# document

id: c95ab3722e870f5b626fe67f7c291a9d
created_at: 2023-03-16 14:02:08
updated_at: 2023-03-16 14:02:08
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"5degC in global warming can result in a significant increase \r\nin extreme precipitation. Projected long-period Rx1day return value \r\nchanges are larger than changes in mean Rx1day and with larger \r\nrelative changes for more rare events (Pendergrass, 2018; Mizuta and \r\nEndo, 2020; Wehner, 2020). The rate of change of moderate extreme \r\nprecipitation may depend more on the forcing agent, similar to the \r\nmean precipitation response to warming (Lin et al., 2016, 2018). \r\nThus, there is high confidence that extreme precipitation that occurs \r\nonce a year or less frequently increases proportionally to the amount \r\nof surface warming, and the rate of change in precipitation is not \r\ndependent on the underlying forcing agents of warming."


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"There is a limited number of studies on the projections of extreme \r\nhourly precipitation. The ability of GCMs to simulate hourly \r\nprecipitation extremes is limited (Morrison et al., 2019) and very \r\nfew modelling centres archive sub-daily and hourly precipitation \r\nprior to CMIP6 experiments. RCM simulations project an increase in \r\nextreme sub-daily precipitation in North America (C. Li et al., 2019b) \r\nand Sweden (Olsson and Foster, 2013), but these models still do not \r\nexplicitly resolve convective processes that are important for properly \r\nsimulating extreme sub-daily precipitation. Simulations by RCMs \r\nthat explicitly resolve convective processes (convection-permitting \r\nmodels) are limited in length and only available in a  few regions \r\nbecause of high computing costs"


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" Yet, a  majority of the available \r\nconvection-permitting simulations project increases in the intensities \r\nof extreme sub-daily precipitation events, with the amount similar to \r\nor higher than the C-C scaling rate (Kendon et al., 2014, 2019; Ban \r\net al., 2015; Prein et al., 2016b; Helsen et al., 2020; Fowler et al., 2021). \r\nAn increase is projected in extreme sub-daily precipitation over Africa \r\n(Kendon et al., 2019); East Africa (Finney et al., 2020) and Western \r\nAfrica (Berthou et al., 2019a; Fitzpatrick et al., 2020), even for areas \r\nwhere parametrized RCMs project a decrease; in Europe (Hodnebrog "


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"there are regional differences, extreme precipitation is projected \r\nto increase in almost all sub-regions, though there can be spatial \r\nheterogeneity within sub-regions, such as in India (Shashikanth et al., \r\n2018) and South East Asia (Ohba and Sugimoto, 2019). In East and \r\nSouth East Asia, there is high confidence that extreme precipitation \r\nis projected to intensify (Seo et al., 2014; Zhou et al., 2014; Y. Xu \r\net al., 2016; Nayak et al., 2017; X. Wang et al., 2017; Y. Wang et al., \r\n2017; Guo et al., 2018; D. Li et al., 2018; Sui et al., 2018). Extreme \r\ndaily precipitation is also projected to increase in South Asia (Xu \r\net al., 2017; Han et al., 2018; Shashikanth et al., 2018). The extreme \r\nprecipitation indices, including Rx5day, R95p, and days of heavy \r\nprecipitation (i.e., R10mm), are all projected to increase under the \r\nRCP4.5 and RCP8.5 scenarios in central and northern Asia (Xu et al., \r\n2017; Han et al., 2018)"


id: 186874bbed6f4eb774c08413cd8cfff5
created_at: 2023-03-16 14:02:08
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" A general wetting across the whole Tibetan \r\nPlateau and the Himalayas is projected, with increases in heavy \r\nprecipitation in the 21st  century (Palazzi et al., 2013; Zhou et al., \r\n2014; Rajbhandari et al., 2015; R. Zhang et al., 2015; Wu et al., 2017; \r\nGao et al., 2018; Paltan et al., 2018). Agreement in projected changes \r\nby different models is low in regions of complex topography such \r\nas Hindu-Kush Himalayas (Roy et al., 2019), but CMIP5, CMIP6 and \r\nCORDEX-CORE simulations consistently project an increase in heavy \r\nprecipitation in higher latitude areas, such as West and East Siberia, \r\nand Russian Far East (high confidence) (Coppola et al., 2021a). "


id: a9b4a4b21ce25b67956633ed5f4812d2
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"et al., 2019; Chan et al., 2020); as well as in the continental USA \r\n(Prein et al., 2016b). Overall, while limited, the available evidence \r\npoints to an increase in extreme sub-daily precipitation in the future. \r\nStudies on future changes in extreme precipitation for a month or \r\nlonger are limited. One study projects an increase in extreme monthly \r\nprecipitation in Japan under 4degC global warming for around 80% of \r\nstations in the summer (Hatsuzuka and Sato, 2019). \r\nIn Africa (Table 11.5), extreme precipitation will likely increase under \r\nwarming levels of 2degC or below (compared to pre-industrial values) \r\nand very likely increase at higher warming levels. Simulations by \r\nCMIP5, CMIP6 and CORDEX regional models project an increase \r\nin daily extreme precipitation between 1.5degC and 2.0degC warming \r\nlevels. The pattern of change in heavy precipitation under different \r\nscenarios or warming levels is similar with larger increases for \r\nhigher warming levels (e.g"


id: 4b989ec4f2801b0c4cbbfd5436daab5a
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", Nikulin et al., 2018; Li et al., 2021). With \r\nincreases in warming, extreme precipitation is projected to increase \r\nin the majority of land regions in Africa (Mtongori et al., 2016; Pfahl \r\net al., 2017; Diedhiou et al., 2018; Dunning et al., 2018; Akinyemi \r\nand Abiodun, 2019; Giorgi et al., 2019). Over Southern Africa, heavy \r\nprecipitation will likely increase by the end of the 21st century under \r\nRCP 8.5 (Dosio, 2016; Pinto et al., 2016; Abiodun et al., 2017; Dosio \r\net al., 2019). However, heavy rainfall amounts are projected to \r\ndecrease over western South Africa (Pinto et al., 2018) as a result \r\nof a projected decrease in the frequency of the prevailing westerly \r\nwinds south of the continent that translates into fewer cold fronts \r\nand closed mid-latitudes cyclones (Engelbrecht et al., 2009; Pinto \r\net al., 2018). Heavy precipitation will likely increase by the end of the \r\ncentury under RCP8.5 in West Africa (Diallo et al., 2016; Dosio, 2016; \r\nSylla et al"


id: be7866a448744beaf0938ca6bc6df503
created_at: 2023-03-16 14:02:08
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", 2016; Abiodun et al., 2017; Akinsanola and Zhou, 2019; \r\nDosio et al., 2019) and is projected to increase (high confidence) \r\nin Central Africa (Fotso-Nguemo et al., 2018, 2019; Sonkoue et al., \r\n2019) and eastern Africa (Thiery et al., 2016; Ongoma et al., 2018a). \r\nIn north-east and central east Africa, extreme precipitation intensity \r\nis projected to increase across CMIP5, CMIP6 and CORDEX-CORE \r\n(high confidence) in most areas annually (Coppola et al., 2021a), \r\nbut the trends differ from season to season in all future scenarios \r\n(Dosio et al., 2019). In northern Africa, there is low confidence in \r\nthe projected changes in heavy precipitation, either due to a lack of \r\nagreement among studies on the sign of changes (Sillmann et al., \r\n2013a; Giorgi et al., 2014) or due to insufficient evidence. "


id: 588a4a169ca54ef19450945284e29028
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"In Australasia (Table  11.11), most CMIP5 models project an \r\nincrease in Rx1day under RCP4.5 and RCP8.5 scenarios for the late \r\n21st century (CSIRO and BOM, 2015; Alexander and Arblaster, 2017; \r\nGrose et al., 2020) and the CMIP6 multi-model median projects an \r\nincrease in the 10- and 50-year return values of Rx1day and Rx5day \r\nat a rate between 5% and 6% per 1degC of near-surface global mean \r\nwarming (Figure 11.7; Li et al., 2021). Yet, there is large uncertainty \r\nin the increase because projected changes in dynamic processes \r\nlead to a  decrease in Rx1day that can offset the thermodynamic \r\nincrease over a large portion of the region (Box 11.1, Figure 1; Pfahl \r\net al., 2017). Projected changes in moderate extreme precipitation \r\n(the 99th percentile of daily precipitation) by RCMs under RCP8.5 \r\nfor 2070-2099 are mixed, with more regions showing decreases \r\nthan increases (Evans et al., 2021)"


id: 898b5863db80ed3acefc06ac801c8916
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" It is likely that daily rainfall \r\nextremes such as Rx1day will increase at the continental scale for \r\nglobal warming levels at or above 3degC. Daily rainfall extremes are \r\nprojected to increase at the 2.0degC global warming level (medium \r\nconfidence), and there is low confidence in changes at the 1.5degC.\r\nProjected changes show important regional differences with very \r\nlikely increases over Northern Australia (Alexander and Arblaster, \r\n2017; Herold et al., 2018; Grose et al., 2020) and New Zealand (MfE, \r\n2018) where projected dynamic contributions are small (Box  11.1 \r\nFigure 1; Pfahl et al., 2017) and medium confidence on increases over \r\ncentral, eastern, and Southern Australia where dynamic contributions \r\nare substantial and can affect local phenomena (CSIRO and BOM, \r\n2015; Pepler et al., 2016; Bell et al., 2019; Dowdy et al., 2019). "


id: 9c077c48393075fe9906432bfb61a065
created_at: 2023-03-16 14:02:08
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"In Asia (Table 11.8), extreme precipitation will likely increase at global \r\nwarming levels of 2degC and below, but very likely increase at higher \r\nwarming levels for the region as whole. The CMIP6 multi-model \r\nmedian projects an increase in the 10- and 50-year return values \r\nof Rx1day and Rx5day over more than 95% of regions, even at the \r\n2degC warming level, with larger increases at higher warming levels, \r\nindependent of emissions scenarios (Li et al., 2021, also Figure 11.7). \r\nThe CMIP5 models produced similar projections. Both heavy rainfall \r\nand rainfall intensity are projected to increase (Zhou et al., 2014; Guo \r\net al., 2016, 2018; Y. Xu et al., 2016; Endo et al., 2017; Han et al., 2018; \r\nG. Kim et al., 2018). A half-degree difference in warming between the \r\n1.5degC and 2.0degC warming levels can result in a detectable increase \r\nin extreme precipitation over the region (Li et al., 2021), in the \r\nAsian-Australian monsoon region (Chevuturi et al"


id: 6b6855264258488ba4f91383713cf8f5
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"and projected changes for more moderate extreme precipitation \r\nare also more uncertain. Extreme precipitation, represented by \r\nthe number of days with daily precipitation exceeding 50 mm and the \r\nannual fraction of precipitation falling during days with the top 10% \r\ndaily precipitation amount, is projected to increase on the eastern \r\ncoast of Southern Central America, but to decrease along the Pacific \r\ncoasts of El Salvador and Guatemala (Imbach et al., 2018). Chou et al. \r\n(2014b) and Giorgi et al. (2014) projected an increase in extreme \r\nprecipitation over South-Eastern South America and the Amazon"


id: f4f9f8ef3f2a9f1c0a08e152d11ed530
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" \r\nProjected changes in moderate extreme precipitation represented \r\nby the 99th percentile of daily precipitation by different models \r\nunder different emissions scenarios, even at high warming levels, \r\nare mixed: increases are projected for all regions by the CORDEX\u0002CORE and CMIP5 simulations, while increases for some regions and \r\ndecreases for other regions are projected by CMIP6 simulations \r\n(Coppola et al., 2021a). Extreme precipitation is projected to increase \r\nin the La Plata basin (Cavalcanti et al., 2015; Carril et al., 2016). Taylor \r\net al. (2018) projected a decrease in days with intense rainfall in the \r\nCaribbean under 2degC global warming by the 2050s under RCP4.5 \r\nrelative to 1971-2000. "


id: 6af14dfa56a5eb6ffaebab9b2c90a2ff
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"In regions with a seasonal snow cover, snowmelt is the main cause \r\nof extreme river flooding over large areas (Pall et al., 2019). Extensive \r\nsnowmelt combined with heavy and/or long-duration precipitation \r\ncan cause significant floods (D. Li et al., 2019; Krug et al., 2020). \r\nChanges in floods in these regions can be uncertain because of \r\nthe compounding and competing effects of the responses of snow \r\nand rain to warming that affect snowpack size: warming results \r\nin an increase in precipitation, but also a  reduction in the time \r\nperiod of snowfall accumulation (Teufel et al., 2019). An increase in \r\natmospheric CO2 enhances water-use efficiency by plants (Roderick \r\net al., 2015; Milly and Dunne, 2016; Swann et al., 2016; Swann, \r\n2018); this could reduce evapotranspiration and contribute to the \r\nmaintenance of soil moisture and streamflow levels under enhanced \r\natmospheric CO2 concentrations (Yang et al., 2019)"


id: 84bb26b474f542cf8ffba62d0f3af57c
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" This mechanism \r\nwould suggest an increase in the magnitude of some floods in the \r\nfuture (Kooperman et al., 2018). But this effect is uncertain as an \r\nincrease in leaf area index, and vegetation coverage could also result \r\nin overall larger water consumption (Matyas and Sun, 2014; Mankin \r\net al., 2019; Teuling et al., 2019), and there are also other CO2-related \r\nmechanisms that come into play (Cross-Chapter Box 5.1). "


id: e212139ef14a972e37bda530d0f69640
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"Floods are the inundation of normally dry land, and are classified \r\ninto types (e.g., pluvial floods, flash floods, river floods, groundwater \r\nfloods, surge floods, coastal floods) depending on the space and time \r\nscales and the major factors and processes involved (Section 8.2.3.2; \r\nNied et al., 2014; Aerts et al., 2018). Flooded area is difficult to \r\nmeasure or quantify and, for this reason, many of the existing studies \r\non changes in floods focus on streamflow. Thus, this section assesses \r\nchanges in flow as a proxy for river floods, in addition to some types \r\nof flash floods. Pluvial and urban floods  - types of flash floods \r\nresulting from the precipitation intensity exceeding the capacity of \r\nnatural and artificial drainage systems - are directly linked to extreme \r\nprecipitation. Because of this link, changes in extreme precipitation \r\nare the main proxy for inferring changes in pluvial and urban floods \r\n(see also Section  12"


id: a7446d08b5b99df219d0f855e8cb34ac
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"4), assuming there is no additional change \r\nin the surface condition. Changes in these types of floods are not \r\nassessed in this section, but can be inferred from the assessment of \r\nchanges in heavy precipitation in Section 11.4. Coastal floods due to \r\nextreme sea levels and flood changes at regional scales are assessed \r\nin Section 12.4. "


id: 90744c9526ea44ef8e8aa8a23615e1a7
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"Various factors, such as extreme precipitation (Cho et al., 2016; Archer \r\nand Fowler, 2018), glacier lake outbursts (Schneider et al., 2014; \r\nSchwanghart et al., 2016), or dam breaks (Biscarini et al., 2016) can \r\ncause flash floods. Very intense rainfall, along with a high fraction \r\nof impervious surfaces can result in flash floods in urban areas \r\n(Hettiarachchi et al., 2018). Because of this direct connection, changes \r\nin very intense precipitation can translate to changes in urban flood \r\npotential (Rosenzweig et al., 2018), though there can be a spectrum \r\nof urban flood responses to this flood potential (Smith et al., 2013), \r\nas many factors, such as the overland flow rate and the design of \r\nurban (Falconer et al., 2009) and storm water drainage systems \r\n(Maksimovic et al., 2009), can play an important role. Nevertheless, \r\nchanges in extreme precipitation are the main proxy for inferring \r\nchanges in some types of flash floods, (which are addressed in \r\nSection 12"


id: fac689e7aa8ff1bbce8fa2559dc0fd69
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"4), given the relation between extreme precipitation and \r\npluvial floods, the very limited literature on urban and pluvial floods \r\n(e.g., Skougaard Kaspersen et al., 2017), and limitations of existing \r\nmethodologies for assessing changes in floods (Archer et al., 2016). "


id: 83cab75d614d4f945d469c2833a936fc
created_at: 2023-03-16 14:02:08
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"The SREX (Seneviratne et al., 2012) assessed low confidence for \r\nobserved changes in the magnitude or frequency of floods at the \r\nglobal scale. This assessment was confirmed by AR5 (Hartmann et al., \r\n2013). The SR1.5 (Hoegh-Guldberg et al., 2018) found increases \r\nin flood frequency and extreme streamflow in some regions, but \r\ndecreases in other regions. While the number of studies on flood \r\ntrends has increased since AR5, and there were also new analyses \r\nafter the release of SR1.5 (Berghuijs et al., 2017; Bloschl et al., 2019; \r\nGudmundsson et al., 2019), hydrological literature on observed \r\nflood changes is heterogeneous, focusing at regional and sub\u0002regional basin scales, making it difficult to synthesize at the global \r\nand sometimes regional scales. The vast majority of studies focus on \r\nriver floods using streamflow as a proxy, with limited attention to \r\nurban floods"


id: 94c5d45eef4f6787183666d93fd6acdc
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" Streamflow measurements are not evenly distributed \r\nover space, with gaps in spatial coverage, and their coverage in many \r\nregions of Africa, South America, and parts of Asia is poor (e.g., Do \r\net al., 2017), leading to difficulties in detecting long-term changes in \r\nfloods (Slater and Villarini, 2017). See also Section 8.3.1.5."


id: 2af6e4ad0e3a8349a29bbafed611de6c
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"Peak flow trends are characterized by high regional variability and \r\nlack overall statistical significance of a decrease or an increase over \r\nthe globe as a  whole. Of more than 3500 streamflow stations in \r\nthe USA, central and Northern Europe, Africa, Brazil, and Australia, \r\n7.1% stations showed a  significant increase, and 11.9% stations \r\nshowed a significant decrease in annual maximum peak flow during \r\n1961-2005 (Do et al., 2017). This is in direct contrast to the global \r\nand continental scale intensification of short-duration extreme \r\nprecipitation (Section 11.4.2). There may be some consistency over \r\nlarge regions (see Gudmundsson et al., 2019), in high streamflows \r\n(>90th percentile), including a decrease in some regions (e.g., in the \r\nMediterranean) and an increase in others (e.g., northern Asia), but \r\ngauge coverage is often limited. On a continental scale, a decrease \r\nseems to dominate in Africa (Tramblay et al., 2020) and Australia \r\n(Ishak et al"


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", 2013; Wasko and Nathan, 2019), an increase in the \r\nAmazon (Barichivich et al., 2018), and trends are spatially variable \r\nin other continents (Q. Zhang et al., 2015b; Bai et al., 2016; Do et al., \r\n2017; Hodgkins et al., 2017). In Europe, flow trends have large spatial \r\ndifferences (Hall et al., 2014; Mediero et al., 2015; Kundzewicz et al., \r\n2018; Mangini et al., 2018), but there appears to be a  pattern of \r\nincrease in north-western Europe, and a decrease in southern and \r\neastern Europe in annual peak flow during 1960-2000 (Bloschl et al., \r\n2019). In North America, peak flow has increased in north-east USA \r\nand decreased in south-west USA (Peterson et al., 2013b; Armstrong \r\net al., 2014; Mallakpour and Villarini, 2015; Archfield et al., 2016; Burn \r\nand Whitfield, 2016; Wehner et al., 2017; Neri et al., 2019)"


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" There are \r\nimportant changes in the seasonality of peak flows in regions where \r\nsnowmelt dominates, such as northern North America (Burn and \r\nWhitfield, 2016; Dudley et al., 2017) and Northern Europe (Bloschl \r\net al., 2017), corresponding to strong winter and spring warming. "


id: 3004c53d5841e306ba5192ab8f49b8aa
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"There are very few studies focused on the attribution of long-term \r\nchanges in floods, but there are studies on changes in flood events. \r\nMost of the studies focus on flash floods and urban floods, which \r\nare closely related to intense precipitation events (Hannaford, 2015). \r\nIn other cases, event attribution focused on runoff using hydrological \r\nmodels, and examples include river basins in the UK (Section 11.4.4; \r\nSchaller et al., 2016; Kay et al., 2018), the Okavango River in Africa \r\n(Wolski et al., 2014), and the Brahmaputra River in Bangladesh (Philip \r\net al., 2019). Findings about anthropogenic influences vary between \r\ndifferent regions and basins. For some flood events, the probability of \r\nhigh floods in the current climate is lower than in a climate without \r\nan anthropogenic influence (Wolski et al., 2014), while in other \r\ncases anthropogenic influence leads to more intense floods (Cho \r\net al., 2016; Pall et al., 2017; van der Wiel et al., 2017; Philip et al"


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", \r\n2018a; Teufel et al., 2019). Factors such as land-cover change and \r\nriver management can also increase the probability of high floods \r\n(Ji et al., 2020). These, along with model uncertainties and the lack \r\nof studies overall, suggest a low confidence in general statements to \r\nattribute changes in flood events to anthropogenic climate change. \r\nA few individual regions have been well studied, which allows for \r\nhigh confidence in the attribution of increased flooding in these \r\ncases. For example, flooding in the UK following increased winter \r\nprecipitation (Schaller et al., 2016; Kay et al., 2018) can be attributed \r\nto anthropogenic climate change (Schaller et al., 2016; Vautard et al., \r\n2016; Yiou et al., 2017; Otto et al., 2018b). "


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"The SREX (Chapter  3, Seneviratne et al., 2012) stressed the low \r\navailability of studies on flood projections under different emissions \r\nscenarios, and concluded that there was low confidence in \r\nprojections of flood events given the complexity of the mechanisms \r\ndriving floods at the regional scale. The AR5 WGII report (Chapter 3, \r\nJimenez Cisneros et al., 2014) assessed with medium confidence the \r\npattern of future flood changes, including flood hazards increasing \r\nover about half of the globe (parts of southern and South East Asia, \r\ntropical Africa, north-east Eurasia, and South America) and flood \r\nhazards decreasing in other parts of the world, despite uncertainties \r\nin GCMs and their coupling to hydrological models. The SR1.5 \r\n(Chapter  3, Hoegh-Guldberg et al., 2018) assessed with medium \r\nconfidence that global warming of 2degC would lead to an expansion \r\nof the fraction of global area affected by flood hazards, compared to \r\nconditions at 1"


id: 5a9e010cc4a337e9fe6fd6ba9ed20448
created_at: 2023-03-16 14:02:08
updated_at: 2023-03-16 14:02:08
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vector_id: 11778
content:
"The majority of new studies that produce future flood projections \r\nbased on hydrological models do not typically consider aspects \r\nthat are also important to actual flood severity or damages, such \r\nas flood prevention measures (Neumann et al., 2015; Sen, 2018), \r\nflood control policies (Barraque, 2017), and future changes in land \r\ncover (see also Section  8.4.1.5). At the global scale, Alfieri et al. \r\n(2017) used downscaled projections from seven GCMs as input to \r\ndrive a hydrodynamic model. They found successive increases in the \r\nfrequency of high floods in all continents except Europe, associated \r\nwith increasing levels of global warming (1.5degC, 2degC, 4degC). These \r\nresults are supported by Paltan et al. (2018), who applied a simplified \r\nrunoff aggregation model forced by outputs from four GCMs. S. Huang \r\net al"


id: f84ab2c1a0b583a0099fca137a8d9780
created_at: 2023-03-16 14:02:08
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vector_id: 36582
content:
" (2018) used three hydrological models forced with bias-adjusted \r\noutputs from four GCMs to produce projections for four river basins \r\nincluding the Rhine, Upper Mississippi, Upper Yellow, and Upper \r\nNiger under 1.5degC, 2degC, and 3degC global warming. This study found \r\ndiverse projections for different basins, including a  shift towards \r\nearlier flooding for the Rhine and the Upper Mississippi, a substantial \r\nincrease in flood frequency in the Rhine only under the 1.5degC and 2degC \r\nscenarios, and a decrease in flood frequency in the Upper Mississippi \r\nunder all scenarios. "


id: 1c312da59917856f27b57a06b6acd0d3
created_at: 2023-03-16 14:02:08
updated_at: 2023-03-16 14:02:08
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vector_id: 1893
content:
"Similar to many other extreme events, droughts occur as \r\na combination of thermodynamic and dynamic processes (Box 11.1). \r\nThermodynamic processes contributing to drought, which are \r\nmodified by greenhouse gas forcing both at global and regional \r\nscales, are mostly related to heat and moisture exchanges, and are \r\nalso partly modulated by plant coverage and physiology. They affect, \r\nfor instance, atmospheric humidity, temperature, and radiation, \r\nwhich in turn affect precipitation and/or evapotranspiration in some \r\nregions and time frames. However, dynamic processes are particularly \r\nimportant to explain drought variability on different time scales, \r\nfrom a few weeks (flash droughts) to multiannual (megadroughts). \r\nThere is low confidence in the effects of greenhouse gas forcing \r\non changes in atmospheric dynamic (Section  2.4; Section  4.3.3), \r\nand on associated changes in drought occurrence"


id: b185f7bcce8d2257a093c83328438285
created_at: 2023-03-16 14:02:08
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index: climate_gpt_only_giec
vector_id: 25424
content:
" Thermodynamic \r\nprocesses are thus the main driver of drought changes in a warming \r\nclimate (high confidence)."


id: f1a91928eb094e121f286aab1d659616
created_at: 2023-03-16 14:02:08
updated_at: 2023-03-16 14:02:08
content_type: text
index: climate_gpt_only_giec
vector_id: 35529
content:
"Droughts refer to periods of time with substantially below\u0002average moisture conditions, usually covering large areas, during \r\nwhich limitations in water availability result in negative impacts \r\nfor various components of natural systems and economic sectors \r\n(Wilhite and Pulwarty, 2017; Ault, 2020). Depending on the \r\nvariables used to characterize it and the systems or sectors being \r\nimpacted, drought may be classified in different types (Figure 8.6 \r\nand Appendix Table 11.A.1) such as meteorological (precipitation \r\ndeficits), agricultural (e.g.,  crop yield reductions or failure, often \r\nrelated to soil moisture deficits), ecological (related to plant water \r\nstress that causes e.g.,  tree mortality), or hydrological droughts \r\n(e.g.,  water shortage in streams or storages such as reservoirs, \r\nlakes, lagoons, and groundwater; see Glossary)"


id: a4d9dad58b713a3c6356947ba6a5e764
created_at: 2023-03-16 14:02:08
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index: climate_gpt_only_giec
vector_id: 23507
content:
" The distinction of \r\ndrought types is not absolute, as drought can affect different sub\u0002domains of the Earth system concomitantly, but sometimes also \r\nasynchronously, including propagation from one drought type to \r\nanother (Brunner and Tallaksen, 2019). Because of this, drought \r\ncannot be characterized using a single universal definition (Lloyd\u0002Hughes, 2014) or directly measured based on a  single variable \r\n(SREX Chapter  3; Wilhite and Pulwarty, 2017). Drought can \r\nhappen on a wide range of timescales - from 'flash droughts' on \r\na scale of weeks, and characterized by a sudden onset and rapid \r\nintensification of drought conditions (Hunt et al., 2014; Otkin et al., \r\n2018; Pendergrass et al., 2020) to multi-year or decadal rainfall \r\ndeficits  - sometimes termed 'megadroughts' (see Glossary; Ault \r\net al., 2014; Cook et al., 2016b; Garreaud et al., 2017). Droughts \r\nare often analysed using indices that are measures of drought \r\nseverity, duration and frequency (Sections 8.3.1"


id: 7d15c24effa3ce2e1bdb006ef57df5c7
created_at: 2023-03-16 14:02:08
updated_at: 2023-03-16 14:02:08
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index: climate_gpt_only_giec
vector_id: 17268
content:
"6, 8.4.1.6, 12.3.2.6 \r\nand 12.3.2.7, and Table  11.A.1). There are many drought indices \r\npublished in the scientific literature, as also highlighted in SREX \r\n(SREX Chapter 3). These can range from anomalies in single variables \r\n(e.g.,  precipitation, soil moisture, runoff, evapotranspiration) to \r\nindices combining different atmospheric variables. "


id: 5b850c47ada9cbca6acff2985a7a4019
created_at: 2023-03-16 14:02:08
updated_at: 2023-03-16 14:02:08
content_type: text
index: climate_gpt_only_giec
vector_id: 11900
content:
"Lack of precipitation is generally the main factor controlling drought \r\nonset. There is high confidence that atmospheric dynamics, which \r\nvary on interannual, decadal and longer time scales, is the dominant \r\ncontributor to variations in precipitation deficits in the majority of \r\nworld regions (Dai, 2013; Miralles et al., 2014b; Seager and Hoerling, \r\n2014; Burgman and Jang, 2015; Dong and Dai, 2015; Schubert et al., \r\n2016; Raymond et al., 2018; Baek et al., 2019; Drumond et al., 2019; \r\nHerrera-Estrada et al., 2019; Gimeno et al., 2020; Mishra, 2020). \r\nPrecipitation deficits are driven by dynamic mechanisms taking \r\nplace on different spatial scales, including synoptic processes  - \r\natmospheric rivers and extratropical cyclones, blocking and ridges \r\n(Section 11.7; Sousa et al., 2017), dominant large-scale circulation \r\npatterns (Kingston et al"


id: 75f9ce992e0670489713e50aa6dd8bae
created_at: 2023-03-16 14:02:08
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vector_id: 16156
content:
", 2015), and global ocean-atmosphere \r\ncoupled patterns such as inter-decadal Pacific Oscillation (IPO), \r\nAtlantic Multi-decadal Oscillation (AMO) and El Nino-Southern \r\nOscillation (ENSO; Dai and Zhao, 2017). These various mechanisms \r\noccur on different scales, are not independent, and substantially \r\ninteract with one another. Also regional moisture recycling and land-\r\natmosphere feedbacks play an important role for some precipitation \r\nanomalies (see below). "


id: 2af4ddcd714fb4398b100ef6fe5fe9e6
created_at: 2023-03-16 14:02:08
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vector_id: 4186
content:
"On subseasonal to decadal scales, temporal variations in AED are \r\nstrongly controlled by circulation variability (Williams et al., 2014; \r\nChai et al., 2018; Martens et al., 2018), but thermodynamic processes \r\nalso play a  fundamental role and, under human-induced climate \r\nchange, dominate the changes in AED. Atmospheric warming due \r\nto increased atmospheric CO2 concentrations increases AED by \r\nmeans of enhanced VPD in the absence of other influences (Scheff \r\nand Frierson, 2015). Because of the greater warming over land than \r\nover oceans (Sections 2.3.1.1 and 11.3), the saturation pressure of \r\nwater vapour increases more over land than over oceans; oceanic \r\nair masses advected over land thus contain insufficient water vapour \r\nto keep pace with the greater increase in saturation vapour pressure \r\nover land (Sherwood and Fu, 2014; Byrne and O'Gorman, 2018; \r\nFindell et al., 2019)"


id: c476a1f4cb7ad7008a261d6b35d5dd72
created_at: 2023-03-16 14:02:08
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vector_id: 28484
content:
" Land-atmosphere feedbacks are also important \r\nin affecting atmospheric moisture content and temperature, with \r\nresulting effects on relative humidity and VPD (Box 11.1; Berg et al., \r\n2016; Haslinger et al., 2019; S. Zhou et al., 2019)."


id: f5df1ef4201ea607613c1233134057c9
created_at: 2023-03-16 14:02:08
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vector_id: 36198
content:
"Atmospheric evaporative demand (AED) quantifies the maximum \r\namount of actual evapotranspiration (ET) that can happen from land \r\nsurfaces if they are not limited by water availability (Table 11.A.1). \r\nAED is affected by radiative and aerodynamic components. For this \r\nreason, the atmospheric dryness, often quantified with the relative \r\nhumidity or the vapour pressure deficit (VPD), is not equivalent to \r\nthe AED, as other variables are also highly relevant, including solar \r\nradiation and wind speed (Hobbins et al., 2012; McVicar et al., 2012a; \r\nSheffield et al., 2012). AED can be estimated using different methods \r\n(McMahon et al., 2013), and those solely based on air temperature \r\n(e.g., Hargreaves, Thornthwaite) usually overestimate it in terms of \r\nmagnitude and temporal trends (Sheffield et al., 2012), in particular, \r\nin the context of substantial background warming"


id: 81de2bc281a6cf076edc4a633e529f55
created_at: 2023-03-16 14:02:08
updated_at: 2023-03-16 14:02:08
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index: climate_gpt_only_giec
vector_id: 17956
content:
" Physically-based \r\ncombination methods such as the Penman-Monteith equation are \r\nmore adequate and recommended since 1998 by the United Nations \r\nFood and Agriculture Oganization (Pereira et al., 2015). For this reason, \r\nthe assessment of this Chapter, when considering atmospheric\u0002based drought indices, only includes AED estimates using the latter \r\n(see also Section 11.9). AED is generally higher than ET, since AED \r\nrepresents an upper bound for ET. Hence, an AED increase does not \r\nnecessarily lead to increased ET (Milly and Dunne, 2016), in particular \r\nunder drought conditions given soil moisture limitation (Bonan et al., \r\n2014; Berg et al., 2016; Konings et al., 2017; Stocker et al., 2018). \r\nIn general, AED is highest in regions where ET is lowest (e.g., desert \r\nareas), further illustrating the decoupling between the two variables \r\nunder limited soil moisture. "


id: 10562b42d072e62d0d8ef902f9dbb19e
created_at: 2023-03-16 14:02:08
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vector_id: 56
content:
"Soil moisture shows an important correlation with precipitation \r\nvariability (Khong et al., 2015; Seager et al., 2019), but ET also plays \r\na substantial role in further depleting moisture from soils, in particular \r\nin humid regions during periods of precipitation deficits (Teuling \r\net al., 2013; Padron et al., 2020). In addition, soil moisture plays \r\na role in drought self-intensification under dry conditions in which ET \r\nis decreased and leads to higher AED (Miralles et al., 2019), an effect \r\nthat can also contribute to triggering flash droughts (Otkin et al., \r\n2016, 2018; DeAngelis et al., 2020; Pendergrass et al., 2020). If soil \r\nmoisture becomes limited, ET is reduced, which may decrease the \r\nrate of soil drying, but can also lead to further atmospheric dryness \r\nthrough various feedback loops (Seneviratne et al., 2010; Miralles \r\net al., 2014a, 2019; Teuling, 2018; Vogel et al., 2018; S. Zhou et al., \r\n2019; Liu et al., 2020)"


id: 25dd5771dad6b0ba17a3f14adcc62681
created_at: 2023-03-16 14:02:08
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index: climate_gpt_only_giec
vector_id: 3381
content:
" The process is complex since vegetation cover \r\nplays a role in modulating albedo and in providing access to deeper \r\nstores of water (both in the soil and groundwater). Also, changes in \r\nland cover and in plant phenology may alter ET (Sterling et al., 2013; \r\nWoodward et al., 2014; Frank et al., 2015; Doll et al., 2016; Ukkola \r\net al., 2016; Trancoso et al., 2017; Hao et al., 2019; Lian et al., 2020). \r\nSnow depth has strong and direct impacts on soil moisture in many \r\nsystems (Gergel et al., 2017; Williams et al., 2020). "


id: 221dfe7ea8d4a2f560f45bfdd113ea0e
created_at: 2023-03-16 14:02:08
updated_at: 2023-03-16 14:02:08
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index: climate_gpt_only_giec
vector_id: 2816
content:
"(Anderegg et al., 2012, 2016; Rowland et al., 2015). Also carbon \r\nassimilation by plants strongly depends on soil moisture (Hartzell \r\net al., 2017), with implications for carbon starvation and plant \r\ndying if soil moisture deficits are prolonged (Sevanto et al., 2014). \r\nThese mechanisms explain that soil moisture deficits are usually \r\nmore relevant than AED excess to explain gross primary production \r\nanomalies and vegetation stress, mostly in sub-humid and semi-arid \r\nregions (Stocker et al., 2018; Liu et al., 2020). High CO2 concentrations \r\nare shown to potentially decrease plant ET and increase plant water\u0002use efficiency, affecting soil moisture levels, but this effect interacts \r\nwith other CO2 physiological and radiative effects (Section 11.6.5.2 \r\nand Cross-Chapter Box 5.1), and has less relevance under low soil \r\nmoisture (Morgan et al., 2011; Z. Xu et al., 2016; Nackley et al., 2018; \r\nDiksaityte et al., 2019)"


id: e6f7cac8836e9d3e3e9e5ca3f43ed7db
created_at: 2023-03-16 14:02:08
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index: climate_gpt_only_giec
vector_id: 33822
content:
" ESMs represent both surface (around 10cm) \r\nand total column soil moisture, whereby total soil moisture is of more \r\ndirect relevance for root water uptake, in particular by trees. There is \r\nevidence that surface soil moisture projections are substantially drier \r\nthan total soil moisture projections, and may overestimate drying of \r\nrelevance for most vegetation (Berg et al., 2017a)."


id: 38bca626513fa16023023f2de086f2e3
created_at: 2023-03-16 14:02:08
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index: climate_gpt_only_giec
vector_id: 6294
content:
"Although there are many atmospheric-based drought indices, two are \r\nassessed in this chapter: the Palmer Drought Severity Index (PDSI) and \r\nthe Standardized Precipitation Evapotranspiration Index (SPEI). The \r\nPDSI has been widely used to monitor and quantify drought severity \r\n(Dai et al., 2018), but is affected by some constraints (SREX Chapter 3; \r\nMukherjee et al., 2018a). Although the calculation of the PDSI is based \r\non a soil water budget, the PDSI is essentially a climate drought index \r\nthat mostly responds to the precipitation and the AED (van der Schrier \r\net al., 2013; Vicente-Serrano et al., 2015; Dai et al., 2018). The SPEI also \r\ncombines precipitation and AED, being equally sensitive to these two \r\nvariables (Vicente-Serrano et al., 2015). The SPEI is more sensitive to \r\nAED than the PDSI (Cook et al., 2014a; Vicente-Serrano et al., 2015), \r\nalthough under humid and normal precipitation conditions, the effects \r\nof AED on the SPEI are small (Tomas-Burguera et al"


id: 56597e900c0edabc65d3f4b64760f6fb
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vector_id: 11066
content:
", 2020). Given \r\nthe limitations associated with temperature-based AED estimates \r\n(Section  11.6.1.2), only studies using the Penman-Monteith-based \r\nSPEI and PDSI (hereafter SPEI-PM and PDSI-PM) are considered in this \r\nassessment and in the regional tables in Section 11.9."


id: 512a30a6650b45edfd1f9dd09690bbea
created_at: 2023-03-16 14:02:08
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index: climate_gpt_only_giec
vector_id: 10225
content:
"Drivers of streamflow and surface water deficits are complex \r\nand strongly depend on the hydrological system analysed \r\n(e.g., streamflows in the headwaters, medium course of the rivers, \r\ngroundwater, highly regulated hydrological basins). Soil hydrological \r\nprocesses, which control the propagation of meteorological droughts \r\nthroughout different parts of the hydrological cycle (Van Loon and \r\nVan Lanen, 2012), are spatially and temporally complex (Herrera\u0002Estrada et al., 2017; S. Huang et al., 2017b) and difficult to quantify \r\n(Van Lanen et al., 2016; Apurv et al., 2017; Caillouet et al., 2017; \r\nKonapala and Mishra, 2017; Hasan et al., 2019). The physiographic \r\ncharacteristics of the basins also affect how droughts propagate \r\nthroughout the hydrological cycle (Van Loon and Van Lanen, 2012; \r\nVan Lanen et al., 2013; Van Loon, 2015; Konapala and Mishra, 2020; \r\nVeettil and Mishra, 2020)"


id: b7b4e12df5da7a05c0d7325cfb23fe65
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index: climate_gpt_only_giec
vector_id: 26435
content:
" In addition, the assessment of groundwater \r\ndeficits is very difficult given the complexity of processes that \r\ninvolve natural and human-driven feedbacks and interactions with \r\nthe climate system (Taylor et al., 2013). Streamflow and surface \r\nwater deficits are affected by land cover, groundwater and soil \r\ncharacteristics (Van Lanen et al., 2013; Van Loon and Laaha, 2015; \r\nBarker et al., 2016; Tijdeman et al., 2018), as well as human activities \r\n(water management and demand, damming) and land-use changes \r\n(Section 11.6.4.3; Van Loon et al., 2016; He et al., 2017; Veldkamp \r\net al., 2017; J. Wu et al., 2018; Y. Xu et al., 2019; Jehanzaib et al., \r\n2020). Finally, snow and glaciers are relevant for water resources \r\nin some regions. For instance, warming affects snowpack levels \r\n(Dierauer et al., 2019; Huning and AghaKouchak, 2020), as well as \r\nthe timing of snow melt, thus potentially affecting the seasonality \r\nand magnitude of low flows (Barnhart et al., 2016)."


id: bcbb11c452e9b8f450d2ec54924da125
created_at: 2023-03-16 14:02:08
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index: climate_gpt_only_giec
vector_id: 27266
content:
"estimates provide an indication of possible trends in the influence of \r\nAED on drought. Given the observed global temperature increases \r\n(Sections 2.3.1.1 and 11.3) and dominant decrease in relative \r\nhumidity over land areas (Simmons et al., 2010; Willett et al., 2014), \r\nVPD has increased globally (Barkhordarian et al., 2019; Yuan et al., \r\n2019). Pan evaporation has increased as a  consequence of VPD \r\nchanges in several AR6 regions, such as East Asia (Li et al., 2013; \r\nZ.  Sun et al., 2018; M.-Z. Yang et al., 2018), Western and Central \r\nEurope (Mozny et al., 2020), the Mediterranean, (Azorin-Molina et al., \r\n2015) and Central and Southern Australia (Stephens et al., 2018). \r\nNevertheless, there is an important regional variability in observed \r\ntrends, and in other AR6 regions pan evaporation has decreased - \r\nfor example, in North Central America (Brena-Naranjo et al., 2017) \r\nand in the Tibetan Plateau (C. Zhang et al., 2018))"


id: 6815979ef1dca98035177137fdb48930
created_at: 2023-03-16 14:02:08
updated_at: 2023-03-16 14:02:08
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index: climate_gpt_only_giec
vector_id: 13962
content:
" Physical models \r\nalso show an important regional diversity, with an increase in New \r\nZealand (Salinger and Porteous, 2014) and the Mediterranean (Gocic \r\nand Trajkovic, 2014; Azorin-Molina et al., 2015; Piticar et al., 2016), \r\na decrease in South Asia (Jhajharia et al., 2015), and strong spatial \r\nvariability in North America (Seager et al., 2015b). This variability is \r\ndriven by the role of other meteorological variables affecting AED. \r\nChanges in solar radiation as a consequence of solar dimming and \r\nbrightening may affect trends (Section  7.2.2.2; Kambezidis et al., \r\n2012; Wang and Yang, 2014; Sanchez-Lorenzo et al., 2015). Wind \r\nspeed is also relevant (McVicar et al., 2012b), and studies suggest \r\na reduction of the wind speed in some regions (Z. Zhang et al., 2019b) \r\nthat could compensate the role of the VPD increase. Nevertheless, the \r\nVPD trend seems to dominate the overall AED trends, compared to \r\nthe effects of trends in wind speed and solar radiation (Wang et al"


id: e043198ccf42239d6f5bd3aa65366787
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vector_id: 32776
content:
"Strong precipitation deficits have been recorded in recent decades \r\nin the Amazon (2005, 2010), south-western China (2009-2010), \r\nsouth-western North America (2011-2014), Australia (1997-2009), \r\nCalifornia (2014), the middle East (2012-2016), Chile (2010-2015), \r\nthe Great Horn of Africa (2011), among others (van Dijk et al., 2013; \r\nMann and Gleick, 2015; Rowell et al., 2015; Marengo and Espinoza, \r\n2016; Dai and Zhao, 2017; Garreaud et al., 2017, 2020; Marengo \r\net al., 2017; Brito et al., 2018; Cook et al., 2018). Global studies \r\ngenerally show no significant trends in SPI time series (Orlowsky \r\nand Seneviratne, 2013; Spinoni et al., 2014), and in derived drought \r\nfrequency and severity data (Spinoni et al., 2019), with very few \r\nregional exceptions (Section  11.9 and Figure  11.17)"


id: 8249a66beba3e1556f9c245b33bd6695
created_at: 2023-03-16 14:02:08
updated_at: 2023-03-16 14:02:08
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index: climate_gpt_only_giec
vector_id: 18024
content:
" Long-term \r\ndecreases in precipitation are found in some AR6 regions in Africa \r\n(Central Africa and East Southern Africa), and several regions in South \r\nAmerica (North-Eastern South America, South American Monsoon, \r\nSouth-Western South America, and Southern South America) \r\n(Section 11.9). Evidence of precipitation-based drying trends is also \r\nfound in Western Africa, consistent with studies based on CDD trends \r\n(Figure 11.17; Chaney et al., 2014; Donat et al., 2014b; Barry et al., \r\n2018; Dunn et al., 2020), however, there is a partial recovery of the \r\nrainfall trends since the 1980s in this region (Section  10.4.2.1). \r\nSome AR6 regions show a  decrease in meteorological drought, \r\nincluding Northern Australia, Central Australia, Northern Europe \r\nand Central North America (Section 11.9)"


id: 69f21fa91bd9953d686586220c4b0bb
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updated_at: 2023-03-16 14:02:08
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index: climate_gpt_only_giec
vector_id: 14268
content:
" Other regions either do \r\nnot show substantial trends in long-term meteorological drought, or \r\nthey display mixed signals depending on the considered time frame \r\nand sub-regions, such as in Southern Australia (Gallant et al., 2013; \r\nDelworth and Zeng, 2014; Alexander and Arblaster, 2017; Spinoni \r\net al., 2019; Dunn et al., 2020; Rauniyar and Power, 2020) and the \r\nMediterranean (Camuffo et al., 2013; Gudmundsson and Seneviratne, \r\n2016; Spinoni et al., 2017; Stagge et al., 2017; Caloiero et al., 2018; \r\nPena-Angulo et al., 2020b; see also Section 11.9 and Atlas.8.2)."


id: 807649676bc0151f169505b0531b1751
created_at: 2023-03-16 14:02:08
updated_at: 2023-03-16 14:02:08
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vector_id: 17740
content:
"There are limited long-term measurements of soil moisture from \r\nground observations (Dorigo et al., 2011; Qiu et al., 2016; Quiring \r\net al., 2016), which impedes their use in the analysis of trends. Among \r\nthe few existing observational studies covering at least two decades, \r\nseveral studies have investigated trends in ground soil moisture in \r\nEast Asia (Section 11.9; Chen and Sun, 2015b; Liu et al., 2015; Qiu \r\net al., 2016). Alternatively, microwave-based satellite measurements \r\nof surface soil moisture have also been used to analyse trends (Dorigo \r\net al., 2012; Jia et al., 2018). Although there is regional evidence that \r\nmicrowave-based soil moisture estimates can capture well drying \r\ntrends in comparison with ground soil moisture observations (Jia \r\net al., 2018), there is only medium confidence in the derived trends, \r\nsince satellite soil moisture data are affected by inhomogeneities \r\n(Dorigo et al., 2015; Rodell et al., 2018; Preimesberger et al., 2021)"


id: 75119f6828bda8ec05b03fda521fe146
created_at: 2023-03-16 14:02:08
updated_at: 2023-03-16 14:02:08
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vector_id: 16012
content:
" \r\nFurthermore, microwave-based satellites only sense surface soil \r\nmoisture, which differs from root-zone soil moisture (Berg et al., \r\n2017a), although relationships can be derived between the two \r\n(Brocca et al., 2011). Several studies have also analysed long-term \r\nsoil moisture time series from observation-driven land-surface \r\nor hydrological models, including land-based reanalysis products \r\n(Albergel et al., 2013; Jia et al., 2018; Gu et al., 2019b; Markonis \r\net al., 2021). Such models have also been used to assess changes in \r\nland water availability, estimated as precipitation minus ET, which \r\nis equal to the sum of soil moisture and runoff (Greve et al., 2014; \r\nPadron et al., 2020). "


id: 3d080e2436bae9614fcb9509d12e57ae
created_at: 2023-03-16 14:02:08
updated_at: 2023-03-16 14:02:08
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vector_id: 6964
content:
"Overall, evidence from global studies suggests that several land \r\nregions have been affected by increased soil moisture drying or water \r\nbalance drying in past decades, despite some spread among products \r\n(Albergel et al., 2013; Greve et al., 2014; Gu et al., 2019b; Padron \r\net al., 2020). Drying has not only occurred in dry regions but also in \r\nhumid regions (Greve et al., 2014). Some studies have specifically \r\naddressed changes in soil moisture at regional scale (Section 11.9). \r\nFor AR6 regions, several studies suggest an increase in the frequency \r\nand areal extent of soil moisture deficits, with examples in East Asia \r\n(Cheng et al., 2015; Y. Qin et al., 2015; Jia et al., 2018), Western and \r\nCentral Europe (Trnka et al., 2015b), and the Mediterranean (Hanel \r\net al., 2018; Moravec et al., 2019; Markonis et al., 2021). Nonetheless, \r\nsome analyses also show no long-term trends in soil drying in some \r\nAR6 regions - for example, in Eastern North America (Park Williams \r\net al"


id: ff11eb88cf10d5396e3bb074acc3ccd1
created_at: 2023-03-16 14:02:08
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content:
", 2017) and Central North America (Seager et al., 2019), as well \r\nas in North Eastern Africa (Kew et al., 2021). The soil moisture drying \r\ntrends identified in both global and regional studies are generally \r\nrelated to increases in ET (associated with higher AED) rather than \r\ndecreases in precipitation, as identified on global land for trends in \r\nwater balance in the dry season (Padron et al., 2020), as well as for \r\nsome regions (Teuling et al., 2013; Cheng et al., 2015; Trnka et al., \r\n2015a; van Der Linden et al., 2019; X. Li et al., 2020). "


id: c634876640407ec9a3ea10db6c33c210
created_at: 2023-03-16 14:02:08
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vector_id: 28759
content:
"flow reconstructions (Caillouet et al., 2017; Barker et al., 2019) and \r\nobservations (Vicente-Serrano et al., 2019). In the Mediterranean \r\nregion, there is high confidence in hydrological drought intensification \r\n(Section  11.9; Giuntoli et al., 2013; Lorenzo-Lacruz et al., 2013; \r\nGudmundsson et al., 2019). In south-eastern South America there \r\nis a  decrease in the severity of hydrological droughts (Rivera and \r\nPenalba, 2018). In North America, depending on the methods, datasets \r\nand study periods, there are differences between studies that suggest \r\nan increase (Shukla et al., 2015; Udall and Overpeck, 2017) versus \r\na  decrease in hydrological drought frequency (Mo and Lettenmaier, \r\n2018), but in general there is strong spatial variability (Poshtiri and \r\nPal, 2016)"


id: f41d8920d4e89f45825c4682c89943d1
created_at: 2023-03-16 14:02:08
updated_at: 2023-03-16 14:02:08
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index: climate_gpt_only_giec
vector_id: 35916
content:
" Streamflow observation reference networks of near-natural \r\ncatchments have also been used to isolate the effect of climate trends \r\non hydrological drought trends in a few regions, but these show limited \r\ntrends in Northern Europe and Western and Central Europe (Stahl et al., \r\n2010; Bard et al., 2015; Harrigan et al., 2018), North America (Dudley \r\net al., 2020) and most of Australia, with the exception of Eastern and \r\nSouthern Australia (X.S. Zhang et al., 2016). Given the low availability \r\nof observations, there are few studies analysing trends of drought \r\nseverity in the groundwater. Nevertheless, some studies suggest \r\na noticeable response of groundwater droughts to climate variability \r\n(Lorenzo-Lacruz et al., 2017) and increased drought frequency and \r\nseverity associated with warming, probably as a  consequence of \r\nenhanced ET induced by higher AED (Maxwell and Condon, 2016). This \r\nis supported by studies in Northern Europe (Bloomfield et al"


id: d14cd0350f89a8ebc3875d061f13e030
created_at: 2023-03-16 14:02:08
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content:
"trends in comparison with observations (Nasrollahi et al., 2015). \r\nMeteorological drought trends in the CMIP5 ensemble showed \r\nsubstantial disagreements compared with observations (Orlowsky \r\nand Seneviratne, 2013; Knutson and Zeng, 2018) including \r\na  tendency to overestimate drying, in particular in mid- to high \r\nlatitudes (Knutson and Zeng, 2018). The CMIP6 models display \r\na better performance in reproducing long-term precipitation trends \r\nor seasonal dynamics in some studies in Southern South America \r\n(Rivera and Arnould, 2020), East Asia (Xin et al., 2020), southern \r\nAsia (Gusain et al., 2020), and south-western Europe (Pena-Angulo \r\net al., 2020b), but there is still too limited evidence to allow for \r\nan assessment of possible differences in performance between \r\nCMIP5 and CMIP6. Furthermore, ESMs are generally found to \r\nunderestimate the severity of precipitation deficits and the dry day \r\nfrequencies in comparison to observations (Fantini et al., 2018; \r\nUkkola et al"


id: bab821eda7220e10c3a403df639ce9b5
created_at: 2023-03-16 14:02:08
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vector_id: 26917
content:
", 2018). This is probably related to shortcomings in \r\nthe simulation of persistent weather events in the mid-latitudes \r\n(Section  10.3.3.3). ESMs also show a  tendency to underestimate \r\nprecipitation-based drought persistence at monthly to decadal \r\ntime scales (Ault et al., 2014; Moon et al., 2018). The overall inter\u0002model spread in the projected frequency of precipitation deficits is \r\nalso substantial (Touma et al., 2015; Zhao et al., 2016; Engstrom \r\nand Keellings, 2018). Moreover, there are spatial differences in the \r\nspread, which is higher in the regions where enhanced drought \r\nconditions are projected and under high-emissions scenarios \r\n(Orlowsky and Seneviratne, 2013). Nonetheless, some event \r\nattribution studies have concluded that droughts at regional scales \r\ncan be adequately simulated by some climate models (Schaller \r\net al., 2016; Otto et al., 2018c)."


id: 9646d8c48cb60b17325aa4d065c6af00
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vector_id: 21151
content:
"increase the severity of agricultural and ecological droughts compared \r\nto meteorological droughts (Garcia-Herrera et al., 2019; Williams et al., \r\n2020), reduce soil moisture during the dry season (Padron et al., 2020), \r\nincrease plant water stress (Allen et al., 2015; Grossiord et al., 2020; \r\nSolander et al., 2020) and trigger more severe forest fires (Abatzoglou \r\nand Williams, 2016; Turco et al., 2019; Nolan et al., 2020). A number \r\nof regional studies based on these drought indices have also shown \r\nstronger drying trends in comparison to trends in precipitation-based \r\nindices in the following AR6 regions (see also Section  11.9): NSA \r\n(R. Fu et al., 2013; Marengo and Espinoza, 2016), SCA (Hidalgo et al., \r\n2017), WCA (Tabari and Aghajanloo, 2013; Sharafati et al., 2020), SAS \r\n(Niranjan Kumar et al., 2013), NEAF (Zeleke et al., 2017), WSAF (Edossa \r\net al., 2016), NWN and NEN (Bonsal et al., 2013), EAS (Yu et al., 2014; \r\nChen and Sun, 2015b; L. Li et al., 2020; Liang et al"


id: df70ce6dccff85c72f760bbf60dd0345
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vector_id: 32655
content:
", 2020; Z. Wu et al., \r\n2020) and MED (Kelley et al., 2015; Stagge et al., 2017; Gonzalez\u0002Hidalgo et al., 2018; Mathbout et al., 2018a). "


id: 9b1b9b6e37eaff96febcf87f317e0130
created_at: 2023-03-16 14:02:08
updated_at: 2023-03-16 14:02:08
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index: climate_gpt_only_giec
vector_id: 21943
content:
"Few AR6 regions show observed increases in meteorological drought \r\n(Section  11.9), mostly in Africa and South America (NES: high \r\nconfidence; WAF, CAF, ESAF, SAM, SWS, SSA, SAS: medium confidence); \r\na  few others show a  decrease (WSB, ESB, NAU, CAU, NEU, CNA: \r\nmedium confidence). There are stronger signals indicating observed \r\nincreases in agricultural and ecological drought (Section 11.9), which \r\nhighlights the role of increased ET, driven by increased AED, for these \r\ntrends (Sections 11.6.2.3 and11.6.2.5). Past increases in agricultural \r\nand ecological droughts are found on all continents and several \r\nregions (WAF, CAF, WSAF, ESAF, WCA, ECA, EAS, SAU, MED, WCE, \r\nNES: medium confidence), while decreases are found only in one \r\nAR6 region (NAU: medium confidence). The more limited availability \r\nof datasets makes it more difficult to assess historical trends in \r\nhydrological drought at regional scale (Section  11.9)"


id: 26462f6400510496cb03e32856181c8e
created_at: 2023-03-16 14:02:08
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index: climate_gpt_only_giec
vector_id: 3437
content:
" Increasing \r\n(MED: high confidence; WAF, EAS, SAU: medium confidence) and \r\ndecreasing (NEU, SES: medium confidence) trends in hydrological \r\ndroughts have only been observed in a few regions."


id: d5f3084cb38b4896e27520c4e1d6ff64
created_at: 2023-03-16 14:02:08
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content:
"Overall, there are contrasting results on the performance of land \r\nsurface models and climate models in representing soil moisture. \r\nSome studies suggest that soil moisture anomalies are well captured \r\nby land surface models driven with observation-based forcing \r\n(Dirmeyer et al., 2006; Albergel et al., 2013; Xia et al., 2014; Balsamo \r\net al., 2015; Reichle et al., 2017; Spennemann et al., 2020), but other \r\nstudies report limited agreement in the representation of interannual \r\nsoil moisture variability (Stillman et al., 2016; Yuan and Quiring, 2017; \r\nFord and Quiring, 2019) and noticeable seasonal differences in model \r\nskill in some regions (Xia et al., 2014, 2015). Models with good skill \r\ncan nonetheless display biases in absolute soil moisture (Xia et al., \r\n2014; Gu et al., 2019a), but these are not necessarily of relevance \r\nfor the simulation of surface water fluxes and drought anomalies \r\n(Koster et al., 2009)"


id: 5e9db98c930b0a2a8a69d05b74ec9b62
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content:
" There is also substantial inter-model spread \r\n(Albergel et al., 2013), particularly for the root-zone soil moisture \r\n(Berg et al., 2017a). "


id: 410be2bf27f0d2406f2146897f39e787
created_at: 2023-03-16 14:02:08
updated_at: 2023-03-16 14:02:08
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vector_id: 7626
content:
"Streamflow and groundwater are not directly simulated by ESMs, \r\nwhich only simulate runoff, but they are generally represented in \r\nhydrological models (Prudhomme et al., 2014; Giuntoli et al., 2015), \r\nwhich are typically driven in a stand-alone manner by observed or \r\nsimulated climate forcing. The simulation of hydrological deficits is \r\nmuch more problematic than the simulation of mean streamflow or \r\npeak flows (Fundel et al., 2013; Stoelzle et al., 2013; Velazquez et al., \r\n2013; Staudinger et al., 2015), since models tend to be too responsive \r\nto the climate forcing and do not satisfactorily capture low flows \r\n(Tallaksen and Stahl, 2014). Simulations of hydrological drought \r\nmetrics show uncertainties related to the contribution of both GCMs \r\nand hydrological models (Bosshard et al., 2013; Giuntoli et al., 2015; \r\nSamaniego et al., 2017; Vetter et al., 2017), but hydrological models \r\nforced by the same climate input data also show a  large spread \r\n(van Huijgevoort et al"


id: 95ca4e10c7e913208439302941ff39ed
created_at: 2023-03-16 14:02:08
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vector_id: 21067
content:
", 2013; Ukkola et al., 2018). At the catchment \r\nscale, the hydrological model uncertainty is higher than both GCM \r\nand downscaling uncertainty (Vidal et al., 2016), and the hydrological \r\nmodels show issues in representing drought propagation throughout \r\nthe hydrological cycle (Barella-Ortiz and Quintana Segui, 2019). \r\nA study on the evaluation of streamflow droughts in seven global \r\n(hydrological and land surface) models compared with observations \r\nin near-natural catchments of Europe showed a substantial spread \r\namong models, an overestimation of the number of drought events, \r\nand an underestimation of drought duration and drought-affected \r\narea (Tallaksen and Stahl, 2014). "


id: 412d63ea4b1f545dd2bf79bfbc7c2b64
created_at: 2023-03-16 14:02:08
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content:
"Regarding the performance of regional and global climate models, an \r\nevaluation of an ensemble of RCM simulations for Europe (Stegehuis \r\net al., 2013) shows that these models display overly strong drying \r\nin early summer, resulting in an excessive decrease of latent heat \r\nfluxes, with potential implications for more severe droughts in dry \r\nenvironments (Teuling, 2018; van Der Linden et al., 2019). Compared \r\nwith a range of observational ET estimates, CMIP5 models show an \r\noverestimation of ET on annual scale, but an ET underestimation in \r\nboreal summer in many Northern Hemisphere mid-latitude regions, \r\nalso suggesting a  tendency towards excessive soil drying (Mueller \r\nand Seneviratne, 2014), consistent with identified biases in soil\u0002moisture-temperature coupling (Donat et al., 2018; Vogel et al., \r\n2018; Selten et al., 2020)"


id: c647f9755427a9e1556d43dd3b965b70
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content:
" Land surface models used in ESMs display \r\na bias in their representation of the sensitivity of interannual land \r\ncarbon uptake to soil moisture conditions, which appears related to \r\na limited range of soil moisture variations compared to observations \r\n(Humphrey et al., 2018)."


id: 818d9a441abc309ebcbbe99d7cc635e7
created_at: 2023-03-16 14:02:08
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vector_id: 17915
content:
"Attribution studies for recent meteorological drought events are \r\navailable for various regions. In Western and Central Europe, a multi\u0002method and multi-model attribution study on the 2015 Central \r\nEuropean drought did not find conclusive evidence for whether \r\nhuman-induced climate change was a driver of the rainfall deficit, \r\nas the results depended on model and method used (Hauser et al., \r\n2017). In the Mediterranean region, a human contribution was found \r\nin the case of the 2014 meteorological drought in the southern Levant \r\nbased on a single-model study (Bergaoui et al., 2015). In Africa, there \r\nis some evidence of a  contribution of human emissions to single \r\nmeteorological drought events, such as the 2015-2017 southern \r\nAfrican drought (Funk et al., 2018a; Yuan et al., 2018a; Pascale et al., \r\n2020), and the three-year (2015-2017) drought in the western Cape \r\nTown region of South Africa (Otto et al., 2018c)"


id: 1d489f18d5f64f8763adbf94131e392d
created_at: 2023-03-16 14:02:08
updated_at: 2023-03-16 14:02:08
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vector_id: 2050
content:
" An attributable \r\nsignal was not found in droughts that occurred in different years \r\nwith different spatial extents in the last decade in North and South \r\nEastern Africa (Marthews et al., 2015; Uhe et al., 2017; Otto et al., \r\n2018a; Philip et al., 2018b; Kew et al., 2021). However, an attributable \r\nincrease in 2011 long rain failure was identified (Lott et al., 2013). \r\nFurther studies have attributed some African meteorological drought \r\nevents to large-scale modes of variability, such as the strong 2015 \r\nEl Nino (Box 11.4; Philip et al., 2018b) and increased SSTs overall \r\n(Funk et al., 2015a, 2018b). Natural variability was dominant in \r\nthe California droughts of 2011-2012 to 2013-2014 (Seager et al., \r\n2015a). In Asia, no climate change signal was found in the record dry \r\nspell over Singapore and Malaysia in 2014 (Mcbride et al., 2015) or \r\nthe drought in central south-west Asia in 2013-2014 (Barlow and \r\nHoell, 2015)"


id: 2fb539dcf2663c1a15fcfa509cd43df
created_at: 2023-03-16 14:02:08
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vector_id: 4927
content:
" Nevertheless, the South East Asia drought of 2015 has \r\nbeen attributed to anthropogenic warming effects (Shiogama et al., \r\n2020). Recent droughts occurring in South America, specifically in \r\nthe southern Amazon region in 2010 (Shiogama et al., 2013) and \r\nin north-east South America in 2014 (Otto et al., 2015b) and 2016 \r\n(Martins et al., 2018) were not attributed to anthropogenic climate \r\nchange. Nevertheless, the central Chile drought between 2010 \r\nand 2018 has been suggested to be partly associated to global \r\nwarming (Boisier et al., 2016; Garreaud et al., 2020). The 2013 New \r\nZealand meteorological drought was attributed to human influence \r\nby Harrington et al. (2014, 2016) based on fully coupled CMIP5 \r\nmodels, but no corresponding change in the dry end of simulated \r\nprecipitation from a stand-alone atmospheric model was found by \r\nAngelil et al. (2017)"


id: 5b77435dc7a9af01820816f3c61141f6
created_at: 2023-03-16 14:02:08
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content:
"There are only two AR6 regions where there is at least medium \r\nconfidence that human-induced climate change has contributed \r\nto changes in meteorological droughts (Section  11.9). In South\u0002Western South America, there is medium confidence that human\u0002induced climate change has contributed to an increase in \r\nmeteorological droughts (Boisier et al., 2016; Garreaud et al., 2020), \r\nwhile in Northern Europe, there is medium confidence that it has \r\ncontributed to a decrease in meteorological droughts (Section 11.9; \r\nGudmundsson and Seneviratne, 2016). In other AR6 regions, there \r\nis inconclusive evidence in the attribution of long-term trends, but \r\na human contribution to single meteorological events or sub-regional \r\ntrends has been identified in some instances (Section 11.9; see also \r\nbelow)"


id: bb539a30ee4000d5783fe3749c49f3df
created_at: 2023-03-16 14:02:08
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index: climate_gpt_only_giec
vector_id: 27026
content:
" In the Mediterranean region, some studies have identified \r\na  precipitation decline or increase in meteorological drought \r\nprobability for time frames since the early or mid 20th century, and \r\na possible human contribution to these trends (Hoerling et al., 2012; \r\nGudmundsson and Seneviratne, 2016; Knutson and Zeng, 2018), also \r\non sub-regional scale in Syria from 1930 to 2010 (Kelley et al., 2015). \r\nOn the contrary, other studies have not identified precipitation and \r\nmeteorological drought trends in the region for the long term (Camuffo \r\net al., 2013; Paulo et al., 2016; Vicente-Serrano et al., 2021) and also \r\nfrom the mid 20th century (Norrant and Douguedroit, 2006; Stagge \r\net al., 2017). There is evidence of substantial internal variability in \r\nlong-term precipitation trends in the region (Section 11.6.2.1), which \r\nlimits the attribution of human influence on variability and trends \r\nof meteorological droughts from observational records (Kelley et al"


id: b8d0e0d29aa7f4749b55aaed8a631d63
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vector_id: 26601
content:
", \r\n2012; Pena-Angulo et al., 2020b). In addition, there are important \r\nsub-regional trends showing mixed signals (Section 11.9; MedECC, \r\n2020). The evidence thus leads to an assessment of low confidence"


id: 2113a5ff4d9814a5cb82dd76e7d9775f
created_at: 2023-03-16 14:02:08
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index: climate_gpt_only_giec
vector_id: 2638
content:
"In other regions, the influence of human water uses can be more \r\nimportant to explain hydrological drought trends (Y. Liu et al., 2016; \r\nMohammed and Scholz, 2016). There is medium confidence that \r\nhuman-induced climate change has contributed to an increase of \r\nhydrological droughts in the Mediterranean (Giuntoli et al., 2013; \r\nVicente-Serrano et al., 2014; Gudmundsson et al., 2017), but also \r\nmedium confidence that changes in land use and terrestrial water \r\nmanagement contributed to these trends (Section 11.9; Teuling et al., \r\n2019; Vicente-Serrano et al., 2019). A  global study with a  single \r\nhydrological model estimated that human water consumption has \r\nintensified the magnitude of hydrological droughts by 20-40% \r\nover the last 50 years, and that the human water use contribution \r\nto hydrological droughts was more important than climatic factors \r\nin the Mediterranean, and central USA, as well as in parts of Brazil \r\n(Wada et al., 2013). However, Gudmundsson et al"


id: ba483ec2376f50a35e6bd554a66555c7
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updated_at: 2023-03-16 14:02:08
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index: climate_gpt_only_giec
vector_id: 26844
content:
" (2021) concluded \r\nthat the contribution of human water use is smaller than that of \r\nanthropogenic climate change to explain spatial differences in the \r\ntrends of low flows based on a multi-model analysis. There is still \r\nlimited evidence and thus low confidence in assessing these trends at \r\nthe scale of single regions, with few exceptions (Section 11.9)."


id: 7660a4626e16fdf61e5454285cf28c7e
created_at: 2023-03-16 14:02:08
updated_at: 2023-03-16 14:02:08
content_type: text
index: climate_gpt_only_giec
vector_id: 16216
content:
"There is a  growing number of studies on the detection and \r\nattribution of long-term changes in soil moisture deficits. Mueller \r\nand Zhang (2016) concluded that anthropogenic forcing contributed \r\nsignificantly to soil moisture drying in the warm season in the \r\nNorthern Hemisphere from 1951 to 2005 and also led to an increase \r\nin the land surface area affected by soil moisture deficits, which can \r\nbe reproduced by CMIP5 models only if anthropogenic forcings are \r\ninvolved. Gu et al. (2019b) similarly identified a  global-scale soil \r\nmoisture drying tendency in land surface model data from the Global \r\nLand Data Assimilation System 2 over the time frame 1948-2005, \r\nwhich was attributed to anthropogenic forcing based on evaluation \r\nwith CMIP5 models using optimal fingerprinting. Padron et al. (2019) \r\nanalysed long-term reconstructed and CMIP5 simulated dry season \r\nwater availability, defined as precipitation minus ET (i.e"


id: 37f8de894e038bdbf20229bccd0dfd48
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vector_id: 6179
content:
", equivalent \r\nto soil moisture and runoff availability), also related to agricultural \r\nand ecological droughts. They found an intensification of dry-season \r\nprecipitation minus evapotranspiration deficits over a predominant \r\nfraction of the land area in the last three decades, which can only be \r\nexplained by anthropogenic forcing and is mostly related to increases \r\nin ET. Similarly, Williams et al. (2020) concluded that human\u0002induced climate change contributed to the strong soil moisture \r\ndeficits recorded in the last two decades in Western North America \r\nthrough VPD increases associated with higher air temperatures and \r\nlower air humidity. There are few studies analysing the attribution \r\nof particular episodes of soil moisture deficits to anthropogenic \r\ninfluence"


id: 59a24bb094647374bab8cf3ba6d69b95
created_at: 2023-03-16 14:02:08
updated_at: 2023-03-16 14:02:08
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index: climate_gpt_only_giec
vector_id: 11602
content:
" Nevertheless, the available modelling studies coincide \r\nin supporting an anthropogenic attribution associated with more \r\nextreme temperatures, exacerbating AED and increasing ET, and \r\nthus depleting soil moisture, as observed in southern Europe in 2017 \r\n(Garcia-Herrera et al., 2019) and in Australia in 2018 (Lewis et al., \r\n2020) and 2019 (van Oldenborgh et al., 2021), the latter event having \r\nstrong implications in the propagation of widespread megafires \r\n(Nolan et al., 2020).  "


id: 382779f61e57faecc2ec5b5806cc4ba0
created_at: 2023-03-16 14:02:08
updated_at: 2023-03-16 14:02:08
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index: climate_gpt_only_giec
vector_id: 6205
content:
"Different studies using atmospheric-based drought indices suggest \r\nan attributable anthropogenic signal, characterized by the increased \r\nfrequency and severity of droughts (Cook et al., 2018), associated to \r\nincreased AED (Section 11.6.4.2). The majority of studies are based on \r\nthe PDSI-PM. Williams et al. (2015) and Griffin and Anchukaitis (2014) \r\nconcluded that increased AED has had an increased contribution to \r\ndrought severity over the last decades, and played a dominant role in \r\nthe intensification of the 2012-2014 drought in California. The same \r\ntemporal pattern and physical mechanism was stressed by Z. Li et al. \r\n(2017) in central Asia. Marvel et al. (2019) compared tree ring-based \r\nreconstructions of the PDSI-PM over the past millennium with PDSI\u0002PM estimates based on output from CMIP5 models"


id: 8d3f8edf703ab7ea38012eea2c73d068
created_at: 2023-03-16 14:02:08
updated_at: 2023-03-16 14:02:08
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index: climate_gpt_only_giec
vector_id: 19747
content:
" The comparisons \r\nsuggested a contribution of greenhouse gas forcing to the changes \r\nsince the beginning of the 20th century, although characterized with \r\ntemporal differences that could be driven by temporal variations \r\nin the aerosol forcing. This was in agreement with the dominant \r\nexternal forcings of aridification at global scale between 1950 and \r\n2014 (Bonfils et al., 2020). In the Mediterranean region, there is \r\nmedium confidence of drying attributable to antropogenic forcing \r\nas a consequence of the strong AED increase (Gocic and Trajkovic, \r\n2014; Azorin-Molina et al., 2015; Liuzzo et al., 2016; Macek et al., \r\n2018), which has enhanced the severity of drought events (Vicente\u0002Serrano et al., 2014; Stagge et al., 2017; Gonzalez-Hidalgo et al., "


id: eb8d40da7ed3b79b9cf1f1b0fd704975
created_at: 2023-03-16 14:02:08
updated_at: 2023-03-16 14:02:08
content_type: text
index: climate_gpt_only_giec
vector_id: 34562
content:
"regional and global scales. The assessment of drought mechanisms \r\nunder future climate change scenarios depends on the model used \r\n(Section  11.6.3). Moreover, uncertainties in drought projections are \r\naffected by the consideration of plant physiological responses to \r\nincreasing atmospheric CO2 (Cross-Chapter Box 5.1; Milly and Dunne, \r\n2016; Greve et al., 2019; Mankin et al., 2019; Yang et al., 2020), the \r\nrole of soil-moisture-atmosphere feedbacks for changes in water \r\nbalance and aridity (Berg et al., 2016; Zhou et al., 2021), and statistical \r\nissues related to considered drought time scales (Vicente-Serrano \r\net al., 2020c). Nonetheless, the extensive literature available since AR5 \r\nallows a substantially more robust assessment of projected changes in \r\ndroughts, also subdivided in different drought types (meteorological \r\ndrought, agricultural and ecological drought, and hydrological drought)"


id: f97d999407c241d0febce73354fbb34c
created_at: 2023-03-16 14:02:08
updated_at: 2023-03-16 14:02:08
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index: climate_gpt_only_giec
vector_id: 36765
content:
" \r\nThis includes assessments of projected changes in droughts, including \r\nchanges at 1.5degC, 2degC and 4degC of global warming, for all AR6 regions \r\n(Section 11.9). Projected changes show increases in drought frequency \r\nand intensity in several regions as function of global warming (high \r\nconfidence). There are also substantial increases in drought hazard \r\nprobability from 1.5degC to 2degC global warming and for further additional \r\nincrements of global warming (high confidence) (Figures 11.18 and \r\n11.19). These findings are based on both CMIP5 and CMIP6 analyses \r\n(Section 11.9; Wartenburger et al., 2017; Greve et al., 2018; L. Xu et al., \r\n2019), and strengthen the conclusions of SR1.5 Chapter 3. "


id: 93e10818d1ab1c3abdf4d1c0f7dc9e59
created_at: 2023-03-16 14:02:08
updated_at: 2023-03-16 14:02:08
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index: climate_gpt_only_giec
vector_id: 20773
content:
"The regional evidence on attribution for single AR6 regions generally \r\nshows low confidence for a human contribution to observed trends \r\nin meteorological droughts at regional scale, with few exceptions \r\n(Section 11.9). There is medium confidence that human influence has \r\ncontributed to increases in agricultural and ecological droughts in \r\nthe dry season in some regions and has led to an overall increase in the \r\naffected land area. At regional scales, there is medium confidence\r\nin a  contribution of human-induced climate change to increases \r\nin agricultural and ecological droughts in the Mediterranean and \r\nWestern North America (Section 11.9). There is medium confidence\r\nthat human-induced climate change has contributed to an increase \r\nin hydrological droughts in the Mediterranean region, but also \r\nmedium confidence in contributions from other human influences, \r\nincluding water management and land use (Section  11.9)"


id: 30ef20db03d7ff5b4eb7ef368da60ce1
created_at: 2023-03-16 14:02:08
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vector_id: 5123
content:
" Several \r\nmeteorological and agricultural and ecological drought events have \r\nbeen attributed to human-induced climate change, even in regions \r\nwhere no long-term changes are detected (medium confidence). \r\nHowever, a lack of attribution to human-induced climate change has \r\nalso been shown for some events (medium confidence)."


id: 7aea03adf8a457749b26f3f86bb0afe0
created_at: 2023-03-16 14:02:08
updated_at: 2023-03-16 14:02:08
content_type: text
index: climate_gpt_only_giec
vector_id: 16932
content:
"Studies based on CMIP5, CMIP6 and Coordinated Regional \r\nClimate Downscaling Experiment (CORDEX) projections show \r\na consistent signal in the sign and spatial pattern of projections of \r\nprecipitation deficits. Global studies based on these multi-model \r\nensemble projections (Orlowsky and Seneviratne, 2013; Martin, \r\n2018; Spinoni et al., 2020; Ukkola et al., 2020; Coppola et al., \r\n2021b) show particularly strong signal-to-noise ratios for increasing \r\nmeteorological droughts in the following AR6 regions: MED, ESAF, \r\nWSAF, SAU, CAU, NCA, SCA, NSA and NES (Section 11.9). There is also \r\nsubstantial evidence of changes in meteorological droughts at 1.5degC \r\nversus 2degC of global warming from global studies (Wartenburger \r\net al., 2017; L. Xu et al., 2019). The patterns of projected changes \r\nin mean precipitation are consistent with the changes in the \r\ndrought duration, but they are not consistent with the changes in \r\ndrought intensity (Ukkola et al., 2020)"


id: c38581f515159fbcc3279ef3f8ff63b8
created_at: 2023-03-16 14:02:08
updated_at: 2023-03-16 14:02:08
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index: climate_gpt_only_giec
vector_id: 28332
content:
" In general, CMIP6 projections \r\nsuggest a stronger increase of the probability of precipitation deficits \r\nthan CMIP5 projections (Cook et al., 2020; Ukkola et al., 2020). \r\nProjections for the number of CDDs in CMIP6 (Figure  11.19) for \r\ndifferent levels of global warming relative to 1850-1900 show similar \r\nspatial patterns as projected precipitation deficits. The robustness of \r\nthe patterns in projected precipitation deficits identified in the global \r\nstudies is also consistent with results from regional studies (Giorgi \r\net al., 2014; Marengo and Espinoza, 2016; Pinto et al., 2016; J. Huang \r\net al., 2018; Maure et al., 2018; Nangombe et al., 2018; Tabari and \r\nWillems, 2018; Abiodun et al., 2019; Dosio et al., 2019). "


id: 576fffdd7b61d4a04f2a87f2849b0fd
created_at: 2023-03-16 14:02:08
updated_at: 2023-03-16 14:02:08
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index: climate_gpt_only_giec
vector_id: 11251
content:
"In summary, human influence has contributed to increases in \r\nagricultural and ecological droughts in the dry season in some \r\nregions due to increases in evapotranspiration (medium confidence). \r\nThe increases in evapotranspiration have been driven by increases in \r\natmospheric evaporative demand induced by increased temperature, \r\ndecreased relative humidity and increased net radiation over affected \r\nland areas (high confidence). There is low confidence that human \r\ninfluence has affected trends in meteorological droughts in most \r\nregions, but medium confidence that they have contributed to the \r\nseverity of some single events. There is medium confidence that \r\nhuman-induced climate change has contributed to increasing trends \r\nin the probability or intensity of recent agricultural and ecological \r\ndroughts, leading to an increase of the affected land area"


id: fbeb10c6874e2078af5ed3a8cdb76511
created_at: 2023-03-16 14:02:08
updated_at: 2023-03-16 14:02:08
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index: climate_gpt_only_giec
vector_id: 37129
content:
" Human\u0002induced climate change has contributed to global-scale change in \r\nlow flow, but human water management and land-use changes are \r\nalso important drivers (medium confidence)."


id: dffb035689a9b534b5e73759d3b3941a
created_at: 2023-03-16 14:02:08
updated_at: 2023-03-16 14:02:08
content_type: text
index: climate_gpt_only_giec
vector_id: 32731
content:
"In Central America, projections suggest an increase in mid-summer \r\nmeteorological drought (Imbach et al., 2018) and increased CDD \r\n(Chou et al., 2014a; Giorgi et al., 2014; Nakaegawa et al., 2014). In \r\nthe Amazon, there is also a projected increase in dryness (Marengo \r\nand Espinoza, 2016), which is the combination of a projected increase \r\nin the frequency and geographic extent of meteorological drought in \r\nthe eastern Amazon, and an opposite trend in the west (Duffy et al., \r\n2015). In South-Western South America, there is a projected increase \r\nof CDD (Chou et al., 2014a; Giorgi et al., 2014) and in Chile, drying \r\nis projected to prevail (Boisier et al., 2018). In the South America \r\nmonsoon region, an increase in CDD is projected (Chou et al., 2014a; \r\nGiorgi et al., 2014), but a decrease is projected in South-Eastern and \r\nSouthern South America (Giorgi et al., 2014)"


id: d528a0895762ee724e74a26cf0644f71
created_at: 2023-03-16 14:02:08
updated_at: 2023-03-16 14:02:08
content_type: text
index: climate_gpt_only_giec
vector_id: 31065
content:
" In Central America, \r\nmid-summer meteorological drought is projected to intensify during \r\n2071-2095 for the RCP8.5 scenario (Corrales-Suastegui et al., 2020). "


id: 19cba644823e5f31f81025b543598e0e
created_at: 2023-03-16 14:02:08
updated_at: 2023-03-16 14:02:08
content_type: text
index: climate_gpt_only_giec
vector_id: 1530
content:
"Several studies suggest that increasing atmospheric CO2 could \r\nlead to reduced leaf stomatal conductance, which would increase \r\nwater-use efficiency and reduce plant water needs, thus limiting ET \r\n(Cross-Chapter Box 5.1; Roderick et al., 2015; Milly and Dunne, 2016; \r\nSwann et al., 2016; Greve et al., 2017; Scheff et al., 2017; Lemordant \r\net al., 2018; Swann, 2018). The implemention of a  CO2-dependent \r\nland resistance parameter has been suggested for the estimation \r\nof AED (Yang et al., 2019). Nevertheless, there are other relevant \r\nmechanisms, as soil moisture deficits and VPD also play an important \r\nrole in the control of the leaf stomatal conductance (Z. Xu et al., 2016; \r\nMenezes-Silva et al., 2019; Grossiord et al., 2020), and a  number \r\nof ecophysiological and anatomical processes affect the response \r\nof plant physiology under higher atmospheric CO2 concentrations \r\n(Cross-Chapter Box  5.1; Mankin et al., 2019; Menezes-Silva et al., \r\n2019)"


id: 4a4e2ced002eb744cd23eddf64a633c2
created_at: 2023-03-16 14:02:08
updated_at: 2023-03-16 14:02:08
content_type: text
index: climate_gpt_only_giec
vector_id: 9096
content:
" The benefits of the atmospheric CO2 for plant stress and \r\nagricultural and ecological droughts would be minimal precisely \r\nduring dry periods given stomatal closure in response to limited \r\nsoil moisture (Allen et al., 2015; Z. Xu et al., 2016). In addition, CO2\r\neffects on plant stomatal conductance could not entirely compensate \r\nfor the increased demand associated with warming (Liu and Sun, \r\n2017); in large tropical and subtropical regions (e.g., southern Africa, \r\nthe Amazon, the Mediterranean and southern North America), AED \r\nis projected to increase, even considering the possible CO2 effects \r\non land resistance (Vicente-Serrano et al., 2020a). Moreover, these \r\nCO2 effects would not affect the direct evaporation from soil and \r\nwater bodies, which is very relevant in the reservoirs of warm areas \r\n(Friedrich et al., 2018)"


id: f50c0feda9dcaf5b57514c6c634f328
created_at: 2023-03-16 14:02:08
updated_at: 2023-03-16 14:02:08
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index: climate_gpt_only_giec
vector_id: 36067
content:
" Because of these uncertainties, there is low \r\nconfidence whether increased CO2-induced water-use efficiency \r\nin vegetation will substantially reduce global plant transpiration \r\nand will diminish the frequency and severity of soil moisture and \r\nstreamflow deficits associated with the radiative effect of higher CO2\r\nconcentrations (Cross-Chapter Box 5.1)."


id: 3f9a46e6ca0889b9bdf87311390171fe
created_at: 2023-03-16 14:02:08
updated_at: 2023-03-16 14:02:08
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index: climate_gpt_only_giec
vector_id: 7395
content:
"(Lu et al., 2019), extending the regions affected by severe soil \r\nmoisture deficits over most of south and central Europe (Lehner \r\net al., 2017; Ruosteenoja et al., 2018; Samaniego et al., 2018; van Der \r\nLinden et al., 2019), southern North America (Cook et al., 2019), South \r\nAmerica (Orlowsky and Seneviratne, 2013), southern Africa (Lu et al., \r\n2019), East Africa (Rowell et al., 2015), Southern Australia (Kirono \r\net al., 2020), India (Mishra et al., 2014a) and East Asia (Figure 11.19; \r\nCheng et al., 2015). Projected changes in total soil moisture display \r\nless widespread drying than those for surface soil moisture (Berg \r\net al., 2017a), but still more than for precipitation (Cross-Chapter \r\nBox 11.1, Figures 2a,b,c). The severity of droughts based on surface \r\nsoil moisture in future projections is stronger than projections \r\nbased on precipitation and runoff (Dai et al., 2018; Vicente-Serrano \r\net al., 2020c)"


id: f92057a63d31c0636113e66cfa17797f
created_at: 2023-03-16 14:02:08
updated_at: 2023-03-16 14:02:08
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index: climate_gpt_only_giec
vector_id: 36704
content:
" Nevertheless, in many parts of the world where soil \r\nmoisture is projected to decrease, the signal-to-noise ratio among \r\nmodels is low; only the projections in the Mediterranean, Europe, \r\nthe south-western USA, and southern Africa show a  high signal\u0002to-noise ratio in soil moisture projections (Figure  11.19; Lu et al., \r\n2019). Increases in soil moisture deficits are found to be statistically \r\nsignicant at regional scale in the Mediterranean region, southern \r\nAfrica and western South America for changes as small as 0.5degC in \r\nglobal warming, based on differences between +1.5degC and +2degC of \r\nglobal warming (Wartenburger et al., 2017). Several other regions \r\nare affected when considering changes in droughts for higher \r\nchanges in global warming (Section 11.9 and Figure 11.19). Seasonal \r\nprojections of drought frequency for boreal winter (December-\r\nJanuary-February) and summer (June-July-August), from CMIP6 \r\nmulti-model ensemble for 1"


id: 76da17cbd0d582360ef40749a2f3962d
created_at: 2023-03-16 14:02:08
updated_at: 2023-03-16 14:02:08
content_type: text
index: climate_gpt_only_giec
vector_id: 16275
content:
"Studies show a stronger drying in projections based on atmospheric\u0002based drought indices compared to ESM projections of changes in \r\nsoil moisture (Berg and Sheffield, 2018) and runoff (Yang et al., 2019). \r\nIt has been suggested that this difference is due to physiological CO2\r\neffects (Section  11.6.5.2; Roderick et al., 2015; Milly and Dunne, \r\n2016; Swann et al., 2016; Lemordant et al., 2018; Scheff, 2018; \r\nSwann, 2018; Greve et al., 2019; Yang et al., 2020). Nonetheless, there \r\nis evidence that differences in projections between atmospheric\u0002based drought indices and water-balance metrics from ESMs are not \r\nalone due to CO2-plant effects (Berg et al., 2016; Scheff et al., 2021). \r\nDifferences can also be related to the fact that AED is an upper bound \r\nfor ET in dry regions and conditions (Section 11.6.1.2) and that soil \r\nmoisture stress limits increases in ET in projections (Section 11.6.5.2; \r\nBerg et al., 2016; Zhou et al., 2021)"


id: 23f192b9b049ae5f1378b4efe3ad204
created_at: 2023-03-16 14:02:08
updated_at: 2023-03-16 14:02:08
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index: climate_gpt_only_giec
vector_id: 3093
content:
" In general, atmospheric-based \r\nindices show more drying than total column soil moisture (Berg and \r\nSheffield, 2018; Cook et al., 2020; Scheff et al., 2021), but are more \r\nconsistent with projected increases in surface soil moisture deficits \r\n(Dirmeyer et al., 2013; Dai et al., 2018; Lu et al., 2019; Cook et al., \r\n2020; Vicente-Serrano et al., 2020c). "


id: 628b8994c7c2b6b8f8f8768a6646d9a
created_at: 2023-03-16 14:02:08
updated_at: 2023-03-16 14:02:08
content_type: text
index: climate_gpt_only_giec
vector_id: 13073
content:
"Some studies support wetting tendencies as a response to a warmer \r\nclimate when considering globally averaged changes in runoff over \r\nland (Roderick et al., 2015; Greve et al., 2017; Y. Yang et al., 2018), \r\nand streamflow projections respond to enhanced CO2 concentrations \r\nin CMIP5 models (Yang et al., 2019). Nevertheless, when focusing \r\nregionally on low-runoff periods, model projections also show an \r\nincrease of hydrological droughts in large world regions (Wanders \r\nand Van Lanen, 2015; Dai et al., 2018; Vicente-Serrano et al., 2020c). \r\nIn general, the frequency of hydrological deficits is projected to \r\nincrease over most of the continents, although with regionally \r\nand seasonally differentiated effects (Section  11.9), with medium \r\nconfidence of increase in the following AR6 regions: WCE, MED, \r\nSAU, WCA, WNA, SCA, NSA, SAM, SWS, SSA, WSAF, ESAF and MDG \r\n(Section 11.9; Forzieri et al., 2014; Prudhomme et al., 2014; Giuntoli \r\net al"


id: 534afea7ab0c11fb6febebcc3268b981
created_at: 2023-03-16 14:02:08
updated_at: 2023-03-16 14:02:08
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index: climate_gpt_only_giec
vector_id: 10573
content:
", 2015; Wanders and Van Lanen, 2015; Roudier et al., 2016; Marx \r\net al., 2018; Cook et al., 2019; Zhao et al., 2020). However, there \r\nare large uncertainties related to the hydrological/impact model used \r\n(Prudhomme et al., 2014; Schewe et al., 2014; Gosling et al., 2017), \r\nlimited signal-to-noise ratio (due to model spread) in several regions \r\n(Giuntoli et al., 2015), and also uncertainties in the projection of future \r\nhuman activities, including water demand and land cover changes, \r\nwhich may represent more than 50% of the projected changes in \r\nhydrological droughts in some regions (Wanders and Wada, 2015). "


id: 792a83a78d543a7767a44373177cf7f0
created_at: 2023-03-16 14:02:08
updated_at: 2023-03-16 14:02:08
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index: climate_gpt_only_giec
vector_id: 16625
content:
"Atmospheric-based drought indices are not metrics of soil moisture \r\nor runoff (Section 11.6.1.5) so their projections may not necessarily \r\nreflect the same trend of online simulated soil moisture and runoff. \r\nIndependently of effects on the land water balance, atmospheric\u0002based drought indices will reflect the potential vegetation stress \r\nresulting from deficits between available water and enhanced \r\nAED, even in conditions with no or low ET. Under dry conditions, \r\nthe enhanced AED associated with human forcing would increase \r\nplant water stress (Brodribb et al., 2020), with effects on widespread \r\nforest dieback and mortality (Anderegg et al., 2013; Williams et al., \r\n2013; Allen et al., 2015; McDowell and Allen, 2015; McDowell et al., \r\n2016, 2020), and stronger risk of megafires (Flannigan et al., 2016; \r\nPodschwit et al., 2018; Clarke and Evans, 2019; Varela et al., 2019)"


id: 5798ddffc4516bb3ef2a5dc7de400ae1
created_at: 2023-03-16 14:02:08
updated_at: 2023-03-16 14:02:08
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index: climate_gpt_only_giec
vector_id: 11276
content:
" \r\nFor these reasons, there is high confidence that the future projections \r\nof enhanced drought severity showed by the PDSI-PM and the SPEI\u0002PM are representative of more frequent and severe plant stress \r\nepisodes and more severe agricultural and ecological drought \r\nimpacts in some regions. "


id: 7e69bef05e8d92e8bfca4c5ac1b0a478
created_at: 2023-03-16 14:02:08
updated_at: 2023-03-16 14:02:08
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index: climate_gpt_only_giec
vector_id: 17454
content:
"WNA and CNA (medium confidence). At 4degC of global warming, \r\neven more regions would be affected by agricultural and ecological \r\ndroughts (WCE, MED, CAU, EAU, SAU, WCA, EAS, SCA, CAR, NSA, \r\nNES, SAM, SWS, SSA, NCA, CNA, ENA, WNA, WSAF, ESAF and MDG). \r\nSome regions are also projected to experience less agricultural \r\nand ecological drought with global warming (medium confidence; \r\nNEAF, SAS). There is high confidence that the projected increases in \r\nagricultural and ecological droughts are strongly affected by AED \r\nincreases in a warming climate, although ET increases are projected \r\nto be smaller than those in AED due to soil moisture limitations and \r\nCO2 effects on leaf stomatal conductance"


id: 8710563ed618ae3a5985f975c89fb489
created_at: 2023-03-16 14:02:08
updated_at: 2023-03-16 14:02:08
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index: climate_gpt_only_giec
vector_id: 18779
content:
" Enhanced atmospheric \r\nCO2 concentrations lead to enhanced water-use efficiency in plants \r\n(medium confidence), but there is low confidence that it can alleviate \r\nagricultural and ecological droughts, or hydrological droughts, at \r\nhigher global warming levels characterized by limited soil moisture \r\nand enhanced AED."


id: 5677b129057a097cb9fb64eed72307dd
created_at: 2023-03-16 14:02:08
updated_at: 2023-03-16 14:02:08
content_type: text
index: climate_gpt_only_giec
vector_id: 11080
content:
"The tables in Section  11.9 provide assessed projected changes in \r\nmetorological drought, agricultural and ecological drought, and \r\nhydrological droughts. The assessment shows that several regions will \r\nbe affected by more severe agricultural and ecological droughts even \r\nif global warming is stabilized at 2degC, including MED, WSAF, SAM and \r\nSSA (high confidence), and ESAF, MDG, EAU, SAU, SCA, CAR, NSA, \r\nNES, SWS, WCE, NCA, WNA and CNA (medium confidence). Some \r\nregions are also projected to be affected by more severe agricultural \r\nand ecological droughts at 1.5degC (MED, WSAF, ESAF, SAU, NSA, SAM, \r\nSSA, can; medium confidence) At 4degC of global warming, even more \r\nregions would be affected by agricultural and ecological droughts \r\n(WCE, MED, CAU, EAU, SAU, WCA, EAS, SCA, CAR, NSA, NES, SAM, \r\nSWS, SSA, NCA, CNA, ENA, WNA, WSAF, ESAF and MDG). NEAF, SAS \r\nare also projected to experience less agricultural and ecological \r\ndrought with global warming (medium confidence)"


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vector_id: 605
content:
" Projected \r\nchanges in meteorological droughts are, overall, less extended but \r\nalso affect several AR6 regions, at 1.5degC and 2degC (MED, EAU, SAU, \r\nSCA, NSA, NCA, WSAF, ESAF, MDG) and 4degC of global warming \r\n(WCE, MED, EAU, SAU, SEA, SCA, CAR, NWS, NSA, NES, SAM, SWS, \r\nSSA, NCA, ENA, WAF, WSAF, ESAF, MDG). Several regions are also \r\nprojected to be affected by more hydrological droughts at 1.5degC and \r\n2degC (WCE, MED, WNA, WSAF, ESAF) and 4degC of global warming (NEU, \r\nWCE, EEU, MED, SAU, WCA, SCA, NSA, SAM, SWS, SSA, WNA, WSAF, \r\nESAF, MDG). To illustrate the changes in both intensity and frequency \r\nof drought in the regions where strongest changes are projected, \r\nFigure  11.18 displays changes in the intensity and frequency of \r\nsoil moisture drought under different global warming levels (1.5degC, \r\n2degC, 4degC) relative to the 1851-1900 baseline based on CMIP6 \r\nsimulations under different SSP forcing scenarios averaged over \r\n\"drying regions\", i.e"


id: 39efdf332de6abed48e2154f9d0f87
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content:
" AR6 regions for which there is at least medium \r\nconfidence in increase in agricultural and ecological drought at 2degC \r\nof global warming. The 90% uncertainty ranges for the projected \r\nchanges in both intensity and frequency are above zero, indicating \r\nsignificant increase in both intensity and frequency of drought in \r\nthese regions as whole."


id: d6cd4bd0637b1bcc5e45ab75f2e7c9ca
created_at: 2023-03-16 14:02:08
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vector_id: 31305
content:
"Projected changes in meteorological droughts are overall less \r\nextended than for agricultural and ecological droughts, but also \r\naffect several AR6 regions, even at 1.5degC and 2degC of global warming. \r\nSeveral regions are also projected to be more strongly affected by \r\nhydrological droughts with increasing global warming (NEU, WCE, \r\nEEU, MED, SAU, WCA, SCA, NSA, SAM, SWS, SSA, WNA, WSAF, ESAF, \r\nMDG). Increased soil moisture limitation and associated changes in \r\ndroughts are projected to lead to increased vegetation stress in many \r\nregions, with implications for the global land carbon sink (Cross\u0002Chapter Box 5)"


id: a871a71c0ea69b6aacafad80cef5ff66
created_at: 2023-03-16 14:02:08
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content:
" There is high confidence that the global land carbon \r\nsink will become less efficient due to soil moisture limitations and \r\nassociated drought conditions in some regions in higher-emissions \r\nscenarios, especially under global warming levels above 4degC; \r\nhowever, there is low confidence on how these water cycle feedbacks \r\nwill play out in lower-emissions scenarios (at 2degC global warming or \r\nlower; Cross-Chapter Box5.1)."


id: 1f50c814014e5567443e40a8eef99756
created_at: 2023-03-16 14:02:08
updated_at: 2023-03-16 14:02:08
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vector_id: 2364
content:
"Figure 11.19 | Projected changes in (a-c) the number of consecutive dry days (CDD), (d-f) annual mean soil moisture over the total column, and (g-l) the \r\nfrequency and intensity of 1-in-10-year soil moisture drought for the June-to-August and December-to-February seasons at 1.5degC, 2degC, and 4degC of global \r\nwarming compared to the 1850-1900 baseline. The unit for soil moisture change is the standard deviation of interannual variability in soil moisture during 1850-1900. \r\nStandard deviation is a widely used metric in characterizing drought severity. A projected reduction in mean soil moisture by one standard deviation corresponds to soil moisture \r\nconditions typical of about 1-in-6-year droughts during 1850-1900 becoming the norm in the future. Results are based on simulations from the Coupled Model Intercomparison \r\nProject Phase 6 (CMIP6) multi-model ensemble under the Shared Socio-economic Pathway (SSP), SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios"


id: 7f9c89fa11c47377d35d5057c6a1d14b
created_at: 2023-03-16 14:02:08
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content:
" The numbers \r\nin the top right indicate the number of simulations included. Uncertainty is represented using the simple approach: no overlay indicates regions with high model agreement, \r\nwhere >=80% of models agree on the sign of change; diagonal lines indicate regions with low model agreement, where <80% of models agree on the sign of change. For more \r\ninformation on the simple approach, please refer to the Cross-Chapter Box Atlas 1. For details on the methods see Supplementary Material 11.SM.2. Changes in CDDs are also \r\ndisplayed in the Interactive Atlas. Further details on data sources and processing are available in the chapter data table (Table 11.SM.9)."


id: 6f3d9ff38df4dfb9e2f24223a58bd625
created_at: 2023-03-16 14:02:08
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content:
"and interannual (e.g., the El Nino-Southern Oscillation and Pacific \r\nand Atlantic Meridional Modes), to inter-decadal (e.g.,  Atlantic \r\nMultidecadal Variability and Pacific Decadal Variability). This broad \r\nrange of natural variability makes detection of anthropogenic \r\neffects difficult, and uncertainties in the projected changes of these \r\nmodes of variability increase uncertainty in the projected changes \r\nin TC activity. Aerosol forcing also affects sea surface temperature \r\n(SST) patterns and cloud microphysics, and it is likely that observed \r\nchanges in TC activity are partly caused by changes in aerosol forcing \r\n(Evan et al., 2011; Ting et al., 2015; Sobel et al., 2016, 2019; Takahashi \r\net al., 2017; Zhao et al., 2018; Reed et al., 2019). Among possible \r\nchanges from these drivers, there is medium confidence that the \r\nHadley cell has widened and will continue to widen in the future \r\n(Sections 2.3, 3.3 and 4.5)"


id: fb76d3485146d2b3689a30c6f45239cd
created_at: 2023-03-16 14:02:08
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content:
" This likely causes latitudinal shifts of TC \r\ntracks (Sharmila and Walsh, 2018). Regional TC activity changes are \r\nalso strongly affected by projected changes in SST warming patterns \r\n(Yoshida et al., 2017), which are highly uncertain (Chapters 4 and 9)."


id: 2fc34062b748bbf871a0f26e3995b5ac
created_at: 2023-03-16 14:02:08
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content:
"Since SREX, several IPCC Reports also assessed storms. The AR5 \r\n(Chapter  2, Hartmann et al., 2013) assessment observed with \r\nlow confidence long-term trends in TC metrics, but revised the \r\nstatement from SREX to state that it is virtually certain that there \r\nare increasing trends in North Atlantic TC activity since the 1970s, \r\nwith medium confidence that anthropogenic aerosol forcing has \r\ncontributed to these trends. The AR5 concluded that it is likely that \r\nTC precipitation and mean intensity will increase and more likely \r\nthan not that the frequency of the strongest storms will increase \r\nwith continued greenhouse gas warming. Confidence in projected \r\ntrends in overall TC frequency remained low. Confidence in observed \r\nand projected trends in hail storm and tornado events also remained \r\nlow. The SROCC (Chapter 6, Collins et al., 2019) assessed past and \r\nprojected TCs and ETCs, supporting the AR5 conclusions with some \r\nadditional detail"


id: b52c02c6f68723420d32002372687356
created_at: 2023-03-16 14:02:08
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content:
" Literature subsequent to AR5 adds support to the \r\nlikelihood of increasing trends in TC intensity, precipitation, and \r\nfrequency of the most intense storms, while some newer studies \r\nhave added uncertainty to projected trends in overall frequency. \r\nA growing body of literature since AR5 on the poleward migration \r\nof TCs led to a  new assessment in SROCC of low confidence that \r\nthe migration in the western North Pacific represents a detectable \r\nclimate change contribution from anthropogenic forcing. The SR1.5 \r\n(Chapter 3, Hoegh-Guldberg et al., 2018) essentially confirmed the \r\nAR5 assessment of TCs and ETCs, adding that heavy precipitation \r\nassociated with TCs is projected to be higher at 2degC compared to \r\n1.5degC global warming (medium confidence)."


id: 7cbdbff0f36e1fd480f8267b26ca0f72
created_at: 2023-03-16 14:02:09
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"There are previous and ongoing efforts to homogenize the best-track \r\ndata (Elsner et al., 2008; Kossin et al., 2013, 2020; Choy et al., 2015; \r\nLandsea, 2015; Emanuel et al., 2018) and there is substantial literature \r\nthat finds positive trends in intensity-related metrics in the best-track \r\nduring the 'satellite period', which is generally limited to around the \r\npast 40 years (Kang and Elsner, 2012; Kishtawal et al., 2012; Kossin \r\net al., 2013, 2020; Mei and Xie, 2016; Zhao et al., 2018; Tauvale and \r\nTsuboki, 2019). When best-track trends are tested using homogenized \r\ndata, the intensity trends generally remain positive, but are smaller in \r\namplitude (Kossin et al., 2013; Holland and Bruyere, 2014). Kossin et al. \r\n(2020) extended the homogenized TC intensity record to the period \r\n1979-2017 and identified significant global increases in major TC \r\nexceedance probability of about 6% per decade"


id: 67292ab076b3866a72825c4476e087cc
created_at: 2023-03-16 14:02:09
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content:
" In addition to trends \r\nin TC intensity, there is evidence that TC intensification rates and the \r\nfrequency of rapid intensification events have increased within the \r\nsatellite era (Kishtawal et al., 2012; Balaguru et al., 2018; Bhatia et al., \r\n2018). The increase in intensification rates is found in the best-track \r\nand the homogenized intensity data."


id: ce921cb4bb1d4ff2eda8aabddddc86a5
created_at: 2023-03-16 14:02:09
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content:
"period. A similarly reliable subset of the data representing TC landfall \r\nfrequency over Australia shows a decreasing trend in Eastern Australia \r\nsince the 1800s (Callaghan and Power, 2011), as well as in other parts \r\nof Australia since 1982 (Chand et al., 2019; Knutson et al., 2019). \r\nA  paleoclimate proxy reconstruction shows that recent levels of TC \r\ninteractions along parts of the Australian coastline are the lowest \r\nin the past 550-1500 years (Haig et al., 2014). Existing TC datasets \r\nshow substantial inter-decadal variations in basin-wide TC frequency \r\nand intensity in the western North Pacific, but a statistically significant \r\nnorth-westward shift in the western North Pacific TC tracks since the \r\n1980s (T.-C. Lee et al., 2020). In the case of the North Indian Ocean, \r\nanalyses of trends are highly dependent on the details of each analysis \r\n(e.g., pre- and/or post-monsoon season period, or Bay of Bengal and/\r\nor Arabian Sea region)"


id: bd16e9c64d19b0d197b3af2d427f5900
created_at: 2023-03-16 14:02:09
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vector_id: 27315
content:
" The most consistent trends are an increase in \r\nthe occurrence of the most intense TCs, and a decrease in the overall \r\nTC frequency, in particular in the Bay of Bengal (Sahoo and Bhaskaran, \r\n2016; Balaji et al., 2018; Singh et al., 2019; Baburaj et al., 2020). In \r\nthe South Indian Ocean (SIO), an increase in the occurrence of the \r\nmost intense TCs has been noted; however, there are well-known \r\ndata quality issues there (Kuleshov et al., 2010; Fitchett, 2018). When \r\nthe SIO data are homogenized, a significant increase is found in the \r\nfractional proportion of global Category 3-5 TC instances (6-hourly \r\nintensity estimates during the lifetime of each TC) to all Category 1-5 \r\ninstances (Kossin et al., 2020). "


id: bbeaf5d4edd5849f059937390780d80a
created_at: 2023-03-16 14:02:09
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vector_id: 27123
content:
"Subsequent to AR5, two metrics have been analysed that are argued \r\nto be comparatively less sensitive to data issues than frequency- and \r\nintensity-based metrics. Trends in these metrics have been identified \r\nover the past 70 years or more (Knutson et al., 2019). The first metric - \r\nthe mean latitude where TCs reach their peak intensity  - exhibits \r\na global and regional poleward migration during the satellite period \r\n(Kossin et al., 2014). The poleward migration can influence TC hazard \r\nexposure and risk (Kossin et al., 2016a) and is consistent with the \r\nindependently observed expansion of the tropics (Lucas et al., 2014). \r\nThe migration has been linked to changes in the Hadley circulation \r\n(Altman et al., 2018; Sharmila and Walsh, 2018; Studholme and \r\nGulev, 2018). The migration is also apparent in the mean locations \r\nwhere TCs exhibit eyes (Knapp et al., 2018), which is when TCs are \r\nmost intense"


id: 22a43eef4f23cad2491c69c38feacbfc
created_at: 2023-03-16 14:02:09
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content:
" Part of the Northern Hemisphere poleward migration \r\nis due to basin-wide changes in TC frequency (Kossin et al., 2014, \r\n2016b; Moon et al., 2015, 2016) and the trends, as expected, can \r\nbe sensitive to the time period chosen (Tennille and Ellis, 2017; \r\nKossin, 2018; Song and Klotzbach, 2018) and to subsetting of the \r\ndata by intensity (Zhan and Wang, 2017). The poleward migration is \r\nparticularly pronounced and well-documented in the western North \r\nPacific basin (Kossin et al., 2016a; Oey and Chou, 2016; Liang et al., \r\n2017; Nakamura et al., 2017; Altman et al., 2018; Daloz and Camargo, "


id: bdae858a0f2d8ad51e00ef64011c971f
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
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index: climate_gpt_only_giec
vector_id: 27408
content:
"Figure 11.20 | Summary schematic of past and projected changes in tropical cyclone (TC), extratropical cyclone (ETC), atmospheric river (AR), and severe \r\nconvective storm (SCS) behaviour. Global changes (blue shading) from top to bottom: (i) Increased mean and maximum rain rates in TCs, ETCs, and ARs [past (low \r\nconfidence due to lack of reliable data) and projected (high confidence)]; (ii) Increased proportion of stronger TCs [past (medium confidence) and projected (high confidence)]; \r\n(iii) Decrease or no change in global frequency of TC genesis [past (low confidence due to lack of reliable data) and projected (medium confidence)]; and (iv) Increased and \r\ndecreased ETC wind speed, depending on the region, as storm tracks change [past (low confidence due to lack of reliable data) and projected (medium confidence)]"


id: d37ce0156691f4f6d804a62e2a061e61
created_at: 2023-03-16 14:02:09
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index: climate_gpt_only_giec
vector_id: 30800
content:
" Regional \r\nchanges, from left to right: (i) Poleward TC migration in the western North Pacific and subsequent changes in TC exposure [past (medium confidence) and projected (medium\r\nconfidence)]; (ii) Slowdown of TC forward translation speed over the contiguous USA and subsequent increase in TC rainfall [past (medium confidence) and projected (low\r\nconfidence due to lack of directed studies)]; and (iii) Increase in mean and maximum SCS rain rate and increase in spring SCS frequency and season length over the contiguous \r\nUSA [past (low confidence due to lack of reliable data) and projected (medium confidence)]. "


id: cd0a4cdc57cfb854a1ec18a4cd463c21
created_at: 2023-03-16 14:02:09
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index: climate_gpt_only_giec
vector_id: 29807
content:
"A second metric that is argued to be comparatively less sensitive \r\nto data issues than frequency- and intensity-based metrics is TC \r\ntranslation speed (Kossin, 2018), which exhibits a global slowdown \r\nin the best-track data over the period 1949-2016. TC translation \r\nspeed is a  measure of the speed at which TCs move across the \r\nEarth's surface, and is very closely related to local rainfall amounts \r\n(i.e.,  a slower translation speed causes greater local rainfall). TC \r\ntranslation speed also affects structural wind damage and coastal \r\nstorm surge by changing the hazard event duration. The slowdown is \r\nobserved in the best-track data from all basins except the Northern \r\nIndian Ocean, and is also found in a number of regions where TCs \r\ninteract directly with land. The slowing trends identified in the best\u0002track data by Kossin (2018) have been argued to be largely due to \r\ndata heterogeneity. Moon et al"


id: 6c687663564930e06fb0fbe34930a685
created_at: 2023-03-16 14:02:09
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content:
" (2019) and Lanzante (2019) provide \r\nevidence that meridional TC track shifts project onto the slowing \r\ntrends, and argue that these shifts are due to the introduction of \r\nsatellite data. Kossin (2019) provides evidence that the slowing trend \r\nis real by focusing on Atlantic TC track data over the contiguous USA \r\nin the 118-year period 1900-2017, which are generally considered \r\nreliable. In this period, mean TC translation speed has decreased by \r\n17%. The slowing TC translation speed is expected to increase local \r\nrainfall amounts, which would increase coastal and inland flooding. \r\nIn combination with slowing translation speed, abrupt TC track \r\ndirection changes  - that can be associated with track 'meanders' \r\nor 'stalls'  - have become increasingly common along the North \r\nAmerican coast since the mid-20th century, leading to more rainfall \r\nin the region (Hall and Kossin, 2019). "


id: f67d15b895e1be1fc31c2ad92ef1b9df
created_at: 2023-03-16 14:02:09
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content:
"Confidence in the projection of intense TCs, such as those of \r\nCategory 4-5, generally becomes higher as the resolution of the \r\nmodels becomes higher. The Coupled Model Intercomparison Project \r\nPhases 5 and 6 (CMIP5/6) class climate models (around 100-200 km \r\ngrid spacing) cannot simulate TCs of Category 4-5 intensity. They do \r\nsimulate storms of relatively high vorticity that are at best described \r\nas 'TC-like', but metrics such as storm counts are highly dependent \r\non tracking algorithms (Camargo, 2013; Wehner et al., 2015; Zarzycki \r\nand Ullrich, 2017; Roberts et al., 2020a). High-resolution GCMs \r\n(around 10-60 km grid spacing), as used in HighResMIP (Haarsma \r\net al., 2016; Roberts et al., 2020a), begin to capture some structures \r\nof TCs more realistically, as well as produce intense TCs of Category \r\n4-5 despite the effects of parametrized deep cumulus convection \r\nprocesses (Murakami et al., 2015; Wehner et al., 2015; Yamada \r\net al., 2017; Roberts et al"


id: 4e3115d662747401306b344ab8fc3ff0
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", 2018; Moon et al., 2020). Convection\u0002permitting models (around 1-10 km grid-spacing), such as used \r\nin some dynamical downscaling studies, provide further realism \r\nwith capturing TC eye-wall structures (Tsuboki et al., 2015). Model \r\ncharacteristics besides resolution, especially details of convective \r\nparametrization, can influence a model's ability to simulate intense \r\nTCs (Reed and Jablonowski, 2011; Zhao et al., 2012; He and Posselt, \r\n2015; D. Kim et al., 2018; Zhang and Wang, 2018; Camargo et al., \r\n2020). However, models' dynamical cores and other physics also \r\naffect simulated TC properties (Reed et al., 2015; Vidale et al., 2021). \r\nBoth wide-area regional and global convection-permitting models \r\nwithout the need for parameterized convection are becoming more \r\nuseful for TC regional model projection studies (Tsuboki et al., 2015; \r\nKanada et al., 2017a; Gutmann et al., 2018) and global model \r\nprojection studies (Satoh et al., 2015, 2017; Yamada et al"


id: 96c394686e9e63700b86147f8b00784
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vector_id: 21238
content:
", 2017), \r\nas they capture more realistic TC eye wall structures (Kinter III et al., \r\n2013) and are becoming more useful for investigating changes in TC \r\nstructures (Kanada et al., 2013; Yamada et al., 2017). Large ensemble \r\nsimulations of GCMs with 60 km grid spacing provide TC statistics \r\nthat allow more reliable detection of changes in the projections, \r\nwhich are not well captured in any single experiment (Yoshida et al., \r\n2017; Yamaguchi et al., 2020). Variable resolution global models offer \r\nan alternative to regional models for individual TC or basin-wide \r\nsimulations (Yanase et al., 2012; Zarzycki et al., 2014; Harris et al., \r\n2016; Reed et al., 2020; Stansfield et al., 2020). Computationally less \r\nintense than equivalent uniform resolution global models, they also \r\ndo not require lateral boundary conditions, thus reducing this source \r\nof error (Hashimoto et al., 2016)"


id: 51ca2d925916fcf6f7abe2584abc1a19
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" Confidence in the projection of TC \r\nstatistics and properties is increased by the use of higher-resolution \r\nmodels with more realistic simulations."


id: 471ee7455eb7af8708ca5d1ede92252b
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
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vector_id: 8570
content:
"Accurate projections of future TC activity have two principal \r\nrequirements: accurate representation of changes in the relevant \r\nenvironmental factors (e.g.,  SSTs) that can affect TC activity, and \r\naccurate representation of actual TC activity in given environmental \r\nconditions. In particular, models' capacity to reproduce historical trends \r\nor interannual variabilities of TC activity is relevant to the confidence in \r\nfuture projections. One test of the models is to evaluate their ability to \r\nreproduce the dependency of the TC statistics in the different basins in \r\nthe real world, in addition to their capability of reproducing atmospheric \r\nand ocean environmental conditions"


id: ac7e3b69fa70280b86138bd4c9215f57
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vector_id: 24680
content:
" For the evaluation of projections \r\nof TC-relevant environmental variables, AR5 confidence statements \r\nwere based on global surface temperature and moisture, but not on \r\nthe detailed regional structure of SST and atmospheric circulation \r\nchanges such as steering flows and vertical shear, which affect \r\ncharacteristics of TCs (genesis, intensity, tracks, etc.). Various aspects of "


id: 2345d865f37486027c3a6ca25df2e4f4
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
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content:
"There is general agreement in the literature that anthropogenic \r\ngreenhouse gases and aerosols have measurably affected observed \r\noceanic and atmospheric variability in TC-prone regions (see \r\nChapter  3). This underpinned the SROCC assessment of medium \r\nconfidence that humans have contributed to the observed increase in \r\nAtlantic hurricane activity since the 1970s (Chapter 5, Bindoff et al., \r\n2013). Literature subsequent to AR5 lends further support to this \r\nstatement (Knutson et al., 2019). However, there is still no consensus \r\non the relative magnitude of human and natural influences on past \r\nchanges in Atlantic hurricane activity, and particularly on which \r\nfactor has dominated the observed increase (Ting et al., 2015) and \r\nit remains uncertain whether past changes in Atlantic TC activity \r\nare outside the range of natural variability"


id: c736265ff68056c477f6f5d100c6c55b
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content:
" A  recent result using \r\nhigh-resolution dynamical model experiments suggested that the \r\nobserved spatial contrast in TC trends cannot be explained only by \r\nmulti-decadal natural variability, and that external forcing plays an \r\nimportant role (Murakami et al., 2020). Observational evidence for \r\nsignificant global increases in the proportion of major TC intensities \r\n(Kossin et al., 2020) is consistent with both theory and numerical \r\nmodelling simulations, which generally indicate an increase in mean \r\nTC peak intensity and the proportion of very intense TCs in a warming \r\nworld (Knutson et al., 2015, 2020; Walsh et al., 2015, 2016). In \r\naddition, high-resolution coupled model simulations provide support \r\nthat natural variability alone is unlikely to explain the magnitude \r\nof the observed increase in TC intensification rates and upward TC \r\nintensity trend in the Atlantic basin since the early 1980s (Bhatia \r\net al., 2019; Murakami et al., 2020)."


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vector_id: 31165
content:
"found no statistically significant impact of anthropogenic climate \r\nchange on storm intensity, while projections in a  warmer world \r\nshowed significant strengthening. However, Magnusson et al. (2014) \r\nfound that, in European Centre for Medium-Range Weather Forecast \r\n(ECMWF) simulations, the simulated cyclone depth and intensity, \r\nas well as precipitation, were larger when the model was driven \r\nby the warmer actual SSTs than the climatological average SSTs. In \r\nSuper Typhoon Haiyan, which struck the Philippines on 8 November \r\n2013, Takayabu et al. (2015) took an event attribution approach \r\nwith cloud system-resolving (around 1  km) downscaling ensemble \r\nexperiments to evaluate the anthropogenic effect on typhoons, and \r\nshowed that the intensity of the simulated worst-case storm in the \r\nactual conditions was stronger than that in a hypothetical condition \r\nwithout historical anthropogenic forcing in the model"


id: ac364c485c61bd33531d240c06362581
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
content_type: text
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vector_id: 24628
content:
" However, \r\nin a  similar approach with two coarser parametrized convection \r\nmodels, Wehner et al. (2019) found conflicting human influences \r\non Haiyan's intensity. Patricola and Wehner (2018) found little \r\nevidence of an attributable change in intensity of hurricanes Katrina \r\n(2005), Irma (2017), and Maria (2017) using a  regional climate \r\nmodel configured between 3  km and 4.5 km resolution. They did, \r\nhowever, find attributable increases in heavy precipitation totals. \r\nThese results imply that higher resolution, such as in a convective \r\npermitting 5 km or less mesh model, is required to obtain a robust \r\nanthropogenic intensification of a strong TC by simulating realistic \r\nrapid intensification (Kanada and Wada, 2016; Kanada et al., 2017a), \r\nand that whether the TC intensification can be attributed to the \r\nrecent warming depends on the case."


id: 708cd45eb0853588333a5084162e2db9
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
content_type: text
index: climate_gpt_only_giec
vector_id: 15325
content:
"The dominant factor in the extreme rainfall amounts during Hurricane \r\nHarvey's passage onto the USA in 2017 was its slow translation speed. \r\nBut studies published after the event have argued that anthropogenic \r\nclimate change contributed to an increase in rain rate, which \r\ncompounded the extreme local rainfall caused by the slow translation. \r\nEmanuel (2017) used a large set of synthetically-generated storms \r\nand concluded that the occurrence of extreme rainfall as observed \r\nin Harvey was substantially enhanced by anthropogenic changes \r\nto the larger-scale ocean and atmosphere characteristics; Trenberth \r\net al. (2018) linked Harvey's rainfall totals to the anomalously large \r\nocean heat content from the Gulf of Mexico; and van Oldenborgh \r\net al. (2017) and Risser and Wehner (2017) applied extreme value \r\nanalysis to extreme rainfall records in the Houston, Texas region, both \r\nattributing large increases to climate change"


id: 19879f14cfc70a442290526f3cc12eff
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
content_type: text
index: climate_gpt_only_giec
vector_id: 1492
content:
" Large precipitation \r\nincreases during Harvey due to global warming were also found \r\nusing climate models (van Oldenborgh et al., 2017; S.-Y.S. Wang et al., \r\n2018). Harvey precipitation totals were estimated in these papers to \r\nbe three to 10 times more probable due to climate change. A best \r\nestimate from a regional climate and flood model is that urbanization \r\nincreased the risk of the Harvey flooding by a factor of 21 (W. Zhang \r\net al., 2018), using a regional climate and flood model, found that \r\nsurface roughness from urbanization increased the risk of the Harvey \r\nflooding by a  factor of 21. Anthropogenic effects on precipitation \r\nincreases were also predicted in advance from a forecast model for \r\nHurricane Florence in 2018 (Reed et al., 2020). "


id: 5375f574c2544dc14112f2b25204f2b9
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
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vector_id: 10604
content:
"The recent active TC seasons in some basins have been studied to \r\ndetermine whether there is anthropogenic influence. For 2015, \r\nMurakami et al. (2017b) explored the unusually high TC frequency \r\nnear Hawaii and in the eastern Pacific basin. W. Zhang et al. (2016b) \r\nconsidered unusually high Accumulated Cyclone Energy (ACE) in the \r\nwestern North Pacific; and S.-H. Yang et al. (2018) and Yamada et al. \r\n(2019) looked at TC intensification in the western North Pacific. These \r\nstudies suggest that the anomalous TC activity in 2015 was not solely \r\nexplained by the effect of an extreme El Nino (see Box  11.4) and \r\nthat there was also an anthropogenic contribution, mainly through \r\nthe effects of SSTs in subtropical regions. In the post-monsoon \r\nseasons of 2014 and 2015, tropical storms with lifetime maximum \r\nwinds greater than 46 m s-1 were first observed over the Arabian \r\nSea, and Murakami et al"


id: f3665d01f0feef6aa646a9ea67bf8018
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
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vector_id: 35802
content:
" (2017a) showed that the probability of late\u0002season severe tropical storms is increased by anthropogenic forcing \r\ncompared to the preindustrial era. Murakami et al. (2018) concluded \r\nthat the active 2017 Atlantic hurricane season was mainly caused \r\nby pronounced SSTs in the tropical North Atlantic and that these \r\ntypes of seasonal events will intensify with projected anthropogenic \r\nforcing. The trans-basin SST change, which might be driven by \r\nanthropogenic aerosol forcing, also affects TC activity. Takahashi \r\net al. (2017) suggested that a decrease in sulphate aerosol emissions \r\ncaused about half of the observed decreasing trends in TC genesis \r\nfrequency in the south-eastern region of the western North Pacific \r\nduring 1992-2011."


id: 8e7a0c7644b0fb08f85ac8f76190cd39
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
content_type: text
index: climate_gpt_only_giec
vector_id: 19938
content:
"Changes in SST and atmospheric temperature and moisture play \r\na  role in tropical cyclogenesis (Walsh et al., 2015). Reductions in \r\nvertical convective mass flux due to increased tropical stability have \r\nbeen associated with a  reduction in cyclogenesis (Held and Zhao, \r\n2011; Sugi et al., 2012). Satoh et al. (2015) further posit that the \r\nrobust simulated increase in the number of intense TCs, and hence \r\nincreased vertical mass flux associated with intense TCs, must lead \r\nto a decrease in overall TC frequency because of this association. The \r\nGenesis Potential Index can be modified to mimic the TC frequency \r\ndecreases of a model by altering the treatment of humidity (Camargo \r\net al., 2014). This supports the idea that increased mid-tropospheric \r\nsaturation deficit (Emanuel et al., 2008) controls TC frequency, \r\nbut the approach remains empirical"


id: adb3fb12808fe27d5194a72665986cf3
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
content_type: text
index: climate_gpt_only_giec
vector_id: 24866
content:
" Other possible controlling \r\nfactors, such as a  decline in the number of seeds (held constant \r\nin Emanuel's downscaling approach, or dependent on the genesis \r\nindex formulation in the approach proposed by C.-Y. Lee et al., 2020) \r\ncaused by increased atmospheric stability have been proposed, \r\nbut questioned as an important factor (Patricola et al., 2018). The \r\nresolution of atmospheric models affects the number of seeds, hence \r\nTC genesis frequency (Vecchi et al., 2019; Sugi et al., 2020; Yamada \r\net al., 2021). The diverse and sometimes inconsistent projected \r\nchanges in global TC frequency by high-resolution models indicate \r\nthat better process understanding and improvement of the models \r\nare needed to raise confidence in these changes. "


id: e99a4028a738c31a58ab54be2b017785
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
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index: climate_gpt_only_giec
vector_id: 34257
content:
"There is not an established theory for the drivers of future changes \r\nin the frequency of TCs. Most, but not all, high-resolution global \r\nsimulations project significant reductions in the total number of TCs, \r\nwith the bulk of the reduction at the weaker end of the intensity \r\nspectrum as the climate warms (Knutson et al., 2020). Recent \r\nexceptions based on high-resolution coupled model results are \r\nnoted in Bhatia et al. (2018) and Vecchi et al. (2019). Vecchi et al. \r\n(2019) showed that the representation of synoptic-scale seeds for TC \r\ngenesis in their high-resolution model causes different projections of \r\nglobal TC frequency, and there is evidence for a decrease in cyclone \r\nseeds in some projected TC simulations (Sugi et al., 2020; Yamada \r\net al., 2011). However, other research indicates that TC seeds are not \r\nan independent control on climatological TC frequency, rather the \r\nseeds covary with the large-scale controls on TCs (Patricola et al., \r\n2018)"


id: a953abf661c3ed228c4864b13be9bb7
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
content_type: text
index: climate_gpt_only_giec
vector_id: 24168
content:
" While empirical genesis indices derived from observations and \r\nreanalysis describe well the observed subseasonal and interannual \r\nvariability of current TC frequency (Camargo et al., 2007, 2009; Tippett \r\net al., 2011; Menkes et al., 2012), they fail to predict the decreased TC \r\nfrequency found in most high-resolution model simulations (Zhang \r\net al., 2010; Camargo, 2013; Wehner et al., 2015), as they generally \r\nproject an increase as the climate warms. This suggests a limitation \r\nof the use of the empirical genesis indices for projections of TC \r\ngenesis, in particular due to their sensitivity to the humidity variable \r\nconsidered in the genesis index for these projections (Camargo et al., \r\n2014)"


id: 40a69dbe71dde5b71caa978df9e7ffa0
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
content_type: text
index: climate_gpt_only_giec
vector_id: 7548
content:
" In a different approach, a statistical-dynamical downscaling \r\nframework assuming a constant seeding rate with warming (Emanuel, \r\n2013, 2021) exhibits increases in TC frequency consistent with \r\ngenesis indices-based projections, while downscaling with a different \r\nmodel leads to two different scenarios depending on the humidity \r\nvariable considered (C.-Y. Lee et al., 2020). This disparity in the sign \r\nof the projected change in global TC frequency, and the difficulty in \r\nexplaining the mechanisms behind the different signed responses, \r\nfurther emphasize the lack of process understanding of future "


id: db71ad145b7db4bd835bf0722817cc1
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
content_type: text
index: climate_gpt_only_giec
vector_id: 32042
content:
"Most TC-permitting model simulations (10-60 km or finer grid \r\nspacing) are consistent in their projection of increases in the \r\nproportion of intense TCs (Category 4-5), as well as an increase in \r\nthe intensity of the strongest TCs defined by maximum wind speed \r\nor central pressure fall (Murakami et al., 2012; Tsuboki et al., 2015; \r\nWehner et al., 2018a; Knutson et al., 2020). The general reduction \r\nin the total number of TCs, which is concentrated in storms weaker \r\nthan or equal to Category 1, contributes to this increase. The models \r\nare somewhat less consistent in projecting an increase in the \r\nfrequency of Category 4-5 TCs (Wehner et al., 2018a; Knutson et al., \r\n2020). The projected increase in the intensity of the strongest TCs is \r\nconsistent with theoretical understanding (e.g., Emanuel, 1987) and \r\nobservations (e.g.,  Kossin et al., 2020)"


id: 327113cd5160a3319991b2648a63c54a
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
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index: climate_gpt_only_giec
vector_id: 5349
content:
" For a  2degC global warming, \r\nthe median proportion of Category 4-5 TCs increases by 13%, \r\nwhile the median global TC frequency decreases by 14%, which \r\nimplies that the median of the global Category 4-5 TC frequency is \r\nslightly reduced by 1% or almost unchanged (Knutson et al., 2020). \r\nMurakami et al. (2020) projected a decrease in TC frequency over the \r\ncoming century in the North Atlantic due to greenhouse warming, \r\nas consistent with Dunstone et al. (2013), and a  reduction in TC \r\nfrequency almost everywhere in the tropics in response to +1% \r\nCO2 forcing. Exceptions include the central North Pacific (Hawaii \r\nregion), east of the Philippines in the North Pacific, and two relatively \r\nsmall regions in the northern Arabian Sea and Bay of Bengal. These \r\nprojections can vary substantially between ocean basins, possibly "


id: b078a2af91d32547fdd3dc0b7f5800a8
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
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vector_id: 25287
content:
"TC occurrence in the central North Pacific (Yamada et al., 2017; \r\nYoshida et al., 2017; Wehner et al., 2018a; Roberts et al., 2020b). \r\nThe observed poleward expansion of the latitude of maximum TC \r\nintensity in the western North Pacific is consistently reproduced \r\nby the CMIP5 models and downscaled models, and these models \r\nshow further poleward expansion in the future; the projected mean \r\nmigration rate of the mean latitude where TCs reach their lifetime\u0002maximum intensity is 0.2+-0.1deg from CMIP5 model results, while it \r\nis 0.13+-0.04deg from downscaled models in the western North Pacific \r\n(Kossin et al., 2014, 2016a). In the North Atlantic, while the location \r\nof TC maximum intensity does not show clear poleward migration \r\nobservationally (Kossin et al., 2014), it tends to migrate poleward \r\nin projections (Garner et al., 2017). The poleward migration is less \r\nrobust among models and observations in the Indian Ocean, eastern \r\nNorth Pacific, and South Pacific (e.g"


id: 2a648690d732a62bd0e187edd1fb3641
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
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vector_id: 4102
content:
",  Tauvale and Tsuboki, 2019; \r\nRamsay et al. 2018; Cattiaux et al. 2020). There is presently no clear \r\nconsensus in projected changes in TC translation speed (Knutson \r\net al., 2020), although recent studies suggest a slowdown outside \r\nof the tropics (Kossin, 2019; Yamaguchi et al., 2020; G. Zhang et al., \r\n2020), but regionally there can even be an acceleration of the storms \r\n(Hassanzadeh et al., 2020)."


id: b1ead0437dfc5ff1f8d4b1044699d764
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
content_type: text
index: climate_gpt_only_giec
vector_id: 25495
content:
"A projected increase in global average TC rain rates of about 12% \r\nfor a 2degC global warming is consistent with the Clausius-Clapeyron \r\nscaling of saturation-specific humidity (Knutson et al., 2020). \r\nIncreases substantially greater than Clausius-Clapeyron scaling are \r\nprojected in some regions, which is caused by increased low-level \r\nmoisture convergence due to projected TC intensity increases in those \r\nregions (Knutson et al., 2015; Phibbs and Toumi, 2016; Patricola and \r\nWehner, 2018; M. Liu et al., 2019a). Projections of TC precipitation \r\nusing large-ensemble experiments (Kitoh and Endo, 2019) show \r\nthat the annual maximum one-day precipitation total is projected \r\nto increase, except for the western North Pacific where only a small \r\nchange (or even a reduction) is projected, mainly due to a projected \r\ndecrease of TC frequency"


id: 5fbabba875b1a25b19d5dee2e3cfd7bd
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
content_type: text
index: climate_gpt_only_giec
vector_id: 12637
content:
" They also show that the 10-year return \r\nvalue of extreme Rx1day associated with TCs will greatly increase \r\nin a region extending from Hawaii to the south of Japan. TC tracks \r\nand the location of topography relative to TCs significantly affect \r\nprecipitation, thus, in general, areas on the eastern and southern \r\nfaces of mountains have more impacts of TC precipitation changes \r\n(Hatsuzuka et al., 2020). Projection studies using variable-resolution \r\nmodels in the North Atlantic (Stansfield et al., 2020) indicate that TC\u0002related precipitation rates within North Atlantic TCs and the amount \r\nof hourly precipitation due to TC are projected to increase by the end \r\nof the century compared to a  historical simulation. However, the \r\nannual average TC-related Rx5day over the eastern USA is projected \r\nto decrease because of a reduction in landfalling TCs. RCM studies \r\nwith around 25-50 km grid spacing are used to study projected \r\nchanges in TCs"


id: 69b9954b2ff3321262584d9892d988f7
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
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index: climate_gpt_only_giec
vector_id: 14230
content:
" The projected changes of TCs in South East Asia \r\nsimulated by RCMs are consistent with those of most GCMs, showing \r\na  decrease in TC frequency and an increase in the amount of TC\u0002associated precipitation or an increase in the frequency of intense \r\nTCs (Redmond et al., 2015; Gallo et al., 2019). \r\nProjected changes in TC tracks or TC areas of occurrence in the \r\nlate 21st  century vary considerably among available studies, \r\nalthough there is better agreement in the western North Pacific. \r\nSeveral studies project either poleward or eastward expansion \r\nof TC occurrence over the western North Pacific region, and more "


id: e250d1ff6d0998096ecaa565ab802244
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
content_type: text
index: climate_gpt_only_giec
vector_id: 33110
content:
"The spatial extent, or 'size', of the TC wind field is an important \r\ndeterminant of storm surge and damage. No detectable anthropogenic \r\ninfluences on TC size have been identified to date, because TCs in \r\nobservations vary in size substantially (Chan and Chan, 2015) and \r\nthere is no definite theory on what controls TC size, although this \r\nis an area of active research (Chavas and Emanuel, 2014; Chan \r\nand Chan, 2018). However, projections by high-resolution models \r\nindicate future broadening of TC wind fields when compared to \r\nTCs of the same categories (Yamada et al., 2017), while Knutson \r\net al. (2015) simulate a reasonable interbasin distribution of TC size \r\nclimatology, but project no statistically significant change in global \r\naverage TC size. A plausible mechanism is that, as the tropopause \r\nheight becomes higher with global warming, the eye wall areas \r\nbecome wider because the eye walls are inclined outward with \r\nheight to the tropopause"


id: 37fb06f67957c898715c81efa72e60ea
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
content_type: text
index: climate_gpt_only_giec
vector_id: 6184
content:
" This effect is only reproduced in high\u0002resolution convection-permitting models capturing eye walls, and \r\nsuch modelling studies are not common. Moreover, the projected TC \r\nsize changes are generally on the order of 10% or less, and these size \r\nchanges are still highly variable between basins and studies. Thus, the \r\nprojected change in both magnitude and sign of TC size is uncertain. "


id: a9ad31c8d9e9f3935227c473d74accba
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
content_type: text
index: climate_gpt_only_giec
vector_id: 24227
content:
"Section 2.3.1.4.3 concluded that there is overall low confidence in \r\nrecent changes in the total number of ETCs over both hemispheres, \r\nand that there is medium confidence in a poleward shift of the storm \r\ntracks over both hemispheres since the 1980s. Overall, there is also \r\nlow confidence in past-century trends in the number and intensity \r\nof the strongest ETCs due to the large interannual and decadal \r\nvariability (Feser et al., 2015; Reboita et al., 2015; Wang et al., 2016; \r\nVarino et al., 2019) and due to temporal and spatial heterogeneities \r\nin the number and type of assimilated data in reanalyses, particularly \r\nbefore the satellite era (Krueger et al., 2013; Tilinina et al., 2013; \r\nBefort et al., 2016; Chang and Yau, 2016; Wang et al., 2016). There \r\nis medium confidence that the agreement among reanalyses and \r\ndetection and tracking algorithms is higher when considering stronger \r\ncyclones (Neu et al., 2013; Pepler et al., 2015; Wang et al., 2016)"


id: 4dfbca58cb418a406e430d66fa3b7a0d
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
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index: climate_gpt_only_giec
vector_id: 9690
content:
" \r\nOver the Southern Hemisphere, there is high confidence that the total \r\nnumber of ETCs with low central pressures (<980 hPa) has increased \r\nbetween 1979 and 2009, with all eight reanalyses considered by \r\nWang et al. (2016) showing positive trends, and five of them showing \r\nstatistically significant trends. Similar results were found by Reboita \r\net al. (2015) using a different detection and tracking algorithm and \r\na  single reanalysis product. Over the Northern Hemisphere, there \r\nis high agreement among reanalyses that the number of cyclones \r\nwith low central pressures (<970 hPa) has decreased in summer and \r\nwinter during the period 1979-2010 (Tilinina et al., 2013; Chang \r\net al., 2016). However, changes exhibit substantial decadal variability \r\nand do not show monotonic trends since the 1980s. For example, \r\nover the Arctic and North Atlantic, Tilinina et al"


id: 86d2e1b8e1f4e35f6d5e8f5e3a85f1d2
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
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index: climate_gpt_only_giec
vector_id: 18747
content:
" (2013) showed that \r\nthe number of cyclones with very low central pressure (<960 hPa) \r\nincreased from 1979 to 1990 and then declined until 2010 in all five \r\nreanalyses considered. Over the North Pacific, the number of cyclones \r\nwith very low central pressure reached a peak around 2000 and then \r\ndecreased until 2010 in the five reanalyses considered (Tilinina et al., \r\n2013). Overall, however, it should be noted that characterising trends \r\nin the dynamical intensity of ETCs (e.g.,  wind speeds) using the \r\nabsolute central pressure is problematic because the central pressure \r\ndepends on the background mean sea level pressure, which varies \r\nseasonally and regionally (e.g., Befort et al., 2016). "


id: 96f293947294039018b3faf7fa2f004
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
content_type: text
index: climate_gpt_only_giec
vector_id: 21265
content:
"In the North Atlantic, vertical wind shear, which inhibits TC genesis \r\nand intensification, varies in a quasi-dipole pattern, with one centre \r\nof action in the tropics and another along the south-east USA \r\ncoast (Vimont and Kossin, 2007). This pattern of variability creates \r\na protective barrier of high shear along the USA coast during periods \r\nof heightened TC activity in the tropics (Kossin, 2017), and appears \r\nto be a  natural part of the Atlantic ocean-atmosphere climate \r\nsystem (Ting et al., 2019). Greenhouse gas forcing in CMIP5 and the \r\nCommunity Earth System Model Large Ensemble (Kay et al., 2015) \r\nsimulations, however, erodes the pattern and degrades the natural \r\nshear barrier along the USA coast. Following the RCP8.5 emissions \r\nscenario, the magnitude of the erosion of the barrier equals the \r\namplitude of past natural variability (time of emergence) by the \r\nmid-21st century (Ting et al., 2019)"


id: e128f5bac15b8f99eb241e4a661a85b8
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
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index: climate_gpt_only_giec
vector_id: 32923
content:
" The projected reduction of shear \r\nalong the USA East Coast with warming is consistent among studies \r\n(e.g., Vecchi and Soden, 2007)."


id: e37fd3985399195b6cdb3ed9af06c8ac
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
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index: climate_gpt_only_giec
vector_id: 33272
content:
"and 6% in December-January-February and June-July-August \r\naccording to the RCP4.5 scenario (2070-2099 vs. 1976-2005). Over \r\nthe North Pacific, Chang (2014) showed that CMIP5 models project \r\na decrease in the frequency of ETCs, with the largest central pressure \r\nperturbation (i.e.,  the depth, strongly related with low-level wind \r\nspeeds) by the end of the century according to simulations using \r\nthe RCP8.5 scenario. Using projections from CMIP5 GCMs under \r\nthe RCP8.5 scenario (1981-2000 to 2081-2100), Seiler and Zwiers \r\n(2016b) projected a  northward shift in the number of explosive \r\nETCs in the northern Pacific, with fewer and weaker events south, \r\nand more frequent and stronger events north of 45degN. Using 19 \r\nCMIP5 GCMs under the RCP8.5 scenario, Kar-Man Chang (2018) \r\nfound a significant decrease in the number of ETCs associated with \r\nextreme wind speeds (2081-2100 vs"


id: b4d517cbd2b9043bc033b8ad22446dbf
created_at: 2023-03-16 14:02:09
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vector_id: 25986
content:
" 1980-99) over the Northern \r\nHemisphere (average decrease of 17%) and over some smaller \r\nregions, including the Pacific and Atlantic regions."


id: f7290ac1213e908a1b2452e619bec539
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vector_id: 36401
content:
"Priestley et al., 2020). There is high confidence that \r\nthe underestimation of the intensity of ETCs is associated with the \r\ncoarse horizontal resolution of climate models, with higher \r\nhorizontal resolution models, including HighResMIP and CORDEX, \r\nusually showing better performance (Colle et al., 2013; Zappa \r\net al., 2013a; Di Luca et al., 2016; Trzeciak et al., 2016; Seiler et al., \r\n2018; Gao et al., 2020; Priestley et al., 2020). The improvement by \r\nhigher-resolution models is found, even when comparing models \r\nand reanalyses after post-processing data to a common resolution \r\n(Zappa et al., 2013a; Di Luca et al., 2016; Priestley et al., 2020). \r\nThe systematic bias in the intensity of ETCs has also been linked to \r\nthe inability of current climate models to resolve diabatic processes, \r\nparticularly those related to the release of latent heat (Willison et al., \r\n2013; Trzeciak et al., 2016) and the formation of clouds (Govekar \r\net al., 2014)"


id: 598eaff3e09f18b68e1505799d92cc34
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
content_type: text
index: climate_gpt_only_giec
vector_id: 11587
content:
" There is medium confidence that climate models \r\nsimulate well the spatial distribution of precipitation associated with \r\nETCs over the Northern Hemisphere, together with some of the main \r\nfeatures of the ETC life cycle, including the maximum in precipitation \r\noccurring just before the peak in dynamical intensity (e.g., vorticity) \r\nas observed in a reanalysis and observations (Hawcroft et al., 2018). \r\nThere is, however, large observational uncertainty in ETC-associated \r\nprecipitation (Hawcroft et al., 2018) and limitations in the simulation \r\nof frontal precipitation, including overly low rainfall intensity over \r\nmid-latitude oceanic areas in both hemispheres (Catto et al., 2015)."


id: 7a7ef39de644b65e3f6d20aaa70b8609
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
content_type: text
index: climate_gpt_only_giec
vector_id: 16843
content:
"Over the Southern Hemisphere, future changes (RCP8.5 scenario; \r\n1980-1999 to 2081-2100) in extreme ETCs were studied by Chang \r\n(2017) using 26 CMIP5 models, and a  variety of intensity metrics \r\n(850-hPa vorticity, 850-hPa wind speed, mean sea level pressure and \r\nnear-surface wind speed). They found that the number of extreme \r\ncyclones is projected to increase by at least 20% and as much as \r\n50%, depending on the specific metric used to define extreme ETCs. \r\nIncreases in the number of strong cyclones appear to be robust across \r\nmodels and for most seasons, although they show strong regional \r\nvariations, with increases occurring mostly over the southern flank of \r\nthe storm track, consistent with a shift and intensification of the storm \r\ntrack. Overall, there is medium confidence that projected changes \r\nin the dynamical intensity of ETCs depend on the resolution and \r\nformulation (e.g.,  explicit or implicit representation of convection) \r\nof climate models (Booth et al"


id: 596f91d59f98e2a5f7c7d38359f5c3f6
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
content_type: text
index: climate_gpt_only_giec
vector_id: 11565
content:
"As reported in AR5 and in Section 8.4.2.8, despite small changes in \r\nthe dynamical intensity of ETCs, there is high confidence that the \r\nprecipitation associated with ETCs will increase in the future (Zappa \r\net al., 2013b; Marciano et al., 2015; Pepler et al., 2016; Michaelis et al., \r\n2017; Yettella and Kay, 2017; Zhang and Colle, 2017; Barcikowska \r\net al., 2018; Hawcroft et al., 2018; Zarzycki, 2018; Kodama et al., \r\n2019; Bevacqua et al., 2020a; Reboita et al., 2021). There is high \r\nconfidence that increases in precipitation will follow increases in \r\nlow-level water vapour (i.e., about 7% per 1degC of surface warming; \r\nsee Box 11.1) and will be larger for higher warming levels (Zhang \r\nand Colle, 2017). There is medium confidence that precipitation \r\nchanges will show regional and seasonal differences due to distinct \r\nchanges in atmospheric humidity and dynamical conditions (Zappa \r\net al., 2015; Hawcroft et al"


id: 26f4005c314a7e90b0fbaee500a87a0f
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
content_type: text
index: climate_gpt_only_giec
vector_id: 3519
content:
", 2018), with decreases in some specific \r\nregions such as the Mediterranean (Zappa et al., 2015; Barcikowska \r\net al., 2018). There is high confidence that snowfall associated \r\nwith winter ETCs will decrease in the future, because increases in \r\ntropospheric temperatures lead to a lower proportion of precipitation \r\nfalling as snow (O'Gorman, 2014; Rhoades et al., 2018; Zarzycki, \r\n2018). However, there is medium confidence that extreme snowfall \r\nevents associated with winter ETCs will change little in regions \r\nwhere snowfall will be supported in the future (O'Gorman, 2014; \r\nZarzycki, 2018)."


id: 39f7aacbbb7d549e875c145e04e0f064
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
content_type: text
index: climate_gpt_only_giec
vector_id: 6494
content:
"The frequency of ETCs is expected to change, primarily following \r\na poleward shift of the storm tracks as discussed in Section 4.5.1.6 \r\n(see also Figure  4.31) and Section  8.4.2.8. There is medium \r\nconfidence that changes in the dynamical intensity (e.g.,  wind \r\nspeeds) of ETCs will be small, although changes in the location of \r\nstorm tracks can lead to substantial changes in local extreme wind \r\nspeeds (Zappa et al., 2013b; Chang, 2014; Li et al., 2014; Seiler and \r\nZwiers, 2016b; Yettella and Kay, 2017; Barcikowska et al., 2018; Kar\u0002Man Chang, 2018). Yettella and Kay (2017) detected and tracked \r\nETCs over both hemispheres in an ensemble of 30 Community Earth \r\nSystem Model Large Ensemble simulations, differing only in their \r\ninitial conditions, and found that changes in mean wind speeds \r\naround ETC centres are often negligible between present (1986-\r\n2005) and future (2081-2100) periods. Using 19 CMIP5 models, \r\nZappa et al"


id: fcb9e6cdc9fab0a1cb125c49cab828d9
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
content_type: text
index: climate_gpt_only_giec
vector_id: 37274
content:
"Johnson, 2005). Many of the recent severe rainfall events in Japan \r\nare associated with band-shaped precipitation systems (Kunii et al., \r\n2016; Oizumi et al., 2018; Tsuguti et al., 2018; Kato, 2020), suggesting \r\ncommon characteristics of severe precipitation, at least in East Asia. \r\nThe convective modes of severe storms in the USA can be classified \r\ninto rotating or linear modes and preferable environmental conditions \r\nfor these modes, such as vertical shear, have been identified (Trapp \r\net al., 2005; Smith et al., 2013; Allen, 2018). Cloud microphysics \r\ncharacteristics of MCSs were examined and the roles of warm rain \r\nprocesses on extreme precipitation were emphasized recently (Sohn \r\net al., 2013; Hamada et al., 2015; Hamada and Takayabu, 2018). \r\nIdealized studies also suggest the importance of ice and mixed-phase \r\nprocesses of cloud microphysics on extreme precipitation (Sandvik \r\net al., 2018; Bao and Sherwood, 2019)"


id: 6a9aefa29c236ee5dfc33ad5bcf18438
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
content_type: text
index: climate_gpt_only_giec
vector_id: 14386
content:
" However, it is unknown \r\nwhether the types of MCS are changing in recent periods or observed \r\nubiquitously all over the world."


id: d04060a77c9873a9abdc7cda29c0db91
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
content_type: text
index: climate_gpt_only_giec
vector_id: 30295
content:
"Severe convective storms are convective systems that are associated \r\nwith extreme phenomena such as tornadoes, hail, heavy precipitation \r\n(rain or snow), strong winds, and lightning. The assessment of changes \r\nin severe convective storms in SREX (Chapter 3, Seneviratne et al., \r\n2012) and AR5 (Chapter 12, Collins et al., 2013) is limited and focused \r\nmainly on tornadoes and hail storms. The SREX assessed that there \r\nis low confidence in observed trends in tornadoes and hail because \r\nof data inhomogeneities and inadequacies in monitoring systems. \r\nSubsequent literature assessed in the Climate Science Special Report \r\n(Kossin et al., 2017) led to the assessment of the observed tornado \r\nactivity over the 2000s in the USA, with a decrease in the number \r\nof days per year with tornadoes and an increase in the number of \r\ntornadoes on these days (medium confidence). However, there \r\nis low confidence in past trends for hail and severe thunderstorm \r\nwinds"


id: cd5de387d074a9454411c3829ebc1be0
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
content_type: text
index: climate_gpt_only_giec
vector_id: 29856
content:
" Climate models consistently project environmental changes \r\nthat would support an increase in the frequency and intensity of \r\nsevere thunderstorms that combine tornadoes, hail, and winds \r\n(high confidence), but there is low confidence in the details of the \r\nprojected increase. Regional aspects of severe convective storms and \r\ndetails of the assessment of tornadoes and hail are also assessed in \r\nSection 12.3.3.2 (tornadoes), Section 12.3.4.5 (hail), Section 12.4.5.3 \r\n(Europe), Section  12.4.6.3 (North America), and Section  12.7.2 \r\n(regional gaps and uncertainties)."


id: 694d849888cd1b549eac96212385a73d
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
content_type: text
index: climate_gpt_only_giec
vector_id: 14164
content:
"Severe convective storms occur under conditions preferable for deep \r\nconvection, that is, conditionally unstable stratification, sufficient \r\nmoisture, both in lower and middle levels of the atmosphere, and \r\na  strong vertical shear. These large-scale environmental conditions \r\nare viewed as necessary conditions for the occurrence of severe \r\nconvective systems, or the resulting tornadoes and lightning, and \r\nthe relevance of these factors strongly depends on the region \r\n(e.g.,  Antonescu et al., 2016a; Allen, 2018; Tochimoto and Niino, \r\n2018). Frequently used metrics are atmospheric static stability, \r\nmoisture content, convective available potential energy (CAPE) and \r\nconvective inhibition, wind shear or helicity, including storm-relative \r\nenvironmental helicity (Tochimoto and Niino, 2018; Elsner et al., \r\n2019)"


id: f906913ce4b1ae1128036e72d9488ed8
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
content_type: text
index: climate_gpt_only_giec
vector_id: 36690
content:
" These metrics, largely controlled by large-scale atmospheric \r\ncirculations or synoptic weather systems, such as TCs and ETCs, \r\nare then generally used to examine severe convective systems. In \r\nparticular, there is high confidence that CAPE in the tropics and the \r\nsubtropics increases in response to global warming (M.S. Singh et al., \r\n2017), as supported by theoretical studies (Singh and O'Gorman, \r\n2013; Seeley and Romps, 2015; Romps, 2016; Agard and Emanuel, \r\n2017). The uncertainty, however, arises from the balance between \r\nfactors affecting severe storm occurrence. For example, the warming \r\nof mid-tropospheric temperatures leads to an increase in the freezing \r\nlevel, which leads to increased melting of smaller hailstones, while \r\nthere may be some offset by stronger updrafts driven by increasing \r\nCAPE, which would favour the growth of larger hailstones, leading to \r\nless melting when falling (Allen, 2018; Mahoney, 2020). "


id: 1bee8f7d964b329f315e0f9907eb205a
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
content_type: text
index: climate_gpt_only_giec
vector_id: 1857
content:
"Severe convective storms are sometimes embedded in synoptic\u0002scale weather systems, such as TCs, ETCs, and fronts (Kunkel et al., \r\n2013). They are also generated as individual events as mesoscale \r\nconvective systems (MCSs) and mesoscale convective complexes \r\n(MCCs, a  special type of a  large, organized and long-lived MCS), \r\nwithout being clearly embedded within larger-scale weather systems. \r\nIn addition to the general vigorousness of precipitation, hail, and \r\nwinds associated with MCSs, characteristics of MCSs are viewed \r\nin new perspectives in recent years, probably because of both the \r\ndevelopment of dense mesoscale observing networks and advances in \r\nhigh-resolution mesoscale modelling (Sections 11.7.3.2 and 11.7.3.3). \r\nThe horizontal scale of MCSs is discussed with their organization \r\nof the convective structure, and  it is examined with a  concept of \r\n'convective aggregation'  in recent years (Holloway et al., 2017)"


id: 8254384b7482656299b261cd70a10938
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
content_type: text
index: climate_gpt_only_giec
vector_id: 18030
content:
"Studies on trends in severe convective storms and their ingredients \r\noutside of the USA are limited. Westra et al. (2014) found that there \r\nis an increase in the intensity of short-duration convective events \r\n(minutes to hours) over many regions of the world, except eastern \r\nChina. In Europe, a climatology of tornadoes shows an increase in \r\ndetected tornadoes between 1800 and 2014, but this trend might be \r\naffected by the density of observations (Antonescu et al., 2016a, b). \r\nAn increase in the trend in extreme daily rainfall is found in south\u0002eastern France, where MCSs play a  key role in this type of event \r\n(Blanchet et al., 2018; Ribes et al., 2019). Trend analysis of the mean \r\nannual number of days with thunderstorms since 1979 in Europe \r\nindicates an increase over the Alps and central, south-eastern, and \r\neastern Europe, with a decrease over the south-west (Taszarek et al., \r\n2019). In the Sahelian region, Taylor et al"


id: 2384a99c125a529d1055b7460bf969f5
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
content_type: text
index: climate_gpt_only_giec
vector_id: 3037
content:
" (2017) analysed MCSs \r\nusing satellite observations since 1982 and showed an increase in the \r\nfrequency of extreme storms. In Bangladesh, the annual number of \r\npropagating MCSs decreased significantly during 1998-2015 based \r\non TRMM precipitation data (Habib et al., 2019). Prein and Holland \r\n(2018) estimated the hail hazard from large-scale environmental \r\nconditions using a statistical approach and showed increasing trends \r\nin the USA, Europe, and Australia. However, trends in hail on regional \r\nscales are difficult to validate because of an insufficient length of \r\nobservations and inhomogeneous records (Allen, 2018). The high \r\nspatial variability of hail suggests it is reasonable that there would be \r\nlocal signals of both positive and negative trends, and the trends that \r\nare occurring in hail globally are uncertain"


id: 8451b426c7b8712bca72131365f13b1c
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
content_type: text
index: climate_gpt_only_giec
vector_id: 18354
content:
" In China, the total number \r\nof days that have either a thunderstorm or hail have decreased by \r\nabout 50% from 1961 to 2010, and the reduction in these severe \r\nweather occurrences correlates strongly with the weakening of the \r\nEast Asian summer monsoon (Q. Zhang et al., 2017). More regional \r\naspects of severe convective storms are detailed in Chapter 12."


id: a86348bd42fd0a3946e06143ed873cd6
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
content_type: text
index: climate_gpt_only_giec
vector_id: 24017
content:
"Observed trends in severe convective storms or MCSs are not well \r\ndocumented, but the climatology of MCSs has been analysed in \r\nspecific regions (North America, South America, Europe, Asia; regional \r\naspects of convective storms are separately assessed in Chapter 12). \r\nAs the definition of severe convective storms varies depending on \r\nthe literature, it is not straightforward to make a synthesizing view \r\nof observed trends in severe convective storms in different regions. \r\nHowever, analysis using satellite observations provides a global view \r\nof MCSs (Kossin et al., 2017). The global distribution of thunderstorms \r\nis captured (Zipser et al., 2006; Liu and Zipser, 2015) by using \r\nthe satellite precipitation measurements by the Tropical Rainfall \r\nMeasuring Mission (TRMM) and Global Precipitation Mission (GPM) \r\n(Hou et al., 2014)"


id: f4e64692994cdd5a001acfb40472e207
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
content_type: text
index: climate_gpt_only_giec
vector_id: 36038
content:
" The climatological characteristics of MCSs are \r\nprovided by satellite analyses in South America (Durkee and Mote, \r\n2010; Rasmussen and Houze, 2011; Rehbein et al., 2018) and those of \r\nMCCs in the Maritime Continent by Trismidianto and Satyawardhana \r\n(2018). Analysis of the environmental conditions favourable for \r\nsevere convective events indirectly indicates the climatology and \r\ntrends of severe convective events (Allen et al., 2018; Taszarek et al., \r\n2018, 2019), though favourable conditions depend on the location, \r\nsuch as the difference for tornadoes associated with ETCs between \r\nthe USA and Japan (Tochimoto and Niino, 2018)."


id: e274f1aa3bec3898a30d73928e7f81bb
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
content_type: text
index: climate_gpt_only_giec
vector_id: 33137
content:
"Observed trends in severe convective storms are highly regionally \r\ndependent. In the USA, it is indicated that there is no significant \r\nincrease in convective storms, and hail and severe thunderstorms \r\n(Kunkel et al., 2013; Kossin et al., 2017). There is an upward trend \r\nin the frequency and intensity of extreme precipitation events in the \r\nUSA (high confidence) (Kunkel et al., 2013; Easterling et al., 2017), and \r\nMCSs have increased in occurrence and precipitation amounts since \r\n1979 (limited evidence) (Feng et al., 2016). Significant interannual \r\nvariability of hailstone occurrences is found in the Southern Great \r\nPlains of the USA (Jeong et al., 2020). The mean annual number \r\nof tornadoes has remained relatively constant, but their variability \r\nof occurrence has increased since the 1970s, particularly over the \r\n2000s, with a  decrease in the number of days per year, but an \r\nincrease in the number of tornadoes on these days (Brooks et al., \r\n2014; Elsner et al"


id: 506c2cf0c9c869d6f4f4730fbe8b83e0
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
content_type: text
index: climate_gpt_only_giec
vector_id: 10099
content:
", 2015, 2019; Kossin et al., 2017; Allen, 2018). There \r\nhas been a shift in the distribution of tornadoes, with increases in the \r\nmid-south of the USA and decreases over the High Plains (Gensini \r\nand Brooks, 2018). Trends in MCSs are relatively more visible for "


id: 80e2d4db69216c6a9228fb201de7a2a5
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
content_type: text
index: climate_gpt_only_giec
vector_id: 17801
content:
"denoted as convection-permitting models or storm-resolving models \r\n(Section  10.3.1). Convection-permitting models are becoming \r\navailable to run over a wide domain, such as a continental scale or even \r\nover the global area, and show realistic climatological characteristics \r\nof MCSs (Prein et al., 2015; Guichard and Couvreux, 2017; Satoh et al., \r\n2019). Such high-resolution simulations are computationally too \r\nexpensive to perform at the larger domain and for long periods, and \r\nalternative methods by using an RCM with dynamical downscaling \r\nare generally used (Section  10.3.1). Convection-permitting models \r\nare used as the flagship project of CORDEX to particularly study \r\nprojections of thunderstorms (Section 10.3.3). Simulations of North \r\nAmerican MCSs by a  convection-permitting model conducted by \r\nPrein et al. (2020) were able to capture the main characteristics of \r\nthe observed MCSs, such as their size, precipitation rate, propagation \r\nspeed, and lifetime"


id: 6477e7ffd847d248bfa39207c6c604be
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
content_type: text
index: climate_gpt_only_giec
vector_id: 13373
content:
" Cloud-permitting model simulations in Europe \r\nalso showed sub-daily precipitation realistically (Ban et al., 2014; \r\nKendon et al., 2014). Evaluation of precipitation conducted using \r\nconvection-permitting simulations around Japan showed that finer \r\nresolution improves intense precipitation (Murata et al., 2017). MCSs \r\nover Africa simulated using convection-permitting models showed \r\nbetter extreme rainfall (Kendon et al., 2019) and diurnal cycles and \r\nconvective rainfall over land than the coarser-resolution RCMs or \r\nGCMs (Stratton et al., 2018; Crook et al., 2019)."


id: faffd60ce034216fe9322e0ca57911b4
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
content_type: text
index: climate_gpt_only_giec
vector_id: 37008
content:
"Future projections of severe convective storms are usually studied \r\neither by analysing the environmental conditions simulated by \r\nclimate models, or by a time-slice approach with higher-resolution \r\nconvection-permitting models by comparing simulations downscaled \r\nwith climate model results under historical conditions and those \r\nunder hypothesized future conditions (Kendon et al., 2017; Allen, \r\n2018). Up to now, individual studies using convection-permitting \r\nmodels gave projections of extreme events associated with severe \r\nconvective storms in local regions, and it is not generally possible \r\nto obtain global or general views of projected changes of severe \r\nconvective storms. Prein et al. (2017) investigated future projections \r\nof North American MCS simulations and showed an increase in \r\nMCS frequency and an increase in total MCS precipitation volume \r\nby the combined effect of increases in maximum precipitation \r\nrates associated with MCSs and increases in their size"


id: 2a2cca30fe2892d4047c0281c18fd8f2
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
content_type: text
index: climate_gpt_only_giec
vector_id: 4060
content:
" Rasmussen \r\net al. (2020) investigated future changes in the diurnal cycle of \r\nprecipitation by capturing organized and propagating convection \r\nand showed that weak-to-moderate convection will decrease, and \r\nstrong convection will increase in frequency in the future. Ban et al. \r\n(2015) found that the day-long and hour-long precipitation events \r\nin summer intensify in the European region covering the Alps. \r\nKendon et al. (2019) showed future increases in extreme three\u0002hourly precipitation in Africa. Murata et al. (2015) investigated \r\nfuture projections of precipitation around Japan and showed \r\na decrease in monthly mean precipitation in the eastern Japan Sea \r\nregion in December, suggesting that convective clouds become \r\nshallower in the future in the winter over the Japan Sea."


id: 78927d0c6911a625e9657a01471ff4eb
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
content_type: text
index: climate_gpt_only_giec
vector_id: 16533
content:
"Extreme winds are defined here in terms of the strongest near\u0002surface wind speeds that are generally associated with extreme \r\nstorms, such as TCs, ETCs, and severe convective storms. In previous \r\nIPCC reports, near-surface wind speed (including extremes), has not \r\nbeen assessed as a variable in its own right, but rather in the context \r\nof other extreme atmospheric or oceanic phenomena. The exception \r\nwas the SREX report (Seneviratne et al., 2012), which specifically \r\nexamined past changes and projections of mean and extreme near\u0002surface wind speeds. A strong decline in extreme winds compared to \r\nmean winds was reported for the continental northern mid-latitudes. \r\nDue to the small number of studies and uncertainties in terrestrial\u0002based surface wind measurements, the findings were assigned low \r\nconfidence in SREX"


id: a48f910b67307eca4b9250e44d341e8a
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
content_type: text
index: climate_gpt_only_giec
vector_id: 23454
content:
" The AR5 reported a  weakening of mean and \r\nmaximum winds from the 1960s or 1970s to the early 2000s in the \r\ntropics and mid-latitudes, and increases in high latitudes, but with \r\nlow confidence in changes in the observed surface winds over land \r\n(Hartmann et al., 2013). Observed trends in mean wind speed over \r\nland and the ocean are assessed in Section  2.3.1.4.4. Aspects of \r\nclimate impact-drivers for winds are addressed in Sections 12.3.3 \r\nand 12.5.2, and their regional changes are assessed in Section 12.4."


id: 682c8c9a3a668866d2724061ba675c9c
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
content_type: text
index: climate_gpt_only_giec
vector_id: 13973
content:
"In the USA, projected changes in the environmental conditions show \r\nan increase in CAPE and no changes or decreases in the vertical \r\nwind shear, suggesting favourable conditions for an increase in \r\nsevere convective storms in the future, but the interpretation of how \r\ntornadoes or hail will change is an open question because  of the \r\nstrong dependence on shear (Brooks, 2013). Diffenbaugh et al. \r\n(2013) showed robust increases in the occurrence of the favourable \r\nenvironments for severe convective storms with increased CAPE \r\nand stronger low-level wind shear in response to future global \r\nwarming. A downscaling approach showed that the variability of the \r\noccurrence of severe convective storms increases in spring in late \r\n21st-century simulations (Gensini and Mote, 2015)"


id: afe0c03355232a85efb942cf3a17086c
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
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vector_id: 25195
content:
" Future changes in \r\nhail occurrence in the USA examined through convection-permitting \r\ndynamical downscaling suggested that the hail season may begin \r\nearlier in the year and exhibit more interannual variability, with \r\nincreases in the frequency of large hail in broad areas over the USA \r\n(Trapp et al., 2019). There is medium confidence that the frequency \r\nand variability of the favourable environments for severe convective \r\nstorms will increase in spring, and low confidence for summer \r\nand autumn (Diffenbaugh et al., 2013; Gensini and Mote, 2015; \r\nHoogewind et al., 2017). The occurrence of hail events in Colorado in \r\nthe USA was examined by comparing both present-day and projected \r\nfuture climates using high-resolution model simulations capable of \r\nresolving hailstorms (Mahoney et al"


id: 11ed74b44cd02eeea893c9a18e38c6f5
created_at: 2023-03-16 14:02:09
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index: climate_gpt_only_giec
vector_id: 318
content:
", 2012), which showed that \r\nhail is almost eliminated at the surface in the future in most of the \r\nsimulations, despite more intense future storms and significantly \r\nlarger amounts of hail generated in-cloud."


id: 6359e43251bf3e3be384a45f0f66120f
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
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vector_id: 13204
content:
"Observationally, although not specifically addressing extreme \r\nwind speed changes, negative surface wind speed trends (stilling) \r\nwere found in the tropics and mid-latitudes of both hemispheres \r\nof -0.014 m s-1 yr-1, while positive trends were reported at high \r\nlatitudes poleward of 70 degrees, based on a review of 148 studies \r\n(McVicar et al., 2012b). An earlier study attributed the stilling to \r\nboth changes in atmospheric circulation and an increase in surface \r\nroughness due to an overall increase in vegetation cover (Vautard \r\net al., 2010). Since then, a number of studies have mostly confirmed \r\nthese general negative mean-wind trends based on anemometer \r\ndata for Spain (Azorin-Molina et al., 2017), Turkey, (Dadaser-Celik \r\nand Cengiz, 2014), the Netherlands, (Wever, 2012), Saudi Arabia, \r\n(Rehman, 2013), Romania, (Marin et al., 2014), and China (Chen \r\net al., 2013). Lin et al"


id: d51e1af0ef37cbb67d8ca1e9801ad7a5
created_at: 2023-03-16 14:02:09
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content:
" (2013) note that wind speed variability over \r\nChina is greater at high-elevation locations compared to those \r\ncloser to mean sea level. Hande et al. (2012), using radiosonde \r\ndata, found an increase in surface wind speed on Macquarie \r\nIsland of Australia. "


id: 11d2d01bf02f7695a85da13a4f63e17b
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
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vector_id: 301
content:
"A number of new studies have examined surface wind speeds over \r\nthe ocean using ship-based measurements, satellite altimeters, and \r\nSpecial Sensor Microwave/Imagers (Tokinaga and Xie, 2011; Zieger \r\net al., 2014). It has been noted that wind speed trends tend to be \r\nstronger in altimeter measurements, although the spatial patterns \r\nof change are qualitatively similar in both instruments (Zieger et al., \r\n2014). Q. Liu et al. (2016) found positive trends in surface wind \r\nspeeds over the Arctic Ocean in 20 years of satellite observations. \r\nSmall positive trends in mean wind speed were found in 33 years \r\nof satellite data, together with larger trends in the 90th percentile \r\nvalues over global oceans (Ribal and Young, 2019). These results \r\nwere consistent with an earlier study that found a positive trend in \r\n1-in-100-year wind speeds (Young et al., 2012)"


id: 475f6af1f353f15a0b0a9d7e8650bfad
created_at: 2023-03-16 14:02:09
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vector_id: 8616
content:
" A positive change \r\nin mean wind speeds was found for the Arabian Sea and the Bay of \r\nBengal (Shanas and Kumar, 2015) and Zheng et al. (2017) found that "


id: 2e03603087b7537b4d520083cfe71a27
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
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index: climate_gpt_only_giec
vector_id: 4674
content:
"Changes in extreme winds are associated with changes in the \r\ncharacteristics (locations, frequencies, and intensities) of extreme \r\nstorms, including TCs, ETCs, and severe convective storms. For TCs, \r\nas assessed in Section  11.7.1.5, it is projected that the average \r\npeak TC wind speeds will increase globally with warming, while the \r\nglobal frequency of TCs over all categories will decrease or remain \r\nunchanged; the average location where TCs reach their peak wind \r\nintensity will migrate poleward in the western North Pacific Ocean \r\nas the tropics expand with warming. Frequency, intensities, and \r\ngeographical distributions of extreme wind events associated with \r\nTCs will change according to these TC changes"


id: 6a07f44dd3798080d7dc81afc4f8adf3
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
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vector_id: 14285
content:
" For ETCs, by the \r\nend of the century, CMIP5 models show that the number of ETCs \r\nassociated with extreme winds will significantly decrease in the mid\u0002and high latitudes of the Northern Hemisphere in winter, with the \r\nprojected decrease being larger over the Atlantic (Kar-Man Chang, \r\n2018), while it will significantly increase irrespective of the season \r\nin the Southern Hemisphere (Section 11.7.2.4; Chang, 2017). Over \r\nthe ocean in the subtropics, a large ensemble of 60-km global model \r\nsimulations indicated that extreme winds associated with storm \r\nsurges will intensify over 15-35degN in the Northern Hemisphere \r\n(Mori et al., 2019). However, extreme surface wind speeds will \r\nmostly decrease due to decreases in the number and intensity of TCs \r\nover most tropical areas of the Southern Hemisphere (Mori et al., \r\n2019). The projected changes in the frequency of extreme winds are \r\nassociated with the future changes in TCs and ETCs."


id: fe637ea69b4c20f56b91649fd3b07162
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
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vector_id: 37562
content:
"Further definitions of compound events have emerged since SREX. \r\nZscheischler et al. (2018) defined compound events broadly as 'the \r\ncombination of multiple drivers and/or hazards that contributes to \r\nsocietal or environmental risk'. This definition is used in the present \r\nassessment, because of its clear focus on the risk framework \r\nestablished by the IPCC, and also highlighting that compound events \r\nmay not necessarily result from dependent drivers. Compound events \r\nhave been classified into: preconditioned events, where a weather\u0002driven or climate-driven precondition aggravates the impacts \r\nof a  climatic impact-driver; multivariate events, where multiple \r\ndrivers and/or climatic impact-drivers lead to an impact; temporally \r\ncompounding events, where a  succession of hazards leads to an \r\nimpact; and spatially compounding events, where hazards in multiple \r\nconnected locations cause an aggregated impact (Zscheischler et al., \r\n2020)"


id: 6087132c00bd59e5777330e21e4522e5
created_at: 2023-03-16 14:02:09
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content:
" Drivers include processes, variables, and phenomena in the \r\nclimate and weather domain that may span over multiple spatial \r\nand temporal scales. Hazards (such as floods, heatwaves, wildfires; \r\nalso termed 'climatic impact-drivers' in this report, see Chapter 12) \r\nare usually the immediate physical precursors to negative impacts, \r\nbut can occasionally have positive outcomes (Flach et al., 2018). The \r\npresent assessment focuses on the physical dimension of changes in \r\ncompound events, as it is part of the IPCC AR6 Working Group I Report."


id: c4dfbed80a308b006b7e9b1ff2d2c211
created_at: 2023-03-16 14:02:09
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content:
"The combination of two or more - not necessarily extreme - weather \r\nor climate events that occur: i) at the same time; ii) in close succession; \r\nor iii) concurrently in different regions, can lead to extreme impacts \r\nthat are much larger than the sum of the impacts due to the occurrence \r\nof individual extremes alone. This is because multiple stressors can \r\nexceed the coping capacity of a system more quickly. The contributing \r\nevents can be of similar types (clustered multiple events) or of \r\ndifferent types (Zscheischler et al., 2020). Many major weather- and \r\nclimate-related catastrophes are inherently of a  compound nature \r\n(Zscheischler et al., 2019). This has been highlighted for a  broad \r\nrange of hazards, such as droughts, heatwaves, wildfires, coastal \r\nextremes, and floods (Westra et al., 2016; AghaKouchak et al., 2020; \r\nRidder et al., 2020). Co-occurring extreme precipitation and extreme \r\nwinds can result in infrastructural damage (Martius et al"


id: 329597471ac9a5bc053a8b25ffa4840a
created_at: 2023-03-16 14:02:09
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content:
", 2016); the \r\ncompounding of storm surge and precipitation extremes can cause \r\ncoastal floods (Wahl et al., 2015); the combination of drought and \r\nheat can lead to tree mortality (Section 11.6; Allen et al., 2015); and \r\nwildfires increase occurrences of hailstorms and lightning (Y. Zhang \r\net al., 2019a). Compound storm types consisting of co-located \r\ncyclone, front and thunderstorm systems have a  higher chance of \r\ncausing extreme rainfall and extreme winds than individual storm \r\ntypes (Dowdy and Catto, 2017). Extremes may occur at similar times \r\nat different locations (De Luca et al., 2020a, b) but affect the same \r\nsystem, for instance, spatially concurrent climate extremes affecting \r\ncrop yields and food prices (Singh et al., 2018; Anderson et al., 2019). \r\nStudies also show an increasing likelihood for breadbasket regions to \r\nbe concurrently affected by climate extremes with increasing global "


id: f9fd27bd1935786468fad1d8776acd2c
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updated_at: 2023-03-16 14:02:09
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content:
"coast and the North Sea (Bevacqua et al., 2019). The probability of \r\nflood occurrence can be assessed via the dependence between storm \r\nsurge and river flow (Bevacqua et al., 2020b, c). For instance, the \r\noccurrence of a North Sea storm surge in close succession with an \r\nextreme Rhine or Meuse river discharge is much more probable due \r\nto their dependence, compared to if both events were independent \r\n(Kew et al., 2013; Klerk et al., 2015). Significant dependence between \r\nhigh sea levels and high river discharge are found for more than \r\nhalf of the available station observations, which are mostly located \r\naround the coasts of North America, Europe, Australia, and Japan \r\n(Ward et al., 2018). Combining global river discharge with a global \r\nstorm surge model, hotspots of compound flooding have been \r\ndiscovered that are not well covered by observations in some regions, \r\nincluding Madagascar, Northern Morocco, Vietnam, and Taiwan of \r\nChina (Couasnon et al., 2020)"


id: 2890f3566a433cb6975d3a75c989298d
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
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vector_id: 3803
content:
"  In  the Dutch Noorderzijlvest area, \r\nthere is more than a two-fold increase in the frequency of exceeding \r\nthe highest warning level compared to the case if storm surge and \r\nheavy precipitation were independent (van den Hurk et al., 2015). In \r\nother regions and seasons, the dependence can be insignificant (W. \r\nWu et al., 2018) and there can be significant seasonal and regional \r\ndifferences in the storm surge-heavy precipitation relationship. \r\nAssessments of flood probabilities are often not based on actual flood \r\nmeasurements; instead, they are estimated from its main drivers, \r\nincluding astronomical tides, storm surge, heavy precipitation, and \r\nhigh streamflow. Such single driver analyses might underestimate \r\nflood probabilities if multiple correlated drivers contribute to flood \r\noccurrence (e.g., van den Hurk et al., 2015). "


id: 5c858e5305d67001a7166b6a0f599923
created_at: 2023-03-16 14:02:09
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vector_id: 12074
content:
"Finally, impacts may occur because of large multivariate anomalies in \r\nthe climate drivers, if systems are adapted to historical multivariate \r\nclimate variability (Flach et al., 2017). For instance, ecosystems \r\nare typically adapted to the local covariability of temperature and \r\nprecipitation such that a bivariate anomaly may have a large impact, \r\neven though neither temperature nor precipitation may be extreme \r\nbased on a  univariate assessment (Mahony and Cannon, 2018). \r\nGiven that almost all systems are affected by weather and climate \r\nphenomena at multiple space-time scales (Raymond et al., 2020), it \r\nis natural to consider extremes in a  compound event framework. \r\nIt should be noted, however, that multi-hazard dependencies can also \r\ndecrease risk, for instance when hazards are negatively correlated \r\n(Hillier et al., 2020). Despite this recognition, the literature on past \r\nand future changes in compound events has been limited, but is \r\ngrowing"


id: fdba7f226585d69fbce44b42ef2ab3ee
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
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index: climate_gpt_only_giec
vector_id: 37438
content:
"Aspects of concurrent extremes in coastal and estuarine environments \r\nhave increased in frequency and/or magnitude over the last century in \r\nsome regions. These include an increase in the dependence between \r\nheavy precipitation and storm surge over the last century, leading \r\nto more frequent co-occurring storm surge and heavy precipitation \r\nevents in the present day along USA coastlines (Wahl et al., 2015). In \r\nEurope, the probability of compound flooding occurrence increases \r\nmost strongly along the Atlantic coast and the North Sea under \r\nstrong warming. This increase is mostly driven by an intensification \r\nof precipitation extremes and aggravated flooding probability due \r\nto sea level rise (Bevacqua et al., 2019)"


id: efa1fe1fea81bc8bc75d8c5e17856576
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index: climate_gpt_only_giec
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content:
" At the global scale and \r\nunder a  high-emissions scenario, the concurrence probability of \r\nmeteorological conditions driving compound flooding would increase \r\nby more than 25%, on average, along coastlines worldwide by 2100, \r\ncompared to the present (Bevacqua et al., 2020c). Sea level extremes \r\nand their physical impacts in the coastal zone arise from a complex \r\nset of atmospheric, oceanic, and terrestrial processes that interact \r\non a range of spatial and temporal scales and will be modified by \r\na changing climate, including sea level rise (McInnes et al., 2016). \r\nInteractions between sea level rise and storm surges (Little et al., \r\n2015), and sea level and fluvial flooding (Moftakhari et al., 2017) are "


id: 3764613d80ce9f29a1b0e17bdb322c9e
created_at: 2023-03-16 14:02:09
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index: climate_gpt_only_giec
vector_id: 6098
content:
"anthropogenic climate change via a  significant increase in vapour \r\npressure deficit, a primary driver of wildfires. A study of the western \r\nUSA examined the correlation between historical water-balance \r\ndeficits and annual area burned, across a range of vegetation types, \r\nfrom temperate rainforest to desert (McKenzie and Littell, 2017). \r\nThe relationship between temperature and dryness, and wildfire, \r\nvaried with ecosystem type, and the fire-climate relationship was \r\nnonstationary and vegetation-dependent. In many fire-prone regions, \r\nsuch as the Mediterranean and China's Daxing'anling region, \r\nprojections for increased severity of future drought and heatwaves \r\nmay lead to an increased frequency of wildfires relative to observed \r\nclimatology (Tian et al., 2017; Ruffault et al., 2018)"


id: c283d9db6f596fcc1fc45f9e9ec2669e
created_at: 2023-03-16 14:02:09
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index: climate_gpt_only_giec
vector_id: 28194
content:
" Observations \r\nshow a long-term trend towards more dangerous weather conditions \r\nfor bushfires in many regions of Australia, which is attributable (at \r\nleast in part) to anthropogenic climate change (Dowdy, 2018). There \r\nis emerging evidence that recent regional surges in wildland fires are \r\nbeing driven by changing weather extremes (Cross-Chapter Box 3; Jia \r\net al., 2019; SRCCL Chapter 2). Between 1979 and 2013, the global \r\nburnable area affected by long fire weather seasons doubled, and \r\nthe mean length of the fire weather season increased by 19% (Jolly \r\net al., 2015). However, at the global scale, the total burned area has \r\nbeen decreasing between 1998 and 2015 due to human activities \r\nmostly related to changes in land use (Andela et al., 2017). Given \r\nthe projected high confidence increase in compound hot and dry \r\nconditions, there is high confidence that fire weather conditions \r\nwill become more frequent at higher levels of global warming in \r\nsome regions"


id: a35e2ccaf8fb54df08a8a43e3f1a1fe8
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
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content:
" This assessment is also consistent with Chapter 12's \r\nexamination of regional projected changes in fire weather. The \r\nSRCCL (Chapter 2) assessed with high confidence that future climate \r\nvariability is expected to enhance the risk and severity of wildfires in \r\nmany biomes such as tropical rainforests."


id: 2651e7e0fb3c90dbc4720c8b0159bf73
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
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index: climate_gpt_only_giec
vector_id: 3445
content:
"The probability of co-occurring meteorological droughts and \r\nheatwaves has increased in the observational period in many regions \r\nand will continue to do so under unabated warming (Herrera-Estrada \r\nand Sheffield, 2017; Zscheischler and Seneviratne, 2017; Hao et al., \r\n2018; Sarhadi et al., 2018; Alizadeh et al., 2020; Wu et al., 2021). Overall, \r\nprojections of increases in co-occurring drought and heatwaves are \r\nreported in northern Eurasia (Schubert et al., 2014), Europe (Orth \r\net al., 2016a; Sedlmeier et al., 2018), south-east Australia (Kirono \r\net al., 2017), multiple regions of the USA (Diffenbaugh et al., 2015; \r\nHerrera-Estrada and Sheffield, 2017), north-west China (X. Li et al., \r\n2019; Kong et al., 2020) and India (Sharma and Mujumdar, 2017). The \r\ndominant signal is related to the increase in heatwave occurrence, \r\nwhich has been attributed to anthropogenic forcing (Section 11.3.4)"


id: c86c05a68c276e1cbdc1a27dfa33722f
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updated_at: 2023-03-16 14:02:09
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vector_id: 29101
content:
" \r\nThis means that, even if drought occurrence is unaffected, compound \r\nhot and dry events will be more frequent (Sarhadi et al., 2018; Yu \r\nand Zhai, 2020). "


id: fa3c37175f2fefc02a834d90f8011e3
created_at: 2023-03-16 14:02:09
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index: climate_gpt_only_giec
vector_id: 36890
content:
"Occurrence of concurrent or near-concurrent extremes in \r\ndifferent parts of a region, or in different locations around the \r\nworld, challenges adaptation and risk management capacity. This \r\ncan occur as a result of natural climate variability, as climates \r\nin different parts of the world are interconnected through large\u0002scale atmospheric-oceanic teleconnections. In addition, in \r\na  warming climate, the probability of having several locations \r\nbeing affected simultaneously by, for example, hot extremes and \r\nheatwaves increases strongly as a function of global warming, \r\nwith detectable changes even for changes as small as +0.5degC \r\nof additional global warming (Sections 11.2.4 and 11.3, Cross\u0002chapter Box  11.1). Recent articles have highlighted the risks \r\nassociated with concurrent extremes over large spatial scales \r\n(e.g.,  Lehner and Stocker, 2015; Boers et al., 2019; Gaupp \r\net al., 2019)"


id: 44d63b2864eb62e2b359ceb74774200c
created_at: 2023-03-16 14:02:09
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content:
" There is evidence that such global-scale extremes \r\nassociated with hot temperature extremes are increasing in \r\noccurrence (Sippel et al., 2015; Vogel et al., 2019). Hereafter, the \r\nfocus is on two case studies of recent global-scale events that \r\nfeatured concurrent extremes in several regions across the world. \r\nThe first focuses on concurrent extremes driven by variability in \r\ntropical Pacific sea surface temperatures (SSTs) associated with the 2015-2016 extreme El Nino, while the second addresses the \r\nimpacts of global warming combined with abnormal atmospheric circulation patterns in the 2018 boreal spring/summer. \r\nBox 11.4, Figure 1 | Analysis of the percentage of land area affected \r\nby temperature extremes larger than two (blue) or three (orange) \r\nstandard deviations in June-July-August (JJA) between 30degN and \r\n80degN using a normalization. This figure shows a substantial increase in the \r\noverall land area affected by very strong hot extremes since 1990"


id: c62c31bf58c499a85e7499a10ca2bdb8
created_at: 2023-03-16 14:02:09
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content:
"El Nino-Southern Oscillation (ENSO) is one of the phenomena that have the ability to bring multitudes of extremes in different \r\nparts of the world, especially in extreme El Nino (Annex IV.2.3) cases. Additionally, the background climate warming associated with \r\ngreenhouse gas forcing can significantly exacerbate extremes in parts of the world, even under normal El Nino conditions. The 2015-\r\n2016 extreme El Nino event was one of the three extreme El Nino events since the 1980s and the availability of satellite rainfall \r\nobservations. According to some measures, it was the strongest El Nino in the past 145 years (Barnard et al., 2017). The 2015-2016 \r\nwarmth was unprecedented at the central equatorial Pacific (Nino4: 5degN-5degS, 150degE-150degW), and this exceptional warmth was \r\nunlikely to have occurred entirely naturally, appearing to reflect an anthropogenically forced trend (Newman et al., 2018)"


id: 3b5cb03f5ccf9f2870041092870ec31b
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content:
" In particular, \r\nits signal was seen in very high monthly global mean surface temperature (GMST) values in late 2015 and early 2016, contributing \r\nto the highest record of GMST in 2016 (Section 2.3.1.1). Both the ENSO amplitude and the frequency of high-magnitude events since \r\n1950 is higher than over the pre-industrial period (medium confidence) (Section 2.4.2), suggesting that global extremes similar to \r\nthose associated with the 2015-2016 extreme El Nino would occur more frequently under further increases in global warming. A brief \r\nsummary of extreme events that happened in 2015-2016 is provided in Sections 6.2.2 and 6.5.1.1 of the Special Report on the Ocean \r\nand Cryosphere in a Changing Climate (SROCC). We provide some highlights illustrating extremes that occurred in different parts of \r\nthe world during the 2015-2016 extreme El Nino in Box 11.4, Table 1, as well as in the short summary that follows."


id: 1fa6db2cbf7c8cb7edb2ae1d3b1c58de
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content:
"Several regions were strongly affected by droughts in 2015, including Indonesia, Australia, the Amazon region, Ethiopia, southern Africa, \r\nand Europe. As a result, global measurements of land water anomalies were particularly low in that year (Humphrey et al., 2018). In 2015, \r\nIndonesia experienced a severe drought and forest fire, causing pronounced impact on economy, ecology and human health due to haze \r\ncrisis (Field et al., 2016; Huijnen et al., 2016; Patra et al., 2017; Hartmann et al., 2018). The northern part of Australia experienced high \r\ntemperatures and low precipitation between late 2015 and early 2016, and the extensive mangrove trees were damaged along the \r\nGulf of Carpentaria in Northern Australia (Duke et al., 2017). The Amazon region experienced the most intense droughts of this century \r\nin 2015-2016. This drought was more severe than the previous major droughts that occurred in the Amazon in 2005 and 2010 (Lewis \r\net al., 2011; Erfanian et al"


id: f11ab78b5358012fa67e950fd0c141ab
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content:
", 2017; Panisset et al., 2018). The 2015-2016 Amazon drought impacted the entirety of South America north \r\nof 20degS during the austral spring and summer (Erfanian et al., 2017). It also increased forest fire incidence by 36% compared to the \r\npreceding 12 years (Aragao et al., 2018) and, as a consequence, increased the biomass burning outbreaks and the carbon monoxide (CO) \r\nconcentration in the area, affecting air quality (Ribeiro et al., 2018). This out-of-season drought affected the water availability for human \r\nconsumption and agricultural irrigation. It also left rivers with very low water levels and large sandbanks, preventing ship transportation \r\nof food, medicines, and fuels (INMET, 2017). Eastern African countries were impacted by drought in 2015. The drought in Ethiopia was the \r\nworst in several decades and was associated with the 2015-2016 extreme El Nino (Blunden and Arndt, 2016; Philip et al., 2018b). It was "


id: aa66419e90c59191205a3b67530ffc1f
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"In 2015, tropical cyclone activity was notably high in the North Pacific (Blunden and Arndt, 2016). Over the western North Pacific, there \r\nwere 13 Category 4 and 5 tropical cyclones (TCs), more than twice the area's typical annual value of 6.3 (W. Zhang et al., 2016b). \r\nSimilarly, a record-breaking number of TCs were observed in the eastern North Pacific, particularly in the western part of that domain \r\n(Collins et al., 2016; Murakami et al., 2017b). These extraordinary TC activities were related to the average SST anomaly during that \r\nyear, which were associated with the 2015-2016 extreme El Nino and the positive phase of the Pacific Meridional Mode (Murakami \r\net al., 2017b; Hong et al., 2018; Yamada et al., 2019). However, it has been suggested that the intense TC activities in both the western \r\nand the eastern North Pacific in 2015 were not only due to the El Nino, but also to a contribution of anthropogenic forcing (Murakami \r\net al., 2017b; S.-H. Yang et al., 2018)"


id: 787b645f76d36f3c92a8c302245d322f
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" The impact of the Indian Ocean SST was also suggested to contribute to the extreme TC activity \r\nin the western North Pacific in 2015 (Zhan et al., 2018). In contrast, in Australia, it was the least active TC season since satellite records \r\nbegan in 1969-1970 (Blunden and Arndt, 2017)."


id: eec8b61f40200a9acf76075949c67f05
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content:
"Global-scale temperature extremes and concurrent precipitation extremes in boreal 2018 spring and summer\r\nIn the 2018 boreal spring-summer season (May-August), wide areas of the mid-latitudes in the Northern Hemisphere experienced \r\nheat extremes and (in part) enhanced drought (Box 11.4, Figure 2; Kornhuber et al., 2019; Vogel et al., 2019). The reported impacts \r\nincluded (Vogel et al., 2019): 90 deaths from heat strokes in Quebec (Canada); 1469 deaths from heat strokes in Japan (Shimpo et al., \r\n2019); 48 heat-related deaths in the Republic of Korea (Min et al., 2020); heat warnings affecting 90,000 students in the USA; fires \r\nin numerous countries (Canada (British Columbia), USA (California), Finland (Lapland), Latvia); crop losses in the UK, Germany and \r\nSwitzerland (Vogel et al., 2019) and overall in central and Northern Europe (leading to yield reductions of up to 50% for the main \r\ncrops (Toreti et al"


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", 2019); fish deaths in Switzerland; and melting of roads in the Netherlands and the UK, among others. In addition \r\nto the numerous hot and dry extremes, an extremely heavy rainfall event occurred over wide areas of Japan from 28 June to 8 July \r\n2018 (Tsuguti et al., 2018), which was followed by a heatwave (Shimpo et al., 2019). The heavy precipitation event caused more than \r\n230 deaths in Japan, and was named 'the Heavy Rain Event of July 2018'. "


id: a8bbbd767f8c88961ab8ccfff2844a38
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"global mean temperature anomaly around that time (for 2017; SR1.5). This study also found that events similar to the 2018 May-July \r\ntemperature extremes would approximately occur two out of three years under +1.5degC of global warming, and every year under +2degC \r\nof global warming. Imada et al. (2019) also suggest that the mean annual occurrence of extreme hot days in Japan will be expected to \r\nincrease by 1.8 times under a global warming level of 2degC above pre-industrial levels. Kawase et al. (2020) showed that the extreme \r\nrainfall in Japan during this event was increased by approximately 7% due to recent rapid warming around Japan. Imada et al. (2020) \r\nshowed that the probability of the Heavy Rain Event of July 2018 in Japan was increased from 0.22% to 2.00% due to anthropogenic \r\nwarming. Hence, it is virtually certain that these 2018 concurrent events would not have occurred without human-induced global \r\nwarming"


id: d76dad5138bca2e108b9612b1618cb8e
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" Concurrent events of this type are also projected to happen more frequently under higher levels of global warming. However, \r\nthere is currently low confidence in projected changes in the frequency or strength of the anomalous circulation patterns leading to \r\nconcurrent extremes (e.g., Cross-Chapter Box 10.1). "


id: 3c403f701a9ef96a2b5706b58f1c6620
created_at: 2023-03-16 14:02:09
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content:
"In summary, the 2015-2016 extreme El Nino and the 2018 boreal spring/summer extremes were two examples of recent concurrent \r\nextremes. The El Nino event in 2015-2016 was one of the three extreme El Nino events since the 1980s, and there are many extreme \r\nevents concurrently observed in this period including droughts, heavy precipitation, and more frequent intense tropical cyclones. Both \r\nthe ENSO amplitude and the frequency of high-magnitude events since 1950 is higher than over the pre-industrial period (medium \r\nconfidence), suggesting that global extremes similar to those associated with the 2015-2016 extreme El Nino would occur more \r\nfrequently under further increases in global warming. The 2018 boreal spring/summer extremes were characterized by heat extremes \r\nand enhanced droughts in wide areas of the mid-latitudes in the Northern Hemisphere and extremely heavy rainfall in East Asia"


id: 1e6135c08ffd990260a25876db5e31cd
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
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" These \r\nconcurrent events were generally related to the abnormal condition of the jet and North Pacific Subtropical High, but also amplified \r\nby background global warming. It is virtually certain that these 2018 concurrent extreme events would not have occurred without \r\nhuman-induced global warming. Recent years have seen a more frequent occurrence of such concurrent events. However, it is still \r\nunknown which types of concurrent extreme events could occur under increasing global warming."


id: 3e0209a806e17f19f384637c847b5d8b
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
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content:
"This section complements the assessments of changes in temperature \r\nextremes (Section  11.3), heavy precipitation (Section  11.4), and \r\ndroughts (Section  11.6), by providing additional regional details. \r\nRegional changes in floods are assessed in Chapter  12. Owing to \r\nthe large number of regions and space limitations, the regional \r\nassessment for each of the AR6 reference regions (see Section 1.5.2.2 \r\nfor a  description) is presented here in a  set of tables. The tables \r\nare organized according to types of extremes (temperature, \r\nheavy precipitation, droughts) for Africa (Tables 11.4-11.6), Asia \r\n(Tables  11.7-11.9), Australasia (Tables 11.10-11.12), Central and \r\nSouth America (Tables 11.13-11.15), Europe (Tables 11.16-11.18), \r\nand North America (Tables 11.19-11.21). Each table contains \r\nregional assessments for observed changes, the human contribution \r\nto the observed changes, and projections of changes in these \r\nextremes at 1.5degC, 2degC and 4degC of global warming"


id: 8c4c631f190930a4dab6ec977df08b2f
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" A synthesis of \r\nregional changes in hot extremes, heavy precipitation, agricultural \r\nand ecological droughts, and hydrological droughts can be found in \r\nthe Chapter 11 Appendix in Table 11.A.2."


id: de05a543a76cc17658532420a4f0594a
created_at: 2023-03-16 14:02:09
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content:
"Tables 11.5, 11.8, 11.11, 11.14, 11.17, and 11.20 include assessments \r\nfor past changes in heavy precipitation events and their attribution, as \r\nwell as future projections. The evidence is mostly drawn from changes \r\nin metrics based on one-day or five-day precipitation amounts, as \r\naddressed in Section  11.4. Similar to temperature extremes, the \r\nassessment of changes in heavy precipitation uses global studies, \r\nincluding Dunn et al. (2020) and Sun et al. (2021) for observed \r\nchanges, and Li et al. (2021) and the Chapter  11 Supplementary \r\nMaterial (11.SM) for projected changes using the CMIP6 multi-model \r\nensemble. For attribution, Paik et al. (2020) provided continental \r\nanalyses where data coverage was sufficient, but no attribution \r\nstudies based on global data are available for the regional scale"


id: dd6c21330cc323997e3fc98683aa7e1b
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content:
" \r\nFor each region, regional studies, and studies based on the CMIP5 \r\nmulti-model ensemble or CORDEX simulations, are also considered in \r\nthe assessments for past changes, attribution, and projections."


id: f08aee43e0ebc3541d4396a792d6be4c
created_at: 2023-03-16 14:02:09
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content:
"Tables 11.4, 11.7, 11.10, 11.13, 11.16, and 11.19 include assessments \r\nfor past temperature extremes and their attribution, as well as future \r\nprojections. The evidence is mostly drawn from changes in metrics based \r\non daily maximum and minimum temperatures, similar to those used \r\nin Section 11.3. The regional assessments start from global studies that \r\nused consistent analyses for all regions globally with sufficient data. \r\nThis includes Dunn et al. (2020) for observed changes, and Li et al. (2021) \r\nand the Chapter 11 Supplementary Material (11.SM) for projections \r\nwith the CMIP6 multi-model ensemble. Evidence from regional studies, \r\nand those based on the CMIP5 multi-model ensemble or CORDEX \r\nsimulations, are then used to refine the confidence assessments. \r\nFor attribution, Seong et al. (2020) provide a consistent analysis for \r\nAR6 regions, and Z. Wang et al. (2017a) for SREX regions. Additional \r\nregional studies, including event attribution analyses (Section  11"


id: 1981987527159636d7e612ab19e16da8
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"2), \r\nare used when available. In some regions that were not analysed \r\nin Seong et al. (2020), and those with no known event attribution \r\nstudies, medium confidence of a  human contribution is assessed: \r\nwhen there is strong evidence of changes from observations that are \r\nin the direction of model-projected changes for the future; when the \r\nmagnitude of projected changes increases with global warming; and \r\nwhere there is no other evidence to the contrary. This assessment is \r\nfurther supported by an understanding of how temperature extremes \r\nchange with the mean temperature and overwhelming evidence of \r\na  human contribution to the observed larger-scale changes in the \r\nmean temperature and temperature extremes. "


id: 8fd7fa036ddab4ceeb9825dfb65efca3
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"Meteorological droughts are assessed based on observed and \r\nprojected changes in precipitation-only metrics such as the \r\nStandardized Precipitation Index (SPI) and Consecutive Dry Days \r\n(CDD). Observed changes are assessed based on two global \r\nstudies, Dunn et al. (2020) for CDD, and Spinoni et al. (2019) for SPI. \r\nFor  projections, evidence for changes at 1.5degC and 2degC of global \r\nwarming is drawn from L. Xu et al. (2019) and Touma et al. (2015) \r\n(based on RCP8.5 for 2010-2054 compared to 1961-2005) for SPI \r\n(CMIP5) and the Chapter  11 Supplementary Material (11.SM) for \r\nCDD (CMIP6). For projections at 4degC of global warming, evidence is \r\ndrawn from several sources, including Touma et al. (2015) and Spinoni \r\net al. (2020) for SPI (from CMIP5 and CORDEX, respectively), and \r\n11.SM for CDD (CMIP6)"


id: f9086daa11728b4075a7b41504b6b265
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vector_id: 36691
content:
" No global-scale studies are available for the \r\nattribution of meteorological drought, so this assessment is based on \r\nregional detection and attribution or event attribution studies."


id: 42f9dcc2ab88d095b043efd7e927908d
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content:
"Climate change will also manifest differently for different weather regimes and can lead to contrasting changes \r\nin average and extreme conditions. Observations of the recent past and climate model projections show that, \r\nin most places, changes in daily temperatures are dominated by a general warming where the climatological \r\naverage and extreme values are shifted towards higher temperatures, making warm extremes more frequent and \r\ncold extremes less frequent. The top panels in FAQ 11.1, Figure 1 show projected changes in surface temperature \r\nfor long-term average conditions (left) and for extreme hot days (right) during the warm season (summer in mid\u0002to high latitudes). Projected increases in long-term average temperature differ substantially between different \r\nplaces, varying from less than 3degC in some places in central South Asia and southern South America to over 7degC \r\nin some places in North America, North Africa and the Middle East"


id: 6e6bfb2a2ce0429882759a446869861a
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content:
" Changes in extreme hot days follow changes \r\nin average conditions quite closely, although, in some places, the warming rates for extremes can be intensified \r\n(e.g., southern Europe and the Amazon basin) or weakened (e.g., northern Asia and Greenland) compared to \r\naverage values."


id: 9a671495ffcd18c2062311d2062d71
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
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content:
"Recent observations and global and regional climate model projections point to changes in precipitation \r\nextremes (including both rainfall and snowfall extremes) differing drastically from those in average precipitation. \r\nThe bottom panels in FAQ 11.1, Figure 1 show projected changes in the long-term average precipitation (left) \r\nand in heavy precipitation (right). Averaged precipitation changes show striking regional differences, with \r\nsubstantial drying in places such as southern Europe and northern South America and wetting in places such as \r\nthe Middle East and southern South America. Changes in extreme precipitation are much more uniform, with \r\nsystematic increases over nearly all land regions. The physical reasons behind the different responses of averaged \r\nand extreme precipitation are now well understood"


id: dbbfd0b3bcac38bb38d2c242e1579a39
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
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content:
" The intensification of extreme precipitation is driven by \r\nthe increase in atmospheric water vapour (about 7% per 1degC of warming near the surface), although this is \r\nmodulated by various dynamical changes. In contrast, changes in average precipitation are driven not only by \r\nmoisture increases but also by slower processes that constrain future changes over the globe to only 2-3% per \r\n1degC of warming near the surface."


id: fd28a4802859d342f26caaa0130f56a3
created_at: 2023-03-16 14:02:09
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vector_id: 37343
content:
"Human-induced climate change has already affected many aspects of the climate system. In addition to the increase \r\nin global surface temperature, many types of weather and climate extremes have changed. In most regions, the \r\nfrequency and intensity of hot extremes have increased and those of cold extremes have decreased. The frequency \r\nand intensity of heavy precipitation events have increased at the global scale and over a majority of land regions. \r\nAlthough extreme events such as land and marine heatwaves, heavy precipitation, drought, tropical cyclones, and \r\nassociated wildfires and coastal flooding have occurred in the past and will continue to occur in the future, they \r\noften come with different magnitudes or frequencies in a warmer world. For example, future heatwaves will last \r\nlonger and have higher temperatures, and future extreme precipitation events will be more intense in several \r\nregions"


id: 8454f472a3ed25c5bd66e26f3296f478
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
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content:
" Certain extremes, such as extreme cold, will be less intense and less frequent with increasing warming. "


id: 2b6a21c21ebb6d6dfa258d46d75df2c6
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
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content:
"It is common to question whether human-caused climate change caused a major weather- and climate-related \r\ndisaster. When extreme weather and climate events do occur, both exposure and vulnerability play an important \r\nrole in determining the magnitude and impacts of the resulting disaster. As such, it is difficult to attribute a specific \r\ndisaster directly to climate change. However, the relatively new science of event attribution enables scientists to \r\nattribute aspects of specific extreme weather and climate events to certain causes. Scientists cannot answer directly \r\nwhether a particular event was caused by climate change, as extremes do occur naturally, and any specific weather \r\nand climate event is the result of a complex mix of human and natural factors. Instead, scientists quantify the relative \r\nimportance of human and natural influences on the magnitude and/or probability of specific extreme weather \r\nevents"


id: 861c418bb2675a17dc08187578aef188
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content:
" Such information is important for disaster risk reduction planning, because improved knowledge about \r\nchanges in the probability and magnitude of relevant extreme events enables better quantification of disaster risks."


id: 69eb3d36a265224d7e7bd0d2acdb2d26
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
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vector_id: 14265
content:
"On a case-by-case basis, scientists can now quantify the contribution of human influences to the magnitude and \r\nprobability of many extreme events. This is done by estimating and comparing the probability or magnitude of \r\nthe same type of event between the current climate - including the increases in greenhouse gas concentrations \r\nand other human influences - and an alternate world \r\nwhere the atmospheric greenhouse gases remained \r\nat pre-industrial levels. FAQ 11.3 Figure  1  illustrates \r\nthis approach using differences in temperature and \r\nprobability between the two scenarios as an example. \r\nBoth the pre-industrial (blue) and current (red) \r\nclimates experience hot extremes, but with different \r\nprobabilities and magnitudes. Hot extremes of a given \r\ntemperature have a  higher probability of occurrence \r\nin the warmer current climate than in the cooler pre\u0002industrial climate"


id: fdfe25b531823296f0140900f75fa0d7
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content:
" Additionally, an extreme hot event \r\nof a particular probability will be warmer in the current \r\nclimate than in the pre-industrial climate. Climate model \r\nsimulations are often used to estimate the occurrence \r\nof a specific event in both climates. The change in the \r\nmagnitude and/or probability of the extreme event \r\nin the current climate compared to the pre-industrial \r\nclimate is attributed to the difference between the two \r\nscenarios, which is the human influence.\r\nFAQ 11.3: Climate change and extreme events\r\nExtreme events have become more probable and more intense. \r\nMany of these changes can be attributed to human influence \r\non the climate.\r\nSame temperature \r\nbut more probable\r\nCurrent climate\r\nSame probability \r\nbut hotter Pre-industrial \r\nclimate\r\nCooler Temperature Warmer\r\nAnnual \r\nchance \r\nof event\r\nMore \r\nprobable\r\nProbabilities are lower \r\nfor hotter (more extreme) events\r\nLess \r\nprobable"


id: b57ec6b99d9f9ef2f46dfad5f509f36a
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
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index: climate_gpt_only_giec
vector_id: 26083
content:
"FAQ 11.3, Figure  1 | Changes in climate result in changes in the \r\nmagnitude and probability of extremes. Example of how temperature \r\nextremes differ between a climate with pre-industrial greenhouse gases (shown \r\nin blue) and the current climate (shown in orange) for a representative region. \r\nThe horizontal axis shows the range of extreme temperatures, while the vertical \r\naxis shows the annual chance of each temperature event's occurrence. Moving \r\ntowards the right indicates increasingly hotter extremes that are more rare (less \r\nprobable). For hot extremes, an extreme event of a particular temperature in the \r\npre-industrial climate would be more probable (vertical arrow) in the current \r\nclimate. An event of a certain probability in the pre-industrial climate would \r\nbe warmer (horizontal arrow) in the current climate"


id: 34e2f441c4a259ead252924b03756104
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updated_at: 2023-03-16 14:02:09
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index: climate_gpt_only_giec
vector_id: 5727
content:
" While the climate under \r\ngreenhouse gases at the pre-industrial level experiences a  range of hot \r\nextremes, such events are hotter and more frequent in the current climate."


id: 7cd3aea22e35e22e8810ab0acebcbc64
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updated_at: 2023-03-16 14:02:09
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vector_id: 17235
content:
"Carley E. Iles (Norway, France/United Kingdom), Jerome Servonnat (France), Gudfinna Adalgeirsdottir \r\n(Iceland), Rita Adrian (Germany), Roxana Bojariu (Romania), Laurent Bopp (France), Audrey Brouillet \r\n(France), Carlo Buontempo (United Kingdom/Italy), Winston Chow (Singapore), Augustin Colette \r\n(France), Cecilia Conde (Mexico), Leticia Cotrim da Cunha (Brazil), Claudine Dereczynski (Brazil), \r\nAlejandro Di Luca (Australia, Canada/Argentina), Fabio Di  Sante (Italy), Arona Diedhiou (Cote \r\nd'Ivoire/Senegal), Aida Diongue-Niang (Senegal), Francisco J. Doblas-Reyes (Spain), Pariva Dobriyal \r\n(India), Sybren S. Drijfhout (The Netherlands), John P. Dunne (United States of America), \r\nTamsin L. Edwards (United Kingdom), Aidan D. Farrell (Trinidad and Tobago/Ireland), Erich Fischer \r\n(Switzerland), John C. Fyfe (Canada), Alexander  Gelfan (Russian Federation), Subimal Ghosh \r\n(India), Irina V"


id: e767c6442b7f15e51414e21e0142356a
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updated_at: 2023-03-16 14:02:09
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content:
" Gorodetskaya (Portugal/Belgium, Russian Federation), Michael Grose (Australia), \r\nJose  Manuel Gutierrez (Spain), David S. Gutzler (United States of America), Rebecca Harris \r\n(Australia), Matthias Hauser (Switzerland), Mark Hemer (Australia), Kevin Hennessy (Australia), \r\nHelene T. Hewitt (United Kingdom), Masao Ishii (Japan), Maialen Iturbide (Spain), Christopher D. Jack \r\n(South Africa), Richard G. Jones (United Kingdom), Nikolay Kadygrov (France/Russian Federation), \r\nEbru Kirezci (Australia/Turkey), Nana Ama Browne Klutse (Ghana), Robert E. Kopp (United States \r\nof America), James Kossin (United States of America), Charles Koven (United States of America), \r\nSvitlana Krakovska (Ukraine), Gerhard  Krinner (France/Germany, France), Benjamin L"


id: 7bc830c9166ecfc1af4f27d9f7af479f
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content:
" Lamptey \r\n(Niger, Ghana/Ghana), Christopher Lennard (South Africa), Xianfu Lu (United Kingdom/China), \r\nDouglas Maraun (Austria/Germany), Simon  McGree (Australia/Fiji, Australia), Glenn McGregor \r\n(United Kingdom/New Zealand, United Kingdom), Kathleen  L.  McInnes (Australia), Dirk Notz \r\n(Germany), Brian O'Neill (United States of America), Ben Orlove (United States of America), \r\nFriederike Otto (United Kingdom/Germany), Carlos Perez Garcia-Pando (Spain), Franz Prettenthaler "


id: 1c571bc26cfc5e91d3692188e9f81cef
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
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vector_id: 1912
content:
"Climate change information is increasingly available and robust \r\nat regional scale for impacts and risk assessment. Climate \r\nservices and vulnerability, impacts and adaptation studies require \r\nregional scale multi-decadal climate observations and projections. \r\nSince the IPCC Fifth Assessment Report (AR5), the increased availability \r\nof coordinated ensemble regional climate model projections and \r\nimprovements in the level of sophistication and resolution of global \r\nand regional climate models, completed by attribution and sectoral \r\nvulnerability studies, have enabled the investigation of past and \r\nfuture evolution of a range of climatic quantities that are relevant to \r\nsocio-economic sectors and natural systems"


id: d02b6fec30c8980c86160894d381113d
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
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vector_id: 30283
content:
" Chapter 12 consolidates \r\ncore physical knowledge from preceding AR6 Working Group I (WGI) \r\nchapters and post-AR5 climate impact assessment literature to \r\nassess the spatio-temporal evolution of the climatic conditions that \r\nmay lead to regional scale impacts and risks (following the sectoral \r\nclasses adopted by AR6 WGII) in the world's regions (presented in \r\nChapter 1). {12.1}"


id: 60dd2f215a957c7372e90d4105640756
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
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vector_id: 12809
content:
"Every region of the world will experience concurrent changes \r\nin multiple CIDs by mid-century (high confidence), challenging \r\nthe resilience and adaptation capacity of the region. Changes in \r\nheat, cold, snow and ice, coastal, oceanic, and CO2 at surface CIDs are \r\nprojected with high confidence in most regions, indicating worldwide \r\nchallenges, while additional region-specific changes are projected in \r\nother CIDs that may lead to more regional challenges. High confidence\r\nincreases in some of the drought, aridity and fire weather CIDs will \r\nchallenge, for example, agriculture, forestry, water systems, health \r\nand ecosystems in Southern Africa, the Mediterranean, North Central \r\nAmerica, Western North America, the Amazon regions, South-Western \r\nSouth America, and Australia"


id: 4be19868d7d221186709a58fd5a1f5c8
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
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content:
" High confidence changes in snow, ice \r\nand pluvial or river flooding will pose challenges for, for example, \r\nenergy production, river transportation, ecosystems, infrastructure \r\nand winter tourism in North America, Arctic regions, Andes regions, \r\nEurope, Siberia, Central, South and East Asia, Southern Australia and \r\nNew Zealand. Only a  few CIDs are projected to change with high \r\nconfidence in the Sahara, Madagascar, Arabian Peninsula, Western \r\nAfrica and Small Islands; however, the lower confidence levels for \r\nCID changes in these regions can originate from knowledge gaps \r\nor model uncertainties, and does not necessarily mean that these \r\nregions have relatively low risk. {12.5}"


id: 1d1c95f055c22c148b793621f98c715
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
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vector_id: 2023
content:
"The current climate in most regions is already different from \r\nthe climate of the early or mid-20th century with respect to \r\nseveral CIDs. Climate change has already altered CID profiles \r\nand resulted in shifts in the magnitude, frequency, duration, \r\nseasonality and spatial extent of associated indices (high \r\nconfidence). Changes in temperature-related CIDs such as mean \r\ntemperatures, growing season length, extreme heat and frost have \r\nalready occurred and many of these changes have been attributed to \r\nhuman activities (medium confidence). Mean temperatures and heat \r\nextremes have emerged above natural variability in all land regions \r\n(high confidence). In tropical regions, recent past temperature \r\ndistributions have already shifted to a  range different to that of \r\nthe early 20th  century (high confidence). Ocean acidification and \r\ndeoxygenation have already emerged over most of the global open \r\nocean, as has reduction in Arctic sea ice (high confidence)"


id: 2c54e7e572e1ec5148bc64a36ac49a93
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
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index: climate_gpt_only_giec
vector_id: 4405
content:
" Using CID \r\nindex distributions and event probabilities accurately in both current \r\nand future risk assessments requires accounting for the climate \r\nchange-induced shifts in distributions that have already occurred. \r\n{12.4, 12.5}"


id: 261bde5f177944dd95e4badaff802641
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
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vector_id: 3416
content:
"Worldwide changes in heat, cold, snow and ice, coastal, oceanic \r\nand CO2-related CIDs will continue over the 21st  century, \r\nalbeit with regionally varying rates of change, regardless of \r\nthe climate scenario (high confidence). In all regions there is high \r\nconfidence that, by 2050, mean temperature and heat extremes will \r\nincrease, and there is high confidence that sea surface temperature \r\nwill increase in all oceanic regions except the North Atlantic. Apart \r\nfrom a few regions with substantial land uplift, relative sea level rise \r\nis very likely to virtually certain (depending on the region) to continue \r\nin the 21st century, contributing to increased coastal flooding in most \r\nlow-lying coastal areas (high confidence) and coastal erosion along \r\nmost sandy coasts (high confidence), while ocean acidification is \r\nvirtually certain to increase"


id: 129c557566f5f29a5943c143c9589ed9
created_at: 2023-03-16 14:02:09
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content:
" It is virtually certain that atmospheric \r\nCO2 at the surface will increase in all emissions scenarios until \r\nnet zero emissions are achieved. Glaciers will continue to shrink \r\nand permafrost to thaw in all regions where they are present \r\n(high confidence). These changes will lead to climate states with no \r\nrecent analogue that are of particular importance for specific regions \r\nsuch as tropical forests or biodiversity hotspots. {12.4}"


id: 6b0fc808b6ac66d377cbb78e54e7c840
created_at: 2023-03-16 14:02:09
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content:
"and storms. Agricultural and ecological drought changes are generally \r\nof higher confidence than hydrological drought changes,  with \r\nincreases projected in North and Southern Africa, Madagascar, \r\nSouthern and Eastern Australia, some regions of Central and South \r\nAmerica, Mediterranean Europe, Western North America and North \r\nCentral America (medium to high confidence). Fire weather conditions \r\nwill increase by 2050 under RCP4.5 or above in several regions in \r\nAfrica, Australia, several regions of South America, Mediterranean \r\nEurope, and North America (medium to high confidence). Extreme \r\nprecipitation and pluvial flooding will increase in many regions \r\naround the world (high confidence). Increases in river flooding are \r\nalso expected in Western and Central Europe and in polar regions \r\n(high confidence), most of Asia, Australasia, North America, the South \r\nAmerican Monsoon region and South-Eastern South America (medium \r\nconfidence)"


id: 98f021949f41cece0ff5174118519533
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
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content:
" Mean winds are projected to slightly decrease by 2050 \r\nover much of Europe, Asia and Western North America, and increase in \r\nmany parts of South America except Patagonia, West and South Africa \r\nand the eastern Mediterranean (medium confidence). Extratropical \r\nstorms are expected to have a decreasing frequency but increasing \r\nintensity over the Mediterranean, increasing intensity over most of \r\nNorth America, and an increase in both intensity and frequency over \r\nmost of Europe (medium confidence). Enhanced convective conditions \r\nare expected in North America (medium confidence). Tropical cyclones \r\nare expected to increase in intensity despite a decrease in frequency \r\nin most tropical regions (medium confidence). Climate change will \r\nmodify multiple CIDs over Small Islands in all ocean basins, most \r\nnotably those related to heat, aridity and droughts, tropical cyclones \r\nand coastal impacts. {12.4}"


id: ac9ee9d7f08a3c84f9464df9906f7726
created_at: 2023-03-16 14:02:09
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content:
"Many global- and regional-scale CIDs have a  direct relation \r\nto global warming levels (GWLs) and can thus inform the \r\nhazard component of 'Representative Key Risks' and 'Reasons \r\nfor Concern' assessed by AR6 WGII. These include both mean \r\nand extreme heat, cold, wet and dry hazards; cryospheric hazards \r\n(snow cover, ice extent, permafrost); and oceanic hazards (marine \r\nheatwaves) (high confidence). For some of these, a  quantitative \r\nrelation can be drawn (high confidence). For example, with each \r\ndegree of global surface air temperature (GSAT) warming, the \r\nmagnitude and intensity of many heat extremes show a  linear \r\nchange, while some changes in frequency of threshold exceedances \r\nare exponential: Arctic temperatures warm about twice as fast as \r\nGSAT; global sea surface temperatures increase by about 80% of \r\nGSAT change; Northern Hemisphere spring snow cover decreases \r\nby about 8% per 1degC. For other hazards (e.g"


id: 22248f9a56b09650075f30e72e620810
created_at: 2023-03-16 14:02:09
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vector_id: 2820
content:
", ice-sheet behaviour, \r\nglacier mass loss, global mean sea level rise, coastal floods and \r\ncoastal erosion) the time and/or scenario dimensions remain critical \r\nand a simple relation with GWLs cannot be drawn (high confidence), \r\nbut still quantitative estimates assuming specific time frames and/\r\nor stabilized GWLs can be derived (medium confidence). Model \r\nuncertainty challenges the link between specific GWLs and tipping \r\npoints and irreversible behaviour, but their occurrence cannot be \r\nexcluded and their chances increase with warming levels (medium \r\nconfidence). {CCB 12.1}"


id: 8426d1e859efc17134d804a84794c4a2
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
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vector_id: 18331
content:
"The level of confidence in the projected direction of \r\nchange in CIDs and the intensity of the signal depend on \r\nmitigation efforts over the 21st century, as reflected by the \r\ndifferences between end-century projections for different \r\nclimate scenarios. Dangerous humid heat thresholds, such as \r\nthe US National Oceanic and Atmospheric Administration Heat \r\nIndex (NOAA HI) of 41degC, will be exceeded much more frequently \r\nunder the SSP5-8.5 scenario than under SSP1-2.6 and will affect \r\nmany more regions (high confidence). In  many tropical regions, \r\nthe number of days per year where an HI of 41degC is exceeded will \r\nincrease by more than 100 days relative to the recent past under \r\nSSP5-8.5, while this increase will be limited to less than 50 days \r\nunder SSP1-2.6 (high confidence). The number of days per year \r\nwhere temperature exceeds 35degC will increase by more than 150 \r\ndays in many tropical areas, such as the Amazon basin and South \r\nEast Asia under SSP5-8"


id: 34600e4aae01ad552965f228eb1eb4fa
created_at: 2023-03-16 14:02:09
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vector_id: 5655
content:
"5, while it is expected to increase by less \r\nthan two months in these areas under SSP1-2.6 (except for the \r\nAmazon Basin). There is high confidence that sandy shorelines will \r\nretreat in most regions of the world, in the absence of additional \r\nsediment sources or physical barriers to shoreline retreat. The total \r\nlength of sandy shorelines around the world that are projected to \r\nretreat by more than 100 m by the end of the century is about 35% \r\ngreater under RCP8.5 (about 130,000 km) compared to that under \r\nRCP 4.5 (about 95,000 km). The frequency of the present-day 1-in\u0002100 year extreme sea level event (represented here by extreme \r\ntotal water level) is also projected to increase substantially in most \r\nregions (high confidence). In a  globally averaged sense, the 1-in\u0002100 year extreme sea level is projected to become an event that "


id: 6dca223e1ae6eab0c8fd35cef3d4b206
created_at: 2023-03-16 14:02:09
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content:
"Impacts of climate change are driven not only by changes in climate \r\nconditions, but also by changes in exposure and vulnerability \r\n(Cross-Chapter Box 1.3). This chapter concentrates on drivers of impacts \r\nthat are of climatic origin (see also the IPCC Special Report on Global \r\nWarming of 1.5degC (SR1.5, IPCC, 2018), and Section 1.3.2 in this Report), \r\nreferred to in WGI as 'climatic impact-drivers' (CIDs). CIDs are physical \r\nclimate system conditions (e.g., means, events, extremes) that affect an \r\nelement of society or ecosystems. Depending on system tolerance, CIDs \r\nand their changes can be detrimental, beneficial, neutral, or a mixture \r\nof each across interacting system elements and regions. However, this \r\nchapter largely focuses on drivers commonly connected to hazards, \r\nand adopts the IPCC risk framework (Cross-Chapter Box  1"


id: 1e9e0887fa67b1011d615dbb9b8249ce
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content:
"3) since \r\nthe main objective of the United Nations Framework Convention on \r\nClimate Change (UNFCCC) is to 'prevent dangerous anthropogenic \r\ninterference with the climate system' (Article 2)."


id: b674a657936283ad4e1342e1e6a2ab98
created_at: 2023-03-16 14:02:09
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"The approach adopted here is consistent with the UN Sendai Framework \r\nfor Disaster Risk Reduction 2015-2030, which aims to face disaster \r\nconsequences (including but not limited to climate disasters) and \r\nreduce risks in natural, managed and built environments (Aitsi-Selmi \r\net  al., 2015; UNISDR, 2015). The classification of climatic impact\u0002drivers in this chapter is largely consistent with the classification of \r\nhazards used in the Sendai Framework. However, the UNISDR hazard \r\nlist spans a  wider range of hazards inducing damage to society, \r\nincluding hazards that are not directly related to climate (such as \r\nvolcanoes and earthquakes), which are excluded from the assessment \r\nherein. Furthermore, the UNISDR classification of hazards does not \r\ninclude mean climatic conditions, which are also discussed as climatic \r\nimpact-drivers in this chapter. The first priority mentioned in the Sendai \r\nFramework is understanding disaster risk as a  necessary step for \r\naction"


id: b10eb9e7aaa4f68c927c9e39472f747f
created_at: 2023-03-16 14:02:09
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content:
"This chapter is new in IPCC WGI assessment reports, in that it represents \r\na  contribution to the 'IPCC risk framework'. Within this framework, \r\nclimate-related impacts and risks are determined through an interplay \r\nbetween the occurrence of climate hazards and their consequences \r\ndepending on the exposure of the affected human or natural system \r\nand its vulnerability to the hazardous conditions. In Chapter 12, we \r\nare assessing climatic impact-drivers that could lead to hazards or to \r\nopportunities, from the literature and model results since AR5. This \r\nwill particularly support the assessment of key risks related to climate \r\nchange by WGII (Chapter 16)"


id: 97e4ad53265913c23bc36c89e6a4aeb3
created_at: 2023-03-16 14:02:09
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content:
" Despite the fact that impacts may also be \r\ninduced by climate adaptation and mitigation policies themselves, as \r\nwell as by socio-economic trends, changes in vulnerability or exposure, \r\nand external geophysical hazards such as volcanoes, the focus here is \r\nonly on climatic impacts and risks induced by shifts in physical climate \r\nphenomena that directly influence human and ecological systems \r\n(Cross-chapter Box 1.3)."


id: 557ec2d38001b4731050e4b8175e084d
created_at: 2023-03-16 14:02:09
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content:
"The narrative of Chapter  12 is illustrated in Figure  12.2. First, \r\nSection 12.2 defines a range of climatic impact-driver categories that \r\nare relevant for regional and sectoral impacts. Next, Section  12.3 \r\nidentifies climatic impact-drivers and their relevant indices that \r\nare frequently used in the context of climate impacts in the WGII \r\nfocus sectors (AR6 WGII Chapters 2-8). The assessment of changes \r\nin regional-scale climatic impact-drivers is then developed within \r\nSection  12.4 by continent, following the structure of the WGII \r\nassessment report regional chapters (AR6 WGII Chapters 9-15), and \r\nadding the polar regions, open/deep ocean and other specific zones \r\ncorresponding to the WGII Cross-Chapter Papers. Section  12.5 then \r\npresents a global perspective (both bottom-up and top-down) on the \r\nchange of regional climatic impact-drivers, including an assessment of \r\nthe 'emergence' of climatic impact-drivers. Section 12"


id: f0e6605a8a45af4fea8a52163214bb61
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content:
"6 discusses how \r\nclimate information is used in 'climate services', which encompasses \r\na range of activities bridging climate science and its use for adaptation \r\nand mitigation decision-making (see also AR6 WGII Chapter  17). \r\nThe chapter concludes with final remarks in Section 12.7."


id: c335172f15cdb15499ebdb1e605881ec
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"Several climatic impact-drivers are reliant on many factors beyond \r\ntheir associated primary climatic phenomenon. For example, river \r\nflooding is heavily dependent on river management and engineering \r\nand could also be affected by tidal water levels due to sea level \r\nrise and/or storm surge. Coastal flooding could be affected by \r\ncoastal protection structures, port and harbour structures, as well \r\nas river flows (on inlet-interrupted coasts). Coastal erosion could \r\nbe influenced by coastal protection measures as well as fluvial \r\nsediment supply to the coast. Furthermore, air pollution weather \r\nis not the only or dominant driver, for instance, of surface ozone \r\npollution, but precursor emissions from anthropogenic sources can \r\nplay a significant role (Section 6.5)"


id: e080e6315ed6d3abb34acfb6e74d0b3d
created_at: 2023-03-16 14:02:09
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content:
" Chapter 12 focuses only on the \r\ninfluence of the atmospheric, land and oceanic conditions associated \r\nwith the climatic impact-drivers and the confidence in the direction \r\nof CID changes given here does not take into account existing or \r\npotential future adaptation measures, unless otherwise stated."


id: 581706b1657ddfea57ab4862c4a5203a
created_at: 2023-03-16 14:02:09
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"This section surveys the links between CIDs and affected sectors; \r\nnot to perform specific climate change impact or risk assessments \r\n(see AR6 WGII), but to describe key indices (among many) that \r\nquantify these links as guidance for stakeholders seeking applicable \r\nclimate information. This survey builds on the work of the World \r\nMeteorological Organization Expert Team on Sector-Specific Climate \r\nIndices (ET-SCI) and previous IPCC assessments, notably AR5 WGII \r\n(Birkmann et al., 2014; IPCC, 2014a) and IPCC Special Reports (IPCC, \r\n2018, 2019b, c) that have assessed climate hazards affecting sectors \r\nbut is organized from a CID perspective drawing also upon recent \r\nsummaries of sectoral hazards (Mora et  al., 2018; ICOMOS, 2019; \r\nYokohata et  al., 2019)"


id: 9eec43bd0b59fab922e037feff9ceb51
created_at: 2023-03-16 14:02:09
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" Impacts, risks and opportunities are rarely \r\nattributable to a single CID index or threshold, but climate shifts that \r\npush conditions outside of expected conditions and beyond tolerance \r\nlevels are indicative of impact, risk or benefit given vulnerability \r\nand exposure. Focus is on direct sectoral connections of a  CID \r\n(Hallegatte and Przyluski, 2010) rather than cascading or secondary \r\neffects (e.g., water-borne diseases following a flood, mental health \r\nchallenges following a  severe storm, or the effects of drought on \r\npoverty), as these are strongly affected by exposure, vulnerability and \r\nresponse, as discussed in the WGII Report."


id: b3e36996307bf90851aab0027f8bbe23
created_at: 2023-03-16 14:02:09
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vector_id: 25816
content:
"For each CID category there can be a range of indices that capture \r\nthe sector- or application-relevant characteristics of a  climatic \r\nimpact-driver as described in Sections 12.3 and 12.4. Indices for \r\nclimatic impact-drivers that are based on absolute or percentile \r\nthresholds (e.g.,  daily maximum temperature above 35degC) can be \r\naffected by biases in climate model simulations, such as local or \r\nregional deviations of a  simulated climate variable from observed \r\nvalues (Sillmann et al., 2014; Dosio, 2016). Where sensible (i.e., where \r\nreliable observational data are available and a climate model that fits \r\nfor the desired purpose), the output of climate model simulations can \r\nbe bias-adjusted, potentially involving advanced methods to account \r\nfor multiple variables and extreme value statistics as assessed in \r\ndetail in Cross-Chapter Box 10.2"


id: b3e399e7c4d654716f352219559b7bbc
created_at: 2023-03-16 14:02:09
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index: climate_gpt_only_giec
vector_id: 25817
content:
" Yet, there is no general agreement \r\nabout which bias adjustment methods to apply, as artefacts can arise \r\nboth from the climate model and from the bias adjustment method, \r\nand the number of available methods has considerably grown in \r\nrecent years (for a  detailed discussion of available methods and \r\ntheir performance see Sections 10.3.1.3.2 and 10.3.3.7.2, and Cross\u0002Chapter Box 10.2). The WGI Interactive Atlas illustrates original and \r\nbias-adjusted CIDs (see Atlas.1.4.5)."


id: 3ad846c89ad61c9b3aa340859bf997ee
created_at: 2023-03-16 14:02:09
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index: climate_gpt_only_giec
vector_id: 6625
content:
"Table 12.2 presents a summary of Section 12.3 connections between \r\nCIDs as defined in Table 12.1 and key sectoral assets, utilizing the \r\nWGII organization of sectors (corresponding to WGII Chapters 2-8). \r\nColours are shown for connections with at least medium confidence\r\nas assessed from sectoral impacts and risk literature, with relevance \r\nassessed according to the prominence of that specific CID/asset \r\nconnection in analyses of current and future impacts and risk. Within \r\neach sector there is a  multitude of specific sectoral systems that \r\nmay be affected by CID increases and decreases, with consequences \r\nfurther distinguished by region, background climate and socio\u0002economic or ecological context of the affected asset"


id: 25128f4a7d666f81cb2a300c149c1c85
created_at: 2023-03-16 14:02:09
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" Our aim is \r\ntherefore to recognize important drivers and the common attributes \r\nof change within each CID that scientists and practitioners monitor to \r\nunderstand current and future challenges for important asset groups, \r\nthereby pointing to the climate information that needs to be tailored \r\nand analysed for impacts and for risk assessment (Section  12.6). \r\nAdditional effects whereby CIDs affect each other (across Table 12.2 \r\ncolumns) are discussed as climatic phenomena within WGI. The ways \r\nsectoral assets affect each other (across Table  12.2 rows) are \r\ndescribed throughout WGII, for example with information about the "


id: 3d7bd354ffc4691cc6060c972804a415
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vector_id: 7031
content:
"Information about increasing mean annual and seasonal air \r\ntemperature is relevant in the determination of suitable species range \r\nfor terrestrial, freshwater and intertidal species (Thomas et al., 2004; \r\nElith et al., 2010; Hincapie and Caicedo, 2013; Cooper, 2014; Krist et al., \r\n2014; Lindner et al., 2014; Saintilan et al., 2014; Lenoir and Svenning, \r\n2015; Myers-Smith et al., 2015; Urban, 2015; Thorne et al., 2017). Ocean \r\necosystems are affected by the ocean temperature CID (described in \r\nSection  12.3.6.1). Species redistribution and extinction studies also \r\nneed information about climate velocity, a comparison of the pace of \r\nwarming to geographical temperature gradients that indicates the rate \r\nat which a species would have to move to maintain its climatological \r\ntemperature (Thomas et  al., 2004; Loarie et  al., 2009; Dobrowski \r\net al., 2013; Burrows et al., 2014; Dobrowski and Parks, 2016; Sittaro \r\net al"


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vector_id: 3088
content:
", 2017) with some studies incorporating additional variables beyond \r\ntemperature (Hamann et al., 2015). Many freshwater ecosystems are \r\nstrongly constrained by stream and lake temperatures (Scheurer et al., \r\n2009; Comte and Grenouillet, 2013; Contador et al., 2014; Knouft and \r\nFicklin, 2017). Warmer and more stratified lake temperatures are more \r\nconducive to cyanobacteria blooms with implications for ecosystem \r\nhealth and water resource quality (Whitehead et al., 2009; Moss et al., \r\n2011; Jones and Brett, 2014; Chapra et al., 2017; Shatwell et al., 2019). \r\nConsideration of nighttime and daytime temperature trends also \r\nelucidates different biophysical effects on vegetation (Peng et al., 2013). \r\nChanges in the seasonal timing caused by warming trends are critical \r\nto species ranges and ecosystem function (Pearce-Higgins et al., 2015; \r\nHughes et al., 2017b), and indices that characterize the onset of spring \r\nshed light on plant emergence and development (Ault et al., 2015)."


id: 45153094293394044ac334fa7aff0992
created_at: 2023-03-16 14:02:09
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vector_id: 8292
content:
"Mean air temperature dictates many aspects of crop cultivation, \r\nlivestock production, agroforestry and output from freshwater \r\naquaculture and fisheries, as well as the potential for food \r\ncontamination. Mean warming alters suitable cultivation zones for crop \r\nspecies (Braganca et al., 2016; Gendron St-Marseille et al., 2019; IPCC, \r\n2019c) and tree species (Hanewinkel et al., 2013; Fei et al., 2017). Crop \r\nand ecosystem service productivity often responds directly to mean \r\ntemperatures, although this is dependent on farming systems (Bassu \r\net al., 2014; Challinor et al., 2014; Lobell and Tebaldi, 2014; Rosenzweig \r\net al., 2014; Asseng et al., 2015; Li et al., 2015; Fleisher et al., 2017; \r\nZhao et al., 2017; Smith and Fazil, 2019). Many studies relate plant \r\ndevelopment (phenology), insect generation cycles and pest outbreaks \r\nto growing degree days, an aggregation of daily thermal units above \r\na threshold (e.g"


id: 11f3342ab39d0bb1e2aae11dd830098d
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
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vector_id: 324
content:
", Tmean >5degC) that accelerates with warmer conditions \r\n(Hof and Svahlin, 2016; Ruosteenoja et al., 2016; Tripathi et al., 2016). \r\nMany plants respond to changes in nighttime temperatures that affect \r\nrespiration and transpiration rates (Narayanan et al., 2015; X. Chen \r\net  al., 2019), and warming of the soil column is also relevant to "


id: c5a649786778e5826009bb0b6742df44
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
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vector_id: 28678
content:
"extreme temperatures and high humidity to capture human health \r\nchallenges (Astrom et al., 2013; Chow et al., 2016; Dahl et al., 2017a; \r\nIm et al., 2017; Coffel et al., 2018; J. Li et al., 2018; Vanos et al., 2020). \r\nDifferent optimum temperatures and extreme heat thresholds based on \r\nlocal distributions are needed to reflect acclimation of different locations \r\nand populations (Hajat et al., 2014; WHO, 2014; Kinney et al., 2015b; \r\nRusso et al., 2015; Petitti et al., 2016; Dosio, 2017; Cheng et al., 2018; \r\nLay et  al., 2018; Schwingshackl et  al., 2021). Hot and humid heat \r\nepisodes can be deadly (Mora et al., 2017), are associated with elevated \r\nhospital intake (Goldie et al., 2017) and lower safety and productivity \r\nof outdoor labourers (Dunne et al., 2013; Graff Zivin and Neidell, 2014; \r\nKjellstrom et al., 2016; Pal and Eltahir, 2016; Y. Zhao et al., 2016; Mora \r\net al., 2017; Watts et al., 2018; Orlov et al., 2019)"


id: b9bcd898578938092d02af13836a51fc
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
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vector_id: 26763
content:
" Elevated nighttime \r\ntemperatures prevent the human body from experiencing relief from \r\nheat stress (Zhang et  al., 2012) and can be tracked over extended \r\nperiods of sequential day and night heat extremes (Murage et al., 2017; \r\nMukherjee and Mishra, 2018). Extreme heat also exacerbates asthma, \r\nrespiratory difficulties and response to airborne allergens such as hay \r\nfever (Upperman et al., 2017). Extreme heat affects outdoor exercise \r\nsuch as the use of bike-share facilities (Heaney et al., 2019; Vanos et al., \r\n2020). Large-scale recreational and sporting events such as marathons \r\nand tennis tournaments monitor heat extremes when determining the \r\nviability of host cities (Smith et al., 2016, 2018)."


id: bccf2c6df44a8dc70c9d33cd84c21386
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
content_type: text
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vector_id: 27277
content:
"Short-term exposure of crops to temperatures beyond a  critical \r\ntemperature threshold can lead to lower yields and above a limiting \r\ntemperature threshold, crops may fail altogether (Schlenker and \r\nRoberts, 2009; Lobell et al., 2012, 2013; Gourdji et al., 2013; Deryng \r\net al., 2014; Schauberger et al., 2017; Tesfaye et al., 2017; Vogel et al., \r\n2019). The exact level of these thresholds depends on species, cultivar \r\nand farm management (Hatfield and Prueger, 2015; Hatfield et al., \r\n2015; Bisbis et al., 2018; Grotjahn, 2021). The timing of heatwaves \r\nis particularly important, as extreme heat is more damaging during \r\ncritical phenological stages (Teixeira et al., 2013; Eyshi Rezaei et al., \r\n2015; Fontana et al., 2015; B. Wang et al., 2017; Makinen et al., 2018). \r\nExtreme canopy temperatures, rather than 2 m air temperatures, \r\nmay be a more robust biophysical indicator of heat impacts on crop \r\nproduction (Siebert et  al., 2017)"


id: 8f3d1f749503ecdfab1aa1fc05d63f23
created_at: 2023-03-16 14:02:09
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vector_id: 20080
content:
" Heat stress indices based upon \r\ntemperature and humidity determine livestock productivity as well \r\nas conception and mortality rates (Key et al., 2014; Dash et al., 2016; \r\nPragna et al., 2016; Rojas-Downing et al., 2017)."


id: f54e8762a99f90139406fe1d943c69b6
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
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vector_id: 36115
content:
"Frost (Tmin <0degC) is a  natural and fundamental aspect of many \r\necosystems, with more extreme conditions defined as ice (or icing) \r\ndays (Tmax <0degC) (L.A. Vincent et  al., 2018). Agricultural systems \r\nplanning (e.g., planting calendars, seed selection or the opportunity \r\nto double-crop) requires information about the start and end of the \r\nfrost-free season (Wypych et  al., 2017; Wolfe et  al., 2018). Crops \r\nand wild plants can be directly damaged by frost, but hard or killing \r\nfrosts (at a threshold several degrees below freezing) can kill crops \r\nor lower harvest quality depending on duration (which relates to \r\nsoil temperature penetration) and plant developmental stage (Crimp \r\net al., 2016a; Cradock-Henry, 2017; G. Li et al., 2018; Makinen et al., \r\n2018; Grotjahn, 2021). Earlier disappearance of snow cover reduces \r\nnatural insulation that protects plants and burrowing animals from \r\nhard frost damages (Trnka et al., 2014; Makinen et al., 2018)"


id: 3412823de62f50a7b645e716ae538e9
created_at: 2023-03-16 14:02:09
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vector_id: 5610
content:
" In some \r\ncases an early season warm spell may reduce plant hardiness or induce \r\nfruit tree flowering that exposes plants to devastating subsequent \r\nfrost impacts (Hufkens et  al., 2012; Hatfield et  al., 2014; Tripathi \r\net  al., 2016; Brunner et  al., 2018; DeGaetano, 2018; Unterberger \r\net al., 2018; Wolfe et al., 2018). Shifts in the seasonality of frozen soils \r\nalso affect groundwater recharge and surface streamflow for water \r\nresource applications, particularly when peak precipitation is shifted \r\nto a season that no longer has frozen soils (Jyrkama and Sykes, 2007)."


id: 5b416ef0b2904f488125fb6052a50e20
created_at: 2023-03-16 14:02:09
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vector_id: 11862
content:
"Figure  12.3 | Conceptual illustration of representative climatic impact\u0002driver thresholds showing how graduating thresholds affect successive \r\nsectoral assets and lead to potentially more acute hazards as conditions \r\nbecome more extreme (exact values are not shown as these must be \r\ntailored to reflect diverse vulnerabilities of regional assets)"


id: d24176b619c95dee70b349c3117fca33
created_at: 2023-03-16 14:02:09
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vector_id: 30606
content:
" Health: Tdeadly = temperature above which prolonged exposure may be deadly \r\n(often combined with humidity for heat indices); Tsevere = temperature above which \r\nprolonged exposure may cause elevated morbidity; T blooms = mean temperature for \r\nharmful algal or cyanobacteria blooms; Tdanger = level of dangerous cold temperatures \r\n(often combined with wind for chill indices); Toverwinter = temperature below which \r\ndisease vector species cannot survive winter. Ecosystems (CID indices for air \r\nand ocean temperature): Thotlim and Tcoldlim = limiting hot and cold temperatures for \r\na given species range; Tfrost = frost threshold; T max and T min = maximum and minimum \r\nsuitable annual mean temperatures for a  given species; Tcrit = critical temperature \r\nabove which a  given species is stressed"


id: 5ad0128104130b4f5b6bc97d7f21288a
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
content_type: text
index: climate_gpt_only_giec
vector_id: 11803
content:
" Agriculture: Thotlim = temperature above \r\nwhich a crop or livestock species dies; Thotpest = maximum (or 'lethal') temperature \r\nabove which an agricultural pest/disease/weed cannot survive; Tcrit = temperature at \r\nwhich productivity for a given crop is depressed; T opt = optimal mean temperature \r\nfor a given plant's productivity; GDDmin = threshold temperature for growing degree \r\ndays determining plant development; Tchill = temperature below which chilling units \r\nare accumulated; Tfrost = temperature below which frost occurs; Thfrost = temperature \r\nbelow which a  hard frost threatens crops or livestock; Tcoldpest = minimum winter \r\ntemperature below which a given agricultural pest cannot survive; Tcoldlim = minimum \r\ntemperature below which a given crop cannot survive."


id: eecfb4c9fc1f99fadceaa7d290313ef2
created_at: 2023-03-16 14:02:09
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index: climate_gpt_only_giec
vector_id: 35042
content:
"A large variety of climate indices and models are utilized to understand \r\nhow river flooding affects both natural or built environments with \r\nhighly variable hazard thresholds, given unique local topography \r\nand engineered defences such as dams and polders (Arnell and \r\nGosling, 2016; Ekstrom et al., 2018). Key transportation routes, built \r\ninfrastructure and agricultural lands are threatened when floods \r\nexceed design standards commonly based around flood magnitudes \r\nof a given historic return period (e.g., 1-in-100-year flood event), an \r\nannual exceedance probability or precipitation intensity-duration\u0002frequency relationships with key indices (e.g.,  10-day cumulative \r\nprecipitation) related to catchment size and properties (Hirabayashi \r\net al., 2013; Arnell and Lloyd-Hughes, 2014; Kundzewicz et al., 2014; \r\nArnell and Gosling, 2016; Dikanski et al., 2016; Gosling and Arnell, \r\n2016; Forzieri et al., 2017; Fluixa-Sanmartin et al., 2018; Koks et al., \r\n2019)"


id: bb32fed62b3b4cc36bef1193e83f8380
created_at: 2023-03-16 14:02:09
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vector_id: 26995
content:
" Floods and high-flow events can scour river beds and elevate \r\nsilt loads, reducing water quality and accelerating deposition in \r\nestuaries and reservoirs (Khan et al., 2018; Parasiewicz et al., 2019). \r\nFloods can knock down, drown or wash away crops and livestock, \r\nand partially submerged plants can have yield reduction depending \r\non water turbidity and their development stage (Ruane et al., 2013; \r\nShrestha et  al., 2019). Basin snowpack properties may also be \r\nimportant during heavy rain events, as rain-on-snow events can \r\nlead to rapid acceleration of flood stages that threaten wildlife and \r\nsociety (Hansen et al., 2014)."


id: ee0d45c4cf669aee8d2db667e0072fd9
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
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index: climate_gpt_only_giec
vector_id: 34932
content:
"Aridity indices may track long-term changes in precipitation, \r\nevapotranspiration demand, surface water, groundwater or soil \r\nmoisture (Sherwood and Fu, 2014; Herrera-Pantoja and Hiscock, 2015; \r\nB.I. Cook et al., 2020). Changes in soil moisture and surface water can \r\nshift the rate of carbon uptake by ecosystems (Humphrey et al., 2018) \r\nand alter suitable climate zones for wild species and agricultural \r\ncultivation (Feng and Fu, 2013; Garcia et  al., 2014; Huang et  al., \r\n2016a; Schlaepfer et al., 2017; Fatemi et al., 2018; IPCC, 2019c) as \r\nwell as the prevalence of related pests and pathogen-carrying vectors \r\n(Paritsis and Veblen, 2011; Smith et al., 2020). Water table depth, in \r\nrelation to rooting depth, is also important for farms and forests under \r\ndry conditions (Feng et  al., 2006)"


id: cb1924a03cbe3e206c271704e0af1997
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
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index: climate_gpt_only_giec
vector_id: 29514
content:
" A  reduction in water availability \r\n(via  aridity or hydrological drought) challenges water supplies \r\nneeded for for municipal, industrial, agriculture and hydropower use \r\n(Schaeffer et al., 2012; Arnell and Lloyd-Hughes, 2014; Schewe et al., \r\n2014; Gosling and Arnell, 2016; van Vliet et al., 2016)."


id: e32359efc7b18753e7d35fbe239f8e79
created_at: 2023-03-16 14:02:09
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vector_id: 33230
content:
"Heavy downpours can lead to pluvial flooding in cities, roadways, \r\nfarmland, subway tunnels and buildings (particularly those with \r\nbasements; Grahn and Nyberg, 2017; Palko, 2017; Pregnolato et al., \r\n2017; Orr et  al., 2018). Heavy precipitation may overwhelm city \r\ntransportation and storm water drainage systems, which are typically \r\ndesigned using intensity-duration-frequency information such as \r\nthe return periods for 1-, 6- or 24-hour rainfall totals (Kermanshah \r\net al., 2017; Depietri and McPhearson, 2018; Rosenzweig et al., 2018; \r\nCourty et al., 2019). Heavy rain events can directly cause leaf loss \r\nand damage, or knock over crops, also driving pollutant entrainment \r\nand erosion hazards in terrestrial ecosystems and farmland, with \r\ndownstream ramifications for water quality (Hatfield et  al., 2014; \r\nSegura et  al., 2014; Li and Fang, 2016; Chhetri et  al., 2019)"


id: 181977513ea83fae83cef4a68ca8870a
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
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index: climate_gpt_only_giec
vector_id: 1284
content:
" The \r\nproportion of total precipitation that falls in heavy events also \r\naffects the percentage that is retained in the soil column, altering \r\ngroundwater recharge and deep soil moisture content for agricultural \r\nuse (Fishman, 2016; Lesk et al., 2020)."


id: e2872b75578bc0c825693bd5ff90326b
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
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index: climate_gpt_only_giec
vector_id: 33146
content:
"Agricultural and ecological drought indices relate to the ability of \r\nplants to meet growth and transpiration needs (Table  11.3; Zargar \r\net al., 2011; Lobell et al., 2015; Pedro-Monzonis et al., 2015; Bachmair \r\net  al.,  2016; Wehner et  al., 2017; Naumann et  al., 2018) and the \r\ntiming and duration of droughts can lead to substantially different \r\nimpacts (Pena-Gallardo et  al., 2019). Drought stress for agriculture \r\nand ecosystems is difficult to directly observe, and therefore scientists \r\nuse a variety of drought indices (Table 11.3), proxy information about \r\nchanges in precipitation supply and reference evapotranspiration \r\ndemand, the ratio of actual/potential evapotranspiration or a deficit in \r\navailable soil water content, particularly at rooting level (Park Williams \r\net al., 2013; Trnka et al., 2014; C.D. Allen et al., 2015; Svoboda and \r\nFuchs, 2017; Makinen et al., 2018; Otkin et al., 2018)"


id: 3c3ab140c993eda20fee189ec8d98e55
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
content_type: text
index: climate_gpt_only_giec
vector_id: 6841
content:
" Severe water \r\nstress can lead to crop failure, in particular when droughts persist for \r\nan extended period or occur during key plant developmental stages \r\n(Hatfield et al., 2014; Jolly et al., 2015; Leng and Hall, 2019). Projections "


id: 30a4c9bceabecd74227de9209f76ab20
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
content_type: text
index: climate_gpt_only_giec
vector_id: 5075
content:
"Complex fire weather indices shed light on conditions that increase \r\nthe likelihood of wildfire and shifts in the fire season (Flannigan \r\net  al., 2013; Bedia et  al., 2015; Jolly et  al., 2015; Harvey, 2016; \r\nLittell et al., 2016; Westerling, 2016; Abatzoglou et al., 2019), which \r\npose particularly acute challenges for indigenous communities \r\n(Christianson and McGee, 2019). Projection of future lightning \r\nfrequency provides information on an important natural triggering \r\nmechanism, particularly when coupled with long-term warming \r\nand drying trends (Romps et al., 2014; Jin et al., 2015; Veraverbeke \r\net al., 2017). Fuel aridity metrics also help determine vegetative fuel \r\ndesiccation and therefore the ignitability, flammability and spread of \r\nfires when they occur (Abatzoglou and Williams, 2016). The presence \r\nof snow cover can influence the length of the fire season and the \r\npenetration of fire danger into new portions of the Arctic tundra \r\n(Young et al"


id: 4df7a812b5dc489e92cb04dbd9b8674f
created_at: 2023-03-16 14:02:09
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vector_id: 9687
content:
", 2017; Abatzoglou et al., 2019). Data on the changing \r\ncharacteristics of local wind circulations like the Santa Ana in \r\nCalifornia shed light on future intensity and spread patterns for fires \r\n(Jin et al., 2015). Fires also produce smoke plumes that reduce air and \r\nwater quality (via deposition), adversely affecting health, visibility \r\nand water resources both near and far downwind (Dennekamp and \r\nAbramson, 2011; McKenzie et  al., 2014; Dreessen et  al., 2016; Liu \r\net al., 2016; Martin, 2016)."


id: b473332fbef2c0b500f47b14687033d4
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
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index: climate_gpt_only_giec
vector_id: 25916
content:
"Sand and dust storms erode soils, damage crops and induce \r\nproblems for health, transportation, mechanical equipment and \r\nbuilt infrastructure corresponding to the magnitude and duration \r\nof high winds and particulate matter concentrations (Goudie, 2014; \r\nO'Loingsigh et al., 2014; Crooks et al., 2016; Barreau et al., 2017; \r\nBhattachan et  al., 2018; Al Ameri et  al., 2019; Middleton et  al., \r\n2019). Dust events may be represented as the number of dust hours \r\nper year and by particulate matter (PM) concentrations (Leys et al., \r\n2011; Spickett et al., 2011; Hand et al., 2016). Photovoltaic panels \r\ncan lose energy production efficiency with dust accumulation \r\n(Patt et al., 2013; Javed et al., 2017). It is also useful to track dust \r\nstorm deposition of nutrients necessary for coral and tropical forest \r\nsystems, but they may also feed algal blooms harming lake and \r\ncoastal ecosystems, health and recreation (Jickells et  al., 2005; \r\nHallegraeff et al., 2014; Gabric et al"


id: b398bb5f82076aabc22e8f45ea591788
created_at: 2023-03-16 14:02:09
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vector_id: 25766
content:
", 2016). Dust storms also cause \r\nair pollution and redistribute the soil-based fungus associated with \r\nValley fever (Barreau et  al., 2017; Coopersmith et  al., 2017; Tong \r\net al., 2017; Gorris et al., 2018)."


id: 44da6d02804ef1800471531dc189cffd
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
content_type: text
index: climate_gpt_only_giec
vector_id: 8255
content:
"Changes in the speed and direction of prevailing winds can alter \r\nthe profile of seed dispersal, windblown pest and disease vectors, \r\nanimal activities, and dust or pollen dispersal affecting ecosystems, \r\nagriculture and human health (Reid and Gamble, 2009; Bullock et al., \r\n2012; Hellberg and Chu, 2016; Nourani et al., 2017). Seasonal winds \r\ninfluence algal blooms, ecosystems and fisheries via lake mixing, \r\nocean currents and coastal upwelling (Bakun et al., 2015; Townhill \r\net  al., 2018; Woolway et  al., 2020). Changes to wind density also \r\nmodify a  region's wind and wave renewable energy endowment \r\n(Schaeffer et al., 2012; Sierra et al., 2017; Craig et al., 2018; Devis \r\net al., 2018; Tobin et al., 2018; Yalew et al., 2020). D. Li et al. (2020) \r\nand Karnauskas et al. (2018a) evaluated wind thresholds at turbine \r\nheight (about 80-100 m above ground) including periods outside of \r\ncut-in (2"


id: 799f09982fb3dcae931b89ce1c53d738
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
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index: climate_gpt_only_giec
vector_id: 16703
content:
"A large number of indices have been used in water resource and \r\necosystem studies to track changes in snow under current and future \r\nclimate conditions, including measurements of the snow water \r\nequivalent at key seasonal dates, the fraction of precipitation falling \r\nas snow, the first and last days of snow cover, and cold season \r\ntemperatures (Mills et al., 2013; Pierce and Cayan, 2013; Berghuijs \r\net al., 2014; Klos et al., 2014; Musselman et al., 2017; Rhoades et al., \r\n2018). Impact studies also examine shifts in seasonal streamflow \r\nfor snow-fed river basins (Mote et al., 2005; Pederson et al., 2011; \r\nBeniston and Stoffel, 2014; Coppola et al., 2014b, 2018; Fyfe et al., \r\n2017; Islam et  al., 2017; Knouft and Ficklin, 2017) as well as the \r\ngeographic extent of snow cover and the depth of frosts when \r\nsnow cover's natural insulation is absent (Scheurer et  al., 2009; \r\nMillar and Stephenson, 2015)"


id: 7f86cb627719148bf603bf1fa0910478
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
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index: climate_gpt_only_giec
vector_id: 17611
content:
" Studies examining the impact of \r\nsnow changes on winter recreation and transportation have used \r\nthresholds of about 30 cm snow depth or snow water equivalent \r\n>10 cm to determine the length of the season for alpine and cross\u0002country skiing and snowmobiling (Damm et al., 2017; Wobus et al., \r\n2017b; Spandre et al., 2019; Steiger et al., 2019; Abegg et al., 2021). \r\nChanges in snow quality also affect recreational activities (Rutty \r\net  al., 2017), and artificial snowmaking can augment recreational \r\nsnowpack depending on the number of suitable snowmaking \r\nhours (e.g.,  where wet bulb globe temperature (WBGT) <-2.2degC; \r\nWobus et al., 2017b). Local detail may also be provided by tracking \r\nthe seasonal rain-snow transition line across space and elevation \r\n(Berghuijs et  al.,  2014) (Pierce and Cayan, 2013; Berghuijs et  al., \r\n2014; Klos et al., 2014; Musselman et al., 2017)."


id: fb55bcc72bd3264b2003b11f03c84a6b
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vector_id: 37051
content:
"Reductions in the duration of thick sea, lake and river ice influence \r\necosystems as well as ice fishing, hunting, dog sledding and \r\nsnowmobiling, which are recreation activities for some but vital aspects \r\nof many traditional indigenous communities (Durkalec et  al.,  2015; \r\nAMAP, 2017; Baztan et  al., 2017; Arp et  al., 2018; Rokaya et  al., \r\n2018; Knoll et al., 2019; Meredith et al., 2019; Sharma et al., 2019). \r\nThe seasonal extent of thin ice and iceberg density also determines the \r\nviability of shipping lanes and seasonal roads (Valsson and Ulfarsson, \r\n2011; Pizzolato et al., 2016; AMAP, 2017; Mullan et al., 2017; Sturm \r\net  al., 2017), oil and gas exploration timing (Schaeffer et  al., 2012) \r\nand the seasonality of phytoplankton blooms (Oziel et al., 2017). Sea \r\nice is a critical aspect of some ecosystems and fisheries (Massom and \r\nStammerjohn, 2010; Jenouvrier et al., 2014; Bindoff et al., 2019; Meredith \r\net al., 2019)"


id: be7aecc3d326e5a678065e108c94be0f
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vector_id: 27519
content:
" Various definitions of 'ice free' Arctic Ocean conditions can \r\nbe tailored to represent transportation needs, including thresholds of \r\nice coverage (<5% or <30% or <1 million km2) in September or over \r\na four-month period (Laliberte et al., 2016; Jahn, 2018)."


id: 45f8614a07092fadff517f5c3395fae4
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
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vector_id: 8395
content:
"Effective management of coastal ecosystems, cities, settlements, \r\nbeaches and infrastructure requires information about coastal \r\nerosion driven by storm surge, waves and sea level rise (Dawson \r\net al., 2009; Hinkel et al., 2013; Harley et al., 2017; Mentaschi et al., \r\n2017). Coastal erosion is generally accompanied by shoreline retreat, \r\nwhich can occur as a  gradual process (e.g.,  due to sea level rise) \r\nor as an episodic event due to storm surge and/or extreme waves, \r\nespecially when combined with high tide (Ranasinghe, 2016). The \r\nmost commonly used shoreline retreat index is the magnitude of \r\nshoreline retreat by a  pre-determined planning horizon such as \r\n50 or 100 years into the future. Commonly used metrics for episodic \r\ncoastal erosion include the beach erosion volume due to the 100-year \r\nrecurrence storm wave height, the full exceedance probability \r\ndistribution of coastal erosion volume (Li et al., 2014a; Pender et al"


id: 632aaba9cc1c135a4eb67311230e62f1
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
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vector_id: 13170
content:
", \r\n2015; Ranasinghe and Callaghan, 2017) and the cumulative storm \r\nenergy and storm power index (Godoi et al., 2018). The destruction \r\nor overtopping of barrier islands may lead to irreversible changes in \r\nthe physical system as well as in coastal ecosystems (Carrasco et al., \r\n2016; Zinnert et  al., 2019). Shoreline position change rates along \r\ninlet-interrupted coasts may also be affected by changes in river \r\nflows and fluvial sediment supply (Hinkel et al., 2013; Bamunawala \r\net al., 2018; Ranasinghe et al., 2019). Permafrost thaw and Arctic sea \r\nice decline also reduce natural coastal protection from wave erosion \r\nfor communities and industry (Forbes, 2011; Melvin et al., 2017)."


id: 71381c5b81ccbf152c4522f190eb5085
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
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vector_id: 15449
content:
"Sea level rise hazards for coastal ecosystems, infrastructure, farmland, \r\ncities and settlements in a  particular region are often driven by \r\nregional changes in relative sea level (RSL) that account for land \r\nuplift or subsidence and thus represent local asset vulnerability better \r\nthan global mean sea level (Box 9.1; Hallegatte et al., 2013; Hinkel \r\net al., 2013; McInnes et al., 2016; Weatherdon et al., 2016; Brown et al., \r\n2018; IPCC, 2019b; Rasoulkhani et  al., 2020). Vertical land motion \r\n(i.e.,  land subsidence) caused by local fluid (gas or groundwater) \r\nextraction can also have a  large influence on relative sea levels \r\n(Minderhoud et  al., 2020)"


id: 29dbe27425c7b37f0d763d4fe07d4759
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
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index: climate_gpt_only_giec
vector_id: 4014
content:
" Several indices have been suggested to \r\nsignify coastal inundation, including a threshold when the local land \r\nelevation falls below the local mean higher high water (MHHW) that \r\nis close to the 'high tide' level (Kulp and Strauss, 2019) or a threshold \r\nwhen flooding occurs about once every two weeks (Sweet and Park, \r\n2014; Dahl et al., 2017b). RSL rise (or RSLR) can drive increased inland \r\npenetration of above-ground and subterranean salt water fronts \r\n(i.e., salinity intrusion) affecting coastal ecosystems, agriculture and \r\nwater resources (Ferguson and Gleeson, 2012; Kirwan and Megonigal, \r\n2013; Rotzoll and Fletcher, 2013; Chen et al., 2016; Colombani et al., \r\n2016; Holding et  al., 2016; Sawyer et  al., 2016; Mohammed and \r\nScholz, 2018). The rate of RSLR can determine the survival and net \r\npressure on niche coastal ecosystems such as mangroves, tidal flats, \r\nsea grasses and coral reefs (Hubbard et al., 2008; Craft et al., 2009; \r\nBell et al"


id: aed8bcba396e7ec3d29ab27ba8989362
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
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vector_id: 25044
content:
", 2013; Kirwan and Megonigal, 2013; Alongi, 2015; Ellison, \r\n2015; Lovelock et al., 2015; Ward et al., 2016; Lee et al., 2018)."


id: d08868ce76e90ff0736156f754b643d6
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
content_type: text
index: climate_gpt_only_giec
vector_id: 30333
content:
"Marine heatwaves (MHW) push water temperatures above key \r\nthresholds and have been associated with coral bleaching episodes, \r\nspecies shifts and harmful algal blooms that can disrupt ecosystems, \r\ntourism and human health (Box  9.2; Wernberg et  al., 2016; Arias\u0002Ortiz et  al., 2018; Oliver et  al., 2018; Frolicher, 2019; Smale et  al., \r\n2019; Sully et al., 2019). The duration and return period of marine \r\nheatwaves provide insight into aggregate stresses on marine species, \r\nfisheries and ecosystems, with various indices gauging cumulative \r\nintensity or the number of days, weeks or months exceeding critical \r\nthresholds (Frieler et al., 2013; Frolicher et al., 2018; Hughes et al., \r\n2018b; Cheung and Frolicher, 2020). Hobday et  al. (2016) defined \r\nmarine heatwaves as the exceedance of the 90th percentile of the sea \r\nsurface temperature (SST) distribution on a given Julian day during \r\nfive or more consecutive days, while Box 9"


id: 81f2ca4c72205a9e84c7497f579f9ba8
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
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index: climate_gpt_only_giec
vector_id: 17967
content:
"2, Figure 1 shows MHW \r\nas an exceedance of 99th-percentile 11-day de-seasonalized SSTs. \r\nThe return period of marine heatwaves is also critical in determining \r\na  coral system's ability to recover before the next event (Hughes \r\net al., 2018a)."


id: 97641be00294365313ae5d8badd2d3cb
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
content_type: text
index: climate_gpt_only_giec
vector_id: 21346
content:
"Although future air pollution will be strongly driven by air quality \r\npolicies, anthropogenically-driven changes to temperature, humidity, \r\nprecipitation and synoptic patterns have the potential to affect the \r\nemissions, production, concentration and transport of particulate matter \r\n(e.g., from dust, fires, pollen) and gaseous pollutants such as sulphur \r\ndioxide, tropospheric ozone and nitrogen dioxide (Section  6.5) with \r\nresulting impacts on human health, agriculture and ecosystems (Ren \r\net al., 2011; Fiore et al., 2015; Kinney et al., 2015a; Tian et al., 2016; \r\nOrru et al., 2017; Emberson et al., 2018; Hayes et al., 2020). Information \r\nabout conditions leading to poor air quality is also important for \r\nvisibility in natural parks and tourist locations (Yue et  al., 2013; Val \r\nMartin et al., 2015), as well as the efficiency of solar photovoltaic panels \r\n(Sweerts et al., 2019)"


id: 12392a6b4645a99c8711aa453d608116
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
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vector_id: 369
content:
" Relevant information about conditions favouring \r\nair pollution includes tracking warmer conditions that accelerate ozone \r\nformation (Peel et al., 2013; Schnell et al., 2016) and the frequency and \r\nduration of stagnant air events (Horton et al., 2014; Fann et al., 2015; \r\nLelieveld et al., 2015; Vautard et al., 2018), although no regional index \r\nhas proven sufficient to capture regional changes or acute events (Kerr \r\nand Waugh, 2018; Schnell et al., 2018). By contrast, precipitation and \r\nmoister air tend to reduce pollution (Section 6.5)."


id: 4471956201579d5a1a796342d5ec891
created_at: 2023-03-16 14:02:09
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vector_id: 8190
content:
"Carbon dioxide (CO2) is a  well-mixed greenhouse gas with global \r\nrepercussions on Earth's energy balance; however, atmospheric \r\nCO2 concentration changes at the land surface also affect plant \r\nfunctions within ecosystems and agriculture (see also Chapter  5). \r\nHigh CO2 concentration can increase photosynthesis rates and \r\nprimary production within natural ecosystems (Norby et  al., 2010; \r\nRatliff et al., 2015; Zhu et al., 2016) and agricultural crops (Hatfield \r\net al., 2011; Leakey et al., 2012; Bell et al., 2013; Glenn et al., 2014; \r\nNagelkerken and Connell, 2015; Behrenfeld et al., 2016; Deryng et al., \r\n2016; Kimball, 2016). High CO2 concentration affects total biomass \r\nand plant sugar content important to bioenergy production (Schaeffer \r\net al., 2012), but also helps some pests and weeds flourish (Hamilton \r\net  al., 2005; Wolfe et  al., 2008; Valerio et  al., 2013; Korres et  al., \r\n2016; Stinson et  al., 2016; Ramesh et  al"


id: 5f9625f22fde46653f8388b354a0b88c
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vector_id: 12616
content:
", 2017), while potentially \r\nshifting the effectiveness of herbicides (Varanasi et al., 2016; Refatti \r\net al., 2019). Higher CO2 concentration reduces transpiration losses \r\nduring drought conditions (Cammarano et al., 2016; Deryng et al., \r\n2016; Swann et al., 2016; Durand et al., 2018), which also changes \r\nthe energy balance within the plant canopy (Webber et al., 2017). \r\nHigher CO2 reduces the nutritional density of crops and forage lands \r\n(Loladze, 2014; Muller et al., 2014; Myers et al., 2014, 2017; X. Li \r\net al., 2016; M.A. Lee et al., 2017; Smith and Myers, 2018; Zhu et al., \r\n2018; Beach et al., 2019) and can increase the production of toxins \r\n(Ziska et al., 2007) and allergenic pollen (Schmidt, 2016)."


id: 65301ac9b341266a92deacbaa0b2c634
created_at: 2023-03-16 14:02:09
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vector_id: 13487
content:
"Regional assessment method and tables: In each section herein \r\n(Sections 12.4.1-12.4.10), we assess changes in sector-relevant CIDs \r\nfollowing the main CID categories defined in Section  12.2 through \r\ncommonly used indices and thresholds relevant for sectors described in \r\n12.3. Sections 12.4.1-12.4.9 each include a summary qualitative CID \r\nassessment table (Tables 12.3-12.11) showing the confidence levels \r\nassociated with the direction of projected CID changes (i.e., increasing \r\nor decreasing) for the mid-century period (2041-2060) relative to \r\nthe recent past, for scenarios RCP4.5, SSP2-4.5, SRES A1B, or above \r\n(RCP6.0, RCP8.5, SSP3-7.0, SSP5-8.5, SRES A2), which approximately \r\nencompasses GWLs of 2.0degC to 2.4degC (as best estimate; see \r\nChapter 4, Table 4.5). For scenarios RCP2.6, SSP1-2.6 or SSP1-1"


id: 5384370b97dc8b817821176c0683722e
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
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index: climate_gpt_only_giec
vector_id: 10608
content:
"9, the \r\nsignal may have lower confidence levels in some cases due to smaller \r\noverall changes, embedded in a similar internal variability, and to the \r\navailability of relatively few studies that account for these scenarios. \r\nNevertheless, CID changes under these lower emissions scenarios are \r\nincluded in the text whenever information is available. For each cell in \r\nTables 12.3-12.11, literature is assessed, aided by global Figure 12.4 or \r\nregional Figures 12.5-12.10. Confidence in projections is established \r\nconsidering evidence emerging from observations, attribution and \r\nprojections, as explained in Cross-Chapter Box 10.3 while considering \r\nthe amount of evidence and agreement across models and studies \r\nand model generations."


id: 7bd00584cd098f081e26e689e44090e8
created_at: 2023-03-16 14:02:09
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content:
"Threats to many sectoral assets and associated systems may also be \r\ncompounded when multiple hazards occur simultaneously in the same \r\nplace, affect multiple regions at the same time, or occur in a sequence \r\nthat may amplify overall impact (Section  11.8; IPCC, 2012; Clarke \r\net al., 2018; Zscheischler et al., 2018; Raymond et al., 2020). There is \r\nemerging literature on many connected extremes and their associated \r\nhazards (e.g.,  climatic conditions that could drive multi-breadbasket \r\nfailures; Trnka et al., 2019; Kornhuber et al., 2020), but a full accounting \r\nis not practical here especially considering the many possible CID \r\ncombinations and the need to assess how exposed systems would be \r\nvulnerable to compound CIDs (assessed in WGII). Table 12.2 is once \r\nagain instructive here in considering hazard-related storylines, as the \r\nmultiple CIDs affecting a given sectoral asset (assessing across a row \r\nof Table  12.2) point to potentially dangerous hazard combinations"


id: 9eb604d68fd66458ec65cf119b98f474
created_at: 2023-03-16 14:02:09
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vector_id: 22507
content:
" \r\nSimilarly, change in a single CID has the potential to affect multiple \r\nsectoral assets (assessing down a column of Table 12.2) in a manner \r\nwith broader systemic implications (AR6 WGII)."


id: 6796aa1adf383da496e931437b1e9460
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
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vector_id: 13886
content:
"The confidence levels associated with the directions of projected CID \r\nchanges are synthesized assessments based on literature that may \r\nutilize different indices and baseline periods or projections by GWLs. \r\nFor extreme heat, cold spell, heavy precipitation and drought CIDs that \r\nare assessed in Chapter 11, here we draw projections from the 2degC \r\nGWL tables in Section 11.9. In some cases, more details are needed in \r\norder to emphasize one aspect of projected CID change. For instance, \r\nthe change in a CID may be different for intensity, duration, frequency; \r\nor there can be strong sub-regional or seasonal signals; or different \r\nCID indices may have conflicting signals. A footnote is added in such \r\ncases, but a confidence level for a direction of projected change is \r\ngiven based on the Section 12.3 assessment of aspects of regional \r\nCID change that are most relevant for impacts and for risks"


id: 80377be98971df2a040571e402841b63
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
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vector_id: 17701
content:
" As an \r\nexample, tropical cyclones are increasing in intensity but decreasing \r\nin frequency in some regions. Here, in assessing the confidence of \r\nthe direction of projected change in the Tropical cyclone CID (i.e., the \r\ncolour of the table cell), we assign more weight to the 'intensity' \r\nrather than the 'frequency', corresponding to the higher relevance \r\nof the intensity of major tropical cyclones for risk assessment. Low \r\nconfidence of changes, arising from lack of evidence, strong spatial \r\nor seasonal heterogeneity, or lack of agreement, are represented by \r\ncolour-less cells, and, for the sake of simplicity, only two categories \r\nof confidence are given: medium confidence and  high  confidence\r\n(and higher). In addition, CID assessment tables also indicate observed "


id: 1041b80ba7d3dbbbcc4f25c961d8a134
created_at: 2023-03-16 14:02:09
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content:
"Figure 12.4 (continued): (a-c) Mean number of days per year with maximum temperature exceeding 35degC; (d-f) mean number of days per year with the NOAA Heat \r\nIndex (HI) exceeding 41degC; (g-i) number of negative precipitation anomaly events per decade using the six-month Standardized Precipitation Index; (j-l) mean soil moisture \r\n(%) and (m-o) mean wind speed (%). (p-r) shows change in extreme sea level (1-in-100-year return period total water level from Vousdoukas et al. (2018)'s CMIP5 based \r\ndataset; metres). Left-hand column is for SSP1-2.6, 2081-2100; middle column is for SSP5-8.5 2041-2060; and right-hand column SSP5-8.5, 2081-2100, all expressed as \r\nchanges relative to 1995-2014. Exception is extreme total water level which is for (p) RCP4.5 2100, (q) RCP8.5 2050 and (r) RCP8.5 2100, each relative to 1980-2014. Bias \r\ncorrection is applied to daily maximum temperature and HI data (Atlas.1.4.5)"


id: e8aeac2a1a230aff1d2804b0d8875c59
created_at: 2023-03-16 14:02:09
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index: climate_gpt_only_giec
vector_id: 34120
content:
" Uncertainty is represented using the simple approach: no overlay indicates regions with high \r\nmodel agreement, where >=80% of models agree on the sign (direction) of change; diagonal lines indicate regions with low model agreement, where <80% of models agree on \r\nthe sign of change. For more information on the simple approach, please refer to the Cross-Chapter Box Atlas.1. See Annex VI for details of indices. Figures 12.SM.1-12.SM.6 \r\nshow regionally averaged values of these indices for the AR6 WGI Reference Regions for various model ensembles, scenarios, time horizons and global warming levels. Further \r\ndetails on data sources and processing are available in the chapter data table (Table 12.SM.1)."


id: 51947a1d40991feaa3b35f90851c15d2
created_at: 2023-03-16 14:02:09
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index: climate_gpt_only_giec
vector_id: 10290
content:
"future ocean acidification will increase given future increases in \r\ngreenhouse gases (Section  5.4). Areas below calcium carbonate \r\nsaturation thresholds expanded from the 1990s to 2010 and Meredith \r\net al. (2019) indicated that both the Southern and Arctic Oceans will \r\nexperience year-round under-saturation conditions by 2100 under \r\nRCP8.5. The vertical level of the aragonite saturation horizon off \r\nthe Pacific coast of North America has risen towards the surface by \r\n30-50 m since pre-industrial times (Mathis et al., 2015b; Feely et al., \r\n2016). In a study of US coastlines, Ekstrom et al. (2015) mapped out \r\nthe projected year when aragonite saturation state drops below 1.5 \r\n(a sublethal threshold for bivalve mollusc larvae), finding hazardous \r\nconditions before 2030 from northern Oregon to Alaska and before \r\n2100 for the Pacific coast and Atlantic coastline north of New Jersey. \r\nMathis et  al"


id: c4bbc6c3bb8681697f7fb707a78cf705
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updated_at: 2023-03-16 14:02:09
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index: climate_gpt_only_giec
vector_id: 28526
content:
" (2015a) found that surface waters in the Beaufort \r\nSea have already dropped below aragonite saturation thresholds, \r\nprojecting further declines and the Chukchi Sea also dropping below \r\nsaturation by about 2030."


id: fc5732bee07cb04b4c57cf5eadebb5f7
created_at: 2023-03-16 14:02:09
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index: climate_gpt_only_giec
vector_id: 37203
content:
"Air pollution weather: The effect of climate change on air quality is \r\nassessed in Section 6.5 with limitations for local planning explained \r\nin Section 6.1.3, and only a brief summary is given here. Section 6.5 \r\nnotes that climate change will have a  small burden on particulate \r\nmatter (PM) pollution (medium confidence) while the main controlling \r\nfactor in determining future concentrations will be future emissions \r\npolicy for PM and their precursors (high confidence). Surface ozone is \r\nsensitive to temperature and water vapour changes, but future levels \r\ndepend on precursor emissions. Although there is low confidence in \r\nprecise regional changes (Section 6"


id: 56ce48bc3fb449c4cbe4b5f5db2b03ad
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
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index: climate_gpt_only_giec
vector_id: 11133
content:
"5), climate change will generally \r\nintroduce a surface ozone (O3) penalty (increasing concentrations with \r\nincreasing warming levels) over regions with high anthropogenic and/\r\nor natural ozone precursor emissions, while in less polluted regions \r\nhigher temperatures and humidity favour destruction of ozone (Schnell \r\net al., 2016). There is low confidence in changes to future stagnation \r\nevents given the lack of robust projections of related atmospheric \r\nconditions, such as future atmospheric blocking events (Sections 3.3.3 \r\nand 8.4.2). The response of regional air pollution to climate change \r\nwill also be affected by other CIDs like fire weather, as well as by \r\necosystem responses such as shifts in emissions by vegetation (Fiore \r\net al., 2015). Section 6.5 assessed medium confidence that climate\u0002driven changes to meteorological conditions generally favour \r\nextreme air pollution episodes in heavily polluted environments, but \r\nnoted strong regional and metric dependencies"


id: ea0c9a14ab2105d2758aba17984b8750
created_at: 2023-03-16 14:02:09
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index: climate_gpt_only_giec
vector_id: 34318
content:
" Given the dominant \r\ninfluence of future air quality policies, uncertainties around stagnation \r\nor blocking events, and the potential contrasting regional changes of \r\nconditions favouring ozone and PM formation, accumulation and \r\ndestruction, cells in Tables 12.3-12.11 for air pollution weather are \r\nmarked as low confidence, and the reader is referred to Section 6.5 \r\nfor further details."


id: f0406abf8035b1fa0c1f93e709b870b1
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
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index: climate_gpt_only_giec
vector_id: 35301
content:
"Atmospheric CO2 at surface: Observations show rising atmospheric \r\nCO2 concentrations at the surface over all Earth regions (robust \r\nevidence, high agreement) (Sections 2.2 and 5.1.1), and it is virtually \r\ncertain that surface atmospheric CO2 concentrations will continue to \r\nincrease without substantial changes to emissions (Section 5.4).\r\nRadiation at surface: Radiation has undergone decadal variations \r\nin past observations, which are mostly responding to the so-called \r\ndimming and brightening phenomenon driven by the increase and \r\ndecrease of aerosols. Over the last two decades or so, brightening \r\ncontinues in Europe and North America and dimming stabilizes \r\nover South and East Asia and increases in some other areas \r\n(Section  7.2.2.3). Future regional shortwave radiation projections \r\ndepend mostly on cloud trends, aerosol and water vapour trends, \r\nand stratospheric ozone when considering UV radiation"


id: 15907b7d8d172e91ba921523da1f1cc9
created_at: 2023-03-16 14:02:09
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vector_id: 907
content:
" Over Africa \r\nin 2050 and beyond, there is medium confidence that radiation will \r\nincrease in North and South Africa and decrease over the Sahara, \r\nNorth Eastern Africa and Western Africa (Wild et  al., 2015, 2017; \r\nSoares et  al., 2019; C. Tang et  al., 2019; Sawadogo et  al., 2020, \r\n2021). Over Asia, the CMIP5 multi-model mean response shows \r\nthat solar radiation will decrease in South Asia and increase in East \r\nAsia (medium confidence) by the mid-century RCP8.5 (Wild et  al., \r\n2015, 2017; Ruosteenoja et  al., 2019b). Projected solar resources \r\nshow an increasing trend throughout the 21st century in East Asia \r\nunder RCP2.6 and RCP8.5 scenarios in CMIP5 simulations (medium \r\nconfidence) (Wild et al., 2015; F. Zhang et al., 2018; Shiogama et al., \r\n2020). More sunshine is projected over Australia in winter and spring \r\nby the end of the century (medium confidence) with the increases \r\nin Southern Australia exceeding 10% (CSIRO and BOM, 2015; \r\nWild et  al., 2015)"


id: 9a4c94a116224a5232f3636621183947
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
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vector_id: 21804
content:
" In  Central and South America, there is medium \r\nconfidence of increasing solar radiation over the Amazon basin and \r\nthe northern part of South America (medium confidence) (Wild et al., \r\n2015, 2017; de Jong et  al., 2019). There is low confidence for an \r\nincrease in surface radiation in central Europe, owing in particular to \r\ndisagreement in cloud cover across global and regional models (Jerez \r\net al., 2015; Bartok et al., 2017; Craig et al., 2018), as well as water \r\nvapour. The treatment of aerosol appears to be key in explaining these \r\ndifferences (Boe, 2016; Undorf et al., 2018; Boe et al., 2020; Gutierrez \r\net al., 2020). Regional and global studies, however, indicate that there \r\nis medium confidence in increasing radiation over southern Europe \r\nand decreasing radiation over Northern Europe. Increasing radiation \r\ntrends are also found over southern and eastern USA, and decreasing \r\ntrends over North-Western North America (Wild et al., 2015; Losada \r\nCarreno et al"


id: 15c6a138fcc7520dfbfb5e34793928b
created_at: 2023-03-16 14:02:09
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vector_id: 946
content:
", 2020), despite large differences between responses \r\nfrom regional climate models (RCMs) and general circulation models \r\n(GCMs) over southern and eastern USA (low confidence), where, as \r\nfor Central Europe, the role of aerosols appears important (Chen, \r\n2021). Over polar regions there is medium confidence of a decrease \r\nin radiation due to increasing moisture in the atmosphere and clouds \r\n(Wild et al., 2015)."


id: e7ee55d64fe91f861274a21bb6a7a7e
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
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index: climate_gpt_only_giec
vector_id: 33986
content:
"Figure 12.5 (continued): (a) Mean change in 1-in-100-year river discharge per unit catchment area (Q100, m3 s-1 km-2) from CORDEX-Africa models for 2041-2060 \r\nrelative to 1995-2014 for RCP8.5. (b) Shoreline position change along sandy coasts by the year 2100 relative to 2010 for RCP8.5 (metres; negative values indicate shoreline \r\nretreat) from the CMIP5-based dataset presented by Vousdoukas et al. (2020b). (c) Bar plots for Q100 (m3 s-1 km-2) averaged over land areas for the AR6 WGI Reference \r\nRegions (defined in Chapter 1). The left-hand column within each panel (associated with the left-hand y-axis) shows the 'recent past' (1995-2014) Q100 absolute values in \r\ngrey shades. The other columns (associated with the right-hand y-axis) show the Q100 changes relative to the recent past values for two time periods ('mid' 2041-2060 and \r\n'long' 2081-2100) and for three global warming levels (GWLs, defined relative to the pre-industrial period 1850-1900): 1"


id: 1abfac9056ba4b2682e01d00eb883ad1
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
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vector_id: 1680
content:
"5degC (purple), 2degC (yellow) and 4degC (brown). The \r\nbars show the median (dots) and the 10-90th percentile range of model ensemble values across each model ensemble. CMIP6 is shown by the darkest colours, CMIP5 by \r\nmedium, and CORDEX by light. SSP5-8.5/RCP8.5 is shown in red and SSP1-2.6/RCP2.6 in blue. (d) Bar plots for shoreline position change show CMIP5-based projections of \r\nshoreline position change along sandy coasts for 2050 and 2100 relative to 2010 for RCP8.5 (red) and RCP4.5 (blue) from Vousdoukas et al. (2020b). Dots indicate regional \r\nmean change estimates and bars show the 5-95th percentile range of associated uncertainty. Note that these shoreline position change projections assume that there are no \r\nadditional sediment sinks/sources or any physical barriers to shoreline retreat. See Technical Annex VI for details of indices. Further details on data sources and processing are \r\navailable in the chapter data table (Table 12.SM.1)."


id: 2e1e03c38644bda0b730da45bb41be83
created_at: 2023-03-16 14:02:09
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vector_id: 4698
content:
"Mortality-related heat stress levels and deadly temperatures are very\r\nlikely to become more frequent in the future in RCP8.5/SSP5-8.5 and \r\nRCP4.5/SSP2-4.5 and for a  2degC global warming (Mora et  al., 2017; \r\nNangombe et al., 2018; Sylla et al., 2018a; Rohat et al., 2019; Q. Sun \r\net al., 2019). In particular the equatorial regions where heat is combined \r\nwith higher humidity levels, but also North Africa, the Sahel and Southern \r\nAfrica (Figure 12.4d-f) are among the regions with the largest increases \r\nof heat stress (Zhao et al., 2015; Ahmadalipour and Moradkhani, 2018; \r\nCoffel et  al., 2018). Mitigation scenarios make a  large difference in \r\nfrequency of exceedance of high heat stress indices thresholds (e.g., HI \r\n> 41degC) by the end of the century (Figure 12.4d-f; Schwingshackl et al., \r\n2021). In West Africa and Central Africa, under SSP5-8.5, the expected \r\nnumber of days per year with HI > 41degC will increase by around 200 \r\ndays while in SSP1-2"


id: 5a313d844cc7606756486c615908449f
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
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index: climate_gpt_only_giec
vector_id: 11699
content:
"6 such exceedances are expected to increase by \r\nless than 50 days per year (Figure 12.4; Figure 12.SM.2)."


id: 5293bdba2c03bf5b0cad6f78683547c8
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
content_type: text
index: climate_gpt_only_giec
vector_id: 10452
content:
"for the A1B scenario (medium confidence) (Figure 12.5; Arnell and \r\nGosling, 2013). Specifically, in Western Africa there is not a univocal \r\npattern of change for future projections (Roudier et  al., 2014); \r\nHowever, under RCP8.5, there is medium confidence of a projected \r\nincrease of 20-year flood magnitudes by 2050 in countries within \r\nthe Niger River basin (Aich et al., 2016b) and low confidence (limited \r\nevidence) of an increase in extreme peak flows and their duration in \r\ncountries of the Volta River basin by 2050 and 2090 (Jin et al., 2018). \r\nA significant median change of flood magnitude for the Gambia River \r\n(-4.5%) and for the Sessandra (+14.4%) and Niger (+6.1%) are \r\nprojected under several scenarios between mid- and end-of-century \r\n(Roudier et al., 2014). In East Africa, extreme flows are projected to \r\nincrease for regions within the Blue Nile basin with low confidence\r\n(limited evidence) (Aich et  al., 2014)"


id: a5b4b5b099467520e7196a3d4a813b0a
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
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vector_id: 23639
content:
" However, uncertainty due to \r\nthe climate scenario dominates the projection of extreme flows (Aich \r\net al., 2014; Krysanova et al., 2017) for the Blue Nile and Niger River \r\nbasins. Averaged over the African continent for different levels of \r\nglobal warming, the present-day 100-year return period flood levels \r\nwill have a return period of 40 years in 1.5degC and 2degC (Alfieri et al., \r\n2017) and 21 years for 4degC warmer climate (Hirabayashi et al., 2013; \r\nAlfieri et al., 2017)."


id: a88487dddc885c7e6e7143507183ddfa
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
content_type: text
index: climate_gpt_only_giec
vector_id: 24039
content:
"Agricultural and ecological drought: Farmers and food security \r\nexperts in East Africa have noted spatial extensions in seasonal \r\nagricultural droughts in recent decades (Elagib, 2014), but it is difficult \r\nto disentangle these trends from climate variability. In Ethiopia, past \r\nsevere agricultural drought conditions in the northern regions are \r\nmoderately common events in recent years (Zeleke et al., 2017). In \r\nSouthern Africa, the number of 'flash' droughts (with rapid onset and \r\ndurations from a few days to couple of months) have increased by \r\n220% between 1961 and 2016 as a result of anthropogenic warming \r\n(Yuan et al., 2018). Section 11.9 notes medium confidence increases \r\nin agricultural and ecological drought trends in North, Western \r\nand Central Africa as well as both Southern Africa regions. The \r\nmost striking drought is the Western Cape drought in 2015-2018, \r\na prolonged drought that resulted in acute water shortages (Wolski, \r\n2018; Burls et  al"


id: 740a1c885fe76257c54680cdeff92dae
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
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vector_id: 15857
content:
", 2019; Section  10.6.2). Anthropogenic climate \r\nchange caused a  threefold increase in the probability of such \r\na drought to occur (Chapters 10 and 11; Botai et al., 2017; Otto et al., \r\n2018). Section 11.9 assesses increases in agricultural and ecological \r\ndrought at 2degC GWL for North Africa and West Southern Africa (high \r\nconfidence) and for East Southern Africa and Madagascar (medium \r\nconfidence), with confidence generally rising for higher emissions \r\nscenarios (Sylla et al., 2016b; Zhao and Dai, 2017; Diedhiou et al., \r\n2018; Abiodun et al., 2019; Todzo et al., 2020; Coppola et al., 2021b). \r\nLiu et al. (2018b) identified the Southern Africa region as the drought \r\n'hottest spot' in Africa in 1.5degC and 2degC global warming scenarios."


id: 838e1c686e1b2a17d668fe09795ccdea
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
content_type: text
index: climate_gpt_only_giec
vector_id: 18231
content:
"Severe wind storm: A limited number of studies allow an assessment \r\nof past trends in wind storms. In West Africa and specifically in the \r\nSahel band, more intense storms have occurred since the 1980s \r\n(low confidence, limited evidence). A persistent and large increase of \r\nfrequency of Sahelian mesoscale convective storms has been found \r\nin several studies (Panthou et  al., 2014; C.M. Taylor et  al., 2017), \r\nwith consequences for extreme rainfalls, and potentially extreme \r\nwinds (low confidence, limited evidence). There is low confidence\r\nof a  general increasing trend in extreme winds across Western, \r\nCentral, Eastern and Southern Africa in a majority of regions by the \r\nmiddle of the century even in high-end scenarios. The frequency \r\nof Mediterranean wind storms reaching North Africa, including \r\nMedicanes, is projected to decrease, but their intensities are projected \r\nto increase, by the mid-century and beyond under SRES A1B, SRES A2 \r\nand RCP8"


id: 6d829099e1d25afa2a8525b6533b0975
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
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index: climate_gpt_only_giec
vector_id: 14837
content:
"5 (medium confidence) (Chapter 11; Cavicchia et al., 2014; \r\nWalsh et al., 2014; Tous et al., 2016; Romera et al., 2017; Romero and \r\nEmanuel, 2017; Gonzalez-Aleman et al., 2019)."


id: 9e6d9cdac4f882f71019db8a67ea7c85
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
content_type: text
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vector_id: 22457
content:
"Snow and glacier: African glaciers are located in East Africa and more \r\nspecifically on Mount Kenya, the Rwenzori Mountains and  Mount \r\nKilimanjaro, with glaciers reducing substantially in each region (high \r\nconfidence) (Taylor et al., 2006; Cullen et al., 2013; Chen et al., 2018; \r\nPrinz et  al., 2018; Wang and Zhou, 2019). Observation and future \r\nprojection of African glacier mass changes are assessed in Section 9.5.1 \r\nwithin the low-latitude glacier region, which is one of the regions with \r\nthe largest mass loss even under low-emissions scenarios (assessment \r\nof this region is dominated by glaciers in the South American Andes, \r\nhowever) (high confidence). Glaciers in the low-latitude region will \r\nlose 67 +- 42%, 86 +- 24% and 94 +- 13% of their mass by the end \r\nof the century for RCP2.6, RCP4.5 and RCP8.5 scenarios respectively \r\n(Marzeion et  al., 2020). Cullen et  al"


id: 3e5d191bcb5fe357b56957c5851e6b27
created_at: 2023-03-16 14:02:09
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vector_id: 7180
content:
" (2013) calculated that even \r\nimbalances between the Mount Kilimanjaro glaciers and present-day \r\nclimate would be enough to eliminate the mountain's glaciers by 2060. \r\nSnow water equivalent and snow cover season duration also decline in \r\nthe East African mountains, Ethiopian Highlands and Atlas Mountains \r\nwith climate change (high confidence) (Lopez-Moreno et al., 2017)."


id: e2b12f2e2515838fb35d3296d2becbaa
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
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index: climate_gpt_only_giec
vector_id: 33168
content:
"Projections indicate that a  vast majority of sandy coasts in the \r\nregion will experience shoreline retreat throughout the 21st century \r\n(high confidence), while parts of the ESAF and western MDG coastline \r\nare projected to prograde over the 21st century, if present ambient \r\ntrends continue. Median shoreline change projections (CMIP5), \r\nrelative to 2010, presented by Vousdoukas et al. (2020b) show that, \r\nunder RCP4.5, shorelines in Africa will retreat by between 30 m (SAH, \r\nNEAF, WSAF, ESAF, MDG) and 55 m (WAF, CAF), by mid-century. By \r\nthe same period but under RCP8.5, the median shoreline retreat is \r\nprojected to be between 35 m (SAH, NEAF, WSAF, ESAF) and 65 m \r\n(WAF, CAF). By 2100, more than 100 m of median retreat is projected \r\nin WAF, CAF and SEAF under RCP4.5, while under RCP8.5, more than \r\n100 m of shoreline retreat is projected in all regions except NEAF \r\nand WSAF. Under RCP8.5 especially, the projected retreat by 2100 \r\nis greater than 150 m in WAF and CAF"


id: afbc3f9f442205f3e6a16f50c3641768
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
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index: climate_gpt_only_giec
vector_id: 25162
content:
" The total length of sandy \r\ncoasts in Africa that is projected to retreat by more than a median of \r\n100 m by 2100 under RCP4.5 and RCP8.5 is about 13,000 km and \r\n17,000 km respectively, an increase of approximately 33%."


id: 75a925ca67dfd6e5a912c28244ee85c4
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
content_type: text
index: climate_gpt_only_giec
vector_id: 16102
content:
"Extreme total water level (ETWL) magnitude and occurrence frequency \r\nare expected to increase throughout the region (high confidence) \r\n(Figure  12.4p-r and Figure  12.SM.6). Across the continent, the \r\n5th-95th percentile range of the 1-in-100-year ETWL is projected to \r\nincrease (relative to 1980-2014) by 7-36 cm and by 14-42 cm by \r\n2050 under RCP4.5 and RCP8.5 respectively. By 2100, this range is \r\nprojected to be 28-86 cm and 43-190 cm under RCP4.5 and RCP8.5 \r\nrespectively (Vousdoukas et al., 2018; Kirezci et al., 2020). In terms \r\nof ETWL occurrence frequencies, the present-day 1-in-100-year ETWL \r\nis projected to have median return periods of around 1-in-10-years \r\nto 1-in-20-years by 2050 and 1-in-1-year to 1-in-5-years by 2100 \r\nin southern and North Africa and occur more than once per year \r\nby 2050 and 2100 in most of East and West Africa under RCP4.5 \r\n(Vousdoukas et  al., 2018)"


id: 70e8175be0803fd45f7bbd9dd09b58b
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
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index: climate_gpt_only_giec
vector_id: 15399
content:
" The present-day 1-in-50-year ETWL is \r\nprojected to occur around three times a year by 2100 with an SLR of \r\n1 m in Africa (Vitousek et al., 2017)."


id: 5e231f0c46770239f001183430fcf788
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
content_type: text
index: climate_gpt_only_giec
vector_id: 12336
content:
"Extreme heat events are very likely to become more intense and/or more \r\nfrequent in SAS, WCA, ARP, EAS, and SEA by the end of 21st century, \r\nespecially under RCP6.0 and RCP8.5 (Figure 12.4a-c and Chapter 11; \r\nLelieveld et al., 2016; Pal and Eltahir, 2016; Guo et al., 2017; Mishra \r\net al., 2017; Dosio et al., 2018; Lin et al., 2018; Nasim et al., 2018; Shin \r\net al., 2018; Hong et al., 2019; Su and Dong, 2019; Khan et al., 2020; \r\nKumar et al., 2020). The exceedance of the dangerous heat stress 41degC \r\nthreshold of the HI is expected to increase by about 250 days in SEA \r\nand by 50-150 days in SAS, WCA, ARP and EAS for SSP5-8.5 at the end \r\nof the century (Figure 12.4d-f and Figure 12.SM.2). Under SSP1-2.6, \r\nthe increase would be restricted to less than 30 days in many of these \r\nregions except SEA, where the number of exceedance days increases by \r\nabout 100 days in some areas. Such increases are already present in the \r\nmiddle of the century (Figure 12.4d-f; Schwingshackl et al"


id: 330c90df5aa7566661195fa8af520f8
created_at: 2023-03-16 14:02:09
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content:
", 2021). In \r\nthese regions, the increase in number of days with exceedance of 35degC \r\nof high heat stress is also expected to increase substantially for the mid\u0002century under SSP5-8.5 (typically by 10-50 days except in Arctic and \r\nSiberian regions), and by more than 60 days in areas of SEA, and a large \r\ndifference is found between low- and high-end scenarios in the end of \r\nthe century (high confidence) (Figure 12.4b). Over WSB, ESB and RFE \r\nalso, an increase of extreme heat durations and frequency is expected in \r\nall scenarios (high confidence) (Kattsov et al., 2017; Khlebnikova et al., \r\n2019b)."


id: 655ac8e5e1b3810249231b010fb842ec
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
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index: climate_gpt_only_giec
vector_id: 13522
content:
"Landslide: The majority of non-seismic fatal landslide events were \r\ntriggered by rainfall, and Asia is the dominant geographical area \r\nof landslide distribution (Froude and Petley, 2018). Floods and \r\nlandslides are the most frequently occurring natural hazards in the \r\neastern Himalayas and hilly regions, particularly caused by torrential \r\nrain during the monsoon season (Gaire et al., 2015; Syed and Al Amin, \r\n2016). They accounted for nearly half of the events recorded in the \r\ncountries of the HKH region (Vaidya et al., 2019). Intense monsoon \r\nrainfall in northern India and western Nepal in 2013, which led to \r\nlandslides and one of the worst floods in history, has been linked \r\nto increased loading of GHG and aerosols (Cho et al., 2016)"


id: 19a2289cca5f9cbf651b09cc392b50d2
created_at: 2023-03-16 14:02:09
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vector_id: 1506
content:
" Due to an \r\nincrease of heavy precipitation and permafrost thawing, an increase in \r\nlandslides is expected in some areas of Asia, such as northern Taiwan \r\n(China), some South Korean mountains, Himalayan mountains, and \r\npermafrost territories of Siberia, and the increase is expected to be \r\nthe greatest over areas covered by current glaciers and glacial lakes \r\n(medium confidence, medium evidence) (Kim et  al., 2015; Kharuk \r\net al., 2016; C.-W. Chen et al., 2019; Kirschbaum et al., 2020)."


id: e4f53693d135425e5497ee6d8765bc5a
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
content_type: text
index: climate_gpt_only_giec
vector_id: 33509
content:
"Aridity: Aridity in West Central Asia and parts of South Asia \r\nincreased in recent decades (medium confidence), as documented in \r\nAfghanistan (Qutbudin et al., 2019), Iran (Zarei et al., 2016; Zolfaghari \r\net  al., 2016; Pour et  al., 2020), most parts of Pakistan (K. Ahmed \r\net al., 2018, 2019), and many parts of India (Roxy et al., 2015; Mallya \r\net al., 2016; Matin and Behera, 2017; Ramarao et al., 2019). Some \r\nspatial and seasonal differences within these regions remain, with \r\nAmbika and Mishra (2020) noting significant aridity declines over \r\nthe Indo-Gangetic Plain in India during 1979-2018 due in part to \r\nthe effect of irrigation, and Araghi et  al. (2018) found that many \r\nparts of Iran show no significant trends in aridity. There was a drying \r\ntendency in the dry season and significant wetting in the wet season \r\nin the Philippines during 1951-2010 (Villafuerte et  al., 2014), and \r\nslight wetting in Vietnam during 1980-2017 (Stojanovic et al., 2020) \r\n(low confidence)"


id: d417adb0b5cb1ee17958a66575e07611
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
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index: climate_gpt_only_giec
vector_id: 30890
content:
" In EAS there is low confidence of broad aridity \r\nchanges, as the frequency of droughts have increased (especially \r\nin spring) along a  strip extending from south-west China to the \r\nwestern part of north-east China; however, there is no evidence \r\nof a significant increase in drought severity over China as a whole \r\nand many parts in the arid north-west China got wetter during \r\n1961-2012 (W. Wang et al., 2015; Zhai et al., 2017; H. Wang et al., \r\n2019; Zhang and Shen, 2019). In Siberia, the number of dry days has \r\ndecreased for much of the region, but increased in its southern parts \r\n(Khlebnikova et al., 2019a)."


id: 7c14398dc1b4efc98d56f3b818c2f39a
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
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index: climate_gpt_only_giec
vector_id: 17124
content:
"Given the increase of heavy precipitation in most Asian regions, the river \r\nflood frequency and intensities will change consequently in Asia. Over \r\nChina floods will increase with different levels under different warming \r\nscenarios (medium confidence) (Lin et al., 2018; Kundzewicz et al., 2019; \r\nLiang et al., 2019; Gu et al., 2020). Monsoon floods will be more intense \r\nin SAS (medium confidence) (Nowreen et al., 2015; Babur et al., 2016; \r\nMohammed et al., 2018). The total flood damage will increase greatly \r\nin river basins in SEA countries under the conditions of climate change \r\nand rapid urbanization in the near future (Dahal et al., 2018; Kefi et al., \r\n2020). A changing snowmelt regime in the mountains may contribute \r\nto a shift of spring floods to earlier periods in Central Asia in future \r\n(medium confidence) (Reyer et al., 2017b)"


id: 7728c9bbc3acdf277c4cdd73d06a7d59
created_at: 2023-03-16 14:02:09
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index: climate_gpt_only_giec
vector_id: 16314
content:
" The annual maximum river \r\ndischarge can almost double by the mid-21st century in major Siberian \r\nrivers, and annual maximum flood area is projected to increase across \r\nSiberia mostly by 2-5% relative to the baseline period (1990-1999) \r\nunder RCP8.5 scenario (medium confidence) (Shkolnik et al., 2018)."


id: 901e2576307a4eb5af94c566642076fe
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
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index: climate_gpt_only_giec
vector_id: 20208
content:
"Hydrological drought: Section 11.9 indicates that limited evidence\r\nand inconsistent regional trends gives low confidence to observed \r\nand projected changes in hydrological drought in all Asian regions at \r\na 2degC GWL (approximately mid-century), although West Central Asia \r\nhydrological droughts increase at the 4degC GWL (approximately end\u0002of-century under higher emissions scenarios) (medium confidence). \r\nHuman activities such as reservoir operation and water abstraction \r\nhave had a  profound effect on low river flow characteristics and \r\ndrought impacts in many Asian regions (Kazemzadeh and Malekian, \r\n2016; Yang et al., 2020b). There was no observed overall long-term \r\nchange of both meteorological droughts and hydrological droughts \r\nover India during 1870-2018 (Mishra, 2020), but there were strong \r\ntrends towards drying of soil moisture in north-central India (Ganeshi \r\net  al"


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vector_id: 17998
content:
", 2020) and intensified droughts in north-west India, parts of \r\nPeninsular India, and Myanmar (Malik et  al., 2016). The frequency \r\nof water scarcity connected with hydrological droughts has increased \r\nsignificantly in southern Russia since the beginning of the 21st century \r\n(Frolova et al., 2017). Higher future temperatures are expected to alter \r\nthe seasonal profile of hydrologic droughts given reduced summer \r\nsnowmelt (medium confidence) downstream of mountains such as \r\nthe Himalayas and the Tibetan Plateau (Sorg et  al., 2014). Several \r\nstudies project more severe future hydrological drought in the Weihe \r\nRiver basin in northern China (Yuan et al., 2016; Sun and Zhou, 2020)."


id: f90df282e1de31cf08db93c8ecaeda05
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
content_type: text
index: climate_gpt_only_giec
vector_id: 36696
content:
"Relative sea level: Around Asia, from 1900-2018, a  new tide \r\ngauge-based reconstruction finds a  regional mean RSL change of \r\n1.33  [0.80  to  1.86]  mm yr-1 in the Indian Ocean-Southern Pacific \r\nand 1.68 [1.27 to 2.09] mm yr-1 in the North-west Pacific (Frederikse \r\net  al., 2020), compared to a  GMSL change of around 1.7  mm yr-1\r\n(Section  2.3.3.3 and Table  9.5). For the period 1993-2018, the \r\nRSLR rates around Asia,  based on satellite altimetry, increased \r\nto 3.65 [3.23  to 4.08]  mm  yr-1 and 3.53 [2.64 to 4.45]  mm yr-1\r\nrespectively (Frederikse et al., 2020), compared to a GMSL change of \r\n3.25 mm yr-1 (Section 2.3.3.3 and Table 9.5). The rate of RSLR along \r\nthe coastline of China ranges from -2.3 +- 1.9 to +5.7 +- 0.4 mm yr-1\r\nduring 1980-2016; after removing the vertical land movement, the \r\naverage rate of sea level rise is 2.9 +- 0.8 mm yr-1 over 1980-2016 \r\nand 3.2 +- 1.1  mm yr-1 since 1993 (Qu et  al., 2019)"


id: a7a39a64553bd2381784ffb7878d71a5
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
content_type: text
index: climate_gpt_only_giec
vector_id: 23882
content:
" However, the \r\nrates of land subsidence reported by Minderhoud et  al. (2017) are \r\nsubstantially higher than those reported by Qu et  al. (2019). RSL \r\nchange in many coastal areas in Asia, especially in EAS, is affected \r\nby land subsidence due to sediment compaction under building mass \r\nand groundwater extraction (high confidence) (Erban et  al., 2014; \r\nNicholls, 2015; Minderhoud et  al., 2019; Qu et  al., 2019). During \r\n1991-2016, the Mekong Delta in Vietnam sank on average about 18 \r\ncm as a consequence of groundwater withdrawal, and the subsidence \r\nrelated to groundwater extraction has gradually increased with \r\nhighest sinking rates estimated to be 11 mm yr-1 in 2015 (Minderhoud \r\net al., 2017)."


id: b8ee2ed2f162d93c1a9de1ea3d63fe2a
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
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index: climate_gpt_only_giec
vector_id: 26629
content:
"Heavy snowfall and ice storm: Observed trends in heavy snowfall \r\nand ice storms are uncertain. Annual maximum snow depth decreased \r\nfor the period between 1962 and 2016 on the western side of both \r\neastern and western Japan, at rates of 12.3% and 14.6% per decade \r\nrespectively (MOE et al., 2018). Observational results generally show \r\na decrease in the frequency and an increase in the mean intensity \r\nof snowfalls in most Chinese regions (medium confidence) (B. Zhou \r\net  al., 2018). Because of the decrease in the snow frequency, the \r\noccurrence of large-scale snow disasters in TIB decreased (low \r\nconfidence) (Qiu et al., 2018; S. Wang et al., 2019). Large parts of \r\nnorthern high-latitude continents (including Siberia and RFE) have \r\nexperienced cold snaps and heavy snowfalls in the past few winters, \r\nand the reduction of Arctic sea ice would increase the chance of heavy \r\nsnowfall events in those regions in the coming decades (medium \r\nconfidence) (Song and Liu, 2017)"


id: 8c53066ab88a6f9fb5dae645ecb5fce3
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
content_type: text
index: climate_gpt_only_giec
vector_id: 19587
content:
" Heavy snowfall is projected to \r\noccur more frequently in Japan's Northern Alps, the inland areas of \r\nHonshu Island and Hokkaido Island (Kawase et al., 2016, 2020; MOE \r\net al., 2018), and the heavy wet snowfall can be enhanced over the \r\nmountainous regions in central Japan and northern part of Japan \r\n(Ohba and Sugimoto, 2020) (medium confidence)."


id: 5adb798e22acc5b0b3a86a86d14c4965
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
content_type: text
index: climate_gpt_only_giec
vector_id: 11810
content:
"The Fourth and Fifth IPCC Assessment Reports (AR4 and AR5) identify \r\nthe most damaging historical hazards in this region to be inland \r\nflooding, drought, wildfire and episodic coastal erosion due to storms \r\n(Hennessy et  al., 2007; Reisinger et  al., 2014). The SR1.5 (Hoegh\u0002Guldberg et al., 2018) projects very likely increases in the intensity \r\nand frequency of warm days and warm nights and decreases in the \r\nintensity and frequency of cold days and cold nights in Australasia. \r\nFurthermore, a likely increase in the frequency and duration of warm \r\nspells is also projected for Australia. The SROCC (IPCC, 2019b) projects \r\na likely global mean sea level rise (RCP8.5) that is up to 0.1 m higher \r\nthan corresponding AR5 projections"


id: 1ee57eb7f20a5df1da961ab7b23edc01
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
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index: climate_gpt_only_giec
vector_id: 2302
content:
" The SROCC also projects an \r\nincrease of mean significant wave height across the Southern Ocean \r\n(high confidence) and an increase in the occurrence of historically rare \r\n(1-in-100-year) extreme sea levels to 1-in-1-year or more frequent \r\nevents all around the Australasian region by 2100 under RCP8.5."


id: 578f319f30d794a20b447f44ba375c4d
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
content_type: text
index: climate_gpt_only_giec
vector_id: 11271
content:
"Projections indicate that a majority of sandy coasts in the Asia region \r\nwill experience shoreline retreat (high confidence) (Udo and Takeda, \r\n2017; Ritphring et al., 2018; Vousdoukas et al., 2020b), while parts of \r\nthe RFE, EAS, SEA and WCA coastline are projected to prograde over \r\nthe 21st century, if present ambient shoreline change trends continue. \r\nMedian shoreline change projections (CMIP5), relative to 2010, \r\npresented by Vousdoukas et al. (2020b) show that, by mid-century, \r\nsandy shorelines in Asia will retreat by between 10-50 m, except in \r\nSAS where shoreline retreat is projected to exceed 100 m, under both \r\nRCP4.5 and RCP8.5. By 2100, and under RCP4.5, shoreline retreats of \r\naround 85, 100 and 300 m are projected along the sandy coastlines \r\nof SEA and WCA, ARP and SAS respectively (50 m or less in other \r\nAsian regions), while under RCP8"


id: f91120452941010a71b8f275c32685c5
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
content_type: text
index: climate_gpt_only_giec
vector_id: 36699
content:
"5, over the same period, sandy \r\nshorelines along all regions with coastlines, except RFE and EAS, are \r\nprojected to retreat by more than 100 m, with the retreat in SAS \r\nreaching 350 m (2100 RCP8.5 projections for RFE and EAS are about \r\n60 m and about 85 m respectively; Figure 12.6)."


id: 426854411fccc3a23e52f12793acf5ae
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
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vector_id: 7846
content:
"Figure 12.7 | Projected changes in selected climatic impact-driver indices for Australasia. (a) Mean change in 1-in-100-year river discharge per unit catchment area \r\n(Q100, m3 s-1 km-2) from CORDEX-Australasia models for 2041-2060 relative to 1995-2014 for RCP8.5. (b) Shoreline position change along sandy coasts by the year 2100 relative \r\nto 2010 for RCP8.5 (metres; negative values indicate shoreline retreat) from the CMIP5-based dataset presented by Vousdoukas et al. (2020b). (c) Bar plots for Q100 (m3 s-1 km-2) \r\naveraged over land areas for the AR6 WGI Reference Regions (defined in Chapter 1). The left-hand column within each panel (associated with the left-hand y-axis) shows the 'recent \r\npast' (1995-2014) Q100 absolute values in grey shades"


id: abec5eeffefcf93899078574dbd6a828
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
content_type: text
index: climate_gpt_only_giec
vector_id: 24570
content:
" The other columns (associated with the right-hand y-axis) show the Q100 changes relative to the recent past values for \r\ntwo time periods ('mid' 2041-2060 and 'long' 2081-2100) and for three global warming levels (defined relative to the pre-industrial period 1850-1900): 1.5degC (purple), 2degC \r\n(yellow) and 4degC (brown). The bars show the median (dots) and the 10-90th percentile range of model ensemble values across each model ensemble. CMIP6 is shown by the darkest \r\ncolours, CMIP5 by medium, and CORDEX by light. SSP5-8.5/RCP8.5 is shown in red and SSP1-2.6/RCP2.6 in blue. (d) Bar plots for shoreline position change show CMIP5-based \r\nprojections of shoreline position change along sandy coasts for 2050 and 2100 relative to 2010 for RCP8.5 (red) and RCP4.5 (blue) from Vousdoukas et al. (2020b). Dots indicate \r\nregional mean change estimates and bars show the 5-95th percentile range of associated uncertainty"


id: fb314616ec00e7a2b745a864005b824a
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
content_type: text
index: climate_gpt_only_giec
vector_id: 37028
content:
" Note that these shoreline position change projections assume that there are \r\nno additional sediment sinks/sources or any physical barriers to shoreline retreat. See Technical Annex VI for details of indices. Further details on data sources and processing are \r\navailable in the chapter data table (Table 12.SM.1)."


id: 84a5c219b045017230b493d75c2b345
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
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index: climate_gpt_only_giec
vector_id: 18396
content:
"Mean temperature in Australasia is projected to continue to rise \r\nthrough the 21st  century (virtually certain) (Atlas.6.4). Projections \r\nfor Australia indicate that the average temperature will increase \r\nby +1.1degC (0.84-1.52degC 10-90th percentile range) by 2041-2060 \r\n(mid-century), and by +1.9degC (1.29-2.58degC) by 2081-2100 (end\u0002century), relative to the baseline period of 1995-2014, under SSP2-4.5 \r\n(Interactive Atlas). For SSP5-8.5, the projected changes are up to \r\n+1.5degC (1.17-1.96degC) and +3.7degC (2.75-4.91degC) for mid- and end\u0002century respectively. For SSP1-2.6, mean temperature is projected \r\nto rise by +0.9degC (0.55-1.26degC) and +1.0degC (0.55-1.54degC) relative \r\nto 1995-2014 by mid- and end-century respectively (Interactive \r\nAtlas). In New Zealand, an increase of mean temperature of +1.0degC \r\n(0.60-1.32degC) relative to 1995-2014 is projected by mid-century, and \r\nan increase of +1.6degC (1.03-2.26degC) by end-century under SSP2-4.5. \r\nFor SSP5-8"


id: 6018da5e1196a51003c21db26336d534
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
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index: climate_gpt_only_giec
vector_id: 12695
content:
"5, the projected increase in mean temperature is +1.3degC \r\n(0.91-1.66degC 10-90th percentile range) and +3.1degC (2.20-4.05degC) \r\nrelative to 1995-2014 by mid- and end-century respectively. For \r\nSSP1-2.6, the projected increase in mean temperature is +0.75degC \r\n(0.39-1.06degC) and +0.8degC (0.47-1.46degC) relative to 1995-2014 by \r\nmid- and end-century, respectively (Interactive Atlas)."


id: 6ae0ce0045764778f52938eb783a1547
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
content_type: text
index: climate_gpt_only_giec
vector_id: 14431
content:
"Extreme heat: The region has a  very likely trend of increasing \r\nfrequency and severity of hot extremes since the 1950s (Table 11.10). \r\nExtreme minimum temperatures have increased in all seasons over \r\nmost of Australia and exceeds the increase in extreme maximum \r\ntemperatures (X.L. Wang et  al., 2013; Jakob and Walland, 2016). \r\nHeatwave characteristics and hot extremes have increased across \r\nmany Australian regions since the mid-20th  century (Table  11.10; \r\nCSIRO and BOM, 2020). The number of days per year with maximum \r\ntemperature greater than 35degC has increased over most parts \r\nof Australia from 1957-2015, with the largest increasing trends of \r\n0.4-1 days/year occurring in north-western, Northern, north-eastern \r\nAustralia and parts of Central Australia (CSIRO and BOM, 2016). \r\nLong-term changes of hot extremes in Australia have been attributed \r\nto anthropogenic influence (Table  11.10)"


id: 3390f79c520bf17be478b420a6673632
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
content_type: text
index: climate_gpt_only_giec
vector_id: 5534
content:
" In New Zealand, the \r\nnumber of annual heatwave days increased at 18 of 30 sites during \r\nthe period 1972-2019 (MfE and Stats NZ, 2020)."


id: 7eb38f0754f813372fc3c53428a95a6c
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
content_type: text
index: climate_gpt_only_giec
vector_id: 17501
content:
"More frequent hot extremes and heatwaves are expected over \r\nthe 21st  century in Australia (virtually certain) (Table  11.10). Heat \r\nthresholds potentially affecting agriculture and health, such as 35degC \r\nor 40degC, are projected to be exceeded more frequently over the \r\n21st century in Australia under all RCPs (high confidence). By 2090 \r\nunder RCP4.5, the average number of days per year with maximum \r\ntemperatures above 35degC is highly spatially variable and is expected \r\nto increase by 50-100%, while the number of days per year with \r\nmaximum temperatures above 40degC is expected to increase by 200%, \r\nrelative to 1985-2005 (CSIRO and BOM, 2015). Under RCP8.5 the \r\ncorresponding projected increases are even greater, with a greater than \r\n100% increase in most of Australia, and far greater increases in Central \r\nand Northern Australia (up to a 20-fold increase in Darwin). Projections \r\nfor New Zealand indicate more frequent hot extremes (virtually \r\ncertain) (Table  11.10)"


id: 321b59b4ff836e72f8035d0315bb8c1a
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
content_type: text
index: climate_gpt_only_giec
vector_id: 5296
content:
" Figure  12.4b, c shows CMIP6 projections of \r\nmean number of days per year with maximum temperature exceeding "


id: 2f0e5ead7576b679e760f85d6ddd26af
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
content_type: text
index: climate_gpt_only_giec
vector_id: 4832
content:
"While median annual runoff is projected to decrease in most of \r\nAustralia (Chiew et  al., 2017), consistent with projected decreases \r\nin average rainfall (CSIRO and BOM, 2015; Alexander and Arblaster, \r\n2017), river floods are projected to increase due to more intense \r\nextreme rainfall events and associated increase in runoff (medium \r\nconfidence). Asadieh and Krakauer (2017) found a  decrease in the \r\nvalue of the 95% percentile of mean streamflow with RCP8.5 by \r\nthe end of the century in all of Australia, except in a small part in centre \r\nof the country. In terms of relative increases, flooding is expected to \r\nincrease more in Northern Australia (driven by convective rainfall \r\nsystems) than in Southern Australia (where more intense extreme \r\nrainfall may be compensated by drier antecedent moisture conditions; \r\nAlexander and Arblaster, 2017; Dey et al"


id: 92ca35890129e687bcfb8c0e0d9199b4
created_at: 2023-03-16 14:02:09
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vector_id: 20608
content:
", 2019) with flood frequency \r\nincreasing in Northern Australia and along parts of the east coast and \r\ndecreasing in south-western Western Australia (Hirabayashi et  al., \r\n2013). Gu et al. (2020) project larger flood magnitude and volumes \r\nunder both RCP2.6 and RCP8.5 in Northern Australia, and smaller flood \r\nmagnitudes and volumes in Southern Australia under the same RCPs. \r\nThese findings are in general agreement with the patterns in peak \r\nflow, corresponding to the 1-in-100-year return period streamflow, \r\nshown in Figure 12.7a,c for mid-21st century under RCP8.5."


id: fc865172ee68b9d5c1971dc16cd19b7d
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
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index: climate_gpt_only_giec
vector_id: 37242
content:
"Aridity is projected to increase, especially during winter and spring, \r\nwith medium confidence in SAU but with high confidence in south-west \r\nWestern Australia (Table 11.11 and Atlas.6.4). In EAU and in the north \r\nand east of NZ, aridity is projected to increase with medium confidence, \r\nwhile a decrease is projected with medium confidence in the south \r\nand west of NZ (Atlas.6.4). Although there is only low confidence in \r\nthe projected decrease of mean annual precipitation in south-western \r\nand eastern Australia and the north and east of New Zealand, there is \r\nhigh confidence of reduced winter and spring precipitation in Australia \r\nin future, mostly in south-western and eastern Australia (Atlas.6.4). \r\nLiu et al. (2018b) show that under 2degC warming, most of Australia is \r\nprojected to become drier based on the Palmer Drought Severity Index \r\n(PDSI), with the exception of the tropical north-east. Ferguson et al"


id: 118b51c850b5e5e7e89a1892b682ce44
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
content_type: text
index: climate_gpt_only_giec
vector_id: 250
content:
"Fire weather: Dowdy and Pepler (2018) examined atmospheric \r\nconditions conducive to pyroconvection in the period 1979-2016, \r\nand found an increased risk in south-east Australia during spring \r\nand summer, due to changes in vertical atmospheric stability and \r\nhumidity, in combination with adverse near-surface fire weather \r\nconditions. CSIRO and BOM (2018) and Dowdy (2018) found that \r\nthe annual 90th percentile daily Forest Fire Danger Index (FFDI) \r\nhas increased from 1950-2016 in parts of Australia, especially in \r\nSouthern Australia (1-2.5 per decade) and in spring and summer. \r\nThese studies indicate an increase in the frequency and magnitude \r\nof FFDI extreme quantiles, as well as a shift of the fire season start \r\ntowards spring, lengthening the fire season"


id: 276c870e6c184ce03a9de8e476279484
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
content_type: text
index: climate_gpt_only_giec
vector_id: 3607
content:
" The unprecedented large \r\nfires of austral spring and summer of 2019 in south-east Australia \r\nwere a  result of extreme hot and dry weather in significantly \r\ndrier than average conditions that had persisted since 2017, in \r\ncombination with consistently stronger than average winds, resulting \r\nin above average to highest on record FFDI values in much of the \r\ncountry (Abram et  al., 2021). These fires have been attributed to \r\nclimate change through the temperature component of fire weather \r\nindices (van Oldenborgh et al., 2021). In New Zealand, days with very \r\nhigh and extreme fire weather increased in 12 out of 28 monitored \r\nsites, and decreased in 8, in the period 1997-2019 (MfE and Stats NZ, \r\n2020). Attribution studies indicate that there is medium confidence of \r\nan anthropogenically driven past increase in fire weather conditions, \r\nessentially due to increase in frequency of extreme heat waves. (Hope \r\net al., 2019; Lewis et al., 2020; van Oldenborgh et al., 2021)."


id: 93e1fc2d80acbfab77cbebfaed49fbd3
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
content_type: text
index: climate_gpt_only_giec
vector_id: 20774
content:
"Hydrological drought: There is low confidence of observed changes \r\nin hydrological droughts in Australasia, except in SAU where there \r\nis medium confidence of an observed increase in the south-east \r\nand south-west. Future projections indicate medium confidence\r\nin further hydrological drought increases for Southern Australia for \r\nwarming levels of 2degC or higher (Section 11.9). Mean annual runoff \r\nin far south-east and far south-west Australia are projected to \r\ndecline by median values of 20 and 50% respectively, by mid-century \r\nunder RCP8.5 (Chiew et al., 2017). Prudhomme et al. (2014) assess \r\nchanges in the Drought Index (DI), defined as areal runoff less \r\nthan the 10th percentile over the reference period 1976-2005, and \r\nproject DI increases for both Australia and New Zealand by 10-20% \r\nby 2070-2099 under RCP8.5, with the greatest effects being in the \r\nsouthern parts of the Australian continent. These projections are \r\nconsistent with the trends shown in Figure 12"


id: 5d02384fdc93ce38847935c2f68c166b
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
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index: climate_gpt_only_giec
vector_id: 12152
content:
"4g-i (Figure 12.SM.3). \r\nThe SPI drought frequency is projected to increase in SAU and \r\nparticularly in south-west Western Australia by mid-century, while by \r\nthe end of the century SPI drought frequency is projected to increase \r\nall over Australia, and particularly strongly in south-west Western \r\nAustralia as well as southern Victoria (see Figure  12.4g-i). For the \r\nMurray-Darling basin, Ferguson et  al. (2018) project effectively no \r\nchange (-1%) in mean precipitation, a 27% decrease in P-E, and 30% \r\nincrease in runoff in 2070-2099 relative to 1976-2005 with RCP8.5."


id: d4991e6fec1793aa0a7274fd1c4e113d
created_at: 2023-03-16 14:02:09
updated_at: 2023-03-16 14:02:09
content_type: text
index: climate_gpt_only_giec
vector_id: 30971
content:
"Fire weather indices are projected to increase in most of Australia \r\n(high confidence) and many parts of New Zealand (medium \r\nconfidence), in particular with respect to extreme fire and induced \r\npyroconvection (Dowdy et al., 2019b). Increasing mean temperature, \r\ncool season rainfall decline, and changes in tropical climate \r\nvariability all contribute to a future increase in extreme fire risk in \r\nAustralia (Abram et al., 2021). Projections indicate that the annual \r\ncumulative FFDI will increase by 31-33% in Southern and Eastern \r\nAustralia, and by 17-25% in Northern Australia and the Rangelands \r\nby 2090 (relative to 1995) under RCP8.5 (CSIRO and BOM, 2015). \r\nUsing a  CMIP5 ensemble of 17 models, Abatzoglou et  al. (2019) \r\nfound a  statistically significant positive trend for fire weather \r\nintensity and fire season length for future mid-century conditions \r\nunder RCP8"


id: f80b39e19ec7705b04bf8ff5ba0d3890
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: 36540
content:
"5, including a  detectable anthropogenic influence \r\non fire risk magnitude and fire season length by 2040 in Western \r\nAustralia and along the Queensland coastline. Using the C-Haines \r\nand FFDI indices with A2 and RCP8.5 respectively, Di Virgilio et al. \r\n(2019) and Clarke et al. (2019) have shown that extreme fire weather \r\nfrequency will increase in south-eastern Australia by the end of the \r\n21st  century. Most of these projections indicate that the biggest \r\nincreases in fire weather conditions will be in late spring, effectively \r\nresulting in longer (stronger) fire seasons in areas where spring \r\nis the shoulder (peak) season. In New Zealand, Watt et  al. (2019) \r\nprojected that the number of days with very high to extreme fire risk \r\nwill increase by 71% by 2040, and by a further 12% by 2090, for the \r\nA1B scenario, with fire risk increase all along the east coast"


id: aff147c036fea4e99787de9fbbba9b88
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: 25208
content:
" The most \r\nmarked relative changes by 2090 were projected for Wellington and \r\nDunedin, where very high to extreme fire risk is projected to increase \r\nby, respectively, 89% to 32 days and 207% to 18 days, compared to \r\nthe baseline period 1970-1999."


id: 12a5ad77fb1860d72af8263f82747b53
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: 432
content:
"Agricultural and ecological drought: There is medium confidence\r\nin observations of agricultural and ecological droughts increasing in \r\nSAU and decreasing in NAU, while there is low confidence of changes \r\nelsewhere in the region (Section  11.9). More regional studies have \r\nobserved an increase in agricultural and ecological drought intensity \r\nin south-west Australia and an increase in drought intensity in parts of \r\nsouth-east Australia, while the length of droughts therein has increased \r\n(Section 11.9). In New Zealand, since 1972-73, soils at 7 of 30 monitored \r\nsites became drier, while the 2012-13 drought was one of the most \r\nextreme in the previous 41 years (MfE and Stats NZ, 2017)"


id: c879090cfe4996aeb9ba417af0be1283
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: 29106
content:
" Future \r\nevaporative demand is projected to lead to medium confidence increases \r\nin agricultural and ecological droughts for 2degC of global warming in \r\nSAU and EAU and low confidence for changes in CAU, NAU and NZ, \r\nalthough there is medium confidence of increases in CAU with 4degC of \r\nglobal warming (Section 11.9). There is medium confidence for more \r\ntime in agricultural and ecological drought in SAU by mid-21st century \r\n(Coppola et al., 2021b) as well as by the end of the 21st century (Herold \r\net al., 2018). The Standardized Precipitation Evapotranspiration Index \r\n(SPEI) shows a  springtime intensification in SAU with moderate and \r\nsevere droughts in the south-west and moderate droughts in the \r\nsouth-east (Herold et al., 2018)"


id: 3a537600f1b8aa8a9a455a05bcee298c
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: 6545
content:
" There is consensus among the different \r\nmodel ensembles (CORDEX-CORE, CMIP5 and CMIP6) that the drought \r\nfrequency (DF), one of several proxies for agricultural and ecological \r\ndrought, will increase in all four Australian regions for both mid-century \r\n(NAU 0.2-2 DF increase, CAU 0.5-2 DF increase, SAU 1-3 DF increase \r\nand EAU 0.8-3 DF increase) and end-century (0.8-2.7 DF increase for \r\nNAU, 1.2-2 DF increase for CAU, 2.2-3.8 for SAU and 0.2-3 for EAU) \r\nfor both RCP8.5 and SSP5-8.5, with CMIP6 showing the lowest increase \r\n(Figure 12.4g-l and Figure 12.SM.4; Coppola et al., 2021b)."


id: 22b52a3974c1a05e30d5bcdd8eb4d398
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: 2916
content:
"Annual mean precipitation is projected to increase in Central \r\nand north-east Australia (low confidence) and in the south and \r\nwest of New Zealand (medium confidence), while it is projected \r\nto decrease in Southern Australia (medium confidence), albeit \r\nwith high confidence in south-west Western Australia, in \r\nEastern Australia (medium confidence), and in the north and \r\neast of New Zealand (medium confidence). Heavy precipitation \r\nand pluvial flooding are projected to increase with medium \r\nconfidence in Northern Australia and Central Australia. There \r\nis medium confidence that river flooding will increase in New \r\nZealand and Australia, with higher increases in Northern \r\nAustralia. Aridity is projected to increase with medium \r\nconfidence in Southern Australia (high confidence in south-west \r\nWestern Australia), Eastern Australia (medium confidence), and \r\nin the north and east of New Zealand (medium confidence)"


id: 9b81fd9289f3745eb7481471536175d5
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: 22019
content:
" \r\nHydrological droughts are projected to increase in Southern \r\nAustralia (medium confidence), while agricultural and \r\necological droughts are projected to increase with medium \r\nconfidence in Southern Australia and Eastern Australia. Fire \r\nweather is projected to increase throughout Australia (high \r\nconfidence) and New Zealand (medium confidence)."


id: 71a0358ab7f3f462d8f5471377db5a25
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: 15509
content:
"Projections indicate that a  majority of sandy coasts in the region \r\nwill experience shoreline retreat, throughout the 21st century (high \r\nconfidence) (Figure  12.7b,d). Median shoreline change projections \r\n(CMIP5) under both RCP4.5 and RCP8.5 presented by Vousdoukas \r\net al. (2020b) show that, by mid-century, sandy shorelines will retreat \r\n(relative to 2010) by between 50 and 80 m all around Australasia, \r\nexcept in SAU and NZ where the projected retreat (relative to 2010) is \r\nbetween 35 and 50 m. By 2100, median shoreline retreats exceeding \r\n100 m (relative to 2010) are projected along the sandy coasts of NAU \r\n(about 150 m), CAU (about 160 m), and EAU (about 110 m) under \r\nRCP4.5m, while projections for SAU and NZ are around 80-90  m. \r\nUnder RCP8.5, shoreline retreat exceeding 100 m is projected all \r\naround the region by 2100 (relative to 2010) with retreats as high \r\nas 220 m in NAU and CAU (about 170 m in EAU and about 130 \r\nm in SAU and NZ; Figure 12.7b,d)"


id: 393aff970a569ac61fd7cf6b7d9e33ec
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: 6378
content:
" The total length of sandy coasts \r\nin Australasia that is projected to retreat by more than a  median \r\nof 100 m by 2100 under RCP4.5 and RCP8.5 is about 12,500 and \r\n16,000 km respectively, an increase of approximately 30%."


id: c8dec8071fc04a514e7215d360c7f200
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: 29165
content:
"Marine heatwave: The mean SST of the ocean around Australia and \r\neast of New Zealand has warmed at a rate of about 0.22degC per decade \r\nbetween 1992 and 2016 (Wijffels et al., 2018), which is higher than \r\nthe global average SST increase of 0.16degC per decade (Oliver et  al., \r\n2018). This mean ocean surface warming is connected to longer and \r\nmore frequent marine heatwaves in the region (Oliver et al., 2018). Over \r\nthe period 1982-2016, the coastal ocean of Australia experienced on \r\naverage more than 1.5 marine heatwaves (MHWs) per year, with the \r\nnorth coast of Western Australia and the Tasman Sea experiencing on \r\naverage 2.5-3 MHWs per year. The average duration was between 10 \r\nand 15 days, with somewhat longer and hotter MHWs in the Tasman Sea. \r\nIn New Zealand, the south-east coast of South Island experiences the \r\nmost MHWs (2.5-3 per year). The duration of MHW in New Zealand is \r\non average 10-15 days (Oliver et al., 2018)"


id: 5da1c34479f877eb78b2dc034b365571
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: 12257
content:
" Changes around Australasia \r\nover the 20th century, derived from MHW proxies, show an increase \r\nin frequency between 0.3 and 1.5 MHW per decade, except along the \r\nsouth-east coast of New Zealand (Box 9.2); an increase in duration per \r\nevent; and the total number of MHW days per decade, with the change \r\nbeing stronger in the Tasman Sea than elsewhere (Oliver et al., 2018)."


id: baab269f040420ec1effe1622124679
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: 26908
content:
"Previous assessments have documented ongoing and projected \r\nchanges in several CIDs. IPCC AR5 projections (IPCC, 2014b) pointed \r\nto increases in mean temperature between 2degC and 6degC by the end \r\nof the century (high confidence) and increases in the occurrence \r\nof warm days and nights under various future climate scenarios \r\n(medium confidence). The AR5 also pointed to patterns of changes \r\nin precipitation (medium confidence), changes in the duration of \r\ndry spells (medium confidence) and decreases in water supply (high \r\nconfidence). The SR1.5 projections indicated expected increases in \r\nriver flooding and extreme runoff at 2degC warming in parts of South \r\nAmerica, and decreases in runoff in Central America, central and \r\nSouthern South America, and the Amazon basin. The SROCC reported \r\nan increased number of landslides, a  decreased volume of lahars \r\nfrom ice and snow-clad volcanoes, and increased frequency of glacier \r\nlake outburst floods"


id: 75cb897915c033d9f5c278233f8856d5
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: 16129
content:
" The SROCC also indicated that regional and \r\nlocal-scale projections point to decreasing trends in glacier runoff."


id: 3b5f0ed05b44f374039309d48771fac8
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: 6692
content:
"There is high confidence that MHWs will increase around most of \r\nAustralasia. Under RCP4.5 and RCP8.5 respectively, mean SST is \r\nprojected to increase by 1degC and 2degC around Australia by 2100, with \r\na hotspot of around 2degC for RCP4.5 and of 4degC for RCP8.5 along the \r\nsouth-east coast between Sydney and Tasmania (Interactive Atlas). \r\nUnder all RCPs, the mean SST around Australia is expected to increase \r\nin the future, with median values of around 0.4degC-1.0degC by 2030 \r\nunder RCP4.5, and 2degC-4degC by 2090 under RCP8.5 (CSIRO and BOM, \r\n2015). Warming is expected to be largest along the north-west coast \r\nof Australia, southern Western Australia, and along the east coast of \r\nTasmania (CSIRO and BOM, 2018). More frequent, extensive, intense \r\nand longer lasting MHWs are projected around Australia and New \r\nZealand for GWLs of 1.5degC, 2degC and 3.5degC relative to the modelled \r\nreference value for 1861-1880 (Frolicher et  al., 2018). Projections \r\nfor SSP1-2.6 and SSP5-8"


id: 7e78da9ff6ab4995ca0f8888d404465
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: 17461
content:
"5 both show an increase in MHWs around \r\nAustralasia by 2081-2100, relative to 1985-2014 (Box 9.2, Figure 1)."


id: b0e5db3801c0184ba4688295424d90d2
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: 25345
content:
"Mean air temperature: New literature confirms a  continuous \r\nwarming since the beginning of the 20th century in the majority of the \r\neight sub-regions (Atlas.7). However, observational datasets in several \r\nareas are still short and trend estimation is hindered by year-to-year \r\nand interannual variability. Atlas projections point to a virtually certain\r\nwarming across all sub-regions, with the largest increases taking place \r\nin the Amazon basin (NSA and SAM; Atlas.7.2.4). A consistent increase \r\nin temperature-related indices linked to several climate-sensitive \r\nsectors (e.g., growing degree days, cooling degree days) is found across \r\nCMIP5, CMIP6 and CORDEX-CORE projections, with smaller increases \r\nfor cooling degree days in mid-latitude regions than in SCA and the \r\nAmazon (Coppola et al., 2021b). Daily mean temperature exceedances \r\nof a  typical 21.5degC threshold for a  successful incubation of disease \r\npathogens inside many mosquito vectors (Lambrechts et  al"


id: de7aeddd2225b1de457c6dce2fba53dd
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: 32506
content:
", 2011; \r\nBlanford et  al., 2013; Mordecai et  al., 2013, 2017) will be crossed \r\nmuch more frequently, potentially driving increases in the incidence \r\nof vector-borne diseases (Laporta et al., 2015; Messina et al., 2019)."


id: d38149455ff972cf1d53eed30cd78286
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: 30803
content:
"Figure 12.8 (continued): (a) Mean change in 1-in-100-year river discharge per unit catchment area (Q100, m3 s-1 km-2) from CORDEX-South and Central America models \r\nfor 2041-2060 relative to 1995-2014 for RCP8.5. (b) Shoreline position change along sandy coasts by the year 2100 relative to 2010 for RCP8.5 (metres; negative values \r\nindicate shoreline retreat) from the CMIP5-based dataset presented by Vousdoukas et al. (2020b). (c) Bar plots for Q100 (m3 s-1 km-2) averaged over land areas for the AR6 \r\nWGI Reference Regions (defined in Chapter 1). The left-hand column within each panel (associated with the left-hand y-axis) shows the 'recent past' (1995-2014) Q100 \r\nabsolute values in grey shades. The other columns (associated with the right-hand y-axis) show the Q100 changes relative to the recent past values for two time periods \r\n('mid' 2041-2060 and 'long' 2081-2100) and for three global warming levels (defined relative to the pre-industrial period 1850-1900): 1"


id: 27f07b169d3ce256bcb4fb6ec3fef084
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: 3693
content:
"5degC (purple), 2degC (yellow) and 4degC \r\n(brown). The bars show the median (dots) and the 10-90th percentile range of model ensemble values across each model ensemble. CMIP6 is shown by the darkest colours, \r\nCMIP5 by medium, and CORDEX by light. SSP5-8.5/RCP8.5 is shown in red and SSP1-2.6/RCP2.6 in blue. (d) Bar plots for shoreline position change show CMIP5-based \r\nprojections of shoreline position change along sandy coasts for 2050 and 2100 relative to 2010 for RCP8.5 (red) and RCP4.5 (blue) from Vousdoukas et al. (2020b). Dots \r\nindicate regional mean change estimates and bars show the 5-95th percentile range of associated uncertainty. Note that these shoreline position change projections assume \r\nthat there are no additional sediment sinks/sources or any physical barriers to shoreline retreat. See Technical Annex VI for details of indices. Further details on data sources and \r\nprocessing are available in the chapter data table (Table 12.SM.1)."


id: 2331772cd6767e00d85033607b5f4110
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: 2984
content:
"Extreme heat: Chapter  11 found high confidence of increased \r\nheatwaves in all regions except SSA over the past decades. There is \r\nevidence of increasing heat stress over summer in much of SES and \r\nSWS using the wet bulb globe temperature (WBGT) index for the \r\nperiod 1973-2012, and this has been attributed to human influence \r\non the climate system (Knutson and Ploshay, 2016). Climate change \r\nprojections point to major increases in several heat indices across \r\nthe region for all scenarios (high confidence). Largest increases in \r\nthe frequency of hot days (maximum temperatures, Tx > 35degC) are \r\nprojected for the Amazon basin under SSP5-8.5 with more than 200 \r\ndays per year at the end of the century under SSP5-8.5 relative to 1995-\r\n2014, while such increases remain moderate (50-100 days) in SSP1-2.6. \r\nFor the dangerous heat threshold of HI > 41degC, increases in frequency \r\nare similar to that in Tx > 35degC (Figure 12.4 and Figures 12.SM.1 and \r\n12.SM.2; Coppola et al"


id: 4dba3b916cd2bd6bd524849f5c9f7c2b
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: 9658
content:
"River flood: Emerging literature in the region documents ongoing \r\nchanges in river floods. Mernild et al. (2018) report decreases and \r\nincreases in annual runoff west of the Andes Cordillera's continental \r\ndivide, with the greatest decreases in the number of low (<10th \r\npercentile) runoff conditions and the greatest increases in high \r\n(>90th  percentile) runoff conditions. In coastal north-east Peru, \r\nextreme precipitation events recently caused devastating river floods \r\nand landslides (Son et al., 2020). In Brazil, floods are becoming more \r\nfrequent and intense in wet regions but less frequent and intense \r\nin drier regions (Bartiko et al., 2019; Borges de Amorim and Chaffe, \r\n2019), with higher propagation of hydrological changes through \r\nanthropogenically modified agricultural basins (Chagas and Chaffe, \r\n2018)"


id: 54c1e053500c606d04b5f1d7fb5b6b37
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: 10789
content:
" Record, catastrophic, unprecedented, and once-in-a-century \r\nflooding events have also been reported in recent decades in the \r\ntributaries of the Amazon River or along its mainstream (Sena \r\net  al., 2012; Espinoza et  al., 2013; Marengo et  al., 2013; Filizola \r\net  al.,  2014), in Argentinean rural and urban areas (Barros et  al., \r\n2015), in the lower reaches of the Atrato, Cauca and Magdalena \r\nrivers in Colombia (Hoyos et al., 2013; Avila et al., 2019), in basins \r\nwhose mainstreams flow through important metropolitan areas \r\nsuch as Concepcion, Chile (Rojas et al., 2017), and even in one of \r\nEarth's driest regions, the Atacama Desert (Wilcox et al., 2016). In the \r\nAmazon basin, the significant increase in extreme flow is associated \r\nwith the strengthening of the Walker circulation (Barichivich et al., \r\n2018)."


id: 41fd3a382085da3122e83781078f08e8
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: 7782
content:
"NSA and SAM are the two regions where the strongest signal of \r\nincreasing number of dry days (NDD) and drought frequency (DF) \r\nis projected compared to other regions of the world (Coppola et al., \r\n2021b). By the end of this century and under RCP8.5, the NSA area \r\naverage value for NDD reaches 43, 32 and 27, within the CORDEX\u0002CORE, CMIP5 and CMIP6 ensembles respectively. For the frequency \r\nof droughts, the NSA area average value is of about 4.6, 3.4 and \r\n3.8. For the same period and scenario, the SAM region shows NDD \r\nand DF values of 29, 20 and 21, and of 4, 3 and 3.5 respectively \r\n(Figure  12.4j-l). In Central America, a  significantly drier northern \r\nregion and a wetter southern region are projected for mid-century \r\nby (Hidalgo et al., 2017), whilst Fuentes-Franco et al. (2015) pointed \r\nto more pronounced dry periods during the rainy season in SCA by \r\nthe end of this century under RCP8.5"


id: 6dd69396b558bff6ece1bc04b6cca7a0
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: 14896
content:
" Increases in the frequency \r\nof meteorological droughts that may initiate other drought types \r\nare projected for the eastern part of the Amazon and the opposite \r\nfor the west under RCP8.5 (Duffy et al., 2015). In central Chile, the "


id: 9ae1cf3dd3bdf5483bb29baa87f6ae7b
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: 21899
content:
"Fire weather: There is evidence of increases in forest fire activity \r\n(number of fires, burned area and fire duration) in central and south\u0002central Chile, where more conducive fire weather conditions have \r\nbeen proposed as the main driver (Gonzalez et  al., 2018; Urrutia\u0002Jalabert et al., 2018). Projections indicate that the Amazon will be one \r\nof the regions in the world with the highest increase in fire weather \r\nindices over the 21st century and under all RCPs (high confidence) \r\n(Betts et  al., 2015; Abatzoglou et  al., 2019; Q. Sun et  al., 2019). \r\nThis is consistent with the large increase in the frequency of joint \r\noccurrence of extreme hot and dry days projected for a 2degC warming \r\nlevel or more (Vogel et al., 2020). Projections of fire weather indices \r\nalso show an increased risk in SWS (high confidence), SSA and SCA \r\n(medium confidence)"


id: 6d33d09b5a13ff086d231f7f050958d4
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: 14801
content:
" However, wildfires highly depend on land use \r\nand appropriate management may help mitigate future increases in \r\nfire risk (Fonseca et al., 2019)."


id: ead1d5e93f696ab8bb9e4d35698ededd
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: 34448
content:
"Snow: Historical studies of seasonal snow cover are limited and \r\nrestricted to the Andes Cordillera. Mernild et al. (2017) indicated that \r\nmuch of the area north of 23degS experienced a decrease in the number \r\nof snow cover days, while the southern half of the Andes Cordillera \r\nexperienced the opposite. A reduction in snow cover of about 15% \r\nwas simulated for areas with altitudes in the range of 3000-5000 \r\nm, whereas in regions with altitude below 1000 m (Patagonia) snow \r\ncover extent increased. The reduced snowfall over the Chilean and \r\nArgentinean Andes mountains, which has resulted in unprecedented \r\nreductions in river flow, reservoir volumes and groundwater levels, led \r\nto the most severe and long-lasting hydrological drought (2010-2015) \r\nreported in the adjacent semi-arid foothills of the central Andes \r\n(Garreaud et  al., 2017; Rivera et  al., 2017; Masiokas et  al., 2020). \r\nProjections (based on process understanding) in Section 9.5.3"


id: 120ccce619dd8c4ed74f02f339f0fd9
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: 339
content:
"3 point \r\nto decreases in seasonal snow cover extent and duration across South \r\nAmerica as global climate continues to warm (high confidence)."


id: a231dcbd0626264a03d3b7459a0f99df
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: 23087
content:
"Coastal flood: The present-day 1-in-100-year ETWL ranges from \r\n0.5 to 2.5 m around most of Central and South America, except in \r\nSSA and SWS, where 1-in-100-year ETWLs can be as large as 5 to \r\n6 m (Vousdoukas et  al., 2018). ETWL magnitude and occurrence \r\nfrequency are expected to increase throughout the region (high \r\nconfidence) (Figure 12.4p-r and Figure 12.SM.6). Across the region, \r\nthe 5-95th percentile range of the 1-in-100-year ETWL is projected \r\nto increase (relative to 1980-2014) by 8-34 cm and by 10-43 cm \r\nby 2050 under RCP4.5 and RCP8.5 respectively (Vousdoukas et al., \r\n2018; Kirezci et  al., 2020). By 2100, this range is projected to be \r\n21-93 cm and 34-190 cm under RCP4.5 and RCP8.5 respectively \r\n(Vousdoukas et  al., 2018; Kirezci et  al., 2020). Furthermore, under \r\nRCP4.5, the present-day 1-in-100-year ETWL is projected to have \r\nmedian return periods of 1-in-10-years to 1-in-50-years by 2050 and \r\n1-in-1-year by 2100 in SES, SSA and SWS"


id: b32f4a668e1575e3ffda225e874d3263
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: 25707
content:
" In other regions of Central \r\nand South America, the present-day 1-in-100-year ETWL is projected \r\nto occur once per year or more by both 2050 and 2100 under RCP4.5. \r\nThe present-day 1-in-50-year ETWL is projected to occur around three \r\ntimes a year by 2100 with a SLR of 1 m (Vitousek et al., 2017)."


id: c47a300169e17232e302eaea63dd4574
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: 28486
content:
"Projections indicate that a  majority of sandy coasts in the region \r\nwill experience shoreline retreat throughout the 21st  century (high \r\nconfidence). Median shoreline change projections (CMIP5) for the \r\nmid-century period show that, relative to 2010, sandy shorelines will \r\nretreat by between 30 and 75 m in SCA, NES, SES and SSA under both \r\nRCP4.5 and RCP8.5, while the projected mid-century retreats are less \r\nthan 30 m in NSA, NWS and SWS for both RCPs (Vousdoukas et al., \r\n2020b). Parts of the coastline in these latter three regions are projected \r\nto prograde over the 21st century, if present ambient shoreline change \r\ntrends continue (Vousdoukas et al., 2020b). By 2100, median retreats \r\nof more than 100 m are projected in SCA, NES, SES and SSA under \r\nboth RCPs, while retreats between 50-100 m are projected for NSA, \r\nNWS and SWS under both RCPs (Figure 12.8). Notably, the projected \r\nshoreline retreats in SCA and SES approach 150 m by 2100 under \r\nRCP4"


id: fa9f1936064efbd939299b8ca533fe1d
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: 36951
content:
"5 and 200 m under RCP8.5. The total length of sandy coasts in \r\nCentral and South America that is projected to retreat by more than \r\na median of 100 m by 2100 under RCP4.5 and RCP8.5 is about 12,000 \r\nand 15,000 km respectively, an increase of approximately 30%."


id: 2aa56042c8136e7e7bfe8e066e5e23d5
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: 4135
content:
"and more frequent marine heatwaves (MHW) in the region (Oliver \r\net al., 2018). Over the period 1982-2016, the coastlines experienced \r\non average more than 1.0 MHW per year, with the Pacific coast of \r\nNorthern Central America and the coast of SES (Atlantic) experiencing \r\non average 2.5-3 MHWs per year. The average duration was between \r\n10 and 15 days, with the notable exception of the equatorial Pacific \r\ncoastline, which experiences MHWs with >30 days average duration \r\nrelated to ENSO conditions. In the south-western Atlantic shelf \r\n(32-38degS), more than half of the days with MHWs have occurred since \r\n2014, and the most intense event (1.7degC above previous maximum) \r\ntook place in the austral summer of 2017 (Manta et al., 2018). Changes \r\nover the 20th century, derived from MHW proxies, show an increase in \r\nfrequency between 0"


id: 685c28e597d2759405727d98abf98a
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: 14002
content:
"5 and 2 MHW per decade in the South Atlantic, \r\nthe Caribbean and the Pacific coast of Northern Central America, an \r\nincrease in intensity per event in the South Atlantic, and a decrease \r\nalong the equatorial Pacific coastline. The total number of MHW \r\ndays per year increases around Central and South America, with the \r\nexception of the equatorial Pacific coastline (Oliver et al., 2018)."


id: 246ae05ab30650e6f0c79dc41b551491
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: 3173
content:
"Using frequency analysis of extreme peak flow events above \r\na  100-year return period as a  threshold, which is the average \r\nprotection level of the European river network (Rojas et al., 2013), \r\nAlfieri et al. (2017) and Alfieri et al. (2015) show that Europe is one of \r\nthe regions where the largest increases in flood risk may occur, with \r\nonly few countries in Eastern Europe showing a decrease (Poland, \r\nLithuania, Belarus) (Osuch et  al., 2017). They find a  significant \r\nincrease of events with peak discharge above 100-year return period \r\n(Q100) in most of Europe in line with Rojas et al. (2012), Hirabayashi \r\net al. (2013), Dankers et al. (2014), Forzieri et al. (2016), Roudier et al. \r\n(2016), Thober et  al. (2018), and an increase in the magnitude of \r\nfloods in southern Europe, although Giuntoli et  al. (2015) projects \r\nno change"


id: 6a664c56376877bda42cf660df8c8a0b
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: 14345
content:
" A modest but significant decrease in the 100-year return \r\nperiod river flood is projected for southern (due to reduction of \r\nprecipitation) and north-eastern European regions, the latter because \r\nof the strong reduction in snowmelt induced river floods (Thober \r\net al., 2018; Di Sante et al., 2021)."


id: c6ddc6b29a5e929f6d50cff47e4ab33b
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: 28870
content:
"Landslide: Rainfall periods connected to landslides are projected to \r\nincrease in central Europe by up to one more period per year in flat \r\nareas in low altitudes and by up to 14 more periods per year at higher \r\naltitudes by mid-century, becoming even more evident by the end of \r\nthe century (Schlogl and Matulla, 2018). An increase of landslides \r\nby up to 45.7% and 21.2% is projected for southern Italy (Calabria \r\nregion) by mid-century under both RCP4.5 and RCP8.5 (Gariano and \r\nGuzzetti, 2016) and by up to 40% in central Italy (Umbria) during \r\nthe winter season (Ciabatta et al., 2016). A decrease of landslides is \r\nprojected in the Peloritani mountains in southern Italy (RCP4.5 and \r\n8.5) by mid-century (Peres and Cancelliere, 2018)"


id: 985930cf1dc4803cf1106c8ffcd74882
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: 21480
content:
" A slight increase \r\nfor a 10-year return period landslide is projected in the eastern \r\nCarpathians, the Moldavian Subcarpathians and the northern part of \r\nthe Moldavian Tableland and a higher increase in the 100-year return \r\nperiod event is projected in the western hilly and plateau areas of \r\nRomania (Jurchescu et al., 2017)."


id: 5ab351564d4a19b0acbcc1a346b8965e
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: 11787
content:
"Recent local studies provide additional risk-relevant context to \r\nchanges in European drought. Agricultural and ecological drought \r\nconditions are expected to intensify in southern Europe by end-of\u0002century based on the 12-month rainfall Drought Severity Index (a soil \r\nmoisture indicator), precipitation deficit SPI and SPEI indices. There will \r\nbe regions in southern Europe where this type of drought could be \r\nup to 14 times worse than the worst drought in the historical period \r\n(Guerreiro et al., 2018). One-in-10-year drought events are projected to \r\nhappen every second year (Mora et al., 2018; Ruosteenoja et al., 2018). \r\nThe Mediterranean region will have 100 additional stress years (years \r\nwith three consecutive months of precipitation deficits greater than \r\n25%; Giorgi et al., 2018); an increase of both drought frequency (up to \r\ntwo events per decade) and severity (Spinoni et al"


id: 6c59e1a42eb4939015b678e9ead42266
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: 14671
content:
", 2014, 2020) and an \r\nincrease of consecutive dry days in the southern part of the MED region \r\n(Lionello and Scarascia, 2020). In contrast, droughts are expected to \r\ndecrease in winter in Northern Europe (Section  11.9; Spinoni et  al., \r\n2018a). These findings are confirmed by the EURO-CORDEX, CMIP5 \r\nand CMIP6 ensemble that show a  change of frequency of drought \r\nevents in the MED between 2-3 events per decade by mid-century for \r\nscenario RCP8.5 (Figure 12.SM.3; Coppola et al., 2021a)."


id: f385b14c4c0b923ac96f211c3beed1b9
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: 35824
content:
"Fire weather: Fire weather conditions have been increasing since \r\nabout 1980 over a few regions in Europe including Mediterranean \r\nareas (low confidence) (Venalainen et al., 2014; Urbieta et al., 2019; \r\nBarbero et al., 2020; Giannaros et al., 2021). However, beyond a few \r\nstudies, evidence is largely missing on attribution of these trends to \r\nanthropogenic climate change (Forzieri et al., 2016). An increase in fire \r\nweather is projected for most of Europe, especially western, eastern \r\nand central regions, by 2080 (current 100-year events will occur every \r\n5-50 years), with a  progressive increase in confidence  and model \r\nagreement along the 21st  century (medium confidence) (Forzieri \r\net al., 2016; Abatzoglou et al., 2019). With increased drying and heat \r\ncombined, in Mediterranean areas, an increase in fire weather indices \r\nis projected under RCP4.5 and RCP8.5, or SRES A1B, as early as by \r\nmid-century (high confidence) (Bedia et al., 2014; Abatzoglou et al"


id: b9189d21bbdf8f9afc5823c831dc5736
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: 26655
content:
", \r\n2019; Dupuy et al., 2020; Fargeon et al., 2020; Ruffault et al., 2020) \r\nand an increase in burned area of 40% and 100% for a 2degC and 3degC \r\nGWL, respectively (Turco et al., 2018)."


id: 29df76f256710b1f9092279fa5dc1308
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: 4018
content:
"There is medium confidence that serial clustering of storms, inducing \r\ncumulated economic losses, in future climate will increase in many \r\nareas in Europe under climate projections over Europe (Karremann \r\net al., 2014; Economou et al., 2015). Strong winds and extratropical \r\nstorms are projected to have a  slightly increasing frequency and \r\namplitude in the future in northern, western and Central Europe \r\n(Outten and Esau, 2013; Feser et al., 2015; Forzieri et al., 2016; Molter \r\net al., 2016; Ruosteenoja et al., 2019a; Vautard et al., 2019) under \r\nRCP8.5 and SRES A1B by the end of the century (medium confidence), \r\nas well as off the European coasts (Martinez-Alvarado et al., 2018) \r\ndue to the increase of intensity of extratropical storms at a 2degC GWL \r\nor above (Zappa et al., 2013) in these areas"


id: bd4381ba32ae78942eaed490f80b1b2c
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: 27340
content:
" The frequency of storms, \r\nincluding Medicanes, is projected to decrease in Mediterranean \r\nregions, and their intensities are projected to increase, by the middle \r\nof the century and beyond for SRES A1B, A2 and RCP8.5 (medium \r\nconfidence) (Nissen et al., 2014; Feser et al., 2015; Forzieri et al., 2016; \r\nMolter et al., 2016; Tous et al., 2016; Romera et al., 2017; Gonzalez\u0002Aleman et al., 2019; MedECC, 2020; Chapter 11)."


id: 7115739c19b7ce9a76dccb25056eac72
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: 15421
content:
"Mean wind speed: Mean surface wind speeds have decreased in \r\nEurope as in many other areas of the Northern Hemisphere over the \r\npast four decades (medium confidence) (AR5 WGI), with a reversal \r\nto an increasing trend in the last decade (low confidence) that is, \r\nhowever, not fully consistent across studies (Tian et  al., 2019; \r\nZeng et  al., 2019; Z. Zhang et  al., 2019; Deng et  al., 2021; see \r\nSection 2.3.1.4.4). Re-analyses also show declining winds in Europe \r\n(Deng et al., 2021) with large interdecadal variability (Laurila et al., \r\n2021). The declining trend has induced a corresponding decline in \r\nwind power potential indices across Europe (low confidence) (Tian \r\net al., 2019). However, there is low agreement and limited evidence\r\nthat climate model historical trends are consistent with observed \r\ntrends (Tian et al., 2019; Deng et al., 2021)"


id: 5976aa087dc115ba0b316ac170d37869
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: 11570
content:
" Several factors have been \r\nattributed to these trends, including forest growth, urbanization, \r\nlocal changes in wind measurement exposure and aerosols (Bichet \r\net al., 2012), as well as natural variability (Zeng et al., 2019)."


id: 57c357186188e06beeceb9c1faadfe7f
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: 11304
content:
"Projections of smaller-scale hazard phenomena such as tornadoes, \r\nwind gusts, hail storms and lightning are currently not directly \r\navailable partly due to the inability of climate models to simulate \r\nsuch phenomena. Observational networks for such phenomena \r\nusually lack homogeneity over long periods, hindering clear trends to \r\nbe detected. For instance, while no robust trends have been identified \r\n(Hermida et al., 2015; Mohr et al., 2015a; Burcea et al., 2016; Curic \r\nand Janc, 2016), hail storm environments (favourable atmospheric \r\nconfigurations) have increased in frequency (low confidence, limited \r\nevidence) (Sanchez et al., 2017). In future climate periods it is more \r\nlikely than not that severe convection environments will become \r\nmore frequent by the end of the century under RCP8.5 (Mohr et al., \r\n2015b; Pucik et al., 2017), and there is medium confidence that such \r\nenvironments will become more frequent by the 2050s in RCP4.5"


id: 9c934d10a8bcc0ca16d1562dc875c773
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: 22180
content:
" \r\nThere is no evidence for changes in tornado frequencies in Europe \r\nin the observations (Groenemeijer and Kuhne, 2014) as well as in \r\nfuture climate projections. Insufficient observational record length for \r\nlightning numbers does not allow an assessment of trends."


id: 7c4eeef6770834d0c1c568ffd9dd0790
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: 17154
content:
"Due to changes in mean surface wind speed patterns (C. Li et al., 2018) \r\nand the poleward shift of the North Atlantic jet stream exit, mean surface \r\nwind speeds are projected to decrease in the Mediterranean areas under \r\nRCP4.5 and RCP8.5 by the middle of the century and beyond, or for \r\nGWLs of 2degC and higher (high confidence), with a subsequent decrease \r\nin wind power potential (medium confidence) (Hueging et al., 2013; \r\nTobin et al., 2015, 2018; Davy et al., 2018; Karnauskas et al., 2018a; \r\nKjellstrom et al., 2018; Moemken et al., 2018; Figure 12.4). However, \r\nsub-regional patterns of change are shown in regional climate models, \r\nsuch as an increase in wind speeds in the Aegean Sea and in the \r\nnorthern Adriatic Sea, where a reduction of Bora events and an increase \r\nof Scirocco events are projected for mid-century and beyond under \r\nRCP4.5 and RCP8.5 (medium confidence) (Tobin et  al., 2016; Davy \r\net al., 2018; Belusic Vozila et al., 2019)"


id: 822cc0850d2ff3979abad80a536a3f2f
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: 18006
content:
" Projections (as cited above) also \r\nindicate a decrease in mean wind speed in Northern Europe (medium \r\nconfidence, medium agreement) (Karnauskas et al., 2018a; Tobin et al., \r\n2018; Jung and Schindler, 2019). Daily and interannual wind variability \r\nis projected to increase under RCP8.5 only in Northern Europe (low \r\nconfidence) (Moemken et  al., 2018), which can influence electrical \r\ngrid management and wind energy production (low confidence). Wind \r\nspeeds are projected to shift towards more frequent occurrences below \r\nthresholds inhibiting wind power production (Weber et  al., 2018). \r\nWind stagnation events may become more frequent in future climate \r\nscenarios in some areas of Europe in the second half of the 21st century \r\n(Horton et al., 2014; Vautard et al., 2018), with potential consequences \r\non air quality (low confidence)."


id: 7917b4c94b309632b2230abc88ae8ae3
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: 16609
content:
"Snow: Widespread and accelerated declines in snow depth (Fontrodona \r\nBach et al., 2018) and snow water equivalent (Marty et al., 2017a; see \r\nFigure 12.9b) have been observed in Europe. In the Pyrenees a slow \r\nsnow cover decline has been observed starting from the industrial \r\nperiod with a sharp increase since 1955 (Lopez-Moreno et al., 2020). \r\nUnder the RCP2.6, RCP4.5 and RCP8.5 scenarios the reliability elevation \r\nfor snowmaking will rise by 200-300 m in the Alps and 400-600 m \r\nin the Pyrenees by mid-century. End of century projections of natural \r\nsnow conditions are highly dependent on the scenario, being stationary \r\nfor the RCP2.6 and continuously decreasing under RCP8.5 to not have \r\nany more natural snow conditions at any of the locations in the French \r\nAlps and Pyrenees (Spandre et al., 2019). Similarly Norway and Austria \r\nwill also see a rising of the natural snow elevation with consequences \r\nfor the ski season (Scott et al., 2020; Steiger and Scott, 2020)"


