emergence and its \r\nconfidence level based on such multiple methods as provided by the \r\nliterature, and unless specified otherwise, emergence here refers to \r\na signal to noise ration S/N > 1 relative to a pre-industrial baseline \r\nand interannual variability (the 'noise')"


id: 3b74f0691cce8b66ce42de585b1aa48
created_at: 2023-03-16 14:02:10
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" Furthermore, observed trends \r\nand attribution are taken into account in combination with climate \r\nsimulations (historical or projections) for assessing whether a trend \r\nhas already emerged in the historical period."


id: 17e27718edc17db2c88a46ecaa340f9f
created_at: 2023-03-16 14:02:10
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"Mean air temperature: Warming of mean annual temperatures \r\nhas already emerged in all land regions, as obtained from past \r\nobservations and confirmed by historical simulations (high \r\nconfidence) (Figure 1.13; King et al., 2015; Hawkins et al., 2020), with \r\nS/N ratios larger than two. In the current climate, the highest S/N \r\nratios exceed five over Central Africa, Amazonia, East and South East \r\nAsia. Seasonal warming emergence depends on the season. Because \r\nthe temperature variability in the mid-latitudes is higher in winter \r\nthan in summer, the emergence of seasonal warming occurs for \r\nsummer but not for winter in most of this part of the world. In Europe, \r\nsummer warming has emerged in all regions (medium confidence, \r\nmedium agreement), and in North America, it has emerged only over \r\nEastern and Western regions while in winter there is low confidence\r\nof an emergence in warming in all regions for both Europe and North \r\nAmerica (Lehner et al., 2017; Hawkins et al., 2020)"


id: c91958eb2bd4f3f9c58ac4984a1639b2
created_at: 2023-03-16 14:02:10
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vector_id: 29200
content:
" When considering \r\nthe climate of the end of the 20th  century (i.e.,  recent past) as \r\na baseline, the emergence of mean temperature is projected at very \r\ndifferent times depending on the scenario. For instance, emergence is \r\nreached by 2050 under RCP8.5 in most areas of Europe, Australia or \r\nEast Asia, but it does not occur within the 21st century under RCP2.6 \r\n(medium confidence) (Sui et al., 2014; Im et al., 2021). This means \r\nthat under RCP2.6, mean temperatures stay within the recent climate \r\nvariability range observed in the mid-latitudes. However, even under \r\nRCP2.6, mean temperatures in tropical regions that have not already \r\nemerged are projected to emerge before 2050 (medium confidence)."


id: 6e0e5f04763d833b1e2936e525ffa172
created_at: 2023-03-16 14:02:10
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content:
"Eastern Europe and decrease in West Africa and Amazonia) from \r\nobservations with an S/N ratio larger than one (low confidence) \r\n(Hawkins et  al., 2020). The emergence of increasing precipitation \r\nbefore the middle of the 21st century is found across scenarios in \r\nSiberian regions, Russian Far East, Northern Europe, Arctic regions \r\nand the northernmost parts of North America (high confidence) \r\nand later in other northern mid-latitude areas, depending on the \r\nscenario, albeit with different methods and emergence definitions \r\nused in climate projections (Chapter 8; Giorgi and Bi, 2009; Maraun, \r\n2013; King et  al., 2015; Akhter et  al., 2018; Kumar and Ganguly, \r\n2018; Nguyen et al., 2018; Barrow and Sauchyn, 2019; Rojas et al., \r\n2019; Kusunoki et  al., 2020; Pohl et  al., 2020; W. Li et  al., 2021)"


id: 569e2411cab8ee9f55fb7af475570c40
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
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content:
" \r\nDecreases in mean precipitation are projected to emerge in parts of \r\nAfrica by the middle of the century, and later in the Mediterranean \r\nand Southern Australia, but the emergence depends on the scenario, \r\nand specific seasons for crop growth (Nguyen et  al., 2018; Rojas \r\net  al., 2019). Mean precipitation does not emerge in any of these \r\nregions at any time in the 21st century under RCP2.6, but emerges \r\nin all under RCP8.5. ToE under RCP4.5 is projected to be around \r\n25 years later relative to RCP8.5 in many of the early emergence \r\nregions, highlighting the importance of mitigation to gain more time \r\nfor adaptation."


id: 178044f69675753696dfefebe559200c
created_at: 2023-03-16 14:02:10
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vector_id: 1198
content:
"Heavy precipitation and floods: There is low confidence in the \r\nemergence of heavy precipitation and pluvial and river flood frequency \r\nin observations, despite trends that have been found in a few regions \r\n(Chapters 8 and Chapter  11, and across Section  12.4). In  climate \r\nprojections, the emergence of increase in heavy precipitation strongly \r\ndepends on the scale of aggregation (Kirchmeier-Young et al., 2019), \r\nwith, in general, no emergence before a 1.5degC or 2degC warming level, \r\nand before the middle of the century (medium confidence), but results \r\ndepend on the method used for the calculation of the ToE (Maraun, \r\n2013; King et al., 2015; Kusunoki et al., 2020). Emergent increases in \r\nheavy precipitation are found in several regions when aggregated \r\nat a regional scale in Northern Europe, Northern Asia and East Asia, \r\nat latest by the end of the century in SRES A1B or RCP8"


id: 16beea25b6c2b05363d34b2027c55223
created_at: 2023-03-16 14:02:10
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"5 scenarios \r\nor when considering the decadal variability as a reference (medium \r\nconfidence) (Maraun, 2013; W. Li et al., 2018, 2021; Kusunoki et al., \r\n2020). There have been few emergence studies for streamflow \r\nand flooding, although one study showed emergence of different \r\nhydrological regimes at different times during the 21st  century \r\nacross the USA (Leng et al., 2016). Variability in extreme streamflows \r\nfrom year to year can be high relative to a trend (Zhuan et al., 2018). \r\nGiven the heterogeneity of methods and results, there is only low \r\nconfidence in the emergence of heavy precipitation and flood signals \r\nin any region when considering the S/N ratio."


id: 1af52df829cf42c1a3a9e292103407ac
created_at: 2023-03-16 14:02:10
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"Extreme heat and cold: An increase in heat extremes has emerged \r\nor will emerge in the coming three decades in most land regions (high \r\nconfidence) (Chapter 11; King et al., 2015; Seneviratne and Hauser, \r\n2020), relative to the pre-industrial period, as found by testing \r\nsignificance of differences in distributions of yearly temperature \r\nmaxima in simulated 20-year periods. In tropical regions, wherever \r\nobserved changes can be established with statistical significance, \r\nand in most mid-latitude regions, there is high confidence that \r\nhot and cold extremes have emerged in the historical period, but \r\nonly medium confidence elsewhere. In other regions emergence \r\nis projected at the latest in the first half of the 21st century under \r\nRCP8.5 (high confidence) (King et al., 2015; Seneviratne and Hauser, \r\n2020)"


id: 1f95746656727dc165e4ab54a9f307d5
created_at: 2023-03-16 14:02:10
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vector_id: 2400
content:
" Relative to the end of 20th-century conditions, changes \r\nin humid heat stress as characterized by wet bulb temperature, \r\nindicates a ToE as early as in the first two decades of the 21st century \r\nin RCP8.5 at least in many tropical regions (most of Africa in the band \r\n20degS-20degN, South Asia and South East Asia) (medium confidence) \r\n(Im et al., 2021). By 2050 and under RCP8.5, wet bulb temperature \r\nis projected to emerge in many other areas such as Southern Africa, \r\nNorth Africa, Europe, and most of Central, Southern and Eastern Asia \r\nand Northern and Eastern Australia, while under RCP2.6, emergence \r\nis either reached later in the century (Europe, Central Asia, Northern \r\nAustralia), or never reached in the century (Im et al., 2021)"


id: e04095a7997751ebaeec9283f02eb21c
created_at: 2023-03-16 14:02:10
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content:
" Decrease \r\nof cold spells has already emerged above the interannual variability \r\nin Australasia, Africa and most of Northern South America, and they \r\nare projected to emerge before 2050 in the northern mid-latitudes \r\nand in Southern South America (King et  al., 2015) under RCP8.5 \r\n(medium confidence, limited evidence and high agreement)."


id: 8ad9476d7ccc9cd6cc551609a426daa1
created_at: 2023-03-16 14:02:10
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vector_id: 19381
content:
"Ocean acidity, ocean salinity and dissolved oxygen: The global \r\nocean pH decline has very likely emerged from natural variability \r\nfor more than 95% of the global open ocean (SROCC, Chapter 2). \r\nThe regional signals are more variable, but in all ocean basins, the \r\nsignal of ocean acidification in the surface ocean is projected to \r\nemerge in the early 21st  century (Chapter  5). The mean ToE for \r\nacidity in the coastal subtropical to temperate north-east Pacific \r\nand north-west Atlantic is above two decades (high agreement,\r\nmedium evidence) (Section  5.3.5.2). Salinity change signals have \r\nalready emerged with 20-45% of the zonally averaged basin in the \r\nAtlantic, 20-55% in the Pacific and 25-50% in the Indian oceans \r\nand will be reaching 35-55% in the Atlantic in 2050 to 55-65% in \r\n2080; 45-65% to 60-75% in the Pacific; and 45-65% to 60-80% \r\nin the Indian oceans (Chapter 9; Silvy et al., 2020). Deoxygenization \r\nhas already emerged in many open oceans"


id: 309c729d6efc161f94ef6e125faad40
created_at: 2023-03-16 14:02:10
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vector_id: 5067
content:
" The signal is most \r\nevident in the Pacific and Southern oceans but not evident in the \r\nNorth Atlantic Ocean (Andrews et al., 2013; Levin, 2018). However, \r\nthere is medium confidence in the emergence of the anthropogenic \r\nsignal in many other oceanic regions by 2050 (Henson et al., 2017; \r\nLevin, 2018)."


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"Snow and ice: The decrease in the Northern Hemisphere snow \r\ncover extent in spring has already emerged from natural variability \r\n(Section 3.4.2). The snow cover duration period is projected to emerge \r\nover large parts of Eastern and Western North America and Europe \r\nby the mid-century both in spring and autumn, and emergence is \r\nexpected in the second half of the 21st century in the Arctic regions in \r\nthe high RCP8.5 scenario (medium confidence) (Chapter 9, SROCC). \r\nFor snow depth or snow water equivalent, there is low confidence \r\n(limited evidence) of the emergence of a  decrease before 2050 \r\nbecause climate change also increases the variability of the snow \r\ndepth signal, for example in Europe (Section 3.4.2; Willibald et al., \r\n2020). Terrestrial permafrost is warming worldwide due to climate \r\nchange (Sections 2.3.2.5 and 9.5.2)"


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" Due to weak interannual \r\nvariability of permafrost temperatures, terrestrial permafrost warming \r\nhas emerged above natural variability in almost all observed time \r\nseries of the Northern Hemisphere (medium confidence, limited \r\nevidence, high agreement) (Biskaborn et  al., 2019), but the active \r\nlayer thickness exhibits considerable interannual variability inhibiting \r\nevidence for emergence (Chapter 9)."


id: 1e27bf492efe075f5d94050fabc3ab66
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"There is high confidence that several CID changes have \r\nalready emerged above historical period natural variability \r\nin many regions (e.g.,  mean temperature in most regions, \r\nheat extremes in tropical areas, sea ice, salinity). Heat and \r\ncold CIDs (excluding frost) that have not already emerged \r\nwill emerge by 2050 whatever the scenario in almost \r\nall land regions (medium confidence). The emergence of \r\nincreasing precipitation before the middle of the century is \r\nalso projected in Siberian regions, Russian Far East, Northern \r\nEurope and the northernmost parts of North America and \r\nArctic regions across scenarios with the various methods \r\nand emergence definitions used (high confidence). Studies \r\nare missing to properly assess S/N emergence for droughts \r\nand for wind CIDs. Arctic sea ice extent declines have mostly \r\nemerged above noise level (medium to high confidence), and \r\nthe emergence of declining snow cover is expected by the end \r\nof the century under RCP8.5"


id: 84b932e13016932e1a4d7b07453decce
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" There is medium confidence that, \r\nunder RCP8.5, the anthropogenic forced signal in near-coast \r\nrelative sea level change will emerge by mid-century in all \r\nregions with coasts, except in the West Antarctic region where \r\nemergence is projected to occur before 2100. In all ocean \r\nbasins, the signal of ocean acidification in the surface ocean is \r\nprojected to emerge before 2050 (high confidence)."


id: 5fda58a40f4ff9020fd511bcc78f6600
created_at: 2023-03-16 14:02:10
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"A consistent risk framework (Reisinger et  al., 2020) has been adopted across the three Working Groups (WGs) in IPCC AR6 while \r\nrecognizing the diversity of risk concepts across disciplines. WGI is assessing changes in climatic impact-drivers (CIDs), which are physical \r\nclimate system conditions (e.g., means, events and extremes) that affect an element of society or ecosystems. Depending on system \r\ntolerance, CIDs and their changes can be detrimental, beneficial, neutral, or a mixture of each across interacting system elements and \r\nregions (Sections 12.1-12.3). In the assessment of Representative Key Risk (RKR) categories and Reasons for Concern (RFCs) in WGII \r\nChapter 16, the focus lies on the adverse consequences of climate change, for which many types of CIDs (i.e., 'hazards' in the context \r\nof identified risks) play a key role"


id: cf0227df1b5b8e21360ec790e534129d
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" This box synthesizes the assessment of such hazards according to global warming levels (GWLs) from \r\nvarious chapters of WGI to inform understanding of their potential changes and associated risks with temperature levels in general, \r\nand in particular to facilitate WGII integrated assessments of RKRs and RFCs. Cross-Chapter Box 11.1, connects the organization of \r\nregional information according to GWLs to the other common dimension along which future projections are organized: that is, scenarios. \r\nSection 1.6 describes all dimensions of integration adopted in this Report, adding cumulative carbon emissions to GWLs and scenarios."


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"user-driven climate services. WMO has created Regional Climate \r\nCentres (RCCs) to facilitate climate service development by regional \r\nand national providers (Hewitt et al., 2020a). The European Union \r\ndeclared its ambition to stimulate 'the creation of a  community \r\nof climate services application developers and users that matches \r\nsupply and demand for climate information and prediction', giving \r\nprimacy to climate services that are user-driven and science-informed \r\n(Lourenco et  al., 2016), thus embracing concepts of co-design, \r\nco-development and co-evaluation of climate services (Street, 2016). \r\nDiverse and action-driven international initiatives allowed climate \r\nservices to progressively shift from mitigation towards adaptation \r\n(Larosa and Mysiak, 2019)"


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" Opportunities for the development of \r\nclimate services have emerged through the 2015 Agendas (Paris \r\nAgreement, Sustainable Development Goals and Sendai Framework), \r\nNationally Determined Contributions, National Adaptation Plans, \r\nMultilateral Development Banks and Task Force on Climate-related \r\nFinancial Disclosure (see Section 1.2.2)."


id: 8e6f7b1f9f2a98e6de0231b5c564116c
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"The AR5 WGII introduced climate services as bridging the generation \r\nand application of climate knowledge, also describing their history \r\nand concepts (Jones et al., 2014). Since then, this transdisciplinary field \r\nhas been growing rapidly (Brasseur and Gallardo, 2016; Hewitt et al., \r\n2020a), with the social sciences in particular pointing out knowledge \r\nrequirements for co-design and co-development of climate services \r\n(Larosa and Mysiak, 2019; Daniels et al., 2020; Steynor et al., 2020). \r\nClimate services differ from more research-driven vulnerability, \r\nimpacts, and adaptation research in their orientation towards decision \r\nsupport (Stone and Meinke, 2005; Ruane et al., 2016; Golding et al., \r\n2019), but overlaps exist (Bruno Soares and Buontempo, 2019)"


id: d3ac4edc8215b3bb5f34852909c40b7c
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" \r\nClimate services are often targeted at building resilience to climate\u0002related hazards from near real-time to seasonal and multi-decadal \r\ntime horizons, to inform adaptation to climate variability and change \r\n(Hewitt et al., 2012), widely recognized as an important challenge for \r\nsustainable development and risk management (Moss et al., 2010; \r\nJones et al., 2014; Vaughan et al., 2018). This section focuses largely \r\non climate change time scales (past, present and future), which are \r\nthe focus of AR6 WGI."


id: fb066fc0e5f7d7418a014eaaa7a79257
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"Scientific advancements in climate services related to meteorology \r\nand climatology are still closely linked to essential climate variables \r\n(Larosa and Mysiak, 2019) and benefit from consistently growing \r\ncomputational power, infrastructure and storage capacity to meet \r\nthe demands of higher spatially and temporally resolved climate \r\ninformation (Buontempo et al., 2020). Climate services also focus on \r\nimpact chains, providing decision makers with information on climate \r\nchange with cross-sectoral impact assessments for adaptation \r\n(Jacob and Solman, 2017). Today there is a  diversity of climate \r\nservices that involve interpretation, analysis, and communication of \r\ndifferent sources of climate data, ideally combining different types \r\nof knowledge (scientific/technical, experiential, indigenous, etc.), to \r\na  targeted group of decision makers (Parris et  al., 2016; Olazabal \r\net  al., 2018; Pezij et  al., 2019)"


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" Jacobs and Street (2020) argue \r\nthat climate services should be expanded to also address societal \r\nchallenges, such as system transformation, that include climate in the \r\ncontext of other risks and development challenges."


id: abfb5f4399b3df092dd92d0166e4f694
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"Climate services are undertaken in public and private sectors at \r\nglobal, regional, national, and local scales (Hewitt et  al., 2012, \r\n2020b; Cortekar et al., 2020). Intermediaries such as private sector \r\nconsulting companies, national climate service providers, research \r\norganizations, government agencies or academic institutions \r\nprovide climate services that translate aspects of climate research \r\nto the specific context of decision makers (see also Section  10.5). \r\nThe EU Roadmap for Climate Services (EC, 2015; Street, 2016) \r\nfocuses on developing a market for climate services comprising of \r\nboth public and private domains. The GFCS, under the leadership \r\nof several United Nations Agencies, emphasizes the public domain by \r\nsupporting national and regional capacity building and development \r\nof climate services mainly through National Meteorological and \r\nHydrological Services (Hewitt et  al., 2012; Domingos et  al., 2016; \r\nSivakumar and Lucio, 2018; WMO, 2018)"


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" There are ongoing debates \r\nabout the commercialization of climate services (M.S. Brooks, 2013; \r\nWMO, 2015; Webber and Donner, 2017; Hoa, 2018; Troccoli et  al., \r\n2018; Bruno Soares and Buontempo, 2019; Hewitt et  al., 2020a). \r\nSome argue that the commercialization of climate services is needed \r\nto meet the diverse needs of specific clients and to drive innovation \r\nin the field (M.S. Brooks, 2013; Troccoli, 2018a). Others argue that if "


id: 5453e528086649e102a24febc31af4a7
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content:
"Some climate adaptation planning already uses climate information \r\nas provided by the IPCC. However, depending on the decision context, \r\nthis information may be too coarse, too broad or too disciplinary \r\nto directly inform decision-making at the scale where adaptation \r\nmeasures are taken (Howarth and Painter, 2016; Nissan et al., 2019). \r\nThus, while the IPCC's role is clearly perceived as that of a reference - \r\nan authoritative starting point - there is a need for complementary \r\ninformation to translate the assessments at the national, local or \r\nsectoral level (Howarth and Painter, 2016; Kjellstrom et al., 2016; van \r\nden Hurk et al., 2018; Vaughan et al., 2018). The AR6 Interactive Atlas \r\n(see Atlas.2) does provide a  collection of observational data and \r\nglobal and regional climate projections"


id: b33c0c2855797ac8267c7b696a321fe8
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" It is designed as a climate \r\nservice towards the needs of WGI and beyond, to assess the state of \r\nthe climate by offering data, maps and a level of expert analysis by \r\naggregation of results to regions, scenarios and warming levels."


id: 8d6f8a9afda3c25bc3bf8b43935369bb
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"Climate services require user engagement and can take various forms \r\nin which climate information and data are delivered or communicated \r\nto the users (very high confidence). Different levels of user engagement \r\nexist, which can range from passive engagement to interactive group \r\nactivities, to focused relationships between climate service provider \r\nand users. These result in different types of climate service products \r\nincluding websites, capacity building, and co-design of tailored climate \r\nindices (Cross-Chapter Box 12.2, Figure 1; Hewitt et al., 2017a). The \r\nfundamental basis for climate service development is the co-production \r\nprocess between climate service provider and user (Valiela, 2006; Briley \r\net al., 2015; Golding et al., 2017a; K. Vincent et al., 2018a; Bruno Soares \r\nand Buontempo, 2019; Schipper et al., 2019), which can be very resource \r\nintensive (Buontempo et al., 2018; Falloon et al., 2018; Kolstad et al"


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", \r\n2019) and varies strongly from case to case (Reinecke, 2015; Bremer \r\net al., 2019; Goodess et al., 2019; Jung and Schindler, 2019). Climate \r\nservices scholars and practitioners can better facilitate and embrace the \r\nknowledge co-production process if it is recognized as a multi-faceted \r\nphenomenon with several dimensions (e.g., constitutive, interactional, \r\ninstitutional, pedagogical, empowerment) (Kruk et al., 2017; Knaggard \r\net al., 2019; Weichselgartner and Arheimer, 2019)."


id: 24e1b9cebc9990d3a27f92e330558721
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"The climate services landscape is fast growing and very broad, as \r\nreflected in the vast diversity of practices and products that can be \r\nfound in the peer-reviewed literature (very high confidence). However, \r\na large part of climate services practices and products is published in \r\n'grey' literature (i.e., non-peer reviewed or non-academic) by private \r\nconsultancy and non-scientific civil organizations, many of which are \r\nnot in the public domain. In addition, the respective climate service \r\ncontext of a specific stakeholder in a sector dictates what climate \r\ninformation is required and on what scales and in what format it \r\nis most usefully provided. The extent and type of engagement \r\nbetween scientists and users is another critical aspect of climate \r\nservices (see Cross-Chapter Box 12.2, Figure 1, and Section 10.5)"


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content:
" The \r\nassessment here can thus only provide a partial and rather general \r\nrepresentation of available practices and products in the evolving \r\nclimate services field."


id: b209ce918e43161ec7c8cddd25687d2
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"User needs and decision-making contexts are very diverse and \r\nthere is no 'one size fits all' solution to climate services (very high \r\nconfidence) (Hewitt et al., 2017b; K. Vincent et al., 2018b). In many \r\ncases this requires recognizing that stakeholders make decisions \r\nthrough a  combination of scientific information and additional \r\nvalues (Vanderlinden et  al., 2017; Parker and Lusk, 2019; see also \r\nSections  1.2.3 and 10.5.4)"


id: 83686854a983fc8bd78e9f341275c0aa
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" The emerging climate service literature \r\nmay clarify some features of climate information requested by users, \r\nfor instance climatic impact-driver identification and prioritization \r\nthrough stakeholder engagement; the specification of thresholds for \r\nvarious regions/sectors; the types of metrics (magnitude/intensity, \r\nfrequency, duration, timing, spatial extent) that are of primary interest; \r\nand decision support systems where informatics allow stakeholders \r\nto custom-make impact-relevant thresholds and then query databases \r\nto understand current and future characteristics (Bachmair et al., 2016; \r\nBuontempo et al., 2020). However, users also ask for capacity building "


id: 8a5148795e263ce533b8dc3b5744ba06
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"(van den Hurk et  al., 2016; Vano et  al., 2018), ocean and coastal \r\necosystems (Weisse et  al., 2015; Le Cozannet et  al., 2017), cities \r\n(Rosenzweig and Solecki, 2014; Rosenzweig et  al., 2015; Gidhagen \r\net al., 2020), and cultural heritage (ICOMOS, 2019). Many countries \r\n(including almost every country in Europe  - see Atlas.8.2) have \r\nestablished a climate service centre, which follow different practices \r\nof user engagement and provide different products (e.g., Kjellstrom \r\net  al., 2016; Skelton et  al., 2017; Kolstad et  al., 2019). Climate \r\nservices in other countries may be distributed across agencies and \r\nprogrammes, although these are often not centrally coordinated (Parris \r\net al., 2016). One of the key pillars of the GFCS is the Climate Services \r\nInformation System (CSIS), which is the principal mechanism through \r\nwhich information about past, present and future climate is archived, \r\nanalysed, modelled, exchanged and processed for users (Hewitt et al., \r\n2020a)"


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" Some national governments also have organized national \r\nclimate projections to be used for official planning (e.g., EEA, 2018). \r\nA list of available national products (e.g., observational datasets) and \r\nprojections can be found in the Atlas (e.g., Atlas.1.4)."


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"The design of climate services involves addressing certain key challenges, \r\nsuch as a  domain challenge where users, tasks and data may be \r\nunknown; or an informational challenge related to the use and adoption \r\nof novel and complex scientific data (Christel et al., 2018). This includes \r\nchallenges in the uptake of climate information in terms of coordinated \r\ndelivery of data, information, expertise and training by public research \r\ninstitutes, the inclusion of climate change adaptation in public and private \r\nregulation, and uncertainties and confidence in climate projections \r\n(Cavelier et al., 2017). Quality control and quality assurance are still weak \r\nelements in the development of climate service products (Jacob, 2020). \r\nQuality criteria or standards (that go beyond good practice) will have \r\nto be developed and agreed (Baldissera Pacchetti et al., 2021)"


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" These \r\nchallenges reflect the dilemma that exists at the interface between the \r\nclimate modelling community and climate services regarding: (i) the \r\npurposes of the models for climate research versus service development; \r\n(ii) the gap between the spatial and temporal scales of the models \r\nversus the scales needed in applications; and (iii) tailoring climate model \r\nresults to real-world applications (Benestad et al., 2017; Hackenbruch \r\net al., 2017; van den Hurk et al., 2018)."


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"Challenges also arise in determining the effectiveness and added \r\nvalue of climate services, particularly in terms of providing quantitative \r\nestimates of economic benefits and making a business case for climate \r\nservices (Bruno Soares, 2017). The market for climate services is still \r\nin its infancy (Cavelier et al., 2017; Bruno Soares et al., 2018b; Tall \r\net al., 2018; Damm et al., 2020). One form of value may be determined \r\nby a  particular user community's willingness to pay (Acquah and \r\nOnumah, 2011; Ouedraogo et  al., 2018; Antwi-Agyei et  al., 2021), \r\nwhich however cannot reflect the value of climate services as a public \r\ngood and for society as a whole (Hewitt et al., 2012). Literature is only \r\nrecently emerging on the socio-economic benefits of weather and \r\nclimate services (Vaughan et al., 2019). Early studies and guidelines \r\nfrom the WMO focus on cost-benefit ratios (Perrels et  al., 2013; \r\nWMO, 2015)"


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" Issues related to demand-driven versus supply-driven \r\nclimate services (Lourenco et  al., 2016; Street, 2016; Daniels et  al., \r\n2020), public versus private climate services (Hewitt et  al., 2020a) \r\nand business models for climate services (Hoa, 2018) have been \r\nraised. A large share of climate services documented in peer-reviewed \r\nliterature is currently provided in non-market frameworks (e.g., public \r\nservice obligations and research and development grants) (Hoa, 2018; \r\nKolstad et al., 2019; Cortekar et al., 2020)."


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"Climate services require a sustained engagement between scientists, \r\nservice providers and users that is often hindered by limited resources \r\nfor the co-design and co-production process (high confidence). \r\nThere are recurring challenges related to successful climate service \r\napplications: (i) climate services are not visible and are poorly \r\nunderstood by 'end users' (Weichselgartner and Arheimer, 2019); \r\n(ii)  data can be of unknown or poor quality, data formats can \r\nbe hard to access or process, and it can be difficult to utilize data \r\ndisseminated from large databases (e.g.,  Section  1.5.4) without \r\nappropriate user guidance; (iii) users are unsure how to choose \r\nfrom available climate services to meet their needs (Rossler et  al., \r\n2019); (iv) building trust between climate service users and providers \r\n(Baztan et al"


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", 2020); (v) the lack of understanding of users and their \r\ncontexts by the climate science and service community (Porter and \r\nDessai, 2017); (vi) the difficulty in scaling up services (Tall et al., 2014; "


id: 37cf73d84d08c0c1d94a20271af9c51e
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vector_id: 6154
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"Climate services involve the provision of climate information in such a way as to assist decision-making. The service needs to have \r\nappropriate engagement from users and providers; be based on scientifically credible information and expertise; have an effective \r\naccess mechanism; and meet the users' needs (Hewitt et al., 2012). Predominantly, climate services are targeted at informing and \r\nenabling risk management in adaptation to climate variability and change (Jones et  al., 2014; Vaughan et  al., 2018). Chapter  1 \r\nintroduces climate services in a broader context of interaction between science and society, including how climate information can be \r\ntailored and co-produced for greatest utility in specific contexts. Chapter 10 assesses the key foundations for the generation of climate \r\ninformation about regional climate change. Chapters 11, 12 and Atlas comprehensively assess regional climate change information"


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" \r\nThe Interactive Atlas gives access to various repositories of quantitative climate information. In WGII, Chapter 17 assesses climate \r\nservices in the context of climate risk management."


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"Climate service contexts are diverse and complex. They can be characterized using different factors such as sectors, regions, purposes, \r\ntime horizons, data sources, level of processing of climate data, background knowledge, type of climate service providers, as well \r\nas the nature of the interactions between providers, users and other stakeholders (Bessembinder et al., 2019). To illustrate the wide \r\ndiversity of climate change information in climate services, a useful categorization is by user -provider engagement of climate services \r\n(Cross-Chapter Box  12.2, Figure  1). One broad category includes 'websites and web tools' which generally focuses on data and \r\ninformation provision (Hewitson et al., 2017). Websites are generally able to reach many users, but engagement is passive through \r\none-way transfer of information"


id: 160410d11172038436e848f6135cf0a6
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" The second broad category involves 'interactive group activities', such as workshops, meetings and \r\ninteractive forums, which create a stronger dialogue between climate service providers and decision makers. Multi-way communication \r\nand regular interaction enable building of trust, co-learning and co-production of products and services. The third broad category involves \r\n'focused relationships' which are tailored, targeted and address very specific needs of the user. Effective engagement arises from an \r\niterative process between the provider and user to ensure the user's needs are being addressed appropriately (Hewitt et al., 2017a)."


id: d1f412d59da236300248222e4aa4ed08
created_at: 2023-03-16 14:02:10
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content:
"To address this audience, the strategy of C3S is to provide free and open access climate data and tools such as historical observations \r\n(both satellite and ground-based), climate data records relevant for a number of Essential Climate Variables (Bojinski et al., 2014), \r\nglobal and regional reanalyses, climate monitoring bulletins, seasonal predictions, as well as both global (a selection of simulations \r\nfrom the Coupled Model Intercomparison Project, CMIP; Taylor et al., 2012; Eyring et al., 2016) and regional climate projections from \r\nthe Coordinated Regional Downscaling Experiment (Euro- and Med-CORDEX; Jacob et al., 2014; Ruti et al., 2016). A number of indices \r\nfor various sectors can be calculated through cloud-based tools. For instance, in order to address the specific needs of key sectoral users, \r\nclimate impact indicators for common variables such as 'heating degree days' can be calculated by the users and made available to \r\nothers (Buontempo et al., 2020)"


id: 7d772d4c42edf286a8c28058dfde4ab3
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" All this material is quality controlled following a standardized, transparent and traceable framework."


id: e4ec5e4eed80afe1123a9f37d8ed83d9
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"Two important elements were deployed in FRACTAL: 'embedded researchers' and 'learning labs'. Embedded researchers were \r\nseconded into the municipality and served as the essential connection for the learning process within each city (Steynor et al., 2020). \r\nLearning labs (Arrighi et al., 2016) were interactive structures in which participants from academia, local city government and councils, \r\nstate-owned enterprises, communities and community development institutions, and others could interact. Embedded researchers and \r\nlearning labs were the backbone of ongoing learning processes within each city and resulted in more focused small-group dialogues, \r\ncapacity development and training processes, and within-city research and engagement activities. Each learning lab focused initially \r\non identifying 'burning issues' without a requirement that they involve strong climate linkages"


id: 5765f65870aa33938a2f073f29e716f6
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" However, with the overarching focus \r\non resilience, discussions evolved in that direction and the burning issues identified often centred around water in peri-urban areas, \r\nfor example in Windhoek, Namibia (Scott et al., 2018)."


id: 2a75c385273f630654adf319eeda478b
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"The learning labs also introduced and developed the concept of Climate Risk Narratives (CRNs) as a process and product to generate \r\nand integrate climate and socio-economic information relevant to adaptation and resilience (see Cross-Chapter Box 12.2, Figure 2, and \r\nBox 10.2 on storylines) (Jack et al., 2020). The first CRNs were informed primarily by climate evidence, but also included some tentative socio\u0002economic impact elements gleaned from literature and other studies. Their content was intentionally provocative and designed to promote \r\ndebate and discussion, and subsequent iteration. Many participants noted that this was the first time that various important conversations \r\nacross governance structures and disciplinary areas had occurred around what climate change may actually mean"


id: f201bfbb9c4346a12de43e90f1418f4d
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" This demonstrates \r\nthe engagement value of CRNs as a key element in an iterative co-production process to ensure important details are included correctly, \r\nsuch as the local context, terms and names as well as providing reality checks on the impacts and societal responses (Jack et al., 2020)."


id: 7549e1253f18a1bb218737cbeb0f1aaa
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"In the wake of the El Nino-induced 2015-2016 severe drought, which caused major damage to agricultural land in the Central Highlands \r\nof Vietnam, the WEIDAP project was initiated to improve water productivity of irrigated agriculture. Proposed project interventions include \r\na package of both 'soft' (e.g., policy, institutional and capacity building, on-farm water efficiency practices) and 'hard' (modernized \r\nirrigation schemes) activities. To ensure that the project delivers expected benefits under a changing climate, consultants were recruited \r\nto carry out a detailed CRA, working as part of the overall project processing team"


id: 7874513d85b8a5d386f449446a0c1e50
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" Through extensive consultations with the rest of the \r\nproject team and review of literature including relevant climate projections, the CRA consultants chose to construct three broad climate \r\nscenarios for the 2050s (a time frame appropriate for the lifetime of the irrigation schemes being proposed under the project): a warm\u0002and-wet, a hot-and-wet, and a hotter future. Outputs from a selection of CMIP5 models were analysed under these three scenarios, to \r\nderive changes in temperature, rainfall and potential evapotranspiration, which in turn were used as inputs to hydrological, crop and \r\nagro-economics models to assess the impacts of climate change on the overall project performance. Table 1 presents the summary of \r\nthe key parameters under the three scenarios"


id: fd24b78b380066cefee6e9957e9268b6
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" Recommendations from the CRA included (largely minor) refinements and additional \r\nactivities for drought planning, detailed engineering design of the relevant project components (such as access roads, river crossings \r\nand foundations), and support for poorer farmers who may not be able to afford access to water and climate-resilient technologies."


id: f49503c96811104f678944b7f5a707fe
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content:
"Climate change can alter many aspects of the climate system, but efforts to identify impacts and risks usually focus \r\non a smaller set of changes known to affect, or potentially affect, things that society cares about. These climatic \r\nimpact-drivers (CIDs) are formally defined in this Report as 'physical climate system conditions (e.g.,  means, \r\nevents, extremes) that affect an element of society or ecosystems. Depending on system tolerance, CIDs and \r\ntheir changes can be detrimental, beneficial, neutral, or a mixture of each across interacting system elements \r\nand regions'. Because people, infrastructure and ecosystems interact directly with their immediate environment, \r\nclimate experts assess CIDs locally and regionally. CIDs may relate to temperature, the water cycle, wind and \r\nstorms, snow and ice, oceanic and coastal processes or the chemistry and energy balance of the climate system"


id: c11f55755dfa360b0db3aaf72a1e2699
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" \r\nFuture impacts and risk may also be directly affected by factors unrelated to the climate (such as socio-economic \r\ndevelopment, population growth, or a viral outbreak) that may also alter the vulnerability or exposure of systems."


id: de2f5c2051a7d8ce145984aa9115d746
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"CIDs capture important characteristics of the average climate and both common and extreme events that shape \r\nsociety and nature (see FAQ 12.2). Some CIDs focus on aspects of the average climate (such as the seasonal \r\nprogression of temperature and precipitation, average winds or the chemistry of the ocean) that determine, \r\nfor example, species distribution, farming systems, the location of tourist resorts, the availability of water \r\nresources and the expected heating and cooling needs for buildings in an average year. CIDs also include \r\ncommon episodic events that are particularly important to systems, such as thaw events that can trigger the \r\ndevelopment of plants in spring, cold spells that are important for fruit crop chill requirements, or frost events \r\nthat eliminate summer vegetation as winter sets in"


id: c154373531ffd00cdd30dc372b4323d
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content:
" Finally, CIDs include many extreme events connected to \r\nimpacts such as hailstorms that damage vehicles, coastal floods that destroy shoreline property, tornadoes that \r\ndamage infrastructure, droughts that increase competition for water resources, and heatwaves that can strain \r\nthe health of outdoor labourers."


id: 9f5c8e68da06527271df35f6cf971b0a
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"FAQ 12.1, Figure 1 tracks example outcomes of seasonal snow cover changes that connect climate science to the \r\nneed for mitigation, adaptation and regional risk management. The length of the season with snow on the ground \r\nis just one of many regional climate conditions that may change in the future, and it becomes a CID because \r\nthere are many elements of society and ecosystems that rely on an expected seasonality of snow cover. Climate \r\nscientists and climate service providers examining human-driven climate change may identify different regions \r\nwhere the length of the season with snow cover could increase, decrease, or stay relatively unaffected. In each \r\nregion, change in seasonal snow cover may affect different systems in beneficial or detrimental ways (in the \r\nlatter case, changing seasonal snow cover would be a 'hazard'), although systems such as coastal aquaculture \r\nremain relatively unaffected"


id: dc5055c561e4887639e9ab60a7b3a507
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content:
" The changing profile of benefits and hazards connected to these changes in the \r\nseasonal snow cover CID affects the profile of impacts, risks and benefits that stakeholders in the region will \r\ngrapple with in response to climate change."


id: cfdf5ec287ccde045851243adbceee9c
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content:
"FAQ 12.2 Figure  1 illustrates how threshold conditions can help us understand climate conditions that are \r\nsuitable for normal system operation and the thresholds beyond which impacts occur. Crops tend to grow most \r\noptimally within a suitable range of daily temperatures that is influenced by the varieties being cultivated and \r\nthe way the farm is managed. As daily temperatures rise above a 'critical' temperature threshold, plants begin \r\nto experience heat stress that reduces growth and may lower resulting yields. If temperatures reach a higher \r\n'limiting' temperature threshold, crops may suffer leaf loss, pollen sterility, or tissue damage that can lead to \r\ncrop failure. Farmers typically select a cropping system with some consideration to the probability of extreme \r\ntemperature events that may occur within a typical season, and so identifying hot temperature thresholds helps \r\nfarmers select their seed and field management strategies as part of their overall risk management"


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content:
" Climate \r\nexperts may therefore aim to assist farm planning by providing information about the climate change-induced \r\nshifts to the expected frequency of daily heat extremes that exceed crop tolerance thresholds."


id: 51a9fcac9610ca7d9f13a4f3a8fb7a3a
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
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vector_id: 10308
content:
"Maialen Iturbide (Spain), Ma. Laurice Preciado Jamero (Philippines), Emilie Vanvyve (United \r\nKingdom/Belgium), Gudfinna Adalgeirsdottir (Iceland), Cecile Agosta (France), Mansour Almazroui \r\n(Saudi Arabia), Jorge Bano-Medina (Spain), Joaquin Bedia (Spain), Maria Laura Bettolli (Argentina), \r\nDonovan Campbell (Jamaica), Ana Casanueva (Spain), Christophe Cassou (France), Tereza Cavazos \r\n(Mexico), Abel Centella-Artola (Cuba), Ruth Cerezo-Mota (Mexico), Haoming Chen (China), \r\nAnnalisa Cherchi (Italy), Erika Coppola (Italy), Faye Abigail Cruz (Philippines), Joseph D. Daron \r\n(United Kingdom), Chirag Dhara (India), Alejandro di  Luca (Australia, Canada/Argentina), \r\nArona  Diedhiou (Cote d'Ivoire/Senegal), Javier Diez Sierra (Spain), Alessandro Dosio (Italy), \r\nJason Evans (Australia), Vincent Favier (France), Erich Fischer (Switzerland), Sebastian Gerland \r\n(Norway/Germany), Subimal Ghosh (India), Natalia Gnatiuk (The Russian Federation/Ukraine), \r\nMelissa I"


id: 2af9e07f7eb886ae58d16ec5f37c9c79
created_at: 2023-03-16 14:02:10
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content:
"Many aspects of the cryosphere either have seen significant \r\nchanges in the recent past or will see them during the \r\n21st century (high confidence). Snow cover duration has very \r\nlikely reduced over Siberia and Eastern and Northern Europe. \r\nAlso, it is virtually certain that snow cover will experience \r\na  decline in these regions and over most of North America \r\nduring the 21st century, in terms of water equivalent, extent \r\nand annual duration. Over the Hindu Kush Himalaya, glacier \r\nmass is likely to decrease considerably (nearly 50%) under \r\nthe RCP4.5 and RCP8.5 scenarios. Snow cover has declined over \r\nAustralia as has annual maximum snow mass over North America \r\n(medium confidence). Some high-latitude regions have experienced \r\nincreases in winter snow (parts of North Asia, medium confidence) or \r\nwill do so in the future (very likely in parts of northern North America) \r\ndue to the effect of increased snowfall prevailing over warming\u0002induced increased snowmelt. {2.3.2.2, 3"


id: 647a29dc18ccab03e5cafb0c7f8437a
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"The regional performance of CMIP6 global climate models \r\n(GCMs) has improved overall compared to CMIP5 in simulating \r\nmean temperature and precipitation, though large errors \r\nstill exist in some regions (high confidence). In particular, \r\nimprovements have been seen over Africa which has belatedly \r\nbecome a  focus for GCM model development. Other specific \r\nimprovements include over East Asia for temperature and the winter \r\nmonsoon, over parts of South Asia for the summer monsoon, over \r\nAustralia (including influences of modes of variability), in simulation \r\nof Antarctic temperatures and Arctic sea ice. Notable errors include \r\nlarge cold biases in mountain ranges in South Asia, a  significant \r\nwet bias over Central Asia, in the East Asia summer monsoon and \r\nin Antarctic precipitation"


id: 920b1fe8b4b9c385df8272569248c419
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" An in-depth evaluation of CMIP6 models \r\nis lacking for several regions (North and South East Asia, parts of \r\nWest Central Asia, Central and South America), though CMIP5 \r\nmodels have been evaluated for many of these. {3.3.1, 3.3.2, \r\nAtlas.4.3, Atlas.5.1.3, Atlas.5.2.3, Atlas.5.3.3, Atlas.5.4.3, Atlas.5.5.3, \r\nAtlas.6.1.3, Atlas.6.2.3, Atlas.7.3, Atlas.8.3, Atlas.9.3, Atlas.10.3, \r\nAtlas.11.1.3, Atlas.11.2.3}"


id: c7cf3bd37a251f446169645eb8b1610d
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
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"Since AR5, the improvement in regional climate modelling \r\nand the growing availability of regional simulations through \r\ncoordinated dynamical downscaling initiatives such as \r\nCORDEX, have advanced the understanding of regional climate \r\nvariability, adding value to CMIP global models, particularly in \r\ncomplex topography zones, coastal areas and small islands, \r\nand in the representation of extremes (high confidence). In \r\nparticular, regional climate models (RCMs) with polar-optimized \r\nphysics are important for estimating the regional and local surface \r\nmass balance and are improved compared to reanalyses and \r\nGCMs when evaluated with observations (high confidence). There \r\nis still a  lack of high-quality and high-resolution observational \r\ndata to assess observational uncertainty in climate studies, and \r\nthis compromises the ability to evaluate models (high confidence). \r\n{Atlas.4.3, Atlas.5.1.3, Atlas.5.2.3, Atlas.5.3.3, Atlas.5.4.3, Atlas.5.5.3, \r\nAtlas.6.1.3, Atlas.6.2"


id: 77af70a451837c18694f20745133e2a7
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"It is very likely that most Small Islands have warmed over \r\nthe period of instrumental records. Precipitation has likely\r\ndecreased since the mid-20th  century in some parts of the \r\nPacific poleward of 20deg latitude in both hemispheres and \r\nin the Caribbean in June-July-August. It is very likely that \r\nsea levels will continue to rise in Small Island regions and \r\nthat this will result in increased coastal flooding. Observed \r\ntemperature trends are generally in the range of 0.15degC-0.2degC per \r\ndecade. Rainfall trends in most other Pacific Ocean and Indian Ocean \r\nSmall Islands are mixed and largely non-significant. There is limited \r\nevidence and low agreement on the cause of the Caribbean drying \r\ntrend, though it is likely that both this and the Pacific drying trends \r\nwill continue in coming decades with drying also projected in the \r\npart of the Western Indian and Atlantic oceans"


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" Small Island regions \r\nin the western and Equatorial Pacific Ocean, and in the northern \r\nIndian Ocean are likely to be wetter in the future. {Cross-Chapter \r\nBox Atlas.2, Atlas.10.2, Atlas.10.4}"


id: f957ea8ffdea6961c3850792400f8877
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"component (Regional Information) includes information from global \r\nobservational (and paleoclimate simulation) datasets assessed \r\nin Chapter  2 and projections of relevant extreme indices (used in \r\nChapter 11) and climatic impact-drivers (CIDs, used in Chapter 12) \r\nallowing for a  regional analysis of the results (Section Atlas.2.2). \r\nIt provides information on CIDs relevant to sectoral and regional \r\nchapters of the Working Group II (WGII) report, being informed by \r\nand complementing the work of Chapter 12 in creating a bridge to \r\nWGII. The second component (Regional Synthesis) provides synthesis \r\ninformation about changes in CIDs in several categories such as heat \r\nand cold, wet and dry, or coastal and oceanic, supporting exploration \r\nof the regional assessment findings summarized in the TS and the \r\nSPM. An overview of the main components of the Atlas chapter is \r\nprovided in Figure Atlas.1. The Interactive Atlas is described in Atlas"


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"The Atlas is the final chapter of this Working Group I (WGI) Sixth \r\nAssessment Report (AR6) and comprises the Atlas Chapter and \r\nan online interactive tool, the Interactive Atlas. The Atlas assesses \r\nfundamental aspects of observed, attributed and projected changes \r\nin regional climate in coordination with other WGI chapters \r\n(Chapters 2, 3, 4, 6, 8, 9, 10, 11 and 12). In particular, it provides \r\nanalyses and assessments of regional changes in mean climate \r\n(specifically surface temperature, precipitation and some cryospheric \r\nvariables, such as snow cover and surface mass balance) and expands \r\non and integrates results from other chapters across different \r\nspatial and temporal scales. The Atlas considers multiple lines of \r\nevidence including assessment of different global and regional \r\nobservational datasets, attribution of observed trends and multiple \r\nmodel simulations from the Coupled Model Intercomparison Projects \r\nCMIP5 (K.E. Taylor et al"


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", 2012) and CMIP6 (Eyring et al., 2016; O'Neill \r\net al., 2016), and the Coordinated Regional Downscaling Experiment \r\n(CORDEX; Gutowski Jr. et al., 2016). The Atlas chapter also assesses \r\nmodel performance and summarizes cross-referenced findings from \r\nother chapters relevant for the different regions."


id: a41705a3d4642c41ec06d34433f7cf4b
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content:
"Figure Atlas.2 (continued): The latter includes both land and ocean regions and it is used as the standard for the regional analysis of atmospheric variables in the Atlas \r\nchapter and the Interactive Atlas. The codes used in the Interactive Atlas are included in the figure. The full description of the regions (grouped by continents) is as follows"


id: a6fb663b00985e03ae6896ab19cc3c7
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updated_at: 2023-03-16 14:02:10
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content:
" The definition of the regions and companion \r\nnotebooks and scripts are available at the Atlas repository (Iturbide et al., 2021). Figure from Iturbide et al. (2020)."


id: 6792b4f2ea88742c38e755c76583f7da
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vector_id: 13884
content:
"those selected in Chapter 2, but considering land-only information \r\nfor the blended products. In particular, for air temperature the Atlas \r\nuses CRUTEM5 - the land component of the HadCRUT5 dataset - \r\n(Osborn et al., 2021), Berkeley Earth (Rohde and Hausfather, 2020) \r\nand the Climatic Research Unit CRU TS4 (version 4.04 used here; \r\nHarris et al., 2020). For precipitation the Atlas includes CRU TS4, the \r\nGlobal Precipitation Climatology Centre (GPCC, v2018 used here; \r\nSchneider et al., 2011), and Global Precipitation Climatology Project \r\n(GPCP; monthly version 2.3 used here; Adler et al., 2018). Although \r\nthe ultimate source of these datasets is surface-station reported \r\nvalues (GPCP also includes satellite information), each has access \r\nto different numbers of stations and lengths of records and employs \r\ndifferent ways of creating the gridded product and ensuring quality \r\ncontrol"


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content:
" For oceanic variables, the most widely used sea surface \r\ntemperature (SST) datasets are HadSST4 (Kennedy et  al., 2019), \r\nwhich is the oceanic component of the HadCRUT5 dataset, ERSST \r\n(B. Huang et al., 2017), and KaplanSST (Kaplan et al., 1998)."


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"There are currently many atmospheric reanalysis datasets with \r\ndifferent spatial resolution and assimilation algorithms (see Annex I \r\nand Section  1.5.2). There are also substantial differences among \r\nthese datasets due to the types of observations assimilated into \r\nthe reanalyses, the assimilation techniques that are used, and the \r\nresolution of the outputs, amongst other reasons. For example, \r\n20CR (Slivinski et  al., 2019) only assimilates surface pressure and \r\nsea surface temperature (SST) to achieve the longest record but \r\nat relatively low resolution, while ERA-20C (Poli et  al., 2016) only \r\nassimilates surface pressure and surface marine winds. At the other \r\nextreme, very sophisticated assimilation systems using multiple \r\nsurface, upper air and Earth observation data sources are employed, \r\nfor example ERA5 (Hersbach et al., 2020) and JRA-55 (Harada et al., \r\n2016), which also have much higher resolutions"


id: 5996b7ced0674999bfae4f1ab0559ebe
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
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vector_id: 11593
content:
" Most reanalysis \r\ndatasets cover the entire globe, but there are also high-resolution \r\nregional reanalysis datasets which provide further regional detail \r\n(Kaiser-Weiss et al., 2019)."


id: 34d7c6266430667be548e9ba532e2450
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
content_type: text
index: climate_gpt_only_giec
vector_id: 5721
content:
"The Atlas chapter (and the Interactive Atlas) uses global model \r\nsimulations from both CMIP5 and CMIP6, mainly historical and future \r\nprojections performed under ScenarioMIP (O'Neill et al., 2016). This \r\nfacilitates backwards comparability and thus the detection of new \r\nsalient features and findings from recent science and the latest \r\nCMIP6 ensemble. The selection of the models is based on availability \r\nof scenario data for the variables assessed in the Atlas chapter and \r\nfor those included in the Interactive Atlas (Atlas.2.2). In particular, in \r\norder to harmonize the results obtained from the different scenarios \r\nas much as possible, only models providing data for the historical \r\nscenario and at least two emissions scenarios, RCP2.6, RCP4.5 and/\r\nor RCP8.5 (for CMIP5), and SSP1-2.6, SSP2-4.5, SSP3-7.0 and/or \r\nSSP5-8.5 (for CMIP6), were chosen, resulting in 29 and 35 models, \r\nrespectively (see Cross-Chapter Box  1.4 for a  description of the \r\nscenarios)"


id: 6780bf19dd4ecf828a0d565db021812f
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
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vector_id: 13872
content:
" In the Atlas chapter (similarly to the regional Chapters 11 \r\nand 12) a single simulation is taken from each model (see Atlas.12 for \r\nlimitations of this choice). Since the RCP and SSP emissions scenarios \r\nare not directly comparable due to different regional forcing \r\n(Section 4.2.2), the Atlas includes GWLs as an alternative dimension \r\nof analysis (Cross-Chapter Box 11.1), which allows intercomparison \r\nof results from different scenarios as an alternative to the standard "


id: 55fa18d9a78dc7399f22aec0ec25fa61
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
content_type: text
index: climate_gpt_only_giec
vector_id: 11016
content:
"Global model data, as generated by the CMIP ensembles, although \r\navailable globally, have spatial resolutions that are limited for \r\nreproducing certain processes and phenomena relevant for regional \r\nanalysis (around 2deg and 1deg for CMIP5 and CMIP6, respectively). The \r\nCoordinated Regional Climate Downscaling Experiment (CORDEX; \r\nGutowski Jr. et  al., 2016) facilitates worldwide application of \r\nRegional Climate Models (RCMs, see Section  10.3.1.2), focusing \r\non a  number of regions (Figure Atlas.6) with a  typical resolution \r\nof 0.44deg (but also at 0.22deg and 0.11deg over some domains, such as \r\nEurope). However, only a  few simulations are available for some \r\ndomains (Annex II, Table AII.1), thus limiting the level of analysis \r\nand assessment that can be performed using CORDEX data in some \r\nregions. Moreover, there are regions where several domains overlap, \r\nthus providing additional lines of evidence"


id: d9701c5282d374a604dfc0e7eb91d76d
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
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index: climate_gpt_only_giec
vector_id: 31721
content:
" The use of multi-domain \r\ngrand ensembles to work globally with CORDEX data have recently \r\nbeen proposed (Legasa et al., 2020; Spinoni et al., 2020). Ongoing \r\nefforts, such as the multi-domain CORDEX-CORE simulations are \r\npromoting more homogeneous coverage and thus more systematic \r\ntreatment of CORDEX domains (Box Atlas.1)."


id: 6c4931e930186f43557f7b962f07a5cf
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
content_type: text
index: climate_gpt_only_giec
vector_id: 14662
content:
"Building on this information, the Interactive Atlas displays a number \r\nof (mean and extreme) indices and climatic impact-drivers (CIDs), \r\nconsidering both atmospheric and oceanic variables (Atlas.2.2). Some \r\nof these indices have been selected in coordination with Chapters 11 \r\nand 12, in order to support and extend the assessment performed in \r\nthese chapters (see Annex VI for details on the indices). In order to \r\nharmonize this information, the indices have been computed for each \r\nindividual model on the original model grids and the results have been \r\ninterpolated to a common 2deg (for CMIP5) and 1deg (CMIP6) horizontal \r\nresolution grids. In addition, for the sake of comparability with CMIP6 \r\nresults (in particular when using baselines going beyond 2005), \r\nthe historical period of the CMIP5 and CORDEX datasets has been \r\nextended to 2006-2014 using the first years of RCP8.5-driven transient \r\nprojections (Atlas.1.3.1)"


id: 37c943d9bbf05ed31af49556c81cb2b4
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
content_type: text
index: climate_gpt_only_giec
vector_id: 6151
content:
" Tables listing the CMIP5 and CMIP6 models \r\nused in the Atlas and in the Interactive Atlas for different scenarios and \r\nvariables are included as Supplementary Material (Tables Atlas.SM.1 \r\nand Atlas.SM.2, respectively); moreover, full inventories including \r\ndetails on the specific Earth System Grid Federation (ESGF) versions \r\nare given in the Atlas GitHub repository (Iturbide et al., 2021)."


id: 6ed79bf98f9d1addef55422889784208
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
content_type: text
index: climate_gpt_only_giec
vector_id: 15061
content:
"Figure Atlas.7 shows mean temperature and precipitation biases over \r\nthe North American domain in RCM simulations driven by reanalysis \r\nand historical GCM simulations (Section 10.3.2.5). Annual and seasonal \r\n(December-January-February (DJF) and June-July-August (JJA)) \r\nbiases are computed for both the RCMs and driving GCMs. Biases in \r\nthe reanalysis-driven RCMs result from intrinsic model errors, with the \r\nresults displayed being spatially aggregated for each reference region. \r\nThis same analysis is performed for the GCM-driven RCM simulations \r\nover the historical period 1986-2005. This allows comparison of \r\nthe intrinsic bias of the RCMs with the biases resulting when driven \r\nby the different GCMs and patterns of behaviour in the RCMs, for \r\nexample intrinsic warm and dry biases in ENA and WNA respectively \r\nor reduced RCM warm biases compared to the CCCma GCM in NEN \r\nand ENA"


id: 6a654f19dc8d9570b8b20492da60ba72
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
content_type: text
index: climate_gpt_only_giec
vector_id: 14342
content:
" Similar results for the other CORDEX domains are included as \r\nSupplementary Material (Figures Atlas.SM.1-Atlas.SM.10)."


id: ca5562e956e2c6d42068f6d854d2d034
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
content_type: text
index: climate_gpt_only_giec
vector_id: 29396
content:
"The main objective of CORDEX-CORE is to provide a global homogeneous foundation of high-resolution regional climate model (RCM) \r\nprojections to improve understanding of local phenomena and facilitate impact and adaptation research worldwide (Gutowski Jr. \r\net al., 2016). The experimental framework is designed to produce homogeneous regional projections for most inhabited land regions \r\nusing nine CORDEX domains at 0.22deg resolution (Figure Atlas.6): North, Central and South America (NAM, CAM, SAM); Europe (EUR); \r\nAfrica (AFR); East, South and Southeast Asia (EAS, WAS, SEA); and Australasia (AUS). Due to computational requirements, three GCMs \r\nwere selected to drive the simulations, HADGEM2-ES, MPI-ESM and NorESM, covering, respectively, the spread of high-, medium\u0002and low-equilibrium climate sensitivities from the CMIP5 ensemble at a global scale (with MIROC5, EC-Earth and GFDL-ES2M as \r\nsecondary GCMs), focusing on two scenarios RCP2.6 and RCP8.5 (see Box Atlas.1, Figure 1)"


id: 8a47007255a7235bba320d870cc2bcf
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
content_type: text
index: climate_gpt_only_giec
vector_id: 19291
content:
" Two RCMs have contributed so far to this \r\ninitiative (REMO and RegCM4) constituting an initial homogeneous downscaled ensemble to analyse mean climate change signals \r\nand hazards (Coppola et al., 2021b; Teichmann et al., 2021), and there are ongoing efforts to extend the CORDEX-CORE ensemble \r\nwith additional regional simulations (e.g., the COSMO-CLM community) to increase the ensemble size. CORDEX-CORE simulations \r\nare distributed as part of the information available for the different CORDEX domains at the Earth System Grid Federation (ESGF)."


id: 5d68548ecdd911b6c0e100dc7a8b330f
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
content_type: text
index: climate_gpt_only_giec
vector_id: 12222
content:
"Spatial information on observed and projected future climate changes has always been a key output of IPCC reports. This information \r\nis typically represented in the form of maps of historical trends (from observational datasets) and of projected changes for future \r\nreference periods and scenarios relative to baseline periods (from multi-model ensembles). These maps usually include information \r\non the robustness or uncertainty of the results such as the significance of trends or the consistency of the change across models. \r\nVisualization of this information combines two aspects that are intertwined: the core methodology (measures and thresholds) and its \r\nvisual implementation. For observed trends, robustness can be simply ascertained by using an appropriate statistical significance test"


id: 828ea51fc5442e9477268398882acef0
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
content_type: text
index: climate_gpt_only_giec
vector_id: 18065
content:
" \r\nHowever, for multi-model mean changes, the consistency across models for the sign of change (model agreement) and the magnitude \r\nof change relative to unforced climate variability (signal-to-noise ratio) provide two complementary measures allowing for simple \r\nor more comprehensive approaches to represent robustness and uncertainty. While they can be visually represented in various ways "


id: c3a087692c3dde499e97bbb40b130518
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
content_type: text
index: climate_gpt_only_giec
vector_id: 28344
content:
"The objectives in AR6 for representing robustness and uncertainty in maps are: 1) adopting a method that can be as coherent as \r\npossible across the different global/regional chapters while accommodating different needs, 2) being visually consistent across WGs, \r\nand 3) making the different layers of information on the maps as accessible as possible for the reader. As a result, a single approach \r\nis selected for observations and two alternative approaches (simple and advanced) are adopted for projected future changes. It is \r\nimportant to highlight that, as in previous reports, these approaches are implemented in maps at a grid-box level and, therefore, are \r\nnot informative for larger spatial scales (e.g., over AR6 reference regions) where the aggregated signals are less affected by small\u0002scale variability leading to an increase in robustness. This is particularly relevant for the AR6 regional assessments and approaches \r\n(e.g"


id: c1d7b395297f40da0e9739c0c0922c53
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
content_type: text
index: climate_gpt_only_giec
vector_id: 28079
content:
", for trend detection and attribution; Cross-Chapter Box 1.4, Section 11.2.4) which are performed for climatological regions and \r\nnot at grid-box scale (Chapters 11 and 12, and Atlas). Both small and large scales are relevant (e.g., adaptation occurs at smaller \r\nscales but also at the level of countries, which are typically larger than a few grid boxes). They are both addressed in the Interactive \r\nAtlas, which implements the above approaches for representing robustness in maps at the grid-box level, but also enables the analysis \r\nof region-wide signals (e.g., AR6 WGI reference regions, monsoon regions, etc.), helping to isolate background changes happening at \r\nlarger scales (Atlas.2.2)."


id: f0389ef86b245ef7ac19c616025ffcde
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
content_type: text
index: climate_gpt_only_giec
vector_id: 35297
content:
"Recent IPCC reports adopted different approaches for mapping uncertainty/robustness, including their calculation method and/or \r\ntheir visual implementation. In AR5 WGI '+' symbols were used to represent significant trends in observations at grid-box level. For \r\nfuture projections, different methods for mapping robustness were assessed (AR5 Box 12.1, Collins et al., 2013), while proposing \r\nas a  reference an approach based on relating the multi-model mean climate change signal to internal variability, calculated as \r\nthe standard deviation of non-overlapping 20-year means in the pre-industrial control runs. Regions where the multi-model mean \r\nchange exceeded two standard deviations of the internal variability and where at least 90% of the models agreed on the sign of \r\nchange  were stippled (as an indication of a  robust signal). Regions where the multi-model change was less than one standard \r\ndeviation were  hatched (small multi-model mean signal)"


id: e58e3cfec1363c7cff6e950708acd6ee
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
content_type: text
index: climate_gpt_only_giec
vector_id: 33624
content:
" However, this category did not distinguish areas with consistent small \r\nchanges from areas of significant but opposing/divergent signals. In addition, the unstippled/unhatched areas were left undefined, \r\nsince the categories were not mutually exclusive."


id: 5dccdfa0c3e3884face52042ec012fb6
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
content_type: text
index: climate_gpt_only_giec
vector_id: 12288
content:
"Since AR5 there has been a growing interest for disentangling small consistent climate change signals from significant divergent \r\nopposite changes resulting in conflicting information (Tebaldi et al., 2011), and different statistical tests have been applied to assess \r\nthe significance of signals working with the individual models forming the ensemble (Dosio and Fischer, 2018; Yang et al., 2018; Morim \r\net al., 2019). Moreover, new approaches have been proposed to identify large changes of opposite sign that compensate in the mean \r\n(Zappa et al., 2021). Recent literature has also highlighted the respective risks of Type I vs Type II errors, which can be associated \r\nwith the determination of robustness in analysed signals (Lloyd and Oreskes, 2018; Knutson et al., 2019). Type I errors are identifying \r\nsignals when there are none, while Type II errors are concluding there is no signal when there is one"


id: dd9767555083a3e10476570cb20260f2
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
content_type: text
index: climate_gpt_only_giec
vector_id: 32369
content:
"Approach B is the simple alternative for model projections. It consists of two categories, one for model agreement (at least 80% of \r\nthe models agree on the sign of change; colour, no overlay) and the other one for non-agreement (hatching). It is noted that model \r\nagreement is computed using 'model democracy' (i.e., without discarding/weighting models), since quantifying and accounting for \r\nmodel interdependence (shared building blocks) still remains challenging (Section 4.2.6). Different thresholds have been used in \r\nprevious reports and in the literature. In CORDEX studies, 80% has been widely used (Dosio and Fischer, 2018; Kjellstrom et al., 2018; \r\nNikulin et al., 2018; Yang et al., 2018; Akperov et al., 2019; Rana et al., 2020), partially due to the small ensemble sizes available \r\nin some cases; this also helps to reduce the impact of model interdependence in the final results"


id: 38c41a2f7f8467d1cc688fc3bdafd986
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
content_type: text
index: climate_gpt_only_giec
vector_id: 6300
content:
" Although 90% (used in AR5 WGI) \r\nprovides high confidence on the forced change, it is deemed too stringent for precipitation-like variables and regional assessments \r\nand was therefore not included (see Cross-Chapter Box Atlas.1, Figure 1). The 66% threshold, which has been used in previous reports \r\n(e.g.,  SR1.5 and SROCC) and in the literature, is not used to avoid communicating weak confidence. Cross-Chapter Box Atlas.1, \r\nFigure 1 illustrates the application of this approach."


id: 47f3078872da54c883576f9236a813bf
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
content_type: text
index: climate_gpt_only_giec
vector_id: 8694
content:
"Approach C is a more advanced alternative for model projections, extending the AR5 WGI and simplifying the AR5 WGII approaches \r\n(fewer categories). It consists of three categories: 'robust change', 'conflicting change', and 'no change or no robust change' (see the \r\ndetails in Cross-Chapter Box Atlas.1, Table 1). The first two categories can be interpreted as areas where the climate change signal likely \r\nemerges from internal variability (i.e., it exceeds the variability threshold in >=66% of the models). The variability threshold is defined \r\nas , where is the standard deviation of 20-year means, computed from non-overlapping periods \r\nin the pre-industrial control (after detrending with a quadratic fit as in AR5 WG1); in cases where this information is not available \r\n(e.g., for CORDEX or HighResMIP), the following approximation is used instead: , where is the \r\ninterannual standard deviation measured in a linearly detrended modern period (note that for white noise )"


id: ed818b8984417755c962625309fea95e
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
content_type: text
index: climate_gpt_only_giec
vector_id: 34833
content:
" \r\nThe factor is used as in the AR5 WGI approach to account for the fact that the variability of a difference in means (the climate \r\nchange signal) is of interest. This approach is an evolution of the AR5 WGI method with three notable differences: (a) AR6 uses \r\na lower threshold for internal variability (1.645 corresponding to a 90% confidence level, instead of 2 as used in AR5 WG1); (b) the \r\nthreshold on agreement in sign is lowered from >=90% to >=80%, leading to more grid boxes classified as robust as opposed to \r\nconflicting signal; (c) the AR6 method compares signal to variability in each individual model and consequently introduces a 66% \r\ncut-off on significant changes, implying that the climate change signal likely emerges from internal variability in the baseline period. "


id: fe5f3ca3dbcbfe1571bd7f5b90e17350
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
content_type: text
index: climate_gpt_only_giec
vector_id: 37558
content:
"Uncertainty at the grid-box and regional scales: interpreting areas with diagonal lines\r\nThere is no one-size-fits-all method for representing robustness or uncertainty in future climate projections from a  multi-model \r\nensemble. One of the main challenges is the dependence of the significance on the spatial scale of interest: while a significant trend \r\nmay not be detected at every location, a fraction of locations showing significant trends can be sufficient to indicate a significant \r\nchange over a region, particularly for extremes (e.g., it is likely that annual maximum one-day precipitation has intensified over \r\nthe land regions globally even though there are only about 10% of weather stations showing significant trends; Figure  11.13). \r\nThe approach adopted in WGI works at a  grid-box level and, therefore, is not informative for assessing climate change signals \r\nover larger spatial scales"


id: 6e20319a9aa28d9f667b035deb9710c8
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
content_type: text
index: climate_gpt_only_giec
vector_id: 14943
content:
" For instance, an assessment of the amount of warming required for a robust climate change signal to \r\nemerge can strongly depend on the considered spatial scale. A robust change in the precipitation extremes averaged over a region or \r\na number of grid boxes emerge at a lower level of warming than at the grid-box level because of larger variability at the smaller scale \r\n(Cross-Chapter Box Atlas.1, Figure 2)."


id: a3280c7a7070f8716a5ac6bee2bcbdf0
created_at: 2023-03-16 14:02:10
updated_at: 2023-03-16 14:02:10
content_type: text
index: climate_gpt_only_giec
vector_id: 23239
content:
"The Interactive Atlas implements the approaches for representing \r\nrobustness in maps at the grid-box level described in Cross-Chapter \r\nBox Atlas.1. These approaches are not necessarily informative for \r\nassessing trends and climate change signals over larger spatial scales \r\nwhere signals are less affected by small-scale variability leading to \r\nan increase in robustness. For regional analysis, the Interactive Atlas \r\nallows the analysis of aggregated region-wide signals and assessing \r\ntheir robustness at a regional scale, thus complementing the previous \r\napproach for grid-box robustness representation. For example, \r\nFigure Atlas.9 shows large hatched areas for maximum five-day \r\nprecipitation in the South Asia region. When aggregated spatially, \r\nthe region exhibits a  robust wetting signal, with most ensemble \r\nmembers agreeing on the sign"


id: 8de5eaca42e0a2a9e3d951dd25400866
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 19856
content:
" This highlights that signals may not \r\nhave emerged at the station or grid-box scale but have clearly at \r\naggregated scales, particularly for variables with high variability \r\n(e.g.,  extreme precipitation or cold extremes; see Cross-Chapter \r\nBox Atlas.1)."


id: 9af9189866a35dc9ab0e2a6861f5f357
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 21920
content:
"All final products visualized in the Interactive Atlas can be exported \r\nin a variety of formats, including PNG and PDF for bitmap and vector \r\ninformation, respectively. Moreover, in the case of the global maps, \r\nthe final data underlying these products can be downloaded in \r\nNetCDF and GIS format (GeoTIFF), thus facilitating reusability of the \r\ninformation. Note that the images are final IPCC products (covered by \r\nthe IPCC terms of use), whereas the underlying data are distributed \r\nby the IPCC-DDC under a  more flexible license which facilitates \r\nreusability. Moreover, a  comprehensive provenance metadata \r\ndescription has been generated, including all details needed for \r\nreproducibility, from the data sources to the different post-processes \r\napplied to obtain the final product. In these cases, there is also \r\nthe possibility to download a  PNG file augmented with attached \r\nmetadata information (in JSON format)"


id: 739aaf01ffdd3a83d2c79a686e1a5eb5
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 15811
content:
" This metadata information \r\n(including the source code generating the product) can be accessed \r\nand interpreted automatically using specific JSON software/libraries. \r\nHowever, for the sake of simplicity, a human-readable version of the \r\nmetadata is accessible directly from the Interactive Atlas, describing \r\nthe key information along the workflow."


id: 4a6ef9662db4e9d1d161c7abccdec018
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 9114
content:
"Figure Atlas.10 | Schematic representation of the Interactive Atlas workflow, from database description, subsetting and data transformation to final \r\ngraphical product generation (maps and plots). Product-dependent workflow steps are depicted with dashed borders. METACLIP specifically considers the different \r\nintermediate steps consisting of various data transformations, bias adjustment, climate index calculation and graphical product generation, providing a semantic description \r\nof each stage and the different elements involved. The different controlled vocabularies describing each stage are indicated by the colours, with gradients indicating several \r\nvocabularies involved, usually meaning that specific individual instances are defined in 'ipcc_terms' extending generic classes of 'datasource'"


id: e5025a123e859750e250eb68019a63df
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 33521
content:
" These two vocabularies, dealing \r\nwith the primary data sources have specific annotation properties linking their own features with the CMIP5, CMIP6 and CORDEX Data Reference Syntax, taking as reference \r\ntheir respective controlled vocabularies. All products generated by the Interactive Atlas provide a METACLIP provenance description, including a persistent link to a reproducible \r\nsource code under version control."


id: 5189377c09b7006d8515a925e84f84e9
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 10279
content:
"Many assessment statements make use of evidence derived \r\nfrom observed changes, model projections, and process-oriented \r\nattribution of changes to human interventions. The Interactive \r\nAtlas shows a  small subset of available observations that \r\ndocument climate change, namely surface air temperature and \r\ntotal precipitation (and thus not including observations of other \r\natmospheric and Earth system components used as part of the \r\nevidence base for the report). Only datasets that have (near) global \r\nor large regional gridded spatial coverage and go back multiple \r\ndecades are used. For each variable multiple datasets are included, \r\nbut some of these have overlapping native ground-station \r\nobservations and so are not independent (Atlas.1.4.1). The datasets \r\nshow patterns of substantial spatial and temporal variability, and \r\nthe empirical evidence of a non-stationary climatology needs to be \r\nfiltered from this information"


id: 3f21012fc231d586e1202166e783671d
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" Issues with quality, representativity \r\nand mutual consistency lead to constraints on their use for \r\nattribution of causes of trends (see Section 10.4.1.2 for examples). \r\nThe practice of attributing trends and extreme events to human \r\ncauses gives confidence that these trends are expected to continue \r\nin the (near) future, provided the human drivers of climate change \r\nremain unchanged. However, large internal variability at decadal \r\ntime scales can be misinterpreted as an anthropogenic influence on \r\nthe likelihood of extreme events, and in that case extrapolation of \r\ntrends cannot be expected to be a reliable predictor for the future \r\n(Schiermeier, 2018)."


id: 3ac438d8e399bda456ea148d0b34a075
created_at: 2023-03-16 14:02:11
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"METACLIP emphasizes the delivery of 'final products' (understood as \r\nany piece of information that is stored in a  file, such as a  plot or \r\na  map) with a  full semantic description of its origin and meaning \r\nattached. METACLIP ensures 'machine readability' through reuse of \r\nwell-defined, standard metadata vocabularies, providing semantic \r\ninteroperability and the possibility of developing database engines \r\nsupporting advanced provenance analytics. Therefore, this framework \r\nhas been adopted to generate provenance information and attach \r\nit as metadata to the products generated by the Interactive Atlas. \r\nA specific vocabulary ('ipcc_terms') is created alongside the inclusion \r\nof new products in the Interactive Atlas and uses the controlled \r\nvocabularies existing from CMIP and CORDEX experiments. As an \r\nexample, Figure Atlas"


id: 4f2b3df066aa3ad112443d7f4bc36165
created_at: 2023-03-16 14:02:11
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"10 shows the semantic vocabularies needed to \r\nencode the information of the typical workflow for computing (from \r\nbias-adjusted data) any of the climate indices (extreme or CIDs) \r\nincluded in the Interactive Atlas."


id: 6f3eafbb618c6b0cb57e0f1c1f0922b5
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"The detailed technical findings in IPCC reports also serve as an \r\nimportant benchmark resource for the research community. The \r\nInteractive Atlas complements the IPCC assessment report as \r\na repository of scientific information on global and regional climate \r\nand its representation in coordinated model ensemble experiments. \r\nRegional climate is governed by a  mixture of drivers, such as \r\ncirculation patterns, seasonal monsoons, annual cycles of snow and \r\nregional land-atmosphere feedbacks. Global warming may affect \r\nregional climate characteristics by altering the dynamics of their \r\ndrivers. The Interactive Atlas allows the comparison of different \r\nlevels of global warming on specific regional climate features but \r\nis not designed for advanced analysis of the relationship between \r\ndrivers and regional climate characteristics"


id: a91a982b3fadbc6189444c83d84caae2
created_at: 2023-03-16 14:02:11
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content:
" For this, tailored analysis \r\nprotocols need to be applied, such as the aggregation of climate \r\nchange information from ensembles of regional climate projections, \r\nand stratification according to drivers of regional climate such as \r\npatterns of atmospheric circulation (Lenderink et  al., 2014). The \r\nanalysis of complex regional climate characteristics resulting from \r\ncompound drivers also require additional expert knowledge and data \r\nprocessing (Thompson et al., 2016). Section 12.6.2 assesses various \r\ncategories of climate services, including tailored analysis of regional \r\nclimate processes."


id: de6693eae215f677561f589acc2d71d7
created_at: 2023-03-16 14:02:11
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"Communicating the full extent of available information on future \r\nclimate for a region, including a quantification of uncertainties, can \r\nact as a barrier to the uptake and use of such information (Lemos \r\net al., 2012; Daron et al., 2018). To address the need to simplify and \r\nincrease the relevance of information for specific contexts, recent \r\nstudies have adopted narrative and storyline approaches (see \r\nSections 1.4.4 and 10.5.3 for definitions and further discussion on \r\nthese concepts; Hazeleger et al., 2015; Shepherd et al., 2018). The \r\nuse of region-specific climate storylines, including a  role for local \r\nmechanisms, drivers and societal impacts generally requires detailed \r\ninformation that is typically not provided by the Interactive Atlas"


id: 4645555f94080a24ff8726edcd7c9cfa
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
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content:
" \r\nHowever, background information and basic (scenario) assumptions \r\ncan be derived from the Interactive Atlas which can be considered \r\nto provide an expert knowledge base from which to build targeted \r\nstorylines and climate information."


id: a44cdcc871579062bdbd5638767ae177
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
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vector_id: 23409
content:
"Figure Atlas.12 demonstrates how temperature is projected to increase \r\nfor all regions, and at a greater rate than the global average over \r\nmany land regions, and with significant amplification in the Arctic. It \r\nalso shows the higher mid-century warming and significantly higher \r\nend-of-century warming under the high-emissions SSP3-7.0 scenario \r\ncompared to the low-emissions SSP1-2.6 scenario. Conversely, \r\ncomparing the projected 2degC global warming level change with that \r\nprojected additional warming compared to the recent past under \r\nthe SSP1-2.6 scenario, demonstrates the much smaller additional \r\nwarming projected under this low-emissions scenario. Finally, \r\nthe maps display the CMIP6 ensemble mean projection, but it is \r\nimportant to explore the full range of outcomes from the ensemble, \r\nfor example when undertaking a  comprehensive risk assessment \r\nin which temperature is an important hazard. This can be explored \r\nregionally in the Interactive Atlas (Atlas"


id: cd93e2931302576b9a984c1eae23fc96
created_at: 2023-03-16 14:02:11
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content:
"2) by viewing the time series \r\nof changes for all of the models within the ensemble over the AR6 \r\nWGI reference regions (Figure Atlas.2)."


id: 31d4aca3c6c2b1639182244e0e2fbc77
created_at: 2023-03-16 14:02:11
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content:
"Figure Atlas.11 | Observed linear trends of signals in annual mean surface air temperature (a, b) and precipitation (e, f) in the Berkeley Earth, CRU TS \r\nand GPCC datasets (see Atlas.1 for dataset details). Trends are calculated for the common 1961-2015 period and are expressed as degC per decade for temperature and \r\nrelative change (with respect to the climatological mean) per decade for precipitation. Crosses indicate regions where trends are not significant (at a 0.1 significance level) \r\nand the black lines mark out the reference regions defined in Atlas.1. Panels (c) and (d) display the period in which the signals of temperature change in data aggregated \r\nover the reference regions emerged from the noise of annual variability in the respective aggregated data. Emergence time is calculated for (c) Berkeley Earth (as used in (a)) \r\nand CRUTEM5. Regions in the CRUTEM5 map are shaded grey when data are available over less than 50% of the land area of the region"


id: ba1815375f349a457d88e91efd9cada4
created_at: 2023-03-16 14:02:11
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content:
" Further details on data sources and \r\nprocessing are available in the chapter data table (Table Atlas.SM.15)."


id: b38132f70878348e06c63006c8b2c5d9
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
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vector_id: 25749
content:
"range of a  climate statistic, it is often useful to generate area\u0002averaged  summaries of these statistics under different future \r\nemissions scenarios or at specific global warming levels. This is \r\ndemonstrated in Figure Atlas.13 and forms the basis of a common \r\nset of analyses, which are presented for the reference regions in \r\nthe regional assessments in Atlas.4 to Atlas.11. It shows the range \r\nof projected changes compared to the 1850-1900 and recent past \r\n1995-2014 baseline periods for the CMIP5 and CMIP6 ensembles. \r\nThe first four panels show: annual mean changes in temperature \r\nglobally and over land only for various global warming levels \r\nand emissions scenarios and time periods (left pair), and then \r\nagain globally and for global land, changes in precipitation and \r\ntemperature at the same global  warming levels (right pair)"


id: 6b93ff50b721eab4a701bbbe0a0d09a1
created_at: 2023-03-16 14:02:11
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content:
" The \r\nsecond four panels provide the same temperature and precipitation \r\ninformation globally and for global land only in the December-\r\nFebruary and July-August seasons. These results demonstrate the \r\nconsensus between the two ensembles for increased warming over \r\nland areas and increases in global precipitation at all warming levels, \r\nand that global land precipitation increases more. They also show \r\nthe increased precipitation response in December-January-February \r\n(DJF), reflecting the large precipitation increases in the Northern "


id: 26bea39945855465f712afe2868c4a1
created_at: 2023-03-16 14:02:11
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vector_id: 3491
content:
"Table Atlas.1 | Regional mapping of the teleconnections associated with the main modes of variability (Annex IV). Fraction of surface air temperature and \r\nprecipitation variance explained at interannual time scale by each mode of variability (columns) for each AR6 region (rows) based on the coefficient of determination R2. Units \r\nare in percent and non-significant values based on t-statistics at the 95% level of confidence are indicated by a white cell with a diagonal line. Grey cells represent regions where \r\nthere is insufficient data to calculate any teleconnection. HadCRUT (HAD), GISTEMP (GIS), Berkeley Earth (BE), and CRU-TS (CRU) observed datasets are used to assess the \r\nstrength of the teleconnection for surface air temperature, and GPCC and CRU-TS are used for precipitation. The colour scale given on label bars shown at the bottom quantifies \r\nthe values of the explained variance and also stands for the sign of the teleconnection for the positive phase of the mode"


id: 31a9776f23ab1b9a7471a1bfef95c56e
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
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index: climate_gpt_only_giec
vector_id: 5221
content:
" All data are linearly detrended prior to the computation \r\nof the regression. Note that results are sensitive to the choice of the detrending function (linear, loess filter, 3-order polynomial function) but by a few percent at most, which is \r\nwell below the range of the observational uncertainty assessed here through the use of several observational products. NAM: Northern Annular Mode; SAM: Southern Annular \r\nMode; ENSO: El Nino-Southern Oscillation; IOB: Indian Ocean basin; IOD: Indian Ocean Dipole; AZM: Atlantic Zonal Mode; AMM: Atlantic Meridional Mode; PDV: Pacific Decadal \r\nVariability; AMV: Atlantic Multi-decadal Variability; DJF: December-January-February; MAM: March-April-May; SON: September-October-November; JJA: June-July-August."


id: 131e1da24093a6697c9381430ca2927d
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
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vector_id: 521
content:
"Figure Atlas.11 shows observed trends in annual mean surface \r\ntemperature and indicates it has been rising rapidly over Africa \r\nfrom 1961 to 2015 and with significant increases in all regions of \r\n0.1degC-0.2degC per decade and higher over some northern, eastern and \r\nsouth-western regions (high confidence) (see also Interactive Atlas). \r\nThis is confirmed by an independent analysis performed for a longer \r\nperiod (1961-2018) over areas where long-term homogeneous \r\ntemperature time series are available (Engelbrecht et  al., 2015). \r\nMore specifically over the Horn of East Africa, the long-term mean \r\nannual temperature change between 1930 and 2014 showed two \r\ndistinct but contrary trends: significant decreases between 1930 \r\nand 1969 and increases from 1970 to 2014 (Ghebrezgabher et al., \r\n2016). North Africa has an overall warming in observed seasonal \r\ntemperature (Barkhordarian et al., 2012; Lelieveld et al"


id: a7c0c96ba4dacb8c3edb447c0e40f989
created_at: 2023-03-16 14:02:11
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", 2016) with \r\npositive trends in annual minimum and maximum temperatures (Vizy \r\nand Cook, 2012). Temperatures over West Africa have increased over \r\nthe last 50 years (Mouhamed et al., 2013; Niang et al., 2014) with \r\na spatially variable warming reaching 0.5degC per decade from 1983 to \r\n2010 (Sylla et al., 2016). West Africa has also experienced a decrease \r\nin the number of cool nights, as well as more frequent warm days and \r\nwarm spells (Mouhamed et al., 2013; Ringard et al., 2016). Similarly, \r\nEast Africa has experienced a  significant increase in temperature \r\nsince the beginning of the early 1980s (Anyah and Qiu, 2012) with an \r\nincrease in seasonal mean temperature. Over South Africa, positive \r\ntrends were found in the annual mean, maximum and minimum \r\ntemperatures for 1960-2003 in all seasons, except for the central \r\ninterior (Kruger and Shongwe, 2004; Zhou et al., 2010; Collins, 2011; \r\nKruger and Sekele, 2013; MacKellar et  al"


id: f8994992cc3fc8f047387267a003bcac
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content:
", 2014), where minimum \r\ntemperatures have decreased significantly (MacKellar et al., 2014). \r\nWithin inland southern Africa, minimum temperatures have increased \r\nmore rapidly than maximum temperatures (New et al., 2006)."


id: 62a6db2332cc1eadadf4490bce413b34
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
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index: climate_gpt_only_giec
vector_id: 13090
content:
"The most recent IPCC reports, AR5 and SR1.5 (Christensen et al., \r\n2013; Hoegh-Guldberg et  al., 2018), state that over most parts \r\nof Africa, minimum temperatures have warmed more rapidly \r\nthan maximum temperatures during the last 50 to 100  years \r\n(medium confidence). In the same period, minimum and maximum \r\ntemperatures have increased by more than 0.5degC relative to \r\n1850-1900 (high confidence). While the quality of ground \r\nobservational temperature measurements tends to be high \r\ncompared to that of measurements for other climate variables, \r\nAfrica remains an under-represented region as reported in SR1.5 \r\n(Hoegh-Guldberg et al., 2018; IPCC, 2018c). Based on the Coupled \r\nModel Intercomparison Project Phase 5 (CMIP5) ensemble and \r\nreported in IPCC AR5 and SR1"


id: b0fbc248d0e27886f0af715228750ad7
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
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index: climate_gpt_only_giec
vector_id: 25354
content:
"5, surface air temperatures in Africa \r\nare projected to rise faster than the global average increase and \r\nare likely to increase by more than 2degC and up to 6degC by the end \r\nof the century, relative to the late 20th century, if global warming \r\nreaches 2degC (Bindoff et  al., 2013; Niang et  al., 2014; Hoegh\u0002Guldberg et  al., 2018). The higher temperature magnitudes are \r\nprojected during boreal summer. Southern Africa is likely to exceed \r\nthe global mean land surface temperature increase in all seasons \r\nby the end of the century. Temperature projections for East Africa \r\nindicate considerable warming under RCP8.5 where average \r\nwarming across all models is approximately 4degC by the end of the \r\ncentury. According to SROCC, eastern Africa like other regions with \r\nsmaller glaciers is projected to lose more than 80% of its glaciers \r\nby 2100 under RCP8.5 (medium confidence) (Hock et al., 2019b)."


id: 5f59d880619c7aa99392b4254e582d26
created_at: 2023-03-16 14:02:11
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index: climate_gpt_only_giec
vector_id: 12573
content:
"Most areas lack enough observational data to draw conclusions \r\nabout trends in annual precipitation over the past century. In addition, \r\nmany regions of Africa have discrepancies between different \r\nobserved precipitation datasets (Sylla et al., 2013; Panitz et al., 2014). \r\nA statistically significant (95% confidence level) decrease in rainfall \r\nand the number of rainy days is reported in autumn over the eastern, \r\ncentral and north-eastern parts of South Africa in spring and summer \r\nduring 1960-2010 (MacKellar et  al., 2014; Kruger and Nxumalo, \r\n2017). Central Africa has experienced a significant decrease in total \r\nprecipitation, which is likely associated with a significant decrease \r\nof the length of the maximum number of consecutive wet days \r\n(Aguilar et al., 2009). Furthermore, rainfall decreased significantly in \r\nthe Horn of Africa (Tierney et al., 2015) with the largest reductions \r\nduring the long rains season from March to May (Lyon and DeWitt, \r\n2012; Viste et al"


id: 4b8137acd2edc23d4d0c0a0bbb887ba
created_at: 2023-03-16 14:02:11
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vector_id: 9298
content:
", 2013; Rowell et al., 2015). Over mountainous areas \r\nsignificant increases are found in the number of rain days around \r\nthe southern Drakensberg in spring and summer during the period \r\n1960-2010 (MacKellar et al., 2014). Similarly, southern West Africa is \r\nobserved to have had more intense rainfall from 1950 to 2014 during \r\nthe second rainy season of September to November (Nkrumah et al., \r\n2019). The Sahel region also had more intense rainfall throughout \r\nthe rainy season (Panthou et al., 2014, 2018a, b; Sanogo et al., 2015; "


id: 200265d52be6aca62ccb6df4b1d8db33
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 2477
content:
"Coordinated Regional Downscaling Experiment (CORDEX) regional \r\nclimate models have been widely evaluated over Africa. They \r\ncapture the occurrence of the West African Monsoon jump and the \r\ntiming and amplitude of the mean annual cycle of precipitation \r\nand temperature over the homogeneous sub-regions of West Africa \r\n(Gbobaniyi et al., 2014), simulate eastern Africa rainfall adequately \r\n(Endris et al., 2013), and over southern Africa capture the observed \r\nclimatological spatial patterns of extreme precipitation (Pinto et al., \r\n2016). They also effectively simulate the phasing and amplitude of \r\nmonthly rainfall evolution and the spatial progression of the wet \r\nseason onset over southern Africa (Shongwe et al., 2015). However, \r\ndiscrepancies and biases in present-day rainfall are reported over \r\nUganda from the RCM-simulated rainfall compared to three gridded \r\nobservational datasets (Kisembe et  al., 2019)"


id: bda76c6932439e497d9336485cc78a1e
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 27402
content:
" Specifically, they \r\nreported that the CORDEX models underestimate the annual rainfall \r\nin Uganda and struggle to reproduce the variability of the long and \r\nshort rainy seasons."


id: 51f653a5d48adfb571a22d49337f1bb9
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
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index: climate_gpt_only_giec
vector_id: 10351
content:
"The drying observed over the Sahel in the 1960s to 1970s has been \r\nattributed to warming of the South Atlantic SST and southern African \r\ndrying as a response to Indian Ocean warming (Hoerling et al., 2006; \r\nDai, 2011). Enhanced rainfall intensity since the mid-1980s over the \r\nSahel (Maidment et al., 2015; Sanogo et al., 2015) is associated with \r\nincreased greenhouse gases suggesting an anthropogenic influence \r\n(medium confidence) (Biasutti, 2019). In the last decade, the changes \r\nin the timing of onset and cessation of rainfall over Africa have \r\nbeen linked to changes in the progression of the tropical rainband \r\nand the Saharan heat low (Dunning et al., 2018; Wainwright et al., \r\n2019). Moreover, later onset and earlier cessation of eastern Africa \r\nrainfall is associated with a delayed and then faster movement of the \r\ntropical rainband northwards during the boreal spring and northward \r\nshift of the Saharan heat low (Wainwright et al"


id: 544e78f9ac56e4b9d0e806d0099afe86
created_at: 2023-03-16 14:02:11
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index: climate_gpt_only_giec
vector_id: 10725
content:
", 2019), driven by \r\nanthropogenic carbon emissions and changing aerosol forcings \r\n(medium confidence). Over East Africa, the drying trend is associated \r\nwith an anthropogenic-forced relatively rapid warming of Indian \r\nOcean SSTs (Williams and Funk, 2011; Hoell et al., 2017); a shift to \r\nwarmer SSTs over the western tropical Pacific and cooler SSTs over \r\nthe central and eastern tropical Pacific (Lyon and DeWitt, 2012); \r\nmulti-decadal variability of SSTs in the tropical Pacific, with cooling in \r\nthe east and warming in the west (Lyon, 2014); and the strengthening \r\nof the 200-mb easterlies (Liebmann et al., 2017). However, decadal \r\nnatural variability from SST variations over the Pacific Ocean has also \r\nbeen associated with the drying trend of East Africa (Wang et  al., \r\n2014; Hoell et al., 2017) with an anthropogenic-forced rapid warming \r\nof Indian Ocean SSTs (medium confidence)."


id: 3e1f7f5095990bccfa260046caf05d77
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 7139
content:
"Research over Africa has improved since AR5, and although SR1.5 \r\n(de Coninck et  al., 2018) has synthesized new information for the \r\ncontinent, there is still not enough literature on specific areas for \r\nassessment. CMIP5 and CMIP6 projections (Figure Atlas.16) are \r\nfor continued warming, with median projected regional warming \r\nfor 2080-2100 compared to 1995-2014 of between 1degC and 2degC \r\nunder SSP1-2.6/RCP2.6 emissions and exceeding 4degC and in some \r\nregions 5degC under SSP5-8.5/RCP8.5 emissions. The central interiors \r\nof southern and northern Africa are likely to warm faster than \r\nequatorial and tropical regions (Interactive Atlas). Projections from \r\nCMIP5 show that East Africa is likely to warm by 1.7degC-2.8degC and \r\n2.2degC-5.4degC under the RCP4.5 and RCP8.5 scenarios respectively \r\nin the period 2071-2100 relative to 1961-1990 (Ongoma et  al., \r\n2018)"


id: 211482a655c7f395da274e2affe26a7d
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
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index: climate_gpt_only_giec
vector_id: 2640
content:
" Over southern Africa, areas in the south-western region \r\nof the sub-continent, covering South Africa and parts of Namibia \r\nand Botswana, are projected to experience the largest increase \r\nin temperature, which are expected to be greater than the global \r\nmean warming (Maure et al., 2018). A large ensemble of CORDEX \r\nAfrica simulations have been used to project the impact of 1.5degC "


id: 7836d0be0815d010543dc56dc6779de3
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 16481
content:
"Africa, summer mean temperatures from CORDEX, CMIP5 (RCP8.5) \r\nand CMIP6 (SSP5-8.5) are projected to increase beyond 6degC by the \r\nend of the century with respect to the period 1970-2000 (Schilling \r\net al., 2012; Ozturk et al., 2018; Almazroui et al., 2020c), see also \r\nthe Interactive Atlas. Note that results for the CORDEX-AFR over \r\nthe Mediterranean (MED) are consistent with those reported from \r\nthe CORDEX-EUR dataset (Figure  Atlas.24; Section Atlas.1.3), in \r\nagreement with Legasa et al. (2020).\r\nProjected rainfall changes over Africa in the mid- and late 21st century \r\nare uncertain in many regions, highly variable spatially and with \r\ndiffering levels of model agreements (Figure Atlas.16) though \r\nwith robust projections of decreases in MED and WSAF and increases \r\nin NEAF and SEAF by 2080-2100 under high emissions (Interactive \r\nAtlas). Some uncertainties are reported over parts of Africa from \r\nCORDEX projections (Dosio and Panitz, 2016; Endris et al"


id: b7e7a86c0ea45917aecbace451adaaa6
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", 2016; Klutse \r\net  al., 2018). For example, large uncertainties are associated with \r\nprojections at 1.5degC and 2degC of global warming over Central Africa \r\n(Mba et al., 2018) and over the Sahel (Gbobaniyi et al., 2014; Sylla \r\net al., 2016). Over southern Africa, enhanced warming is projected to \r\nresult in a reduction in mean rainfall across the region (Maure et al., \r\n2018), and in particular over the Limpopo basin and smaller areas of \r\nthe Zambezi basin in Zambia, and also in parts of the Western Cape \r\nin South Africa, under a  global warming of 2degC. The projections of \r\nreduced precipitation in summer rainfall regions of southern Africa \r\nare associated with delayed wet season onset in spring (Dunning \r\net  al., 2018) due to a  northward shift and delayed breakdown of \r\nthe Congo Air Boundary (Howard and Washington, 2020). However, \r\nprojected rainfall intensity over southern Africa is likely to increase \r\nand be magnified under RCP8.5 compared with RCP4"


id: f95ffb2c6f8b1dae5fbb6933a631c2f8
created_at: 2023-03-16 14:02:11
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content:
"5 for the period \r\n2069-2098 relative to the reference period 1976-2005 (Pinto et al., \r\n2018). For West Africa, rainfall projection is uncertain because of the \r\ncontrasting signals from models (Dosio et al., 2019). Nonetheless, West \r\nAfrica river basin-scale irrigation potential would decline under 2degC \r\nof global warming even for areas where water availability increases \r\n(Sylla et al., 2018). The western and eastern Sahel are projected as \r\nhotspots for delayed rainfall onset dates of about four days and six \r\ndays causing reduced length of rainy season in the 1.5degC-2degC warmer \r\nclimates under RCP4.5 and RCP8.5 scenarios (Kumi and Abiodun, \r\n2018). Projected delay in rainfall cessation dates and a longer length \r\nof rainy season over the western part of the Guinea coast is likely\r\nunder the same scenarios (Figure Atlas.16; Sellami et  al., 2016; \r\nKumi and Abiodun, 2018)"


id: 7e2f7791ffff41a696dc4b75af797db9
created_at: 2023-03-16 14:02:11
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content:
" There is a tendency towards an increase \r\nin annual mean precipitation over central Sahel and eastern Africa \r\n(Interactive Atlas, Figure Atlas.16, (Nikulin et al., 2018), especially over \r\nthe Ethiopian Highlands with up to 0.5 mm day-1 (Osima et al., 2018)."


id: 144d80c96be756c329c4c18c38ba2990
created_at: 2023-03-16 14:02:11
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content:
"There are contrasting signals in the projections of rainfall over some \r\nparts of Africa until the end of the 21st  century (high confidence) \r\nthough changes in any given region are generally projected with\r\nmedium confidence. In regions of high or complex topography such as \r\nthe Ethiopian Highlands, downscaled projections indicate increases in \r\nrainfall by the end of the 21st century. However, northern Africa and \r\nthe south-western parts of South Africa are likely to have a reduction \r\nin precipitation under higher warming levels (high confidence). \r\nOver Western Africa, rainfall is projected to decrease in the western \r\nSahel  sub-region (medium confidence) and increase in the central \r\nSahel sub-region (low confidence) and along the Guinea coast \r\nsub-region (medium confidence). Rainfall amounts are projected to \r\nincrease over Eastern Africa (medium confidence)"


id: d8c6033a9f485dd25abb8e8c93a9a07e
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" Southern Africa is \r\nprojected to have a reduction in annual mean rainfall but increases \r\nin rainfall intensity by 2100 (medium confidence)."


id: a7e1273907e5840c5ceafca87c1f737e
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
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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 11.7-11.9) and climatic impact-drivers in \r\nChapter 12 (Table 12.4). It covers most Asian territories of the region \r\n(Figure Atlas.17) with the exception of the Russian Arctic (RAR), \r\nwhich is assessed as part of the Arctic in Section 11.2. These include \r\nWest and East Siberia (WSB, ESB) and the Russian Far East (RFE) \r\nin the north; West and East Central Asia (WCA, ECA), the Tibetan \r\nPlateau (TIB) and East Asia (EAS); and the Arabian Peninsula (ARP), \r\nSouth and South East Asia (SAS, SEA) in the south. Figure Atlas"


id: c18a653f419affc8954192fb2415e5e8
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"17 \r\nsupports the assessment of regional mean changes in annual \r\nmean surface air temperature and precipitation over Asia. Due to \r\nthe high climatological and geographical heterogeneity of Asia, the \r\nassessment is performed over five sub-continental areas: East Asia \r\n(EAS and ECA), North Asia (WSB, ESB and RFE), South Asia  (SAS), "


id: d14b8b50f17cb971b6c5146e0e959070
created_at: 2023-03-16 14:02:11
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"The climatic regions defined for East Asia include central and eastern \r\nChina, Japan and the Korea Peninsula (regions ECA and EAS in \r\nFigure  Atlas.17). East Asia is significantly influenced by monsoon \r\nsystems (Section 8.3.2.4.2). The seasonal advance or retreat of the East \r\nAsian summer monsoon (EASM) rainband is crucial to local climate. \r\nThe East Asian winter monsoon (EAWM) has significant influence on \r\nthe weather and climate over East Asia and plays an important role \r\nin regulating winter temperatures including strong cold events and \r\nsnowstorms (Wang and Chen, 2014; Wang and Lu, 2016). The East \r\nAsian monsoons exhibit considerable variability on a wide range of \r\ntime scales, including notable interannual variabilities that includes an \r\neffect of the El Nino-Southern Oscillation (ENSO; Wang et al., 2000) \r\nand the Indian Ocean Dipole (IOD; Takaya et al"


id: 4e919aef594ca72d1e37a21571cf4d33
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", 2020), and significant \r\ninter-decadal variabilities in the 20th century resulted from the effect \r\nof Pacific Decadal Variability (PDV; Zhou et al., 2009), see also Annex IV \r\nand Table Atlas.1. The thermal conditions of both the Tibetan Plateau \r\nand related ocean regions play key roles in modulating the intensity of \r\nthe monsoon circulation. The East Asian monsoons are mainly driven \r\nby land-sea thermal contrast and, thus, are deeply affected by global \r\nclimate change (Ding et al., 2014; Gong et al., 2018)."


id: 53242ad44c434d332a5ef2255a8951f0
created_at: 2023-03-16 14:02:11
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"Summer (June-August) mean temperature in eastern China has \r\nincreased by 0.82degC since reliable observations were established \r\nin the 1950s (Sun et al., 2014). Based on historical meteorological \r\nobservations, the best estimate of the linear trend of annual mean \r\nsurface air temperature (SAT) for China with 95% uncertainty ranges \r\nis 0.38degC +- 0.05degC per decade for 1979-2015 (Li et al., 2017). From \r\n1960 to 2010, the increasing trend of temperature was about 0.34degC \r\nper decade in the arid region of north-west China, higher than the \r\naverage over China (B. Li et al., 2012; Xu et al., 2015). Over South \r\nKorea, warming is 1.4-2.6 times larger than global trends. The increase \r\nis 1.90degC during 1912-2014 and 0.99degC during 1973-2014 (Park \r\net al., 2017) with a 25-45% urbanization contribution. The annual \r\ntemperature increased in large cities at a rate of 0.29degC +- 0.08degC per \r\ndecade compared with 0.11degC +- 0"


id: 3dcf2f1eaa8c6fa91d3a0f8a902f69c8
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"08degC per decade in other stations \r\nin South Korea from 1960 to 2010 (H.-S. Kim et al., 2016). A relatively \r\nhigh increase in annual mean temperature at the rate of 3.0degC per \r\ncentury was detected in the Tokyo metropolitan area for the period \r\n1901-2015 (Matsumoto et al., 2017). Trends of annual temperature \r\nfor the period of 1961-2015 are shown in Figure Atlas.11. Most areas \r\nof East Asia have significant warming trends exceeding 0.1degC per \r\ndecade, and the strongest warming (0.3degC-0.4degC per decade) occurs \r\nin northern China."


id: 488cf84a448ccb4533e6392eca737f6e
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content:
"The findings of the IPCC AR5 (Christensen et al., 2013) stated that \r\nthe EASM and EAWM circulations have experienced an inter-decadal \r\nscale weakening since the 1970s, leading to a  warmer climate in \r\nwinter and enhanced mean precipitation along the Yangtze River \r\nValley (30degN) but deficient mean precipitation in northern China in \r\nsummer. Since the middle of the 20th century, it is likely that there has \r\nbeen an increasing trend in winter temperatures across much of Asia \r\n(Christensen et al., 2013). The numbers of cold days and nights have \r\ndecreased and the numbers of warm days and nights have increased \r\nover Asia (Hartmann et al., 2013). It is likely that there are decreasing \r\nnumbers of snowfall events where increased winter temperatures have \r\nbeen observed (Hartmann et al., 2013). The SRCCL reports a land-use\u0002change-induced cooling as large as -1.5degC in eastern China between \r\n1871 and 2007 (Hartmann et al., 2013)"


id: f7b1f6d1b32a4737fe91767cd26d4391
created_at: 2023-03-16 14:02:11
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content:
"1961 to 2014 (Zhou and Wang, 2017). The summer precipitation trends \r\nover eastern China display a dipole pattern, characterized by positive \r\nanomalies in central-eastern China along the Yangtze River Valley and \r\nnegative anomalies in north China since the 1950s (Section 8.3.2.4.2). \r\nThis pattern has changed with the enhanced rainfall in the Huaihe \r\nRiver Valley and decreased in the regions south of the middle and \r\nlower reaches of the Yangtze River Valley since the 2000s (Liu et al., \r\n2012; Zhao et  al., 2015). The climate in north-west China changed \r\nfrom 'warm-dry' to 'warm-wet' condition in the mid-1980s (Peng \r\nand Zhou, 2017; Wang et al., 2020), with an increased rate of annual \r\nprecipitation of about 3.7% per decade from 1961 to 2015 (P. Wu \r\net  al., 2019) and 11.2  mm per decade between 1960 and 2011 in \r\nnorthern Xinjiang (Xu et  al., 2015)"


id: 6262bd095f4172f6e9be8847bbeb7a63
created_at: 2023-03-16 14:02:11
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content:
" Mean rainfall and the number \r\nof rainy days during the Meiyu-Baiu-Changma period from June to \r\nSeptember have increased during 1973-2015 in Korea (Lee et  al., \r\n2017). The precipitation trend has caused a large increase in summer \r\nprecipitation at a rate of 40.6 +- 4.3 mm per decade, resulting in an \r\nincrease of annual precipitation of 27.7 +- 5.5  mm per decade in \r\nSouth Korea from 1960 to 2010 (H.-S. Kim et al., 2016). Precipitation \r\namounts exhibited a slight decrease at both the annual and seasonal \r\nscales in Japan for the period 1901-2012 (Duan et al., 2015)."


id: bacddcd2380890f683a7d9c16ae9f92e
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
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vector_id: 26927
content:
"Recent studies using CORDEX-EA models with resolution of about \r\n12-25 km showed that the RCMs produce relatively more detailed \r\nregional features of the temperature distribution compared with the \r\ndriving GCMs (Tang et al., 2016). Over China, RCMs provide more \r\nspatial details and in general reduce the biases of their driving GCMs, \r\nin particular in DJF (December-January-February) and over areas with \r\ncomplex topography (Wu and Gao, 2020). However, RCMs also show \r\nbiases in simulating East Asian precipitation and its variability (Park \r\net al., 2016; Zhou et al., 2016; Zou and Zhou, 2016), and do not always \r\nshow added value compared to the driving GCMs (Li et al., 2018b). \r\nFor example, by comparing inter-GCM and inter-RCM differences \r\naround the Japan archipelago, it was found that RCM  generate \r\nrelatively large differences in precipitation (Suzuki-Parker et  al., \r\n2018)"


id: b413aa5e4edd54a245f1d27382840f21
created_at: 2023-03-16 14:02:11
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content:
" The RCM multi-model ensemble produces superior simulation \r\ncompared to that of a single model (Jin et al., 2016; D.-L. Guo et al., \r\n2018). A comparative study of RCMs at different spatial resolutions \r\nshowed that with coarse resolution they present some limitations \r\nand high-resolution RCMs offer added value for several evaluation \r\nmetrics (Park et al., 2020)."


id: 6f73122ed9ae109c398d4bd10d91dfaa
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
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vector_id: 15163
content:
"The development of climate models provides a  solid basis for \r\nprojection of future monsoon changes under different global warming \r\nscenarios. Coupled model simulations indicate that East Asia and the \r\nTibetan Plateau will likely experience higher warming than the global \r\nmean conditions across all global warming levels (Figure Atlas.17) and \r\nwith the projected warming greater in ECA and TIB than EAS. Also, \r\nin the CMIP6 ensemble, the multi-model mean and 90th percentile \r\nwarming for a given period and emissions scenario are consistently \r\ngreater than in the CMIP5 ensemble. Larger warming magnitudes are \r\nprojected to occur in the southern, north-western, and north-eastern \r\nregions of China, parts of Mongolia, the Korean Peninsula, and Japan \r\nthan in other regions (Li et al., 2018a). Projections indicate winter \r\nincreases in SAT over the East Asian continent and in precipitation \r\nover the northern East Asian continent with 1.5degC and 2.0degC global \r\nwarming under the RCP4"


id: 7b07b523451a8000074b100b6756d342
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vector_id: 16949
content:
"Current climate models perform poorly in simulating the mean \r\nprecipitation in East Asia, including the phase of the northward \r\nprogression of the seasonal rainband (M. Zhang et al., 2018). Although \r\nthere has been an improvement in the simulation of mean states, \r\ninterannual variability and past climate changes in the progression \r\nfrom CMIP3 to CMIP5, some previously documented biases (such \r\nas the ridge position of the western North Pacific Subtropical High \r\nand the associated rainfall bias) are still evident in CMIP5 models \r\n(Sperber et al., 2013; Zhou et al., 2017). Most models capture the \r\nmain characteristics of the winter mean circulation over East Asia \r\nreasonably well, but they still suffer from difficulty in predicting the \r\ninterannual variability of the EAWM (Shin and Moon, 2018). Models \r\nhave improved from CMIP5 to CMIP6 for climatological temperature \r\nand EAWM (D. Jiang et al., 2020)"


id: 46f46b3e4dff0727076f1d0864ab9f5
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content:
" Some CMIP6 models also show \r\nimprovements in simulating the annual mean and interannual \r\nvariation of precipitation (Sellar et  al., 2019; Tatebe et  al., 2019; "


id: 2342f7e6144aceb2a3461b33a4d2ea66
created_at: 2023-03-16 14:02:11
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content:
"from CMIP5 models show that compared with the current summer \r\nclimate, both SAT and precipitation increase significantly over the \r\nEast Asian continent during the 1.5degC warming period (L. Chen et al., \r\n2019) and that the main mode of EASM precipitation changes from \r\ntripolar to dipolar (Wang et al., 2018). The increase in precipitable \r\nwater in the wet EASM region is only slightly greater than the global \r\naverage but the increase in precipitation is much greater (Z.  Li \r\net  al., 2019). The monsoon circulation in the lower troposphere is \r\nprojected to strengthen due to the enhanced thermal forcing by the \r\nTibetan Plateau (He et al., 2019; He and Zhou, 2020), which causes \r\nthe increased summer precipitation over the East Asian continent. \r\nPrecipitation over eastern China increases for almost all months \r\nunder global warming in projections from GCMs with different \r\nhorizontal resolutions (Kusunoki, 2018a)"


id: 651fcad7ef740696fa52e555284b219
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updated_at: 2023-03-16 14:02:11
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content:
" Also, under RCP scenarios, \r\nin the 21st century, mean precipitation is projected to increase (Kim \r\net al., 2020), especially in the late afternoons (Oh and Suh, 2018), over \r\nthe Korean Peninsula due to global warming and associated changes \r\nin EASM. Increase in JJA mean precipitation is projected in northern \r\nEast Asia consistently among the CMIP models, while northward \r\nmigration of early summer East Asian rainbands such as the Meiyu\u0002Baiu-Changma is delayed along with that of the mid-latitude westerly \r\njet in the future (Horinouchi et al., 2019). However, the geographical \r\ndistribution of precipitation change tends to depend more on the \r\ncumulus convection scheme (Ose, 2017) and horizontal resolution \r\nof models rather than on SST distributions. Under the RCP4.5 and \r\nthe RCP8.5 scenarios, the interannual variability in EASM rainfall \r\nis projected by the multi-model ensemble mean to increase in the \r\n21st  century (Ren et  al., 2017)"


id: 6a7c786f70874ff4260cf1d28e6f2751
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" Further studies show a  projected \r\nincrease in heavy rainfall together with increases in rainfall intensity \r\n(Endo et al., 2017). Multi-model intercomparison indicates significant \r\nuncertainties in future projections of climate change in East Asia, \r\nalthough precipitation increases consistently across models (Zhou \r\net al., 2017). Simulations under the RCP4.5 scenario project that the \r\nnumber of snow days will be reduced by the end of the 21st century \r\nrelative to 1986-2005, primarily owing to the decline of light snowfall \r\nevents. The total amount is projected to increase in north-western \r\nChina but decrease in the other sub-regions (Zhou et al., 2018)."


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"in the southern part of WSB and ESB (Cohen et al., 2014; Ippolitov \r\net al., 2014; Gruza et al., 2015; Kharyutkina et al., 2016; Overland \r\net al., 2016; Perevedentsev et al., 2017; Wegmann et al., 2018). These \r\ntemperature decreases were strongly correlated with significant \r\nwarming over the Barents-Kara Sea (greater than 2.5degC per decade \r\nduring 2003-2017) and sea ice loss, suggesting a causal link (Outten \r\nand Esau, 2012; Semenov et al., 2012; Overland et al., 2016; Semenov, \r\n2016; Wegmann et al., 2018; Meleshko et al., 2019; Susskind et al., \r\n2019), though recent studies (Blackport et  al., 2019; Clark and \r\nLee, 2019) have shown that both phenomena result from mid-latitude \r\ncirculation variability (see also Cross-Chapter Box 10.1). In addition, \r\nsignificant warming in the last decade has halved the cooling trend in \r\nsouthern WSB from -0.6degC per decade during 1976-2012 to -0.3degC \r\nper decade during 1976-2018 (high confidence) (Frolov et al"


id: 2373e52c9111073902494ad186b9dc7
created_at: 2023-03-16 14:02:11
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vector_id: 3028
content:
"The SRCCL identified aridification of the climate in southern East \r\nSiberia between 1976 and 2016 as causing an extension of the steppes \r\npolewards whilst climate change also extended the vegetation \r\nseason, increasing forest productivity in most of boreal Siberia, \r\nbut increasing risk of wildfire and tree mortality (Mirzabaev et al., \r\n2019). The SROCC noted the warming climate has caused permafrost \r\nthaw and loss of ground ice, and thus land subsidence and collapse, \r\ndisturbing ecosystems and human infrastructure. Permafrost stability, \r\nhydrology and vegetation were also impacted by recent extensive \r\nfires burning into the organic soil layer (Meredith et al., 2019). The \r\nSR1.5 noted that future, higher levels of warming lead to greater \r\nimpacts in key systems such as the Siberian ecosystems, identified \r\nas one of the threatened systems ('Reason for Concern 1 - RFC1'; \r\nHoegh-Guldberg et  al., 2018) with impacts at 2degC expected to be \r\ngreater than those at 1"


id: 6721da4c358974c1a15a0be5adbc502e
created_at: 2023-03-16 14:02:11
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index: climate_gpt_only_giec
vector_id: 13820
content:
"Annual surface air temperature and precipitation have very likely \r\nincreased and maximum snow depth has likely increased over most \r\nof North Asia since the mid-1970s. The highest warming has been \r\nfound in spring in ESB and RFE, strengthening from south to north \r\nwith linear trends of 0.8degC-1.2degC per decade over the 1976-2014 \r\nperiod (high confidence). A  temperature decrease was identified \r\njust in winter in the southern part of WSB and ESB as a  result of \r\nnatural variability, but halved from -0.6degC per decade in 1976-2012 \r\nto -0.3degC per decade for the longer 1976-2018 period due to \r\nrecent warmer winters (high confidence). Over North Asia annual \r\nprecipitation increases with estimated trends of 5-15 mm per decade \r\nin the 1976-2014 period have been recorded with an exception over \r\nthe Kamchatka and the Chukchi peninsulas, where decreases of up \r\nto -20 mm per decade in the same period have been found (medium \r\nconfidence)"


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content:
" Snow cover duration has very likely decreased over \r\nSiberia and increases in maximum snow depths of 1.8 cm, 1.1 cm \r\nand 4.6 cm per decade have been observed for WSB, ESB and RFE \r\nrespectively from 1976 to 2016 (limited evidence)."


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content:
"Recent IPCC reports assessed that it is very likely that the mean \r\nannual temperature over South Asia has increased during the \r\npast  century (Figure  2.21 in Hartmann et  al., 2013, Figure  24-2 in \r\nHijioka et al., 2014), and the frequency of cold (warm) days and nights \r\nhave decreased (increased) across most of Asia since about 1950 \r\n(Figure 2.32 in Hartmann et al., 2013). The AR5 assessed that there is \r\nhigh confidence that the large-scale patterns of surface temperature \r\nare generally well simulated by the CMIP5 models though with \r\nproblems in some regions, particularly at higher elevations over \r\nthe Himalayas (Flato et  al., 2013). CMIP5 models projected for \r\nthe 21st  century a  significant increase in temperature over South \r\nAsia (high confidence from robust evidence) and in projections of \r\nincreased summer monsoon precipitation (medium confidence) \r\n(Collins et al., 2013)"


id: add1c41578f35046bb890d301aa3d698
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 24886
content:
" The AR5 assessed there is high confidence that \r\nhigh-resolution regional downscaling, which generate results \r\ncomplementary to those from global climate models, adds value to \r\nthe simulation of spatial variations in climate in regions with highly \r\nvariable topography (e.g.,  distinct orography, coastlines), and for \r\nmesoscale phenomena and extremes (Flato et al., 2013)."


id: 10fb0ffa3ded829da36e98d055caeb74
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 146
content:
"There has been a noticeable declining trend in rainfall with monsoon \r\ndeficits occurring with higher frequency in different regions in \r\nSouth Asia (see also Section 8.3.2.4 on the South Asian monsoon). \r\nConcurrently, the frequency of heavy precipitation events has \r\nincreased over India, while the frequency of moderate rain events \r\nhas decreased since 1950 (high confidence) (Goswami et al., 2006; \r\nDash et  al., 2009; Christensen et  al., 2013; Krishnan et  al., 2016; \r\nKulkarni et al., 2017; Roxy et al., 2017). There is a considerable spread \r\nin the seasonal and annual mean precipitation climatology and \r\ninterannual variability among the different observed precipitation \r\ndatasets over India (Collins et  al., 2013; Prakash et  al., 2014; Kim \r\net  al., 2018; Ramarao et  al., 2019). Yet, the regions of agreement \r\namong datasets lend high confidence that there has been a decrease \r\nin mean rainfall over most parts of the eastern and central north \r\nregions of India (Singh et al"


id: 93bd6ef3b05546598f2a77f613d9bfc2
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
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vector_id: 20756
content:
", 2014; Roxy et al., 2015; Juneng et al., \r\n2016; Krishnan et al., 2016; Guhathakurta and Revadekar, 2017; Jin \r\nand Wang, 2017; Latif et al., 2017). A global modelling study with \r\nhigh resolution over South Asia (Sabin et  al., 2013) indicated that \r\na juxtaposition of regional land-use changes, anthropogenic-aerosol \r\nforcing and the rapid warming signal of the Equatorial Indian Ocean \r\nwas crucial to simulate the observed Indian summer monsoon \r\nweakening in recent decades (medium confidence)."


id: f66cf88be52232009db73716d6c55758
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 36281
content:
"Whilst simulations of Indian summer monsoon rainfall (ISMR) have \r\nimproved in CMIP5 compared to CMIP3 in terms of northward \r\npropagation, time for peak monsoon and withdrawal (Sperber et al., \r\n2013), they fail to simulate the trends in monsoon rainfall and the \r\npost-1950 weakening of monsoon circulation (Saha et al., 2014). This \r\nis partially attributed to the failure of coarse-resolution CMIP5 models \r\nto simulate fine-resolution processes such as orographic effects or \r\nland surface feedback, and problems in cloud parametrization result \r\nin an overestimation of convective precipitation fraction (M.S. Singh \r\net al., 2017). In CMIP6, a significant improvement is found in capturing \r\nthe monsoon spatio-temporal patterns over India, particularly in \r\nthe Western Ghats and north-eastern Himalayan foothills (Gusain \r\net  al., 2020). Over Pakistan the CMIP6 models simulate surface \r\ntemperature better in JJA than DJF (Karim et al., 2020)"


id: dc4d54ba8aa24fc287e95aeea32a9b3a
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 32165
content:
" The CMIP6 \r\nensemble underestimates annual mean temperature over all of \r\nSouth Asian with mixed results for precipitation (Almazroui et  al., \r\n2020b). The CMIP6 GCMs have a large cold bias in both mean annual \r\nmaximum and minimum temperatures in the complex Karakorum \r\nand Himalayan mountain ranges but exhibit warm biases in mean \r\nannual minimum temperature in most of the rest of South Asia."


id: 6b90a270939b2e9257bde6cc94c7defc
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 14542
content:
"Regional climate model (RCM) downscaling of CMIP5 models as part \r\nof CORDEX South Asia uses higher resolution (50 km) and improved \r\nsurface fields such as topography and coastlines to resolve better \r\nthe complexities of the monsoon and other hydrological processes \r\n(Giorgi et  al., 2009). The added value of their simulations, relative \r\nto the driving GCMs, presents a  complex picture. CORDEX RCMs \r\nbetter represent spatial patterns of temperature (Sanjay et  al., \r\n2017), the spatial features of precipitation distribution associated \r\nwith the Indian summer monsoon (Choudhary and Dimri, 2018), \r\nand the simulation of monsoon active- and break-phase composite \r\nprecipitation (Karmacharya et al., 2017b). The RCMs follow the driving \r\nGCMs in underestimating seasonal mean surface air temperature \r\nand overestimating spatial variability in precipitation"


id: 107e6b14147475379d1530f3cf71332a
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 78
content:
" They amplify \r\nCMIP5 cold biases over almost the entire region, including over the \r\nHKH region, Afghanistan and south-west Pakistan during winter \r\n(Iqbal et  al., 2017), and substantial cold biases of 6degC-10degC are \r\nfound over the Himalayan watersheds of the Indus basin (Nengker \r\net  al., 2018; Hasson et  al., 2019). Neither RCMs nor their driving \r\nCMIP5 GCMs reproduce well the region's precipitation climatology \r\n(Mishra, 2015). In addition, important characteristics of ISMR such \r\nas northward and eastward propagation, onset, seasonal rainfall \r\npatterns, intra-seasonal oscillations and patterns of extremes did \r\nnot show consistent improvement (S. Singh et al., 2017)"


id: ca2dacea4dd93173852221ffe51de4a0
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 29364
content:
" Also, these \r\nRCM simulations have not demonstrated added value in capturing \r\nthe observed changes in ISMR characteristics over recent decades, \r\nthough RegCM4 simulations at 25  km showed high accuracy in \r\ncapturing monsoon precipitation characteristics and atmospheric \r\ndynamics in historical simulations (Ashfaq et al., 2021)."


id: 5406695fcd4664fe061dbcf7638f5d6a
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 10683
content:
"With continued global warming and anticipated reductions in \r\nanthropogenic aerosol emissions in the future, CMIP5 models \r\nproject an increase in the mean and variability of summer monsoon \r\nprecipitation over India by the end of the 21st  century, together \r\nwith substantial increases in daily precipitation extremes (medium \r\nconfidence) (Krishnan et al., 2020), see also Section 8.4.2.4 on changes \r\nin the South Asian monsoon. The CMIP5 GCMs consistently project \r\nan increase in moisture transport over the Arabian Sea and Bay of \r\nBengal towards the end of the 21st century, an increase in moisture \r\nconvergence and consequent increases in monsoon rainfall over the \r\nIndo-Pakistan region which are higher under RCP8.5 than RCP4.5 \r\n(Srivastava and Delsole, 2014; Mei et  al., 2015; Latif et  al., 2018). \r\nOut of 20 CMIP5 GCMs, four showed an increase in magnitude and \r\nlengthening of the summer monsoon across India under RCP8.5"


id: 431f4cb14764fe06fcae673c263b5abe
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 7961
content:
" The \r\nintensity of both strong and weak monsoons is projected to increase \r\nduring the period 2051-2099 (Srivastava and Delsole, 2014)."


id: 2dc060dc3371c9375a89a8620f4522a3
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 4638
content:
"Evaluation of four global reanalysis products (ERA5 and ERA-Interim, \r\nJRA-55 and MERRA-2; Atlas.1.4.2) for snow depth and snow cover \r\nover TIB was performed against 33 in situ station observations, \r\nInteractive Multisensor Snow and Ice Mapping System (IMS) snow \r\ncover and a satellite microwave snow-depth dataset (Orsolini et al., \r\n2019). Most of the reanalyses showed a systematic overestimation. \r\nOnly ERA-Interim assimilated IMS snow cover at high altitudes, \r\nwhereas ERA5 did not and the excessive snowfall, snow depth and \r\nsnow cover in ERA5 was attributed to this difference. The analysis \r\nof annual maximum consecutive snow-covered days for the period \r\n1980-2018 over TIB using JRA-55 and passive microwave satellite \r\nobservations showed a decreasing trend in all time periods and in \r\nrecent snow seasons for MERRA-2 (Bian et al., 2020)"


id: 1d18152826e972e34677c80fdecb2300
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 2021
content:
" The uncertainty \r\nassessment of model physics in snow modelling over TIB using \r\nground-based observations and high-resolution snow cover satellite \r\nproducts from the Moderate Resolution Imaging Spectroradiometer \r\n(MODIS) and FengYun-3B suggests that errors can be overcome by \r\noptimizing parametrizations of the snow cover fraction rather than \r\noptimizing physics-scheme options (Y. Jiang et al., 2020)."


id: 63fe3a7c6171df9146d3f8cdac2a33f3
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 13302
content:
"Within the last decade, there has been an increasing number of studies \r\non climatic trends over South East Asia, carried out on a regional basis \r\n(Thirumalai et al., 2017; Cheong et al., 2018) or focused on specific \r\ncountries (Cinco et al., 2014; Villafuerte et al., 2014; Mayowa et al., \r\n2015; Villafuerte and Matsumoto, 2015; Guo et  al., 2017a; Supari \r\net  al.,  2017; Sa'adi et  al., 2019; Tan et  al., 2021). They document \r\nvirtually certain significant increases in mean as well as extreme \r\ntemperature. The minimum temperature extremes very likely warmed \r\nfaster compared to the maximum temperature. Temperatures, including \r\nextremes, are strongly influenced by ENSO in the region (Cinco et al., \r\n2014; Thirumalai et al., 2017; Cheong et al., 2018). Over much of the \r\nregion, extreme high temperatures occurred mostly in April and almost \r\nall April extreme temperatures occur in El Nino years (Thirumalai et al., \r\n2017)"


id: 1e5d8096b9578137d484e1d149856f78
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 2217
content:
" In most of South East Asia (except for the north-eastern areas), \r\nthere was likely an increase in the number of warm nights with El Nino \r\nepisodes within the period 1972-2010 (Cheong et al., 2018)."


id: 7a6a2371bbc4d3eda5d7096f9174a2b2
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 16830
content:
"physics (Manomaiphiboon et al., 2013; Kwan et al., 2014) and/or the \r\nbiases in the SST forcing (Ngo-Duc et al., 2014). A few simulations \r\nrevealed a  warm bias over some areas such as in the Maritime \r\nContinent (Cruz et  al., 2017) or Vietnam (Van Khiem et al., 2014). \r\nThe biases for rainfall in GCMs and RCMs over South East Asia were \r\nfound to be less systematic with wet or dry biases depending on \r\nthe sub-regions (Manomaiphiboon et al., 2013; Kwan et al., 2014; \r\nVan Khiem et  al., 2014; Juneng et  al., 2016; Supari et  al., 2020; \r\nTangang et al., 2020; Nguyen-Thi et al., 2021), although wet biases \r\nwere more pronounced in RCMs (Kwan et al., 2014; Van Khiem et al., \r\n2014; Kirono et  al., 2015; Juneng et  al., 2016; Supari et  al., 2020; \r\nTangang et al., 2020). Some RCMs overestimated rainfall interannual \r\nvariability (Juneng et al., 2016) while some others underestimated \r\nit (Kirono et al., 2015)"


id: 7af91e755ebabc9e2bed2577f51cd4b3
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 16937
content:
" Simulated rainfall amount is sensitive to the \r\nchoice of convective scheme (Juneng et  al., 2016; Ngo-Duc et  al., \r\n2017) and the choice of land surface scheme (Chung et al., 2018). \r\nRainfall biases in current climate simulations can be greatly reduced \r\nif a  bias adjustment method such as quantile mapping is applied \r\n(Trinh-Tuan et al., 2018). The pattern of tropical cyclone numbers in \r\nthe region were reasonable represented by RCM outputs (Van Khiem \r\net al., 2014; Kieu-Thi et al., 2016; Herrmann et al., 2020)."


id: c800c90265aba5e0c2fdb6e6f73790e3
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 29046
content:
"Mean temperature in South East Asia is projected to continue to rise \r\nthrough the 21st  century (virtually certain, very high confidence). \r\nProjections by multi-model regional climate simulations of \r\nCORDEX-SEA showed a temperature increment over land under RCP8.5 \r\nto range from 3degC-5degC by the end of the 21st century relative to the \r\npre-1986-2005 period (Tangang et al., 2018). For the same periods, \r\nthe average mean temperature increase over land projected by CMIP5 \r\n(CMIP6) varies, with 10th-90th percentile ranges, from 0.7degC to 1.3degC \r\n(0.7degC to 1.8degC) under RCP2.6 (SSP1-2.6) to 2.8degC to 4.4degC (2.6degC to \r\n4.8degC) under RCP8.5 (SSP5-8.5) (Interactive Atlas). For all GWLs the \r\nland region is projected to warm by a slightly smaller amount than the \r\nglobal average, with 10th-90th percentile ranges for CMIP5 (CMIP6) \r\nof 1.2degC-1.6degC (1.2degC-1.5degC) for the 1.5degC GWL and of 3.3degC-4.0degC \r\n(3.3degC-3"


id: 17b171b1a43e8213645c0373c1997bef
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 1224
content:
"9degC) for the 4degC GWL relative to the 1850-1900 baseline \r\n(calculated from RCP8.5 (SSP5-8.5) projections). Changes for other \r\nwarming levels, periods and emissions pathways are shown in Figure \r\nAtlas.17 and can be explored in the Interactive Atlas."


id: b8f097cd32e1bfde0c11aa4962496971
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 26632
content:
"RCMs have been intensively used over the region in recent years in \r\na series of single or multi-model experiments and there is medium \r\nconfidence that they reproduce reasonably well seasonal climate \r\npatterns of temperature, precipitation and large-scale circulation \r\nover the different sub-regions of South East Asia with added values \r\ncompared to their host GCMs (Kwan et  al., 2014; Ngo-Duc et  al., \r\n2014, 2017; Van Khiem et  al., 2014; Juneng et  al., 2016; Katzfey \r\net al., 2016; Loh et al., 2016; Raghavan et al., 2016; Cruz et al., 2017; \r\nRatna et al., 2017; Trinh-Tuan et al., 2018; Nguyen-Thuy et al., 2021). \r\nRCM ensemble means tend to outperform the individual models in \r\nrepresenting the climatological mean state (Ngo-Duc et  al., 2014; \r\nTrinh-Tuan et al., 2018; Nguyen-Thi et al., 2021)"


id: 21cadeafc9907da81fda5b8429451f9b
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 2750
content:
" There is relatively \r\nhigh consistency among the simulations of historical climate over \r\nmainland South East Asia compared to those over the Maritime \r\nContinent for both seasonal and interannual variability (Ngo-Duc \r\net al., 2017). The consistency in rainfall simulations was lower than \r\nfor temperature simulations."


id: 4b41d36d2373d9ab54a2e4b3e8eebf7b
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 9255
content:
"Projections of future rainfall changes are highly variable among \r\nsub-regions of South East Asia and among the models (high \r\nconfidence). The CMIP5 and CMIP6 ensembles showed an increase \r\nin annual mean precipitation over most land areas by the mid- and \r\nlate 21st century, although only with a strong model agreement for \r\nhigher warming levels (Figure Atlas.17 and the Interactive Atlas), \r\nwhile CORDEX produces a general decrease in projected precipitation \r\n(Figure Atlas.17). Based on CORDEX South East Asia multi-model \r\nsimulations, significant and robust increases of mean rainfall over \r\nIndochina and the Philippines were projected while there is a drying \r\ntendency over the Maritime Continent during DJF for the early, mid \r\nand end of the 21st century periods under both RCP4.5 and RCP8.5 \r\n(Figure Atlas.19; Tangang et al., 2020). At the end of the 21st century \r\nduring DJF and under RCP8"


id: 1e87d9383275180c760538a8d7ce1796
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 2245
content:
"South West Asia includes the Arabian Peninsula (ARP) and West \r\nCentral Asia (WCA) reference regions (Figure Atlas.17). ARP has \r\na  semi-arid or arid desert climate with very low annual mean \r\nprecipitation and very high temperature. Its temperature is influenced \r\nby SST variations over the tropical ocean (e.g., ENSO) and the NAO \r\nand AO (see Annex IV for these and subsequent modes of variability; \r\nAttada et al., 2019). Rainfall is influenced by the IOD and ENSO, with \r\nmore rainfall during El Nino (Kang et al., 2015; Kumar et al., 2015; \r\nAbid et al., 2018; Kamil et al., 2019) and less during La Nina (Atif \r\net  al., 2020). The wet season in ARP is mainly from November to \r\nApril and the dry season is from June to August. Rainfall is confined \r\nmostly to the south-western part of the peninsula and contribution \r\nof extreme events to the total rainfall varies within 20-70% from \r\nregion to region and season to season (Almazroui, 2020b; Almazroui \r\nand Saeed, 2020)"


id: 398a6d100c0c3f41c8a548844cbd6374
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 6429
content:
" WCA is separated from Eastern Europe by the \r\nCaucasus Mountains, is adjacent to ARP, with South Asia (SAS) to \r\nthe south and West Siberia (WSB) to the north, and lies between \r\nthe Mediterranean (MED), Tibetan Plateau (TIB) and East Central \r\nAsia (ECA) regions. WCA is heterogeneous in terrain with the Zagros \r\nMountains and Iranian Plateau in the west and south-west, the \r\nCaspian Sea and lowland with deserts in the north and north-east. \r\nThe regional climate of WCA is influenced by the NAO and ENSO \r\nand it is typically semi-arid or arid with a strong gradient in both \r\nprecipitation and temperature from the mountains to the plains and \r\nfrom north to south."


id: e23b7a287f831eb3c4d4060ad03f1b0e
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
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index: climate_gpt_only_giec
vector_id: 33088
content:
"The SR1.5 stated that even for 1.5degC and 2degC of global warming, South \r\nWest Asia is among the regions with the strongest projected increase \r\nin hot extremes with more urban populations exposed to severe \r\ndroughts in West Asia, while an increase of heavy precipitation events \r\nis projected in mountainous regions of Central Asia (Hoegh-Guldberg \r\net al., 2018; IPCC, 2018c). Higher temperatures with less precipitation \r\nwill likely result in higher risks of desertification, wildfires and dust \r\nstorms exacerbated by land-use and land-cover changes in the region \r\nwith consequent effects on human health. Further drying of the Aral \r\nSea in Central Asia will likely have negative effects on the regional \r\nmicroclimate, adding to the growing wind erosion in adjacent deltaic \r\nareas and deserts that is already resulting in a  reduction of the \r\nvegetation productivity including croplands"


id: 55991fac67e9be3259930a1fe55c3648
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 10948
content:
" There is also a projected \r\nincrease of precipitation intensity in the Arabian Peninsula which is \r\nlikely to lead to higher soil erosion particularly in winter and spring \r\ndue to floods (Mirzabaev et al., 2019). WCA includes high mountains \r\nwith enhanced warming above 500 m where, regardless of the \r\nemissions scenario, decreases in snow cover are projected due to \r\nincreased winter snowmelt and more precipitation falling as rain \r\n(high confidence). A very strong interannual and decadal variability, \r\nas well as scarce in situ records for mountain snow cover, have \r\nprevented a quantification of recent trends in High Mountain Asia \r\n(Hock et al., 2019b)."


id: 251baa597c64f1b08fabaf1083fb5000
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 3277
content:
"An overall increasing trend of annual precipitation (0.66 mm per \r\ndecade) was found over Central Asia based on GPCC v7 data \r\nfor the period 1901-2013 (Hu et  al., 2017), but annual trends \r\nwere found not significant over the shorter period 1960-2013 \r\n(Figure Atlas.11 and Interactive Atlas). Winter precipitation saw \r\na significant increase of 1.1 mm per decade (Song and Bai, 2016). \r\nThese estimates have low to medium confidence since the satellite \r\nprecipitation products have large systematic and random errors \r\nin mountainous regions. Moreover CMORPH and TRMM products \r\nfail to capture the precipitation events in the ice/snow covered \r\nregions in winter and show a  substantial false-alarm percentage \r\nin summer, but the gauge-corrected GSMAP performs better than \r\nother products (Song and Bai, 2016; Guo et al., 2017b; Hu et al., \r\n2017; S. Chen et al., 2019). Over the elevated part of eastern WCA \r\nprecipitation increases in the range of 1.3-4"


id: 47c353d7ce7a5709a7762eec199822a4
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 8668
content:
"8  mm per decade \r\nduring 1960-2013 were observed (very high confidence) (Han \r\nand Yang, 2013; Li et al., 2013; Hu et al., 2014, 2017; Huang et al., \r\n2014; Deng and Chen, 2017; Zhang et  al., 2017, 2019a; H.  Guo \r\net  al., 2018; Haag et  al., 2019; Yu et  al., 2019). Reductions in \r\nspring precipitation and increases in winter have been reported \r\nfor Uzbekistan over the period 1979-2013 based on station data \r\nbut these are not significant (Khaydarov and Gerlitz, 2019). There \r\nis very low confidence of the impact of the Aral Sea shrinking on \r\nprecipitation (Chen et al., 2011; Jin et al., 2017)."


id: 18192e70532b9ff69444e926d1e5c5f
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
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vector_id: 1283
content:
"In WCA, projections for different GWLs are consistent not only in \r\nannual and seasonal warming but in the ranges of the projections. \r\nUnder RCP8.5, annual mean temperature will likely exceed 2degC by \r\nmid-century (compared with 1995-2014) and reach up to 4.8degC-6degC \r\nby the end of the century (Yang et al., 2017), with faster warming \r\nprojected by the CMIP6 ensemble under SSP5-8.5. In individual \r\ncounty-level studies on GCM future climate projections, temperatures \r\nincreased by up to 7degC by the end of the century, depending on \r\nseason and emissions scenario (Allaberdiyev, 2010; MENRPG, 2015; \r\nMNP, 2015; Gevorgyan et al., 2016; Osborn et al., 2016; Aalto et al., \r\n2017; IDOE, 2017; Salman et  al., 2017). Statistical downscaling of \r\n18 CMIP5 GCMs projected an annual temperature increase of 0.37degC \r\nper decade (under RCP4.5) with the maximum in northern WCA \r\nand warming most conspicuous in summer (Luo et al., 2019)"


id: 4a97d4f6c1cc7e25ffd8eaaf7b149d28
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
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index: climate_gpt_only_giec
vector_id: 9138
content:
" RCM \r\ndownscaling of GCMs over Central Asia projected a larger increase of \r\ntemperature under RCP8.5 for the 2071-2100 period, ranging from \r\n5degC to 8degC (Ozturk et al., 2017)."


id: d077f03f2a6c090e114a7191ebc9db9f
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 30326
content:
"RCM simulations using the CORDEX-MENA domain reproduce \r\nthe main features of the mean surface climatology over ARP with \r\nmoderate biases (high confidence). RegCM4 driven by five GCMs \r\n(HadGEM2, GFDL, CNRM, CanESM2 and ECHAM6) showed an \r\nensemble-mean cold bias of about -0.7degC and a dry bias of -13% \r\nover ARP (Almazroui, 2016) with a  cold (warm) bias over western \r\n(south-eastern) areas (Syed et  al., 2019). Temperature biases in \r\n30-year historical simulations with WRF using three different radiation \r\nparametrizations were within +-2degC and mostly caused by surface \r\nlong-wave radiation errors which affected nighttime minimum \r\ntemperatures over 70% of the domain (Zittis and Hadjinicolaou, \r\n2017). Mean absolute errors in COSMO-CLM driven by ERA-Interim \r\nwere about 1"


id: 5112d91641653317443aeed94c24ecb
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
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vector_id: 10202
content:
"2degC for temperature, 15 mm per month for precipitation \r\nand 9% for total cloud cover, and with new parametrizations of \r\nalbedo and aerosols optimized for the region the RCM simulated the \r\nmain climate features of this very complex area (Bucchignani et al., \r\n2016). RegCM4.4 also simulated the main features of the observed \r\nclimatology (especially for dry regions) with temperature biases \r\nwithin +-3.0degC. Annual precipitation was overestimated with winter \r\nand spring underestimated (Ozturk et al., 2018)."


id: 11ae0ae5776a89532056afc3d63c2fc6
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 273
content:
"Future projections of precipitation in South West Asia have large \r\nuncertainties and thus low confidence. There are few significant \r\nchanges, little consensus on the sign and with a  tendency for \r\nreduction in CMIP5 being reversed in CMIP6 across all warming \r\nlevels (Ozturk et  al., 2018). Statistical downscaling of 18 CMIP5 \r\nGCMs under RCP4.5 projected an increase in precipitation of \r\n4.6 mm per decade in South West Asia during 2021-2060 relative \r\nto 1965-2004 (Luo et  al., 2019). CMIP5 simulations project \r\na general decrease in precipitation over lowlands in Turkey, Iran, \r\nAfghanistan and Pakistan (Ozturk et  al., 2017), and an increase \r\nover high-mountain regions (Aalto et  al., 2017; Salman et  al., \r\n2018). At a  4degC global warming level, the multi-model mean \r\nannual precipitation for Turkmenistan and parts of Tajikistan and \r\nUzbekistan is projected to decrease by 20%, with somewhat \r\nstronger relative decreases in summer (Reyer et  al., 2017)"


id: e60a6e1150436855af1ec79c2ed8b383
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 33682
content:
" Over \r\nnorthern WCA, the CMIP5 ensemble mean projects increases of \r\nover 3 mm per decade under RCP2.6 and over 6 mm per decade \r\nunder RCP4.5 and RCP8.5 over the 21st  century (Huang et  al., \r\n2014). Mean annual precipitation is projected to rise by 5.2% at the \r\nend of the 21st century (2070-2099) under RCP8.5, compared to \r\n1976-2005, while mean annual snowfall is projected to decrease "


id: daf02b6833de925eeba465d5233855a1
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 31966
content:
"Australasia is divided into five regions for the Atlas (Figure Atlas.21), \r\nas follows: New Zealand (NZ), with a  varied climate with diverse \r\nlandscapes, mainly maritime temperate with four distinct seasons; \r\nNorthern Australia (NAU), which is mainly tropical with monsoonal \r\nsummer-dominated rainfall (monsoon season December to March, \r\nsee Annex V), but with a hot, semi-arid climate in the south of the \r\nregion; Central Australia (CAU) with a predominantly hot, dry desert \r\nclimate; Eastern Australia (EAU) with a temperate oceanic climate at \r\nthe coast to semi-arid inland; and Southern Australia (SAU), which \r\nranges from Mediterranean and semi-arid in the west to mainly cool \r\ntemperate maritime climate in the south-east. Various remote drivers \r\nhave notable teleconnections to regions within Australasia, including \r\nan effect of the El Nino-Southern Oscillation (ENSO) and the Indian \r\nOcean Dipole (Table Atlas.1 and Annex IV)"


id: 6c821dd10766dad7ead3acee63c927b
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
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vector_id: 14696
content:
" Much of southern NZ \r\nand SAU are affected by systems within the westerly mid-latitude \r\ncirculation, in turn affected by the Southern Annular Mode (SAM). \r\nThe monsoon and the Madden-Julian Oscillation (MJO) affect rainfall \r\nvariability in northern Australia."


id: ae3d67a617a355563a89af3e2680fe31
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 24944
content:
"The AR5 WGI and WGII reports (IPCC, 2013c; Stocker et al., 2013; \r\nReisinger et al., 2014) give very high confidence that air and sea \r\ntemperatures in the region have warmed; cool extremes have \r\nbecome rarer in Australia and New Zealand since 1950, while hot \r\nextremes have become more frequent and intense (e.g., it is very \r\nlikely that the number of warm days and nights have increased). \r\nThe AR5 reported that it is virtually certain that mean air and sea \r\ntemperatures will continue to increase, with very high confidence\r\nthat the greatest increase will be experienced by inland Australia \r\nand the smallest increase by coastal areas and New Zealand. \r\nThe AR5 reported a range of different precipitation trends within \r\nthe region"


id: cf5fa74415feda2d9da3ea9168b7bf20
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
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vector_id: 30158
content:
" For example, while annual rainfall has been significantly \r\nincreasing in north-western Australia since the 1950s (very high \r\nconfidence), it has been decreasing in the north-east of the South \r\nIsland of New Zealand over 1950-2004 (very high confidence) and \r\nover the south-west of the state of Western Australia. In line with \r\nthese trends, AR5 reported it is likely that drought has decreased in \r\nnorth-west Australia. Future projections for precipitation extremes \r\nindicate an increase in most of Australia and New Zealand, in \r\nterms of rare daily rainfall extremes (i.e., current 20-year return "


id: f1432c5e8440fbedb77f9f9d7d2ecbd5
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 35460
content:
"For a  longer-term perspective based on high-quality regional \r\ndatasets, Figure Atlas.20 shows Australasia has warmed over the \r\nlast century (very high confidence). Australian mean temperature \r\nhas increased by 1.44degC +- 0.24degC during the period 1910-2019 \r\nusing the updated observed temperature dataset ACORN-SATv2.1, \r\nwith 2019 Australia's hottest year on record and nine out of 10 of \r\nthe warmest years on record occurring since 2005 (Trewin et al., \r\n2020). Much of the warming has occurred since 1960, there is \r\nclear anthropogenic attribution of this change and emergence of \r\nthe signal from the1850-1900 climate (BOM and CSIRO, 2020; \r\nHawkins et  al., 2020). Warming has been more rapid than the \r\nnational average in central and eastern Australia, with a warming \r\nminimum and non-significant trends since the 1960s in the north\u0002west (CSIRO and BOM, 2015; BOM and CSIRO, 2020)"


id: a25aa6e0c6a72a8758d6f21659653fd4
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 23107
content:
" The National \r\nInstitute of Water and Atmospheric Research temperature record, \r\nNIWA NZ, shows a warming of 1.13degC +- 0.27degC during the period \r\n1909-2019, although several stations show non-significant trends \r\nsince 1960 (Figure Atlas.20), including a warming minimum in the \r\nsouth-east at least partly due to a persistent shift in atmospheric \r\ncirculation (Sturman and Quenol, 2013; MfE and Stats NZ, \r\n2017, 2020)."


id: fc1daf44b8fd5cf1721426e6c877283c
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
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vector_id: 37166
content:
"The AR5 reported mean sea levels have also increased in Australia \r\nand New Zealand at average rates of relative sea level rise of \r\n1.4 +- 0.6 mm yr-1 from 1900 to 2011, and 1.7 +- 0.1 mm yr-1 from \r\n1900 to 2009, respectively (very high confidence). The assessment \r\nfound that the volume of ice in New Zealand has declined by 36-61% \r\nfrom the mid- to late 1800s to the late 1900s (high confidence), while \r\nlate-season significant snow depth has also declined in three out of \r\nfour Snowy Mountain sites in Australia between 1957 and 2002 \r\n(high confidence). As mean sea level rise is projected to continue \r\nfor at least several more centuries, there is very high confidence that \r\nthis will lead to large increases in the frequency of extreme sea level \r\nevents in Australia and New Zealand"


id: cebb5d4aea4aa974c3d878afbd9f3bec
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
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vector_id: 30064
content:
" On the other hand, the volume \r\nof winter snow and the number of days with low-elevation snow \r\ncover in New Zealand are projected to decrease in the future (very \r\nhigh confidence), while both snow depth and area are projected to \r\ndecline in Australia (very high confidence)."


id: 8c20fae1a1a6ae5356aa157928ad98ae
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 19555
content:
"Since 1960, precipitation has increased in much of mainland Australia \r\nin austral summer and decreased in many regions of southern and \r\neastern Australia in austral winter (Figure Atlas.20). A  detectable \r\nanthropogenic signal of increases in precipitation in Australia has \r\nbeen reported particularly for north central Australia and for a few \r\nregions along the south-central coast for the period 1901-2010 \r\n(Knutson and Zeng, 2018). Seasonally, there is a significant decline \r\nin winter rainfall in the south-west of the state of Western Australia \r\n(Figure Atlas.20), with an attributable human influence (high \r\nconfidence, robust evidence, medium agreement) (Section 10.4 and \r\nreferences therein, e.g.,  Delworth and Zeng, 2014). Rainfall trends \r\nin the south-east are not significant since 1960 but have shown \r\na notable reduction since the 1990s, and there is limited evidence\r\nfor the attribution of this change to human influence (e.g., Rauniyar \r\nand Power, 2020)"


id: ebea97d3a43840b159ddad1650280e3e
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 34617
content:
" In New Zealand between 1960 and 2019 in both \r\nsummer and winter, rainfall increased in some stations in the South \r\nIsland and decreased at many stations in the North Island, however \r\nmost station trends are not statistically significant (Figure Atlas.20; \r\nMfE and Stats NZ, 2020). In JJA, Milford Sound (increasing) and \r\nWhangaparaoa (decreasing) trends are significant."


id: 7c498af692edaba3c5b658a5db2ed3fc
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 17153
content:
"In line with recent trends, a  significant reduction in annual mean \r\nrainfall in south-west Australia is projected, with the greatest reduction \r\nin winter and spring (very likely, high confidence). There is more than \r\n80% model agreement for projected mean annual rainfall decrease \r\nin the south-west of the state of Western Australia for both the mid- \r\n(2041-2060) and far (2081-2100) future, and for all warming levels \r\n(Interactive Atlas). Rainfall decreases, mainly in winter and spring, \r\nare also projected for other regions within southern Australia with \r\nonly medium confidence (medium evidence, medium agreement). \r\nAlmost all models project continued drying in SAU in winter (JJA) \r\nand also in spring (SON), but a few models show little change. CMIP5 \r\nand CMIP6 results are similar or with a slightly narrower range in the \r\nlatter (Figure Atlas.21). CORDEX produces a similar range of change \r\nin winter rainfall change for SAU as a whole"


id: 8a09713f81863b70659566256b9a2642
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 19246
content:
" Circulation change is \r\nthe dominant driver of these projected reductions, explaining the \r\nrange of model results for southern Australia (CSIRO and BOM, 2015; \r\nMindlin et al., 2020). Studies of winter rainfall change and circulation \r\nin southern Australia suggest the wettest changes in winter rainfall \r\nchange may possibly be rejected (Grose et al., 2017, 2019a)."


id: 8bac264f835520ea45604ad2af0066f8
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
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index: climate_gpt_only_giec
vector_id: 19497
content:
"The CORDEX Australasia simulations produce some regional detail \r\nin projected precipitation change associated with important features \r\nsuch as orography. Areas where there is coincident 'added value' in \r\nthe simulation of the current climate and 'potential added value' as \r\nnew information in the projected climate change signal (collectively \r\ntermed 'realized added value') in Australia include the Australian \r\nAlps, Tasmania and parts of northern Australia (Di Virgilio et  al., \r\n2020). There have been several studies of regional climate change for \r\nNew Zealand and states within Australia at fine resolution (5-12 km) \r\nthat have produced important insights. One is enhanced drying in \r\ncool seasons on the windward slopes of the southern Australian Alps \r\n(decreases of 20-30% compared to 10-15% in the driving models), \r\nand conversely a chance of enhanced rainfall increase on the peaks \r\nof mountains in summer (Grose et  al"


id: 6fb40d278a3e570e90b883c16a928ad7
created_at: 2023-03-16 14:02:11
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vector_id: 15197
content:
", 2019b), with the summer \r\nfinding in line with those for the European Alps (Giorgi et al., 2016)."


id: bb076ef43618af436003e873654322a7
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 26969
content:
"The model mean projection of northern Australian wet-season \r\nprecipitation (a period including DJF) is for little change under all \r\nSSPs and warming levels, with low confidence in the direction of \r\nchange as the projections include both large and significant decrease \r\nand increases (Figure Atlas.21 and Interactive Atlas). Evidence \r\nfrom warming patterns suggests a  constraint on the dry end of \r\nprojections (Brown et al., 2016), and the CMIP6 ensemble suggests \r\nthat the projection follows the zonally averaged rainfall response in \r\nthe Southern Hemisphere rather than changes in the western Pacific \r\n(Narsey et al., 2020). There is also evidence for a projected increase \r\nin rainfall variability in northern Australia in scales from days to \r\ndecades (Brown et al., 2017). Liu et al. (2018) find that under 1.5degC \r\nwarming, central and north-east Australia are projected to become \r\nwetter, however this projection has low confidence"


id: e9308fdefd22c90771b711fe4025e264
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 34206
content:
"Pool and the Caribbean low-level jet (CLLJ), while the Atlantic ITCZ is \r\nresponsible for the unimodal rainfall cycle of the central and southern \r\nLesser Antilles (Martinez et  al., 2019). The CLLJ is a  persistent \r\nclimatological feature of the low-level circulation in the Central \r\nCaribbean, with a characteristic semi-annual cycle with maxima in \r\nthe summer (main) and winter (secondary) (Amador, 1998; Magana \r\net al., 1999; Whyte et al., 2008). Temporal variability is influenced by \r\nseveral large-scale atmospheric modes (Annex IV and Table Atlas.1). \r\nA significant positive correlation between precipitation rates in CAR \r\nand the Atlantic Multi-decadal Variability (AMV) was found (Enfield \r\net al., 2001). A similar result was found in southern Mexico (north of \r\nSCA) in the MSD region (see case-study discussion in Section 10.4.2.3; \r\nMendez and Magana, 2010; Cavazos et al., 2020)"


id: 399e46da016c7ee142e06c891234d100
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
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index: climate_gpt_only_giec
vector_id: 6442
content:
" On the other hand, \r\nENSO favours wet conditions in NCA, but its effect is modulated by \r\nPacific Decadal Variability (PDV; Maldonado et al., 2016)."


id: b912f9c2aa6aff93cebd3c89ee7aa3c5
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
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index: climate_gpt_only_giec
vector_id: 26652
content:
"Precipitation in most of SCA is characterized by two maxima in \r\nJune and September, an extended dry season from November to \r\nMay, and a shorter relatively dry season between July and August \r\nknown as the midsummer drought (MSD; Chapter 10; Magana et al., \r\n1999; Perdigon-Morales et al., 2018). To some extent, precipitation \r\nseasonality is explained by the migration of the Inter-tropical \r\nConvergence Zone (ITCZ) (Taylor and Alfaro, 2005). The climate of \r\nNCA is temperate to the north of the Tropic of Cancer, with a marked \r\ndifference between winter and summer, modulated by the North \r\nAmerican Monsoon (NAmerM, Section  8.3.2.4.4). The CAR region \r\nhas two main seasons, characterized by differences in temperature \r\nand precipitation. The wet or rainy season, with higher values of \r\ntemperature and accumulated precipitation, occurs during the boreal \r\nsummer and part of spring and autumn (Gouirand et  al., 2020)"


id: 5c0eaff7933d5c740fb3c7583c8f7dd2
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
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index: climate_gpt_only_giec
vector_id: 11991
content:
"1.6degC (SCA). For the 3degC GWL, the corresponding projected ensemble \r\nmean regional warming values are 2.7degC (CAR), 3.5degC (NCA) and \r\n3.1degC (SCA). CAR average annual warming is below the level of \r\nglobal warming, while the two continental reference regions are \r\nclose to the global warming level with CMIP6 and CMIP5 showing \r\nvery consistent results (Figure Atlas.22). However, when focusing on \r\ntime slices instead of warming levels, the CMIP6 projections show \r\nsystematically higher median values than CMIP5. CORDEX results \r\nare also consistent with the previous findings, though the subset of \r\ndriving models spans a smaller range of uncertainty, particularly over \r\nCAR. Results have also been reported for this region based on CMIP5, \r\nCMIP6 and downscaled simulations over the CORDEX CAM domain or \r\nsimilar smaller domains (Taylor et al., 2013b; Nakaegawa et al., 2014; \r\nImbach et al., 2018; Vichot-Llano et al., 2019; Almazroui et al., 2021)"


id: d4165e330b40c627879ca7eae32de826
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updated_at: 2023-03-16 14:02:11
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index: climate_gpt_only_giec
vector_id: 30887
content:
" \r\nStatistical downscaling methods have been also applied to CMIP5 \r\nprojections to obtain bias-adjusted regional projections (Colorado\u0002Ruiz et al., 2018; Taylor et al., 2018; Vichot-Llano et al., 2019)."


id: a2d2bcbfdf587bb30d11bc7c89407d17
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 23184
content:
"Regarding precipitation, it is likely that the annual average \r\nprecipitation changes for the 1.5degC GWL will be in the ranges of -11 \r\nto 0% in CAR, from -12 to 0% in SCA, and from -10 to +3% in \r\nNCA (Interactive Atlas). For the 3degC GWL, the corresponding annual \r\naverage precipitation changes will be from -17 to -2% in CAR, from \r\n-16 to +2% in NCA, and from -23 to 0% in SCA. A  clear drying \r\ntendency is observed for the 3degC GWL relative to the 1.5degC GWL. \r\nMaloney et al. (2014) examined 21st-century climate projections of \r\nNorth American climate in CMIP5 models under RCP8.5, including \r\nCentral America and the Caribbean. Summer drying was projected \r\nin CAR and SCA for most of the models, with good agreement. \r\nThe strongest drying is projected to occur during July and August \r\nwhich are the months when the MSD occurs in many sub-regions \r\n(Figure  Atlas.22 and the Interactive Atlas)"


id: 3ab282700808958d75a1416eae810604
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 6605
content:
" Intensification of the \r\nMSD in SCA was also projected by using the Rossby Centre Regional \r\nClimate Model (RCA4; Corrales-Suastegui et  al., 2020), but with \r\na future decrease in area and frequency (Cross-Chapter Box Atlas.2). \r\nThey also found a  projected intensification of CLLJ and drying for \r\nthe future time slice of 2071-2095, relative to their baseline of \r\n1981-2005. Decreased precipitation was also projected for SCA \r\n(Imbach et al., 2018) with the 8-km resolution Eta RCM during the \r\nrainy season, including an intensification of the MSD, although no \r\nsignificant change was projected for the CLLJ."


id: de38d868d74c0bd615cff3e470bc5c3c
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
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index: climate_gpt_only_giec
vector_id: 32452
content:
"Regional climate models (RCMs) forced with reanalyses and \r\natmosphere-only global climate models provide simulations with \r\na  reasonably good performance over the core North American \r\nMonsoon region, mostly in NCA (high confidence) (Bukovsky \r\net  al., 2013; Cerezo-Mota et  al., 2016). RCMs also reproduce the \r\nseasonal spatial patterns of temperature and the bimodal rainfall \r\ncharacteristics of the NCA, SCA and CAR (high confidence) (Karmalkar \r\net al., 2013; Centella-Artola et al., 2015; Martinez-Castro et al., 2018; \r\nCavazos et  al., 2020; Vichot-Llano et  al., 2021b), though in some \r\nsub-regions specific models overestimate and shift the month of the \r\nmaxima. RCM simulations in the region do not necessarily improve \r\nwith the size of the domain, as important features of the regional \r\ncirculation and key rainfall climate features, such as the CLLJ and MSD, \r\nare well represented for a variety of domains of different sizes (high \r\nconfidence) (Centella-Artola et al"


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vector_id: 19556
content:
", 2015; Martinez-Castro et al., 2018; \r\nCabos et al., 2019; Cavazos et al., 2020; Vichot-Llano et al., 2021b)."


id: ec0e3cfd995df96661d8b21885e4a7b3
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
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index: climate_gpt_only_giec
vector_id: 34634
content:
"RCP4.5 and RCP8.5 scenarios respectively. The greatest decrease in \r\nprecipitation is projected during summer reaching 13%, especially \r\nin southern Mexico, Central America and the Caribbean. Dynamically \r\ndownscaled simulations (Bukovsky et  al., 2015) also projected \r\na decrease of precipitation for the middle of the century (2041-2069) \r\nrelative to 1971-1999 for the north of Mexico, though despite good \r\nagreement amongst the models, these results must be considered of \r\nlow confidence, because of their poor simulation of important monsoon \r\nphysical processes. Vichot-Llano et al. (2021a) used a multi-parameter \r\nensemble of RegCM4, driven by the CMIP5 global model HagGEM2-ES \r\nprojections to conclude that, relative to the 1975-2004 baseline, in \r\nthe near (2020-2049) and more prominently in the far (2070-2099) \r\nfuture, drier conditions will prevail at over the eastern Caribbean"


id: 53033aca6268595a020f30db58b91f69
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
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index: climate_gpt_only_giec
vector_id: 10517
content:
" The \r\nprojected future warming trend was statistically significant at the 95% \r\nconfidence level over CAR and SCA. Almazroui et al. (2021) used an \r\nensemble of 31 CMIP6 models to estimate climate change signals of \r\ntemperature and precipitation in six reference regions in North and \r\nCentral America and the Caribbean, finding a decrease in precipitation \r\n(10-30%) over Central America and the Caribbean under three \r\nscenarios with regional and seasonal variations."


id: 84ba0e5bc72e102ecc26cfcdd251a9ec
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 18410
content:
"The regional assessment in this section emphasizes the seven new \r\nSouth American reference regions (Figure Atlas.22; Iturbide et  al., \r\n2020) that have a largely consistent climate and response to climate \r\nchange, and can be used for analysis and impact studies (Solman \r\net al., 2008; Neukom et al., 2010; Barros et al., 2015; Nobre et al., \r\n2016). At the sub-regional scale, several phenomena drive climate \r\nvariability. Brazil's north-east (North-Eastern South America; NES) \r\nis the most densely populated dryland globally and recurrently \r\naffected by climatic extremes. The climate variability, particularly \r\nthe precipitation, is marked by strong interannual variability \r\nrelated to ENSO, the ITCZ, and the North Tropical Atlantic Ocean \r\nSSTs (Marengo et  al., 2018a). Northern (NSA) and North-Western \r\nSouth America (NWS) are part of the Amazonia region. Its most \r\nrecognizable features are the high rainfall, high humidity and high \r\ntemperatures that prevail in the region"


id: 63f7b6cfaadc78233ce8cc3ec3fd962e
created_at: 2023-03-16 14:02:11
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vector_id: 13297
content:
" Rainfall variability in these \r\nregions results from the interplay between regional atmospheric \r\ncirculation, the SST variations in both the Pacific and Atlantic \r\noceans, among other regional-to-local interactions (Marengo and \r\nEspinoza, 2016; Espinoza et al., 2020). The South American Monsoon \r\n(SAM) region has distinct wet (summer) and dry (winter) periods. \r\nKey drivers include the South Atlantic Convergence Zone (Marengo \r\net  al., 2012), the Bolivian High, the 40- to 60-day intra-seasonal \r\noscillation, and the forcing of the high Andes Mountains to the west \r\n(Almeida et  al., 2017). The geographic position of South-Western "


id: 4aaf92151739301dcbbc8e12d77a40d9
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
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content:
"South America (SWS) results in very specific climatic characteristics \r\nsince SWS contains subtropical climates as well as sub-Antarctic and \r\nAntarctic climates. The climate of SWS is driven by seasonal changes \r\nin the position of subtropical high-pressure air masses in the South \r\nAtlantic and South Pacific oceans, the Southern Annular Mode, the \r\ndynamics of the cold Humboldt ocean current, and icy cold fronts \r\nand mid-latitude westerlies (Valdes-Pineda et al., 2016). The densely \r\npopulated, highly productive sub-region of South-Eastern South \r\nAmerica (SES) has cool winters and hot summers typical of the \r\ntemperate zone, and climatic conditions are strongly tied to ENSO, \r\nwhose influence is moderated by local air-sea thermodynamics in the \r\nSouth Atlantic (Barreiro, 2010)"


id: 670f26eb3514a98f5fe6c148d5c0592c
created_at: 2023-03-16 14:02:11
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content:
" Lastly, the climate of the southern \r\ntip of South America (SSA) is cold and dry, and is influenced by the \r\nSouthern Annular Mode, and the interaction between the wetter \r\nPacific winds and the Andean Cordillera (Aceituno, 1988; Silvestri \r\nand Vera, 2009)."


id: 287bc3b0566f79000892f1bc497b0b0e
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
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content:
"Regionally, analyses of temperatures point to an increased warming \r\ntrend (high confidence) over Amazonia over the last 40 years, \r\nwhich reached approximately 0.6degC-0.7degC (Figure Atlas.11 and the \r\nInteractive Atlas) and with stronger warming during the dry season \r\nand over the south-east. The analyses also showed that 2016 was the \r\nwarmest year since at least 1950 (Marengo et al., 2018b). Andean \r\ntemperatures showed significant warming trends, especially at \r\ninland and higher-elevation sites, while trends are non-significant \r\nor negative at coastal sites (high confidence) (Vuille et  al., 2015; \r\nBurger et  al., 2018; Vicente-Serrano et  al., 2018; Pabon-Caicedo \r\net al., 2020). Over central Chile, positive trends are largely restricted \r\nto austral spring, summer and autumn seasons for mean, maximum \r\nand minimum temperatures (Burger et  al., 2018; Vicente-Serrano \r\net  al., 2018)"


id: 4aa201c0c8a628b160a11b46a7a78e2b
created_at: 2023-03-16 14:02:11
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vector_id: 9148
content:
" Over Peru, trends of maximum air temperature were \r\nmainly amplified during the austral summer, but trends of cold\u0002season minimum air temperature showed an opposite pattern, with \r\nthe strongest warming being recorded in the austral winter (Vicente\u0002Serrano et al., 2018)."


id: c7e10b79fe541f1b226eb7c98d095a22
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
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vector_id: 29026
content:
"In general, the spatial patterns of observed trends in temperature \r\nare more consistent than for precipitation across the whole of South \r\nAmerica (medium confidence) (Interactive Atlas; de Barros Soares \r\net al., 2017). In south-east Brazil there is a region of highly significant \r\ndecrease of rainfall in both wet and dry seasons recorded in the \r\nperiod 1979-2011 (Interactive Atlas; Rao et  al., 2016). The most \r\nconsistent evidence of positive rainfall trend occurs in the southern \r\npart of the La Plata basin (high confidence) (southern Brazil, Uruguay, \r\nand north-eastern Argentina; de Barros Soares et  al., 2017). By \r\ncontrast, there is high confidence that annual rainfall has decreased \r\nover north-east Brazil during the last decades (Carvalho et al., 2020). \r\nContrary to temperature changes, trends in annual precipitation \r\nexhibit different signs across sectors in the Andes"


id: f0f9c3992b9089953a0db133eb20e10f
created_at: 2023-03-16 14:02:11
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vector_id: 35406
content:
" For instance, \r\nannual precipitation trends in the north tropical (north of 8degS) and \r\nsouth tropical (8degS-27degS) Andes do not show a  homogeneous \r\npattern. Over the subtropical Andes, central Chile shows a  robust \r\nsignal of declining precipitation since 1970 (high confidence) (Pabon\u0002Caicedo et al., 2020)."


id: 6dcc39540279337bc20ee89550393250
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
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vector_id: 14884
content:
"Observational studies show that the dry-season length over southern \r\nAmazonia has increased significantly since 1979 (high confidence) \r\n(Fu et al., 2013; Alves, 2016). In the Peruvian Amazon-Andes basin, \r\nthere is no trend in mean rainfall during the period 1965-2007 (Lavado \r\nCasimiro et  al., 2012) though statistically significant decreases in \r\ntotal annual rainfall in the central and southern Peruvian Andes from \r\n1966 to 2010 were found (Heidinger et al., 2018). Despite that, recent \r\nanalyses of Amazon hydrological and precipitation data suggest an \r\nintensification of the hydrological cycle over the past few decades \r\n(Gloor et al., 2015). In general, these changes are attributed mainly \r\nto decadal climate fluctuations (high confidence), ENSO, the Atlantic \r\nSST north-south gradient, feedbacks between fire and land-use \r\nchange mainly across southern south-eastern Amazon, and changes \r\nin the frequency of organized deep convection (Fernandes et  al., \r\n2015; Sanchez et al"


id: e890c9f3d4dc844aaf059b16c594a96d
created_at: 2023-03-16 14:02:11
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content:
"the tropical Andes of NWS, CMIP5 models tend to underestimate \r\nprecipitation which is associated with the misrepresentation of \r\nthe Pacific ITCZ and local low-level jets (Sierra et al., 2015, 2018), \r\nwhereas over the subtropical central Andes in SWS, the models are \r\nfound to overestimate both mean temperature and precipitation \r\nvalues (limited evidence, high agreement) (Zazulie et  al., 2017; \r\nRivera and Arnould, 2020; Diaz et  al., 2021). Most models show \r\na dry bias over SES (Diaz and Vera, 2017; Barros and Doyle, 2018; \r\nSolman and Blazquez, 2019; Diaz et  al., 2021) associated with an \r\nunderestimation of the northern flow that brings water vapour into \r\nthe region (medium confidence) (Gulizia et al., 2013; Zazulie et al., \r\n2017; Barros and Doyle, 2018)"


id: eed80824d0264c1de90568e8b1413a95
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
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vector_id: 35048
content:
" The biases in seasonal precipitation, \r\nannual precipitation and climate extremes over several regions of \r\nSouth America were reduced, including the Amazon, central South \r\nAmerica, Bolivia, eastern Argentina and Uruguay, in the CMIP5 \r\nmodels when compared to those of CMIP3 (medium confidence) \r\n(Joetzjer et  al., 2013; Gulizia and Camilloni, 2015; Diaz and Vera, \r\n2017). The evidence is still insufficient to determine whether \r\nCMIP6 biases are reduced when compared with CMIP5 simulations \r\nregarding precipitation and its variability in South America. The \r\ntemperature and precipitation patterns of anomalies associated \r\nwith ENSO in tropical South America (NWS, NSA and NES) are better \r\ncaptured by GCMs in tropical South America (NWS, NSA and NES) \r\nthan in extratropical South America (SES), particularly during austral \r\nsummer and autumn (limited evidence, high agreement) (Tedeschi \r\nand Collins, 2016; Perry et al., 2020)."


id: d4a1781aa1e625adff53e05b91a7db5c
created_at: 2023-03-16 14:02:11
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vector_id: 30978
content:
"Based on regional simulations, studies showed that some RCMs \r\nimprove the quality of the simulated climate when compared with \r\nthe driving GCM (medium evidence, high agreement) (Llopart et al., \r\n2014; Sanchez et al., 2015; Falco et al., 2019; Solman and Blazquez, \r\n2019; Ciarlo` et al., 2021; Teichmann et al., 2021). Regional climate \r\nmodel (RCM) simulations over South America can reproduce the \r\nmain features of temperature and precipitation in terms of both \r\nspatial distributions (Solman et  al., 2013; Falco et  al., 2019) and \r\nseasonal cycles over the different climate regimes, including the main \r\nSAmerM features (high confidence) (Jacob et al., 2012; Solman, 2013; \r\nLlopart et al., 2014; Reboita et al., 2014; de Jesus et al., 2016; Lyra \r\net al., 2018; Bozkurt et al., 2019; Ashfaq et al., 2021)"


id: 773e0110387216761c7f380500dfdee8
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
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vector_id: 16323
content:
" However, RCMs \r\nshowed systematic biases such as precipitation overestimations and \r\ntemperature underestimations along the Andes throughout the year \r\n(high confidence), although these biases may be artificially amplified \r\nby the lack of a dense observational station network (Jacob et al., \r\n2012; Solman et al., 2013; Bozkurt et al., 2019; Falco et al., 2019). \r\nRCMs tended to show dry biases over the Amazon and the northern \r\npart of the continent (SAM, NSA) during DJF and during the maximum \r\nprecipitation associated with the ITCZ over NSA during JJA (medium \r\nevidence, high agreement) (Solman et al., 2013; Falco et al., 2019). \r\nTemperature overestimation and precipitation underestimation over \r\nLa Plata basin (in SES) are also RCM common biases, with the warm \r\nbias amplified for austral summer and the dry bias amplified for the \r\nrainy season (high confidence) (Solman et al., 2013; Reboita et al., \r\n2014; Solman, 2016; Falco et al., 2019)"


id: 7d2bc4a7f7b73a70f6848277bb026a75
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
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vector_id: 17277
content:
" Despite their relevance, RCM \r\nsimulations at very high resolution (less than 10  km) are still few \r\nin South America (high confidence) and are mainly designed for \r\nspecific regions or purposes (Lyra et al., 2018; Bozkurt et al., 2019; \r\nBettolli et al., 2021)."


id: 67df4be5f8e5009d00cea35564f51946
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
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index: climate_gpt_only_giec
vector_id: 13932
content:
"(Figure Atlas.22 and the Interactive Atlas). In the medium and long \r\nterm, under the high-emissions scenario, the CMIP5 multi-model \r\nensemble projected an increase in precipitation (generally greater \r\nthan 10%) in SES and NWS, and a decrease (less than 10%) in NSA \r\nacross seasons (high confidence, robust evidence) (Solman, 2013; \r\nChou et al., 2014; Coppola et al., 2014; Llopart et al., 2014, 2021; \r\nReboita et al., 2014, 2021; Sanchez et al., 2015; Menendez et al., 2016; \r\nRuscica et al., 2016; Bozkurt et al., 2018a; Zaninelli et al., 2019). Also, \r\nin parts of SWS, annual precipitation is projected to decrease (up to \r\n30%) by the late 21st  century (Souvignet et  al., 2010; Palomino\u0002Lemus et al., 2017, 2018; Bozkurt et al., 2018a)"


id: abe78f7727e8be02388df7a0a93cdd1
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
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index: climate_gpt_only_giec
vector_id: 24566
content:
" Under high RCPs, the \r\nCMIP5 ensemble projects that all Brazilian regions will experience \r\nmore rainfall variability in the future, so drier dry periods and wetter \r\nwet periods on daily, weekly, monthly and seasonal time scales, \r\ndespite the future changes in mean rainfall being currently uncertain \r\n(medium confidence) (Alves et  al., 2021). Regarding the SAmerM, \r\nit is very likely that the monsoon will experience changes in its life \r\ncycle by the end of the 21st  century for both RCP4.5 and RCP8.5 \r\nemissions and, in particular, delayed onset. However there is low \r\nagreement on the projected changes in terms of extreme and total \r\nprecipitation of the monsoon season in South America (Llopart et al., \r\n2014; Ashfaq et al., 2021). Changes in the SAmerM are assessed in \r\nSection 8.3.2.4.5."


id: d1347ca9663bb08f315ddac3384e2a44
created_at: 2023-03-16 14:02:11
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vector_id: 30454
content:
"The AR5 (Collins et al., 2013) reports that the ability of models to \r\nsimulate the climate in Europe has improved in many important \r\naspects. Particularly relevant for this region are increased model \r\nresolution and a better representation of the land surface processes \r\nin many of the models that participated in CMIP5. The spread in \r\nclimate model projections is still substantial, partly due to pronounced \r\ninternal variability in this region (particularly NAO and AMO). In the \r\nwinter half year, NEU and WCE are likely to have increased mean \r\nprecipitation associated with increased atmospheric moisture and \r\nmoisture convergence, and intensification in extratropical cyclone \r\nactivity. No change or a  moderate reduction is projected for \r\nMED. In the summer half year, it is likely that NEU and WCE mean \r\nprecipitation will have only small changes with a notable reduction \r\nin MED. According to SR1.5 (Hoegh-Guldberg et  al"


id: 762e384ad7fa2d1385b1f2494ffe2d98
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content:
", 2018), these \r\nprecipitation changes are more pronounced at 2degC than at 1.5degC of \r\nglobal warming. For a 2degC global warming level, an increase in runoff \r\nis projected for north-eastern Europe while decreases are projected \r\nin the Mediterranean region, where runoff differences between 1.5degC \r\nand 2degC global warming will be most prominent (medium confidence). \r\nAccording to SROCC (Hock et al., 2019b) the RCP8.5 projections lead \r\nto a loss of more than 80% of the ice mass from small glaciers by the \r\nend of century in Central Europe (high confidence). Snow cover and \r\nglaciers are projected to decrease throughout the 21st century."


id: e4596f8e4968238f8b799f4086391a
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
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vector_id: 33401
content:
"To support climatological analyses and model evaluation, national \r\nmeteorological and hydrological services are increasingly making \r\navailable high spatial and temporal resolution gridded and in situ \r\nhomogenized and quality-checked datasets (Deque and Somot, 2008; \r\nVidal et al., 2010; Rauthe et al., 2013; Noel et al., 2015; Spinoni et al., \r\n2015b; Ruti et  al., 2016; Fantini et  al., 2018; Lussana et  al., 2018; \r\nHerrera et al., 2019; Skrynyk et al., 2020). The inclusion of additional \r\nstation data and data rescue activities lead to a better representation \r\nof extreme precipitation statistics than the global- or continental\u0002scale datasets (Atlas.1.4.1). Recent gridded products merging radar \r\nand station data allow higher spatial and temporal resolutions to be \r\nreached (Haiden et al., 2011; Tabary et al., 2012; Berg et al., 2016; \r\nFumiere et al., 2020). A number of regional reanalysis products has \r\nbecome available for the European region (Bollmeyer et  al"


id: a1c979944bf9c9110ccbe49ed6d0e944
created_at: 2023-03-16 14:02:11
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vector_id: 23018
content:
", 2015; \r\nBach et  al., 2016; Dahlgren et  al., 2016; Landelius et  al., 2016). \r\nA  European ensemble of regional reanalyses from 1961 to 2019 "


id: c286aec795dcd2f18affc938a3d3e31d
created_at: 2023-03-16 14:02:11
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index: climate_gpt_only_giec
vector_id: 28197
content:
"The large-scale spatial patterns of the E-OBS annual mean \r\nprecipitation trend between 1980 and 2015 shown in Figure Atlas.23 \r\nis broadly consistent with trends derived from CRU, GPCP and \r\nGPCC (Figure Atlas.11) but with more explicit spatial detail. Trends \r\ncalculated for regional averages are sensitive to the selection of the \r\ntime window: for 1980-2015 annual mean precipitation averaged \r\nover the regions shows a positive trend (not significant at p = 0.05), \r\nwhile for CRU and GPCC the trend calculated over 1901-2015 \r\nis positive for NEU, EEU and WCE, and non-significant for MED. \r\nPrecipitation trends in the MED are significant only in selected areas \r\n(Lionello et al., 2012; MedECC, 2020). Also the NEU trends show large \r\nspatial variability and are subject to decadal variability related to \r\nNAO (Heikkila and Sorteberg, 2012), but are generally positive over \r\nthe 20th century (Figure Atlas.23)"


id: 13768d218ac5ca2c89121bb6583b220b
created_at: 2023-03-16 14:02:11
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content:
" 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, observed land \r\nprecipitation trends show pronounced variability within the region, \r\nwith magnitude and sign of trend in the past century depending on \r\ntime period and exact study region (medium confidence)."


id: 775e1d6240b1799db7be7d7a9f9dd653
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
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index: climate_gpt_only_giec
vector_id: 16340
content:
"In NEU continued warming has been observed, particularly during \r\nspring. An annual mean temperature increase of 0.4degC per decade \r\nwas reported between 1970 and 2008 (Rutgersson et al., 2015). In \r\nWCE temperature increases since the mid-20th  century have been \r\ndocumented for Poland (Degirmendzic et  al., 2004) and Ukraine \r\n(Boychenko et  al., 2016; Balabukh and Malitskaya, 2017). Land\u0002only observations indicate a  rapid increase in summer (JJA) mean \r\nsurface air temperature since the mid-1990s (Dong et al., 2017). In \r\nEastern Europe no significant trend in winter mean air temperatures \r\nwas found between 1881 and 2016 in Belarus (Loginov et al., 2018). \r\nIn parts of the European area of the MED, spring and summer \r\ntemperatures are reported to increase faster than in the other \r\nseasons (see the Mediterranean case study in Section  10.6.4 and \r\nFigure 10.18; Brunetti et al., 2006; Homar et al., 2009; Lionello et al., \r\n2012; Philandras et al., 2015; Gonzalez-Hidalgo et al"


id: f7ef93a7f9ae7baaf038c174cf036ae0
created_at: 2023-03-16 14:02:11
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index: climate_gpt_only_giec
vector_id: 36522
content:
", 2016; Vicente\u0002Serrano et al., 2017). Figure Atlas.23 shows that since 1980 in each \r\nEuropean region all datasets show a consistent warming of annual \r\nmean temperature of 0.04degC yr -1 to 0.05degC yr -1. Trends in European \r\nland temperature  cannot be explained without accounting for \r\nanthropogenic warming offset by anthropogenic aerosol emissions \r\n(Section 3.3.1.1 and Figure 3.9). It is virtually certain that annual mean \r\ntemperature continues to increase in each European subdomain."


id: 3133cee3c670b8afb3096bdd0210f0a3
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
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index: climate_gpt_only_giec
vector_id: 5163
content:
"Trends in snowfall and snowmelt are related to seasonal changes \r\nin both temperature and precipitation. In EEU, melt onset dates \r\nhave advanced by one to two weeks in the 1979-2012 period \r\n(Mioduszewski et  al., 2015). Over Eurasia, trends in spring and \r\nearly summer snow cover extent increased over the 1971-2014 \r\nperiod (Hernandez-Henriquez et al., 2015). Between 1966 and 2012, \r\naveraged over entire Eurasia, monthly mean snow depth decreased \r\nin autumn and increased in winter and spring (Zhong et al., 2018), \r\nwhile the snow cover extent was reported to have decreased during \r\nthe past 40 years (Bulygina et al., 2011). In NEU late winter and early \r\nspring snow depth and snow cover decreases since the early 1960s \r\nare reported over Finland (Luomaranta et al., 2019) and Norway (Rizzi \r\net al., 2018) with a dependence on altitude (Skaugen et al., 2012), \r\nwhile winter snow depth increased in northern Sweden (Kohler et al., \r\n2006)"


id: bedae0bf2448ae02bfe513dc7b982819
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
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index: climate_gpt_only_giec
vector_id: 27582
content:
" It is very likely that since the early 1980s in snow-dominant \r\nareas in NEU and EEU the length of the snowfall season is reduced \r\nwith regional warming, and the melt onset dates have advanced."


id: 52ddf4c93475660544366e2b02cef291
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updated_at: 2023-03-16 14:02:11
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vector_id: 10493
content:
"Regional climate models (RCMs; Section  10.3.1.2) have been \r\nextensively evaluated for a  range of climate features over Europe \r\n(Casanueva et  al., 2016; Vaittinada Ayar et  al., 2016; Krakovska \r\net  al., 2017; Terzago et  al., 2017; Cavicchia et  al., 2018; Drobinski \r\net al., 2018; Fantini et al., 2018; Harzallah et al., 2018; Ivanov et al., \r\n2018; Panthou et al., 2018a). Standard assessments of RCMs driven \r\nby reanalyses, typically run at 12-25 km spatial resolution, confirm \r\nthat the Euro-CORDEX and Med-CORDEX ensembles are capable of \r\nreproducing the salient features of European climate (Kotlarski et al., \r\n2014; Krakovska, 2018) and represent European circulation features \r\nrealistically (Cardoso et al., 2016; Drobinski et al., 2018; Flaounas et al., \r\n2018; Sanchez-Gomez and Somot, 2018). Seasonal and regionally \r\naveraged temperature biases generally do not exceed 1.5degC, while \r\nprecipitation biases can be up to +-40% (Kotlarski et  al., 2014)"


id: 78c8ced2826516af6d2c19a96882677b
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
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vector_id: 16562
content:
" \r\nExtensive evaluation of a large collection of RCM-GCM combinations \r\nshow a general wet, cold and windy bias compared to observations \r\nand reanalyses, but none of the models is systematically performing \r\nbest or worst (Vautard et  al., 2021). Higher-resolution simulations \r\ndo show improved performance in reproducing the spatial patterns \r\nand seasonal cycle of not only extreme precipitation but also mean \r\nprecipitation over all European regions (see Sections  10.3.3.4 and \r\n10.3.3.5 for an extensive evaluation of the added value of increased \r\nsimulation resolution; Mayer et al., 2015; Fantini et al., 2018; Soares \r\nand Cardoso, 2018; Ciarlo` et al., 2021)."


id: c96783481e20416bc840e67b0e3e1570
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
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vector_id: 29249
content:
"New, or updated, higher-resolution, coupled atmosphere-ocean-ice \r\nmodel systems have been found to improve simulations of observed \r\nclimate features over the Baltic area compared to atmosphere-only \r\nmodel versions, including correlation between precipitation and SST, \r\nbetween surface heat-flux components and SST, and weather events \r\nlike convective snow bands over the Baltic Sea (e.g., Tian et al., 2013; \r\nVan Pham et  al., 2014; Groger et  al., 2015; S. Wang et  al., 2015; \r\nPham et al., 2017). Coupled atmosphere-land-river-ocean regional \r\nclimate system models (RCSMs) from Med-CORDEX have similar skill \r\nas the ENSEMBLES and the Euro-CORDEX ensembles to represent \r\ndecadal variability of Mediterranean climate and its extremes \r\n(Cavicchia et al., 2018; Dell'Aquila et al., 2018; Gaertner et al., 2018). \r\nPanthou et al"


id: bc1c9ec0181fc657c243d7d473b0af9c
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
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vector_id: 27164
content:
" (2018a) showed that, over land, differences between \r\natmosphere-only and coupled RCMs are confined to coastal areas \r\nthat are directly influenced by SST anomalies. In contrast, Van \r\nPham et al. (2014) showed significant differences in seasonal mean \r\ntemperature across a widespread continental domain."


id: 9ea89147eb5d60a9e1960be33f4fc50
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
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vector_id: 22499
content:
"SSP5-8.5 emissions in CMIP6 exceeds the warming projected by \r\nRCP8.5 emissions in CMIP5 (Section  4.3; Forster et  al., 2020). In \r\nselected regions in Europe CMIP6 also projects a  systematically \r\nhigher mean temperature than CMIP5 (Seneviratne and Hauser, \r\n2020). The annual mean projections from CMIP5, CMIP6 and 0.11deg \r\nresolution EURO-CORDEX contained in the Interactive Atlas are \r\nshown for the four European regions in Figure Atlas.24. For each \r\nregion and season a  warming offset between the pre-industrial \r\n(1850-1900) and the recent past (1995-2014) baselines is also \r\nshown. The results confirm higher CMIP6 long-term annual mean \r\nwarming rates for WCE, EEU and MED and a  larger inter-model \r\nspread for each region. For given GWLs, regional annual mean \r\ntemperature change in CMIP5 and CMIP6 are largely consistent \r\nand higher than the global average, most prominently in EEU"


id: f3fb970f848b284248c91fc912ba2cd9
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
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vector_id: 35897
content:
" \r\nFor high warming levels the CMIP5 subset of eight GCMs used to \r\ndrive the EURO-CORDEX simulations show a  lower annual mean \r\ntemperature change than the full CMIP5 ensemble in each of the \r\nEuropean sub-regions. This illustrates the large inter-model spread \r\nand implications for subsampling a relatively small subset from the \r\nfull ensemble. Regional warming is strongest in continental EEU \r\naway from the Atlantic and in MED during summer (Lionello and \r\nScarascia, 2018). The assessment of EURO-CORDEX projections for \r\nlevels of global warming of 1.5degC and 2.0degC indicate enhanced local \r\nwarming even at relatively low global warming levels, particularly \r\ntowards the north in winter (Schaller et al., 2016; Dosio and Fischer, \r\n2018; Kjellstrom et al., 2018; Teichmann et al., 2018)."


id: df260fb285f3b1cf3a011ec7c19174df
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
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index: climate_gpt_only_giec
vector_id: 32606
content:
"Regional warming is virtually certain to extend the observed \r\ndownward trends in snow accumulation, snow water equivalent \r\nand length of the snow cover season in NEU and at low altitudes in \r\nmountainous areas in the Alps and Pyrenees (very high confidence). \r\nThis is supported by regional and global multi-model and/or \r\nsingle-model ensemble projections including CMIP5, PRUDENCE, \r\nENSEMBLES and EURO-CORDEX (Jylha et  al., 2008; Steger et  al., \r\n2013; Mankin and Diffenbaugh, 2015; Schmucki et  al., 2015; \r\nMarty et al., 2017; Frei et al., 2018), and attributed to changes in \r\nthe snowfall fraction of precipitation and to increased snowmelt. \r\nIn mountain areas a  strong dependence of projected snow \r\ntrends on altitude is shown, with most pronounced effects below \r\n1500 m (Lopez-Moreno et al., 2009). Terzago et al. (2017) showed \r\na  large positive bias in the amplitude of the annual snow cycle \r\nof EURO-CORDEX 0"


id: c485c68305b23c46e95131b6b9ad3cf1
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
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index: climate_gpt_only_giec
vector_id: 28496
content:
"11deg simulations driven by GCM projections, \r\nwhile reanalysis-driven RCMs showed good agreement with in \r\nsitu observations."


id: b0bc6017d2fec7c0dd206c6bedf3e7a
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 25325
content:
"Some signatures of climate change projected by GCMs are modified \r\nby RCMs and CPRCMs. Projections of temperature, precipitation and \r\nwind in RCMs may deviate from GCM signals dependent on the \r\ndominant atmospheric circulation (Kjellstrom et al., 2018). In many \r\nareas RCMs produce lower warming rates and higher precipitation \r\n(less drying) in summer (Fernandez et al., 2019; Boe et al., 2020a). \r\nAlso, for mean surface shortwave radiation, systematic differences \r\nbetween GCM and RCM outputs are found (Bartok et  al., 2017; \r\nGutierrez et  al., 2020). Although RCMs generally have a  smaller \r\nbias for the present climate (Sorland et al., 2018) and better cloud \r\nrepresentation (Bartok et  al., 2017), the representation of aerosol \r\nforcing (Boe et  al., 2020a; Gutierrez et  al., 2020), air-sea coupling \r\n(Boe et al., 2020a) or vegetation response to elevated atmospheric \r\nCO2 (Schwingshackl et  al., 2019) give rise to systematic biases in \r\nRCM projections"


id: e7561a4c3d0fb75e806f81dc5c02fc99
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 33887
content:
" The comparison between EURO-CORDEX and the \r\nCMIP5 subset shown in Figure Atlas.24 illustrates that the RCMs \r\nprimarily modify the climate change warming signal from the driving \r\nGCMs for MED and WCE in summer (Boe et al., 2020a)."


id: fdd8a9ec7e3039823fa18c22b356c8e9
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 37457
content:
"The recent-past climate of North America is characterized by \r\nhigh spatial heterogeneity and by variability at diverse temporal \r\nscales. Considering the traditional Koppen-Geiger classification, \r\nNorth America covers all main climate types (see reference region \r\ndescriptions below). Important geographical features influence \r\nlocal climates over various distances, like the Rocky Mountains \r\nthrough cyclogenesis (Grise et  al., 2013) and the Great Lakes \r\nthrough  lake-effect snowfall (Wright et  al., 2013). The cryosphere \r\nis an important component of the climate system in North America, \r\nwith fundamental roles for sea ice cover, snow cover and permafrost. \r\nThe ocean surrounding the continent also influences its climate, \r\nwith water temperatures strongly influencing hurricane activity \r\nwhich impacts the coasts of eastern Mexico and south-eastern USA \r\n(Walsh et  al., 2010). Temporal variability is influenced by several \r\nlarge-scale atmospheric modes (Table Atlas"


id: bc88cd4965e99724b1d4fb2d88104353
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
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vector_id: 27241
content:
"1 and Annex IV) with \r\nthe North Atlantic Oscillation (NAO) affecting north-eastern USA and \r\neastern Canada precipitation (Whan and Zwiers, 2017), and El Nino-\r\nSouthern Oscillation (ENSO) affecting temperature and precipitation \r\nin California, although in a  complex and not yet fully understood \r\nmanner (Yoon et al., 2015; Yeh et al., 2018)."


id: ba2c2c9d5de2869f9412469014673d92
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 26831
content:
"The observed trends in annual mean surface temperature \r\n(Figure Atlas.11 and the Interactive Atlas) across near-Arctic latitudes \r\nare exceptionally pronounced (>0.5degC per decade), significant \r\nand consistent across datasets except for far north-east Canada \r\nwhere trends are not significant in the CRU dataset. Significant \r\npositive trends are seen across the rest of North America during \r\n1961-2015 (Figure Atlas.11) though over the shorter 1980-2015 \r\nperiod the regional dataset Daymet (Thornton et al., 2016) records \r\nnon-significant changes over southern Alaska, western and south\u0002central Canada, and north-central USA (Interactive Atlas). An analysis \r\nof annual mean surface temperature in the Berkeley Earth dataset \r\naggregated over the reference regions (Figure Atlas.11) demonstrates \r\nthat a  temperature change signal has emerged over all regions \r\nof North America"


id: 484099800096de4a7ea3cc8566022396
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 8738
content:
" There is a  detectable anthropogenic influence \r\n(medium confidence) on the observed upward annual temperature \r\ntrends in Western and northern North America (Vose et  al., 2017; \r\nZ. Wang et al., 2017; Smith et al., 2019)."


id: e47becd111e4727d513e128a69f19b38
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
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index: climate_gpt_only_giec
vector_id: 33428
content:
"The reference regions defined for summarising North America climate \r\nchange (Figure Atlas.26) include: North-Western North America (NWN), \r\ncharacterized by a sub-Arctic climate with cool summers and rainfall \r\nall year round; North-Eastern North America (NEN), which also has \r\na sub-Arctic climate with sections of tundra climate in the far north \r\n(these two northern regions are also discussed in Section Atlas.11"


id: 462f580c62eefc2e5161189ecfe1ccf5
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
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index: climate_gpt_only_giec
vector_id: 8427
content:
"2, \r\nPolar Arctic); Western North America (WNA), which has a  complex \r\nbut mainly cold semi-arid climate; Central North America (CNA) with \r\na mainly continental climate in the northern part of the region and \r\na  humid subtropical climate in the southern portion; Eastern North \r\nAmerica (ENA) with a humid continental climate in the northern half \r\nand a humid subtropical climate to the south; Northern Central America \r\n(northern Mexico; NCA), has a temperate climate to the north of the \r\nTropic of Cancer, with marked differences between winter and summer, \r\nmodulated by the North American Monsoon (Peel et al., 2007). "


id: 1550c6225b2f23155c53e1a669df7da0
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 873
content:
"There is evidence of a recent decline in the overall North American \r\nannual maximum snow mass, with a  trend for non-alpine regions \r\nabove 40degN during 1980-2018 estimated from the bias-corrected \r\nGlobSnow 3.0 data (medium confidence) (Pulliainen et  al., 2020). \r\nThis is despite technical challenges with in situ measurements and \r\nremote-sensing retrievals of snow variables (Larue et  al., 2017; \r\nSmith et al., 2017; X.L. Wang et al., 2017; Zeng et al., 2018), spatial \r\nheterogeneity and interpolation assumptions that affect gridded \r\nreference products, notably over alpine and forested areas (Mudryk \r\net al., 2015; Dozier et al., 2016; Cantet et al., 2019), and breaks in \r\ninstruments and procedures (Kunkel et  al., 2007; Mortimer et  al., \r\n2020). Changes in snow cover have evolved in a complex way, with \r\nboth positive and negative trends, and differing from one metric \r\nto another (Knowles, 2015; Brown et al., 2019)"


id: 55b885fe966bdd976761584bf51c7e96
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 10968
content:
" Evidence of snow \r\ncover decline includes decreases in annual maximum snow depth \r\nand in snow water equivalent (Vincent et  al., 2015; Kunkel et  al., \r\n2016; Mote et al., 2018), as well as a shortening of the snow-season \r\nduration (Knowles, 2015; Vincent et  al., 2015). However, reported \r\nsnow-decline trends are statistically significant only for a fraction of \r\nthe concerned areas or locations (low confidence) (Figure Atlas.25). \r\nSee also Sections 2.3.2.2 and 9.5.3.1."


id: 17b4c5d79c52c28d496fed64d982db01
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 1227
content:
"CMIP6 models have been evaluated in the literature, although these \r\nstudies have not included the full set of CMIP6 simulations. Fan et al. \r\n(2020) established on a  continental basis for North America that \r\ntemperature pattern correlations were quite accurate. Thorarinsdottir \r\net al. (2020) compared maximum and minimum temperatures over \r\nEurope and North America with several observational datasets \r\nand found that the CMIP6 ensemble agreed better with ERA5 \r\ndata than did CMIP5. Srivastava et  al. (2020) evaluated historical \r\nCMIP6 simulations for precipitation, comparing them with several \r\nobservational datasets over the continental US. Most models show \r\na  wet bias over the eastern half of the continental USA and the \r\nnorth-east region, while dry biases persist in the central part of \r\nthe country (Akinsanola et  al., 2020a; Almazroui et  al., 2021). The \r\nspatial structure of biases is similar in CMIP5 and CMIP6, but with \r\nlower magnitudes in CMIP6"


id: 8502a3143f7798d48738b870f5edcd8d
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 18449
content:
" Agel and Barlow (2020) examined \r\n16 CMIP6 models over the north-eastern USA for precipitation and \r\ndid not find a distinct improvement over CMIP5, although they did \r\nfind the higher-resolution models tended to perform better. On the \r\nbasis of the evidence so far, there is medium confidence that CMIP6 \r\nmodels are improved compared to CMIP5 in terms of biases in mean \r\ntemperature and precipitation over North America."


id: 1e2fad4b749eb0b750c9ab8396feb7b
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 2187
content:
"Yoon et al., 2012), and NA-CORDEX (Bukovsky and Mearns, 2020). \r\nTherefore, much performance evaluation has been conducted with \r\na focus on specific climate features in North America. For the North \r\nAmerican Monsoon region, multi-model performance evaluation \r\n(Bukovsky et al., 2013; Tripathi and Dominguez, 2013; Cerezo-Mota \r\net al., 2016) or a single-member performance (Lucas-Picher et al., \r\n2013; Martynov et al., 2013; Separovic et al., 2013) demonstrated \r\nthe added value of RCMs, particularly more recent CORDEX \r\nsimulations, through improved simulation of summer precipitation \r\nand the climatological winter storm tracks across the western USA. \r\nNA-CORDEX simulations were more successful at reproducing \r\nweather types compared to a single model-based large perturbed\u0002physics ensemble (Prein et al., 2019)"


id: 7915590d959f1ffd629361183d02435f
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
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index: climate_gpt_only_giec
vector_id: 16606
content:
" The application of a complex \r\nevaluation tool to the full suite of NA-CORDEX simulations found \r\nthat the higher-resolution simulations (25 km compared with 50 km) \r\nof precipitation were improved, particularly for daily intensity \r\n(Gibson et al., 2019)."


id: ca2bb671a535fb21dd9674f1ced08534
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 29363
content:
"CMIP5 and CMIP6 surface temperature and precipitation projections \r\nover the region are similar, with all regions warming more than the \r\nglobal average, most prominently those in the north (Figure Atlas.26). \r\nCMIP6 projects, for all scenarios and time periods, higher temperature \r\nchanges (Chapter 4), with this contrast more accentuated in the long\u0002term future and at higher global warming levels. The higher warming \r\nin the north (Interactive Atlas) is clear when comparing NEN, with \r\nincreases from 2degC to over 8.5degC on an annual basis for SSP5-8.5 \r\n(near term to long term compared to a 1995-2014 baseline), to NCA, \r\nwhere changes range from 1.5degC to 6degC across the same periods. \r\nMaps showing changes in temperature and precipitation, and their \r\nrobustness, are available in the Interactive Atlas. The number of model \r\nresults (i.e"


id: a4c0c55d54360a44f4199b13597d591a
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 23498
content:
", ensemble size used to generate these figures) differs, and \r\nthis sample size difference may affect the results, but the patterns \r\nand magnitudes of change are generally consistent and thus it is very \r\nlikely that temperatures will increase throughout the 21st century in \r\nall land areas, with stronger warming in the far north."


id: efd200a68ad7bac3f95d02eb6a9b3a2d
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 35237
content:
"CMIP5 results have been analysed extensively (e.g., Maloney et al., \r\n2014) and used in major climate change assessments. The most \r\nrecent US National Climate Assessment analysis of CMIP5 focusing \r\non RCP4.5 and RCP8.5 for two future time periods stated that the \r\nUSA would continue to warm regardless of the scenario, but is likely\r\nto be higher with higher-emissions scenarios (e.g., RCP8.5). Projected \r\nchanges in precipitation are somewhat complex, but increased \r\nprecipitation dominates in winter and spring, whereas in summer \r\nchanges are more variable and uncertain. Canada's Changing Climate \r\nReport (Bush and Lemmen, 2019) presents changes in temperature \r\nand precipitation, as well as other variables, such as snow, for \r\nfuture periods in Canada using results from CMIP5. It indicates that \r\nannual and winter precipitation is projected to increase everywhere \r\nin Canada over the 21st  century with larger percentage increases \r\nin the north"


id: 86469ddde199bbb7ad971c24806bbc0f
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 18669
content:
" Temperature is also projected to increase, regardless of \r\nthe scenario, and with larger changes occurring in the north."


id: 51d6842cbb27afdbda80279f6ccf7e57
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 10337
content:
"To provide the basis for generating additional information compared to \r\nthat derived from CMIP5 the NA-CORDEX experiments were designed \r\nto involve a  GCM-RCM matrix which included multiple  GCMs that \r\nsampled the full range of climate sensitivity, multiple RCMs, at \r\ntwo different spatial resolutions (25 and 50  km) and a  range of \r\nemissions scenarios (in most cases RCP4.5 and RCP8.5; Mearns et al., \r\n2017). Karmalkar (2018) noted that the NA-CORDEX models cover \r\nsub-regional ranges of temperature change from the CMIP5 GCMs \r\nbetter than NARCCAP did for the CMIP3 models. This structural design \r\nshift provides greater confidence in the NA-CORDEX results in terms \r\nof sampling the uncertainty across the CMIP5 models (Figure Atlas.27; \r\nBukovsky and Mearns, 2020)"


id: 9c6419a4f772b6ae632d0e889fdd1ca1
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
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index: climate_gpt_only_giec
vector_id: 22158
content:
" The pattern of warming is as seen in \r\nCMIP5 and CMIP6, which also builds confidence that the RCMs \r\ngenerate high-resolution results consistent with CMIP5 on large \r\nscales whilst providing added value over regions such as the complex \r\ntopography of the Rocky Mountains in the western USA, which are not \r\nwell resolved in the GCMs. There is high confidence that downscaling \r\na subset of CMIP models that spans the range of climate sensitivities \r\nin the full ensemble is critical for producing a representative range of \r\ndynamically downscaled projections."


id: 8acad340710ef67f8eaf6a3fd01c33ca
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 19374
content:
"Recent studies have examined RCMs' simulation of SWE, a quantity \r\nof primary importance notably for hydrological modelling, though \r\nits ground measurements are restricted by relatively high time \r\nand monetary costs (Smith et  al., 2017; Odry et  al., 2020) which \r\nlimit model assessment. Also, studies often emphasize that a false \r\nimpression of model skill for SWE can be obtained by compensating \r\ntemperature and precipitation biases. Assessment frameworks have \r\ndealt with these issues by considering observational uncertainty \r\n(Mccrary et  al., 2017) and by decomposing SWE biases into their \r\ncontributing processes (Rhoades et al., 2018; Xu et al., 2019). SWE \r\nbiases exceed observational uncertainty in several 50-km reanalysis\u0002driven NARCCAP simulations over several regions, for all cold \r\nmonths (Mccrary et al., 2017)"


id: efb8a9f92fb1e2c680520f0fb6ca76b4
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
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index: climate_gpt_only_giec
vector_id: 35214
content:
" Analyses of NA-CORDEX simulations \r\nshow that refining spatial resolution from 50 to 12  km improves \r\ncertain (but not all) aspects of SWE, stemming from improved mean \r\nprecipitation and topography-related temperature (Xu et al., 2019). \r\nSimilarly an assessment of RCM simulations of freezing rain over \r\neastern Canada found a mix of improved and deteriorated aspects \r\nfrom higher resolution (St-Pierre et al., 2019)."


id: 399449abc49fae64bc51db6efe6cd81e
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vector_id: 6436
content:
"The fraction of precipitation falling as snow is projected to decrease \r\npractically everywhere over North America, including over the \r\nwestern USA and south-western Canada (Mahoney et  al., 2021), \r\nand in the Great Lakes basin where lake-effect precipitation is \r\nimportant (Suriano and Leathers, 2016). In this basin, the frequency \r\nof heavy lake-effect snowstorms is expected to decrease during \r\nthe 21st  century, except for a  possible temporary increase around \r\nLake Superior by mid-century, if local air temperatures remain low \r\nenough (Notaro et  al., 2015). CMIP5 simulations of the periods \r\n1981-2000 and 2081-2100 over the central and eastern USA \r\nsuggest a  northward shift in the transition zone between rain\u0002dominated and snow-dominated areas, by about 2deg latitude under \r\nthe RCP4.5 scenario and 4deg latitude under the RCP8.5 scenario \r\n(Ning and Bradley, 2015)"


id: ac6712d512af82d514fa37437ecf277f
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
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vector_id: 24660
content:
" Rain-on-snow event properties over North \r\nAmerica should also evolve during the 21st century, with non-trivial \r\ndependencies on the positioning relative to the freezing line (Jeong \r\nand Sushama, 2018) and on elevation (Musselman et al., 2018)."


id: 4ef1c9ff2eca88f337a5d6553e092ce
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
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vector_id: 9860
content:
"As discussed in Section 10.3.3.4, an important advance in regional \r\nmodelling over the past decade or so is the use of convection\u0002permitting regional models (CPMs; Prein et al., 2015, 2017a). There \r\nhave been a number of experiments using CPMs over North America \r\n(e.g.,  Rasmussen et  al., 2014; Prein et  al., 2015, 2019; Liu et  al., \r\n2017; Komurcu et al., 2018). A CPM study over North America that \r\ninvestigated changes in Mesoscale Convective Systems projected \r\nthat by the end of the century, assuming an RCP8.5 scenario, \r\ntheir frequency more than tripled and associated precipitation \r\nincreased by 80% (Prein et al., 2017b). A multiple nesting of WRF \r\nover the north-eastern USA, downscaling to 3  km a  CESM GCM \r\nclimate projection assuming an RCP8.5 scenario, found a different \r\npattern of precipitation change of mixed increases and decreases \r\ncompared to the GCM projection of increases every month (Komurcu \r\net  al., 2018)"


id: 4c9a1bc0b117d39cb9813433e2f4d43f
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 9477
content:
" These investigations demonstrate the potential of \r\nvery-high-resolution simulations to add important dimensions to our \r\nunderstanding of regional climate change, though not necessarily to \r\nreduce uncertainty (high confidence)."


id: ce8bf49ca223ae41dc425c433cc00c5e
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 30037
content:
"Ocean warming rates have likely increased in recent decades with \r\nmarine heatwaves increasing and very likely to have become longer\u0002lasting, more intense and extensive as a  result of anthropogenic \r\nwarming. Open ocean oxygen levels have very likely decreased and \r\noxygen minimum zones have likely increased in extent. There is very \r\nhigh confidence that global mean sea level rise has accelerated in \r\nrecent decades which, combined with increases in tropical cyclone \r\nwinds and rainfall and increases in extreme waves, has exacerbated \r\nextreme sea level events and coastal hazards (high confidence). It is \r\nvirtually certain that during the 21st century, the ocean will transition \r\nto unprecedented conditions with further warming and acidification \r\nvirtually certain, increased upper ocean stratification very likely and \r\ncontinued oxygen decline (medium confidence)"


id: 25bcc0b8dc17971928bccd04c7ca251d
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 3363
content:
" There is very high \r\nconfidence that marine heatwaves and medium confidence that \r\nextreme El Nino and La Nina events will become more frequent. It \r\nis very likely that these changes will be smaller under scenarios with \r\nlow greenhouse gas emissions. Global mean sea level will continue \r\nto rise and there is high confidence that the consequent increases \r\nin extreme levels will result in local sea levels in most locations that \r\nhistorically occurred once per century occurring at least annually by \r\nthe end of the century under all RCP scenarios (high confidence). In \r\nparticular, many small islands are projected to experience historical \r\ncentennial events at least annually by 2050 under RCP2.6 and higher \r\nemissions. The proportion of Category 4 and 5 tropical cyclones, and \r\nassociated precipitation rates and storm surges, along with average \r\ntropical cyclone intensity are projected to increase with a 2degC global \r\ntemperature rise, thereby exacerbating coastal hazards."


id: 65c7c8cb211f4de4399902e57bbad81c
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 13596
content:
"Many small islands lie in tropical regions and their climate varies \r\ndepending on a range of factors with location, extent and topography \r\nhaving major influences. In general, their climate is determined \r\nby that of the broader region in which they lie as they have little \r\ninfluence on the regional climate, although steep topography can \r\ninduce higher rainfall totals locally. Temperature variability tends to \r\nbe low due to the influence of the surrounding ocean, most marked \r\nin the tropics where oceanic temperature ranges are small. However, \r\nseasonal rainfall variability can often be significant, both through \r\nthe annual cycle and also interannually through the influence of \r\nmany modes of variability (Cross-Chapter Box Atlas.2:, Annex IV \r\nand Atlas.7.1 for the Caribbean)"


id: 978de6f61f36ec8838144785fd14df56
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 21374
content:
" Many small islands are exposed to \r\ntropical cyclones and the associated hazards of high winds, storm \r\nsurges and extreme rainfall, and many low-lying islands are exposed \r\nto regular flooding from natural high-tide and wave activity. In the \r\nPacific, phases of the El Nino-Southern Oscillation result in periods \r\nof warmer or cooler than average temperatures following the upper \r\nocean warming of El Nino events or cooling of La Nina events, and \r\nrespectively weaker and stronger trade winds. El Nino conditions also \r\nlead to drought in Melanesian islands and increased tropical cyclones \r\nand storm surges in French Polynesia with La Nina conditions causing \r\ndrought in Kiribati. Other islands experience increased rainfall during \r\nthese periods."


id: 50629198af5b0644697692dccb3f53f9
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 10093
content:
"period and was strongly related to anthropogenic warming, which \r\nwould have increased the severity of the event by 17% and its spatial \r\nextent by 7% (Herrera et al., 2018). However, a seasonal analysis \r\nof observations grouped into large sub-regions of the Caribbean \r\nrevealed no significant long-term trends in rainfall over 1901-2012 \r\nbut significant inter-decadal variability (P.D. Jones et  al., 2016b). \r\nDeclines in summer rainfall (-4.4% per decade) and maximum \r\nfive-day rainfall (-32.6  mm per decade) over 1960-2005 were \r\nreported for Jamaica (CSGM, 2012), and an insignificant decrease \r\nin summer precipitation was observed for Cuba for 1960-1995 \r\n(Naranjo-Diaz and Centella, 1998). Three of four stations examined \r\nfor Puerto Rico exhibited declining JJA rainfall over 1955-2009 with \r\nthe trend statistically significant at the 95% level for Canovana \r\n(Mendez-Lazaro et  al., 2014)"


id: f0a65b56df22b9a16ccaf4f75d970a86
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
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vector_id: 35362
content:
" In the Caribbean, positive regional \r\ntrends in precipitation and trends in extremes during 1961-2010 \r\nwere found to be not statistically significant (at the 5% level; \r\nStephenson et  al., 2014; Beharry et  al., 2015). Positive trends in \r\nJJA rainfall over Cuba and Jamaica are seen in CRU, whereas they \r\nare negative over Cuba for GPCC; over eastern Hispaniola they are \r\npositive in CRU and negative in CHIRPS (Cavazos et al., 2020)."


id: c04b545cde6f64a2928453c28db2747e
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
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vector_id: 27815
content:
"(June-August) based on studies using GCMs (Rauscher et al., 2008; \r\nKarmalkar et  al., 2013; Karmacharya et  al., 2017a; Taylor et  al., \r\n2018). The future summer drying over the Caribbean is associated \r\nwith a projected future strengthening of the Caribbean low-level jet \r\n(Taylor et al., 2013a). Rauscher et al. (2008) hypothesized that the \r\nsimulated 21st-century drying over Central America represents an \r\nearly onset and intensification of the MSD. The westward expansion \r\nand intensification of the NASH associated with the MSD occurs \r\nearlier with stronger low-level easterlies. Rauscher et  al. (2008) \r\nfurther suggested that the eastern Pacific ITCZ is also located further \r\nsouthward and that there are some indications that these changes \r\ncould be forced by ENSO-like warming of the tropical eastern Pacific \r\nand increased land-ocean heating contrasts over the North American \r\ncontinent"


id: c0bf7249ec4acafa5c1245c903d9c439
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 27888
content:
" Other studies also suggest a future intensification of the \r\nNASH due to changes in land-sea temperature contrast resulting \r\nfrom increased greenhouse-gas concentrations (W. Li et al., 2012)."


id: 2348ef408009af67c7a4b71111ba3e01
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 3003
content:
"The CMIP5 ensemble median projected precipitation decreases of \r\nup to -16% over the Caribbean, parts of the Atlantic and Indian \r\noceans, and the southern subtropical and eastern Pacific Ocean, and \r\nincreases of up to 10% over parts of the western Pacific and Southern \r\noceans, and up to 55% in the equatorial Pacific Islands under RCP6.0 \r\nin the period 2081-2100 relative to 1986-2005 (Harter et al., 2015). \r\nA projected decrease in annual precipitation is also noted over the \r\nLesser Antilles under the RCP4.5 and RCP8.5 scenarios (Cantet \r\net al., 2014). Seasonal rainfall is projected to decrease in most areas \r\nin Hawaii, except for the climatically wet windward side of the \r\nmountains, which would increase the wet to dry gradient over the \r\narea (Timm et al., 2015). The average precipitation changes in Hawaii \r\nare estimated to be about -11% to -28% under RCP4.5 during \r\nthe wet season, and about -4% to -28% under RCP4"


id: e2de1e019c609c089cacb71fad2fef54
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 33191
content:
"5 during the \r\ndry season in the period 2041-2071 relative to 1975-2005, with \r\nlarger changes under RCP8.5 (Timm et  al., 2015). There are still \r\nuncertainties in the projected changes, which have been attributed to \r\nfactors including insufficient model skill in representing topography \r\nin the small islands, and high variability in climate drivers. However, \r\nthe broad-scale pattern of projected wetter conditions in the western \r\nand equatorial Pacific, and the north Indian and Southern oceans, and \r\nof drier conditions over the Caribbean, and in parts of the Atlantic, \r\nIndian and southern subtropical and eastern Pacific oceans are \r\nfurther strengthened in the CMIP6 ensemble (Figure Atlas.28), which \r\nare thus likely regional responses as the climate continues to warm."


id: 573fc3d9348d6c8bfdfebe78a3852e8a
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
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vector_id: 11218
content:
"Small Islands are predominantly located in the Pacific, Atlantic and Indian oceans, and in the Caribbean (Nurse et al., 2014; Shultz et al., \r\n2019). They are characterized by their small physical size, being surrounded by large ocean expanses, vulnerability to natural disasters \r\nand extreme events, and relative isolation (Section 12.4.7, Atlas.10 and Glossary; Nurse et al., 2014). These and nearby larger islands \r\n(e.g., Madagascar and Cuba) are often water-scarce with low water volumes due to increasing demand (from population growth and \r\ntourism), aging and poorly designed infrastructure (Burns, 2002), and decreasing supply (from pollution, changes in precipitation \r\npatterns, drought, saltwater intrusion, regional sea level rise, inadequate water governance (Belmar et al., 2016; Mycoo, 2018) and \r\ncompeting and conflicting uses (Section 8.1.1.1; Cashman, 2014; Gheuens et al., 2019)"


id: e8fca3102d7e60da9ad7d725dfa4dfcc
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 34175
content:
" In the Caribbean, groundwater is the main \r\nfreshwater source and depends strongly on rainfall variability (Post et al., 2018), while rain, ground or surface water are the primary \r\nsources for the Pacific Islands depending on island type (volcanic or atoll), size and quality of groundwater reserves (Burns, 2002). \r\nGroundwater pumping and increasing sea levels also affect water availability by increasing the salinity of the aquifer (e.g., Bailey \r\net al., 2015, 2016), thus reinforcing negative drought effects from reduced rainfall and increased evaporative demand from higher \r\ntemperatures. For example, in 54% of the Marshall Islands, groundwater is highly vulnerable to droughts (Barkey and Bailey, 2017)."


id: 7a6e5737da7eed64bb0428f3de7b98f7
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 16835
content:
"The climate of Small Islands and findings from previous IPCC assessments\r\nIntra-seasonal to interannual rainfall in the Caribbean and in the Indian and Pacific oceans is influenced by the trade winds, the passage \r\nof tropical cyclones (TCs), Madden-Julian Oscillation (MJO), easterly waves, migrations of the Inter-tropical Convergence Zone (ITCZ) \r\nand the North Atlantic Subtropical High (NASH) for the Caribbean; the South Pacific Convergence Zone (SPCZ) and western North \r\nPacific summer monsoon for the Pacific; and the South Asian monsoons for the Indian Ocean. The relevant dominant modes of climate \r\nvariability (Section 8.3.2.9 and Annex IV) are El Nino-Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) which have been \r\nassociated with extreme events in the islands (Annex IV; Stephenson et al., 2014; Kruk et al., 2015; Frazier et al., 2018)"


id: dc8372691c271c629fdc5dc5cc1bc04
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 32198
content:
" The modes of \r\nclimate variability are modulated by Pacific Decadal Variability (PDV), Inter-decadal Pacific Oscillation (IPO) and Atlantic Multi-decadal \r\nVariability (AMV). These modes show no sustained trend since the late 19th century (high confidence) (Section 2.4)."


id: cabd631e5931627d81090600d41e61cc
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 29458
content:
"The AR5 WGI reports observed temperature increases of 0.1degC-0.2degC per decade in the Pacific Islands with these trends very likely to \r\ncontinue under high emissions, and projects a drier rainy season for many islands in the south-west Pacific (Christensen et al., 2013). \r\nThe AR5 WGII reports rainfall reductions over the Caribbean, increases over the Seychelles, streamflow reductions over the Hawaiian \r\nIslands and saltwater intrusion into groundwater reserves in the Pacific Islands resulting from storm surges and high tides (Nurse et al., \r\n2014). The SROCC (IPCC, 2019a) finds very high confidence that global mean sea level rise has accelerated in recent decades which \r\nhas exacerbated extreme sea level events and flooding (high confidence). It will continue to rise with consequent increases in extreme \r\nlevels so that the historical one-in-a-century extreme local sea level will become an annual event by the end of the century under all \r\nRCP scenarios (high confidence)"


id: ae1ebe5de149f09a2bd14de5bbd99557
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 24929
content:
" In particular, many Small Islands are projected to experience historical centennial events at least \r\nannually by 2050 under RCP2.6, RCP4.5 and RCP8.5 emissions. The proportion of Category 4 and 5 TCs and associated precipitation \r\nrates along with their average intensity are projected to increase with a 2degC global temperature rise which will further increase the \r\nmagnitude of resultant storm surges and flooding. The SROCC Cross-Chapter Box on Low-lying Islands and Coasts (Magnan et al., \r\n2019) focused on sea level rise and oceanic changes and their impacts, therefore the assessment presented here on climate changes \r\nrelevant to water resources, including precipitation and temperature, is complementary."


id: 797693e3f164fe34d72225205124fb9a
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 16682
content:
"Rainfall is very likely to decline over the Caribbean, in the annual mean and especially in JJA, with a stronger and more coherent \r\nsignal in CMIP6 compared to CMIP5 (Figure Atlas.28 and Interactive Atlas) and reductions of 20-30% by the end of the century \r\nunder high future emissions (SSP5-8.5). This JJA drying has been linked to a  future strengthening of the Caribbean low level \r\njet (CLLJ) (Taylor et al., 2013a), a westward expansion and intensification of the NASH, stronger low-level easterlies over the \r\nregion, a southwardly-placed eastern Pacific ITCZ (Rauscher et al., 2008), and changing dynamics due to increased greenhouse \r\ngas concentrations (very high confidence) (W. Li et al., 2012). Projections from 15 GCM and two RCM experiments for 2080-2089 \r\nrelative to 1970-1989 were for a generally drier Caribbean and a robust summer drying (Karmalkar et al., 2013). More recent \r\ndownscaling studies (e.g., Taylor et  al., 2018; Vichot-Llano et  al"


id: 9e256359b667531b5f6e45d40791aa67
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 22404
content:
"Summary of information distilled from multiple lines of evidence\r\nIt is very likely that most Small Islands have warmed over the period of instrumental records. The clearest precipitation trend is a likely\r\ndecrease in JJA rainfall over the Caribbean since 1950. There is limited evidence and low agreement for the cause of the observed \r\ndrying trend, whether it is mainly caused by decadal-scale internal variability or anthropogenic forcing, but it is likely that it will \r\ncontinue over coming decades. It is likely that drying has occurred since the mid-20th century in some parts of the Pacific poleward \r\nof 20deg latitude in both the Northern Hemisphere and the Southern Hemisphere and that these changes will continue over coming \r\ndecades. Rainfall trends in most other Pacific Ocean and Indian Ocean Small Islands are mixed and largely non-significant"


id: 9b22c8bb6adf434e34cf364888af6555
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 21949
content:
" It is very \r\nlikely that sea levels will continue to rise in all Small Island regions, and this will result in increased coastal flooding with the potential \r\nto increase saltwater intrusion into aquifers in Small Islands."


id: 5f2df15dfcf3b325bb693feba019210e
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 12536
content:
"The assessment in this section focuses on changes in average \r\ntemperature, precipitation (rainfall and snow) and surface mass \r\nbalance over the polar regions, Antarctica and the Arctic, including \r\nthe most recent years of observations, updates to observed datasets, \r\nthe consideration of recent studies using CMIP5 simulations and \r\nthose using CMIP6 and CORDEX simulations. Findings are presented \r\nfor West Antarctica (WAN) and East Antarctica (EAN), and three \r\nArctic regions: Arctic Ocean (ARO), Greenland/Iceland (GIC) and \r\nRussian Arctic (RAR; Figure Atlas.29) with some reference also to \r\nNorth-Eastern North America (NEN), North-Western North America \r\n(NWN) and Northern Europe (NEU), which are covered more \r\nextensively in Atlas.9 and Atlas.8 respectively. Sub-regional changes \r\nare discussed when relevant, for example the Antarctic Peninsula \r\n(AP) as a  sub-region of WAN"


id: 42286f77b85c721f0512b6ec5d7727c2
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 7807
content:
" The Southern Ocean (SOO) region \r\nis assessed in Chapter  9 with changes in climatic impact-drivers \r\nassessed in Chapter 12 (Section 12.4.9 and Table 12.11) and some \r\nextremes in Chapter 11 (Tables 11.7-9 for RAR). Chapter 9 provides "


id: e8fac48c8d5fc9c3a8941ab4d7eeb7d5
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 34174
content:
"of snowfall and diamond dust, with sporadic coastal rainfall during \r\nthe summer over the Antarctic Peninsula and sub-Antarctic islands. \r\nDrizzle events sometimes occur during warm air intrusions (Nicolas \r\net  al., 2017) at relatively low temperatures (Silber et  al., 2019). \r\nPrecipitation constitutes the largest component of the surface \r\nmass balance (SMB), which also includes sublimation (from the \r\nsurface or drifting snow), meltwater runoff and redistribution by \r\nwind (Lenaerts et al., 2019). SMB can be considered as a proxy of \r\nprecipitation if averaged over an annual cycle (Gorodetskaya et al., \r\n2015; Bracegirdle et al., 2019). Precipitation and SMB exhibit spatial \r\nand temporal variability controlled by atmospheric large-scale low\u0002pressure systems and moisture advection from lower latitudes. SMB \r\nis an important component of the total ice-sheet mass balance \r\n(Section 9.4.2.1)"


id: cca57d9505f8fe47f2f61e24b002231
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 29734
content:
" The Antarctic contribution to sea level results from \r\nthe imbalance between net snow accumulation and ice discharge \r\ninto the ocean (Box 9.1). Ice shelves buttress the ice sheet and are \r\ninfluenced by oceanic and atmospheric drivers (Box 9.1)."


id: 6792f69e273b5a832947de6372b83759
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 13885
content:
"Figure Atlas.30 (Antarctic map inset) shows near-surface air \r\ntemperature trends for 1957-2016 and 1979-2016 at the stations \r\nwhere observations are available for at least 50 years and the \r\ndetected trends have statistical significance of at least 90% according \r\nto the most recent (after SROCC) studies (Jones et al., 2019; Turner \r\net al., 2020). It is very likely that the western and northern AP has \r\nbeen warming significantly since the 1950s (0.49degC +- 0.28degC per \r\ndecade during 1957-2016 and 0.46degC +- 0.15degC during 1951-2018 \r\nat Faraday-Vernadsky station; 0.29degC +- 0.16degC per decade during \r\n1957-2016 at Esperanza station), with no significant trends reported \r\nin the eastern AP during the same period (Gonzalez and Fortuny, \r\n2018; Jones et al., 2019; Turner et al., 2020)"


id: 77afde0a3a382a8c369de47411404826
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 16389
content:
" Short-term cooling trends, \r\nstrongest during austral summer, have been reported at AP stations \r\nduring 1999-2016, but the absence of warming and cooling at some \r\nstations during 1999-2016 is consistent with natural variability, and \r\nthere is no evidence of a shift in the overall warming trend observed \r\nsince the 1950s (Turner et  al., 2016, 2020; Gonzalez and Fortuny, \r\n2018; Jones et al., 2019; Bozkurt et al., 2020)."


id: 446d116b13610accb1bb1f98ec8deed1
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 8187
content:
"While a trend towards a positive phase of the SAM since the 1970s \r\nlikely explains a significant part of the warming at the northern AP, it \r\nhad a cooling effect on continental WAN and EAN (particularly strong \r\nin DJF; Table Atlas.1). Warming in western AP and over WAN during \r\n1957-2016 (Figure Atlas.30) and through to 2020 (Figure  2.11) is \r\nlikely due to significant contribution of other factors, such as tropical \r\nPacific forcing through PDV, ENSO, Amundsen Sea Low position/\r\nstrength and also anthropogenic climate change (Jones et al., 2019; \r\nScott et al., 2019; Wille et al., 2019; Donat-Magnin et al., 2020; Turner \r\net al., 2020). Since SROCC, new studies confirmed the influence of \r\nfoehn wind and cloud radiative forcing on Larsen C surface melt \r\n(Elvidge et al., 2020; Gilbert et al., 2020; Turton et al., 2020). In WAN, \r\nsummer surface-melt occurrence over ice shelves may have increased \r\nsince the late 2000s (Scott et  al., 2019)"


id: 1e80c06a4c381d9aca67414b66dcb351
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 2240
content:
" It is likely that increased \r\nmeltwater ponding and resulting hydrofracturing have been \r\nimportant mechanisms of the rapid disintegration of the Larsen B ice \r\nshelf (Banwell et al., 2013; MacAyeal and Sergienko, 2013; Robel and \r\nBanwell, 2019). Ice-shelf disintegration and relevant processes are \r\ndiscussed in Sections 9.4.2.1 and 9.4.2.3."


id: 5008ec2bc0e0412d5337a94ac89e497c
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 10035
content:
"The geographic pattern of accumulation changes since the 1950s \r\nbears a strong imprint of a trend towards a more positive phase of the \r\nSAM (e.g., Medley and Thomas, 2019), which could be linked to ozone \r\ndepletion (Lenaerts et al., 2018) or large-scale atmospheric warming \r\n(Frieler et al., 2015; Medley and Thomas, 2019). More evidence has \r\nemerged showing the importance of the Pacific-South American \r\npattern, ENSO and Pacific Ocean convection, and large-scale blocking \r\ncausing warm-air intrusions and both extreme precipitation and \r\nmelt events, responsible for large interannual SMB variability (high \r\nconfidence) (Gorodetskaya et al., 2014; Bodart and Bingham, 2019; \r\nScott et al., 2019; Turner et al., 2019; Wille et al., 2019; Adusumilli \r\net al., 2021). This strengthens evidence for an important connection \r\nbetween Antarctic climate and tropical sea surface temperature \r\nstated by SROCC (Meredith et al., 2019). Section 3.4.3 and SROCC \r\n(Meredith et al"


id: db2bb835bf011d6e2c06eb150bc1baad
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 31996
content:
"Recent higher-resolution simulations covering the entire Antarctic Ice \r\nSheet with a grid spacing of 12 to 50 km include five Polar-CORDEX \r\nRCMs  - RACMO2 (van Wessem et  al., 2018), MAR (Agosta et  al., \r\n2019; Kittel et  al., 2021), COSMO-CLM2 (Souverijns et  al., 2019), \r\nHIRHAM5 (Lucas-Picher et  al., 2012) and MetUM (Walters et  al., \r\n2017; Mottram et al., 2021) - and one stretched-grid GCM - ARPEGE \r\n(Beaumet et  al., 2019). RCM simulations forced by ERA-Interim \r\nagree well with automatic weather station temperatures, with \r\nhigh correlation (R2 > 0.9) and low bias (<1.5degC) except for high\u0002resolution HIRHAM5 (-2.1degC) and MetUM (-3.4degC), which are not \r\ninternally nudged models (Mottram et  al., 2021). RCMs generally \r\nunderestimate the observed SMB but with biases lower than \r\n20%, except for COSMO-CLM2 at lower elevations (<1200 m) and \r\nHIRHAM5 and MetUM at higher elevations (>2200 m) (Mottram \r\net  al., 2021)"


id: dc78c51678547b62f7644a73e99d9a7d
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 32188
content:
" These RCM simulations lead to estimates of the \r\ngrounded Antarctic Ice Sheet SMB ranging from 2133 Gt yr-1 to \r\n2328 Gt yr -1 when considering the four simulations compatible with \r\nthe IMBIE2 Antarctic total mass budget (IMBIE team et  al., 2018; \r\nMottram et al., 2021). However, the simulated spatial pattern of SMB \r\ndiffers widely between models, suggesting the importance of missing \r\nor under-represented processes in the models, such as drifting-snow \r\ntransport and sublimation (Agosta et al., 2019), cloud-precipitation \r\nmicrophysical processes (van Wessem et  al., 2018) and snowpack \r\nmodelling (Mottram et  al., 2021). Comparisons of integrated \r\nSMB estimates between models are also complicated by different \r\nresolutions and continental ice masks, with models showing large \r\ndifferences in the absolute SMB (Mottram et  al., 2021) but better \r\nagreement for SMB annual rates (Figure Atlas.30)."


id: 29f75abc51f073f88a631e1fc02d2107
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
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vector_id: 4033
content:
"There is high confidence that projected future surface air temperature \r\nincrease over Antarctica will be accompanied by precipitation \r\nincrease (Figure Atlas.29). CMIP6 models show a similar or larger but \r\nmore constrained increase in precipitation (more models agreeing \r\nwith larger precipitation increase) for the same GWLs compared to \r\nCMIP5. For example, over WAN during JJA for 3degC GWL, CMIP6 and \r\nCMIP5 models project a median 15% increase in precipitation with \r\na 10th-90th percentile range of 7-25% in CMIP6 models and of 3-24% \r\nin CMIP5. Average precipitation changes relative to 1995-2014 over \r\nWAN and EAN are largely similar; they show projected increases for \r\nSSP2-4.5 (SSP5-8.5) of around 5% (5%) for 2021-2040, 7% (10%) \r\nfor 2041-2060, and 12% (25%) for 2081-2100 with smaller \r\nincreases projected for SSP1-2.6 emissions, reaching around 5% in \r\n2081-2100"


id: f79fe989f1f7905849cf7d192d5b73e8
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
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vector_id: 36469
content:
" Regionally, the largest relative precipitation increase \r\nis projected (under all scenarios) for the eastern part of WAN, the \r\nwestern AP, large parts of the EAN plateau and over coastal EAN \r\nwithin 0degE-90degE longitudinal sector (Interactive Atlas). The largest \r\nincrease in absolute precipitation amount is projected along the \r\ncoastal regions, with the largest increase over coastal WAN and \r\nthe western AP, and is projected to be largely driven by the increase \r\nin maximum five-day precipitation (Interactive Atlas), which is in line \r\nwith the dominant contribution of extreme snowfall events to the \r\ntotal annual precipitation in the present Antarctic climate (Boening \r\net al., 2012; Gorodetskaya et al., 2014; Turner et al., 2020)"


id: 788ead1e368488a0d42fa567d63e48c6
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 16528
content:
" Under all \r\nemissions scenarios, the coastal precipitation increase corresponds to \r\nthe snowfall increase, except for the northern and central part of the \r\nwestern AP, where snowfall is projected to decrease and rainfall to \r\nincrease (similarly to the tendency towards increased precipitation, \r\ndecreased snowfall and increase in rainfall over the Southern Ocean; \r\nInteractive Atlas)."


id: d9a6b94277a3fd38fecfc0129b8fc3fd
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 31758
content:
"The Antarctic region is very likely to experience a significant increase \r\nin annual mean temperature and precipitation by the end of this \r\ncentury under all emissions scenarios used in CMIP5 and CMIP6 \r\n(Figure Atlas.29; Bracegirdle et  al., 2015, 2020b; Frieler et  al., 2015; \r\nLenaerts et al., 2016; Previdi and Polvani, 2016; Palerme et al., 2017). \r\nEnsemble means (and 10th-90th percentile ranges) of end-of-century \r\n(2081-2100) projected Antarctic surface air temperature change from \r\n35 CMIP6 models and relative to 1995-2014 are 1.2degC (0.5degC-2.0degC) \r\nfor the SSP1-2.6 emissions scenarios, 2.3degC (1.3degC-3.4degC) for SSP2-4.5, \r\n3.5degC (2degC-5degC) for SSP3-7.0, and 4.4degC (2.8degC-6.4degC) for SSP5-8.5 \r\n(Interactive Atlas). Both temperature and precipitation projections are \r\ncharacterized by a relatively large multi-model range (Figure Atlas.29 \r\nand the Interactive Atlas)"


id: 8f93abb5f49013b62968c0881b58939b
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 20137
content:
" A strong regional variability is present with \r\nthe projected changes over coastal Antarctica not scaling linearly \r\nwith  global forcing. While continental mean temperatures are \r\nlinearly  related to global mean temperatures in CMIP6 models, the \r\nrelative increase in coastal temperatures are higher for low-emissions \r\nscenarios due to stronger relative Southern Ocean warming and \r\nrelatively stronger effects of ozone recovery (Bracegirdle et al., 2020b). \r\nA  higher multi-model average increase in temperature is projected \r\nby CMIP6 models compared to CMIP5, with a  1.3degC higher mean \r\nAntarctic near-surface temperature at the end of the 21st  century \r\n(Kittel et  al., 2021). While similar median temperature changes are \r\nprojected for WAN and EAN, the former shows larger spread and \r\nhigher projected temperature range in both CMIP5 and CMIP6 models \r\nand for all scenarios (Figure Atlas.29)"


id: 625250909205a85c3da87eee057c24eb
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 13020
content:
" CORDEX-Antarctica simulations \r\nshow a mean and range in the future temperature changes similar to \r\nthe subset of CMIP5 models used to drive them for the RCP8.5 scenario \r\nand 1.5degC, 2degC and 3degC GWLs (Figure Atlas.29)."


id: d4c762bb7c283c35c55e7114b735f396
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 31004
content:
"From 2000 to 2100, the grounded Antarctic SMB is projected to \r\nmitigate sea level rise for RCP4.5 (RCP8.5) by the following sea \r\nlevel equivalents (SLEs), 0.03 +- 0.02 m (0.08 +- 0.04 m SLE) from \r\n30 CMIP5 models and for SSP2-4.5 (SSP5-8.5) by 0.03 +- 0.03  m \r\nSLE (0.07 +- 0.04 m SLE) from 24 CMIP6 models (Gorte et al., 2020). \r\nSubsets or downscaling of CMIP AOGCMs lead to 21st-century \r\ncumulative projections in the range of 0.05 +- 0.03 m SLE for \r\nCMIP5 RCP8.5 and 0.08 +- 0.04 m SLE for CMIP6 SSP5-8.5 (Gorte \r\net al., 2020; Nowicki et al., 2020; Seroussi et al., 2020; Kittel et al., \r\n2021). Use of model subsets reduces spread leading to either lower \r\nor higher climate sensitivity in the Antarctic depending on the \r\nselection method. For example, models selected by Gorte et al. (2020) \r\nbased on SMB ice-core reconstruction from Medley and Thomas \r\n(2019) tend to underestimate strongly winter sea ice area (Agosta \r\net  al., 2015; Roach et  al"


id: b08afe2d3188c715a296e32b991395e8
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 25297
content:
", 2020) and show reduced 21st-century \r\nincrease in Antarctic SMB compared to the full ensembles (Agosta \r\net al., 2015; Bracegirdle et al., 2015). A different subset of models \r\nis used for ISMIP6 (Section 9.4.2.3) which gives a lower increase in \r\nAntarctic SMB than the full ensemble for CMIP5 but a larger increase \r\nfor CMIP6."


id: 5c067ffef2d0d926c05b7cb37b8df9e9
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
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index: climate_gpt_only_giec
vector_id: 11987
content:
"in Europe (NEU) and North America (NEN, NWN) (Figure Atlas.29). \r\nThe region is one of the coldest and driest regions on Earth and \r\nplays a  key role influencing global and regional climates and the \r\nhydrological cycle. A  number of physical processes contribute to \r\namplified Arctic temperature variations as compared to the global \r\ntemperature, in particular thermodynamic changes that include \r\nthe increase in surface absorption of solar radiation due to surface \r\nalbedo feedbacks related with sea ice, ice, and snow cover retreat \r\nas well as poleward energy transports, water-vapour-radiation and \r\ncloud-radiation feedbacks (Screen and Simmonds, 2010; Serreze \r\nand Barry, 2011; Pithan and Mauritsen, 2014; Bintanja and Krikken, \r\n2016; Graversen and Burtu, 2016; Franzke et  al., 2017; Stuecker \r\net  al., 2018). Precipitation in the Arctic is dominated by snowfall, \r\nwith rainfall present mostly during the summer period"


id: 9f211bc6bfe784d957697afaedeaf02d
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 22571
content:
" Arctic \r\nclimate is influenced by the North Atlantic Oscillation (NAO), the \r\nleading mode of atmospheric variability in the North Atlantic basin \r\nwith a northward extension into the Arctic affecting temperature, \r\nprecipitation and sea ice over the region, with ENSO and Atlantic \r\nMulti-decadal Variability (AMV) also affecting parts of the region \r\n(Annex IV). Further, the Greenland Ice Sheet contribution to sea \r\nlevel results from the imbalance between mass gain by net snow \r\naccumulation and mass loss by meltwater runoff and ice discharge \r\ninto the ocean (IMBIE team, 2020), highlighting that the ice sheet is \r\na major contributor to sea level changes."


id: 450ba454b268fccb9974be130b405c2b
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 8288
content:
"The Arctic has warmed at more than twice the global rate over the \r\npast 50 years with the greatest warming during the cold season \r\n(high confidence) (Davy et al., 2018; Box et al., 2019; Przybylak and \r\nWyszynski, 2020; Xiao et al., 2020). This is based on various Arctic \r\namplification processes, in particular the combined effect of several \r\nrelated feedback processes, including between various radiation \r\ncomponents and (a) the albedo of sea ice and snow, (b) water vapour, \r\nand (c) clouds, as well as poleward energy transports. The annual \r\naverage Arctic surface air temperature increased by 2.7degC from 1971 \r\nto 2017, with a  3.1degC increase in the cold season (October-May) \r\nand a 1.8degC increase in the warm season (June-September) (AMAP, \r\n2019). Satellite-based data estimate the rate of annual warming for \r\n1981-2012 over sea ice covered regions to be 0.47degC per decade, \r\nwhereas the trend was significantly higher at 0"


id: 963ba7a56f32d1f72cb485b0a53a77bc
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 21139
content:
"77degC per decade over \r\nGreenland and amplified in the northern Barents and Kara seas \r\n(Comiso and Hall, 2014). The largest Arctic warming in 2003-2017 \r\nwas reported over the Barents and Kara seas with trends larger than \r\n2.5degC per decade (Susskind et  al., 2019), and Arctic temperatures \r\nfrom 2014 to 2018 have exceeded all previous records since 1900 \r\n(Blunden and Arndt, 2019)."


id: f4d26752c0d3bc39edb4536b34f34cf6
created_at: 2023-03-16 14:02:11
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index: climate_gpt_only_giec
vector_id: 36027
content:
"Over the ARO, long-term temperature records are available from \r\nSpitsbergen (Svalbard Airport). For the period 1898-2018, the annual \r\nmean warming was 0.32degC per decade, about 3.5 times the global \r\nmean temperature for the same period and since 1991, it was 1.7degC \r\nper decade or about seven times the global average for the same \r\nperiod (Nordli et al., 2020). There is a positive trend in the annual \r\ntemperature for all stations across Svalbard (Gjelten et  al., 2016; \r\nHanssen-Bauer et al., 2019; Dahlke et al., 2020) of 0.64degC-1.01degC \r\nper decade for 1971-2017 (Hanssen-Bauer et al., 2019), co-varying \r\nwith regional changes in sea ice conditions (Dahlke et al., 2020). The \r\nlargest temperature trends very likely occur in winter, with Svalbard \r\nAirport warming at 0.43degC per decade during 1898-2018 and 3.19degC \r\nper decade during 1991-2018 (Nordli et al., 2020), and Isaksen et al"


id: f86b297846b9b0bb02cec6e63861e293
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
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index: climate_gpt_only_giec
vector_id: 36598
content:
" \r\n(2016) reporting on substantial warming in western Spitsbergen, \r\nparticularly in winter, while the summer warming is moderate."


id: 9f4996d7ee50a8e024ea5dec14c47716
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 22603
content:
"The CMIP5 models reproduce the observed Arctic warming over the \r\npast  century (medium confidence) (Chylek et  al., 2016; Hao et  al., \r\n2018; Huang et al., 2019). The simulated mean Arctic warming for \r\n1900-2014 averaged over 40 CMIP5 models is 2.7degC compared \r\nto the observed values of 2.2degC (NASA GISS data smoothed using \r\na  1200-km radius) or 1.7degC (using a  250-km smoothing radius) \r\n(Chylek et al., 2016). However, there are large inter-model differences \r\nin the simulated warming which ranges from 1.2degC to 5.0degC. Although \r\nthe  CMIP5 models reproduce the spatially averaged observed \r\nwarming over the past 50 to 100 years, the pattern is different \r\nfrom that of observations and reanalysis (Xie et al., 2016; Franzke \r\net al., 2017; Hao et al., 2018). Zonal mean temperature trends in the \r\nCMIP5 models overestimate the warming in the cold season over \r\nhigh latitudes in the Northern Hemisphere (Xie et al., 2016)"


id: cf08ff18fd99470d2dad1b382ad69c5d
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 30107
content:
" Overall, \r\nthe amplified Arctic warming in recent decades is overestimated by \r\nCMIP5 models (Huang et al., 2019). Possible reasons are modelled \r\nsea surface temperature biases and an overestimated temperature \r\nresponse to the Arctic sea ice decline. Furthermore, some models, "


id: ef298b37c0d30a291fd495eefde06a6f
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 35099
content:
"runoff. Mouginot et al. (2019) stated that SMB played a strong role \r\nin the ice-sheet mass loss, where SMB dominated in the last two \r\ndecades. Mottram et al. (2019) found that SMB processes dominate \r\nthe ice-sheet mass budget over most of the interior, highlighting that \r\nthe ice sheet is a contributor to global mean sea level rise between \r\n1991 and 2015. More specifically, SMB models have improved \r\n(Fettweis et al., 2020; Hanna et al., 2021) due to increased availability \r\nand quality of remotely sensed (Koenig et al., 2016; Overly et al., 2016) \r\nand in situ observations (Machguth et al., 2016; Fausto et al., 2018; \r\nVandecrux et al., 2019, 2020). Fettweis et al. (2020) showed that the \r\nmodels' ensemble mean provides the best estimate of the present\u0002day SMB relative to observations. This is the case for the patterns \r\nin all seven regions (regional division after Mouginot et al"


id: 6c05a037561b1b8456a64cee7a2d4e2
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
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index: climate_gpt_only_giec
vector_id: 14620
content:
", 2019) \r\napart from the SE accumulation zone where large discrepancies in \r\nmodelled snowfall accumulation occurred where the spread can reach \r\n2-m water equivalent per year. Montgomery et al. (2020) confirmed \r\nthis, highlighting that RCMs (MAR and RACMO) are underestimating \r\naccumulation in south-east Greenland and that models misrepresent \r\nspatial heterogeneity due to an orographically forced bias in snowfall \r\nnear the coast. Further, for north-east Greenland, Karlsson et  al. \r\n(2020) found RCMs underestimate snow accumulation rates by up \r\nto 35%. The regional time series show that SMB has been gradually \r\ndecreasing in all seven regions (1979-2017), although the trend \r\nis less strong in central-eastern and south-eastern regions. In the \r\nsouth-west, north-east and north-west, SMB turns negative or close \r\nto zero after 2000 and remains above zero in other regions (medium \r\nconfidence) (Figure Atlas.30)."


id: cd21c96a1d00c93812c32758ef8b5ac4
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
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index: climate_gpt_only_giec
vector_id: 29823
content:
"Overland et al. (2014) highlighted the difference between the near\u0002term 'adaptation timescale' and the long-term 'mitigation timescale' \r\nfor the Arctic. Only in the latter half of the century do the projections \r\nunder RCP4.5 and RCP8.5 noticeably separate. End-of-the-century \r\nwarming is approximately twice as large under RCP8.5 demonstrating \r\nthe impact of the lower emissions under RCP4.5 (high confidence) \r\n(AMAP, 2017). More specifically under the strong forcing scenario, \r\nannual mean surface air temperature in the Arctic is projected \r\nto increase by 8.5degC +- 2.1degC over the course of the 21st  century \r\n(Bintanja and Andry, 2017), and emerges as a 'new Arctic' climate \r\nbeing significantly different from that of the mid-20th  century \r\n(Landrum and Holland, 2020). The end-of-the-century warming \r\n(2080-2099 relative to 1980-1999, RCP8.5) can exceed 15degC in \r\nautumn and winter over the Arctic Ocean (Koenigk et  al., 2015)"


id: e7a26fef9bdec3fbd42342376d94d97e
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
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index: climate_gpt_only_giec
vector_id: 33945
content:
" \r\nProjections averaged over the four best-performing CMIP5 models \r\nshow an Arctic annual warming of 4.1degC (RCP2.6), 5.7degC (RCP4.5) \r\nand 10.6degC (RCP8.5) by 2100 compared to 1951-1980 (Hao et al., \r\n2018). Also, for neighbouring Arctic regions, for example NEU, WSB \r\nand ESB, temperature is projected to increase towards the end of the \r\ncentury under both RCP4.5 and RCP8.5 (Atlas.8 and Atlas.5.2)."


id: fb80d5f990fa08fdd11334602399b767
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 37068
content:
"Mean precipitation in ARO, GIC and RAR is projected to rise in \r\na  warming climate (Figure Atlas.29), with different rates for the \r\ndifferent seasons and scenarios. For NWN and NEN, see Atlas.9. \r\nThe CMIP5 multi-model mean projected precipitation increase in \r\nthe Arctic is likely of the order of 50% under RCP8.5 by the end of \r\nthe 21st century, which is among the highest globally (Bintanja and \r\nSelten, 2014). Over 70degN-90degN, the precipitation increase is likely \r\n62 +- 20% and 56 +- 13% for RCP4.5 and RCP8.5 respectively. For \r\nARO (Svalbard), the increase in annual precipitation by 2100 is \r\nestimated to be about 45% for RCP4.5 and 65% for RCP8.5 (CMIP5 \r\nensemble median; Van der Bilt et  al., 2019). However, importantly \r\nthe simulated Arctic precipitation increase varies by a factor of three \r\nto four between models (Bintanja and Selten, 2014). The projected \r\nincrease is strongest in late autumn and winter (Vihma et al., 2016)"


id: 44d7ee0b5ca04eb2f666d4e891a5459a
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
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index: climate_gpt_only_giec
vector_id: 8252
content:
" \r\nThe interannual variability of Arctic precipitation will likely increase \r\nmarkedly (up to 40% over the 21st century), especially in summer \r\n(medium confidence) (Bintanja et al., 2020)."


id: 1189bc584ee97c7cf7004b28b03d5431
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
content_type: text
index: climate_gpt_only_giec
vector_id: 248
content:
"Detailed and structured information about climate models, \r\nsimulations and their conformance to common experimental \r\nprotocols is not only important for scientific interpretation but, \r\nunder increased scrutiny from society, it is also demanded of climate \r\nscience that purports to be mature, credible, open, transparent and \r\nreproducible (Guilyardi et al., 2013). Scientific publications remain an \r\nessential way of documenting models but remain largely scattered \r\nand not easily accessible by the growing community of users of model \r\noutput. To address these challenges, the Earth System Documentation \r\n(ES-DOC) project offers an ecosystem of tools and services in support \r\nof Earth system modelling documentation creation, analysis and \r\ndissemination"


id: 79e7527b781b80f6f1aa360c87cf0dfc
created_at: 2023-03-16 14:02:11
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" ES-DOC is coordinated with other community efforts \r\nsuch as the Coupled Model Intercomparison Project (CMIP) via the \r\nWorld Climate Research Programme work group on Climate Modelling \r\n(WGCM) and its Infrastructure Panel WIP (Balaji et al., 2018)."


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"The key new model developments since AR5 are summarized in \r\nChapter 1, Section 1.3.5, and model results are assessed in multiple \r\nchapters of this report. In this annex, Table AII.5 presents the main \r\nfeatures of the CMIP6 coupled models, in a  format comparable \r\nwith AR5 Table 9.A.1 for CMIP5 (Flato et al., 2013). At the date of \r\nMarch 2021, 136 models had registered for CMIP6, including the 23 \r\nCMIP6-endorsed MIPs (Chapter 1, Table 1.3). For conciseness, Table \r\nAII.5 documents only the coupled models used in the CMIP6 'DECK' \r\nexperiments and ScenarioMIP, excluding atmosphere-only and \r\nocean-only components (AMIP and OMIP), radiative transfer models \r\n(RFMIP), and other MIPs. Registered coupled models that have not \r\nsubmitted data in time to be used in this report are not included. The \r\nhigh-resolution models used for HighResMIP (Haarsma et al., 2016) \r\nare listed in Table AII.6, and ice-sheet models are documented in \r\nSection AII.3"


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" The citation information for all CMIP6 model datasets \r\nis compiled in Section AII.4, Table AII.10."


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"The objective of CMIP is to better understand past, present and future \r\nglobal climate changes arising from natural, unforced variability or \r\nin response to changes in radiative forcing in a multi-model context \r\n(Chapter 1, Section 1.5.4). This understanding includes assessments \r\nof model performance during the historical period (Chapter 3) and \r\nquantifications of the causes of the spread in future projections \r\n(Chapters 4 and 7). Idealized experiments are also used to increase \r\nunderstanding of the model responses. In addition to these long \r\ntime scale responses, experiments are performed to investigate the \r\npredictability of the climate system on various time and space scales, \r\nas well as making predictions from initialized climate states. The \r\ndifferent activities (MIP) endorsed by CMIP6 are listed in Chapter 1, \r\nTable  1.3 (Eyring et  al., 2016)"


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" A set of common experiments, the \r\nDECK (Diagnostic, Evaluation and Characterization of Klima) and \r\nCMIP historical simulations (1850-2014), were introduced that will \r\nmaintain continuity with previous CMIP phases and help document \r\nbasic and evolving characteristics of models. ScenarioMIP is the \r\nframework for future climate projections (O'Neill et  al., 2016). \r\nThe infrastructure panel of the World Climate Research Programme \r\ncoordinates framework developments and defines data standards \r\nfor CMIP. A key aspect is the dissemination of the data via the \r\nEarth System Grid Federation (ESGF; Williams et  al., 2016; Petrie \r\net al., 2021)."


id: 52f31f19a98a68e267666d9ae56dff70
created_at: 2023-03-16 14:02:11
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"index time series, that is, principal component (PC), describing the \r\ntemporal evolution tied to the spatial pattern. PCA/EOF is widely \r\nused with gridded datasets and model outputs. Correlation or \r\nregression maps between the climate index time series and other \r\nspace-time variables, such as precipitation, temperature or wind, are \r\nthen calculated to quantify the teleconnection patterns. Altogether, \r\nbeyond their physical relevance, modes of climate variability and \r\ntheir teleconnection patterns provide a  simplified representation \r\nof climate variation affecting weather, from regional (i.e., oceanic \r\nor continental scale) to worldwide scales (i.e., for some specific \r\nphenomena like ENSO), having important impacts on human \r\nactivities and ecosystems. Modes are often associated with climatic \r\nimpact-drivers (CIDs, as defined in Chapter  12, Section  12"


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"1) \r\nincluding droughts, heavy rains, storms, heatwaves and cold spells, \r\naffecting agriculture, water resources, availability and consumption \r\nof energy, and fire risks."


id: 5556e23a485d235eabd492e43ebe7f6e
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"This annex describes the fundamental features of the main modes \r\nof large-scale climate variability assessed across chapters in the AR6 \r\nWGI Report. Modes are defined as recurrent space-time structures \r\nof variability of the climate system with intrinsic spatial patterns, \r\nseasonality and time scales. They can arise through the dynamical \r\ncharacteristics of atmospheric circulation, but also through coupling \r\nbetween the ocean and the atmosphere, with some interactions with \r\nland surfaces and sea ice. The variability of the climate system at \r\nocean- or continental-basin scales, and in particular, on seasonal\u0002to-multi-decadal time scales, can be described to a large extent by \r\nthe occurrence and often combination of several modes of climate \r\nvariability which lead to local impacts and remote responses through \r\nteleconnection processes on top of externally forced trends"


id: ee6aa01db1422ee83d4218a95e0320ae
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" More \r\nprecisely, the concept of 'teleconnection' refers to the ability of modes \r\nof variability to relate climate in remote regions through associated \r\natmospheric or oceanic pathways. Regional climate variations \r\nare thus the complex outcome of local physical processes, such as \r\nthermodynamical and land-atmosphere feedback processes,  and \r\nnon-local large-scale phenomena."


id: 2193e55a44b1798d39c0511c53b7861e
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"Historically, modes have been identified based on evidence of \r\ncorrelations between regional climate variations at widely separated, \r\nmostly geographically fixed spatial locations. One of the oldest known \r\nspace-time structured patterns of variability is the North Atlantic \r\nOscillation (NAO; Section AIV.2.1), with some early description from \r\nseafaring activities available since several centuries ago (Stephenson \r\net  al., 2003). Similarly, empirical, albeit remarkably precise, \r\ndocumentation of trans-Pacific coherent variability in atmospheric \r\nand oceanic conditions can be traced back to the 19th century for the \r\nEl Nino-Southern Oscillation (ENSO; Section AIV.2.3). The Southern \r\nOscillation was named in 1924 and the first use of the name 'El Nino' \r\nto describe a  climatic phenomenon appeared in South America in \r\n1893 (see for instance Philander, 1983 for ENSO history)"


id: fcc885473db01d96c003833296bb8c7e
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" Since then, \r\nthe increased spatial coverage of observations and the measurement \r\nof new climatic parameters have improved significantly. These \r\nadvancements, along with the development of new theoretical \r\nbackground and statistical methods for combining different sources \r\nof information, have allowed substantial improvements in our \r\nunderstanding of the characteristic spatial scale of the coherent \r\nstructures of variability as well as of the characteristic temporal \r\nexpression of the modes."


id: c195a7f29a08e00a635b738228f64873
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"Previous IPCC reports have described the observed and modelled \r\nbehaviour of a  large number of modes of variability over the \r\ninstrumental period and in a  paleoclimate context. The projected \r\nevolution of those modes has been also documented based on \r\nfuture climate scenarios. In AR5, large-scale modes of variability \r\nand teleconnections were assessed in depth in Chapter 14, setting \r\nthe physical basis of future regional climate changes contingent on \r\nglobal mean temperature increase. Modes were defined through \r\nso-called 'indices of climate variability' in AR5 Chapter 2, dedicated to \r\nobserved atmospheric and surface changes. Two boxes (AR5 Box 2.5 \r\nand AR5 Box 14.1) introduced the main concepts. AR4 had a similar \r\nstructure, with a specific section on modes in the chapter dedicated \r\nto the observations (Chapter 3)"


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" In AR6, a different perspective has \r\nbeen adopted because the modes are discussed in various chapters \r\nand assessed with respect to the specific objectives of each of them. \r\nSpace-time past changes of the modes using proxy records and \r\ninstrumental data are assessed in Chapter  2. Human influence on \r\nmodes is assessed in Chapter 3 over the instrumental period, while \r\ntheir future changes are addressed in Chapter 4. Their influence at \r\nregional scale through large-scale teleconnection is assessed in \r\nChapters 10, 11 and 12, and in the Atlas, while the contribution \r\nof the modes in past and future hydrological changes and related \r\nuncertainties are assessed in Chapter 8. Chapter 9 focuses on oceanic \r\nexpression of the modes. The aim of this annex is to set a common \r\nbackground for the assessment of the modes of climate variability \r\nand related regional climate anomalies for all the AR6 WG1 chapters, \r\nand to provide this information for the readers of the report"


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" In doing \r\nso, the annex provides a more accessible summary of the main modes \r\nof variability allowing a more coherent assessment of this important \r\naspect of the climate system."


id: fe0c382c70d8c497dd0b8e598b03931
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"The NAO can be viewed as the regional expression of a hemispheric\u0002scale mode of atmospheric variability that was identified in the late \r\n1990s known as the Arctic Oscillation (AO; Thompson and Wallace, \r\n1998) or Northern Annular Mode (NAM; Thompson and \r\nWallace,  2000; Ambaum et  al., 2001) to reflect the longitudinal \r\nscale that transcends the entire hemisphere. An additional centre \r\nof action is present for NAM over the North Pacific, albeit much \r\nweaker than its Atlantic counterpart, with a slackened Aleutian Low \r\nin the positive phase (Figure AIV.1b). The AO/NAM can be defined as \r\nthe leading mode of variability across a range of atmospheric levels \r\nfrom the surface to the stratosphere (Baldwin and Dunkerton, 1999; \r\nGerber et al., 2010). In the stratosphere, the AO/NAM corresponds \r\nto fluctuations in the strength of the boreal winter polar vortex over \r\nthe Arctic"


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" In the troposphere, the AO/NAM primarily corresponds to \r\nfluctuations in the position of the mid-latitude westerly jet stream. \r\nInteractions between the stratospheric and tropospheric annular \r\nmodes have been extensively studied and are important for the \r\nvariability and predictability of the mode at intra-seasonal time \r\nscales (i.e., see Domeisen, 2019)."


id: b3913238340ac2fd67fcaab676e8f370
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"Despite the fact that NAM and NAO indices are strongly correlated \r\n(Figure AIV.1c), the debate on the relevance of the NAM as a mode of \r\nvariability per se (Deser, 2000; Honda and Nakamura, 2001; Itoh, 2008) \r\nis not closed yet, despite progress in understanding the dynamical \r\nmechanisms linking the Pacific and Atlantic SLP cores on the basis \r\nof wave breaking theory that prevails at intra-seasonal time scales \r\n(e.g., Riviere and Drouard, 2015). Consequently, NAO and NAM are \r\ntreated as indistinguishable in some studies (Feldstein and Franzke, \r\n2006) while others still consider the NAO as a  stand-alone mode \r\neven if associated circumpolar patterns exist (Garcia-Serrano \r\nand Haarsma, 2017). In the latter case, the NAM is interpreted as \r\na statistical artefact of two regional independent modes, namely the \r\nNAO and the Pacific-North American pattern (Ambaum et al., 2001; \r\nDouville et al., 2017)"


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" Throughout the report, NAO and NAM are often \r\nconsidered as the same entity because the NAO explains most of \r\nthe variance on the zonally-averaged hemispheric circulation and \r\nbecause the associated time series are highly correlated."


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"The NAO/NAM arises as large-scale signals primarily driven by the \r\ninternal atmospheric variability of mid-latitude dynamics (e.g., Lorenz \r\nand Hartmann, 2003). By nature, these signals correspond to \r\nbarotropic phenomena being partly controlled by eddy-mean flow \r\ninteraction (Feldstein and Franzke, 2017). Their fingerprint can be \r\nfound along the entire atmospheric column with maximum amplitude \r\nin the winter season when storminess is the most pronounced. In \r\naddition, the tropospheric NAO/NAM variability has been shown \r\nin boreal winter to be linked to the strength of the stratospheric \r\npolar vortex (Baldwin and Dunkerton, 1999), including sudden \r\nstratospheric warmings (Domeisen, 2019)"


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" On intra-seasonal time \r\nscales, downward propagation of associated geopotential height \r\nanomalies occurs from the lower stratosphere to the troposphere \r\nthrough wave-mean flow interaction resulting in some predictability \r\nat the surface for CIDs, through temperature, precipitation and \r\nwind anomalies at large spatial scales (e.g., Karpechko et al., 2017 \r\nover Europe)."


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"The NAO is the main mode of climate variability over a broad North \r\nAtlantic-European region in all seasons (Hurrell and Deser, 2009) for \r\nintra-seasonal to multi-decadal time scales, albeit with seasonally \r\ndependent physical characteristics (fraction of explained variance, \r\nlatitudinal shift and intensity of the related anomalous atmospheric \r\ncirculation and SST fingerprint, etc.; Folland et al., 2009; Woollings \r\net al., 2015). The phases of the NAO control a significant fraction of \r\nthe variance of temperature and precipitation over the North Atlantic \r\nand surrounding continents, and of the prevailing westerly winds and \r\nthe related storm tracks (Woollings et al., 2014). It is also strongly \r\nrelated to the occurrence and intensity of blocking conditions, \r\nespecially over Greenland (Woollings et al., 2010; Davini et al"


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", 2012), \r\nand controls a large part of the incidence of extremes (cold waves, \r\nvery strong wind episodes related to explosive storminess, heavy \r\nprecipitation events, etc.) over Europe (Matthews et al., 2014; Yiou \r\net al., 2017) and eastern North America (Durkee et al., 2008; Whan \r\nand Zwiers, 2017)."


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content:
"The NAO/NAM includes a  forced component arising from SST \r\nanomalies: key oceanic regions are identified but are dependent on the \r\ntime scale of variability that is investigated (Baker et al., 2019). There \r\nis also growing evidence for the importance of cryosphere-atmosphere \r\ncoupling to explain part of the observed variability of the NAO in \r\nresponse to recent Arctic sea ice decline (Cohen et al., 2014), even if \r\ndiscrepancies remain in model estimates (Screen et al., 2018) and may \r\nnot be stationary in time (Blackport and Screen, 2020). Finally, part of \r\nthe NAO/NAM variability has also been documented to be influenced \r\nby external forcing such as volcanic eruptions (Swingedouw et  al., \r\n2017) and solar forcing (Ineson et al., 2011; Gray et al., 2016; Le Mouel \r\net  al., 2019) as well as anthropogenic factors (Gillett et  al., 2003)"


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" \r\nHowever, internal variability is predominant and, because of signal\u0002to-noise issues, very long observational records and large ensembles \r\nof climate simulations are needed to clearly evaluate the fraction \r\nof the forced component in the observed NAO/NAM, as assessed in \r\ndetail in Sections 2.4.1.1 and 3.7.1. Sea ice-NAO/NAM coupling is \r\ncomprehensively assessed in Cross-Chapter Box 10.1."


