# document

id: 4f75d2ef8e2b1ca64fcff4bf423fcf27
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
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"Carley E. Iles (Norway, France/United Kingdom), Jerome Servonnat (France), Gudfinna Adalgeirsdottir \r (Iceland), Rita Adrian (Germany), Roxana Bojariu (Romania), Laurent Bopp (France), Audrey Brouillet \r (France), Carlo Buontempo (United Kingdom/Italy), Winston Chow (Singapore), Augustin Colette \r (France), Cecilia Conde (Mexico), Leticia Cotrim da Cunha (Brazil), Claudine Dereczynski (Brazil), \r Alejandro Di Luca (Australia, Canada/Argentina), Fabio Di  Sante (Italy), Arona Diedhiou (Cote \r d'Ivoire/Senegal), Aida Diongue-Niang (Senegal), Francisco J. Doblas-Reyes (Spain), Pariva Dobriyal \r (India), Sybren S. Drijfhout (The Netherlands), John P. Dunne (United States of America), \r Tamsin L. Edwards (United Kingdom), Aidan D. Farrell (Trinidad and Tobago/Ireland), Erich Fischer \r (Switzerland), John C. Fyfe (Canada), Alexander  Gelfan (Russian Federation), Subimal Ghosh \r (India), Irina V. Gorodetskaya (Portugal/Belgium, Russian Federation), Michael Grose (Australia), \r [...]"


id: 5748a7ea0702fd2c8b35d9f51650acd6
created_at: 2023-04-19 13:22:47
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"[...] Jose  Manuel Gutierrez (Spain), David S. Gutzler (United States of America), Rebecca Harris \r (Australia), Matthias Hauser (Switzerland), Mark Hemer (Australia), Kevin Hennessy (Australia), \r Helene T. Hewitt (United Kingdom), Masao Ishii (Japan), Maialen Iturbide (Spain), Christopher D. Jack \r (South Africa), Richard G. Jones (United Kingdom), Nikolay Kadygrov (France/Russian Federation), \r Ebru Kirezci (Australia/Turkey), Nana Ama Browne Klutse (Ghana), Robert E. Kopp (United States \r of America), James Kossin (United States of America), Charles Koven (United States of America), \r Svitlana Krakovska (Ukraine), Gerhard  Krinner (France/Germany, France), Benjamin L. Lamptey \r (Niger, Ghana/Ghana), Christopher Lennard (South Africa), Xianfu Lu (United Kingdom/China), \r Douglas Maraun (Austria/Germany), Simon  McGree (Australia/Fiji, Australia), Glenn McGregor \r (United Kingdom/New Zealand, United Kingdom), Kathleen  L.  McInnes (Australia), Dirk Notz \r [...]"


id: 2c896387ba8ae36032086439a1c41de4
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
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"[...] (Germany), Brian O'Neill (United States of America), Ben Orlove (United States of America), \r Friederike Otto (United Kingdom/Germany), Carlos Perez Garcia-Pando (Spain), Franz Prettenthaler "


id: ea2ef359e9c787c90a89f66b7d1b811b
created_at: 2023-04-19 13:22:47
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"Climate change information is increasingly available and robust \r at regional scale for impacts and risk assessment. Climate \r services and vulnerability, impacts and adaptation studies require \r regional scale multi-decadal climate observations and projections. \r Since the IPCC Fifth Assessment Report (AR5), the increased availability \r of coordinated ensemble regional climate model projections and \r improvements in the level of sophistication and resolution of global \r and regional climate models, completed by attribution and sectoral \r vulnerability studies, have enabled the investigation of past and \r future evolution of a range of climatic quantities that are relevant to \r socio-economic sectors and natural systems. Chapter 12 consolidates \r core physical knowledge from preceding AR6 Working Group I (WGI) \r chapters and post-AR5 climate impact assessment literature to \r assess the spatio-temporal evolution of the climatic conditions that \r [...]"


id: 894c91541250002e7562515f1cea0c7e
created_at: 2023-04-19 13:22:47
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"[...] may lead to regional scale impacts and risks (following the sectoral \r classes adopted by AR6 WGII) in the world's regions (presented in \r Chapter 1). {12.1}"


id: cc53f692b880a6866eee7760e8236a01
created_at: 2023-04-19 13:22:47
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"Every region of the world will experience concurrent changes \r in multiple CIDs by mid-century (high confidence), challenging \r the resilience and adaptation capacity of the region. Changes in \r heat, cold, snow and ice, coastal, oceanic, and CO2 at surface CIDs are \r projected with high confidence in most regions, indicating worldwide \r challenges, while additional region-specific changes are projected in \r other CIDs that may lead to more regional challenges. High confidence\r increases in some of the drought, aridity and fire weather CIDs will \r challenge, for example, agriculture, forestry, water systems, health \r and ecosystems in Southern Africa, the Mediterranean, North Central \r America, Western North America, the Amazon regions, South-Western \r South America, and Australia. High confidence changes in snow, ice \r and pluvial or river flooding will pose challenges for, for example, \r energy production, river transportation, ecosystems, infrastructure \r [...]"


id: d23d26c939e3005c14c87a7638d24460
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
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"[...] and winter tourism in North America, Arctic regions, Andes regions, \r Europe, Siberia, Central, South and East Asia, Southern Australia and \r New Zealand. Only a  few CIDs are projected to change with high \r confidence in the Sahara, Madagascar, Arabian Peninsula, Western \r Africa and Small Islands; however, the lower confidence levels for \r CID changes in these regions can originate from knowledge gaps \r or model uncertainties, and does not necessarily mean that these \r regions have relatively low risk. {12.5}"


id: a47f2455d3ceebd256886e227ca33c61
created_at: 2023-04-19 13:22:47
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"The current climate in most regions is already different from \r the climate of the early or mid-20th century with respect to \r several CIDs. Climate change has already altered CID profiles \r and resulted in shifts in the magnitude, frequency, duration, \r seasonality and spatial extent of associated indices (high \r confidence). Changes in temperature-related CIDs such as mean \r temperatures, growing season length, extreme heat and frost have \r already occurred and many of these changes have been attributed to \r human activities (medium confidence). Mean temperatures and heat \r extremes have emerged above natural variability in all land regions \r (high confidence). In tropical regions, recent past temperature \r distributions have already shifted to a  range different to that of \r the early 20th  century (high confidence). Ocean acidification and \r deoxygenation have already emerged over most of the global open \r ocean, as has reduction in Arctic sea ice (high confidence). Using CID \r [...]"


id: 2ad2e1b437bae4de9576ea9313ba7202
created_at: 2023-04-19 13:22:47
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"[...] index distributions and event probabilities accurately in both current \r and future risk assessments requires accounting for the climate \r change-induced shifts in distributions that have already occurred. \r {12.4, 12.5}"


id: a38945bf4fce5a136ac6c046795e71d1
created_at: 2023-04-19 13:22:47
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"Worldwide changes in heat, cold, snow and ice, coastal, oceanic \r and CO2-related CIDs will continue over the 21st  century, \r albeit with regionally varying rates of change, regardless of \r the climate scenario (high confidence). In all regions there is high \r confidence that, by 2050, mean temperature and heat extremes will \r increase, and there is high confidence that sea surface temperature \r will increase in all oceanic regions except the North Atlantic. Apart \r from a few regions with substantial land uplift, relative sea level rise \r is very likely to virtually certain (depending on the region) to continue \r in the 21st century, contributing to increased coastal flooding in most \r low-lying coastal areas (high confidence) and coastal erosion along \r most sandy coasts (high confidence), while ocean acidification is \r virtually certain to increase. It is virtually certain that atmospheric \r CO2 at the surface will increase in all emissions scenarios until \r [...]"


id: e4f1eb89f8921056049847436f32b809
created_at: 2023-04-19 13:22:47
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"[...] net zero emissions are achieved. Glaciers will continue to shrink \r and permafrost to thaw in all regions where they are present \r (high confidence). These changes will lead to climate states with no \r recent analogue that are of particular importance for specific regions \r such as tropical forests or biodiversity hotspots. {12.4}"


id: adea00ac5a039971a5e0686cb0d4d78f
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
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"and storms. Agricultural and ecological drought changes are generally \r of higher confidence than hydrological drought changes,  with \r increases projected in North and Southern Africa, Madagascar, \r Southern and Eastern Australia, some regions of Central and South \r America, Mediterranean Europe, Western North America and North \r Central America (medium to high confidence). Fire weather conditions \r will increase by 2050 under RCP4.5 or above in several regions in \r Africa, Australia, several regions of South America, Mediterranean \r Europe, and North America (medium to high confidence). Extreme \r precipitation and pluvial flooding will increase in many regions \r around the world (high confidence). Increases in river flooding are \r also expected in Western and Central Europe and in polar regions \r (high confidence), most of Asia, Australasia, North America, the South \r American Monsoon region and South-Eastern South America (medium \r [...]"


id: 3bbbab93fe1e99f21ffb0b45ca7c058d
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
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"[...] confidence). Mean winds are projected to slightly decrease by 2050 \r over much of Europe, Asia and Western North America, and increase in \r many parts of South America except Patagonia, West and South Africa \r and the eastern Mediterranean (medium confidence). Extratropical \r storms are expected to have a decreasing frequency but increasing \r intensity over the Mediterranean, increasing intensity over most of \r North America, and an increase in both intensity and frequency over \r most of Europe (medium confidence). Enhanced convective conditions \r are expected in North America (medium confidence). Tropical cyclones \r are expected to increase in intensity despite a decrease in frequency \r in most tropical regions (medium confidence). Climate change will \r modify multiple CIDs over Small Islands in all ocean basins, most \r notably those related to heat, aridity and droughts, tropical cyclones \r and coastal impacts. {12.4}"


id: c4eeb56cf869bfdc9580c7b89b135df0
created_at: 2023-04-19 13:22:47
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content:
"Many global- and regional-scale CIDs have a  direct relation \r to global warming levels (GWLs) and can thus inform the \r hazard component of 'Representative Key Risks' and 'Reasons \r for Concern' assessed by AR6 WGII. These include both mean \r and extreme heat, cold, wet and dry hazards; cryospheric hazards \r (snow cover, ice extent, permafrost); and oceanic hazards (marine \r heatwaves) (high confidence). For some of these, a  quantitative \r relation can be drawn (high confidence). For example, with each \r degree of global surface air temperature (GSAT) warming, the \r magnitude and intensity of many heat extremes show a  linear \r change, while some changes in frequency of threshold exceedances \r are exponential: Arctic temperatures warm about twice as fast as \r GSAT; global sea surface temperatures increase by about 80% of \r GSAT change; Northern Hemisphere spring snow cover decreases \r by about 8% per 1degC. For other hazards (e.g., ice-sheet behaviour, \r [...]"


id: 5a0938ea02db97110f53341278e4d138
created_at: 2023-04-19 13:22:47
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"[...] glacier mass loss, global mean sea level rise, coastal floods and \r coastal erosion) the time and/or scenario dimensions remain critical \r and a simple relation with GWLs cannot be drawn (high confidence), \r but still quantitative estimates assuming specific time frames and/\r or stabilized GWLs can be derived (medium confidence). Model \r uncertainty challenges the link between specific GWLs and tipping \r points and irreversible behaviour, but their occurrence cannot be \r excluded and their chances increase with warming levels (medium \r confidence). {CCB 12.1}"


id: bfa01f394796c93e43a455bdcf04db2a
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
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"The level of confidence in the projected direction of \r change in CIDs and the intensity of the signal depend on \r mitigation efforts over the 21st century, as reflected by the \r differences between end-century projections for different \r climate scenarios. Dangerous humid heat thresholds, such as \r the US National Oceanic and Atmospheric Administration Heat \r Index (NOAA HI) of 41degC, will be exceeded much more frequently \r under the SSP5-8.5 scenario than under SSP1-2.6 and will affect \r many more regions (high confidence). In  many tropical regions, \r the number of days per year where an HI of 41degC is exceeded will \r increase by more than 100 days relative to the recent past under \r SSP5-8.5, while this increase will be limited to less than 50 days \r under SSP1-2.6 (high confidence). The number of days per year \r where temperature exceeds 35degC will increase by more than 150 \r days in many tropical areas, such as the Amazon basin and South \r [...]"


id: 10f9f7fcd6979560ba2add09e1c38012
created_at: 2023-04-19 13:22:47
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"[...] East Asia under SSP5-8.5, while it is expected to increase by less \r than two months in these areas under SSP1-2.6 (except for the \r Amazon Basin). There is high confidence that sandy shorelines will \r retreat in most regions of the world, in the absence of additional \r sediment sources or physical barriers to shoreline retreat. The total \r length of sandy shorelines around the world that are projected to \r retreat by more than 100 m by the end of the century is about 35% \r greater under RCP8.5 (about 130,000 km) compared to that under \r RCP 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 total water level) is also projected to increase substantially in most \r regions (high confidence). In a  globally averaged sense, the 1-in\u0002100 year extreme sea level is projected to become an event that "


id: 16c12441393245b46024594388ca87d6
created_at: 2023-04-19 13:22:47
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"Impacts of climate change are driven not only by changes in climate \r conditions, but also by changes in exposure and vulnerability \r (Cross-Chapter Box 1.3). This chapter concentrates on drivers of impacts \r that are of climatic origin (see also the IPCC Special Report on Global \r Warming of 1.5degC (SR1.5, IPCC, 2018), and Section 1.3.2 in this Report), \r referred to in WGI as 'climatic impact-drivers' (CIDs). CIDs are physical \r climate system conditions (e.g., means, events, extremes) that affect an \r element of society or ecosystems. Depending on system tolerance, CIDs \r and their changes can be detrimental, beneficial, neutral, or a mixture \r of each across interacting system elements and regions. However, this \r chapter largely focuses on drivers commonly connected to hazards, \r and adopts the IPCC risk framework (Cross-Chapter Box  1.3) since \r the main objective of the United Nations Framework Convention on \r Climate Change (UNFCCC) is to 'prevent dangerous anthropogenic \r [...]"


id: e06fe12971d51c3331d79abf767067be
created_at: 2023-04-19 13:22:47
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"The approach adopted here is consistent with the UN Sendai Framework \r for Disaster Risk Reduction 2015-2030, which aims to face disaster \r consequences (including but not limited to climate disasters) and \r reduce risks in natural, managed and built environments (Aitsi-Selmi \r et  al., 2015; UNISDR, 2015). The classification of climatic impact\u0002drivers in this chapter is largely consistent with the classification of \r hazards used in the Sendai Framework. However, the UNISDR hazard \r list spans a  wider range of hazards inducing damage to society, \r including hazards that are not directly related to climate (such as \r volcanoes and earthquakes), which are excluded from the assessment \r herein. Furthermore, the UNISDR classification of hazards does not \r include mean climatic conditions, which are also discussed as climatic \r impact-drivers in this chapter. The first priority mentioned in the Sendai \r Framework is understanding disaster risk as a  necessary step for \r [...]"


id: 2b05fc36f144bf390a2152d72f777fd8
created_at: 2023-04-19 13:22:47
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"This chapter is new in IPCC WGI assessment reports, in that it represents \r a  contribution to the 'IPCC risk framework'. Within this framework, \r climate-related impacts and risks are determined through an interplay \r between the occurrence of climate hazards and their consequences \r depending on the exposure of the affected human or natural system \r and its vulnerability to the hazardous conditions. In Chapter 12, we \r are assessing climatic impact-drivers that could lead to hazards or to \r opportunities, from the literature and model results since AR5. This \r will particularly support the assessment of key risks related to climate \r change by WGII (Chapter 16). Despite the fact that impacts may also be \r induced by climate adaptation and mitigation policies themselves, as \r well as by socio-economic trends, changes in vulnerability or exposure, \r and external geophysical hazards such as volcanoes, the focus here is \r [...]"


id: 318bb568e5bcecff01cb52164a07634d
created_at: 2023-04-19 13:22:47
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"[...] only on climatic impacts and risks induced by shifts in physical climate \r phenomena that directly influence human and ecological systems \r (Cross-chapter Box 1.3)."


id: 144b383f0f4380c154bcd864aaba8afc
created_at: 2023-04-19 13:22:47
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"The narrative of Chapter  12 is illustrated in Figure  12.2. First, \r Section 12.2 defines a range of climatic impact-driver categories that \r are relevant for regional and sectoral impacts. Next, Section  12.3 \r identifies climatic impact-drivers and their relevant indices that \r are frequently used in the context of climate impacts in the WGII \r focus sectors (AR6 WGII Chapters 2-8). The assessment of changes \r in regional-scale climatic impact-drivers is then developed within \r Section  12.4 by continent, following the structure of the WGII \r assessment report regional chapters (AR6 WGII Chapters 9-15), and \r adding the polar regions, open/deep ocean and other specific zones \r corresponding to the WGII Cross-Chapter Papers. Section  12.5 then \r presents a global perspective (both bottom-up and top-down) on the \r change of regional climatic impact-drivers, including an assessment of \r the 'emergence' of climatic impact-drivers. Section 12.6 discusses how \r [...]"


id: a9c8ad59b53309bb02503629eba3ba15
created_at: 2023-04-19 13:22:47
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"[...] climate information is used in 'climate services', which encompasses \r a range of activities bridging climate science and its use for adaptation \r and mitigation decision-making (see also AR6 WGII Chapter  17). \r The chapter concludes with final remarks in Section 12.7."


id: 8efd0c4de30247598ef0b972672c2157
created_at: 2023-04-19 13:22:47
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"Several climatic impact-drivers are reliant on many factors beyond \r their associated primary climatic phenomenon. For example, river \r flooding is heavily dependent on river management and engineering \r and could also be affected by tidal water levels due to sea level \r rise and/or storm surge. Coastal flooding could be affected by \r coastal protection structures, port and harbour structures, as well \r as river flows (on inlet-interrupted coasts). Coastal erosion could \r be influenced by coastal protection measures as well as fluvial \r sediment supply to the coast. Furthermore, air pollution weather \r is not the only or dominant driver, for instance, of surface ozone \r pollution, but precursor emissions from anthropogenic sources can \r play a significant role (Section 6.5). Chapter 12 focuses only on the \r influence of the atmospheric, land and oceanic conditions associated \r with the climatic impact-drivers and the confidence in the direction \r [...]"


id: 3eb43a089e9e552218e4db7ab4097bcd
created_at: 2023-04-19 13:22:47
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"[...] of CID changes given here does not take into account existing or \r potential future adaptation measures, unless otherwise stated."


id: da782d1786c33a6a813e98c7b607127a
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
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"This section surveys the links between CIDs and affected sectors; \r not to perform specific climate change impact or risk assessments \r (see AR6 WGII), but to describe key indices (among many) that \r quantify these links as guidance for stakeholders seeking applicable \r climate information. This survey builds on the work of the World \r Meteorological Organization Expert Team on Sector-Specific Climate \r Indices (ET-SCI) and previous IPCC assessments, notably AR5 WGII \r (Birkmann et al., 2014; IPCC, 2014a) and IPCC Special Reports (IPCC, \r 2018, 2019b, c) that have assessed climate hazards affecting sectors \r but is organized from a CID perspective drawing also upon recent \r summaries of sectoral hazards (Mora et  al., 2018; ICOMOS, 2019; \r Yokohata et  al., 2019). Impacts, risks and opportunities are rarely \r attributable to a single CID index or threshold, but climate shifts that \r push conditions outside of expected conditions and beyond tolerance \r [...]"


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created_at: 2023-04-19 13:22:47
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"[...] levels are indicative of impact, risk or benefit given vulnerability \r and exposure. Focus is on direct sectoral connections of a  CID \r (Hallegatte and Przyluski, 2010) rather than cascading or secondary \r effects (e.g., water-borne diseases following a flood, mental health \r challenges following a  severe storm, or the effects of drought on \r poverty), as these are strongly affected by exposure, vulnerability and \r response, as discussed in the WGII Report."


id: 283730bb5263587102443888f7041232
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
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"For each CID category there can be a range of indices that capture \r the sector- or application-relevant characteristics of a  climatic \r impact-driver as described in Sections 12.3 and 12.4. Indices for \r climatic impact-drivers that are based on absolute or percentile \r thresholds (e.g.,  daily maximum temperature above 35degC) can be \r affected by biases in climate model simulations, such as local or \r regional deviations of a  simulated climate variable from observed \r values (Sillmann et al., 2014; Dosio, 2016). Where sensible (i.e., where \r reliable observational data are available and a climate model that fits \r for the desired purpose), the output of climate model simulations can \r be bias-adjusted, potentially involving advanced methods to account \r for multiple variables and extreme value statistics as assessed in \r detail in Cross-Chapter Box 10.2. Yet, there is no general agreement \r about which bias adjustment methods to apply, as artefacts can arise \r [...]"


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created_at: 2023-04-19 13:22:47
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"[...] both from the climate model and from the bias adjustment method, \r and the number of available methods has considerably grown in \r recent years (for a  detailed discussion of available methods and \r their 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 bias-adjusted CIDs (see Atlas.1.4.5)."


id: f223959a17c0b726fe82e008fc7cdc95
created_at: 2023-04-19 13:22:47
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"Table 12.2 presents a summary of Section 12.3 connections between \r CIDs as defined in Table 12.1 and key sectoral assets, utilizing the \r WGII organization of sectors (corresponding to WGII Chapters 2-8). \r Colours are shown for connections with at least medium confidence\r as assessed from sectoral impacts and risk literature, with relevance \r assessed according to the prominence of that specific CID/asset \r connection in analyses of current and future impacts and risk. Within \r each sector there is a  multitude of specific sectoral systems that \r may be affected by CID increases and decreases, with consequences \r further distinguished by region, background climate and socio\u0002economic or ecological context of the affected asset. Our aim is \r therefore to recognize important drivers and the common attributes \r of change within each CID that scientists and practitioners monitor to \r understand current and future challenges for important asset groups, \r [...]"


id: 43a3dd37bdef02eb89323796a1987a68
created_at: 2023-04-19 13:22:47
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"[...] thereby pointing to the climate information that needs to be tailored \r and analysed for impacts and for risk assessment (Section  12.6). \r Additional effects whereby CIDs affect each other (across Table 12.2 \r columns) are discussed as climatic phenomena within WGI. The ways \r sectoral assets affect each other (across Table  12.2 rows) are \r described throughout WGII, for example with information about the "


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vector_id: 15257
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"Information about increasing mean annual and seasonal air \r temperature is relevant in the determination of suitable species range \r for terrestrial, freshwater and intertidal species (Thomas et al., 2004; \r Elith et al., 2010; Hincapie and Caicedo, 2013; Cooper, 2014; Krist et al., \r 2014; Lindner et al., 2014; Saintilan et al., 2014; Lenoir and Svenning, \r 2015; Myers-Smith et al., 2015; Urban, 2015; Thorne et al., 2017). Ocean \r ecosystems are affected by the ocean temperature CID (described in \r Section  12.3.6.1). Species redistribution and extinction studies also \r need information about climate velocity, a comparison of the pace of \r warming to geographical temperature gradients that indicates the rate \r at which a species would have to move to maintain its climatological \r temperature (Thomas et  al., 2004; Loarie et  al., 2009; Dobrowski \r et al., 2013; Burrows et al., 2014; Dobrowski and Parks, 2016; Sittaro \r [...]"


id: f6801a39e0c4d6f5c2837e1dc8e58a59
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 40739
content:
"[...] et al., 2017) with some studies incorporating additional variables beyond \r temperature (Hamann et al., 2015). Many freshwater ecosystems are \r strongly constrained by stream and lake temperatures (Scheurer et al., \r 2009; Comte and Grenouillet, 2013; Contador et al., 2014; Knouft and \r Ficklin, 2017). Warmer and more stratified lake temperatures are more \r conducive to cyanobacteria blooms with implications for ecosystem \r health and water resource quality (Whitehead et al., 2009; Moss et al., \r 2011; Jones and Brett, 2014; Chapra et al., 2017; Shatwell et al., 2019). \r Consideration of nighttime and daytime temperature trends also \r elucidates different biophysical effects on vegetation (Peng et al., 2013). \r Changes in the seasonal timing caused by warming trends are critical \r to species ranges and ecosystem function (Pearce-Higgins et al., 2015; \r Hughes et al., 2017b), and indices that characterize the onset of spring \r [...]"


id: 291832235411e2bcf0b72661d7a2732b
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 4440
content:
"Mean air temperature dictates many aspects of crop cultivation, \r livestock production, agroforestry and output from freshwater \r aquaculture and fisheries, as well as the potential for food \r contamination. Mean warming alters suitable cultivation zones for crop \r species (Braganca et al., 2016; Gendron St-Marseille et al., 2019; IPCC, \r 2019c) and tree species (Hanewinkel et al., 2013; Fei et al., 2017). Crop \r and ecosystem service productivity often responds directly to mean \r temperatures, although this is dependent on farming systems (Bassu \r et al., 2014; Challinor et al., 2014; Lobell and Tebaldi, 2014; Rosenzweig \r et al., 2014; Asseng et al., 2015; Li et al., 2015; Fleisher et al., 2017; \r Zhao et al., 2017; Smith and Fazil, 2019). Many studies relate plant \r development (phenology), insect generation cycles and pest outbreaks \r to growing degree days, an aggregation of daily thermal units above \r a threshold (e.g., Tmean >5degC) that accelerates with warmer conditions \r [...]"


id: c4ac10657f9fec0e510787917282f0ca
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
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index: climate_gpt_v2
vector_id: 31906
content:
"[...] (Hof and Svahlin, 2016; Ruosteenoja et al., 2016; Tripathi et al., 2016). \r Many plants respond to changes in nighttime temperatures that affect \r respiration and transpiration rates (Narayanan et al., 2015; X. Chen \r et  al., 2019), and warming of the soil column is also relevant to "


id: 7a6bf297b4525901731dbd7dbfa9e593
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 18735
content:
"extreme temperatures and high humidity to capture human health \r challenges (Astrom et al., 2013; Chow et al., 2016; Dahl et al., 2017a; \r Im et al., 2017; Coffel et al., 2018; J. Li et al., 2018; Vanos et al., 2020). \r Different optimum temperatures and extreme heat thresholds based on \r local distributions are needed to reflect acclimation of different locations \r and populations (Hajat et al., 2014; WHO, 2014; Kinney et al., 2015b; \r Russo et al., 2015; Petitti et al., 2016; Dosio, 2017; Cheng et al., 2018; \r Lay et  al., 2018; Schwingshackl et  al., 2021). Hot and humid heat \r episodes can be deadly (Mora et al., 2017), are associated with elevated \r hospital intake (Goldie et al., 2017) and lower safety and productivity \r of outdoor labourers (Dunne et al., 2013; Graff Zivin and Neidell, 2014; \r Kjellstrom et al., 2016; Pal and Eltahir, 2016; Y. Zhao et al., 2016; Mora \r et al., 2017; Watts et al., 2018; Orlov et al., 2019). Elevated nighttime \r [...]"


id: a66af4f614d063b69372889d9e58d808
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 26501
content:
"[...] temperatures prevent the human body from experiencing relief from \r heat stress (Zhang et  al., 2012) and can be tracked over extended \r periods of sequential day and night heat extremes (Murage et al., 2017; \r Mukherjee and Mishra, 2018). Extreme heat also exacerbates asthma, \r respiratory difficulties and response to airborne allergens such as hay \r fever (Upperman et al., 2017). Extreme heat affects outdoor exercise \r such as the use of bike-share facilities (Heaney et al., 2019; Vanos et al., \r 2020). Large-scale recreational and sporting events such as marathons \r and tennis tournaments monitor heat extremes when determining the \r viability of host cities (Smith et al., 2016, 2018)."


id: 7b3f7bb9f52880aa598363c1800aa371
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 18885
content:
"Short-term exposure of crops to temperatures beyond a  critical \r temperature threshold can lead to lower yields and above a limiting \r temperature threshold, crops may fail altogether (Schlenker and \r Roberts, 2009; Lobell et al., 2012, 2013; Gourdji et al., 2013; Deryng \r et al., 2014; Schauberger et al., 2017; Tesfaye et al., 2017; Vogel et al., \r 2019). The exact level of these thresholds depends on species, cultivar \r and farm management (Hatfield and Prueger, 2015; Hatfield et al., \r 2015; Bisbis et al., 2018; Grotjahn, 2021). The timing of heatwaves \r is particularly important, as extreme heat is more damaging during \r critical phenological stages (Teixeira et al., 2013; Eyshi Rezaei et al., \r 2015; Fontana et al., 2015; B. Wang et al., 2017; Makinen et al., 2018). \r Extreme canopy temperatures, rather than 2 m air temperatures, \r may be a more robust biophysical indicator of heat impacts on crop \r production (Siebert et  al., 2017). Heat stress indices based upon \r [...]"


id: 1d363315958b90c373f438bc8d94cf5c
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 2314
content:
"[...] temperature and humidity determine livestock productivity as well \r as conception and mortality rates (Key et al., 2014; Dash et al., 2016; \r Pragna et al., 2016; Rojas-Downing et al., 2017)."


id: 9f915d2f974bec2afb4cbd3873ba1967
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 25287
content:
"Frost (Tmin <0degC) is a  natural and fundamental aspect of many \r ecosystems, with more extreme conditions defined as ice (or icing) \r days (Tmax <0degC) (L.A. Vincent et  al., 2018). Agricultural systems \r planning (e.g., planting calendars, seed selection or the opportunity \r to double-crop) requires information about the start and end of the \r frost-free season (Wypych et  al., 2017; Wolfe et  al., 2018). Crops \r and wild plants can be directly damaged by frost, but hard or killing \r frosts (at a threshold several degrees below freezing) can kill crops \r or lower harvest quality depending on duration (which relates to \r soil temperature penetration) and plant developmental stage (Crimp \r et al., 2016a; Cradock-Henry, 2017; G. Li et al., 2018; Makinen et al., \r 2018; Grotjahn, 2021). Earlier disappearance of snow cover reduces \r natural insulation that protects plants and burrowing animals from \r hard frost damages (Trnka et al., 2014; Makinen et al., 2018). In some \r [...]"


id: d138ac6f95dc523c080bd953b6bf31cb
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 34162
content:
"[...] cases an early season warm spell may reduce plant hardiness or induce \r fruit tree flowering that exposes plants to devastating subsequent \r frost impacts (Hufkens et  al., 2012; Hatfield et  al., 2014; Tripathi \r et  al., 2016; Brunner et  al., 2018; DeGaetano, 2018; Unterberger \r et al., 2018; Wolfe et al., 2018). Shifts in the seasonality of frozen soils \r also affect groundwater recharge and surface streamflow for water \r resource applications, particularly when peak precipitation is shifted \r to a season that no longer has frozen soils (Jyrkama and Sykes, 2007)."


id: 53ea4f66b09249114d4c847edda01de5
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 12034
content:
"Figure  12.3 | Conceptual illustration of representative climatic impact\u0002driver thresholds showing how graduating thresholds affect successive \r sectoral assets and lead to potentially more acute hazards as conditions \r become more extreme (exact values are not shown as these must be \r tailored to reflect diverse vulnerabilities of regional assets). Representative \r threshold definitions (T = instantaneous temperature; T = mean temperature): Cities \r and Infrastructures: Ttrans = temperature at which energy transmission lines efficiency \r reduced; Taircraft = temperature at which aircraft become weight-restricted for takeoff; \r Thotroads = temperature above which roads begin to warp; Tstream = temperature at which \r streams are not capable of adequately cooling thermal plants; CDDmin = minimum \r temperature for calculating cooling degree days; HDDmax  =  maximum temperature \r for calculating heating degree days; Tice = temperature at which ice threatens \r [...]"


id: 2ba4c175873d252c2a90afe33d873c4d
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 4936
content:
"[...] transportation; T permafrost = mean seasonal temperature above which permafrost thaws \r at critical depths; Tcoldroads = temperature below which road asphalt performance \r suffers. Health: Tdeadly = temperature above which prolonged exposure may be deadly \r (often combined with humidity for heat indices); Tsevere = temperature above which \r prolonged exposure may cause elevated morbidity; T blooms = mean temperature for \r harmful algal or cyanobacteria blooms; Tdanger = level of dangerous cold temperatures \r (often combined with wind for chill indices); Toverwinter = temperature below which \r disease vector species cannot survive winter. Ecosystems (CID indices for air \r and ocean temperature): Thotlim and Tcoldlim = limiting hot and cold temperatures for \r a given species range; Tfrost = frost threshold; T max and T min = maximum and minimum \r suitable annual mean temperatures for a  given species; Tcrit = critical temperature \r [...]"


id: 465c60d66b46c047fe59338330b2a610
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 9602
content:
"[...] above which a  given species is stressed. Agriculture: Thotlim = temperature above \r which a crop or livestock species dies; Thotpest = maximum (or 'lethal') temperature \r above which an agricultural pest/disease/weed cannot survive; Tcrit = temperature at \r which productivity for a given crop is depressed; T opt = optimal mean temperature \r for a given plant's productivity; GDDmin = threshold temperature for growing degree \r days determining plant development; Tchill = temperature below which chilling units \r are accumulated; Tfrost = temperature below which frost occurs; Thfrost = temperature \r below which a  hard frost threatens crops or livestock; Tcoldpest = minimum winter \r temperature below which a given agricultural pest cannot survive; Tcoldlim = minimum \r temperature below which a given crop cannot survive."


id: 3e47d7e68dee23dc09003b22b32b2c66
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 8134
content:
"A large variety of climate indices and models are utilized to understand \r how river flooding affects both natural or built environments with \r highly variable hazard thresholds, given unique local topography \r and engineered defences such as dams and polders (Arnell and \r Gosling, 2016; Ekstrom et al., 2018). Key transportation routes, built \r infrastructure and agricultural lands are threatened when floods \r exceed design standards commonly based around flood magnitudes \r of a given historic return period (e.g., 1-in-100-year flood event), an \r annual exceedance probability or precipitation intensity-duration\u0002frequency relationships with key indices (e.g.,  10-day cumulative \r precipitation) related to catchment size and properties (Hirabayashi \r et al., 2013; Arnell and Lloyd-Hughes, 2014; Kundzewicz et al., 2014; \r Arnell and Gosling, 2016; Dikanski et al., 2016; Gosling and Arnell, \r 2016; Forzieri et al., 2017; Fluixa-Sanmartin et al., 2018; Koks et al., \r [...]"


id: e5424e08f4acbe068c995591d354df84
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 37638
content:
"[...] 2019). Floods and high-flow events can scour river beds and elevate \r silt loads, reducing water quality and accelerating deposition in \r estuaries and reservoirs (Khan et al., 2018; Parasiewicz et al., 2019). \r Floods can knock down, drown or wash away crops and livestock, \r and partially submerged plants can have yield reduction depending \r on water turbidity and their development stage (Ruane et al., 2013; \r Shrestha et  al., 2019). Basin snowpack properties may also be \r important during heavy rain events, as rain-on-snow events can \r lead to rapid acceleration of flood stages that threaten wildlife and \r society (Hansen et al., 2014)."


id: 72fc1f00e43893f267fede456161df45
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 17479
content:
"Aridity indices may track long-term changes in precipitation, \r evapotranspiration demand, surface water, groundwater or soil \r moisture (Sherwood and Fu, 2014; Herrera-Pantoja and Hiscock, 2015; \r B.I. Cook et al., 2020). Changes in soil moisture and surface water can \r shift the rate of carbon uptake by ecosystems (Humphrey et al., 2018) \r and alter suitable climate zones for wild species and agricultural \r cultivation (Feng and Fu, 2013; Garcia et  al., 2014; Huang et  al., \r 2016a; Schlaepfer et al., 2017; Fatemi et al., 2018; IPCC, 2019c) as \r well as the prevalence of related pests and pathogen-carrying vectors \r (Paritsis and Veblen, 2011; Smith et al., 2020). Water table depth, in \r relation to rooting depth, is also important for farms and forests under \r dry conditions (Feng et  al., 2006). A  reduction in water availability \r (via  aridity or hydrological drought) challenges water supplies \r needed for for municipal, industrial, agriculture and hydropower use \r [...]"


id: db55dbf5885adf4241ba93fbfa005d1e
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 35858
content:
"[...] (Schaeffer et al., 2012; Arnell and Lloyd-Hughes, 2014; Schewe et al., \r 2014; Gosling and Arnell, 2016; van Vliet et al., 2016)."


id: e90918de5ab29cb64834785ce4fbb02a
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 38354
content:
"Heavy downpours can lead to pluvial flooding in cities, roadways, \r farmland, subway tunnels and buildings (particularly those with \r basements; Grahn and Nyberg, 2017; Palko, 2017; Pregnolato et al., \r 2017; Orr et  al., 2018). Heavy precipitation may overwhelm city \r transportation and storm water drainage systems, which are typically \r designed using intensity-duration-frequency information such as \r the return periods for 1-, 6- or 24-hour rainfall totals (Kermanshah \r et al., 2017; Depietri and McPhearson, 2018; Rosenzweig et al., 2018; \r Courty et al., 2019). Heavy rain events can directly cause leaf loss \r and damage, or knock over crops, also driving pollutant entrainment \r and erosion hazards in terrestrial ecosystems and farmland, with \r downstream ramifications for water quality (Hatfield et  al., 2014; \r Segura et  al., 2014; Li and Fang, 2016; Chhetri et  al., 2019). The \r proportion of total precipitation that falls in heavy events also \r [...]"


id: 299bed2b6ab478429e48985520181e02
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 4547
content:
"[...] affects the percentage that is retained in the soil column, altering \r groundwater recharge and deep soil moisture content for agricultural \r use (Fishman, 2016; Lesk et al., 2020)."


id: 2d2ac5dbe69a56186d6c5a8e26cbdfb6
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 5229
content:
"Agricultural and ecological drought indices relate to the ability of \r plants to meet growth and transpiration needs (Table  11.3; Zargar \r et al., 2011; Lobell et al., 2015; Pedro-Monzonis et al., 2015; Bachmair \r et  al.,  2016; Wehner et  al., 2017; Naumann et  al., 2018) and the \r timing and duration of droughts can lead to substantially different \r impacts (Pena-Gallardo et  al., 2019). Drought stress for agriculture \r and ecosystems is difficult to directly observe, and therefore scientists \r use a variety of drought indices (Table 11.3), proxy information about \r changes in precipitation supply and reference evapotranspiration \r demand, the ratio of actual/potential evapotranspiration or a deficit in \r available soil water content, particularly at rooting level (Park Williams \r et al., 2013; Trnka et al., 2014; C.D. Allen et al., 2015; Svoboda and \r Fuchs, 2017; Makinen et al., 2018; Otkin et al., 2018). Severe water \r [...]"


id: db7aa66284090055f633b92f51718911
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 35891
content:
"[...] stress can lead to crop failure, in particular when droughts persist for \r an extended period or occur during key plant developmental stages \r (Hatfield et al., 2014; Jolly et al., 2015; Leng and Hall, 2019). Projections "


id: fefbbdf7a3c18b2551d6aa74a6de4a06
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 42259
content:
"Complex fire weather indices shed light on conditions that increase \r the likelihood of wildfire and shifts in the fire season (Flannigan \r et  al., 2013; Bedia et  al., 2015; Jolly et  al., 2015; Harvey, 2016; \r Littell et al., 2016; Westerling, 2016; Abatzoglou et al., 2019), which \r pose particularly acute challenges for indigenous communities \r (Christianson and McGee, 2019). Projection of future lightning \r frequency provides information on an important natural triggering \r mechanism, particularly when coupled with long-term warming \r and drying trends (Romps et al., 2014; Jin et al., 2015; Veraverbeke \r et al., 2017). Fuel aridity metrics also help determine vegetative fuel \r desiccation and therefore the ignitability, flammability and spread of \r fires when they occur (Abatzoglou and Williams, 2016). The presence \r of snow cover can influence the length of the fire season and the \r penetration of fire danger into new portions of the Arctic tundra \r [...]"


id: 159c8afeafde841f4390a0b4e676cf7d
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 1017
content:
"[...] (Young et al., 2017; Abatzoglou et al., 2019). Data on the changing \r characteristics of local wind circulations like the Santa Ana in \r California shed light on future intensity and spread patterns for fires \r (Jin et al., 2015). Fires also produce smoke plumes that reduce air and \r water quality (via deposition), adversely affecting health, visibility \r and water resources both near and far downwind (Dennekamp and \r Abramson, 2011; McKenzie et  al., 2014; Dreessen et  al., 2016; Liu \r et al., 2016; Martin, 2016)."


id: f7e5114640169afb17ffa3dd3f0456fa
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 40990
content:
"Sand and dust storms erode soils, damage crops and induce \r problems for health, transportation, mechanical equipment and \r built infrastructure corresponding to the magnitude and duration \r of high winds and particulate matter concentrations (Goudie, 2014; \r O'Loingsigh et al., 2014; Crooks et al., 2016; Barreau et al., 2017; \r Bhattachan et  al., 2018; Al Ameri et  al., 2019; Middleton et  al., \r 2019). Dust events may be represented as the number of dust hours \r per year and by particulate matter (PM) concentrations (Leys et al., \r 2011; Spickett et al., 2011; Hand et al., 2016). Photovoltaic panels \r can lose energy production efficiency with dust accumulation \r (Patt et al., 2013; Javed et al., 2017). It is also useful to track dust \r storm deposition of nutrients necessary for coral and tropical forest \r systems, but they may also feed algal blooms harming lake and \r coastal ecosystems, health and recreation (Jickells et  al., 2005; \r [...]"


id: 3a7d084f385178d190cb61d0ef1dac03
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 7490
content:
"[...] Hallegraeff et al., 2014; Gabric et al., 2016). Dust storms also cause \r air pollution and redistribute the soil-based fungus associated with \r Valley fever (Barreau et  al., 2017; Coopersmith et  al., 2017; Tong \r et al., 2017; Gorris et al., 2018)."


id: b9e19ad01862d6ae87aebe423e7d746f
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 29923
content:
"Changes in the speed and direction of prevailing winds can alter \r the profile of seed dispersal, windblown pest and disease vectors, \r animal activities, and dust or pollen dispersal affecting ecosystems, \r agriculture and human health (Reid and Gamble, 2009; Bullock et al., \r 2012; Hellberg and Chu, 2016; Nourani et al., 2017). Seasonal winds \r influence algal blooms, ecosystems and fisheries via lake mixing, \r ocean currents and coastal upwelling (Bakun et al., 2015; Townhill \r et  al., 2018; Woolway et  al., 2020). Changes to wind density also \r modify a  region's wind and wave renewable energy endowment \r (Schaeffer et al., 2012; Sierra et al., 2017; Craig et al., 2018; Devis \r et al., 2018; Tobin et al., 2018; Yalew et al., 2020). D. Li et al. (2020) \r and Karnauskas et al. (2018a) evaluated wind thresholds at turbine \r height (about 80-100 m above ground) including periods outside of \r cut-in (2.5-3 m s-1) and cut-out (about 25 m s-1) levels beyond which \r [...]"


id: 7844645bbd9771265193330895a80c4c
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 18371
content:
"A large number of indices have been used in water resource and \r ecosystem studies to track changes in snow under current and future \r climate conditions, including measurements of the snow water \r equivalent at key seasonal dates, the fraction of precipitation falling \r as snow, the first and last days of snow cover, and cold season \r temperatures (Mills et al., 2013; Pierce and Cayan, 2013; Berghuijs \r et al., 2014; Klos et al., 2014; Musselman et al., 2017; Rhoades et al., \r 2018). Impact studies also examine shifts in seasonal streamflow \r for snow-fed river basins (Mote et al., 2005; Pederson et al., 2011; \r Beniston and Stoffel, 2014; Coppola et al., 2014b, 2018; Fyfe et al., \r 2017; Islam et  al., 2017; Knouft and Ficklin, 2017) as well as the \r geographic extent of snow cover and the depth of frosts when \r snow cover's natural insulation is absent (Scheurer et  al., 2009; \r Millar and Stephenson, 2015). Studies examining the impact of \r [...]"


id: fbc2b99810ef340ebeaf7944e0cd1ff3
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 41661
content:
"[...] snow changes on winter recreation and transportation have used \r thresholds of about 30 cm snow depth or snow water equivalent \r >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 2017b; Spandre et al., 2019; Steiger et al., 2019; Abegg et al., 2021). \r Changes in snow quality also affect recreational activities (Rutty \r et  al., 2017), and artificial snowmaking can augment recreational \r snowpack depending on the number of suitable snowmaking \r hours (e.g.,  where wet bulb globe temperature (WBGT) <-2.2degC; \r Wobus et al., 2017b). Local detail may also be provided by tracking \r the seasonal rain-snow transition line across space and elevation \r (Berghuijs et  al.,  2014) (Pierce and Cayan, 2013; Berghuijs et  al., \r 2014; Klos et al., 2014; Musselman et al., 2017)."


id: e3206b163c2431bd336b9afbc9b47c45
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 37257
content:
"Reductions in the duration of thick sea, lake and river ice influence \r ecosystems as well as ice fishing, hunting, dog sledding and \r snowmobiling, which are recreation activities for some but vital aspects \r of many traditional indigenous communities (Durkalec et  al.,  2015; \r AMAP, 2017; Baztan et  al., 2017; Arp et  al., 2018; Rokaya et  al., \r 2018; Knoll et al., 2019; Meredith et al., 2019; Sharma et al., 2019). \r The seasonal extent of thin ice and iceberg density also determines the \r viability of shipping lanes and seasonal roads (Valsson and Ulfarsson, \r 2011; Pizzolato et al., 2016; AMAP, 2017; Mullan et al., 2017; Sturm \r et  al., 2017), oil and gas exploration timing (Schaeffer et  al., 2012) \r and the seasonality of phytoplankton blooms (Oziel et al., 2017). Sea \r ice is a critical aspect of some ecosystems and fisheries (Massom and \r Stammerjohn, 2010; Jenouvrier et al., 2014; Bindoff et al., 2019; Meredith \r [...]"


id: ea3a65117a5d9bb45fd53be0d1fbbd
created_at: 2023-04-19 13:22:47
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vector_id: 38561
content:
"[...] et al., 2019). Various definitions of 'ice free' Arctic Ocean conditions can \r be tailored to represent transportation needs, including thresholds of \r ice coverage (<5% or <30% or <1 million km2) in September or over \r a four-month period (Laliberte et al., 2016; Jahn, 2018)."


id: 2195ac0ed6760f8167bcd647f04b861
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 3118
content:
"Effective management of coastal ecosystems, cities, settlements, \r beaches and infrastructure requires information about coastal \r erosion driven by storm surge, waves and sea level rise (Dawson \r et al., 2009; Hinkel et al., 2013; Harley et al., 2017; Mentaschi et al., \r 2017). Coastal erosion is generally accompanied by shoreline retreat, \r which can occur as a  gradual process (e.g.,  due to sea level rise) \r or as an episodic event due to storm surge and/or extreme waves, \r especially when combined with high tide (Ranasinghe, 2016). The \r most commonly used shoreline retreat index is the magnitude of \r shoreline retreat by a  pre-determined planning horizon such as \r 50 or 100 years into the future. Commonly used metrics for episodic \r coastal erosion include the beach erosion volume due to the 100-year \r recurrence storm wave height, the full exceedance probability \r distribution of coastal erosion volume (Li et al., 2014a; Pender et al., \r [...]"


id: 86a8347a25fc32007ee4b1278f43ec76
created_at: 2023-04-19 13:22:47
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vector_id: 20855
content:
"[...] 2015; Ranasinghe and Callaghan, 2017) and the cumulative storm \r energy and storm power index (Godoi et al., 2018). The destruction \r or overtopping of barrier islands may lead to irreversible changes in \r the physical system as well as in coastal ecosystems (Carrasco et al., \r 2016; Zinnert et  al., 2019). Shoreline position change rates along \r inlet-interrupted coasts may also be affected by changes in river \r flows and fluvial sediment supply (Hinkel et al., 2013; Bamunawala \r et al., 2018; Ranasinghe et al., 2019). Permafrost thaw and Arctic sea \r ice decline also reduce natural coastal protection from wave erosion \r for communities and industry (Forbes, 2011; Melvin et al., 2017)."


id: a79d70fe456dbdcaf68eb00fe7e1cd3a
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 26702
content:
"Sea level rise hazards for coastal ecosystems, infrastructure, farmland, \r cities and settlements in a  particular region are often driven by \r regional changes in relative sea level (RSL) that account for land \r uplift or subsidence and thus represent local asset vulnerability better \r than global mean sea level (Box 9.1; Hallegatte et al., 2013; Hinkel \r et al., 2013; McInnes et al., 2016; Weatherdon et al., 2016; Brown et al., \r 2018; IPCC, 2019b; Rasoulkhani et  al., 2020). Vertical land motion \r (i.e.,  land subsidence) caused by local fluid (gas or groundwater) \r extraction can also have a  large influence on relative sea levels \r (Minderhoud et  al., 2020). Several indices have been suggested to \r signify coastal inundation, including a threshold when the local land \r elevation falls below the local mean higher high water (MHHW) that \r is close to the 'high tide' level (Kulp and Strauss, 2019) or a threshold \r when flooding occurs about once every two weeks (Sweet and Park, \r [...]"


id: e5e50e95ad68c8f9f309a811ac8425f3
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
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index: climate_gpt_v2
vector_id: 37746
content:
"[...] 2014; Dahl et al., 2017b). RSL rise (or RSLR) can drive increased inland \r penetration of above-ground and subterranean salt water fronts \r (i.e., salinity intrusion) affecting coastal ecosystems, agriculture and \r water resources (Ferguson and Gleeson, 2012; Kirwan and Megonigal, \r 2013; Rotzoll and Fletcher, 2013; Chen et al., 2016; Colombani et al., \r 2016; Holding et  al., 2016; Sawyer et  al., 2016; Mohammed and \r Scholz, 2018). The rate of RSLR can determine the survival and net \r pressure on niche coastal ecosystems such as mangroves, tidal flats, \r sea grasses and coral reefs (Hubbard et al., 2008; Craft et al., 2009; \r Bell et al., 2013; Kirwan and Megonigal, 2013; Alongi, 2015; Ellison, \r 2015; Lovelock et al., 2015; Ward et al., 2016; Lee et al., 2018)."


id: 1fe2269295c603e725484ed2c8e249d9
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
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index: climate_gpt_v2
vector_id: 2816
content:
"Marine heatwaves (MHW) push water temperatures above key \r thresholds and have been associated with coral bleaching episodes, \r species shifts and harmful algal blooms that can disrupt ecosystems, \r tourism 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 2019; Sully et al., 2019). The duration and return period of marine \r heatwaves provide insight into aggregate stresses on marine species, \r fisheries and ecosystems, with various indices gauging cumulative \r intensity or the number of days, weeks or months exceeding critical \r thresholds (Frieler et al., 2013; Frolicher et al., 2018; Hughes et al., \r 2018b; Cheung and Frolicher, 2020). Hobday et  al. (2016) defined \r marine heatwaves as the exceedance of the 90th percentile of the sea \r surface temperature (SST) distribution on a given Julian day during \r five or more consecutive days, while Box 9.2, Figure 1 shows MHW \r [...]"


id: c5bd7940edf0a726e1d0e227b6848cb0
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
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index: climate_gpt_v2
vector_id: 32098
content:
"[...] as an exceedance of 99th-percentile 11-day de-seasonalized SSTs. \r The return period of marine heatwaves is also critical in determining \r a  coral system's ability to recover before the next event (Hughes \r et al., 2018a)."


id: c7f8297cd4b8317ffde6eabd34492b46
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 32486
content:
"Although future air pollution will be strongly driven by air quality \r policies, anthropogenically-driven changes to temperature, humidity, \r precipitation and synoptic patterns have the potential to affect the \r emissions, production, concentration and transport of particulate matter \r (e.g., from dust, fires, pollen) and gaseous pollutants such as sulphur \r dioxide, tropospheric ozone and nitrogen dioxide (Section  6.5) with \r resulting impacts on human health, agriculture and ecosystems (Ren \r et al., 2011; Fiore et al., 2015; Kinney et al., 2015a; Tian et al., 2016; \r Orru et al., 2017; Emberson et al., 2018; Hayes et al., 2020). Information \r about conditions leading to poor air quality is also important for \r visibility in natural parks and tourist locations (Yue et  al., 2013; Val \r Martin et al., 2015), as well as the efficiency of solar photovoltaic panels \r (Sweerts et al., 2019). Relevant information about conditions favouring \r [...]"


id: 7f1920c943b20f27b176e3b6f13ebe3a
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
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index: climate_gpt_v2
vector_id: 19512
content:
"[...] air pollution includes tracking warmer conditions that accelerate ozone \r formation (Peel et al., 2013; Schnell et al., 2016) and the frequency and \r duration of stagnant air events (Horton et al., 2014; Fann et al., 2015; \r Lelieveld et al., 2015; Vautard et al., 2018), although no regional index \r has proven sufficient to capture regional changes or acute events (Kerr \r and Waugh, 2018; Schnell et al., 2018). By contrast, precipitation and \r moister air tend to reduce pollution (Section 6.5)."


id: a81cdb1888f168cf727f27c87199ad5f
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 26789
content:
"Carbon dioxide (CO2) is a  well-mixed greenhouse gas with global \r repercussions on Earth's energy balance; however, atmospheric \r CO2 concentration changes at the land surface also affect plant \r functions within ecosystems and agriculture (see also Chapter  5). \r High CO2 concentration can increase photosynthesis rates and \r primary production within natural ecosystems (Norby et  al., 2010; \r Ratliff et al., 2015; Zhu et al., 2016) and agricultural crops (Hatfield \r et al., 2011; Leakey et al., 2012; Bell et al., 2013; Glenn et al., 2014; \r Nagelkerken and Connell, 2015; Behrenfeld et al., 2016; Deryng et al., \r 2016; Kimball, 2016). High CO2 concentration affects total biomass \r and plant sugar content important to bioenergy production (Schaeffer \r et al., 2012), but also helps some pests and weeds flourish (Hamilton \r et  al., 2005; Wolfe et  al., 2008; Valerio et  al., 2013; Korres et  al., \r 2016; Stinson et  al., 2016; Ramesh et  al., 2017), while potentially \r [...]"


id: 5d9a059edd839a8ac830b7b83eb1bd84
created_at: 2023-04-19 13:22:47
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vector_id: 13755
content:
"[...] shifting the effectiveness of herbicides (Varanasi et al., 2016; Refatti \r et al., 2019). Higher CO2 concentration reduces transpiration losses \r during drought conditions (Cammarano et al., 2016; Deryng et al., \r 2016; Swann et al., 2016; Durand et al., 2018), which also changes \r the energy balance within the plant canopy (Webber et al., 2017). \r Higher CO2 reduces the nutritional density of crops and forage lands \r (Loladze, 2014; Muller et al., 2014; Myers et al., 2014, 2017; X. Li \r et al., 2016; M.A. Lee et al., 2017; Smith and Myers, 2018; Zhu et al., \r 2018; Beach et al., 2019) and can increase the production of toxins \r (Ziska et al., 2007) and allergenic pollen (Schmidt, 2016)."


id: f61f363ed9d22324961d6d8644a86144
created_at: 2023-04-19 13:22:47
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content:
"Regional assessment method and tables: In each section herein \r (Sections 12.4.1-12.4.10), we assess changes in sector-relevant CIDs \r following the main CID categories defined in Section  12.2 through \r commonly used indices and thresholds relevant for sectors described in \r 12.3. Sections 12.4.1-12.4.9 each include a summary qualitative CID \r assessment table (Tables 12.3-12.11) showing the confidence levels \r associated with the direction of projected CID changes (i.e., increasing \r or decreasing) for the mid-century period (2041-2060) relative to \r the recent past, for scenarios RCP4.5, SSP2-4.5, SRES A1B, or above \r (RCP6.0, RCP8.5, SSP3-7.0, SSP5-8.5, SRES A2), which approximately \r encompasses GWLs of 2.0degC to 2.4degC (as best estimate; see \r Chapter 4, Table 4.5). For scenarios RCP2.6, SSP1-2.6 or SSP1-1.9, the \r signal may have lower confidence levels in some cases due to smaller \r overall changes, embedded in a similar internal variability, and to the \r [...]"


id: 6a6ce75ab2cd9dc616bc77a0f6ae80fe
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
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"[...] availability of relatively few studies that account for these scenarios. \r Nevertheless, CID changes under these lower emissions scenarios are \r included in the text whenever information is available. For each cell in \r Tables 12.3-12.11, literature is assessed, aided by global Figure 12.4 or \r regional Figures 12.5-12.10. Confidence in projections is established \r considering evidence emerging from observations, attribution and \r projections, as explained in Cross-Chapter Box 10.3 while considering \r the amount of evidence and agreement across models and studies \r and model generations."


id: 1f100db92bc9b7f328a744837c2e9159
created_at: 2023-04-19 13:22:47
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index: climate_gpt_v2
vector_id: 2670
content:
"Threats to many sectoral assets and associated systems may also be \r compounded when multiple hazards occur simultaneously in the same \r place, affect multiple regions at the same time, or occur in a sequence \r that may amplify overall impact (Section  11.8; IPCC, 2012; Clarke \r et al., 2018; Zscheischler et al., 2018; Raymond et al., 2020). There is \r emerging literature on many connected extremes and their associated \r hazards (e.g.,  climatic conditions that could drive multi-breadbasket \r failures; Trnka et al., 2019; Kornhuber et al., 2020), but a full accounting \r is not practical here especially considering the many possible CID \r combinations and the need to assess how exposed systems would be \r vulnerable to compound CIDs (assessed in WGII). Table 12.2 is once \r again instructive here in considering hazard-related storylines, as the \r multiple CIDs affecting a given sectoral asset (assessing across a row \r of Table  12.2) point to potentially dangerous hazard combinations. \r [...]"


id: 3057710afcc8b9621f129973b714c4ef
created_at: 2023-04-19 13:22:47
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index: climate_gpt_v2
vector_id: 5758
content:
"[...] Similarly, change in a single CID has the potential to affect multiple \r sectoral assets (assessing down a column of Table 12.2) in a manner \r with broader systemic implications (AR6 WGII)."


id: 2ac993e49c8e3257d271c2eb651e4a1d
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
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index: climate_gpt_v2
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content:
"The confidence levels associated with the directions of projected CID \r changes are synthesized assessments based on literature that may \r utilize different indices and baseline periods or projections by GWLs. \r For extreme heat, cold spell, heavy precipitation and drought CIDs that \r are assessed in Chapter 11, here we draw projections from the 2degC \r GWL tables in Section 11.9. In some cases, more details are needed in \r order to emphasize one aspect of projected CID change. For instance, \r the change in a CID may be different for intensity, duration, frequency; \r or there can be strong sub-regional or seasonal signals; or different \r CID indices may have conflicting signals. A footnote is added in such \r cases, but a confidence level for a direction of projected change is \r given based on the Section 12.3 assessment of aspects of regional \r CID change that are most relevant for impacts and for risks. As an \r example, tropical cyclones are increasing in intensity but decreasing \r [...]"


id: 5a3387d49c13c9d6ddc88a309a859d3d
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
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index: climate_gpt_v2
vector_id: 13119
content:
"[...] in frequency in some regions. Here, in assessing the confidence of \r the direction of projected change in the Tropical cyclone CID (i.e., the \r colour of the table cell), we assign more weight to the 'intensity' \r rather than the 'frequency', corresponding to the higher relevance \r of the intensity of major tropical cyclones for risk assessment. Low \r confidence of changes, arising from lack of evidence, strong spatial \r or seasonal heterogeneity, or lack of agreement, are represented by \r colour-less cells, and, for the sake of simplicity, only two categories \r of confidence are given: medium confidence and  high  confidence\r (and higher). In addition, CID assessment tables also indicate observed "


id: 6c2b8ab937084483826d80e67a09ac1a
created_at: 2023-04-19 13:22:47
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index: climate_gpt_v2
vector_id: 16328
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 Index (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 (%) 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 dataset; 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 changes 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 [...]"


id: ebd8a47398cb9233a6d2a383eb9f2940
created_at: 2023-04-19 13:22:47
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content_type: text
index: climate_gpt_v2
vector_id: 38859
content:
"[...] correction is applied to daily maximum temperature and HI data (Atlas.1.4.5). Uncertainty is represented using the simple approach: no overlay indicates regions with high \r model 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 the 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 show 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 details on data sources and processing are available in the chapter data table (Table 12.SM.1)."


id: b0b147d1fdefd8abaf989276761fa7fa
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vector_id: 28313
content:
"future ocean acidification will increase given future increases in \r greenhouse gases (Section  5.4). Areas below calcium carbonate \r saturation thresholds expanded from the 1990s to 2010 and Meredith \r et al. (2019) indicated that both the Southern and Arctic Oceans will \r experience year-round under-saturation conditions by 2100 under \r RCP8.5. The vertical level of the aragonite saturation horizon off \r the Pacific coast of North America has risen towards the surface by \r 30-50 m since pre-industrial times (Mathis et al., 2015b; Feely et al., \r 2016). In a study of US coastlines, Ekstrom et al. (2015) mapped out \r the projected year when aragonite saturation state drops below 1.5 \r (a sublethal threshold for bivalve mollusc larvae), finding hazardous \r conditions before 2030 from northern Oregon to Alaska and before \r 2100 for the Pacific coast and Atlantic coastline north of New Jersey. \r Mathis et  al. (2015a) found that surface waters in the Beaufort \r [...]"


id: 25bbba2bff4e600c5d82ddf9042770be
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vector_id: 3823
content:
"[...] Sea have already dropped below aragonite saturation thresholds, \r projecting further declines and the Chukchi Sea also dropping below \r saturation by about 2030."


id: a78b346817154acb4d18fc3162f63aae
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
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index: climate_gpt_v2
vector_id: 26690
content:
"Air pollution weather: The effect of climate change on air quality is \r assessed in Section 6.5 with limitations for local planning explained \r in Section 6.1.3, and only a brief summary is given here. Section 6.5 \r notes that climate change will have a  small burden on particulate \r matter (PM) pollution (medium confidence) while the main controlling \r factor in determining future concentrations will be future emissions \r policy for PM and their precursors (high confidence). Surface ozone is \r sensitive to temperature and water vapour changes, but future levels \r depend on precursor emissions. Although there is low confidence in \r precise regional changes (Section 6.5), climate change will generally \r introduce a surface ozone (O3) penalty (increasing concentrations with \r increasing warming levels) over regions with high anthropogenic and/\r or natural ozone precursor emissions, while in less polluted regions \r higher temperatures and humidity favour destruction of ozone (Schnell \r [...]"


id: 9904080b8f42a3ee34a2bf0be956abb8
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 24137
content:
"[...] et al., 2016). There is low confidence in changes to future stagnation \r events given the lack of robust projections of related atmospheric \r conditions, such as future atmospheric blocking events (Sections 3.3.3 \r and 8.4.2). The response of regional air pollution to climate change \r will also be affected by other CIDs like fire weather, as well as by \r ecosystem responses such as shifts in emissions by vegetation (Fiore \r et al., 2015). Section 6.5 assessed medium confidence that climate\u0002driven changes to meteorological conditions generally favour \r extreme air pollution episodes in heavily polluted environments, but \r noted strong regional and metric dependencies. Given the dominant \r influence of future air quality policies, uncertainties around stagnation \r or blocking events, and the potential contrasting regional changes of \r conditions favouring ozone and PM formation, accumulation and \r destruction, cells in Tables 12.3-12.11 for air pollution weather are \r [...]"


id: 8fef9ee9071e225be83073e41af08a56
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
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index: climate_gpt_v2
vector_id: 22531
content:
"Atmospheric CO2 at surface: Observations show rising atmospheric \r CO2 concentrations at the surface over all Earth regions (robust \r evidence, high agreement) (Sections 2.2 and 5.1.1), and it is virtually \r certain that surface atmospheric CO2 concentrations will continue to \r increase without substantial changes to emissions (Section 5.4).\r Radiation at surface: Radiation has undergone decadal variations \r in past observations, which are mostly responding to the so-called \r dimming and brightening phenomenon driven by the increase and \r decrease of aerosols. Over the last two decades or so, brightening \r continues in Europe and North America and dimming stabilizes \r over South and East Asia and increases in some other areas \r (Section  7.2.2.3). Future regional shortwave radiation projections \r depend mostly on cloud trends, aerosol and water vapour trends, \r and stratospheric ozone when considering UV radiation. Over Africa \r [...]"


id: ecfc03343256ec2a9d4c6c26fffd72b
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 39064
content:
"[...] in 2050 and beyond, there is medium confidence that radiation will \r increase in North and South Africa and decrease over the Sahara, \r North Eastern Africa and Western Africa (Wild et  al., 2015, 2017; \r Soares et  al., 2019; C. Tang et  al., 2019; Sawadogo et  al., 2020, \r 2021). Over Asia, the CMIP5 multi-model mean response shows \r that solar radiation will decrease in South Asia and increase in East \r Asia (medium confidence) by the mid-century RCP8.5 (Wild et  al., \r 2015, 2017; Ruosteenoja et  al., 2019b). Projected solar resources \r show an increasing trend throughout the 21st century in East Asia \r under RCP2.6 and RCP8.5 scenarios in CMIP5 simulations (medium \r confidence) (Wild et al., 2015; F. Zhang et al., 2018; Shiogama et al., \r 2020). More sunshine is projected over Australia in winter and spring \r by the end of the century (medium confidence) with the increases \r in Southern Australia exceeding 10% (CSIRO and BOM, 2015; \r [...]"


id: 75184e4096c04cf322f268c93423d4e6
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 17839
content:
"[...] Wild et  al., 2015). In  Central and South America, there is medium \r confidence of increasing solar radiation over the Amazon basin and \r the northern part of South America (medium confidence) (Wild et al., \r 2015, 2017; de Jong et  al., 2019). There is low confidence for an \r increase in surface radiation in central Europe, owing in particular to \r disagreement in cloud cover across global and regional models (Jerez \r et al., 2015; Bartok et al., 2017; Craig et al., 2018), as well as water \r vapour. The treatment of aerosol appears to be key in explaining these \r differences (Boe, 2016; Undorf et al., 2018; Boe et al., 2020; Gutierrez \r et al., 2020). Regional and global studies, however, indicate that there \r is medium confidence in increasing radiation over southern Europe \r and decreasing radiation over Northern Europe. Increasing radiation \r trends are also found over southern and eastern USA, and decreasing \r [...]"


id: 3151d170ed58884065a52ec6b798ff1f
created_at: 2023-04-19 13:22:47
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vector_id: 5926
content:
"[...] trends over North-Western North America (Wild et al., 2015; Losada \r Carreno et al., 2020), despite large differences between responses \r from regional climate models (RCMs) and general circulation models \r (GCMs) over southern and eastern USA (low confidence), where, as \r for Central Europe, the role of aerosols appears important (Chen, \r 2021). Over polar regions there is medium confidence of a decrease \r in radiation due to increasing moisture in the atmosphere and clouds \r (Wild et al., 2015)."


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"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 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 indicate shoreline \r 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 WGI Reference \r 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 absolute values in \r 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 ('mid' 2041-2060 and \r [...]"


id: fdf59d559a7e70b3f46b4d3bee1cbe16
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
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content:
"[...] 'long' 2081-2100) and for three global warming levels (GWLs, defined relative to the pre-industrial period 1850-1900): 1.5degC (purple), 2degC (yellow) and 4degC (brown). The \r 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, CMIP5 by \r 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 projections of \r 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 regional \r mean 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 [...]"


id: 1d5adeaf152bc5fcf2d7c158fb99a234
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 2347
content:
"[...] 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 available in the chapter data table (Table 12.SM.1)."


id: 2948143f304f34c85d7b258a952f2667
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 4483
content:
"Mortality-related heat stress levels and deadly temperatures are very\r likely to become more frequent in the future in RCP8.5/SSP5-8.5 and \r RCP4.5/SSP2-4.5 and for a  2degC global warming (Mora et  al., 2017; \r Nangombe et al., 2018; Sylla et al., 2018a; Rohat et al., 2019; Q. Sun \r et al., 2019). In particular the equatorial regions where heat is combined \r with higher humidity levels, but also North Africa, the Sahel and Southern \r Africa (Figure 12.4d-f) are among the regions with the largest increases \r of heat stress (Zhao et al., 2015; Ahmadalipour and Moradkhani, 2018; \r Coffel et  al., 2018). Mitigation scenarios make a  large difference in \r frequency of exceedance of high heat stress indices thresholds (e.g., HI \r > 41degC) by the end of the century (Figure 12.4d-f; Schwingshackl et al., \r 2021). In West Africa and Central Africa, under SSP5-8.5, the expected \r number of days per year with HI > 41degC will increase by around 200 \r [...]"


id: cd8a637018bc33eccf421bdae8c9231c
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 33496
content:
"[...] days while in SSP1-2.6 such exceedances are expected to increase by \r less than 50 days per year (Figure 12.4; Figure 12.SM.2)."


id: 3145b2ddaa510947adb51d7f1f7f3f21
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 5920
content:
"for the A1B scenario (medium confidence) (Figure 12.5; Arnell and \r Gosling, 2013). Specifically, in Western Africa there is not a univocal \r pattern of change for future projections (Roudier et  al., 2014); \r However, under RCP8.5, there is medium confidence of a projected \r increase of 20-year flood magnitudes by 2050 in countries within \r the Niger River basin (Aich et al., 2016b) and low confidence (limited \r evidence) of an increase in extreme peak flows and their duration in \r countries of the Volta River basin by 2050 and 2090 (Jin et al., 2018). \r A significant median change of flood magnitude for the Gambia River \r (-4.5%) and for the Sessandra (+14.4%) and Niger (+6.1%) are \r projected under several scenarios between mid- and end-of-century \r (Roudier et al., 2014). In East Africa, extreme flows are projected to \r increase for regions within the Blue Nile basin with low confidence\r (limited evidence) (Aich et  al., 2014). However, uncertainty due to \r [...]"


id: b1e20821a0119805bdb09e25891b14fd
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
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index: climate_gpt_v2
vector_id: 28521
content:
"[...] the climate scenario dominates the projection of extreme flows (Aich \r et al., 2014; Krysanova et al., 2017) for the Blue Nile and Niger River \r basins. Averaged over the African continent for different levels of \r global warming, the present-day 100-year return period flood levels \r will have a return period of 40 years in 1.5degC and 2degC (Alfieri et al., \r 2017) and 21 years for 4degC warmer climate (Hirabayashi et al., 2013; \r Alfieri et al., 2017)."


id: ba5525ddf648e35e90bdc21f718efa16
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 29999
content:
"Agricultural and ecological drought: Farmers and food security \r experts in East Africa have noted spatial extensions in seasonal \r agricultural droughts in recent decades (Elagib, 2014), but it is difficult \r to disentangle these trends from climate variability. In Ethiopia, past \r severe agricultural drought conditions in the northern regions are \r moderately common events in recent years (Zeleke et al., 2017). In \r Southern Africa, the number of 'flash' droughts (with rapid onset and \r durations from a few days to couple of months) have increased by \r 220% between 1961 and 2016 as a result of anthropogenic warming \r (Yuan et al., 2018). Section 11.9 notes medium confidence increases \r in agricultural and ecological drought trends in North, Western \r and Central Africa as well as both Southern Africa regions. The \r most striking drought is the Western Cape drought in 2015-2018, \r a prolonged drought that resulted in acute water shortages (Wolski, \r [...]"


id: 27b404153b95ea5584a2760d4659e0ed
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
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index: climate_gpt_v2
vector_id: 4170
content:
"[...] 2018; Burls et  al., 2019; Section  10.6.2). Anthropogenic climate \r change caused a  threefold increase in the probability of such \r a drought to occur (Chapters 10 and 11; Botai et al., 2017; Otto et al., \r 2018). Section 11.9 assesses increases in agricultural and ecological \r drought at 2degC GWL for North Africa and West Southern Africa (high \r confidence) and for East Southern Africa and Madagascar (medium \r confidence), with confidence generally rising for higher emissions \r scenarios (Sylla et al., 2016b; Zhao and Dai, 2017; Diedhiou et al., \r 2018; Abiodun et al., 2019; Todzo et al., 2020; Coppola et al., 2021b). \r Liu et al. (2018b) identified the Southern Africa region as the drought \r 'hottest spot' in Africa in 1.5degC and 2degC global warming scenarios."


id: 71e29a5532ffce3da9ac8a1e838032da
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 17279
content:
"Severe wind storm: A limited number of studies allow an assessment \r of past trends in wind storms. In West Africa and specifically in the \r Sahel band, more intense storms have occurred since the 1980s \r (low confidence, limited evidence). A persistent and large increase of \r frequency of Sahelian mesoscale convective storms has been found \r in several studies (Panthou et  al., 2014; C.M. Taylor et  al., 2017), \r with consequences for extreme rainfalls, and potentially extreme \r winds (low confidence, limited evidence). There is low confidence\r of a  general increasing trend in extreme winds across Western, \r Central, Eastern and Southern Africa in a majority of regions by the \r middle of the century even in high-end scenarios. The frequency \r of Mediterranean wind storms reaching North Africa, including \r Medicanes, is projected to decrease, but their intensities are projected \r to increase, by the mid-century and beyond under SRES A1B, SRES A2 \r [...]"


id: 843795b6ae76a41465eeaffca7930cd3
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
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index: climate_gpt_v2
vector_id: 20415
content:
"[...] and RCP8.5 (medium confidence) (Chapter 11; Cavicchia et al., 2014; \r Walsh et al., 2014; Tous et al., 2016; Romera et al., 2017; Romero and \r Emanuel, 2017; Gonzalez-Aleman et al., 2019)."


id: 7b0360011385ba84247ea2ca8696fbb2
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 18847
content:
"Snow and glacier: African glaciers are located in East Africa and more \r specifically on Mount Kenya, the Rwenzori Mountains and  Mount \r Kilimanjaro, with glaciers reducing substantially in each region (high \r confidence) (Taylor et al., 2006; Cullen et al., 2013; Chen et al., 2018; \r Prinz et  al., 2018; Wang and Zhou, 2019). Observation and future \r projection of African glacier mass changes are assessed in Section 9.5.1 \r within the low-latitude glacier region, which is one of the regions with \r the largest mass loss even under low-emissions scenarios (assessment \r of this region is dominated by glaciers in the South American Andes, \r however) (high confidence). Glaciers in the low-latitude region will \r lose 67 +- 42%, 86 +- 24% and 94 +- 13% of their mass by the end \r of the century for RCP2.6, RCP4.5 and RCP8.5 scenarios respectively \r (Marzeion et  al., 2020). Cullen et  al. (2013) calculated that even \r imbalances between the Mount Kilimanjaro glaciers and present-day \r [...]"


id: b727ed942c08df8f92d887957075c5b6
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 29434
content:
"[...] climate would be enough to eliminate the mountain's glaciers by 2060. \r Snow water equivalent and snow cover season duration also decline in \r the East African mountains, Ethiopian Highlands and Atlas Mountains \r with climate change (high confidence) (Lopez-Moreno et al., 2017)."


id: 6c3a10b49e4a1aee5707b12c6001ff49
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 16335
content:
"Projections indicate that a  vast majority of sandy coasts in the \r region will experience shoreline retreat throughout the 21st century \r (high confidence), while parts of the ESAF and western MDG coastline \r are projected to prograde over the 21st century, if present ambient \r trends continue. Median shoreline change projections (CMIP5), \r relative to 2010, presented by Vousdoukas et al. (2020b) show that, \r under RCP4.5, shorelines in Africa will retreat by between 30 m (SAH, \r NEAF, WSAF, ESAF, MDG) and 55 m (WAF, CAF), by mid-century. By \r the same period but under RCP8.5, the median shoreline retreat is \r projected to be between 35 m (SAH, NEAF, WSAF, ESAF) and 65 m \r (WAF, CAF). By 2100, more than 100 m of median retreat is projected \r in WAF, CAF and SEAF under RCP4.5, while under RCP8.5, more than \r 100 m of shoreline retreat is projected in all regions except NEAF \r and WSAF. Under RCP8.5 especially, the projected retreat by 2100 \r [...]"


id: 7977b72a4e0e0b612d01b74f5e25971a
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
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vector_id: 18570
content:
"[...] is greater than 150 m in WAF and CAF. The total length of sandy \r coasts in Africa that is projected to retreat by more than a median of \r 100 m by 2100 under RCP4.5 and RCP8.5 is about 13,000 km and \r 17,000 km respectively, an increase of approximately 33%."


id: 8f45b9071fb893f561ec25836778161b
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
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vector_id: 22416
content:
"Extreme total water level (ETWL) magnitude and occurrence frequency \r are expected to increase throughout the region (high confidence) \r (Figure  12.4p-r and Figure  12.SM.6). Across the continent, the \r 5th-95th percentile range of the 1-in-100-year ETWL is projected to \r increase (relative to 1980-2014) by 7-36 cm and by 14-42 cm by \r 2050 under RCP4.5 and RCP8.5 respectively. By 2100, this range is \r projected to be 28-86 cm and 43-190 cm under RCP4.5 and RCP8.5 \r respectively (Vousdoukas et al., 2018; Kirezci et al., 2020). In terms \r of ETWL occurrence frequencies, the present-day 1-in-100-year ETWL \r is projected to have median return periods of around 1-in-10-years \r to 1-in-20-years by 2050 and 1-in-1-year to 1-in-5-years by 2100 \r in southern and North Africa and occur more than once per year \r by 2050 and 2100 in most of East and West Africa under RCP4.5 \r (Vousdoukas et  al., 2018). The present-day 1-in-50-year ETWL is \r [...]"


id: 55962e854ad1089e50f8ec6ba7a217c5
created_at: 2023-04-19 13:22:47
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index: climate_gpt_v2
vector_id: 12328
content:
"[...] projected to occur around three times a year by 2100 with an SLR of \r 1 m in Africa (Vitousek et al., 2017)."


id: de285d1a244f527724bf2bf224aa1de1
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 36346
content:
"Extreme heat events are very likely to become more intense and/or more \r frequent in SAS, WCA, ARP, EAS, and SEA by the end of 21st century, \r especially under RCP6.0 and RCP8.5 (Figure 12.4a-c and Chapter 11; \r Lelieveld et al., 2016; Pal and Eltahir, 2016; Guo et al., 2017; Mishra \r et al., 2017; Dosio et al., 2018; Lin et al., 2018; Nasim et al., 2018; Shin \r et al., 2018; Hong et al., 2019; Su and Dong, 2019; Khan et al., 2020; \r Kumar et al., 2020). The exceedance of the dangerous heat stress 41degC \r threshold of the HI is expected to increase by about 250 days in SEA \r and by 50-150 days in SAS, WCA, ARP and EAS for SSP5-8.5 at the end \r of the century (Figure 12.4d-f and Figure 12.SM.2). Under SSP1-2.6, \r the increase would be restricted to less than 30 days in many of these \r regions except SEA, where the number of exceedance days increases by \r about 100 days in some areas. Such increases are already present in the \r [...]"


id: c87f7703c780cce2e2155cfc3d816f3f
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
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index: climate_gpt_v2
vector_id: 32578
content:
"[...] middle of the century (Figure 12.4d-f; Schwingshackl et al., 2021). In \r these regions, the increase in number of days with exceedance of 35degC \r of 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 Siberian regions), and by more than 60 days in areas of SEA, and a large \r difference is found between low- and high-end scenarios in the end of \r the century (high confidence) (Figure 12.4b). Over WSB, ESB and RFE \r also, an increase of extreme heat durations and frequency is expected in \r all scenarios (high confidence) (Kattsov et al., 2017; Khlebnikova et al., \r 2019b)."


id: ee246105ced78865a597479d2290c305
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 39268
content:
"Landslide: The majority of non-seismic fatal landslide events were \r triggered by rainfall, and Asia is the dominant geographical area \r of landslide distribution (Froude and Petley, 2018). Floods and \r landslides are the most frequently occurring natural hazards in the \r eastern Himalayas and hilly regions, particularly caused by torrential \r rain during the monsoon season (Gaire et al., 2015; Syed and Al Amin, \r 2016). They accounted for nearly half of the events recorded in the \r countries of the HKH region (Vaidya et al., 2019). Intense monsoon \r rainfall in northern India and western Nepal in 2013, which led to \r landslides and one of the worst floods in history, has been linked \r to increased loading of GHG and aerosols (Cho et al., 2016). Due to an \r increase of heavy precipitation and permafrost thawing, an increase in \r landslides is expected in some areas of Asia, such as northern Taiwan \r (China), some South Korean mountains, Himalayan mountains, and \r [...]"


id: 808d746128cfe78e9f0d58cd531e8ff0
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vector_id: 19745
content:
"[...] permafrost territories of Siberia, and the increase is expected to be \r the greatest over areas covered by current glaciers and glacial lakes \r (medium confidence, medium evidence) (Kim et  al., 2015; Kharuk \r et al., 2016; C.-W. Chen et al., 2019; Kirschbaum et al., 2020)."


id: 74538b54eb75cc40191033ef5828275b
created_at: 2023-04-19 13:22:47
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vector_id: 17701
content:
"Aridity: Aridity in West Central Asia and parts of South Asia \r increased in recent decades (medium confidence), as documented in \r Afghanistan (Qutbudin et al., 2019), Iran (Zarei et al., 2016; Zolfaghari \r et  al., 2016; Pour et  al., 2020), most parts of Pakistan (K. Ahmed \r et al., 2018, 2019), and many parts of India (Roxy et al., 2015; Mallya \r et al., 2016; Matin and Behera, 2017; Ramarao et al., 2019). Some \r spatial and seasonal differences within these regions remain, with \r Ambika and Mishra (2020) noting significant aridity declines over \r the Indo-Gangetic Plain in India during 1979-2018 due in part to \r the effect of irrigation, and Araghi et  al. (2018) found that many \r parts of Iran show no significant trends in aridity. There was a drying \r tendency in the dry season and significant wetting in the wet season \r in the Philippines during 1951-2010 (Villafuerte et  al., 2014), and \r slight wetting in Vietnam during 1980-2017 (Stojanovic et al., 2020) \r [...]"


id: cb0d200f28fa99d8f2b900b4b3ba9001
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 33065
content:
"[...] (low confidence). In EAS there is low confidence of broad aridity \r changes, as the frequency of droughts have increased (especially \r in spring) along a  strip extending from south-west China to the \r western part of north-east China; however, there is no evidence \r of a significant increase in drought severity over China as a whole \r and many parts in the arid north-west China got wetter during \r 1961-2012 (W. Wang et al., 2015; Zhai et al., 2017; H. Wang et al., \r 2019; Zhang and Shen, 2019). In Siberia, the number of dry days has \r decreased for much of the region, but increased in its southern parts \r (Khlebnikova et al., 2019a)."


id: 80ca6ff8ad8dc4a500d52c7a6f712dff
created_at: 2023-04-19 13:22:47
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index: climate_gpt_v2
vector_id: 19792
content:
"Given the increase of heavy precipitation in most Asian regions, the river \r flood frequency and intensities will change consequently in Asia. Over \r China floods will increase with different levels under different warming \r scenarios (medium confidence) (Lin et al., 2018; Kundzewicz et al., 2019; \r Liang et al., 2019; Gu et al., 2020). Monsoon floods will be more intense \r in SAS (medium confidence) (Nowreen et al., 2015; Babur et al., 2016; \r Mohammed et al., 2018). The total flood damage will increase greatly \r in river basins in SEA countries under the conditions of climate change \r and rapid urbanization in the near future (Dahal et al., 2018; Kefi et al., \r 2020). A changing snowmelt regime in the mountains may contribute \r to a shift of spring floods to earlier periods in Central Asia in future \r (medium confidence) (Reyer et al., 2017b). The annual maximum river \r discharge can almost double by the mid-21st century in major Siberian \r [...]"


id: 1bf71254512a32d1098f9436c4dc35f8
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index: climate_gpt_v2
vector_id: 2108
content:
"[...] rivers, and annual maximum flood area is projected to increase across \r Siberia mostly by 2-5% relative to the baseline period (1990-1999) \r under RCP8.5 scenario (medium confidence) (Shkolnik et al., 2018)."


id: 390817bcfe77a15695cfbe268acbc816
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 7240
content:
"Hydrological drought: Section 11.9 indicates that limited evidence\r and inconsistent regional trends gives low confidence to observed \r and projected changes in hydrological drought in all Asian regions at \r a 2degC GWL (approximately mid-century), although West Central Asia \r hydrological droughts increase at the 4degC GWL (approximately end\u0002of-century under higher emissions scenarios) (medium confidence). \r Human activities such as reservoir operation and water abstraction \r have had a  profound effect on low river flow characteristics and \r drought impacts in many Asian regions (Kazemzadeh and Malekian, \r 2016; Yang et al., 2020b). There was no observed overall long-term \r change of both meteorological droughts and hydrological droughts \r over India during 1870-2018 (Mishra, 2020), but there were strong \r trends towards drying of soil moisture in north-central India (Ganeshi \r et  al., 2020) and intensified droughts in north-west India, parts of \r [...]"


id: 6aaa875c5cf92d5789828a5c2cce1bb9
created_at: 2023-04-19 13:22:47
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index: climate_gpt_v2
vector_id: 16050
content:
"[...] Peninsular India, and Myanmar (Malik et  al., 2016). The frequency \r of water scarcity connected with hydrological droughts has increased \r significantly in southern Russia since the beginning of the 21st century \r (Frolova et al., 2017). Higher future temperatures are expected to alter \r the seasonal profile of hydrologic droughts given reduced summer \r snowmelt (medium confidence) downstream of mountains such as \r the Himalayas and the Tibetan Plateau (Sorg et  al., 2014). Several \r studies project more severe future hydrological drought in the Weihe \r River basin in northern China (Yuan et al., 2016; Sun and Zhou, 2020)."


id: 2559ca78e3919f740d388f49afcb5071
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 3761
content:
"Relative sea level: Around Asia, from 1900-2018, a  new tide \r gauge-based reconstruction finds a  regional mean RSL change of \r 1.33  [0.80  to  1.86]  mm yr-1 in the Indian Ocean-Southern Pacific \r and 1.68 [1.27 to 2.09] mm yr-1 in the North-west Pacific (Frederikse \r et  al., 2020), compared to a  GMSL change of around 1.7  mm yr-1\r (Section  2.3.3.3 and Table  9.5). For the period 1993-2018, the \r RSLR rates around Asia,  based on satellite altimetry, increased \r to 3.65 [3.23  to 4.08]  mm  yr-1 and 3.53 [2.64 to 4.45]  mm yr-1\r respectively (Frederikse et al., 2020), compared to a GMSL change of \r 3.25 mm yr-1 (Section 2.3.3.3 and Table 9.5). The rate of RSLR along \r the coastline of China ranges from -2.3 +- 1.9 to +5.7 +- 0.4 mm yr-1\r during 1980-2016; after removing the vertical land movement, the \r average rate of sea level rise is 2.9 +- 0.8 mm yr-1 over 1980-2016 \r and 3.2 +- 1.1  mm yr-1 since 1993 (Qu et  al., 2019). However, the \r [...]"


id: d5c2750abe5d3995cddb531f315d091d
created_at: 2023-04-19 13:22:47
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index: climate_gpt_v2
vector_id: 34921
content:
"[...] rates of land subsidence reported by Minderhoud et  al. (2017) are \r substantially higher than those reported by Qu et  al. (2019). RSL \r change in many coastal areas in Asia, especially in EAS, is affected \r by land subsidence due to sediment compaction under building mass \r and groundwater extraction (high confidence) (Erban et  al., 2014; \r Nicholls, 2015; Minderhoud et  al., 2019; Qu et  al., 2019). During \r 1991-2016, the Mekong Delta in Vietnam sank on average about 18 \r cm as a consequence of groundwater withdrawal, and the subsidence \r related to groundwater extraction has gradually increased with \r highest sinking rates estimated to be 11 mm yr-1 in 2015 (Minderhoud \r et al., 2017)."


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vector_id: 11497
content:
"Heavy snowfall and ice storm: Observed trends in heavy snowfall \r and ice storms are uncertain. Annual maximum snow depth decreased \r for the period between 1962 and 2016 on the western side of both \r eastern and western Japan, at rates of 12.3% and 14.6% per decade \r respectively (MOE et al., 2018). Observational results generally show \r a decrease in the frequency and an increase in the mean intensity \r of snowfalls in most Chinese regions (medium confidence) (B. Zhou \r et  al., 2018). Because of the decrease in the snow frequency, the \r occurrence of large-scale snow disasters in TIB decreased (low \r confidence) (Qiu et al., 2018; S. Wang et al., 2019). Large parts of \r northern high-latitude continents (including Siberia and RFE) have \r experienced cold snaps and heavy snowfalls in the past few winters, \r and the reduction of Arctic sea ice would increase the chance of heavy \r snowfall events in those regions in the coming decades (medium \r [...]"


id: 4b149a52b4bf67868cad0c738723b67f
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 10451
content:
"[...] confidence) (Song and Liu, 2017). Heavy snowfall is projected to \r occur more frequently in Japan's Northern Alps, the inland areas of \r Honshu Island and Hokkaido Island (Kawase et al., 2016, 2020; MOE \r et al., 2018), and the heavy wet snowfall can be enhanced over the \r mountainous regions in central Japan and northern part of Japan \r (Ohba and Sugimoto, 2020) (medium confidence)."


id: 35ef0ae7d784ae4d1f83ca9ee464a65d
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 6736
content:
"The Fourth and Fifth IPCC Assessment Reports (AR4 and AR5) identify \r the most damaging historical hazards in this region to be inland \r flooding, drought, wildfire and episodic coastal erosion due to storms \r (Hennessy et  al., 2007; Reisinger et  al., 2014). The SR1.5 (Hoegh\u0002Guldberg et al., 2018) projects very likely increases in the intensity \r and frequency of warm days and warm nights and decreases in the \r intensity and frequency of cold days and cold nights in Australasia. \r Furthermore, a likely increase in the frequency and duration of warm \r spells is also projected for Australia. The SROCC (IPCC, 2019b) projects \r a likely global mean sea level rise (RCP8.5) that is up to 0.1 m higher \r than corresponding AR5 projections. The SROCC also projects an \r increase of mean significant wave height across the Southern Ocean \r (high confidence) and an increase in the occurrence of historically rare \r (1-in-100-year) extreme sea levels to 1-in-1-year or more frequent \r [...]"


id: 769aab49c7278e437d7cbe04ad8be6e9
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 18087
content:
"Projections indicate that a majority of sandy coasts in the Asia region \r will experience shoreline retreat (high confidence) (Udo and Takeda, \r 2017; Ritphring et al., 2018; Vousdoukas et al., 2020b), while parts of \r the RFE, EAS, SEA and WCA coastline are projected to prograde over \r the 21st century, if present ambient shoreline change trends continue. \r Median shoreline change projections (CMIP5), relative to 2010, \r presented by Vousdoukas et al. (2020b) show that, by mid-century, \r sandy shorelines in Asia will retreat by between 10-50 m, except in \r SAS where shoreline retreat is projected to exceed 100 m, under both \r RCP4.5 and RCP8.5. By 2100, and under RCP4.5, shoreline retreats of \r around 85, 100 and 300 m are projected along the sandy coastlines \r of SEA and WCA, ARP and SAS respectively (50 m or less in other \r Asian regions), while under RCP8.5, over the same period, sandy \r shorelines along all regions with coastlines, except RFE and EAS, are \r [...]"


id: 87324f8a5ef8a9289dd20e41bb761e03
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 20947
content:
"[...] projected to retreat by more than 100 m, with the retreat in SAS \r reaching 350 m (2100 RCP8.5 projections for RFE and EAS are about \r 60 m and about 85 m respectively; Figure 12.6)."


id: e782577d45351a0a180d9cf30ab20141
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 38039
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 (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 to 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 averaged 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 past' (1995-2014) Q100 absolute 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 \r [...]"


id: 47750ab7e38b449775bd7187240f0d3b
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 9787
content:
"[...] two 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 (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 colours, 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 projections 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 regional mean change estimates and bars show the 5-95th percentile range of associated uncertainty. Note that these shoreline position change projections assume that there are \r [...]"


id: d3b47b8484f7eea2d8dc1c68d8bd4a03
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 34584
content:
"[...] 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 processing are \r available in the chapter data table (Table 12.SM.1)."


id: 564a6a7c432cdfe62c6a500f73386fd6
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 12447
content:
"Mean temperature in Australasia is projected to continue to rise \r through the 21st  century (virtually certain) (Atlas.6.4). Projections \r for Australia indicate that the average temperature will increase \r by +1.1degC (0.84-1.52degC 10-90th percentile range) by 2041-2060 \r (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 (Interactive Atlas). For SSP5-8.5, the projected changes are up to \r +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 to rise by +0.9degC (0.55-1.26degC) and +1.0degC (0.55-1.54degC) relative \r to 1995-2014 by mid- and end-century respectively (Interactive \r Atlas). In New Zealand, an increase of mean temperature of +1.0degC \r (0.60-1.32degC) relative to 1995-2014 is projected by mid-century, and \r an increase of +1.6degC (1.03-2.26degC) by end-century under SSP2-4.5. \r [...]"


id: 27018d061c68c8268431f9847f53c46d
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 4027
content:
"[...] For SSP5-8.5, the projected increase in mean temperature is +1.3degC \r (0.91-1.66degC 10-90th percentile range) and +3.1degC (2.20-4.05degC) \r relative to 1995-2014 by mid- and end-century respectively. For \r SSP1-2.6, the projected increase in mean temperature is +0.75degC \r (0.39-1.06degC) and +0.8degC (0.47-1.46degC) relative to 1995-2014 by \r mid- and end-century, respectively (Interactive Atlas)."


id: b36fcfc75b33a5d9b6cc29be19341120
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 28809
content:
"Extreme heat: The region has a  very likely trend of increasing \r frequency and severity of hot extremes since the 1950s (Table 11.10). \r Extreme minimum temperatures have increased in all seasons over \r most of Australia and exceeds the increase in extreme maximum \r temperatures (X.L. Wang et  al., 2013; Jakob and Walland, 2016). \r Heatwave characteristics and hot extremes have increased across \r many Australian regions since the mid-20th  century (Table  11.10; \r CSIRO and BOM, 2020). The number of days per year with maximum \r temperature greater than 35degC has increased over most parts \r of Australia from 1957-2015, with the largest increasing trends of \r 0.4-1 days/year occurring in north-western, Northern, north-eastern \r Australia and parts of Central Australia (CSIRO and BOM, 2016). \r Long-term changes of hot extremes in Australia have been attributed \r to anthropogenic influence (Table  11.10). In New Zealand, the \r [...]"


id: b99d335f1d287d63bda2568e6bd83be2
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 29864
content:
"[...] number of annual heatwave days increased at 18 of 30 sites during \r the period 1972-2019 (MfE and Stats NZ, 2020)."


id: 3e736c0dadd10441d25e50723d1fad55
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 8170
content:
"More frequent hot extremes and heatwaves are expected over \r the 21st  century in Australia (virtually certain) (Table  11.10). Heat \r thresholds potentially affecting agriculture and health, such as 35degC \r or 40degC, are projected to be exceeded more frequently over the \r 21st century in Australia under all RCPs (high confidence). By 2090 \r under RCP4.5, the average number of days per year with maximum \r temperatures above 35degC is highly spatially variable and is expected \r to increase by 50-100%, while the number of days per year with \r maximum temperatures above 40degC is expected to increase by 200%, \r relative to 1985-2005 (CSIRO and BOM, 2015). Under RCP8.5 the \r corresponding projected increases are even greater, with a greater than \r 100% increase in most of Australia, and far greater increases in Central \r and Northern Australia (up to a 20-fold increase in Darwin). Projections \r for New Zealand indicate more frequent hot extremes (virtually \r [...]"


id: 9944aa56122e096d1c007c0a0c2c41db
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 24183
content:
"[...] certain) (Table  11.10). Figure  12.4b, c shows CMIP6 projections of \r mean number of days per year with maximum temperature exceeding "


id: d04ed82e61e111043e8a7c29a929bb18
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 34007
content:
"While median annual runoff is projected to decrease in most of \r Australia (Chiew et  al., 2017), consistent with projected decreases \r in average rainfall (CSIRO and BOM, 2015; Alexander and Arblaster, \r 2017), river floods are projected to increase due to more intense \r extreme rainfall events and associated increase in runoff (medium \r confidence). Asadieh and Krakauer (2017) found a  decrease in the \r value of the 95% percentile of mean streamflow with RCP8.5 by \r the end of the century in all of Australia, except in a small part in centre \r of the country. In terms of relative increases, flooding is expected to \r increase more in Northern Australia (driven by convective rainfall \r systems) than in Southern Australia (where more intense extreme \r rainfall may be compensated by drier antecedent moisture conditions; \r Alexander and Arblaster, 2017; Dey et al., 2019) with flood frequency \r increasing in Northern Australia and along parts of the east coast and \r [...]"


id: 5654d026d17f984092215e2046c9833b
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 12455
content:
"[...] decreasing in south-western Western Australia (Hirabayashi et  al., \r 2013). Gu et al. (2020) project larger flood magnitude and volumes \r under both RCP2.6 and RCP8.5 in Northern Australia, and smaller flood \r magnitudes and volumes in Southern Australia under the same RCPs. \r These findings are in general agreement with the patterns in peak \r flow, corresponding to the 1-in-100-year return period streamflow, \r shown in Figure 12.7a,c for mid-21st century under RCP8.5."


id: fc71259a042e8d9a7170a7341d2ce7a0
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 41783
content:
"Aridity is projected to increase, especially during winter and spring, \r with medium confidence in SAU but with high confidence in south-west \r Western Australia (Table 11.11 and Atlas.6.4). In EAU and in the north \r and east of NZ, aridity is projected to increase with medium confidence, \r while a decrease is projected with medium confidence in the south \r and west of NZ (Atlas.6.4). Although there is only low confidence in \r the projected decrease of mean annual precipitation in south-western \r and eastern Australia and the north and east of New Zealand, there is \r high confidence of reduced winter and spring precipitation in Australia \r in future, mostly in south-western and eastern Australia (Atlas.6.4). \r Liu et al. (2018b) show that under 2degC warming, most of Australia is \r projected to become drier based on the Palmer Drought Severity Index \r (PDSI), with the exception of the tropical north-east. Ferguson et al. \r [...]"


id: 9f2fb9874f23da913dece4b0dabd41d2
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 25228
content:
"Fire weather: Dowdy and Pepler (2018) examined atmospheric \r conditions conducive to pyroconvection in the period 1979-2016, \r and found an increased risk in south-east Australia during spring \r and summer, due to changes in vertical atmospheric stability and \r humidity, in combination with adverse near-surface fire weather \r conditions. CSIRO and BOM (2018) and Dowdy (2018) found that \r the annual 90th percentile daily Forest Fire Danger Index (FFDI) \r has increased from 1950-2016 in parts of Australia, especially in \r Southern Australia (1-2.5 per decade) and in spring and summer. \r These studies indicate an increase in the frequency and magnitude \r of FFDI extreme quantiles, as well as a shift of the fire season start \r towards spring, lengthening the fire season. The unprecedented large \r fires of austral spring and summer of 2019 in south-east Australia \r were a  result of extreme hot and dry weather in significantly \r [...]"


id: 96a506d4bcbd5e948f963a1fe58fff41
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 23707
content:
"[...] drier than average conditions that had persisted since 2017, in \r combination with consistently stronger than average winds, resulting \r in above average to highest on record FFDI values in much of the \r country (Abram et  al., 2021). These fires have been attributed to \r climate change through the temperature component of fire weather \r indices (van Oldenborgh et al., 2021). In New Zealand, days with very \r high and extreme fire weather increased in 12 out of 28 monitored \r sites, and decreased in 8, in the period 1997-2019 (MfE and Stats NZ, \r 2020). Attribution studies indicate that there is medium confidence of \r an anthropogenically driven past increase in fire weather conditions, \r essentially due to increase in frequency of extreme heat waves. (Hope \r et al., 2019; Lewis et al., 2020; van Oldenborgh et al., 2021)."


id: 49533c2f8d565020c0397c8423c15efe
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 10123
content:
"Hydrological drought: There is low confidence of observed changes \r in hydrological droughts in Australasia, except in SAU where there \r is medium confidence of an observed increase in the south-east \r and south-west. Future projections indicate medium confidence\r in further hydrological drought increases for Southern Australia for \r warming levels of 2degC or higher (Section 11.9). Mean annual runoff \r in far south-east and far south-west Australia are projected to \r decline by median values of 20 and 50% respectively, by mid-century \r under RCP8.5 (Chiew et al., 2017). Prudhomme et al. (2014) assess \r changes in the Drought Index (DI), defined as areal runoff less \r than the 10th percentile over the reference period 1976-2005, and \r project DI increases for both Australia and New Zealand by 10-20% \r by 2070-2099 under RCP8.5, with the greatest effects being in the \r southern parts of the Australian continent. These projections are \r [...]"


id: ea3c7c44481d23c1e24f5086bb55d1bf
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 38566
content:
"[...] consistent with the trends shown in Figure 12.4g-i (Figure 12.SM.3). \r The SPI drought frequency is projected to increase in SAU and \r particularly in south-west Western Australia by mid-century, while by \r the end of the century SPI drought frequency is projected to increase \r all over Australia, and particularly strongly in south-west Western \r Australia as well as southern Victoria (see Figure  12.4g-i). For the \r Murray-Darling basin, Ferguson et  al. (2018) project effectively no \r change (-1%) in mean precipitation, a 27% decrease in P-E, and 30% \r increase in runoff in 2070-2099 relative to 1976-2005 with RCP8.5."


id: aeeddc92b403dc4a39c91be90afa420c
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 28018
content:
"Fire weather indices are projected to increase in most of Australia \r (high confidence) and many parts of New Zealand (medium \r confidence), in particular with respect to extreme fire and induced \r pyroconvection (Dowdy et al., 2019b). Increasing mean temperature, \r cool season rainfall decline, and changes in tropical climate \r variability all contribute to a future increase in extreme fire risk in \r Australia (Abram et al., 2021). Projections indicate that the annual \r cumulative FFDI will increase by 31-33% in Southern and Eastern \r Australia, and by 17-25% in Northern Australia and the Rangelands \r by 2090 (relative to 1995) under RCP8.5 (CSIRO and BOM, 2015). \r Using a  CMIP5 ensemble of 17 models, Abatzoglou et  al. (2019) \r found a  statistically significant positive trend for fire weather \r intensity and fire season length for future mid-century conditions \r under RCP8.5, including a  detectable anthropogenic influence \r [...]"


id: 94efeb26379097f28b686af75be645a2
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 23407
content:
"[...] on fire risk magnitude and fire season length by 2040 in Western \r Australia and along the Queensland coastline. Using the C-Haines \r and FFDI indices with A2 and RCP8.5 respectively, Di Virgilio et al. \r (2019) and Clarke et al. (2019) have shown that extreme fire weather \r frequency will increase in south-eastern Australia by the end of the \r 21st  century. Most of these projections indicate that the biggest \r increases in fire weather conditions will be in late spring, effectively \r resulting in longer (stronger) fire seasons in areas where spring \r is the shoulder (peak) season. In New Zealand, Watt et  al. (2019) \r projected that the number of days with very high to extreme fire risk \r will increase by 71% by 2040, and by a further 12% by 2090, for the \r A1B scenario, with fire risk increase all along the east coast. The most \r marked relative changes by 2090 were projected for Wellington and \r Dunedin, where very high to extreme fire risk is projected to increase \r [...]"


id: 36e27a7bc130bee46fb6a6fd8fcf9c0f
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 6883
content:
"[...] by, respectively, 89% to 32 days and 207% to 18 days, compared to \r the baseline period 1970-1999."


id: 6cb0e9bf2ff199568ffda4d3eaea676e
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 16407
content:
"Agricultural and ecological drought: There is medium confidence\r in observations of agricultural and ecological droughts increasing in \r SAU and decreasing in NAU, while there is low confidence of changes \r elsewhere in the region (Section  11.9). More regional studies have \r observed an increase in agricultural and ecological drought intensity \r in south-west Australia and an increase in drought intensity in parts of \r south-east Australia, while the length of droughts therein has increased \r (Section 11.9). In New Zealand, since 1972-73, soils at 7 of 30 monitored \r sites became drier, while the 2012-13 drought was one of the most \r extreme in the previous 41 years (MfE and Stats NZ, 2017). Future \r evaporative demand is projected to lead to medium confidence increases \r in agricultural and ecological droughts for 2degC of global warming in \r SAU and EAU and low confidence for changes in CAU, NAU and NZ, \r although there is medium confidence of increases in CAU with 4degC of \r [...]"


id: 6852a5815c280162b8d04e8cb7a76d3d
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 15623
content:
"[...] global warming (Section 11.9). There is medium confidence for more \r time in agricultural and ecological drought in SAU by mid-21st century \r (Coppola et al., 2021b) as well as by the end of the 21st century (Herold \r et al., 2018). The Standardized Precipitation Evapotranspiration Index \r (SPEI) shows a  springtime intensification in SAU with moderate and \r severe droughts in the south-west and moderate droughts in the \r south-east (Herold et al., 2018). There is consensus among the different \r model ensembles (CORDEX-CORE, CMIP5 and CMIP6) that the drought \r frequency (DF), one of several proxies for agricultural and ecological \r drought, will increase in all four Australian regions for both mid-century \r (NAU 0.2-2 DF increase, CAU 0.5-2 DF increase, SAU 1-3 DF increase \r and EAU 0.8-3 DF increase) and end-century (0.8-2.7 DF increase for \r NAU, 1.2-2 DF increase for CAU, 2.2-3.8 for SAU and 0.2-3 for EAU) \r [...]"


id: ebc7a67e4e29054db78dfe12d3330346
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 38849
content:
"[...] for both RCP8.5 and SSP5-8.5, with CMIP6 showing the lowest increase \r (Figure 12.4g-l and Figure 12.SM.4; Coppola et al., 2021b)."


id: f74e6241771242af275d563d33f71bc6
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 40888
content:
"Annual mean precipitation is projected to increase in Central \r and north-east Australia (low confidence) and in the south and \r west of New Zealand (medium confidence), while it is projected \r to decrease in Southern Australia (medium confidence), albeit \r with high confidence in south-west Western Australia, in \r Eastern Australia (medium confidence), and in the north and \r east of New Zealand (medium confidence). Heavy precipitation \r and pluvial flooding are projected to increase with medium \r confidence in Northern Australia and Central Australia. There \r is medium confidence that river flooding will increase in New \r Zealand and Australia, with higher increases in Northern \r Australia. Aridity is projected to increase with medium \r confidence in Southern Australia (high confidence in south-west \r Western Australia), Eastern Australia (medium confidence), and \r in the north and east of New Zealand (medium confidence). \r Hydrological droughts are projected to increase in Southern \r [...]"


id: d9bc9dd579cd4b988507c0b568e19358
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 35604
content:
"[...] Australia (medium confidence), while agricultural and \r ecological droughts are projected to increase with medium \r confidence in Southern Australia and Eastern Australia. Fire \r weather is projected to increase throughout Australia (high \r confidence) and New Zealand (medium confidence)."


id: 11d81d20639f94856202155dffa692bb
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 333
content:
"Projections indicate that a  majority of sandy coasts in the region \r will experience shoreline retreat, throughout the 21st century (high \r confidence) (Figure  12.7b,d). Median shoreline change projections \r (CMIP5) under both RCP4.5 and RCP8.5 presented by Vousdoukas \r et al. (2020b) show that, by mid-century, sandy shorelines will retreat \r (relative to 2010) by between 50 and 80 m all around Australasia, \r except in SAU and NZ where the projected retreat (relative to 2010) is \r between 35 and 50 m. By 2100, median shoreline retreats exceeding \r 100 m (relative to 2010) are projected along the sandy coasts of NAU \r (about 150 m), CAU (about 160 m), and EAU (about 110 m) under \r RCP4.5m, while projections for SAU and NZ are around 80-90  m. \r Under RCP8.5, shoreline retreat exceeding 100 m is projected all \r around the region by 2100 (relative to 2010) with retreats as high \r as 220 m in NAU and CAU (about 170 m in EAU and about 130 \r [...]"


id: 4426f17d59c86cb19844dde3d4ee32e8
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 9228
content:
"[...] m in SAU and NZ; Figure 12.7b,d). The total length of sandy coasts \r in Australasia that is projected to retreat by more than a  median \r of 100 m by 2100 under RCP4.5 and RCP8.5 is about 12,500 and \r 16,000 km respectively, an increase of approximately 30%."


id: d3fa90ed822d883d06b52667fcae7742
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 34627
content:
"Marine heatwave: The mean SST of the ocean around Australia and \r east of New Zealand has warmed at a rate of about 0.22degC per decade \r between 1992 and 2016 (Wijffels et al., 2018), which is higher than \r the global average SST increase of 0.16degC per decade (Oliver et  al., \r 2018). This mean ocean surface warming is connected to longer and \r more frequent marine heatwaves in the region (Oliver et al., 2018). Over \r the period 1982-2016, the coastal ocean of Australia experienced on \r average more than 1.5 marine heatwaves (MHWs) per year, with the \r north coast of Western Australia and the Tasman Sea experiencing on \r average 2.5-3 MHWs per year. The average duration was between 10 \r and 15 days, with somewhat longer and hotter MHWs in the Tasman Sea. \r In New Zealand, the south-east coast of South Island experiences the \r most MHWs (2.5-3 per year). The duration of MHW in New Zealand is \r on average 10-15 days (Oliver et al., 2018). Changes around Australasia \r [...]"


id: 7f9f3ad6ad3e32f63281cc23eb923c10
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 19591
content:
"[...] over the 20th century, derived from MHW proxies, show an increase \r in frequency between 0.3 and 1.5 MHW per decade, except along the \r south-east coast of New Zealand (Box 9.2); an increase in duration per \r event; and the total number of MHW days per decade, with the change \r being stronger in the Tasman Sea than elsewhere (Oliver et al., 2018)."


id: 8d292d04ea1239b19e263fe6b3413ae2
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 22016
content:
"Previous assessments have documented ongoing and projected \r changes in several CIDs. IPCC AR5 projections (IPCC, 2014b) pointed \r to increases in mean temperature between 2degC and 6degC by the end \r of the century (high confidence) and increases in the occurrence \r of warm days and nights under various future climate scenarios \r (medium confidence). The AR5 also pointed to patterns of changes \r in precipitation (medium confidence), changes in the duration of \r dry spells (medium confidence) and decreases in water supply (high \r confidence). The SR1.5 projections indicated expected increases in \r river flooding and extreme runoff at 2degC warming in parts of South \r America, and decreases in runoff in Central America, central and \r Southern South America, and the Amazon basin. The SROCC reported \r an increased number of landslides, a  decreased volume of lahars \r from ice and snow-clad volcanoes, and increased frequency of glacier \r [...]"


id: cc21a6700091987a1dea9591ea0aeb92
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 33231
content:
"[...] lake outburst floods. The SROCC also indicated that regional and \r local-scale projections point to decreasing trends in glacier runoff."


id: 984d2d120a6f33fe282d4439d7090ab5
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 24004
content:
"There is high confidence that MHWs will increase around most of \r Australasia. Under RCP4.5 and RCP8.5 respectively, mean SST is \r projected to increase by 1degC and 2degC around Australia by 2100, with \r a hotspot of around 2degC for RCP4.5 and of 4degC for RCP8.5 along the \r south-east coast between Sydney and Tasmania (Interactive Atlas). \r Under all RCPs, the mean SST around Australia is expected to increase \r in the future, with median values of around 0.4degC-1.0degC by 2030 \r under RCP4.5, and 2degC-4degC by 2090 under RCP8.5 (CSIRO and BOM, \r 2015). Warming is expected to be largest along the north-west coast \r of Australia, southern Western Australia, and along the east coast of \r Tasmania (CSIRO and BOM, 2018). More frequent, extensive, intense \r and longer lasting MHWs are projected around Australia and New \r Zealand for GWLs of 1.5degC, 2degC and 3.5degC relative to the modelled \r reference value for 1861-1880 (Frolicher et  al., 2018). Projections \r [...]"


id: 452f39963d90f1cdc4930fbe8602cd43
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 9401
content:
"[...] for SSP1-2.6 and SSP5-8.5 both show an increase in MHWs around \r Australasia by 2081-2100, relative to 1985-2014 (Box 9.2, Figure 1)."


id: 295a8b9ad1828a1ef45165ca508fdfc9
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 4492
content:
"Mean air temperature: New literature confirms a  continuous \r warming since the beginning of the 20th century in the majority of the \r eight sub-regions (Atlas.7). However, observational datasets in several \r areas are still short and trend estimation is hindered by year-to-year \r and interannual variability. Atlas projections point to a virtually certain\r warming across all sub-regions, with the largest increases taking place \r in the Amazon basin (NSA and SAM; Atlas.7.2.4). A consistent increase \r in temperature-related indices linked to several climate-sensitive \r sectors (e.g., growing degree days, cooling degree days) is found across \r CMIP5, CMIP6 and CORDEX-CORE projections, with smaller increases \r for cooling degree days in mid-latitude regions than in SCA and the \r Amazon (Coppola et al., 2021b). Daily mean temperature exceedances \r of a  typical 21.5degC threshold for a  successful incubation of disease \r pathogens inside many mosquito vectors (Lambrechts et  al., 2011; \r [...]"


id: f8981d84353411f8237e478179188ab0
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 41098
content:
"[...] Blanford et  al., 2013; Mordecai et  al., 2013, 2017) will be crossed \r much more frequently, potentially driving increases in the incidence \r of vector-borne diseases (Laporta et al., 2015; Messina et al., 2019)."


id: 16a78d824c0af5ddb5c620f1f21859bd
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 1198
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 for 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 indicate 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 WGI 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 absolute 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 [...]"


id: aaf3e7df64df554bf7a16f39416709fc
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 27303
content:
"[...] ('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 (yellow) and 4degC \r (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 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 projections 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 indicate regional mean change estimates and bars show the 5-95th percentile range of associated uncertainty. Note that these shoreline position change projections assume \r [...]"


id: 41a4883941aeb566b2b7a6cdbd3d3ea8
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 8737
content:
"[...] that 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 processing are available in the chapter data table (Table 12.SM.1)."


id: 72e7166e29a62d3c83322ed3649b7588
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 17462
content:
"Extreme heat: Chapter  11 found high confidence of increased \r heatwaves in all regions except SSA over the past decades. There is \r evidence of increasing heat stress over summer in much of SES and \r SWS using the wet bulb globe temperature (WBGT) index for the \r period 1973-2012, and this has been attributed to human influence \r on the climate system (Knutson and Ploshay, 2016). Climate change \r projections point to major increases in several heat indices across \r the region for all scenarios (high confidence). Largest increases in \r the frequency of hot days (maximum temperatures, Tx > 35degC) are \r projected for the Amazon basin under SSP5-8.5 with more than 200 \r days per year at the end of the century under SSP5-8.5 relative to 1995-\r 2014, while such increases remain moderate (50-100 days) in SSP1-2.6. \r For the dangerous heat threshold of HI > 41degC, increases in frequency \r are similar to that in Tx > 35degC (Figure 12.4 and Figures 12.SM.1 and \r [...]"


id: ffcc106c396a58efb7acfcc758bde1f8
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 42397
content:
"River flood: Emerging literature in the region documents ongoing \r changes in river floods. Mernild et al. (2018) report decreases and \r increases in annual runoff west of the Andes Cordillera's continental \r divide, with the greatest decreases in the number of low (<10th \r percentile) runoff conditions and the greatest increases in high \r (>90th  percentile) runoff conditions. In coastal north-east Peru, \r extreme precipitation events recently caused devastating river floods \r and landslides (Son et al., 2020). In Brazil, floods are becoming more \r frequent and intense in wet regions but less frequent and intense \r in drier regions (Bartiko et al., 2019; Borges de Amorim and Chaffe, \r 2019), with higher propagation of hydrological changes through \r anthropogenically modified agricultural basins (Chagas and Chaffe, \r 2018). Record, catastrophic, unprecedented, and once-in-a-century \r flooding events have also been reported in recent decades in the \r [...]"


id: 773564fa95bc2b89a7d179bb4b6ae54
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 18178
content:
"[...] tributaries of the Amazon River or along its mainstream (Sena \r et  al., 2012; Espinoza et  al., 2013; Marengo et  al., 2013; Filizola \r et  al.,  2014), in Argentinean rural and urban areas (Barros et  al., \r 2015), in the lower reaches of the Atrato, Cauca and Magdalena \r rivers in Colombia (Hoyos et al., 2013; Avila et al., 2019), in basins \r whose mainstreams flow through important metropolitan areas \r such as Concepcion, Chile (Rojas et al., 2017), and even in one of \r Earth's driest regions, the Atacama Desert (Wilcox et al., 2016). In the \r Amazon basin, the significant increase in extreme flow is associated \r with the strengthening of the Walker circulation (Barichivich et al., \r 2018)."


id: 24171b30a85bdcdab1cafbdf449248b5
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 3531
content:
"NSA and SAM are the two regions where the strongest signal of \r increasing number of dry days (NDD) and drought frequency (DF) \r is projected compared to other regions of the world (Coppola et al., \r 2021b). By the end of this century and under RCP8.5, the NSA area \r average value for NDD reaches 43, 32 and 27, within the CORDEX\u0002CORE, CMIP5 and CMIP6 ensembles respectively. For the frequency \r of droughts, the NSA area average value is of about 4.6, 3.4 and \r 3.8. For the same period and scenario, the SAM region shows NDD \r and DF values of 29, 20 and 21, and of 4, 3 and 3.5 respectively \r (Figure  12.4j-l). In Central America, a  significantly drier northern \r region and a wetter southern region are projected for mid-century \r by (Hidalgo et al., 2017), whilst Fuentes-Franco et al. (2015) pointed \r to more pronounced dry periods during the rainy season in SCA by \r the end of this century under RCP8.5. Increases in the frequency \r [...]"


id: aa38f6c20c7fdd0b9bcda73b6b1d798d
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 27178
content:
"[...] of meteorological droughts that may initiate other drought types \r are projected for the eastern part of the Amazon and the opposite \r for the west under RCP8.5 (Duffy et al., 2015). In central Chile, the "


id: 98acf6684097efcdfaa6664f843cca6d
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 24077
content:
"Fire weather: There is evidence of increases in forest fire activity \r (number of fires, burned area and fire duration) in central and south\u0002central Chile, where more conducive fire weather conditions have \r been proposed as the main driver (Gonzalez et  al., 2018; Urrutia\u0002Jalabert et al., 2018). Projections indicate that the Amazon will be one \r of the regions in the world with the highest increase in fire weather \r indices over the 21st century and under all RCPs (high confidence) \r (Betts et  al., 2015; Abatzoglou et  al., 2019; Q. Sun et  al., 2019). \r This is consistent with the large increase in the frequency of joint \r occurrence of extreme hot and dry days projected for a 2degC warming \r level or more (Vogel et al., 2020). Projections of fire weather indices \r also show an increased risk in SWS (high confidence), SSA and SCA \r (medium confidence). However, wildfires highly depend on land use \r and appropriate management may help mitigate future increases in \r [...]"


id: e9ffaefcaeda3d4285f559e6481baf60
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 38519
content:
"Snow: Historical studies of seasonal snow cover are limited and \r restricted to the Andes Cordillera. Mernild et al. (2017) indicated that \r much of the area north of 23degS experienced a decrease in the number \r of snow cover days, while the southern half of the Andes Cordillera \r experienced the opposite. A reduction in snow cover of about 15% \r was simulated for areas with altitudes in the range of 3000-5000 \r m, whereas in regions with altitude below 1000 m (Patagonia) snow \r cover extent increased. The reduced snowfall over the Chilean and \r Argentinean Andes mountains, which has resulted in unprecedented \r reductions in river flow, reservoir volumes and groundwater levels, led \r to the most severe and long-lasting hydrological drought (2010-2015) \r reported in the adjacent semi-arid foothills of the central Andes \r (Garreaud et  al., 2017; Rivera et  al., 2017; Masiokas et  al., 2020). \r Projections (based on process understanding) in Section 9.5.3.3 point \r [...]"


id: a12eb4b1d2e7c0b9f14f1ef9488f95b8
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 25580
content:
"[...] to decreases in seasonal snow cover extent and duration across South \r America as global climate continues to warm (high confidence)."


id: 3b5266ef3c4369035cd1788fd03a0b31
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 7625
content:
"Coastal flood: The present-day 1-in-100-year ETWL ranges from \r 0.5 to 2.5 m around most of Central and South America, except in \r SSA and SWS, where 1-in-100-year ETWLs can be as large as 5 to \r 6 m (Vousdoukas et  al., 2018). ETWL magnitude and occurrence \r frequency are expected to increase throughout the region (high \r confidence) (Figure 12.4p-r and Figure 12.SM.6). Across the region, \r the 5-95th percentile range of the 1-in-100-year ETWL is projected \r to increase (relative to 1980-2014) by 8-34 cm and by 10-43 cm \r by 2050 under RCP4.5 and RCP8.5 respectively (Vousdoukas et al., \r 2018; Kirezci et  al., 2020). By 2100, this range is projected to be \r 21-93 cm and 34-190 cm under RCP4.5 and RCP8.5 respectively \r (Vousdoukas et  al., 2018; Kirezci et  al., 2020). Furthermore, under \r RCP4.5, the present-day 1-in-100-year ETWL is projected to have \r median return periods of 1-in-10-years to 1-in-50-years by 2050 and \r [...]"


id: 4da4d43142384eb065c830e86574a95d
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 10938
content:
"[...] 1-in-1-year by 2100 in SES, SSA and SWS. In other regions of Central \r and South America, the present-day 1-in-100-year ETWL is projected \r to occur once per year or more by both 2050 and 2100 under RCP4.5. \r The present-day 1-in-50-year ETWL is projected to occur around three \r times a year by 2100 with a SLR of 1 m (Vitousek et al., 2017)."


id: 7d7e725aaf239608af350e89cb21e4d1
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 19248
content:
"Projections indicate that a  majority of sandy coasts in the region \r will experience shoreline retreat throughout the 21st  century (high \r confidence). Median shoreline change projections (CMIP5) for the \r mid-century period show that, relative to 2010, sandy shorelines will \r retreat by between 30 and 75 m in SCA, NES, SES and SSA under both \r RCP4.5 and RCP8.5, while the projected mid-century retreats are less \r than 30 m in NSA, NWS and SWS for both RCPs (Vousdoukas et al., \r 2020b). Parts of the coastline in these latter three regions are projected \r to prograde over the 21st century, if present ambient shoreline change \r trends continue (Vousdoukas et al., 2020b). By 2100, median retreats \r of more than 100 m are projected in SCA, NES, SES and SSA under \r both RCPs, while retreats between 50-100 m are projected for NSA, \r NWS and SWS under both RCPs (Figure 12.8). Notably, the projected \r shoreline retreats in SCA and SES approach 150 m by 2100 under \r [...]"


id: 2284575ef2a92a16fdeaa85d31a5a6e5
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 3289
content:
"[...] RCP4.5 and 200 m under RCP8.5. The total length of sandy coasts in \r Central and South America that is projected to retreat by more than \r a median of 100 m by 2100 under RCP4.5 and RCP8.5 is about 12,000 \r and 15,000 km respectively, an increase of approximately 30%."


id: 82d6a616d6d09021b938474ad98775a9
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 20145
content:
"and more frequent marine heatwaves (MHW) in the region (Oliver \r et al., 2018). Over the period 1982-2016, the coastlines experienced \r on average more than 1.0 MHW per year, with the Pacific coast of \r Northern Central America and the coast of SES (Atlantic) experiencing \r on average 2.5-3 MHWs per year. The average duration was between \r 10 and 15 days, with the notable exception of the equatorial Pacific \r coastline, which experiences MHWs with >30 days average duration \r related to ENSO conditions. In the south-western Atlantic shelf \r (32-38degS), more than half of the days with MHWs have occurred since \r 2014, and the most intense event (1.7degC above previous maximum) \r took place in the austral summer of 2017 (Manta et al., 2018). Changes \r over the 20th century, derived from MHW proxies, show an increase in \r frequency between 0.5 and 2 MHW per decade in the South Atlantic, \r the Caribbean and the Pacific coast of Northern Central America, an \r [...]"


id: be025368e17513531e7b667ce39263d3
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 30693
content:
"[...] increase in intensity per event in the South Atlantic, and a decrease \r along the equatorial Pacific coastline. The total number of MHW \r days per year increases around Central and South America, with the \r exception of the equatorial Pacific coastline (Oliver et al., 2018)."


id: 27496c4d761f1e9d027570fa805a0cb9
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 4082
content:
"Using frequency analysis of extreme peak flow events above \r a  100-year return period as a  threshold, which is the average \r protection level of the European river network (Rojas et al., 2013), \r Alfieri et al. (2017) and Alfieri et al. (2015) show that Europe is one of \r the regions where the largest increases in flood risk may occur, with \r only few countries in Eastern Europe showing a decrease (Poland, \r Lithuania, Belarus) (Osuch et  al., 2017). They find a  significant \r increase of events with peak discharge above 100-year return period \r (Q100) in most of Europe in line with Rojas et al. (2012), Hirabayashi \r et al. (2013), Dankers et al. (2014), Forzieri et al. (2016), Roudier et al. \r (2016), Thober et  al. (2018), and an increase in the magnitude of \r floods in southern Europe, although Giuntoli et  al. (2015) projects \r no change. A modest but significant decrease in the 100-year return \r period river flood is projected for southern (due to reduction of \r [...]"


id: bce8d52958fb6f340928b58bd89690a8
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 30474
content:
"[...] precipitation) and north-eastern European regions, the latter because \r of the strong reduction in snowmelt induced river floods (Thober \r et al., 2018; Di Sante et al., 2021)."


id: 6ef7154d3e1d286b18a607bb954a94df
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 16796
content:
"Landslide: Rainfall periods connected to landslides are projected to \r increase in central Europe by up to one more period per year in flat \r areas in low altitudes and by up to 14 more periods per year at higher \r altitudes by mid-century, becoming even more evident by the end of \r the century (Schlogl and Matulla, 2018). An increase of landslides \r by up to 45.7% and 21.2% is projected for southern Italy (Calabria \r region) by mid-century under both RCP4.5 and RCP8.5 (Gariano and \r Guzzetti, 2016) and by up to 40% in central Italy (Umbria) during \r the winter season (Ciabatta et al., 2016). A decrease of landslides is \r projected in the Peloritani mountains in southern Italy (RCP4.5 and \r 8.5) by mid-century (Peres and Cancelliere, 2018). A slight increase \r for a 10-year return period landslide is projected in the eastern \r Carpathians, the Moldavian Subcarpathians and the northern part of \r the Moldavian Tableland and a higher increase in the 100-year return \r [...]"


id: 4031b4744a3266b27e3c774d8dc09367
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 8482
content:
"[...] period event is projected in the western hilly and plateau areas of \r Romania (Jurchescu et al., 2017)."


id: 62a28715bdca6d1e0fe9431831900341
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 14645
content:
"Recent local studies provide additional risk-relevant context to \r changes in European drought. Agricultural and ecological drought \r conditions are expected to intensify in southern Europe by end-of\u0002century based on the 12-month rainfall Drought Severity Index (a soil \r moisture indicator), precipitation deficit SPI and SPEI indices. There will \r be regions in southern Europe where this type of drought could be \r up to 14 times worse than the worst drought in the historical period \r (Guerreiro et al., 2018). One-in-10-year drought events are projected to \r happen every second year (Mora et al., 2018; Ruosteenoja et al., 2018). \r The Mediterranean region will have 100 additional stress years (years \r with three consecutive months of precipitation deficits greater than \r 25%; Giorgi et al., 2018); an increase of both drought frequency (up to \r two events per decade) and severity (Spinoni et al., 2014, 2020) and an \r [...]"


id: 8c3a073ed9c67484810da27647f24946
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 21843
content:
"[...] increase of consecutive dry days in the southern part of the MED region \r (Lionello and Scarascia, 2020). In contrast, droughts are expected to \r decrease in winter in Northern Europe (Section  11.9; Spinoni et  al., \r 2018a). These findings are confirmed by the EURO-CORDEX, CMIP5 \r and CMIP6 ensemble that show a  change of frequency of drought \r events in the MED between 2-3 events per decade by mid-century for \r scenario RCP8.5 (Figure 12.SM.3; Coppola et al., 2021a)."


id: 9aa967471987aded32738d3d1a8c5aa3
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 24449
content:
"Fire weather: Fire weather conditions have been increasing since \r about 1980 over a few regions in Europe including Mediterranean \r areas (low confidence) (Venalainen et al., 2014; Urbieta et al., 2019; \r Barbero et al., 2020; Giannaros et al., 2021). However, beyond a few \r studies, evidence is largely missing on attribution of these trends to \r anthropogenic climate change (Forzieri et al., 2016). An increase in fire \r weather is projected for most of Europe, especially western, eastern \r and central regions, by 2080 (current 100-year events will occur every \r 5-50 years), with a  progressive increase in confidence  and model \r agreement along the 21st  century (medium confidence) (Forzieri \r et al., 2016; Abatzoglou et al., 2019). With increased drying and heat \r combined, in Mediterranean areas, an increase in fire weather indices \r is projected under RCP4.5 and RCP8.5, or SRES A1B, as early as by \r mid-century (high confidence) (Bedia et al., 2014; Abatzoglou et al., \r [...]"


id: 276920082342dc4499fb2663c570fad9
created_at: 2023-04-19 13:22:47
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content_type: text
index: climate_gpt_v2
vector_id: 4113
content:
"[...] 2019; Dupuy et al., 2020; Fargeon et al., 2020; Ruffault et al., 2020) \r and an increase in burned area of 40% and 100% for a 2degC and 3degC \r GWL, respectively (Turco et al., 2018)."


id: 45754d83eb0147edad3d1c81d71f82b2
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 9437
content:
"There is medium confidence that serial clustering of storms, inducing \r cumulated economic losses, in future climate will increase in many \r areas in Europe under climate projections over Europe (Karremann \r et al., 2014; Economou et al., 2015). Strong winds and extratropical \r storms are projected to have a  slightly increasing frequency and \r amplitude in the future in northern, western and Central Europe \r (Outten and Esau, 2013; Feser et al., 2015; Forzieri et al., 2016; Molter \r et al., 2016; Ruosteenoja et al., 2019a; Vautard et al., 2019) under \r RCP8.5 and SRES A1B by the end of the century (medium confidence), \r as well as off the European coasts (Martinez-Alvarado et al., 2018) \r due to the increase of intensity of extratropical storms at a 2degC GWL \r or above (Zappa et al., 2013) in these areas. The frequency of storms, \r including Medicanes, is projected to decrease in Mediterranean \r regions, and their intensities are projected to increase, by the middle \r [...]"


id: c904f318fbad11a08b55e0131e29491b
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 32687
content:
"[...] of the century and beyond for SRES A1B, A2 and RCP8.5 (medium \r confidence) (Nissen et al., 2014; Feser et al., 2015; Forzieri et al., 2016; \r Molter et al., 2016; Tous et al., 2016; Romera et al., 2017; Gonzalez\u0002Aleman et al., 2019; MedECC, 2020; Chapter 11)."


id: 50e19c09ca36f985a2c5d3305a762a19
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 11502
content:
"Mean wind speed: Mean surface wind speeds have decreased in \r Europe as in many other areas of the Northern Hemisphere over the \r past four decades (medium confidence) (AR5 WGI), with a reversal \r to an increasing trend in the last decade (low confidence) that is, \r however, not fully consistent across studies (Tian et  al., 2019; \r Zeng et  al., 2019; Z. Zhang et  al., 2019; Deng et  al., 2021; see \r Section 2.3.1.4.4). Re-analyses also show declining winds in Europe \r (Deng et al., 2021) with large interdecadal variability (Laurila et al., \r 2021). The declining trend has induced a corresponding decline in \r wind power potential indices across Europe (low confidence) (Tian \r et al., 2019). However, there is low agreement and limited evidence\r that climate model historical trends are consistent with observed \r trends (Tian et al., 2019; Deng et al., 2021). Several factors have been \r attributed to these trends, including forest growth, urbanization, \r [...]"


id: fb336c3dda300f791950e0618bc6656e
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 41565
content:
"[...] local changes in wind measurement exposure and aerosols (Bichet \r et al., 2012), as well as natural variability (Zeng et al., 2019)."


id: b14c9a983b5f45c015b0ce5d1d32bfb5
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 28424
content:
"Projections of smaller-scale hazard phenomena such as tornadoes, \r wind gusts, hail storms and lightning are currently not directly \r available partly due to the inability of climate models to simulate \r such phenomena. Observational networks for such phenomena \r usually lack homogeneity over long periods, hindering clear trends to \r be detected. For instance, while no robust trends have been identified \r (Hermida et al., 2015; Mohr et al., 2015a; Burcea et al., 2016; Curic \r and Janc, 2016), hail storm environments (favourable atmospheric \r configurations) have increased in frequency (low confidence, limited \r evidence) (Sanchez et al., 2017). In future climate periods it is more \r likely than not that severe convection environments will become \r more frequent by the end of the century under RCP8.5 (Mohr et al., \r 2015b; Pucik et al., 2017), and there is medium confidence that such \r environments will become more frequent by the 2050s in RCP4.5. \r [...]"


id: e2a5c44886a6a5d46e6a0b9fbc554edd
created_at: 2023-04-19 13:22:47
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content_type: text
index: climate_gpt_v2
vector_id: 37177
content:
"[...] There is no evidence for changes in tornado frequencies in Europe \r in the observations (Groenemeijer and Kuhne, 2014) as well as in \r future climate projections. Insufficient observational record length for \r lightning numbers does not allow an assessment of trends."


id: 3f31c028eb82ff02070bf3d9a6921648
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 8301
content:
"Due to changes in mean surface wind speed patterns (C. Li et al., 2018) \r and the poleward shift of the North Atlantic jet stream exit, mean surface \r wind speeds are projected to decrease in the Mediterranean areas under \r RCP4.5 and RCP8.5 by the middle of the century and beyond, or for \r GWLs of 2degC and higher (high confidence), with a subsequent decrease \r in wind power potential (medium confidence) (Hueging et al., 2013; \r Tobin et al., 2015, 2018; Davy et al., 2018; Karnauskas et al., 2018a; \r Kjellstrom et al., 2018; Moemken et al., 2018; Figure 12.4). However, \r sub-regional patterns of change are shown in regional climate models, \r such as an increase in wind speeds in the Aegean Sea and in the \r northern Adriatic Sea, where a reduction of Bora events and an increase \r of Scirocco events are projected for mid-century and beyond under \r RCP4.5 and RCP8.5 (medium confidence) (Tobin et  al., 2016; Davy \r [...]"


id: 836d10c134ec919f3215c9155da18bcc
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 20257
content:
"[...] et al., 2018; Belusic Vozila et al., 2019). Projections (as cited above) also \r indicate a decrease in mean wind speed in Northern Europe (medium \r confidence, medium agreement) (Karnauskas et al., 2018a; Tobin et al., \r 2018; Jung and Schindler, 2019). Daily and interannual wind variability \r is projected to increase under RCP8.5 only in Northern Europe (low \r confidence) (Moemken et  al., 2018), which can influence electrical \r grid management and wind energy production (low confidence). Wind \r speeds are projected to shift towards more frequent occurrences below \r thresholds inhibiting wind power production (Weber et  al., 2018). \r Wind stagnation events may become more frequent in future climate \r scenarios in some areas of Europe in the second half of the 21st century \r (Horton et al., 2014; Vautard et al., 2018), with potential consequences \r on air quality (low confidence)."


id: b49afce4e256977cae04bdc86772dd38
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 29023
content:
"Snow: Widespread and accelerated declines in snow depth (Fontrodona \r Bach et al., 2018) and snow water equivalent (Marty et al., 2017a; see \r Figure 12.9b) have been observed in Europe. In the Pyrenees a slow \r snow cover decline has been observed starting from the industrial \r period with a sharp increase since 1955 (Lopez-Moreno et al., 2020). \r Under the RCP2.6, RCP4.5 and RCP8.5 scenarios the reliability elevation \r for snowmaking will rise by 200-300 m in the Alps and 400-600 m \r in the Pyrenees by mid-century. End of century projections of natural \r snow conditions are highly dependent on the scenario, being stationary \r for the RCP2.6 and continuously decreasing under RCP8.5 to not have \r any more natural snow conditions at any of the locations in the French \r Alps and Pyrenees (Spandre et al., 2019). Similarly Norway and Austria \r will also see a rising of the natural snow elevation with consequences \r for the ski season (Scott et al., 2020; Steiger and Scott, 2020). In the \r [...]"


id: 4af20d9b62c0e6a2ccab9f1658629a0c
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 10426
content:
"[...] Alps, recent simulations project a reduction in snow water equivalent \r (SWE) at 1500 m above sea level of 80-90% by 2100 under the A1B \r scenario and a snow season that would start 2-4 weeks later and end \r 5-10 weeks earlier than the 1992-2012 average (Schmucki et  al., \r 2015), which is equivalent to a shift in elevation of about 700 m (Marty \r et al., 2017b). For elevations above 3000 m above sea level, a decline in \r SWE of at least 10% is expected by the end of the century even when \r assuming the largest projected precipitation increase. Similar trends \r are observed for the Pyrenees and Scandinavia (Lopez-Moreno et al., \r 2009; Raisanen and Eklund, 2012). For the northern French Alps above \r 1500 m and the Otztal locations in the Austrian alps SWE has a similar \r decreasing trend altitudinally dependent for RCP2.6, RCP4.5 and \r RCP8.5 until mid-century and with significant differentiation among \r them in the second half of the century up to snow-free conditions \r [...]"


id: c897f424c850b1eeea4bc9fdba0724af
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 32607
content:
"Glacier: Observations and future projections of European glacier \r mass changes are assessed in Section  9.5.1 grouped in two main \r regions: Scandinavia and central Europe regions. It is virtually certain\r that glaciers will shrink in the future and there is medium confidence\r in the timing and mass change rates (Section 9.5.1). Central Europe \r is one of the regions where glaciers are projected to lose substantial \r mass even under low-emissions scenarios (Section 9.5.1.3; MedECC, \r 2020). GlacierMIP projections indicate that glaciers in the central \r Europe region will lose 63 +- 31%, 80 +- 22% and 93 +- 13% of their \r 2015 mass by the end of the century under RCP2.6, RCP4.5 and \r RCP8.5 respectively (Marzeion et al., 2020). For the same scenarios, \r glaciers in Scandinavia are projected to lose 55 +- 33%, 66 +- 34% and \r 82 +- 24% of their 2015 mass. The virtually certain shrink in glaciers \r is bolstered by RCM simulations from the EURO-CORDEX ensemble, \r [...]"


id: fa24022f155d59da6c3617c22cb5573c
created_at: 2023-04-19 13:22:47
updated_at: 2023-04-19 13:22:47
content_type: text
index: climate_gpt_v2
vector_id: 41380
content:
"[...] with the Global Glacier Evolution Model (GloGEM) indicating \r a substantial reduction of glacier ice volumes in the European Alps \r by 2050 (47-52% with respect to 2017 for RCP2.6, RCP4.5 and \r RCP8.5). Under RCP2.6, about two-thirds (63+-11%) of the present\u0002"


id: 66a90a52c082e9b7cb519dc92784c620
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 15318
content:
"Projections indicate that sandy coasts throughout the continent (except \r those bordering the northern Baltic Sea) will experience shoreline retreat \r through the 21st century (high confidence). Median shoreline change \r projections (CMIP5) relative to 2010, show that, by mid-century, \r shorelines will retreat by between 25 m and 60 m along sandy coasts \r in WCE and MED under both RCP4.5 and RCP8.5 (Athanasiou et al., \r 2020; Vousdoukas et al., 2020b). Mid-century median projections for \r NEU indicate virtually no shoreline retreat under RCP4.5, but a retreat \r of around 40 m under RCP8.5. By 2100, median shoreline retreats of \r around 50 m are projected in NEU and MED under RCP4.5, increasing \r to around 80 m under RCP8.5. End-century median projections for \r WCE are far higher at 100 m (RCP4.5) and 160 m (RCP8.5). The total \r length of sandy coasts in Europe that is projected to retreat by more \r than a median of 100 m by 2100 under RCP4.5 and RCP8.5 is about \r [...]"


id: cca2ade1ec146160ad6881a7c230bf9d
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 33326
content:
"[...] 12,000 km and 18,000 km respectively, an increase of approximately \r 54% (Vousdoukas et al., 2020b)."


id: abfb17b0be4fc56d75b9cadaf4cce198
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 27500
content:
"There is high confidence that extreme total water level (ETWL) \r magnitude and occurrence frequency will increase throughout Europe \r (see Figure  12.4p-r), except in the northern Baltic Sea. Across the \r region, the 5-95th percentile range of the 1-in-100-year ETWL is \r projected to increase (relative to 1980-2014) by 4-40 cm and by \r 6-47 cm by 2050 under RCP4.5 and RCP8.5, respectively. By 2100, this \r range is projected to be 6-88 cm and 25-186 cm under RCP4.5 and \r RCP8.5, respectively (Figure 12.SM.6; Vousdoukas et al., 2018; Kirezci \r et al., 2020). Mass addition across the Gibraltar Strait may play a role, \r although the extent of this contribution is currently unclear (Lionello \r et al., 2017). Furthermore, under RCP4.5, the present day 1-in-100-year \r ETWL is projected to have median return periods of between 1-in-5 and \r 1-in-20 years by 2050 and occur at least once per year by 2100 in the \r Mediterranean and Black Sea, while in the rest of Europe it is mostly \r [...]"


id: 223a20e252f97deb562be0079d119c14
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 3245
content:
"[...] projected to have median return periods of between 1-in-20-years and \r 1-in-50-years by 2050 and between 1-in-5-years and 1-in-20-years \r by 2100 (Vousdoukas et al., 2018). Under RCP8.5, occurrence of the \r present day 1-in-100-year ETWL is projected to increase further to \r median return periods of 1-in-1-year to 1-in-5-years by 2050 and occur \r more than once per year by 2100 in the Mediterranean and Black Sea, \r while in the rest of Europe it is mostly projected to have median return \r periods between 1-in-5-years and more than once per year by 2100."


id: c65d1e588822af04986a481d0e0b68a
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 32210
content:
"Mean air temperature: Atlas.9.2 assessed very likely mean warming \r in observations across North America, with highest increases at \r higher latitudes and in the winter season. Atlas.9.4 assessed very \r likely mean warming in future decades in all North American \r regions, with CMIP and CORDEX models showing median increases \r exceeding 2degC in much of the continental interior under RCP8.5 \r (2041-2060 compared to 1995-2014) and higher increases towards \r the north. Mean temperatures at the end of century show strong \r scenario dependence, rising between 1degC and 2.5degC in RCP2.6 and \r about 4degC to 8degC in RCP8.5 (Figures Atlas.12, Atlas.26 and Atlas.27). \r Warming also raises stream temperatures across the continent \r (DOE, 2015; Trtanj et al., 2016; van Vliet et al., 2016; Chapra et al., \r 2017), and Hill et al. (2014) projected US stream warming by 0.6degC \r (+-0.3degC) per 1degC increase in local air temperature. Mean warming \r [...]"


id: 7fe1a4d2069962c0ca8cdd1c610eb067
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 19638
content:
"[...] drives shifts in the seasonal timing of temperature thresholds, \r including increasing growing degree days (Mu et al., 2017), longer \r growing seasons (Gowda et al., 2018; G. Li et al., 2018; L.A. Vincent \r et al., 2018), reduced chill hours (Luedeling, 2012; Lee and Sumner, \r 2015; Xie et  al., 2015; Parker and Abatzoglou, 2019), and longer \r pollen and allergy seasons (Fann et al., 2016; Anenberg et al., 2017; \r Sapkota et al., 2019). Warmer temperatures reduce heating degree \r days and increase cooling degree days (high confidence) (Bartos et \r al., 2016; US EPA, 2016; Craig et al., 2018; X. Zhang et al., 2019; \r Coppola et al., 2021b) "


id: a9f895c34f3f659164b0c31e040a73cc
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 27126
content:
"Figure  12.4b shows over a  month of additional days at CMIP6 \r SSP5-8.5 mid-century where temperatures exceed 35degC across \r much of southern Mexico and regions near the US-Mexico border, \r and these extreme temperatures occur at least once per year up to \r southern Canada. Coppola et al. (2021b) found similar patterns in \r CMIP5 and CORDEX-Core. Using locally tailored heat thresholds, \r Maxwell et  al. (2018) found that 'very hot' days in five US cities \r will occur a median of three to five times more often by 2036-2065 \r under RCP8.5 (2 to 3.5 times more often in RCP4.5), Oleson et al. \r (2018) projected that annual heatwave duration will exceed \r one month in Houston in RCP8.5 2061-2080, and Anderson et al. \r (2018) projected 7 to 12  times more exceedances of thresholds \r associated with high-mortality by 2061-2080 under RCP8.5 (6 to \r 7 times more exceedances in RCP4.5). Schwingshackl et al. (2021) \r found that Central and Eastern North America are among the regions \r [...]"


id: 610e8a4eeb0c730ff002b3fe52c54941
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 14369
content:
"[...] with the strongest trend in heat stress indicators. Studies also project \r increasingly surpassed heat extreme thresholds for North American \r crops (Gourdji et al., 2013), airplane weight restrictions (Coffel et al., \r 2017), and peak load energy systems (Auffhammer et al., 2017)."


id: 8af89d8ebac564e247e4055dcee2fb36
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 21620
content:
"in the future (high confidence). Significant decreases in frost days, \r consecutive frost days, and ice days were identified in 1948-2016 \r station observations across Canada, along with a  resulting \r lengthening of the frost-free season by more than a month in many \r regions (L.A. Vincent et al., 2018). Frost days also declined in nearly \r all Mexican cities from 1980 to 2010 (Garcia-Cueto et al., 2019), and \r a 1917-2016 decline of about three weeks in frigid winter conditions \r challenges ecosystems in the north-east USA and south-east Canada \r (Contosta et  al., 2020). Studies connect projections of a  longer \r frost-free season in North America to a longer outdoor construction \r season, shifts in frost variance to orchard damages, and lower weight \r tolerances for runways (Daniel et al., 2018; DeGaetano, 2018; Jacobs \r et al., 2018). Frosts are projected to persist as an episodic hazard in \r many regions given natural variability and cold air outbreaks even as \r [...]"


id: a1aea1e58050724843a96b665810fff2
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 25666
content:
"Cold spells are projected to decrease over North America under climate \r change, with the largest decreases most common in the winter season \r (high confidence) (Section 11.9). Minimum winter temperatures are \r projected to rise faster than the mean winter temperature (Underwood \r et  al., 2017) and alter cold-hardiness zones used to determine \r agricultural suitability (Parker and Abatzoglou, 2016). Wuebbles \r et  al. (2014) projections for RCP8.5 end-of-century show that the \r four-day cold spell that happens on average once every five years \r is projected to warm by more than 10degC and CMIP5 models do not \r project current 1-in-20-year annual minimum temperature extremes \r to recur over much of the continent. Multiple studies have shown \r that Arctic warming can alter large-scale variability and change the \r frequency and duration of mid-latitude cold air outbreaks, potentially \r leading to increasing cold hazards in some regions (low agreement) \r [...]"


id: 95460c660a57c5265149c3d9277cec5d
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 23462
content:
"the USA (Dudley et al., 2017; Wehner et al., 2017, Neri et al., 2020). \r Some rivers show ice-jam floods occurring a week earlier, but changes \r are mixed, given localized positive and negative changes across the \r continent (Rokaya et  al., 2018). There is medium confidence that \r climate change will increase river floods over the USA and Canada \r but low confidence for changes in Mexico. Wobus et  al. (2017a) \r used a  regional hydrologic model for 57,000 streams to project \r more than a doubling in the frequency of current 1-in-100-year flow \r events in many portions of the USA for RCP8.5 2050 with additional \r contributions from earlier snowmelt. CMIP6 projections for SSP5-8.5 \r 2065-2099 show strongest peak USA runoff increases in the east \r (Villarini and Zhang, 2020); however, several studies applying global \r hydrological models disagree with regional streamflow projections, \r indicating a decrease in the magnitude or frequency of floods over \r [...]"


id: af38c2f4e5bdd0902866a10b4b03a453
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 28067
content:
"[...] a large portion of North America (e.g., Hirabayashi et al., 2013; Arnell \r and Gosling, 2016; see Figure 12.10a,c)."


id: feb062590651ab7b8ac83538738b657d
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 42195
content:
"Aridity: Chapter 8 showed that aridity in North America generally \r moves opposite to mean precipitation change with an added \r evaporative demand from warmer temperatures (high confidence in \r aridity increase for Northern Central America; medium confidence for \r an increase in Central North America; high confidence for a decrease \r in North-Eastern North America; medium confidence decreases in \r Eastern and North-Western North America; see also Section  11.9). \r Projected soil moisture declines (Figure 12.4j-l) are most widespread \r across North America during the summer, with the largest declines in \r Mexico and the southern Great Plains but also extending into Canada \r (Section  8.4.1.6; Swain and Hayhoe, 2015; Easterling et  al., 2017; \r Bonsal et al., 2019; J. Lu et al., 2019). Yoon et al. (2018) found net \r reduction in southern Great Plains groundwater storage in RCP8.5 \r mid-century projections despite increases in mean precipitation \r [...]"


id: 1c1e2b872bc6451cb34685423679eb81
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 2126
content:
"[...] and both wet and dry extremes. Soil moisture drying could reach \r unprecedented levels by the CMIP6 RCP8.5 end-of-century, even \r when evaluating deeper soil columns relevant for crop rooting depth \r (B.I. Cook et al., 2020). Projected changes in the aridity index portray \r a shift the geographic range of temperate drylands northward and \r eastward in Central and Western North America (Schlaepfer et al., \r 2017; Seager et  al., 2018) which also diminishes aquifer recharge \r rates in the southern Great Plains and in some western regions \r where snowpack is reduced (Meixner et al., 2016)."


id: 80a29f508d734527c138613e0cc122e3
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
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index: climate_gpt_v2
vector_id: 19762
content:
"Section 11.4 found that high precipitation is projected to increase \r across North America (high confidence) except for portions of Western \r North America where projections are mixed (medium confidence of \r increase). Maxwell et al. (2018) identified regional 'heavy precipitation \r day' thresholds for five cities across the USA and projected that \r a tripling (or more) of these events is possible by RCP8.5 mid-century. \r Projections indicate changes to intensity-duration-frequency (IDF) \r curves typically used for construction design and automobile hazards, \r as well as increases in the 10-year recurrence level of 24-hour \r rainfall intensities that challenge storm water drainage systems \r (Hambly et al., 2013; Cheng and AghaKouchak, 2015; J.E. Neumann \r et  al.,  2015; Prein et  al., 2017b; Hettiarachchi et  al., 2018; Ragno \r et al., 2018). Precise levels of regional IDF characteristics may still \r depend substantially on the method and resolution of downscaling \r [...]"


id: 1aa2a7f1fde74655b01367a39903b795
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 1886
content:
"Agricultural and ecological drought: Section  11.9 assessed \r medium confidence for an increase in agricultural and ecological \r drought in Western North America but otherwise found limited \r evidence for broadly observed changes in North American agricultural \r and ecological drought, even as increasing evaporative demand \r intensified vegetation stress and soil moisture deficits in recent events \r (Sections 11.6, 11.9). Section 11.9 asssessed medium confidence for \r more intense agricultural and ecological drought conditions over \r North Central America, Western North America and Central North \r America in a  2degC global warming level (about mid-century), with \r medium confidence extending to Eastern North America and high \r confidence for Northern Central America and Central North America \r under a 4degC global warming level associated with higher emissions \r scenarios past 2050. Figure  12.4g-i shows that the frequency \r [...]"


id: e06d5f7f584b3a0b05ec9c59df4d1067
created_at: 2023-04-19 13:22:48
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index: climate_gpt_v2
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content:
"[...] of meteorological droughts (which often initiate hydrological, \r agricultural and ecological droughts) is largely projected to increase \r in North American areas where total precipitation decreases (and \r vice versa; see Section 11.9 and Coppola et al., 2021b), and higher "


id: 569cc0971f9b5432cf6692f728c738f4
created_at: 2023-04-19 13:22:48
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content_type: text
index: climate_gpt_v2
vector_id: 12496
content:
"Landslide: There is growing yet limited evidence for unique climate\u0002driven changes in landslide and rockfall hazards in North America, \r even as theory suggests decreases in slope and rockface stability \r due to more intense rainfall, rain-on-snow events, mean warming, \r permafrost thaw, glacier retreat, and coastal erosion (Cloutier \r et al., 2017; Coe et al., 2018; Handwerger et al., 2019; Hock et al., \r 2019; Patton et al., 2019) although dry trends can decelerate mass \r movements (Bennett et al., 2016). Landslide frequency has increased \r in British Columbia (Canada; Geertsema et al., 2006) and is expected \r to increase in North-Western North America given the combination \r of these factors (medium confidence) (Gariano and Guzzetti, 2016). \r Cloutier et al. (2017) projected an increase in landslides in western \r Canada due to wetter overall conditions and reduced return period \r for extreme rainfall. Robinson et  al. (2017) used scenarios based \r [...]"


id: 3f60b89a53accedc313dd1e4a96f6004
created_at: 2023-04-19 13:22:48
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index: climate_gpt_v2
vector_id: 8340
content:
"[...] upon projection of 50-year recurrence of 7-day precipitation periods \r to highlight the potential for increased landslide hazards near Seattle \r (USA). Broad projections for the USA are more uncertain given "


id: e4cec2bb6066f8a5e4cd2a0b35b6dc8e
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 37538
content:
"Fire weather: Climatic conditions conducive to wildfire have increased \r in Mexico, Western and North-Western North America, primarily due \r to warming (high confidence). Abatzoglou and Williams (2016) found \r climate change led to higher values for eight fuel aridity indices over \r the western USA in recent decades, with 2000-2015 changes exposing \r 75% more forested area to high fuel aridity and adding nine more high \r fire-potential days each year, similar to 1979-2013 western USA and \r Mexico fire season expansion reported in Jolly et al. (2015). Increases \r in lightning-initiated fires have been distinguished from trends in man\u0002made fire in western Canada and the USA (Balch et al., 2017; Hanes \r et al., 2019). Jain et al. (2017) identified a 1979-2015 expansion in \r fire weather season in eastern Canada and the south-western USA \r (with a smaller reduction in the northern Mountain West) along with \r regional shifts in the 99th percentile Canadian Fire Weather Index (FWI) \r [...]"


id: 7807b9fd2a984ea3fcf5920bcdcb3665
created_at: 2023-04-19 13:22:48
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index: climate_gpt_v2
vector_id: 18328
content:
"[...] and potential fire-spread days. Girardin and Wotton (2009) noted that \r 1951-2002 trends in the Monthly Drought Code fire index in eastern \r Canada could hardly be distinguished from decadal variability."


id: 81bcad892da2bd5af50a455795d65c49
created_at: 2023-04-19 13:22:48
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content_type: text
index: climate_gpt_v2
vector_id: 19954
content:
"Climate change drives future increases in North American fire \r weather, particularly in the south-west (high confidence), although \r further studies on shifts in exposure and vulnerability are needed \r to understand overall fire risks (see WGII Chapter  14). A  significant \r increase of FWI is apparent before 2050 under RCP8.5 in much of \r North America, including the frequency of 95th-percentile FWI days, \r peak seasonal FWI average, fire weather season length, and maximum \r fire weather index (Abatzoglou et al., 2019), and fire season across \r North America expands dramatically beyond 2degC global warming \r levels (Q. Sun et al., 2019; Jain et al., 2020). X. Wang et al. (2017b) \r simulated fire-spread days across Canada and found increases across \r most of the areas studied by 2071-2100, with median changes of \r -20 to +140% (RCP2.6), -20 to +250% (RCP4.5) and 40 to 360% \r (RCP8.5) compared to 1976-2015. Prestemon et al. (2016) found more \r [...]"


id: 33c667c96073f6f6eda35e666f07e7fb
created_at: 2023-04-19 13:22:48
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index: climate_gpt_v2
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content:
"[...] conducive conditions for lightning-ignited fires in  the south-eastern \r USA by mid-century, while warming conditions in Alaska increasingly \r push July temperatures above 13.4degC, a  threshold for fire danger \r across Alaska's tundra and boreal forest (Partain et al., 2016; Young \r et al., 2017). Longer and more intense fire seasons would also raise \r particulate matter and black carbon concentrations in the western \r USA, reducing visibility at many National Parks (Yue et al., 2013; Val \r Martin et al., 2015)."


id: ac68f8a0e8077d87577ccc4b53daff5d
created_at: 2023-04-19 13:22:48
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content_type: text
index: climate_gpt_v2
vector_id: 27570
content:
"There is medium confidence of a general decrease in the number of \r extratropical cyclones producing high wind speeds in North America, \r except over northernmost parts, for a global warming level of 2degC or \r by the end of the century under RCP4.5 and RCP8.5 (Kumar et al., \r 2015; Jeong and Sushama, 2018a; C. Li et al., 2018). GCMs cannot \r directly resolve tornadoes and severe thunderstorms, however \r projections of favourable environments for severe storms (based \r on convective available potential energy and wind shear) indicate \r medium confidence for more severe storms and a longer convective \r storm season in the USA, weaker increases extending north and east \r (Seeley and Romps, 2015; Glazer et al., 2021), and a corresponding \r increase in autumn and winter tornadic storms (H.E. Brooks, 2013; \r Diffenbaugh et al., 2013; Brooks et al., 2014; see also Section 11.7.2). \r Prein et al. (2017a) used a convection-permitting model to project \r [...]"


id: 2d1241967d60b357d1831559ab1a0a2c
created_at: 2023-04-19 13:22:48
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content_type: text
index: climate_gpt_v2
vector_id: 5205
content:
"Sand and dust storm: Land-use change has increased dust \r emissions in the western USA in the past 200 years (Neff et  al., \r 2008). However, there is medium confidence for observed increases \r in Western North American sand and dust storm activity since 1980. \r In their study of Valley Fever spread, Tong et  al. (2017) identified \r a rapid intensification of dust storm activity using PM10 and PM2.5\r observations from 1980-2011 across 29 monitoring sites in the \r south-western USA, similar to contiguous USA observations by \r Brahney et al. (2013). Hand et al. (2016) attributed the earlier onset \r of spring dusts in the south-west in large part to the Pacific Decadal \r Oscillation, however. The increasing trend in dust since the 1990s \r in the south-western USA can be explained by precipitation deficit \r and surface bareness (Pu and Ginoux, 2018). Projections of future \r sand and dust storms over North America are based on aridity as \r [...]"


id: c8d184137e8eb56134d4888840d0f739
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 32658
content:
"[...] a  primary proxy for conducive conditions which lends medium \r confidence of an increase over Mexico and the south-western USA. \r Pu and Ginoux (2017) project about five more dusty days in spring \r and summer in the southern Great Plains under RCP8.5 at the end \r of the century, while dusty days decrease in northern regions where \r mean precipitation tends towards wetter conditions."


id: e9dedc29578f1703bdccb30b55e76325
created_at: 2023-04-19 13:22:48
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index: climate_gpt_v2
vector_id: 38493
content:
"Glacier: Section  9.5.1 assessed that glaciers in Alaska, western \r Canada and the western USA are expected to continue to lose mass \r and areal extent (high confidence). Compared to their 2015 state, \r glaciers in the western Canada and the USA region will lose 62 +- 30%, \r 75 +- 29% and 85 +- 23%, of their mass by the end of the century for \r RCP2.6, RCP4.5 and RCP8.5 scenarios, respectively (Marzeion et al., \r 2020). Meanwhile glaciers in Alaska will lose 26 +- 21%, 31 +- 24% \r and 44 +- 27%, of their 2015 mass under the same scenarios. \r The overall loss of glacial mass can act as a meltwater supply for \r freshwater resources, although this is expected to peak in the middle \r of the century and then fade as glaciers disappear (Fyfe et al., 2017; \r Derksen et al., 2018). Continued shrinkage of glaciers is projected \r to create further glacial lakes (medium confidence) similar to those \r that have led to outburst floods in Alaska and Canada (Carrivick and \r [...]"


id: cbd09035b9daed696d9db369b466f509
created_at: 2023-04-19 13:22:48
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index: climate_gpt_v2
vector_id: 33182
content:
"Heavy snowfall: There is low agreement (limited evidence) for \r observed changes in heavy snowfall in North America. Kluver and \r Leathers (2015) noted a  1930-2008 frequency increase for all \r snow intensities in the northern Great Plains but declines in heavier \r snow  events in the Pacific Northwest and declines in the south\u0002eastern USA. Changnon (2018) found that most extreme 30-day \r high-snowfall periods in the 1900-2016 record over the eastern USA \r occurred in the 1959-1987 period, which lies between the 1930s \r Dust Bowl and recent warming. There is low agreement and medium \r evidence for broad projected changes to heavy snowfall over North \r America given increased heavy precipitation and warmer winter \r temperatures. Several recent regional studies have projected that \r low-intensity events decrease more rapidly than heavy snowfall \r events, resulting in an increase in the snowfall proportion from \r heavy snowfall events even as the number of such events decreases \r [...]"


id: b9cb75efa4a886f0522fa2b5c91a248b
created_at: 2023-04-19 13:22:48
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content:
"[...] (O'Gorman, 2014; Lute et  al., 2015; Zarzycki, 2016; Janoski et  al., \r 2018; Ashley et al., 2020)."


id: 2d9aaaa9e99280223755e1929b2b02a
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
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vector_id: 5317
content:
"Relative sea level: Chapter  9 found that observations indicate \r increasing sea levels along most North American coasts (robust \r evidence, high agreement), although there is substantial regional \r variation in relative sea level rise (robust evidence, high agreement). \r Around North America, over 1900-2018, a  new tide gauge-based \r reconstruction finds a  regional mean RSL change of 1.08  [0.79  to \r 1.38] mm yr-1 in the subpolar North Atlantic, 2.49 [1.89 to 3.06] mm yr-1\r in the subtropical North Atlantic, and 1.20 [0.76 to 1.62] in the East \r Pacific (Frederikse et  al., 2020), compared to a  GMSL change of \r around 1.7 mm yr-1 (Section 2.3.3.3 and Table 9.5). For the period \r 1993-2018, these RSLR rates, based on satellite altimetry, increased \r to 2.17 [1.66 to 2.66]  mm yr-1, 4.04  [2.77  to 5.24]  mm yr-1 and \r 2.35  [0.70 to 4.06]  mm  yr-1,  respectively (Frederikse et  al., 2020), \r compared to a GMSL change of 3.25 mm yr-1 (Section 2.3.3.3 and \r [...]"


id: c14bcfd2008cb497eea09a5e50fad5c1
created_at: 2023-04-19 13:22:48
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index: climate_gpt_v2
vector_id: 31307
content:
"[...] Table 9.5). Relative sea level (RSL) is falling in portions of southern \r Alaska (Sweet et al., 2018) and much of the northern part of north\u0002eastern Canada and around Hudson Bay (where land is rising by \r >10 mm/year; Greenan et al., 2018)."


id: d59cc616267f0e8ad5ed2cd056d76e53
created_at: 2023-04-19 13:22:48
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index: climate_gpt_v2
vector_id: 34895
content:
"many North American regions under future climate change (high \r confidence) although land uplift from glacial isostatic adjustment in \r northern and Hudson Bay portions of North-Eastern North America \r leads to only medium confidence of coastal flood increases in that \r region. Sweet et al. (2018) found 2000-2015 observed increases of \r about 125% in high-tide flooding frequencies along the southern \r Atlantic USA coastline, with 75% increases along the USA Gulf \r Coast and USA northern Atlantic coastlines. That same study noted \r that a GMSL of 0.5 m in 2100 would increase high tide ('nuisance') \r flooding from current rates of about once a month for most coastal \r regions to about once every other day along the USA Atlantic and Gulf \r coasts and smaller increases in frequency along the Pacific coast, and \r Dahl et al. (2017a) found similar trends on the USA East Coast prior to \r mid-century. The present day 1-in-50-year ETWL is projected to occur \r [...]"


id: 94c5d96d042df26c71b28d5ed2a669cf
created_at: 2023-04-19 13:22:48
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content:
"[...] around three times per year by 2100 with an SLR of 1 m all around \r North America, except in most of Eastern North America where \r it is expected to have return periods of 1-in-1-year to 1-in-2-years \r (Vitousek et al., 2017). Ghanbari et al. (2019) projected corresponding \r shifts towards higher frequencies of major flooding events for 20 US \r cities. Figure 12.4r and Figure 12.SM.6 show increases of 70 cm or \r more in the 100-year return period extreme total water level (ETWL) \r over much of the USA East Coast, British Columbia, Alaska, and the \r Hudson Bay under RCP8.5 by 2100 (relative to 1980-2014), with \r lower increases in northern Mexico, northern Canada, Labrador, and \r the Pacific and Gulf coasts of the USA (Vousdoukas et  al., 2018). \r Projected increases in coastal flooding generally follow patterns of \r RSL change, although sea ice loss in the north also increases open \r water storm surge (Greenan et al., 2018)."


id: b4e36b3cd7690b7e9f19cfb38eca409a
created_at: 2023-04-19 13:22:48
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index: climate_gpt_v2
vector_id: 29071
content:
"This section covers the climatic impact-drivers affecting small islands \r around the world (see definition of SIDS in the Glossary; Cross\u0002Chapter Box Atlas.2) with a particular focus on small islands in the \r Caribbean (CAR) Sea and the Pacific Ocean. Caribbean and Pacific \r small islands have mostly tropical climates and local conditions are \r also influenced by diverse topography ranging from low-lying islands \r and atolls to volcanic and mountainous terrain. Climate variability in \r these islands is influenced by the trade winds, easterly waves, tropical \r cyclones (TC), and the migrations of the Inter-tropical Convergence \r Zone (ITCZ), the North Atlantic Subtropical High, and the South Pacific \r Convergence Zone (SPCZ), and other modes of climate variability as \r discussed in Cross-Chapter Box Atlas.2. Furthermore, changes in the \r ocean temperature and chemistry, and relative sea level have strong \r impacts on these small islands given their geographical location and \r [...]"


id: d0f93f348ddb216e8ba8eeae62821733
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content:
"The AR5 recognized the heterogeneity in these small islands in terms \r of physical geography, socio-economic and cultural backgrounds, as \r well as their vulnerability to the impacts of climate change. Similar \r to previous reports, these regions have been assessed together \r in this section, given the similarities in the challenges they face in \r addressing climate change impacts and risk, which were thought - \r until AR4  - to be dominated by sea level rise (Nurse et  al., 2014; \r Betzold, 2015). Since then there has been a  substantial increase \r in the number and complexity of the literature on the drivers and \r impacts of climate change on small islands (BOM and CSIRO, 2011, \r 2014; Nurse et al., 2014; Gould et al., 2018; Keener et al., 2018). There \r are also increasing efforts being made to produce higher resolution \r climate projections for small islands through downscaling methods \r (Elison Timm et al., 2015; McLean et al., 2015; Khalyani et al., 2016; \r [...]"


id: 3621ccff93e102688c20e277ab84dd46
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vector_id: 6774
content:
"[...] C. Zhang et al., 2016; Stennett-Brown et al., 2017; Bhardwaj et al., \r 2018; Bowden et al., 2021)."


id: 3485545926fafcd8816ee8309fd3e42a
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 6478
content:
"Heavy precipitation and pluvial flood: Heavy precipitation days \r in CAR have increased in magnitude, and have been more frequent \r in the northern part during the latter part of the 20th century (low \r confidence) (Section 11.4.2 and Table 11.14). The direction of change \r in extreme precipitation varies across the Pacific and depends on \r the season (low confidence) (Section  11.4.2 and Cross-Chapter \r Box Atlas.2, Table  1). Although pluvial flooding events have been \r observed in some islands, there is limited evidence for an assessment \r on past changes in pluvial flooding, unlike in other regions. There is \r low confidence in the projected change in magnitude of very heavy \r precipitation days in CAR across different GWLs (Table 11.14). On the \r other hand, there is high confidence in the increase in frequency and \r intensity of extreme rainfall events (i.e., 1-in-20-year rainfall events) \r in the western tropical Pacific in the 21st century, even for RCP2.6 \r [...]"


id: aefc0f1321412c6a7c095b6e3febcdb0
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index: climate_gpt_v2
vector_id: 28030
content:
"[...] scenario, based on model agreement and mechanistic understanding \r but low confidence in the magnitude of change in extreme rainfall \r due to model bias (BOM and CSIRO, 2014)."


id: 3c8fd0890aeb645fcd5d01153ecfb7
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 7847
content:
"Agricultural and ecological drought: Recent trends toward more \r frequent and severe droughts have been noted in the small islands but \r only with low confidence in broad trend patterns, given high spatial \r variability including heightened drought on the leeward side of islands \r (e.g., Frazier and Giambelluca, 2017; Herrera and Ault, 2017; McGree \r et  al., 2019; see Table  11.15, Cross-Chapter Box  Atlas.2, Table  1). \r Agricultural and ecological droughts are projected to increase in \r frequency, duration, magnitude, and extent in small islands, such as \r in CAR (medium confidence) and parts of the Pacific (low confidence), \r particularly where future declines in precipitation are compounded \r by higher evapotranspiration, under increasing levels of warming \r (Naumann et al., 2018; Taylor et al., 2018; Vichot-Llano et al., 2021). \r Relative to the period 1985-2014, decreases in annual surface and \r total column soil moisture become more robust in more areas in CAR \r [...]"


id: 2b84115429fd4030931c3f1d2bda5c99
created_at: 2023-04-19 13:22:48
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index: climate_gpt_v2
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content:
"[...] by 2071-2100 under SSP3-7.0 and SSP5-8.5 scenarios (B.I. Cook \r et al., 2020), but reliably representing drought features in small island \r domains with global simulations is challenging (see also Section 11.9)."


id: 474ba170bdab0cfd031359cad3705f5a
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 9750
content:
"Aridity: Current estimates identify many small islands as being under \r water stress and thus particularly sensitive to variations in rainfall and \r groundwater, population growth and demand, and land-use change, \r among others (Cross-Chapter Box Atlas.2; Holding et al., 2016). From \r 1950 to 2016, a heterogeneous but prevalent drying trend is found \r in CAR (low confidence), where drought variability is modulated \r by the tropical Pacific and North Atlantic oceans (Table 11.15 and \r Cross-Chapter Box Atlas.2, Table 1; Herrera and Ault, 2017). In the \r future, increased aridity and decreased freshwater availability are \r projected in many small islands due to higher evapotranspiration \r in a warmer climate that partially offsets increases or exacerbates \r reductions in precipitation (Karnauskas et al., 2016, 2018b; Hoegh\u0002Guldberg et al., 2018). Increased aridity is projected for the majority \r of the small islands, such as in CAR, southern Pacific and western \r [...]"


id: 2d5625b1c1cb19a653f51389f0a644f3
created_at: 2023-04-19 13:22:48
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index: climate_gpt_v2
vector_id: 5262
content:
"[...] Indian Ocean, by 2041-2059 relative to 1981-1999 under RCP8.5 or \r at 1.5degC and 2degC GWLs, which will further intensify by 2081-2099 \r (medium confidence) (Karnauskas et al., 2016, 2018b). Groundwater \r recharge is projected to increase in Maui, Hawaii except on the \r leeward side of the island, which underscores the importance of \r topography and elevation on freshwater availability in different \r island microclimates (Brewington et al., 2019; Mair et al., 2019)."


id: 4fd7af79d04f5adf72926c1dbc8f0a34
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updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 11340
content:
"Mean wind speed: Scarcity of observations limits assessment of \r long-term changes in winds over the small islands in the Pacific and \r CAR. Records indicate that average daily wind speeds have slowly \r declined in Hawaii, but have remained constant across western and \r southern Pacific sites since the mid-20th century (Marra and Kruk, \r 2017). Recent studies of reanalyses and hindcast simulations indicate \r an intensification of the Pacific trade winds during the 1992-2011 \r period, which contributed to the ocean cooling in the tropical central \r and eastern Pacific (England et al., 2014; Takahashi and Watanabe, \r 2016). Projections estimate up to 0.4 m s-1 (8%) increase in annual \r winds in CAR under RCP8.5, which is associated with changes in \r the extension of the North Atlantic Subtropical High that enhances \r the Caribbean low-level jet during the wet season, and stronger local \r easterlies due to enhanced land-ocean temperature differences in \r [...]"


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vector_id: 5005
content:
"Projections of global changes in tropical cyclones indicate more \r frequent Category  4-5 storms (high confidence) and increased rain \r rates (high confidence) (Knutson et al., 2020), with relative sea level rise \r exacerbating storm surge potential, but with large regional differences \r (see Section 11.7.1.5). By the late 21st century, tropical cyclones are \r projected to be less frequent in the basins of the western and eastern \r North Pacific, Bay of Bengal, Caribbean Sea and in the Southern \r Hemisphere, but will be more frequent in the subtropical central \r Pacific (Murakami et al., 2014; Yoshida et al., 2017; Bell et al., 2019; \r Knutson et al., 2020). Over CAR, tropical cyclone intensity is expected \r to increase by the end of the century under RCP8.5 due to higher sea \r surface temperatures but can be inhibited by increases in vertical wind \r shear in the region (medium confidence) (Kossin, 2017; Ting et  al., \r [...]"


id: 6ecbbe58a1b779b17ffcd70d44deb6bb
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 16762
content:
"[...] 2019). The poleward movement of the area in which tropical cyclones \r reach peak intensity in the western North Pacific is likely to continue, \r which affects the tropical cyclone frequency over the small islands in \r the area (Section 11.7.1.5; Kossin et al., 2016). Projections also indicate \r an increase (decrease) in the tropical cyclone frequency during El Nino \r (La Nina) events in the Pacific at the end of the 21st century (Chand \r et al., 2017). RCP8.5 2080-2099 projections indicate a 2% increase in \r the number of tropical cyclones in the north-central Pacific relative to \r 1980-1999, with tracks shifting northward towards Hawaii (N. Li et al., \r 2018). Given projected reductions to the overall number of tropical \r cyclones but increases in storm intensity, total rainfall and storm surge \r potential, we assess medium confidence of overall changes to tropical \r cyclones affecting the Caribbean and Pacific small islands."


id: 29aa1a466d1bfabb4458aff4965cdb12
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 4558
content:
"Coastal erosion: Recent studies have indicated variable and dynamic \r changes in shorelines of reef islands (medium confidence), including \r both erosion and accretion, which suggest factors other than SLR \r affecting shoreline changes, such as in the central and western Pacific \r within the past 50-to-60-year timeframe (Webb and Kench, 2010; \r Le  Cozannet et  al., 2014; Ford and Kench, 2015; Duvat and Pillet, \r 2017). For example, islands on atolls in the central and western Pacific \r have not substantially eroded or reduced in size in the past decades \r while sea level has been rising, but their position and morphology have \r changed due to anthropogenic factors (e.g.,  seawalls, reclamation) \r and climate-ocean processes (Biribo and Woodroffe, 2013; McLean \r and  Kench, 2015). Analysis of aerial and satellite imagery revealed \r severe shoreline retreat in six islands and the disappearance of five \r vegetated reef islands in Solomon Islands in the western Pacific \r [...]"


id: a9b8f5ddfd8bca465582eeb39175d21b
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 27080
content:
"[...] between 1947 and 2014, which may be due to the interaction between \r SLR and waves (Albert et al., 2016). In French Polynesia, changes in \r shoreline and island area have been observed since the 1960s, partly \r due to the effect of TCs on sediment changes and human activities \r (Duvat and Pillet, 2017; Duvat et al., 2017). Coastal erosion has also \r been noted over the small, low-lying, sandy islands, such as in French \r Polynesia and Solomon Islands, among others (Luijendijk et al., 2018; \r Mentaschi et  al., 2018). Average shoreline retreat rates between 1 \r and 2 m yr-1 are estimated for the islands in the equatorial Pacific \r and in CAR, while a retreat rate of 0.5 m yr-1 is estimated for islands \r in the South Pacific, based on satellite observations from 1984-2016 \r (Luijendijk et al., 2018; Mentaschi et al., 2018). There was also a loss of \r 610 km2 compared with a gain of 520 km2 in coastal area in Oceania \r during the 1984-2015 period (Mentaschi et al., 2018)."


id: c581e64e1f5c56ef168ea5375154373a
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 32052
content:
"Oceans face challenges from anthropogenic perturbations to the global \r Earth system, which cause increasing ocean warming, carbon dioxide\u0002induced acidification and oxygen loss (Bindoff et al., 2019). Climate \r change will affect the major oceanic CIDs described in Section 12.2: \r mean ocean temperature, marine heatwave, ocean acidity, ocean \r salinity, and dissolved oxygen (O2), as well as severe wind storm and \r sea ice. These changes result in a shifting profile of hazards relevant \r to impact and risk assessments (Section  12.3). New evidence, the \r SROCC (IPCC 2019b) assessments and advances in the new CMIP6 \r climate simulations reinforce confidence in projected changes in \r climatic impact-drivers in the global oceans. As the ocean has taken \r up about 90% of the global warming for the period 1971-2018 \r (Section  7.2.2.2), the emergence of the sea surface temperature \r increase signal has already been observed in global oceans over the \r [...]"


id: 6216a56a1b1d2097c28004c06daeff10
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 14528
content:
"[...] last century (Hawkins et al., 2020). The signal in sea ice extent decrease \r has already emerged in the Arctic Ocean (Landrum and Holland, 2020), \r while ocean acidification and low oxygen have also already emerged \r in many oceanic regions and will emerge in all global oceans by 2050 \r under RCP8.5 (Section 12.5.2 and Table 12.10). This section assesses \r key climatic impact-drivers that can be linked with sectoral and \r regional vulnerability and exposure in open and deep oceans, drawing \r from previous Chapters (Chapters 2, 3, 4, 5 and 9)."


id: c55da0a29af42d6b3408962b6f6300f1
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 32031
content:
"Projections indicate that shoreline retreat will occur over most of \r the small islands in the Pacific and CAR throughout the 21st century \r with spatial variability (high confidence). Median shoreline change \r projections (CMIP5) relative to 2010, presented by Vousdoukas et al. \r (2020b), show that, by mid-century, shorelines in the islands in the \r equatorial Pacific and South Pacific will retreat by around 40 m, \r under both RCP4.5 and RCP8.5. In CAR islands, sandy shorelines are \r projected to retreat by about 80 m by mid-century under both RCPs. \r By 2100, more than 100 m of median shoreline retreat is projected \r for all small islands under both RCPs; notably in CAR where retreats \r approaching 200 m (relative to 2010) are projected under both RCPs. \r The total length of sandy coasts in CAR and Pacific small islands that \r is projected to retreat by more than a median of 100 m by 2100 under \r RCP4.5 and RCP8.5 is about 1100 km and 1200 km respectively, an \r [...]"


id: 7080623438de7396d6faa5f9b2432606
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 17049
content:
"from 1850-1900 to 2011-2020, and 0.60 [0.44 to 0.74] degC from \r 1980 to 2020 (Section  2.3.1.1.6 and Table  2.4). There is very high \r confidence that the Indian Ocean, the western equatorial Pacific \r Ocean, and western boundary currents have warmed faster than the \r global average, while the Southern Ocean, the eastern equatorial \r Pacific, and the North Atlantic Ocean have warmed more slowly or \r slightly cooled (Section 9.2.1.1). It is virtually certain that global mean \r SST will continue to increase in the 21st century at a rate depending \r on future emissions scenario, with CMIP6 projections indicating an \r increase of 0.86degC (likely range 0.43degC-1.47degC) under SSP1-2.6 \r and 2.89degC (2.01degC-4.07degC) under SSP5-8.5, by 2081-2100, \r relative to 1995-2014 (Section  9.2.1.1). Global warming of 2degC \r above pre-industrial levels is projected to increase SST, resulting \r in the exceedance of numerous hazard thresholds for pathogens, \r [...]"


id: b9eae1a6d9a6762e77d90022147524e4
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 29931
content:
"[...] seagrasses, mangroves, kelp forests, rocky shores, coral reefs \r and other marine ecosystems (medium confidence) (Poloczanska \r et al., 2013a, b, 2016; Liu et al., 2014; Portner et al., 2014; Graham \r et al., 2015; Schoepf et al., 2015; Gobler et al., 2017; Henson et al., \r 2017; Hoegh-Guldberg et  al., 2017; Krueger et  al., 2017; Hughes \r et al., 2018a, b; Perry et al., 2018). It is virtually certain that upper\u0002ocean stratification has increased at a  rate of 4.9 +- 1.5% during \r 1970-2018 and that this will continue to increase in the 21st century \r (Section 9.2.1.3), potentially leading to reduced nutrient supply and \r total productivity (low confidence) (Moore et al., 2018)."


id: 30a9df6f3505624e42daccd5d1a1f9cf
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 5816
content:
"At the regional scale, by 2100 the average Arctic surface salinity \r is projected to decrease by 1.5 +- 1.1 psu (practical salinity units), \r and the liquid freshwater column in the Arctic Ocean is projected \r to increase by 5.4 +- 3.8 m under RCP8.5, (Shu et al., 2018). In the \r Indian Ocean, sea surface salinity is projected to decrease by \r between 0.49  and 0.75 psu by 2080, compared to 2015, under \r RCP2.6 and RCP2.6, respectively (Akhiljith et al., 2019). Projections \r for the North and South Atlantic oceans indicate increasing salinity \r in the upper layer (0-500 m) under both RCP4.5 and RCP8.5, due to \r the decreasing freshwater input from the equator and increasing net \r evaporation (Skliris et  al., 2020). There is medium confidence that \r fresh ocean regions (Pacific, Southern and Indian oceans) will get \r fresher and salty ocean regions (Atlantic Ocean) will get saltier over \r the 21st century (Section 9.2.2.2; IPCC, 2019b). Ocean warming and \r [...]"


id: 9227d534464cd67ffe6b54e5eb5c4199
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 22930
content:
"[...] high-latitude surface freshening is projected to continue to increase \r upper-ocean stratification in the 21st century (Section 9.2.1.3)."


id: 11548f6130fe5bde90dc163e05d96cba
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 246
content:
"Marine heatwave: Marine heatwaves (MHWs) have increased \r in frequency over the 20th century, with an approximate doubling \r since the 1980s (high confidence), and their intensity and duration \r have also increased (medium confidence) (Box  9.2). Projections \r show that this increasing trend likely continues with 2-9 times more \r frequent MHWs (at global scale) projected by 2081-2100, relative to \r 1995-2014 under SSP1-2.6, and 4-18 times more frequent MWHs \r under SSP5-8.5. The largest changes in MHW frequency are likely to \r occur in the tropical ocean and the Arctic, while there is medium \r confidence of moderate increases in the mid-latitudes, and of small \r increases in the Southern Ocean (Box 9.2). Permanent MHWs (more \r than 360 days per year, relative to the historical climate conditions) \r are projected to occur in the 21st  century in parts of the tropical \r ocean, in the Arctic Ocean, and around latitude 45degS, under SSP5-8.5 \r [...]"


id: 9a2ae12bf6f7dfbda908680b30cafdac
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 24363
content:
"[...] (Box 9.2). The occurrence of such permanent MHWs can be largely \r avoided under the SSP1-2.6 scenario (Box  9.2). MHWs can have \r devastating and long-term impacts on ecosystems (Oliver et al., 2018), \r making them an emerging hazard for marine ecosystems (Frolicher \r and Laufkotter, 2018; Smale et  al., 2019). A  series of MHWs that \r occurred in 2010-2011 had consequences for seagrass in western \r Australia (Wernberg et al., 2013; Arias-Ortiz et al., 2018), and for the \r lobster fishery in the Gulf of Maine (Pershing et al., 2018). The MHWs \r that occured western Australia in 2015/2016 led to the third-highest \r mass coral bleaching globally (Le Nohaic et al., 2017)."


id: 5d9e6ad253eba72c15f98a2bb91afb8e
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 13765
content:
"Dissolved oxygen: Since the middle of the 20th  century, oxygen \r concentrations of open and coastal waters have been declining, \r and such deoxygenation affects biological and biogeochemical \r processes in the ocean (Schmidtko et al., 2017). In recent decades, \r low-oxygen zones in ocean ecosystems have expanded, and \r projections indicate an acceleration with global warming (medium \r confidence) (Diaz and Rosenberg, 2008; Gilly et  al., 2013; Gobler \r et  al., 2014). A  2%  loss (4.8  +- 2.1 pmoles O2) in total dissolved \r oxygen in the upper ocean layer (100-600 m) has been observed \r during 1970-2010 (Section 2.3.4.2), with the highest oxygen loss of \r up to 30 mol  m-2 per decade in the equatorial and North Pacific, \r the Southern Ocean and the South Atlantic Ocean (Section 5.3.3.2). \r Global mean ocean oxygen concentration is projected to decrease by \r 6.36 +- 2.92 mmol m-3 under SSP1-2.6 and by 13.27 +- 5.28 mmol m-3\r under SSP5-8.5 in the subsurface (100-600 m) by 2080-2099, \r [...]"


id: 6c725dedb2257c93f3d728fbd06d7aef
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 16373
content:
"[...] compared to 1870-1899, which is respectively 71% and 40% greater \r than previous estimates based on CMIP5 models (Section 5.3.3.2). \r In the benthic ocean, projected future losses of dissolved oxygen \r concentration by 2080-2099, compared to 1870-1899, are \r -5.14 +- 2.04 mmol m-3 under SSP1-2.6 and -6.04 +- 2.19 mmol m-3\r under SSP5-8.5 (Kwiatkowski et al., 2020). Section 5.3.3.2 assessed \r very likely global decreases in ocean oxygen concentrations although "


id: 5acff640d0e41ccd1b110551f5738e4b
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 13229
content:
"Extreme heat, cold spell and frost: Ecosystem and societal \r temperature thresholds in polar regions often reflect lower tolerance \r to heat and higher tolerance to cold. Extreme heat events have \r increased around the Arctic and Iceland since 1979, including \r increases in cold season warm days and nights, melt days, and Arctic \r winter warm events (T > -10degC) as well as decreases in cold days and \r nights (Mernild et al., 2014; Matthes et al., 2015; Vikhamar-Schuler \r et al., 2016; Graham et al., 2017; Sui et al., 2017; Dobricic et al., 2020; \r Pena-Angulo et  al., 2020). Heatwaves causing high temperature \r records have been recently documented in West and East Antarctica \r (Wille et al., 2019; Robinson et al., 2020). There is high confidence\r that polar amplification will drive increases in Arctic heat extremes \r as well as continuing declines in the magnitude and frequency of \r cold extremes (Matthes et al., 2015; Kharin et al., 2018), although \r [...]"


id: 342d0e0456989d8219723018c0de3ac0
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 6416
content:
"[...] dynamical effects will still bring substantial cold air anomalies over \r the Arctic (Wu and Francis, 2019). There is medium confidence for \r equivalent changes in extreme heat in Antarctica based primarily "


id: a01e05cbb2ca9d97cdf23a2bb2e9e304
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 25384
content:
"Permafrost: Observations from recent decades (assessed in \r Section  9.5.2 and Section  2.3.2.5) show increases in permafrost \r temperature (very high confidence) and active layer thickness (medium \r confidence) across the Arctic (AMAP, 2017; Derksen et  al.,  2018; \r Markon et  al., 2018; Biskaborn et  al., 2019; Farquharson et  al., \r 2019; Meredith et al., 2019; Romanovsky et al., 2020). Section 9.5.2 \r noted that observations of active layer thickness in Antarctica are \r too limited to assess long-term trends (see also Hrbacek et  al., \r 2018; Biskaborn et al., 2019). Future projections indicate continuing \r increases in permafrost temperature and active layer thickness \r with loss of permafrost across the Arctic (Section 9.5.2). Streletskiy \r et al. (2019) noted that changes to Russian permafrost temperature \r and active layer thickness are most pronounced in areas where \r permafrost is continuous (underlying >90% of landmass). CMIP5 \r [...]"


id: 330438cd23e13ef84231e7c4487375a0
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 6241
content:
"[...] analyses by Slater and Lawrence (2013) projected that, by RCP8.5 \r 2100, shallow (<3 m) permafrost would be most probable only in \r portions of the Canadian Arctic Archipelago and the Russian Arctic \r coastal and eastern upland regions."


id: 99cbad29869f9cb63264074ec3966008
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 24288
content:
"Sea ice: Consistent with SROCC (Meredith et al., 2019), Section 9.3.1 \r and Section 2.3.2.1.1 assess very high confidence that Arctic sea ice \r thickness, extent, and average age have significantly decreased over \r the past four decades, with largest declines in September (when \r sea ice is at an annual minimum). Declines in landfast ice are most \r rapid in the Laptev Sea (Selyuzhenok et  al., 2015), with warming \r also breaking perennial landfast ice blocking ocean channels ('ice \r plugs') in the Canadian Archipelago (Pope et al., 2017), and landfast \r ice declining in the cold season by 7% per decade across the Arctic \r (1976-2007; Yu et  al., 2014). Observed trends and projections \r suggest that perennial sea ice is being replaced by thin, seasonal ice, \r although multi-year ice will persist above the Canadian Archipelago \r and drift into sea transportation lanes (Howell et al., 2016; Derksen \r et al., 2018). Trends from 1979 to 2013 show slightly earlier spring \r [...]"


id: a6f3f2af9b241f031851b5b6bfc918ca
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 26580
content:
"[...] melt for Arctic sea ice, but substantially delayed autumn freeze-up \r and a melt season lengthened by more than 3 days per decade off \r northern Alaska and Canada with the exception of portions of the \r Bering Sea (Parkinson, 2014; Stroeve et  al., 2014). Section  9.3.2 \r assessed low confidence in long-term trends in sea ice extent or \r thickness near Antarctica."


id: b0d6f59feb2c8642d53af2076b05a4e3
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 28339
content:
"Glacier and ice sheet: Section  9.5.1 and Section  2.3.2.3 found \r that glaciers have lost mass in all polar regions since 2000 (high \r confidence), and Section 9.4 assessed high confidence in Greenland \r Ice Sheet mass losses since 1980 and Antarctic Ice Sheet losses \r since 1992 (dominated by West Antarctica, with losses in parts of \r East Antarctica in the past two decades). New simulations from \r GlacierMIP (Marzeion et al., 2020) indicate glaciers in Iceland will \r lose 31 +- 35%, 41 +- 46% and 53 +- 45% of their mass in 2015 \r by the end of the century for RCP2.6, RCP4.5 and RCP8.5 scenarios, \r respectively. Marzeion et  al. (2020) projected mass losses (high \r confidence) for those same scenarios in the Greenland Periphery: \r 22 +- 23%, 29 +- 26%, and 42 +- 28%; Svalbard: 35 +- 34%, 50 +- 36%, \r and 66 +- 35%; Russian Arctic: 26 +- 26%, 38 +- 28%, and 52 +- 30%; \r Northern Arctic Canada: 12 +- 13%, 18 +- 12%, and 27 +- 18%; \r [...]"


id: 9dc4bc1822f977609f2c8a63b175fc28
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 24990
content:
"Climate change has caused and will continue to induce an \r enhanced warming trend, increasing heat-related extremes \r and decreasing cold spells and frosts in the Arctic (high \r confidence), with similar changes in Antarctica but medium \r confidence for extreme heat increases and West Antarctic \r frost change decreases and low confidence for cold spell \r changes and East Antarctica frost. The water cycle is projected \r to intensify in polar regions, leading to more rainfall, \r higher river flood potential and more intense precipitation \r (high confidence). Projections indicate reductions in glaciers \r at both poles, with sea ice loss, enhanced permafrost \r warming, decreasing permafrost extent, and decreasing \r seasonal duration and extent of snow cover in the Arctic \r (high confidence) even as some of the coldest regions will see \r higher total snowfall given increased precipitation (medium \r confidence). Projections indicate relative sea level rises in \r [...]"


id: 8faba048272720ae47c7a7e491f7ffc2
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 22487
content:
"[...] polar regions (high confidence), with the exception of regions \r with substantial land uplift including North-Eastern North \r America (high confidence), western Greenland, the northern \r Baltic Sea, and portions of West Antarctica. Higher sea levels \r also contribute to high confidence for projected increases of \r Arctic coastal flooding and higher coastal erosion (aided by \r sea ice loss) (medium confidence) with lower confidence for \r those CIDs in regions with substantial land uplift."


id: e2ad6042e8530d8281b6a3d285d42c7a
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 37180
content:
"Mean temperature increase is a  major climatic impact-driver \r for biodiversity hotspots, and it is very likely that it will affect all \r hotspot areas identified in the literature, at various rates in all \r climate scenarios, except those located in the North Atlantic where \r warming is uncertain (see Chapter  4). Terrestrial ecosystems will \r experience an enhanced warming compared to ocean ecosystems, \r because land temperatures are warming faster than ocean \r temperatures (Chapter 4). Marine ecoregions will experience ocean \r acidification and temperatures that increase faster in high latitudes \r (high confidence), but critical temperature and oxygen thresholds \r are projected to be crossed earlier (by mid-century RCP8.5) in \r tropical areas (Hughes et al., 2017a; Bruno et al., 2018). A warming \r trend is also expected for freshwater ecosystems, with different \r local magnitudes due to combined effects of groundwater system \r [...]"


id: 4890cf0cf1f80d6f6058e4bdc587787b
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 9981
content:
"[...] inertia as well as hydrology changes (Knouft and Ficklin, 2017). \r In tropical land areas, because interannual temperature variability \r is weak compared to mean changes, the temperature distribution \r range is likely to be shifted to a very different range in all projection \r scenarios, with unprecedented values relative to pre-industrial \r conditions. High climate velocities are particularly noteworthy for \r biodiversity hotspots given complex ecosystem dynamics and niche \r climates not easily replicated under shifted geographies (Burrows \r et  al., 2014; Halpern et  al., 2015; Dobrowski and Parks, 2016). In \r some regions (e.g., Central Africa, Amazon, South East Asia) the \r mean temperature change is already beyond the normal range of \r variations as it has reached levels higher than three (and up to \r six) times larger than the standard deviation of the interannual \r variations (Hawkins et  al., 2020). Together with global warming, \r [...]"


id: c7e8b676fc4e66014b923ab485159ece
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 32480
content:
"[...] land and marine heatwaves are very likely to increase in the future \r climate in biodiversity hotspots (Sections 12.4.1-12.4.7)."


id: f09e1a1cb40b6fac30bf35337463809f
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 39707
content:
"Climate change-related variations in oceanic drivers (e.g., relative sea \r level, storm surge, ocean waves), combined with tropical cyclones, \r extreme precipitation and river flooding, are expected to lead to \r more frequent and more intense coastal flooding and erosion (very \r high confidence) impacting cities and settlements located especially \r in low-elevation coastal zones and mega-deltas (Chan et  al., 2012, \r 2018; Karymbalis et al., 2012; Hemer et al., 2013; Aerts et al., 2014; B. \r Neumann et al., 2015; Hauer et al., 2016; Ranasinghe, 2016; Hinkel et al., \r 2018; Mavromatidi et al., 2018; Marcos et al., 2019; see also Sections \r 12.3, 12.4.1-12.4.7 and 12.4.9). Coastal erosion and flooding also \r pose challenges to critical infrastructure such as roads, subway tunnels, \r electricity and phone networks, wastewater management plants and \r buildings (Grahn and Nyberg, 2017; Pregnolato et al., 2017). Compound \r flooding due to simultaneous storm surges and high river flows have \r [...]"


id: 2a65d049715bd9c84c295ac630026b17
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
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index: climate_gpt_v2
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content:
"[...] been found to be increasingly frequent in several cities and/or low\u0002lying areas in Europe and the USA (high confidence) (Wahl et al., 2015; \r Bevacqua et al., 2019; Ganguli and Merz, 2019). Chapter 11 found that \r the frequency of such compound flood events is projected to increase \r (high confidence). In addition to changes induced by sea level change, \r many cities and settlements by the sea are in regions where tropical "


id: 5b23594b92e02d1ed6aa3b4f6d9d8171
created_at: 2023-04-19 13:22:48
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index: climate_gpt_v2
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"Deserts and semi-arid areas are prone to dust storms, which can \r drive impacts on health and several other sectors (X. Zhang et al., \r 2016; Tong et al., 2017). The SRCCL indicated that the evolution of \r dust under climate change is uncertain (Mirzabaev et  al., 2019), \r and there is a lack of evidence and agreement of a change in their \r frequency or intensity so far in most regions (Sections 12.4.1-12.4.9). \r Model projections of future changes in dust are hindered by the \r uncertainties in future regional wind and precipitation as the climate \r warms (Evan et al., 2016); in the effect of CO2 fertilization on source \r extent (Huang et al., 2017); and in the impact of human activities \r upon the land surface (Ginoux et al., 2012; see Chapter 10). Projected \r trends in dust storms and dust loads in deserts and semi-arid areas \r vary from region to region. Dust loadings are expected to decrease \r over most of the Sahara and Sahel (low confidence) (Section 12.4.1), \r [...]"


id: 46b8f53cf5b157cbd18eddeb90d1632b
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"[...] increase over Mexico and the south-west USA (medium confidence) \r (Section 12.4.6), and there is low confidence of a future trend due to \r climate change in other continents (Sections 12.4.2-12.4.5)."


id: 2269d761fa780cae9f3ee5ca7a3369a1
created_at: 2023-04-19 13:22:48
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"Elevation-dependent warming could speed up the observed, rapid \r upward shifts of the freezing level height (FLH) in several mountainous \r regions of the world and lead to faster changes in the snowline, the \r glacier equilibrium-line altitude and the snow/rain transition height \r (high confidence). In the Indus, Ganges and Brahmaputra basins in \r Asia, the FLH is projected to rise at a rate of 4.4 to 10.0 m yr-1 under \r RCP8.5 (Viste and Sorteberg, 2015). In the Argentinian Andes, FLH \r is projected under RCP8.5 to move up more than twice as much by \r 2070 as during the entire Holocene under the worst case scenario \r (Drewes et al., 2018). On the western slope of the subtropical Andes \r (30degS-38degS) in central Chile, the mean value of the free tropospheric \r height of the 0degC isotherm under wet conditions is projected to be \r close to or higher than the upper quartile of the distribution in the \r current climate, towards the end of the century and under RCP8.5 \r [...]"


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"[...] (Mardones and Garreaud, 2020). In the Alps and the Pyrenees, Spandre \r et al. (2019) projected a rise in the natural snow elevation of between \r 200-300 m and 400-600 m by mid-century under RCP2.6 and RCP8.5, \r respectively. In the same region, the environmental equilibrium-line \r altitude is projected to exceed the maximum elevation of 69%, 81% \r and 92% of the glaciers by the end of the century under RCPs 2.6, \r 4.5 and 8.5, respectively (Zebre et al., 2021)."


id: ecd1a4f711e279ad5ce72c9fac00c2b3
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"Orographic effects enhance convection and stratiform heavy \r precipitation (due to uplift) and make mountains prone to extreme \r precipitation events. These events are projected to increase in major \r mountainous regions (Alps, parts of the Andes, British Columbia, North\u0002Western North America, Calabria, Carpathian, Hindu-Kush-Himalaya, \r Rocky Mountains, Umbria; medium to high confidence depending on \r location), with potential cascading consequences of floods, landslides \r and lake outbursts in mountainous areas in all scenarios (medium \r confidence) (Chapter  11 and Sections 12.4.1-12.4.9; Geertsema \r et al., 2006; Gaire et al., 2015; Kim et al., 2015; Ciabatta et al., 2016; \r Gariano and Guzzetti, 2016; Kharuk et al., 2016; Syed and Al Amin, \r 2016; Cloutier et al., 2017; Gadek et al., 2017; Jurchescu et al., 2017; \r Rajczak and Schar, 2017; Alvioli et al., 2018; Coe et al., 2018; Schlogl \r and Matulla, 2018; C.-W. Chen et al., 2019; Handwerger et al., 2019; \r [...]"


id: a9eea58c3ba1c3f761a552bf5eca1cac
created_at: 2023-04-19 13:22:48
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index: climate_gpt_v2
vector_id: 27118
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"[...] Hock et al., 2019; Patton et al., 2019; Vaidya et al., 2019; Kirschbaum \r et al., 2020; Coppola et al., 2021b)."


id: 3da1e8b16f30fa7a23cfc06db468a6d
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"(almost half) under SSP1-2.6. Several global-scale studies have \r shown that high temperature extremes will increase everywhere \r (high confidence) (Gourdji et  al., 2013; Perkins-Kirkpatrick and \r Gibson, 2017; Harrington et  al., 2018; Jones et  al., 2018; Lehner \r et al., 2018; Shi et al., 2018; Tebaldi and Wehner, 2018; Arnell et al., \r 2019; Russo et  al., 2019; Schwingshackl et  al., 2021), although \r the change depends on the indicator (see also Chapter  11). For \r example, by 2080, at least 80% of the land surface is expected to \r experience average summer temperatures greater than the historical \r (1920-2014) maximum with high (RCP8.5) emissions (Lehner et al., \r 2018). The areas of rice and maize cropland with damaging extreme \r temperatures during the reproductive season will increase by \r a factor of three under RCP8.5 (Gourdji et al., 2013). Under a high \r emissions scenario, heatwaves that are currently considered rare \r [...]"


id: 7f347cc60ed641699dcc41e1a7225b58
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"[...] are projected to become the norm almost everywhere by the end of \r the century (Russo et al., 2014). Heat stress as a combined function \r of temperature and humidity also increases at the global scale, \r especially with high emissions (e.g., Matthews et al., 2017). Growing \r degree-days and cooling degree-days also increase everywhere \r (Arnell et  al., 2019) with the absolute and proportional changes \r depending on temperature threshold. Increases in temperatures \r will result in reductions in heating degree days (Arnell et al., 2019; \r Coppola et al., 2021b) and a widespread reduced frequency of cold \r extremes (high confidence)."


id: 5832ba9002b07866928b71182729bd1c
created_at: 2023-04-19 13:22:48
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"Tropical forests typically reach peak fire weather conditions in the dry \r season (Taufik et al., 2017), in particular during long-lived droughts \r (Brando et al., 2014; Marengo et al., 2018), with consequences for \r tree mortality, forest and carbon sink loss (Brando et al., 2019), and \r on the hydrological cycle in South America (Martinez and Dominguez, \r 2014; Espinoza et al., 2020). Observations and reanalyses over the \r past three to four decades, combined into fire risk indices, show that \r the fire weather season length has been increasing by about 20% \r globally (Jolly et al., 2015), and this index exhibits particularly high \r trend values over tropical forest areas of South and Central America \r and Africa. There is generally low confidence in future projections of \r general fire weather risk evolution in tropical forests and evolutions \r depend on the region (Abatzoglou et al., 2019). Over the Amazon \r basin the fire risk increase is projected to emerge well before 2050 \r [...]"


id: 15a4b74e0b64f19baee27bf75e33386e
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"[...] while for other equatorial forests no significant evolution is found. \r In Savanna areas the risk increase is found to be more general."


id: b9b28d5fc4a27ae7299bca643a083521
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
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index: climate_gpt_v2
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content:
"Integrating the results of the regional assessments in Section  12.4 \r shows that changes in CIDs linked with the water cycle or atmospheric \r dynamics (e.g., storms) vary more among regions, largely due to the \r spatial pattern of changes in atmospheric circulation and changes \r in precipitation and evaporation (Chapters 8 and 11). There is high \r confidence that heavy precipitation and pluvial floods will be increasing \r in a  majority of land regions, primarily due to the well-understood \r Clausius-Clapeyron relationship describing the  increase in moisture \r content with air temperature (Chapters 8 and 11), but there is \r a  large spatial variability in fluvial flood hazards. Top-down global\u0002scale studies show that although fluvial flood hazards are projected \r to decrease in regions where there are large reductions in seasonal \r rainfall totals or where warmer temperatures mean less accumulated \r snow, at the global scale, fluvial flood hazard (characterized as the \r [...]"


id: edc77dff0cf930421f0344092416e3ad
created_at: 2023-04-19 13:22:48
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"[...] area affected, size of peak or likelihood of an event) is projected to \r increase substantially through the century (Giuntoli et al., 2015; Arnell \r and Gosling, 2016; Winsemius et al., 2016; Alfieri et al., 2017; Dottori \r et al., 2018; Arnell et al., 2019). Projected changes in agricultural and \r hydrological drought characteristics are dependent on the indicator \r used to define drought (Sections 11.6 and Section 12.3.2), but there \r is at least medium confidence of an increase in the drought hazard in \r many parts of the world. This is also reflected in global-scale studies, \r with Naumann et  al. (2018), for example, showing that the global \r mean average drought duration (based on the SPEI index which is \r calculated from the difference between precipitation and potential \r evaporation) increased from 7  months with the current climate to \r 18.5 months for a global warming level of 3degC. The apparent global \r [...]"


id: bb6a2b98f3322db6d0422c56e0eb266d
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"[...] increase in drought occurrence is greater when evaporation is captured \r in the drought indicator (e.g., SPEI) than when the indicator is based on \r precipitation alone (as in SPI; Carrao et al., 2018). There is evidence that \r the likelihood of simultaneous events in several locations will increase: \r Trnka et al. (2019) found that the proportion of wheat-growing areas "


id: 5cb8d26058b721e4ed9a1530c3c610b3
created_at: 2023-04-19 13:22:48
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"Figure 12.11 | Synthesis of the climatic impact-driver (CID) changes projected by 2050 (2041-2060) with high confidence, relative to reference period \r (1995-2014), together with the sign (direction) of change. Information is taken from the CID tables in Section 12.4. Some CIDs are grouped in order to streamline the \r information in order to fit in all information in the figure. Mean temperature, extreme heat, cold spells and frost are grouped under a single 'heat' icon, as they are projected to \r change simultaneously, albeit heat and cold are changing in opposite directions. Coastal CIDs (relative sea level, coastal flooding and coastal erosion at sandy beaches) are also \r grouped. In the figure, the 'coastal' icon indicates regions where at least two of the three individual coastal CIDs are projected to change with high confidence. Cases where \r [...]"


id: 385c43b80f2342347d74ff141c2231fe
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"[...] only two of the three CIDs increase with high confidence are in Arctic Northern Europe, Russian Arctic and Arctic North-Western North America. A single icon is used for aridity, \r hydrological drought, and agricultural and ecological drought, and only the number of drought types that change is indicated. For the 'Snow, ice' icon, information is taken from \r the evolution of the 'Snow, glacier and ice sheet' CID; most regions also have similar changes for 'permafrost' and 'lake, river and sea ice'. Exceptions are for North-Eastern \r North America, Russian Arctic and Arctic North-Western North America where snow is decreasing with medium confidence (thus not appearing in the figure), while permafrost \r and lake, river and sea ice is decreasing with high confidence. The location of the icons within the regions is arbitrary. Icon sources: https://www.flaticon.com/authors/freepik."


id: f8f714a33a23f11ca60f60be65e3dd33
created_at: 2023-04-19 13:22:48
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"The emergence of a climate change signal occurs when that signal \r exceeds some critical threshold (usually taken to be a  measure of \r natural variability; see for example, Hawkins and Sutton, 2012) \r or when the probability distribution of an indicator becomes \r significantly different to that over a reference period (e.g., Chadwick \r et al., 2019; see also Chapter 10 and Section 1.4.2), in which case \r external anthropogenic forcings can be detected as causal factors. \r The 'time of emergence' (ToE) or 'temperature of emergence' is \r the time or global warming level thresholds associated with this \r exceedance. Emergence is particularly relevant to impacts, risk \r assessment and adaptation because human and natural systems \r are largely adapted to natural variability but may be vulnerable if \r exposed to changes that go beyond this variability range; this is not \r to say that changes within natural variability have no impact, as \r [...]"


id: 35739dcc76f17cea372fbb35f8424c88
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"[...] occurrence of damaging extremes proves. Emergence also informs \r the timing of adaptation measures. The emergence of a change is \r always relative to a reference period (e.g., the pre-industrial period \r or a recent past), depending on the framing question. In the former \r case, the goal is to estimate the amplitude of an anthropogenically \r driven change while in the latter, it is to estimate the amplitude of \r change relative to a baseline that is familiar to stakeholders. Both \r questions are important for risk assessment, but the former may be \r more directly interpretable in a  mitigation context. The variability \r also refers to a time scale, generally interannual to inter-decadal. See \r Section 1.4.2 and Chapter 10 for more details about how emergence \r is defined and used in the literature."


id: c7f80d5046c4c4de255e3a8569cf9c15
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"Figure 12.11 highlights that each region will, with high confidence, \r experience changes in multiple CIDs, challenging the vulnerability of \r the region and its adaptation and mitigation capacity. All non-polar \r regions with a  coastline will see an increase in relative sea level, \r extreme sea level and coastal erosion, and will also see an increase in \r hot extremes, a decrease in cold extremes, and many will experience \r an increase in heavy precipitation. One cluster of regions  - East \r Southern and West Southern Africa regions, the Mediterranean, \r Northern Central America, Western North America, several regions \r in South America and Australia - will experience, in addition to the \r aforementioned globally changing CIDs, increases in either drought/\r aridity or fire weather (high confidence). This will impact upon \r agricultural resources, infrastructure and health and ecosystems. \r A second cluster of regions including mountainous areas or regions \r [...]"


id: ee6fe4b13b9e20dc9352229f362d6ddb
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index: climate_gpt_v2
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"[...] with seasonal snow cover will experience (in addition to increases \r in heat extremes, more intense short-duration rainfall, and increases in \r coastal hazards where coasts exist) reductions in snow and ice cover \r and/or increases in river flooding in many cases (Western, North\u0002Western, Central and Eastern North America, Arctic regions, Andes \r regions, Europe, Siberia, Central and East Asia, Southern Australia \r and New Zealand) (high confidence). These are places where energy \r production, ski tourism, river transportation, and infrastructure could \r in particular face increased risks."


id: 9969a5a47e85d40b0d85628ff9f3a5ae
created_at: 2023-04-19 13:22:48
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index: climate_gpt_v2
vector_id: 24214
content:
"Estimations of ToE must be done with caution given the many sources of \r inherent uncertainties, such as observations representing only a single \r realization of climate history, internal variability (whose frequency - \r e.g., annual or decadal - needs to be precisely defined), model biases, \r and potential low-frequency changes in variability (Chapter  10; \r Lehner et  al., 2017). In addition, a  homogeneous interpretation of \r multiple studies is hampered by heterogeneous methodologies used \r to calculate emergence. In this section, we assess emergence and its \r confidence level based on such multiple methods as provided by the \r literature, and unless specified otherwise, emergence here refers to \r a signal to noise ration S/N > 1 relative to a pre-industrial baseline \r and interannual variability (the 'noise'). Furthermore, observed trends \r and attribution are taken into account in combination with climate \r simulations (historical or projections) for assessing whether a trend \r [...]"


id: 64c7d9fee56ca246ebb2f36e8d7250c5
created_at: 2023-04-19 13:22:48
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"Mean air temperature: Warming of mean annual temperatures \r has already emerged in all land regions, as obtained from past \r observations and confirmed by historical simulations (high \r confidence) (Figure 1.13; King et al., 2015; Hawkins et al., 2020), with \r S/N ratios larger than two. In the current climate, the highest S/N \r ratios exceed five over Central Africa, Amazonia, East and South East \r Asia. Seasonal warming emergence depends on the season. Because \r the temperature variability in the mid-latitudes is higher in winter \r than in summer, the emergence of seasonal warming occurs for \r summer but not for winter in most of this part of the world. In Europe, \r summer warming has emerged in all regions (medium confidence, \r medium agreement), and in North America, it has emerged only over \r Eastern and Western regions while in winter there is low confidence\r of an emergence in warming in all regions for both Europe and North \r [...]"


id: e660e803192390ee441e242884d2c5a5
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"[...] America (Lehner et al., 2017; Hawkins et al., 2020). When considering \r the climate of the end of the 20th  century (i.e.,  recent past) as \r a baseline, the emergence of mean temperature is projected at very \r different times depending on the scenario. For instance, emergence is \r reached by 2050 under RCP8.5 in most areas of Europe, Australia or \r East Asia, but it does not occur within the 21st century under RCP2.6 \r (medium confidence) (Sui et al., 2014; Im et al., 2021). This means \r that under RCP2.6, mean temperatures stay within the recent climate \r variability range observed in the mid-latitudes. However, even under \r RCP2.6, mean temperatures in tropical regions that have not already \r emerged are projected to emerge before 2050 (medium confidence)."


id: b6e910b37fa9803bb9cdcff909a5a83e
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"Eastern Europe and decrease in West Africa and Amazonia) from \r observations with an S/N ratio larger than one (low confidence) \r (Hawkins et  al., 2020). The emergence of increasing precipitation \r before the middle of the 21st century is found across scenarios in \r Siberian regions, Russian Far East, Northern Europe, Arctic regions \r and the northernmost parts of North America (high confidence) \r and later in other northern mid-latitude areas, depending on the \r scenario, albeit with different methods and emergence definitions \r used in climate projections (Chapter 8; Giorgi and Bi, 2009; Maraun, \r 2013; King et  al., 2015; Akhter et  al., 2018; Kumar and Ganguly, \r 2018; Nguyen et al., 2018; Barrow and Sauchyn, 2019; Rojas et al., \r 2019; Kusunoki et  al., 2020; Pohl et  al., 2020; W. Li et  al., 2021). \r Decreases in mean precipitation are projected to emerge in parts of \r Africa by the middle of the century, and later in the Mediterranean \r [...]"


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"[...] and Southern Australia, but the emergence depends on the scenario, \r and specific seasons for crop growth (Nguyen et  al., 2018; Rojas \r et  al., 2019). Mean precipitation does not emerge in any of these \r regions at any time in the 21st century under RCP2.6, but emerges \r in all under RCP8.5. ToE under RCP4.5 is projected to be around \r 25 years later relative to RCP8.5 in many of the early emergence \r regions, highlighting the importance of mitigation to gain more time \r for adaptation."


id: e1ffb660be82fd29a7a77d388d42c931
created_at: 2023-04-19 13:22:48
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index: climate_gpt_v2
vector_id: 37043
content:
"Heavy precipitation and floods: There is low confidence in the \r emergence of heavy precipitation and pluvial and river flood frequency \r in observations, despite trends that have been found in a few regions \r (Chapters 8 and Chapter  11, and across Section  12.4). In  climate \r projections, the emergence of increase in heavy precipitation strongly \r depends on the scale of aggregation (Kirchmeier-Young et al., 2019), \r with, in general, no emergence before a 1.5degC or 2degC warming level, \r and before the middle of the century (medium confidence), but results \r depend on the method used for the calculation of the ToE (Maraun, \r 2013; King et al., 2015; Kusunoki et al., 2020). Emergent increases in \r heavy precipitation are found in several regions when aggregated \r at a regional scale in Northern Europe, Northern Asia and East Asia, \r at latest by the end of the century in SRES A1B or RCP8.5 scenarios \r or when considering the decadal variability as a reference (medium \r [...]"


id: c7124328348327acd42128d73716dfea
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content:
"[...] confidence) (Maraun, 2013; W. Li et al., 2018, 2021; Kusunoki et al., \r 2020). There have been few emergence studies for streamflow \r and flooding, although one study showed emergence of different \r hydrological regimes at different times during the 21st  century \r across the USA (Leng et al., 2016). Variability in extreme streamflows \r from year to year can be high relative to a trend (Zhuan et al., 2018). \r Given the heterogeneity of methods and results, there is only low \r confidence in the emergence of heavy precipitation and flood signals \r in any region when considering the S/N ratio."


id: c6c5b0d16119c0eadb161634e3a13d2c
created_at: 2023-04-19 13:22:48
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vector_id: 32279
content:
"Extreme heat and cold: An increase in heat extremes has emerged \r or will emerge in the coming three decades in most land regions (high \r confidence) (Chapter 11; King et al., 2015; Seneviratne and Hauser, \r 2020), relative to the pre-industrial period, as found by testing \r significance of differences in distributions of yearly temperature \r maxima in simulated 20-year periods. In tropical regions, wherever \r observed changes can be established with statistical significance, \r and in most mid-latitude regions, there is high confidence that \r hot and cold extremes have emerged in the historical period, but \r only medium confidence elsewhere. In other regions emergence \r is projected at the latest in the first half of the 21st century under \r RCP8.5 (high confidence) (King et al., 2015; Seneviratne and Hauser, \r 2020). Relative to the end of 20th-century conditions, changes \r in humid heat stress as characterized by wet bulb temperature, \r [...]"


id: 24e6c9282aeb3a350a9761021ce3e94
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"[...] indicates a ToE as early as in the first two decades of the 21st century \r in RCP8.5 at least in many tropical regions (most of Africa in the band \r 20degS-20degN, South Asia and South East Asia) (medium confidence) \r (Im et al., 2021). By 2050 and under RCP8.5, wet bulb temperature \r is projected to emerge in many other areas such as Southern Africa, \r North Africa, Europe, and most of Central, Southern and Eastern Asia \r and Northern and Eastern Australia, while under RCP2.6, emergence \r is either reached later in the century (Europe, Central Asia, Northern \r Australia), or never reached in the century (Im et al., 2021). Decrease \r of cold spells has already emerged above the interannual variability \r in Australasia, Africa and most of Northern South America, and they \r are projected to emerge before 2050 in the northern mid-latitudes \r and in Southern South America (King et  al., 2015) under RCP8.5 \r (medium confidence, limited evidence and high agreement)."


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"Ocean acidity, ocean salinity and dissolved oxygen: The global \r ocean pH decline has very likely emerged from natural variability \r for more than 95% of the global open ocean (SROCC, Chapter 2). \r The regional signals are more variable, but in all ocean basins, the \r signal of ocean acidification in the surface ocean is projected to \r emerge in the early 21st  century (Chapter  5). The mean ToE for \r acidity in the coastal subtropical to temperate north-east Pacific \r and north-west Atlantic is above two decades (high agreement,\r medium evidence) (Section  5.3.5.2). Salinity change signals have \r already emerged with 20-45% of the zonally averaged basin in the \r Atlantic, 20-55% in the Pacific and 25-50% in the Indian oceans \r and will be reaching 35-55% in the Atlantic in 2050 to 55-65% in \r 2080; 45-65% to 60-75% in the Pacific; and 45-65% to 60-80% \r in the Indian oceans (Chapter 9; Silvy et al., 2020). Deoxygenization \r has already emerged in many open oceans. The signal is most \r [...]"


id: 3d03ea011062d66056eee13461c47f27
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"[...] evident in the Pacific and Southern oceans but not evident in the \r North Atlantic Ocean (Andrews et al., 2013; Levin, 2018). However, \r there is medium confidence in the emergence of the anthropogenic \r signal in many other oceanic regions by 2050 (Henson et al., 2017; \r Levin, 2018)."


id: 79982385f9c13b68141051a57671e10e
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content:
"Snow and ice: The decrease in the Northern Hemisphere snow \r cover extent in spring has already emerged from natural variability \r (Section 3.4.2). The snow cover duration period is projected to emerge \r over large parts of Eastern and Western North America and Europe \r by the mid-century both in spring and autumn, and emergence is \r expected in the second half of the 21st century in the Arctic regions in \r the high RCP8.5 scenario (medium confidence) (Chapter 9, SROCC). \r For snow depth or snow water equivalent, there is low confidence \r (limited evidence) of the emergence of a  decrease before 2050 \r because climate change also increases the variability of the snow \r depth signal, for example in Europe (Section 3.4.2; Willibald et al., \r 2020). Terrestrial permafrost is warming worldwide due to climate \r change (Sections 2.3.2.5 and 9.5.2). Due to weak interannual \r variability of permafrost temperatures, terrestrial permafrost warming \r [...]"


id: 45e07bcad15b2941439066334c1e79dc
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"[...] has emerged above natural variability in almost all observed time \r series of the Northern Hemisphere (medium confidence, limited \r evidence, high agreement) (Biskaborn et  al., 2019), but the active \r layer thickness exhibits considerable interannual variability inhibiting \r evidence for emergence (Chapter 9)."


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content:
"There is high confidence that several CID changes have \r already emerged above historical period natural variability \r in many regions (e.g.,  mean temperature in most regions, \r heat extremes in tropical areas, sea ice, salinity). Heat and \r cold CIDs (excluding frost) that have not already emerged \r will emerge by 2050 whatever the scenario in almost \r all land regions (medium confidence). The emergence of \r increasing precipitation before the middle of the century is \r also projected in Siberian regions, Russian Far East, Northern \r Europe and the northernmost parts of North America and \r Arctic regions across scenarios with the various methods \r and emergence definitions used (high confidence). Studies \r are missing to properly assess S/N emergence for droughts \r and for wind CIDs. Arctic sea ice extent declines have mostly \r emerged above noise level (medium to high confidence), and \r the emergence of declining snow cover is expected by the end \r [...]"


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index: climate_gpt_v2
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content:
"[...] of the century under RCP8.5. There is medium confidence that, \r under RCP8.5, the anthropogenic forced signal in near-coast \r relative sea level change will emerge by mid-century in all \r regions with coasts, except in the West Antarctic region where \r emergence is projected to occur before 2100. In all ocean \r basins, the signal of ocean acidification in the surface ocean is \r projected to emerge before 2050 (high confidence)."


id: ce245c1ba3349a77afa0f1a672802a76
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content:
"A consistent risk framework (Reisinger et  al., 2020) has been adopted across the three Working Groups (WGs) in IPCC AR6 while \r recognizing the diversity of risk concepts across disciplines. WGI is assessing changes in climatic impact-drivers (CIDs), which are physical \r climate system conditions (e.g., means, events and extremes) that affect an element of society or ecosystems. Depending on system \r tolerance, CIDs and their changes can be detrimental, beneficial, neutral, or a mixture of each across interacting system elements and \r regions (Sections 12.1-12.3). In the assessment of Representative Key Risk (RKR) categories and Reasons for Concern (RFCs) in WGII \r Chapter 16, the focus lies on the adverse consequences of climate change, for which many types of CIDs (i.e., 'hazards' in the context \r of identified risks) play a key role. This box synthesizes the assessment of such hazards according to global warming levels (GWLs) from \r [...]"


id: ba602eb0f71117e5640c5a0f84063dad
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content:
"[...] various chapters of WGI to inform understanding of their potential changes and associated risks with temperature levels in general, \r and in particular to facilitate WGII integrated assessments of RKRs and RFCs. Cross-Chapter Box 11.1, connects the organization of \r regional information according to GWLs to the other common dimension along which future projections are organized: that is, scenarios. \r Section 1.6 describes all dimensions of integration adopted in this Report, adding cumulative carbon emissions to GWLs and scenarios."


id: 4d37db449eb38810af91b7561c471a1b
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updated_at: 2023-04-19 13:22:48
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content:
"user-driven climate services. WMO has created Regional Climate \r Centres (RCCs) to facilitate climate service development by regional \r and national providers (Hewitt et al., 2020a). The European Union \r declared its ambition to stimulate 'the creation of a  community \r of climate services application developers and users that matches \r supply and demand for climate information and prediction', giving \r primacy to climate services that are user-driven and science-informed \r (Lourenco et  al., 2016), thus embracing concepts of co-design, \r co-development and co-evaluation of climate services (Street, 2016). \r Diverse and action-driven international initiatives allowed climate \r services to progressively shift from mitigation towards adaptation \r (Larosa and Mysiak, 2019). Opportunities for the development of \r climate services have emerged through the 2015 Agendas (Paris \r Agreement, Sustainable Development Goals and Sendai Framework), \r [...]"


id: e5d6d0f17b3505ec4e419370b764117c
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updated_at: 2023-04-19 13:22:48
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vector_id: 37738
content:
"[...] Nationally Determined Contributions, National Adaptation Plans, \r Multilateral Development Banks and Task Force on Climate-related \r Financial Disclosure (see Section 1.2.2)."


id: 360050a635943c684e0e15fee325dd11
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
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content:
"The AR5 WGII introduced climate services as bridging the generation \r and application of climate knowledge, also describing their history \r and concepts (Jones et al., 2014). Since then, this transdisciplinary field \r has been growing rapidly (Brasseur and Gallardo, 2016; Hewitt et al., \r 2020a), with the social sciences in particular pointing out knowledge \r requirements for co-design and co-development of climate services \r (Larosa and Mysiak, 2019; Daniels et al., 2020; Steynor et al., 2020). \r Climate services differ from more research-driven vulnerability, \r impacts, and adaptation research in their orientation towards decision \r support (Stone and Meinke, 2005; Ruane et al., 2016; Golding et al., \r 2019), but overlaps exist (Bruno Soares and Buontempo, 2019). \r Climate services are often targeted at building resilience to climate\u0002related hazards from near real-time to seasonal and multi-decadal \r time horizons, to inform adaptation to climate variability and change \r [...]"


id: a37e30ae0bf48ac4c42eb4ab6ceb8538
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updated_at: 2023-04-19 13:22:48
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content:
"[...] (Hewitt et al., 2012), widely recognized as an important challenge for \r sustainable development and risk management (Moss et al., 2010; \r Jones et al., 2014; Vaughan et al., 2018). This section focuses largely \r on climate change time scales (past, present and future), which are \r the focus of AR6 WGI."


id: 597fb63831344da9076659c4ad960e62
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content:
"Scientific advancements in climate services related to meteorology \r and climatology are still closely linked to essential climate variables \r (Larosa and Mysiak, 2019) and benefit from consistently growing \r computational power, infrastructure and storage capacity to meet \r the demands of higher spatially and temporally resolved climate \r information (Buontempo et al., 2020). Climate services also focus on \r impact chains, providing decision makers with information on climate \r change with cross-sectoral impact assessments for adaptation \r (Jacob and Solman, 2017). Today there is a  diversity of climate \r services that involve interpretation, analysis, and communication of \r different sources of climate data, ideally combining different types \r of knowledge (scientific/technical, experiential, indigenous, etc.), to \r a  targeted group of decision makers (Parris et  al., 2016; Olazabal \r et  al., 2018; Pezij et  al., 2019). Jacobs and Street (2020) argue \r [...]"


id: ec1c03337a21ee407ced6836b551234e
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content:
"[...] that climate services should be expanded to also address societal \r challenges, such as system transformation, that include climate in the \r context of other risks and development challenges."


id: 7ddabc9d999ac17cdd0b8e25b66de7fb
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content:
"Climate services are undertaken in public and private sectors at \r global, regional, national, and local scales (Hewitt et  al., 2012, \r 2020b; Cortekar et al., 2020). Intermediaries such as private sector \r consulting companies, national climate service providers, research \r organizations, government agencies or academic institutions \r provide climate services that translate aspects of climate research \r to the specific context of decision makers (see also Section  10.5). \r The EU Roadmap for Climate Services (EC, 2015; Street, 2016) \r focuses on developing a market for climate services comprising of \r both public and private domains. The GFCS, under the leadership \r of several United Nations Agencies, emphasizes the public domain by \r supporting national and regional capacity building and development \r of climate services mainly through National Meteorological and \r Hydrological Services (Hewitt et  al., 2012; Domingos et  al., 2016; \r [...]"


id: 1b448c773a37329cb9e780ef93360503
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content:
"[...] Sivakumar and Lucio, 2018; WMO, 2018). There are ongoing debates \r about the commercialization of climate services (M.S. Brooks, 2013; \r WMO, 2015; Webber and Donner, 2017; Hoa, 2018; Troccoli et  al., \r 2018; Bruno Soares and Buontempo, 2019; Hewitt et  al., 2020a). \r Some argue that the commercialization of climate services is needed \r to meet the diverse needs of specific clients and to drive innovation \r in the field (M.S. Brooks, 2013; Troccoli, 2018a). Others argue that if "


id: 1b7c8dcfeb250b07b8a3794de49534a9
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index: climate_gpt_v2
vector_id: 2023
content:
"Some climate adaptation planning already uses climate information \r as provided by the IPCC. However, depending on the decision context, \r this information may be too coarse, too broad or too disciplinary \r to directly inform decision-making at the scale where adaptation \r measures are taken (Howarth and Painter, 2016; Nissan et al., 2019). \r Thus, while the IPCC's role is clearly perceived as that of a reference - \r an authoritative starting point - there is a need for complementary \r information to translate the assessments at the national, local or \r sectoral level (Howarth and Painter, 2016; Kjellstrom et al., 2016; van \r den Hurk et al., 2018; Vaughan et al., 2018). The AR6 Interactive Atlas \r (see Atlas.2) does provide a  collection of observational data and \r global and regional climate projections. It is designed as a climate \r service towards the needs of WGI and beyond, to assess the state of \r the climate by offering data, maps and a level of expert analysis by \r [...]"


id: d82f6306e57b925bd7653283c88c4976
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vector_id: 35334
content:
"Climate services require user engagement and can take various forms \r in which climate information and data are delivered or communicated \r to the users (very high confidence). Different levels of user engagement \r exist, which can range from passive engagement to interactive group \r activities, to focused relationships between climate service provider \r and users. These result in different types of climate service products \r including websites, capacity building, and co-design of tailored climate \r indices (Cross-Chapter Box 12.2, Figure 1; Hewitt et al., 2017a). The \r fundamental basis for climate service development is the co-production \r process between climate service provider and user (Valiela, 2006; Briley \r et al., 2015; Golding et al., 2017a; K. Vincent et al., 2018a; Bruno Soares \r and Buontempo, 2019; Schipper et al., 2019), which can be very resource \r intensive (Buontempo et al., 2018; Falloon et al., 2018; Kolstad et al., \r [...]"


id: d4f47860db5a9c275ab81d3a92bcf03a
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content:
"[...] 2019) and varies strongly from case to case (Reinecke, 2015; Bremer \r et al., 2019; Goodess et al., 2019; Jung and Schindler, 2019). Climate \r services scholars and practitioners can better facilitate and embrace the \r knowledge co-production process if it is recognized as a multi-faceted \r phenomenon with several dimensions (e.g., constitutive, interactional, \r institutional, pedagogical, empowerment) (Kruk et al., 2017; Knaggard \r et al., 2019; Weichselgartner and Arheimer, 2019)."


id: 201a599335a2315f3e38ddd3f320edb0
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updated_at: 2023-04-19 13:22:48
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content:
"The climate services landscape is fast growing and very broad, as \r reflected in the vast diversity of practices and products that can be \r found in the peer-reviewed literature (very high confidence). However, \r a large part of climate services practices and products is published in \r 'grey' literature (i.e., non-peer reviewed or non-academic) by private \r consultancy and non-scientific civil organizations, many of which are \r not in the public domain. In addition, the respective climate service \r context of a specific stakeholder in a sector dictates what climate \r information is required and on what scales and in what format it \r is most usefully provided. The extent and type of engagement \r between scientists and users is another critical aspect of climate \r services (see Cross-Chapter Box 12.2, Figure 1, and Section 10.5). The \r assessment here can thus only provide a partial and rather general \r representation of available practices and products in the evolving \r [...]"


id: ecbad754af61c4a2b3573bb992cd772e
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content:
"User needs and decision-making contexts are very diverse and \r there is no 'one size fits all' solution to climate services (very high \r confidence) (Hewitt et al., 2017b; K. Vincent et al., 2018b). In many \r cases this requires recognizing that stakeholders make decisions \r through a  combination of scientific information and additional \r values (Vanderlinden et  al., 2017; Parker and Lusk, 2019; see also \r Sections  1.2.3 and 10.5.4). The emerging climate service literature \r may clarify some features of climate information requested by users, \r for instance climatic impact-driver identification and prioritization \r through stakeholder engagement; the specification of thresholds for \r various regions/sectors; the types of metrics (magnitude/intensity, \r frequency, duration, timing, spatial extent) that are of primary interest; \r and decision support systems where informatics allow stakeholders \r to custom-make impact-relevant thresholds and then query databases \r [...]"


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"[...] to understand current and future characteristics (Bachmair et al., 2016; \r Buontempo et al., 2020). However, users also ask for capacity building "


id: e36c73db3ae241a1f6efec0aa9df05b5
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content:
"(van den Hurk et  al., 2016; Vano et  al., 2018), ocean and coastal \r ecosystems (Weisse et  al., 2015; Le Cozannet et  al., 2017), cities \r (Rosenzweig and Solecki, 2014; Rosenzweig et  al., 2015; Gidhagen \r et al., 2020), and cultural heritage (ICOMOS, 2019). Many countries \r (including almost every country in Europe  - see Atlas.8.2) have \r established a climate service centre, which follow different practices \r of user engagement and provide different products (e.g., Kjellstrom \r et  al., 2016; Skelton et  al., 2017; Kolstad et  al., 2019). Climate \r services in other countries may be distributed across agencies and \r programmes, although these are often not centrally coordinated (Parris \r et al., 2016). One of the key pillars of the GFCS is the Climate Services \r Information System (CSIS), which is the principal mechanism through \r which information about past, present and future climate is archived, \r analysed, modelled, exchanged and processed for users (Hewitt et al., \r [...]"


id: b2d8f4b516d8f2e880851484372849e9
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content:
"[...] 2020a). Some national governments also have organized national \r climate projections to be used for official planning (e.g., EEA, 2018). \r A list of available national products (e.g., observational datasets) and \r projections can be found in the Atlas (e.g., Atlas.1.4)."


id: 768a9b54d7cd0386179228dffb567ea7
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content:
"The design of climate services involves addressing certain key challenges, \r such as a  domain challenge where users, tasks and data may be \r unknown; or an informational challenge related to the use and adoption \r of novel and complex scientific data (Christel et al., 2018). This includes \r challenges in the uptake of climate information in terms of coordinated \r delivery of data, information, expertise and training by public research \r institutes, the inclusion of climate change adaptation in public and private \r regulation, and uncertainties and confidence in climate projections \r (Cavelier et al., 2017). Quality control and quality assurance are still weak \r elements in the development of climate service products (Jacob, 2020). \r Quality criteria or standards (that go beyond good practice) will have \r to be developed and agreed (Baldissera Pacchetti et al., 2021). These \r challenges reflect the dilemma that exists at the interface between the \r [...]"


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updated_at: 2023-04-19 13:22:48
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content:
"[...] climate modelling community and climate services regarding: (i) the \r purposes of the models for climate research versus service development; \r (ii) the gap between the spatial and temporal scales of the models \r versus the scales needed in applications; and (iii) tailoring climate model \r results to real-world applications (Benestad et al., 2017; Hackenbruch \r et al., 2017; van den Hurk et al., 2018)."


id: 88869ff4d89bd61cb2f0caee55ee94d7
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updated_at: 2023-04-19 13:22:48
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content:
"Challenges also arise in determining the effectiveness and added \r value of climate services, particularly in terms of providing quantitative \r estimates of economic benefits and making a business case for climate \r services (Bruno Soares, 2017). The market for climate services is still \r in its infancy (Cavelier et al., 2017; Bruno Soares et al., 2018b; Tall \r et al., 2018; Damm et al., 2020). One form of value may be determined \r by a  particular user community's willingness to pay (Acquah and \r Onumah, 2011; Ouedraogo et  al., 2018; Antwi-Agyei et  al., 2021), \r which however cannot reflect the value of climate services as a public \r good and for society as a whole (Hewitt et al., 2012). Literature is only \r recently emerging on the socio-economic benefits of weather and \r climate services (Vaughan et al., 2019). Early studies and guidelines \r from the WMO focus on cost-benefit ratios (Perrels et  al., 2013; \r WMO, 2015). Issues related to demand-driven versus supply-driven \r [...]"


id: def13f8ced64630d59a0f8f2763b0ed3
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content:
"[...] climate services (Lourenco et  al., 2016; Street, 2016; Daniels et  al., \r 2020), public versus private climate services (Hewitt et  al., 2020a) \r and business models for climate services (Hoa, 2018) have been \r raised. A large share of climate services documented in peer-reviewed \r literature is currently provided in non-market frameworks (e.g., public \r service obligations and research and development grants) (Hoa, 2018; \r Kolstad et al., 2019; Cortekar et al., 2020)."


id: e50f8a3e814991b9ee0a28f940598ae5
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vector_id: 37597
content:
"Climate services require a sustained engagement between scientists, \r service providers and users that is often hindered by limited resources \r for the co-design and co-production process (high confidence). \r There are recurring challenges related to successful climate service \r applications: (i) climate services are not visible and are poorly \r understood by 'end users' (Weichselgartner and Arheimer, 2019); \r (ii)  data can be of unknown or poor quality, data formats can \r be hard to access or process, and it can be difficult to utilize data \r disseminated from large databases (e.g.,  Section  1.5.4) without \r appropriate user guidance; (iii) users are unsure how to choose \r from available climate services to meet their needs (Rossler et  al., \r 2019); (iv) building trust between climate service users and providers \r (Baztan et al., 2020); (v) the lack of understanding of users and their \r contexts by the climate science and service community (Porter and \r [...]"


id: 664e33925dae70d24ae8d6998f17f0c1
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updated_at: 2023-04-19 13:22:48
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index: climate_gpt_v2
vector_id: 15263
content:
"Climate services involve the provision of climate information in such a way as to assist decision-making. The service needs to have \r appropriate engagement from users and providers; be based on scientifically credible information and expertise; have an effective \r access mechanism; and meet the users' needs (Hewitt et al., 2012). Predominantly, climate services are targeted at informing and \r enabling risk management in adaptation to climate variability and change (Jones et  al., 2014; Vaughan et  al., 2018). Chapter  1 \r introduces climate services in a broader context of interaction between science and society, including how climate information can be \r tailored and co-produced for greatest utility in specific contexts. Chapter 10 assesses the key foundations for the generation of climate \r information about regional climate change. Chapters 11, 12 and Atlas comprehensively assess regional climate change information. \r [...]"


id: 4f1e33d79bf0494df7c5a95513189b5a
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updated_at: 2023-04-19 13:22:48
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content:
"[...] The Interactive Atlas gives access to various repositories of quantitative climate information. In WGII, Chapter 17 assesses climate \r services in the context of climate risk management."


id: e413cb8fcd956b14ecb97bb44aaad711
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 37429
content:
"Climate service contexts are diverse and complex. They can be characterized using different factors such as sectors, regions, purposes, \r time horizons, data sources, level of processing of climate data, background knowledge, type of climate service providers, as well \r as the nature of the interactions between providers, users and other stakeholders (Bessembinder et al., 2019). To illustrate the wide \r diversity of climate change information in climate services, a useful categorization is by user -provider engagement of climate services \r (Cross-Chapter Box  12.2, Figure  1). One broad category includes 'websites and web tools' which generally focuses on data and \r information provision (Hewitson et al., 2017). Websites are generally able to reach many users, but engagement is passive through \r one-way transfer of information. The second broad category involves 'interactive group activities', such as workshops, meetings and \r [...]"


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updated_at: 2023-04-19 13:22:48
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content:
"[...] interactive forums, which create a stronger dialogue between climate service providers and decision makers. Multi-way communication \r and regular interaction enable building of trust, co-learning and co-production of products and services. The third broad category involves \r 'focused relationships' which are tailored, targeted and address very specific needs of the user. Effective engagement arises from an \r iterative process between the provider and user to ensure the user's needs are being addressed appropriately (Hewitt et al., 2017a)."


id: 311e951fb7c6d923bdfc13227c304cc1
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"To address this audience, the strategy of C3S is to provide free and open access climate data and tools such as historical observations \r (both satellite and ground-based), climate data records relevant for a number of Essential Climate Variables (Bojinski et al., 2014), \r global and regional reanalyses, climate monitoring bulletins, seasonal predictions, as well as both global (a selection of simulations \r from the Coupled Model Intercomparison Project, CMIP; Taylor et al., 2012; Eyring et al., 2016) and regional climate projections from \r the Coordinated Regional Downscaling Experiment (Euro- and Med-CORDEX; Jacob et al., 2014; Ruti et al., 2016). A number of indices \r for various sectors can be calculated through cloud-based tools. For instance, in order to address the specific needs of key sectoral users, \r climate impact indicators for common variables such as 'heating degree days' can be calculated by the users and made available to \r [...]"


id: a57d4cdb2c5404d1de112dac6d9d4b4b
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"[...] others (Buontempo et al., 2020). All this material is quality controlled following a standardized, transparent and traceable framework."


id: f8305d54b58917c15a2f55a55b80ccf8
created_at: 2023-04-19 13:22:48
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"Two important elements were deployed in FRACTAL: 'embedded researchers' and 'learning labs'. Embedded researchers were \r seconded into the municipality and served as the essential connection for the learning process within each city (Steynor et al., 2020). \r Learning labs (Arrighi et al., 2016) were interactive structures in which participants from academia, local city government and councils, \r state-owned enterprises, communities and community development institutions, and others could interact. Embedded researchers and \r learning labs were the backbone of ongoing learning processes within each city and resulted in more focused small-group dialogues, \r capacity development and training processes, and within-city research and engagement activities. Each learning lab focused initially \r on identifying 'burning issues' without a requirement that they involve strong climate linkages. However, with the overarching focus \r [...]"


id: c98ff33327656a01c6d5369dfeb44168
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"[...] on resilience, discussions evolved in that direction and the burning issues identified often centred around water in peri-urban areas, \r for example in Windhoek, Namibia (Scott et al., 2018)."


id: 82840f302a59b8946395cc8773bcaaae
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
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"The learning labs also introduced and developed the concept of Climate Risk Narratives (CRNs) as a process and product to generate \r and integrate climate and socio-economic information relevant to adaptation and resilience (see Cross-Chapter Box 12.2, Figure 2, and \r Box 10.2 on storylines) (Jack et al., 2020). The first CRNs were informed primarily by climate evidence, but also included some tentative socio\u0002economic impact elements gleaned from literature and other studies. Their content was intentionally provocative and designed to promote \r debate and discussion, and subsequent iteration. Many participants noted that this was the first time that various important conversations \r across governance structures and disciplinary areas had occurred around what climate change may actually mean. This demonstrates \r the engagement value of CRNs as a key element in an iterative co-production process to ensure important details are included correctly, \r [...]"


id: 4f2b77562d8b39c58c0ce7479d2248ae
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"[...] such as the local context, terms and names as well as providing reality checks on the impacts and societal responses (Jack et al., 2020)."


id: 2ed325777df1a8f651faf29a91d319c
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"In the wake of the El Nino-induced 2015-2016 severe drought, which caused major damage to agricultural land in the Central Highlands \r of Vietnam, the WEIDAP project was initiated to improve water productivity of irrigated agriculture. Proposed project interventions include \r a package of both 'soft' (e.g., policy, institutional and capacity building, on-farm water efficiency practices) and 'hard' (modernized \r irrigation schemes) activities. To ensure that the project delivers expected benefits under a changing climate, consultants were recruited \r to carry out a detailed CRA, working as part of the overall project processing team. Through extensive consultations with the rest of the \r project team and review of literature including relevant climate projections, the CRA consultants chose to construct three broad climate \r [...]"


id: 5a9172f73b200a8a03ff66bdfc248d05
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content:
"[...] scenarios for the 2050s (a time frame appropriate for the lifetime of the irrigation schemes being proposed under the project): a warm\u0002and-wet, a hot-and-wet, and a hotter future. Outputs from a selection of CMIP5 models were analysed under these three scenarios, to \r derive changes in temperature, rainfall and potential evapotranspiration, which in turn were used as inputs to hydrological, crop and \r agro-economics models to assess the impacts of climate change on the overall project performance. Table 1 presents the summary of \r the key parameters under the three scenarios. Recommendations from the CRA included (largely minor) refinements and additional \r activities for drought planning, detailed engineering design of the relevant project components (such as access roads, river crossings \r and foundations), and support for poorer farmers who may not be able to afford access to water and climate-resilient technologies."


id: eb516e98ee538fdaa52a48308a1a185d
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
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"Climate change can alter many aspects of the climate system, but efforts to identify impacts and risks usually focus \r on a smaller set of changes known to affect, or potentially affect, things that society cares about. These climatic \r impact-drivers (CIDs) are formally defined in this Report as 'physical climate system conditions (e.g.,  means, \r events, extremes) that affect an element of society or ecosystems. Depending on system tolerance, CIDs and \r their changes can be detrimental, beneficial, neutral, or a mixture of each across interacting system elements \r and regions'. Because people, infrastructure and ecosystems interact directly with their immediate environment, \r climate experts assess CIDs locally and regionally. CIDs may relate to temperature, the water cycle, wind and \r storms, snow and ice, oceanic and coastal processes or the chemistry and energy balance of the climate system. \r [...]"


id: b60940e62c1b909caae206b092217a86
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content:
"[...] Future impacts and risk may also be directly affected by factors unrelated to the climate (such as socio-economic \r development, population growth, or a viral outbreak) that may also alter the vulnerability or exposure of systems."


id: 9dc50fa7d2efb50cbb9183e56099d703
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updated_at: 2023-04-19 13:22:48
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content:
"CIDs capture important characteristics of the average climate and both common and extreme events that shape \r society and nature (see FAQ 12.2). Some CIDs focus on aspects of the average climate (such as the seasonal \r progression of temperature and precipitation, average winds or the chemistry of the ocean) that determine, \r for example, species distribution, farming systems, the location of tourist resorts, the availability of water \r resources and the expected heating and cooling needs for buildings in an average year. CIDs also include \r common episodic events that are particularly important to systems, such as thaw events that can trigger the \r development of plants in spring, cold spells that are important for fruit crop chill requirements, or frost events \r that eliminate summer vegetation as winter sets in. Finally, CIDs include many extreme events connected to \r impacts such as hailstorms that damage vehicles, coastal floods that destroy shoreline property, tornadoes that \r [...]"


id: de5e8a73b96de2f1a92dd316d64cd64a
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"[...] damage infrastructure, droughts that increase competition for water resources, and heatwaves that can strain \r the health of outdoor labourers."


id: a15acdd1b812f501d7a88bbd98f9dea
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
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content:
"FAQ 12.1, Figure 1 tracks example outcomes of seasonal snow cover changes that connect climate science to the \r need for mitigation, adaptation and regional risk management. The length of the season with snow on the ground \r is just one of many regional climate conditions that may change in the future, and it becomes a CID because \r there are many elements of society and ecosystems that rely on an expected seasonality of snow cover. Climate \r scientists and climate service providers examining human-driven climate change may identify different regions \r where the length of the season with snow cover could increase, decrease, or stay relatively unaffected. In each \r region, change in seasonal snow cover may affect different systems in beneficial or detrimental ways (in the \r latter case, changing seasonal snow cover would be a 'hazard'), although systems such as coastal aquaculture \r [...]"


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"[...] remain relatively unaffected. The changing profile of benefits and hazards connected to these changes in the \r seasonal snow cover CID affects the profile of impacts, risks and benefits that stakeholders in the region will \r grapple with in response to climate change."


id: 13b8aae629620d4910f6bec19abd0e2d
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content:
"FAQ 12.2 Figure  1 illustrates how threshold conditions can help us understand climate conditions that are \r suitable for normal system operation and the thresholds beyond which impacts occur. Crops tend to grow most \r optimally within a suitable range of daily temperatures that is influenced by the varieties being cultivated and \r the way the farm is managed. As daily temperatures rise above a 'critical' temperature threshold, plants begin \r to experience heat stress that reduces growth and may lower resulting yields. If temperatures reach a higher \r 'limiting' temperature threshold, crops may suffer leaf loss, pollen sterility, or tissue damage that can lead to \r crop failure. Farmers typically select a cropping system with some consideration to the probability of extreme \r temperature events that may occur within a typical season, and so identifying hot temperature thresholds helps \r [...]"


id: a6148a2295ea6ffc657e1963fe4f36d6
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content:
"[...] farmers select their seed and field management strategies as part of their overall risk management. Climate \r experts may therefore aim to assist farm planning by providing information about the climate change-induced \r shifts to the expected frequency of daily heat extremes that exceed crop tolerance thresholds."


id: aa6799afff15cfb326d9ea214af7a832
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updated_at: 2023-04-19 13:22:48
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content:
"Maialen Iturbide (Spain), Ma. Laurice Preciado Jamero (Philippines), Emilie Vanvyve (United \r Kingdom/Belgium), Gudfinna Adalgeirsdottir (Iceland), Cecile Agosta (France), Mansour Almazroui \r (Saudi Arabia), Jorge Bano-Medina (Spain), Joaquin Bedia (Spain), Maria Laura Bettolli (Argentina), \r Donovan Campbell (Jamaica), Ana Casanueva (Spain), Christophe Cassou (France), Tereza Cavazos \r (Mexico), Abel Centella-Artola (Cuba), Ruth Cerezo-Mota (Mexico), Haoming Chen (China), \r Annalisa Cherchi (Italy), Erika Coppola (Italy), Faye Abigail Cruz (Philippines), Joseph D. Daron \r (United Kingdom), Chirag Dhara (India), Alejandro di  Luca (Australia, Canada/Argentina), \r Arona  Diedhiou (Cote d'Ivoire/Senegal), Javier Diez Sierra (Spain), Alessandro Dosio (Italy), \r Jason Evans (Australia), Vincent Favier (France), Erich Fischer (Switzerland), Sebastian Gerland \r (Norway/Germany), Subimal Ghosh (India), Natalia Gnatiuk (The Russian Federation/Ukraine), \r [...]"


id: 2746fa518e74098182a49ce834514311
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content:
"[...] Melissa I. Gomis (France/Switzerland), Patrick Grenier (Canada), David S. Gutzler (United States \r of America), Rein Haarsma (The Netherlands), Rafiq Hamdi (Belgium), Cedric Hananel (Belgium/\r France), Ed Hawkins (United Kingdom), Mark Hemer (Australia), Kevin  Hennessy (Australia), \r Nazrul Islam (Bangladesh/Saudi Arabia), Sanjay Jayanarayanan (India), Liew Juneng (Malaysia), \r Eleni Katragkou (Greece), Elena Kharyutkina (The Russian Federation), Megan Kirchmeier-Young \r (Canada/United States of America), Akio Kitoh (Japan), Erik  Kjellstrom (Sweden), Yu Kosaka \r (Japan), James Kossin (United States of America), Kenneth Kunkel (United States of America), \r June-Yi Lee (Republic of Korea), Christopher Lennard (South Africa), Piero  Lionello (Italy), \r Marta  Pereira Llopart (Brazil), Ian  Macadam (Australia/United Kingdom), Douglas Maraun \r (Austria/Germany), Seth McGinnis (United States of America), Simon McGree (Australia/Fiji, \r [...]"


id: 3766a98f4e3adbd1f9b301b812ed840a
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updated_at: 2023-04-19 13:22:48
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content:
"[...] Australia), Wilfran Moufouma-Okia (France), Grigory Nikulin (Sweden/The Russian Federation), \r Francis  Nkrumah (Ghana), Dirk Notz (Germany), Andrew  Orr (United Kingdom), Sarah  Osima "


id: 52b5e592d8bbfb9cd5029bd6141a773f
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
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content:
"Many aspects of the cryosphere either have seen significant \r changes in the recent past or will see them during the \r 21st century (high confidence). Snow cover duration has very \r likely reduced over Siberia and Eastern and Northern Europe. \r Also, it is virtually certain that snow cover will experience \r a  decline in these regions and over most of North America \r during the 21st century, in terms of water equivalent, extent \r and annual duration. Over the Hindu Kush Himalaya, glacier \r mass is likely to decrease considerably (nearly 50%) under \r the RCP4.5 and RCP8.5 scenarios. Snow cover has declined over \r Australia as has annual maximum snow mass over North America \r (medium confidence). Some high-latitude regions have experienced \r increases in winter snow (parts of North Asia, medium confidence) or \r will do so in the future (very likely in parts of northern North America) \r [...]"


id: 7f1f2d24c8e162c975e05ae140d45390
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content:
"[...] due to the effect of increased snowfall prevailing over warming\u0002induced increased snowmelt. {2.3.2.2, 3.4.2, Atlas.5.2.2, Atlas.5.3.4, \r Atlas.6.2, Atlas.8.2, Atlas.8.4, Atlas.9.2, Atlas.9.4}"


id: c10ca6f696f147e5eb8971602822f420
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updated_at: 2023-04-19 13:22:48
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index: climate_gpt_v2
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content:
"The regional performance of CMIP6 global climate models \r (GCMs) has improved overall compared to CMIP5 in simulating \r mean temperature and precipitation, though large errors \r still exist in some regions (high confidence). In particular, \r improvements have been seen over Africa which has belatedly \r become a  focus for GCM model development. Other specific \r improvements include over East Asia for temperature and the winter \r monsoon, over parts of South Asia for the summer monsoon, over \r Australia (including influences of modes of variability), in simulation \r of Antarctic temperatures and Arctic sea ice. Notable errors include \r large cold biases in mountain ranges in South Asia, a  significant \r wet bias over Central Asia, in the East Asia summer monsoon and \r in Antarctic precipitation. An in-depth evaluation of CMIP6 models \r is lacking for several regions (North and South East Asia, parts of \r West Central Asia, Central and South America), though CMIP5 \r [...]"


id: 6cd141b3b4739d47c69fcca29aed004b
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updated_at: 2023-04-19 13:22:48
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content:
"[...] models have been evaluated for many of these. {3.3.1, 3.3.2, \r Atlas.4.3, Atlas.5.1.3, Atlas.5.2.3, Atlas.5.3.3, Atlas.5.4.3, Atlas.5.5.3, \r Atlas.6.1.3, Atlas.6.2.3, Atlas.7.3, Atlas.8.3, Atlas.9.3, Atlas.10.3, \r Atlas.11.1.3, Atlas.11.2.3}"


id: db715bcb0d96b54af3f82bf481d87c1e
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
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content:
"Since AR5, the improvement in regional climate modelling \r and the growing availability of regional simulations through \r coordinated dynamical downscaling initiatives such as \r CORDEX, have advanced the understanding of regional climate \r variability, adding value to CMIP global models, particularly in \r complex topography zones, coastal areas and small islands, \r and in the representation of extremes (high confidence). In \r particular, regional climate models (RCMs) with polar-optimized \r physics are important for estimating the regional and local surface \r mass balance and are improved compared to reanalyses and \r GCMs when evaluated with observations (high confidence). There \r is still a  lack of high-quality and high-resolution observational \r data to assess observational uncertainty in climate studies, and \r this compromises the ability to evaluate models (high confidence). \r {Atlas.4.3, Atlas.5.1.3, Atlas.5.2.3, Atlas.5.3.3, Atlas.5.4.3, Atlas.5.5.3, \r [...]"


id: 518550b3ae8c4f813e30b9fdb6dfcc71
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content:
"[...] Atlas.6.1.3, Atlas.6.2.3, Atlas.7.3, Atlas.8.3, Atlas.9.3, Atlas.10.3, \r Atlas.11.1.3, Atlas.11.2.3}"


id: f67ee05c371bd943ff05b52f9a6b8542
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updated_at: 2023-04-19 13:22:48
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content:
"It is very likely that most Small Islands have warmed over \r the period of instrumental records. Precipitation has likely\r decreased since the mid-20th  century in some parts of the \r Pacific poleward of 20deg latitude in both hemispheres and \r in the Caribbean in June-July-August. It is very likely that \r sea levels will continue to rise in Small Island regions and \r that this will result in increased coastal flooding. Observed \r temperature trends are generally in the range of 0.15degC-0.2degC per \r decade. Rainfall trends in most other Pacific Ocean and Indian Ocean \r Small Islands are mixed and largely non-significant. There is limited \r evidence and low agreement on the cause of the Caribbean drying \r trend, though it is likely that both this and the Pacific drying trends \r will continue in coming decades with drying also projected in the \r part of the Western Indian and Atlantic oceans. Small Island regions \r in the western and Equatorial Pacific Ocean, and in the northern \r [...]"


id: ff392dd7115f2831e5d4f27c69b1739e
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content:
"[...] Indian Ocean are likely to be wetter in the future. {Cross-Chapter \r Box Atlas.2, Atlas.10.2, Atlas.10.4}"


id: 41ff6c632aa145eb9f16ed9d32728a80
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updated_at: 2023-04-19 13:22:48
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index: climate_gpt_v2
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content:
"component (Regional Information) includes information from global \r observational (and paleoclimate simulation) datasets assessed \r in Chapter  2 and projections of relevant extreme indices (used in \r Chapter 11) and climatic impact-drivers (CIDs, used in Chapter 12) \r allowing for a  regional analysis of the results (Section Atlas.2.2). \r It provides information on CIDs relevant to sectoral and regional \r chapters of the Working Group II (WGII) report, being informed by \r and complementing the work of Chapter 12 in creating a bridge to \r WGII. The second component (Regional Synthesis) provides synthesis \r information about changes in CIDs in several categories such as heat \r and cold, wet and dry, or coastal and oceanic, supporting exploration \r of the regional assessment findings summarized in the TS and the \r SPM. An overview of the main components of the Atlas chapter is \r provided in Figure Atlas.1. The Interactive Atlas is described in Atlas.2 \r [...]"


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content:
"The Atlas is the final chapter of this Working Group I (WGI) Sixth \r Assessment Report (AR6) and comprises the Atlas Chapter and \r an online interactive tool, the Interactive Atlas. The Atlas assesses \r fundamental aspects of observed, attributed and projected changes \r in regional climate in coordination with other WGI chapters \r (Chapters 2, 3, 4, 6, 8, 9, 10, 11 and 12). In particular, it provides \r analyses and assessments of regional changes in mean climate \r (specifically surface temperature, precipitation and some cryospheric \r variables, such as snow cover and surface mass balance) and expands \r on and integrates results from other chapters across different \r spatial and temporal scales. The Atlas considers multiple lines of \r evidence including assessment of different global and regional \r observational datasets, attribution of observed trends and multiple \r model simulations from the Coupled Model Intercomparison Projects \r [...]"


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"[...] CMIP5 (K.E. Taylor et al., 2012) and CMIP6 (Eyring et al., 2016; O'Neill \r et al., 2016), and the Coordinated Regional Downscaling Experiment \r (CORDEX; Gutowski Jr. et al., 2016). The Atlas chapter also assesses \r model performance and summarizes cross-referenced findings from \r other chapters relevant for the different regions."


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created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
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index: climate_gpt_v2
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content:
"Figure Atlas.2 (continued): The latter includes both land and ocean regions and it is used as the standard for the regional analysis of atmospheric variables in the Atlas \r chapter and the Interactive Atlas. The codes used in the Interactive Atlas are included in the figure. The full description of the regions (grouped by continents) is as follows. North \r America: NWN (North-Western North America), NEN (North-Eastern North America), WNA (Western North America), CNA (Central North America), ENA (Eastern North America); \r Central America: NCA (Northern Central America), SCA (Southern Central America), CAR (Caribbean); South America: NWS (North-Western South America), NSA (Northern \r South America), NES (North-Eastern South America), SAM (South American Monsoon), SWS (South-Western South America), SES (South-Eastern South America), SSA (Southern \r [...]"


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content:
"[...] South America); Europe: GIC (Greenland/Iceland), NEU (Northern Europe), WCE (Western and Central Europe), EEU (Eastern Europe), MED (Mediterranean); Africa: MED \r (Mediterranean), SAH (Sahara), WAF (Western Africa), CAF (Central Africa), NEAF (North Eastern Africa), SEAF (South Eastern Africa), WSAF (West Southern Africa), ESAF (East \r Southern Africa), MDG (Madagascar); Asia: RAR (Russian Arctic), WSB (West Siberia), ESB (East Siberia), RFE (Russian Far East), WCA (West Central Asia), ECA (East Central Asia), \r TIB (Tibetan Plateau), EAS (East Asia), ARP (Arabian Peninsula), SAS (South Asia), SEA (South East Asia); Australasia: NAU (Northern Australia), CAU (Central Australia), EAU \r (Eastern Australia), SAU (Southern Australia), NZ (New Zealand); Antarctica: WAN (Western Antarctica), EAS (Eastern Antarctica). The definition of the regions and companion \r notebooks and scripts are available at the Atlas repository (Iturbide et al., 2021). Figure from Iturbide et al. (2020)."


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updated_at: 2023-04-19 13:22:48
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vector_id: 5427
content:
"those selected in Chapter 2, but considering land-only information \r for the blended products. In particular, for air temperature the Atlas \r uses CRUTEM5 - the land component of the HadCRUT5 dataset - \r (Osborn et al., 2021), Berkeley Earth (Rohde and Hausfather, 2020) \r and the Climatic Research Unit CRU TS4 (version 4.04 used here; \r Harris et al., 2020). For precipitation the Atlas includes CRU TS4, the \r Global Precipitation Climatology Centre (GPCC, v2018 used here; \r Schneider et al., 2011), and Global Precipitation Climatology Project \r (GPCP; monthly version 2.3 used here; Adler et al., 2018). Although \r the ultimate source of these datasets is surface-station reported \r values (GPCP also includes satellite information), each has access \r to different numbers of stations and lengths of records and employs \r different ways of creating the gridded product and ensuring quality \r control. For oceanic variables, the most widely used sea surface \r [...]"


id: 6d473fc0dca61d6e74b1b4da01975550
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
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"[...] temperature (SST) datasets are HadSST4 (Kennedy et  al., 2019), \r which is the oceanic component of the HadCRUT5 dataset, ERSST \r (B. Huang et al., 2017), and KaplanSST (Kaplan et al., 1998)."


id: 23c2def3b58b337309df2e9aef12939d
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
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"There are currently many atmospheric reanalysis datasets with \r different spatial resolution and assimilation algorithms (see Annex I \r and Section  1.5.2). There are also substantial differences among \r these datasets due to the types of observations assimilated into \r the reanalyses, the assimilation techniques that are used, and the \r resolution of the outputs, amongst other reasons. For example, \r 20CR (Slivinski et  al., 2019) only assimilates surface pressure and \r sea surface temperature (SST) to achieve the longest record but \r at relatively low resolution, while ERA-20C (Poli et  al., 2016) only \r assimilates surface pressure and surface marine winds. At the other \r extreme, very sophisticated assimilation systems using multiple \r surface, upper air and Earth observation data sources are employed, \r for example ERA5 (Hersbach et al., 2020) and JRA-55 (Harada et al., \r 2016), which also have much higher resolutions. Most reanalysis \r [...]"


id: e17816be103457fd169c23bad1418196
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
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"[...] datasets cover the entire globe, but there are also high-resolution \r regional reanalysis datasets which provide further regional detail \r (Kaiser-Weiss et al., 2019)."


id: 4ae41d9a0da2a204598a310d3569c821
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
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"The Atlas chapter (and the Interactive Atlas) uses global model \r simulations from both CMIP5 and CMIP6, mainly historical and future \r projections performed under ScenarioMIP (O'Neill et al., 2016). This \r facilitates backwards comparability and thus the detection of new \r salient features and findings from recent science and the latest \r CMIP6 ensemble. The selection of the models is based on availability \r of scenario data for the variables assessed in the Atlas chapter and \r for those included in the Interactive Atlas (Atlas.2.2). In particular, in \r order to harmonize the results obtained from the different scenarios \r as much as possible, only models providing data for the historical \r scenario and at least two emissions scenarios, RCP2.6, RCP4.5 and/\r or RCP8.5 (for CMIP5), and SSP1-2.6, SSP2-4.5, SSP3-7.0 and/or \r SSP5-8.5 (for CMIP6), were chosen, resulting in 29 and 35 models, \r respectively (see Cross-Chapter Box  1.4 for a  description of the \r [...]"


id: 4e6df691e172df09c32ef1d82ae26ded
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
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"[...] scenarios). In the Atlas chapter (similarly to the regional Chapters 11 \r and 12) a single simulation is taken from each model (see Atlas.12 for \r limitations of this choice). Since the RCP and SSP emissions scenarios \r are not directly comparable due to different regional forcing \r (Section 4.2.2), the Atlas includes GWLs as an alternative dimension \r of analysis (Cross-Chapter Box 11.1), which allows intercomparison \r of results from different scenarios as an alternative to the standard "


id: 8f15adedc11abf39ca12a89480bb1b78
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
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index: climate_gpt_v2
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content:
"Global model data, as generated by the CMIP ensembles, although \r available globally, have spatial resolutions that are limited for \r reproducing certain processes and phenomena relevant for regional \r analysis (around 2deg and 1deg for CMIP5 and CMIP6, respectively). The \r Coordinated Regional Climate Downscaling Experiment (CORDEX; \r Gutowski Jr. et  al., 2016) facilitates worldwide application of \r Regional Climate Models (RCMs, see Section  10.3.1.2), focusing \r on a  number of regions (Figure Atlas.6) with a  typical resolution \r of 0.44deg (but also at 0.22deg and 0.11deg over some domains, such as \r Europe). However, only a  few simulations are available for some \r domains (Annex II, Table AII.1), thus limiting the level of analysis \r and assessment that can be performed using CORDEX data in some \r regions. Moreover, there are regions where several domains overlap, \r thus providing additional lines of evidence. The use of multi-domain \r [...]"


id: d3c34daa5dd6380d6edf9f80374e336e
created_at: 2023-04-19 13:22:48
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"[...] grand ensembles to work globally with CORDEX data have recently \r been proposed (Legasa et al., 2020; Spinoni et al., 2020). Ongoing \r efforts, such as the multi-domain CORDEX-CORE simulations are \r promoting more homogeneous coverage and thus more systematic \r treatment of CORDEX domains (Box Atlas.1)."


id: 3fdb33cc3aeafe5bcaf1d9f891d154f7
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
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"Building on this information, the Interactive Atlas displays a number \r of (mean and extreme) indices and climatic impact-drivers (CIDs), \r considering both atmospheric and oceanic variables (Atlas.2.2). Some \r of these indices have been selected in coordination with Chapters 11 \r and 12, in order to support and extend the assessment performed in \r these chapters (see Annex VI for details on the indices). In order to \r harmonize this information, the indices have been computed for each \r individual model on the original model grids and the results have been \r interpolated to a common 2deg (for CMIP5) and 1deg (CMIP6) horizontal \r resolution grids. In addition, for the sake of comparability with CMIP6 \r results (in particular when using baselines going beyond 2005), \r the historical period of the CMIP5 and CORDEX datasets has been \r extended to 2006-2014 using the first years of RCP8.5-driven transient \r projections (Atlas.1.3.1). Tables listing the CMIP5 and CMIP6 models \r [...]"


id: bb723857c1758c23aa3c6b281821fec6
created_at: 2023-04-19 13:22:48
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"[...] used in the Atlas and in the Interactive Atlas for different scenarios and \r variables are included as Supplementary Material (Tables Atlas.SM.1 \r and Atlas.SM.2, respectively); moreover, full inventories including \r details on the specific Earth System Grid Federation (ESGF) versions \r are given in the Atlas GitHub repository (Iturbide et al., 2021)."


id: 4ae226e3d8db6a84641023cd6ad0eee1
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
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content:
"Figure Atlas.7 shows mean temperature and precipitation biases over \r the North American domain in RCM simulations driven by reanalysis \r and historical GCM simulations (Section 10.3.2.5). Annual and seasonal \r (December-January-February (DJF) and June-July-August (JJA)) \r biases are computed for both the RCMs and driving GCMs. Biases in \r the reanalysis-driven RCMs result from intrinsic model errors, with the \r results displayed being spatially aggregated for each reference region. \r This same analysis is performed for the GCM-driven RCM simulations \r over the historical period 1986-2005. This allows comparison of \r the intrinsic bias of the RCMs with the biases resulting when driven \r by the different GCMs and patterns of behaviour in the RCMs, for \r example intrinsic warm and dry biases in ENA and WNA respectively \r or reduced RCM warm biases compared to the CCCma GCM in NEN \r and ENA. Similar results for the other CORDEX domains are included as \r [...]"


id: cafb2267beaf5bd124c6a6e1a0654003
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
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content:
"The main objective of CORDEX-CORE is to provide a global homogeneous foundation of high-resolution regional climate model (RCM) \r projections to improve understanding of local phenomena and facilitate impact and adaptation research worldwide (Gutowski Jr. \r et al., 2016). The experimental framework is designed to produce homogeneous regional projections for most inhabited land regions \r using nine CORDEX domains at 0.22deg resolution (Figure Atlas.6): North, Central and South America (NAM, CAM, SAM); Europe (EUR); \r Africa (AFR); East, South and Southeast Asia (EAS, WAS, SEA); and Australasia (AUS). Due to computational requirements, three GCMs \r were selected to drive the simulations, HADGEM2-ES, MPI-ESM and NorESM, covering, respectively, the spread of high-, medium\u0002and low-equilibrium climate sensitivities from the CMIP5 ensemble at a global scale (with MIROC5, EC-Earth and GFDL-ES2M as \r [...]"


id: 541a2b761033d8ce73cc9eb1d66183d6
created_at: 2023-04-19 13:22:48
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"[...] secondary GCMs), focusing on two scenarios RCP2.6 and RCP8.5 (see Box Atlas.1, Figure 1). Two RCMs have contributed so far to this \r initiative (REMO and RegCM4) constituting an initial homogeneous downscaled ensemble to analyse mean climate change signals \r and hazards (Coppola et al., 2021b; Teichmann et al., 2021), and there are ongoing efforts to extend the CORDEX-CORE ensemble \r with additional regional simulations (e.g., the COSMO-CLM community) to increase the ensemble size. CORDEX-CORE simulations \r are distributed as part of the information available for the different CORDEX domains at the Earth System Grid Federation (ESGF)."


id: 19715ec154327b0249996ff0da9051fa
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
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"Spatial information on observed and projected future climate changes has always been a key output of IPCC reports. This information \r is typically represented in the form of maps of historical trends (from observational datasets) and of projected changes for future \r reference periods and scenarios relative to baseline periods (from multi-model ensembles). These maps usually include information \r on the robustness or uncertainty of the results such as the significance of trends or the consistency of the change across models. \r Visualization of this information combines two aspects that are intertwined: the core methodology (measures and thresholds) and its \r visual implementation. For observed trends, robustness can be simply ascertained by using an appropriate statistical significance test. \r However, for multi-model mean changes, the consistency across models for the sign of change (model agreement) and the magnitude \r [...]"


id: 3159d7b6d7f5132ea8514e05c51fa2d5
created_at: 2023-04-19 13:22:48
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content:
"[...] of change relative to unforced climate variability (signal-to-noise ratio) provide two complementary measures allowing for simple \r or more comprehensive approaches to represent robustness and uncertainty. While they can be visually represented in various ways "


id: 10176f1183ad8075289a7c719c619ee2
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 17
content:
"The objectives in AR6 for representing robustness and uncertainty in maps are: 1) adopting a method that can be as coherent as \r possible across the different global/regional chapters while accommodating different needs, 2) being visually consistent across WGs, \r and 3) making the different layers of information on the maps as accessible as possible for the reader. As a result, a single approach \r is selected for observations and two alternative approaches (simple and advanced) are adopted for projected future changes. It is \r important to highlight that, as in previous reports, these approaches are implemented in maps at a grid-box level and, therefore, are \r not informative for larger spatial scales (e.g., over AR6 reference regions) where the aggregated signals are less affected by small\u0002scale variability leading to an increase in robustness. This is particularly relevant for the AR6 regional assessments and approaches \r [...]"


id: ae8e989d62ef717a41a6ebddc9a6204a
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
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"[...] (e.g., for trend detection and attribution; Cross-Chapter Box 1.4, Section 11.2.4) which are performed for climatological regions and \r not at grid-box scale (Chapters 11 and 12, and Atlas). Both small and large scales are relevant (e.g., adaptation occurs at smaller \r scales but also at the level of countries, which are typically larger than a few grid boxes). They are both addressed in the Interactive \r Atlas, which implements the above approaches for representing robustness in maps at the grid-box level, but also enables the analysis \r of region-wide signals (e.g., AR6 WGI reference regions, monsoon regions, etc.), helping to isolate background changes happening at \r larger scales (Atlas.2.2)."


id: 9c9b17ffd9fe255e60eccb2ba5f56e0
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
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"Recent IPCC reports adopted different approaches for mapping uncertainty/robustness, including their calculation method and/or \r their visual implementation. In AR5 WGI '+' symbols were used to represent significant trends in observations at grid-box level. For \r future projections, different methods for mapping robustness were assessed (AR5 Box 12.1, Collins et al., 2013), while proposing \r as a  reference an approach based on relating the multi-model mean climate change signal to internal variability, calculated as \r the standard deviation of non-overlapping 20-year means in the pre-industrial control runs. Regions where the multi-model mean \r change exceeded two standard deviations of the internal variability and where at least 90% of the models agreed on the sign of \r change  were stippled (as an indication of a  robust signal). Regions where the multi-model change was less than one standard \r [...]"


id: 859d4cfa8383fc6b9daecf8104973024
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"[...] deviation were  hatched (small multi-model mean signal). However, this category did not distinguish areas with consistent small \r changes from areas of significant but opposing/divergent signals. In addition, the unstippled/unhatched areas were left undefined, \r since the categories were not mutually exclusive."


id: a851b964ef3f380860609455e5efaf58
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content:
"Since AR5 there has been a growing interest for disentangling small consistent climate change signals from significant divergent \r opposite changes resulting in conflicting information (Tebaldi et al., 2011), and different statistical tests have been applied to assess \r the significance of signals working with the individual models forming the ensemble (Dosio and Fischer, 2018; Yang et al., 2018; Morim \r et al., 2019). Moreover, new approaches have been proposed to identify large changes of opposite sign that compensate in the mean \r (Zappa et al., 2021). Recent literature has also highlighted the respective risks of Type I vs Type II errors, which can be associated \r with the determination of robustness in analysed signals (Lloyd and Oreskes, 2018; Knutson et al., 2019). Type I errors are identifying \r signals when there are none, while Type II errors are concluding there is no signal when there is one. In the case of grid-box level \r [...]"


id: db1bd0f4c99496424b0137c5558d7164
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content:
"[...] analysis, the focus on small-scale features with inherently large signal-to-noise ratio may emphasize noise even though signals are "


id: f9b6d62e4e428273b7e89500d5d49c9a
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updated_at: 2023-04-19 13:22:48
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"Approach B is the simple alternative for model projections. It consists of two categories, one for model agreement (at least 80% of \r the models agree on the sign of change; colour, no overlay) and the other one for non-agreement (hatching). It is noted that model \r agreement is computed using 'model democracy' (i.e., without discarding/weighting models), since quantifying and accounting for \r model interdependence (shared building blocks) still remains challenging (Section 4.2.6). Different thresholds have been used in \r previous reports and in the literature. In CORDEX studies, 80% has been widely used (Dosio and Fischer, 2018; Kjellstrom et al., 2018; \r Nikulin et al., 2018; Yang et al., 2018; Akperov et al., 2019; Rana et al., 2020), partially due to the small ensemble sizes available \r in some cases; this also helps to reduce the impact of model interdependence in the final results. Although 90% (used in AR5 WGI) \r [...]"


id: 93235a28f0b7d593843391be43c53b7c
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"[...] provides high confidence on the forced change, it is deemed too stringent for precipitation-like variables and regional assessments \r and was therefore not included (see Cross-Chapter Box Atlas.1, Figure 1). The 66% threshold, which has been used in previous reports \r (e.g.,  SR1.5 and SROCC) and in the literature, is not used to avoid communicating weak confidence. Cross-Chapter Box Atlas.1, \r Figure 1 illustrates the application of this approach."


id: 75614f4f622df17ad94f0bfa4c97db0b
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
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index: climate_gpt_v2
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content:
"Approach C is a more advanced alternative for model projections, extending the AR5 WGI and simplifying the AR5 WGII approaches \r (fewer categories). It consists of three categories: 'robust change', 'conflicting change', and 'no change or no robust change' (see the \r details in Cross-Chapter Box Atlas.1, Table 1). The first two categories can be interpreted as areas where the climate change signal likely \r emerges from internal variability (i.e., it exceeds the variability threshold in >=66% of the models). The variability threshold is defined \r as , where is the standard deviation of 20-year means, computed from non-overlapping periods \r in the pre-industrial control (after detrending with a quadratic fit as in AR5 WG1); in cases where this information is not available \r (e.g., for CORDEX or HighResMIP), the following approximation is used instead: , where is the \r interannual standard deviation measured in a linearly detrended modern period (note that for white noise ). \r [...]"


id: 20933aa2525db9c82e8ad59e1c1b0e43
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content:
"[...] The factor is used as in the AR5 WGI approach to account for the fact that the variability of a difference in means (the climate \r change signal) is of interest. This approach is an evolution of the AR5 WGI method with three notable differences: (a) AR6 uses \r a lower threshold for internal variability (1.645 corresponding to a 90% confidence level, instead of 2 as used in AR5 WG1); (b) the \r threshold on agreement in sign is lowered from >=90% to >=80%, leading to more grid boxes classified as robust as opposed to \r conflicting signal; (c) the AR6 method compares signal to variability in each individual model and consequently introduces a 66% \r cut-off on significant changes, implying that the climate change signal likely emerges from internal variability in the baseline period. "


id: acaed9f5af3be089b2c7041f9ede3f02
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"Uncertainty at the grid-box and regional scales: interpreting areas with diagonal lines\r There is no one-size-fits-all method for representing robustness or uncertainty in future climate projections from a  multi-model \r ensemble. One of the main challenges is the dependence of the significance on the spatial scale of interest: while a significant trend \r may not be detected at every location, a fraction of locations showing significant trends can be sufficient to indicate a significant \r change over a region, particularly for extremes (e.g., it is likely that annual maximum one-day precipitation has intensified over \r the land regions globally even though there are only about 10% of weather stations showing significant trends; Figure  11.13). \r The approach adopted in WGI works at a  grid-box level and, therefore, is not informative for assessing climate change signals \r [...]"


id: 566b51bac29c740b362310f5d3df91b9
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
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index: climate_gpt_v2
vector_id: 12465
content:
"[...] over larger spatial scales. For instance, an assessment of the amount of warming required for a robust climate change signal to \r emerge can strongly depend on the considered spatial scale. A robust change in the precipitation extremes averaged over a region or \r a number of grid boxes emerge at a lower level of warming than at the grid-box level because of larger variability at the smaller scale \r (Cross-Chapter Box Atlas.1, Figure 2)."


id: a508430d46d76039be088a3e275e745f
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
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content:
"The Interactive Atlas implements the approaches for representing \r robustness in maps at the grid-box level described in Cross-Chapter \r Box Atlas.1. These approaches are not necessarily informative for \r assessing trends and climate change signals over larger spatial scales \r where signals are less affected by small-scale variability leading to \r an increase in robustness. For regional analysis, the Interactive Atlas \r allows the analysis of aggregated region-wide signals and assessing \r their robustness at a regional scale, thus complementing the previous \r approach for grid-box robustness representation. For example, \r Figure Atlas.9 shows large hatched areas for maximum five-day \r precipitation in the South Asia region. When aggregated spatially, \r the region exhibits a  robust wetting signal, with most ensemble \r members agreeing on the sign. This highlights that signals may not \r have emerged at the station or grid-box scale but have clearly at \r [...]"


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index: climate_gpt_v2
vector_id: 14620
content:
"[...] aggregated scales, particularly for variables with high variability \r (e.g.,  extreme precipitation or cold extremes; see Cross-Chapter \r Box Atlas.1)."


id: 153fcd8103be2dac8d502bdeba5c83d2
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
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content:
"All final products visualized in the Interactive Atlas can be exported \r in a variety of formats, including PNG and PDF for bitmap and vector \r information, respectively. Moreover, in the case of the global maps, \r the final data underlying these products can be downloaded in \r NetCDF and GIS format (GeoTIFF), thus facilitating reusability of the \r information. Note that the images are final IPCC products (covered by \r the IPCC terms of use), whereas the underlying data are distributed \r by the IPCC-DDC under a  more flexible license which facilitates \r reusability. Moreover, a  comprehensive provenance metadata \r description has been generated, including all details needed for \r reproducibility, from the data sources to the different post-processes \r applied to obtain the final product. In these cases, there is also \r the possibility to download a  PNG file augmented with attached \r metadata information (in JSON format). This metadata information \r [...]"


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updated_at: 2023-04-19 13:22:48
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index: climate_gpt_v2
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content:
"[...] (including the source code generating the product) can be accessed \r and interpreted automatically using specific JSON software/libraries. \r However, for the sake of simplicity, a human-readable version of the \r metadata is accessible directly from the Interactive Atlas, describing \r the key information along the workflow."


id: 5126af82a9e2129b079dc43ec7553639
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updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 11564
content:
"Figure Atlas.10 | Schematic representation of the Interactive Atlas workflow, from database description, subsetting and data transformation to final \r graphical product generation (maps and plots). Product-dependent workflow steps are depicted with dashed borders. METACLIP specifically considers the different \r intermediate steps consisting of various data transformations, bias adjustment, climate index calculation and graphical product generation, providing a semantic description \r of each stage and the different elements involved. The different controlled vocabularies describing each stage are indicated by the colours, with gradients indicating several \r vocabularies involved, usually meaning that specific individual instances are defined in 'ipcc_terms' extending generic classes of 'datasource'. These two vocabularies, dealing \r [...]"


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"[...] with the primary data sources have specific annotation properties linking their own features with the CMIP5, CMIP6 and CORDEX Data Reference Syntax, taking as reference \r their respective controlled vocabularies. All products generated by the Interactive Atlas provide a METACLIP provenance description, including a persistent link to a reproducible \r source code under version control."


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"Many assessment statements make use of evidence derived \r from observed changes, model projections, and process-oriented \r attribution of changes to human interventions. The Interactive \r Atlas shows a  small subset of available observations that \r document climate change, namely surface air temperature and \r total precipitation (and thus not including observations of other \r atmospheric and Earth system components used as part of the \r evidence base for the report). Only datasets that have (near) global \r or large regional gridded spatial coverage and go back multiple \r decades are used. For each variable multiple datasets are included, \r but some of these have overlapping native ground-station \r observations and so are not independent (Atlas.1.4.1). The datasets \r show patterns of substantial spatial and temporal variability, and \r the empirical evidence of a non-stationary climatology needs to be \r filtered from this information. Issues with quality, representativity \r [...]"


id: b1ddc37d3c453cade2b54809263f5b0
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
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content:
"[...] and mutual consistency lead to constraints on their use for \r attribution of causes of trends (see Section 10.4.1.2 for examples). \r The practice of attributing trends and extreme events to human \r causes gives confidence that these trends are expected to continue \r in the (near) future, provided the human drivers of climate change \r remain unchanged. However, large internal variability at decadal \r time scales can be misinterpreted as an anthropogenic influence on \r the likelihood of extreme events, and in that case extrapolation of \r trends cannot be expected to be a reliable predictor for the future \r (Schiermeier, 2018)."


id: 8d81ed86fbd9366507a61f6c80bd0d19
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
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index: climate_gpt_v2
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content:
"METACLIP emphasizes the delivery of 'final products' (understood as \r any piece of information that is stored in a  file, such as a  plot or \r a  map) with a  full semantic description of its origin and meaning \r attached. METACLIP ensures 'machine readability' through reuse of \r well-defined, standard metadata vocabularies, providing semantic \r interoperability and the possibility of developing database engines \r supporting advanced provenance analytics. Therefore, this framework \r has been adopted to generate provenance information and attach \r it as metadata to the products generated by the Interactive Atlas. \r A specific vocabulary ('ipcc_terms') is created alongside the inclusion \r of new products in the Interactive Atlas and uses the controlled \r vocabularies existing from CMIP and CORDEX experiments. As an \r example, Figure Atlas.10 shows the semantic vocabularies needed to \r encode the information of the typical workflow for computing (from \r [...]"


id: a741d0480b2e237a3017e7a589e75abf
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
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index: climate_gpt_v2
vector_id: 26644
content:
"[...] bias-adjusted data) any of the climate indices (extreme or CIDs) \r included in the Interactive Atlas."


id: 9853bb42139882afb1afa848ba402cc1
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
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"The detailed technical findings in IPCC reports also serve as an \r important benchmark resource for the research community. The \r Interactive Atlas complements the IPCC assessment report as \r a repository of scientific information on global and regional climate \r and its representation in coordinated model ensemble experiments. \r Regional climate is governed by a  mixture of drivers, such as \r circulation patterns, seasonal monsoons, annual cycles of snow and \r regional land-atmosphere feedbacks. Global warming may affect \r regional climate characteristics by altering the dynamics of their \r drivers. The Interactive Atlas allows the comparison of different \r levels of global warming on specific regional climate features but \r is not designed for advanced analysis of the relationship between \r drivers and regional climate characteristics. For this, tailored analysis \r protocols need to be applied, such as the aggregation of climate \r [...]"


id: 4c1a5d894444743d903cba619fe2dd37
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 10647
content:
"[...] change information from ensembles of regional climate projections, \r and stratification according to drivers of regional climate such as \r patterns of atmospheric circulation (Lenderink et  al., 2014). The \r analysis of complex regional climate characteristics resulting from \r compound drivers also require additional expert knowledge and data \r processing (Thompson et al., 2016). Section 12.6.2 assesses various \r categories of climate services, including tailored analysis of regional \r climate processes."


id: b6ee8ee174144f4fe739ece7247d7a06
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 29389
content:
"Communicating the full extent of available information on future \r climate for a region, including a quantification of uncertainties, can \r act as a barrier to the uptake and use of such information (Lemos \r et al., 2012; Daron et al., 2018). To address the need to simplify and \r increase the relevance of information for specific contexts, recent \r studies have adopted narrative and storyline approaches (see \r Sections 1.4.4 and 10.5.3 for definitions and further discussion on \r these concepts; Hazeleger et al., 2015; Shepherd et al., 2018). The \r use of region-specific climate storylines, including a  role for local \r mechanisms, drivers and societal impacts generally requires detailed \r information that is typically not provided by the Interactive Atlas. \r However, background information and basic (scenario) assumptions \r can be derived from the Interactive Atlas which can be considered \r to provide an expert knowledge base from which to build targeted \r [...]"


id: 432c73ad6880a72a2433015a544a0336
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
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index: climate_gpt_v2
vector_id: 9027
content:
"Figure Atlas.12 demonstrates how temperature is projected to increase \r for all regions, and at a greater rate than the global average over \r many land regions, and with significant amplification in the Arctic. It \r also shows the higher mid-century warming and significantly higher \r end-of-century warming under the high-emissions SSP3-7.0 scenario \r compared to the low-emissions SSP1-2.6 scenario. Conversely, \r comparing the projected 2degC global warming level change with that \r projected additional warming compared to the recent past under \r the SSP1-2.6 scenario, demonstrates the much smaller additional \r warming projected under this low-emissions scenario. Finally, \r the maps display the CMIP6 ensemble mean projection, but it is \r important to explore the full range of outcomes from the ensemble, \r for example when undertaking a  comprehensive risk assessment \r in which temperature is an important hazard. This can be explored \r [...]"


id: 203da89d2c7752704459d27161b42357
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 2889
content:
"[...] regionally in the Interactive Atlas (Atlas.2) by viewing the time series \r of changes for all of the models within the ensemble over the AR6 \r WGI reference regions (Figure Atlas.2)."


id: 32436ab5ba229223ded9b16088976b99
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 6097
content:
"Figure Atlas.11 | Observed linear trends of signals in annual mean surface air temperature (a, b) and precipitation (e, f) in the Berkeley Earth, CRU TS \r and GPCC datasets (see Atlas.1 for dataset details). Trends are calculated for the common 1961-2015 period and are expressed as degC per decade for temperature and \r relative change (with respect to the climatological mean) per decade for precipitation. Crosses indicate regions where trends are not significant (at a 0.1 significance level) \r and the black lines mark out the reference regions defined in Atlas.1. Panels (c) and (d) display the period in which the signals of temperature change in data aggregated \r over the reference regions emerged from the noise of annual variability in the respective aggregated data. Emergence time is calculated for (c) Berkeley Earth (as used in (a)) \r [...]"


id: aec9be5b6fb40205ee49d72b37818ca9
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 27995
content:
"[...] and CRUTEM5. Regions in the CRUTEM5 map are shaded grey when data are available over less than 50% of the land area of the region. Further details on data sources and \r processing are available in the chapter data table (Table Atlas.SM.15)."


id: 707e304937129045d1aa04c7e3525e10
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 17047
content:
"range of a  climate statistic, it is often useful to generate area\u0002averaged  summaries of these statistics under different future \r emissions scenarios or at specific global warming levels. This is \r demonstrated in Figure Atlas.13 and forms the basis of a common \r set of analyses, which are presented for the reference regions in \r the regional assessments in Atlas.4 to Atlas.11. It shows the range \r of projected changes compared to the 1850-1900 and recent past \r 1995-2014 baseline periods for the CMIP5 and CMIP6 ensembles. \r The first four panels show: annual mean changes in temperature \r globally and over land only for various global warming levels \r and emissions scenarios and time periods (left pair), and then \r again globally and for global land, changes in precipitation and \r temperature at the same global  warming levels (right pair). The \r second four panels provide the same temperature and precipitation \r information globally and for global land only in the December-\r [...]"


id: 24a1258a5ed15ffc986419e403e1dc3f
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 3635
content:
"[...] February and July-August seasons. These results demonstrate the \r consensus between the two ensembles for increased warming over \r land areas and increases in global precipitation at all warming levels, \r and that global land precipitation increases more. They also show \r the increased precipitation response in December-January-February \r (DJF), reflecting the large precipitation increases in the Northern "


id: dc65623d15e3705f71835d4d904e0ee8
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 36042
content:
"Table Atlas.1 | Regional mapping of the teleconnections associated with the main modes of variability (Annex IV). Fraction of surface air temperature and \r precipitation variance explained at interannual time scale by each mode of variability (columns) for each AR6 region (rows) based on the coefficient of determination R2. Units \r are in percent and non-significant values based on t-statistics at the 95% level of confidence are indicated by a white cell with a diagonal line. Grey cells represent regions where \r there is insufficient data to calculate any teleconnection. HadCRUT (HAD), GISTEMP (GIS), Berkeley Earth (BE), and CRU-TS (CRU) observed datasets are used to assess the \r strength of the teleconnection for surface air temperature, and GPCC and CRU-TS are used for precipitation. The colour scale given on label bars shown at the bottom quantifies \r [...]"


id: 97aadc9ed7f58c23bcc557a49d2c8d8f
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
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index: climate_gpt_v2
vector_id: 23902
content:
"[...] the values of the explained variance and also stands for the sign of the teleconnection for the positive phase of the mode. All data are linearly detrended prior to the computation \r of the regression. Note that results are sensitive to the choice of the detrending function (linear, loess filter, 3-order polynomial function) but by a few percent at most, which is \r well below the range of the observational uncertainty assessed here through the use of several observational products. NAM: Northern Annular Mode; SAM: Southern Annular \r Mode; ENSO: El Nino-Southern Oscillation; IOB: Indian Ocean basin; IOD: Indian Ocean Dipole; AZM: Atlantic Zonal Mode; AMM: Atlantic Meridional Mode; PDV: Pacific Decadal \r Variability; AMV: Atlantic Multi-decadal Variability; DJF: December-January-February; MAM: March-April-May; SON: September-October-November; JJA: June-July-August."


id: 68121165fd3222ecd5348b56fa08168f
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 15578
content:
"Figure Atlas.11 shows observed trends in annual mean surface \r temperature and indicates it has been rising rapidly over Africa \r from 1961 to 2015 and with significant increases in all regions of \r 0.1degC-0.2degC per decade and higher over some northern, eastern and \r south-western regions (high confidence) (see also Interactive Atlas). \r This is confirmed by an independent analysis performed for a longer \r period (1961-2018) over areas where long-term homogeneous \r temperature time series are available (Engelbrecht et  al., 2015). \r More specifically over the Horn of East Africa, the long-term mean \r annual temperature change between 1930 and 2014 showed two \r distinct but contrary trends: significant decreases between 1930 \r and 1969 and increases from 1970 to 2014 (Ghebrezgabher et al., \r 2016). North Africa has an overall warming in observed seasonal \r temperature (Barkhordarian et al., 2012; Lelieveld et al., 2016) with \r [...]"


id: f46802dc7e395c8da6791bb786887be8
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 40375
content:
"[...] positive trends in annual minimum and maximum temperatures (Vizy \r and Cook, 2012). Temperatures over West Africa have increased over \r the last 50 years (Mouhamed et al., 2013; Niang et al., 2014) with \r a spatially variable warming reaching 0.5degC per decade from 1983 to \r 2010 (Sylla et al., 2016). West Africa has also experienced a decrease \r in the number of cool nights, as well as more frequent warm days and \r warm spells (Mouhamed et al., 2013; Ringard et al., 2016). Similarly, \r East Africa has experienced a  significant increase in temperature \r since the beginning of the early 1980s (Anyah and Qiu, 2012) with an \r increase in seasonal mean temperature. Over South Africa, positive \r trends were found in the annual mean, maximum and minimum \r temperatures for 1960-2003 in all seasons, except for the central \r interior (Kruger and Shongwe, 2004; Zhou et al., 2010; Collins, 2011; \r Kruger and Sekele, 2013; MacKellar et  al., 2014), where minimum \r [...]"


id: 5112c9c21bb8e495eaed47b1cb6933f
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 11541
content:
"[...] temperatures have decreased significantly (MacKellar et al., 2014). \r Within inland southern Africa, minimum temperatures have increased \r more rapidly than maximum temperatures (New et al., 2006)."


id: f79afd6aa58e40e2763b0c3dfa24f040
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 40933
content:
"The most recent IPCC reports, AR5 and SR1.5 (Christensen et al., \r 2013; Hoegh-Guldberg et  al., 2018), state that over most parts \r of Africa, minimum temperatures have warmed more rapidly \r than maximum temperatures during the last 50 to 100  years \r (medium confidence). In the same period, minimum and maximum \r temperatures have increased by more than 0.5degC relative to \r 1850-1900 (high confidence). While the quality of ground \r observational temperature measurements tends to be high \r compared to that of measurements for other climate variables, \r Africa remains an under-represented region as reported in SR1.5 \r (Hoegh-Guldberg et al., 2018; IPCC, 2018c). Based on the Coupled \r Model Intercomparison Project Phase 5 (CMIP5) ensemble and \r reported in IPCC AR5 and SR1.5, surface air temperatures in Africa \r are projected to rise faster than the global average increase and \r are likely to increase by more than 2degC and up to 6degC by the end \r [...]"


id: 4ea4f0126139951e3be80c93c510ed57
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 11106
content:
"[...] of the century, relative to the late 20th century, if global warming \r reaches 2degC (Bindoff et  al., 2013; Niang et  al., 2014; Hoegh\u0002Guldberg et  al., 2018). The higher temperature magnitudes are \r projected during boreal summer. Southern Africa is likely to exceed \r the global mean land surface temperature increase in all seasons \r by the end of the century. Temperature projections for East Africa \r indicate considerable warming under RCP8.5 where average \r warming across all models is approximately 4degC by the end of the \r century. According to SROCC, eastern Africa like other regions with \r smaller glaciers is projected to lose more than 80% of its glaciers \r by 2100 under RCP8.5 (medium confidence) (Hock et al., 2019b)."


id: 71d7a89472e25c3f00d4d8eecab714ad
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 17271
content:
"Most areas lack enough observational data to draw conclusions \r about trends in annual precipitation over the past century. In addition, \r many regions of Africa have discrepancies between different \r observed precipitation datasets (Sylla et al., 2013; Panitz et al., 2014). \r A statistically significant (95% confidence level) decrease in rainfall \r and the number of rainy days is reported in autumn over the eastern, \r central and north-eastern parts of South Africa in spring and summer \r during 1960-2010 (MacKellar et  al., 2014; Kruger and Nxumalo, \r 2017). Central Africa has experienced a significant decrease in total \r precipitation, which is likely associated with a significant decrease \r of the length of the maximum number of consecutive wet days \r (Aguilar et al., 2009). Furthermore, rainfall decreased significantly in \r the Horn of Africa (Tierney et al., 2015) with the largest reductions \r during the long rains season from March to May (Lyon and DeWitt, \r [...]"


id: aa38bc4c250c1812ca855c8401aba044
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 27177
content:
"[...] 2012; Viste et al., 2013; Rowell et al., 2015). Over mountainous areas \r significant increases are found in the number of rain days around \r the southern Drakensberg in spring and summer during the period \r 1960-2010 (MacKellar et al., 2014). Similarly, southern West Africa is \r observed to have had more intense rainfall from 1950 to 2014 during \r the second rainy season of September to November (Nkrumah et al., \r 2019). The Sahel region also had more intense rainfall throughout \r the rainy season (Panthou et al., 2014, 2018a, b; Sanogo et al., 2015; "


id: f194f4324d95ba86b395273f86ebfe62
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 39886
content:
"Coordinated Regional Downscaling Experiment (CORDEX) regional \r climate models have been widely evaluated over Africa. They \r capture the occurrence of the West African Monsoon jump and the \r timing and amplitude of the mean annual cycle of precipitation \r and temperature over the homogeneous sub-regions of West Africa \r (Gbobaniyi et al., 2014), simulate eastern Africa rainfall adequately \r (Endris et al., 2013), and over southern Africa capture the observed \r climatological spatial patterns of extreme precipitation (Pinto et al., \r 2016). They also effectively simulate the phasing and amplitude of \r monthly rainfall evolution and the spatial progression of the wet \r season onset over southern Africa (Shongwe et al., 2015). However, \r discrepancies and biases in present-day rainfall are reported over \r Uganda from the RCM-simulated rainfall compared to three gridded \r observational datasets (Kisembe et  al., 2019). Specifically, they \r [...]"


id: 72d4d38c47334f7c1d3bb7bd270590ea
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 17451
content:
"[...] reported that the CORDEX models underestimate the annual rainfall \r in Uganda and struggle to reproduce the variability of the long and \r short rainy seasons."


id: 7ff20637b28c3a02867d3b69a0a8fe1
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 19647
content:
"The drying observed over the Sahel in the 1960s to 1970s has been \r attributed to warming of the South Atlantic SST and southern African \r drying as a response to Indian Ocean warming (Hoerling et al., 2006; \r Dai, 2011). Enhanced rainfall intensity since the mid-1980s over the \r Sahel (Maidment et al., 2015; Sanogo et al., 2015) is associated with \r increased greenhouse gases suggesting an anthropogenic influence \r (medium confidence) (Biasutti, 2019). In the last decade, the changes \r in the timing of onset and cessation of rainfall over Africa have \r been linked to changes in the progression of the tropical rainband \r and the Saharan heat low (Dunning et al., 2018; Wainwright et al., \r 2019). Moreover, later onset and earlier cessation of eastern Africa \r rainfall is associated with a delayed and then faster movement of the \r tropical rainband northwards during the boreal spring and northward \r shift of the Saharan heat low (Wainwright et al., 2019), driven by \r [...]"


id: 2ded886d7b1f791437ade2fce1cc258c
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 5360
content:
"[...] anthropogenic carbon emissions and changing aerosol forcings \r (medium confidence). Over East Africa, the drying trend is associated \r with an anthropogenic-forced relatively rapid warming of Indian \r Ocean SSTs (Williams and Funk, 2011; Hoell et al., 2017); a shift to \r warmer SSTs over the western tropical Pacific and cooler SSTs over \r the central and eastern tropical Pacific (Lyon and DeWitt, 2012); \r multi-decadal variability of SSTs in the tropical Pacific, with cooling in \r the east and warming in the west (Lyon, 2014); and the strengthening \r of the 200-mb easterlies (Liebmann et al., 2017). However, decadal \r natural variability from SST variations over the Pacific Ocean has also \r been associated with the drying trend of East Africa (Wang et  al., \r 2014; Hoell et al., 2017) with an anthropogenic-forced rapid warming \r of Indian Ocean SSTs (medium confidence)."


id: e40532904d44d3230640df89e99d005d
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 37417
content:
"Research over Africa has improved since AR5, and although SR1.5 \r (de Coninck et  al., 2018) has synthesized new information for the \r continent, there is still not enough literature on specific areas for \r assessment. CMIP5 and CMIP6 projections (Figure Atlas.16) are \r for continued warming, with median projected regional warming \r for 2080-2100 compared to 1995-2014 of between 1degC and 2degC \r under SSP1-2.6/RCP2.6 emissions and exceeding 4degC and in some \r regions 5degC under SSP5-8.5/RCP8.5 emissions. The central interiors \r of southern and northern Africa are likely to warm faster than \r equatorial and tropical regions (Interactive Atlas). Projections from \r CMIP5 show that East Africa is likely to warm by 1.7degC-2.8degC and \r 2.2degC-5.4degC under the RCP4.5 and RCP8.5 scenarios respectively \r in the period 2071-2100 relative to 1961-1990 (Ongoma et  al., \r 2018). Over southern Africa, areas in the south-western region \r [...]"


id: ed13eee0842f551537ae5c7870b3e249
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 39084
content:
"[...] of the sub-continent, covering South Africa and parts of Namibia \r and Botswana, are projected to experience the largest increase \r in temperature, which are expected to be greater than the global \r mean warming (Maure et al., 2018). A large ensemble of CORDEX \r Africa simulations have been used to project the impact of 1.5degC "


id: 47ee0634c50322050b513e3ee9b7ed16
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 9867
content:
"Africa, summer mean temperatures from CORDEX, CMIP5 (RCP8.5) \r and CMIP6 (SSP5-8.5) are projected to increase beyond 6degC by the \r end of the century with respect to the period 1970-2000 (Schilling \r et al., 2012; Ozturk et al., 2018; Almazroui et al., 2020c), see also \r the Interactive Atlas. Note that results for the CORDEX-AFR over \r the Mediterranean (MED) are consistent with those reported from \r the CORDEX-EUR dataset (Figure  Atlas.24; Section Atlas.1.3), in \r agreement with Legasa et al. (2020).\r Projected rainfall changes over Africa in the mid- and late 21st century \r are uncertain in many regions, highly variable spatially and with \r differing levels of model agreements (Figure Atlas.16) though \r with robust projections of decreases in MED and WSAF and increases \r in NEAF and SEAF by 2080-2100 under high emissions (Interactive \r Atlas). Some uncertainties are reported over parts of Africa from \r CORDEX projections (Dosio and Panitz, 2016; Endris et al., 2016; Klutse \r [...]"


id: bbcf24132a8061c56c2e1d063ef5cb4b
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 30271
content:
"[...] et  al., 2018). For example, large uncertainties are associated with \r projections at 1.5degC and 2degC of global warming over Central Africa \r (Mba et al., 2018) and over the Sahel (Gbobaniyi et al., 2014; Sylla \r et al., 2016). Over southern Africa, enhanced warming is projected to \r result in a reduction in mean rainfall across the region (Maure et al., \r 2018), and in particular over the Limpopo basin and smaller areas of \r the Zambezi basin in Zambia, and also in parts of the Western Cape \r in South Africa, under a  global warming of 2degC. The projections of \r reduced precipitation in summer rainfall regions of southern Africa \r are associated with delayed wet season onset in spring (Dunning \r et  al., 2018) due to a  northward shift and delayed breakdown of \r the Congo Air Boundary (Howard and Washington, 2020). However, \r projected rainfall intensity over southern Africa is likely to increase \r and be magnified under RCP8.5 compared with RCP4.5 for the period \r [...]"


id: ade33167be695b7f220d72c11f9a0832
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 27836
content:
"[...] 2069-2098 relative to the reference period 1976-2005 (Pinto et al., \r 2018). For West Africa, rainfall projection is uncertain because of the \r contrasting signals from models (Dosio et al., 2019). Nonetheless, West \r Africa river basin-scale irrigation potential would decline under 2degC \r of global warming even for areas where water availability increases \r (Sylla et al., 2018). The western and eastern Sahel are projected as \r hotspots for delayed rainfall onset dates of about four days and six \r days causing reduced length of rainy season in the 1.5degC-2degC warmer \r climates under RCP4.5 and RCP8.5 scenarios (Kumi and Abiodun, \r 2018). Projected delay in rainfall cessation dates and a longer length \r of rainy season over the western part of the Guinea coast is likely\r under the same scenarios (Figure Atlas.16; Sellami et  al., 2016; \r Kumi and Abiodun, 2018). There is a tendency towards an increase \r in annual mean precipitation over central Sahel and eastern Africa \r [...]"


id: 2d948932b8eea24d817a2b4217b0c240
created_at: 2023-04-19 13:22:48
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vector_id: 5312
content:
"[...] (Interactive Atlas, Figure Atlas.16, (Nikulin et al., 2018), especially over \r the Ethiopian Highlands with up to 0.5 mm day-1 (Osima et al., 2018)."


id: 772f78ede3e4dc2b4b8c42fe48e1d11f
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 18173
content:
"There are contrasting signals in the projections of rainfall over some \r parts of Africa until the end of the 21st  century (high confidence) \r though changes in any given region are generally projected with\r medium confidence. In regions of high or complex topography such as \r the Ethiopian Highlands, downscaled projections indicate increases in \r rainfall by the end of the 21st century. However, northern Africa and \r the south-western parts of South Africa are likely to have a reduction \r in precipitation under higher warming levels (high confidence). \r Over Western Africa, rainfall is projected to decrease in the western \r Sahel  sub-region (medium confidence) and increase in the central \r Sahel sub-region (low confidence) and along the Guinea coast \r sub-region (medium confidence). Rainfall amounts are projected to \r increase over Eastern Africa (medium confidence). Southern Africa is \r projected to have a reduction in annual mean rainfall but increases \r [...]"


id: a58c6c42a7b069d6680c3522bb82cc85
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
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index: climate_gpt_v2
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content:
"The assessment in this section focuses on changes in average \r temperature and precipitation (rainfall and snow), including the \r most recent years of observations, updates to observed datasets, \r the consideration of recent studies using CMIP5 and those using \r CMIP6 and CORDEX simulations. Assessment of changes in extremes \r is in Chapter  11 (Tables 11.7-11.9) and climatic impact-drivers in \r Chapter 12 (Table 12.4). It covers most Asian territories of the region \r (Figure Atlas.17) with the exception of the Russian Arctic (RAR), \r which is assessed as part of the Arctic in Section 11.2. These include \r West and East Siberia (WSB, ESB) and the Russian Far East (RFE) \r in the north; West and East Central Asia (WCA, ECA), the Tibetan \r Plateau (TIB) and East Asia (EAS); and the Arabian Peninsula (ARP), \r South and South East Asia (SAS, SEA) in the south. Figure Atlas.17 \r supports the assessment of regional mean changes in annual \r [...]"


id: c54ccc64cd729f931f1c104b54981330
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
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index: climate_gpt_v2
vector_id: 32017
content:
"[...] mean surface air temperature and precipitation over Asia. Due to \r the high climatological and geographical heterogeneity of Asia, the \r assessment is performed over five sub-continental areas: East Asia \r (EAS and ECA), North Asia (WSB, ESB and RFE), South Asia  (SAS), "


id: 1bf2fa0b8f330571ecc517efd6f1db1f
created_at: 2023-04-19 13:22:48
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index: climate_gpt_v2
vector_id: 2098
content:
"The climatic regions defined for East Asia include central and eastern \r China, Japan and the Korea Peninsula (regions ECA and EAS in \r Figure  Atlas.17). East Asia is significantly influenced by monsoon \r systems (Section 8.3.2.4.2). The seasonal advance or retreat of the East \r Asian summer monsoon (EASM) rainband is crucial to local climate. \r The East Asian winter monsoon (EAWM) has significant influence on \r the weather and climate over East Asia and plays an important role \r in regulating winter temperatures including strong cold events and \r snowstorms (Wang and Chen, 2014; Wang and Lu, 2016). The East \r Asian monsoons exhibit considerable variability on a wide range of \r time scales, including notable interannual variabilities that includes an \r effect of the El Nino-Southern Oscillation (ENSO; Wang et al., 2000) \r and the Indian Ocean Dipole (IOD; Takaya et al., 2020), and significant \r inter-decadal variabilities in the 20th century resulted from the effect \r [...]"


id: c33467287e119f521806dff4c66ce828
created_at: 2023-04-19 13:22:48
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content:
"[...] of Pacific Decadal Variability (PDV; Zhou et al., 2009), see also Annex IV \r and Table Atlas.1. The thermal conditions of both the Tibetan Plateau \r and related ocean regions play key roles in modulating the intensity of \r the monsoon circulation. The East Asian monsoons are mainly driven \r by land-sea thermal contrast and, thus, are deeply affected by global \r climate change (Ding et al., 2014; Gong et al., 2018)."


id: 2e43e8ad12ad365ea983e05633fcd20d
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
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index: climate_gpt_v2
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content:
"Summer (June-August) mean temperature in eastern China has \r increased by 0.82degC since reliable observations were established \r in the 1950s (Sun et al., 2014). Based on historical meteorological \r observations, the best estimate of the linear trend of annual mean \r surface air temperature (SAT) for China with 95% uncertainty ranges \r is 0.38degC +- 0.05degC per decade for 1979-2015 (Li et al., 2017). From \r 1960 to 2010, the increasing trend of temperature was about 0.34degC \r per decade in the arid region of north-west China, higher than the \r average over China (B. Li et al., 2012; Xu et al., 2015). Over South \r Korea, warming is 1.4-2.6 times larger than global trends. The increase \r is 1.90degC during 1912-2014 and 0.99degC during 1973-2014 (Park \r et al., 2017) with a 25-45% urbanization contribution. The annual \r temperature increased in large cities at a rate of 0.29degC +- 0.08degC per \r decade compared with 0.11degC +- 0.08degC per decade in other stations \r [...]"


id: 87a093ba3e90a28bc609af8bfbe2f409
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
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content:
"[...] in South Korea from 1960 to 2010 (H.-S. Kim et al., 2016). A relatively \r high increase in annual mean temperature at the rate of 3.0degC per \r century was detected in the Tokyo metropolitan area for the period \r 1901-2015 (Matsumoto et al., 2017). Trends of annual temperature \r for the period of 1961-2015 are shown in Figure Atlas.11. Most areas \r of East Asia have significant warming trends exceeding 0.1degC per \r decade, and the strongest warming (0.3degC-0.4degC per decade) occurs \r in northern China."


id: 17cc0eb1ce0511a71328e820b6625f93
created_at: 2023-04-19 13:22:48
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index: climate_gpt_v2
vector_id: 1395
content:
"The findings of the IPCC AR5 (Christensen et al., 2013) stated that \r the EASM and EAWM circulations have experienced an inter-decadal \r scale weakening since the 1970s, leading to a  warmer climate in \r winter and enhanced mean precipitation along the Yangtze River \r Valley (30degN) but deficient mean precipitation in northern China in \r summer. Since the middle of the 20th century, it is likely that there has \r been an increasing trend in winter temperatures across much of Asia \r (Christensen et al., 2013). The numbers of cold days and nights have \r decreased and the numbers of warm days and nights have increased \r over Asia (Hartmann et al., 2013). It is likely that there are decreasing \r numbers of snowfall events where increased winter temperatures have \r been observed (Hartmann et al., 2013). The SRCCL reports a land-use\u0002change-induced cooling as large as -1.5degC in eastern China between \r 1871 and 2007 (Hartmann et al., 2013). The summer rainfall amount \r [...]"


id: 2dd1e7ebc43adea09e0b2cd835a699f3
created_at: 2023-04-19 13:22:48
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index: climate_gpt_v2
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content:
"1961 to 2014 (Zhou and Wang, 2017). The summer precipitation trends \r over eastern China display a dipole pattern, characterized by positive \r anomalies in central-eastern China along the Yangtze River Valley and \r negative anomalies in north China since the 1950s (Section 8.3.2.4.2). \r This pattern has changed with the enhanced rainfall in the Huaihe \r River Valley and decreased in the regions south of the middle and \r lower reaches of the Yangtze River Valley since the 2000s (Liu et al., \r 2012; Zhao et  al., 2015). The climate in north-west China changed \r from 'warm-dry' to 'warm-wet' condition in the mid-1980s (Peng \r and Zhou, 2017; Wang et al., 2020), with an increased rate of annual \r precipitation of about 3.7% per decade from 1961 to 2015 (P. Wu \r et  al., 2019) and 11.2  mm per decade between 1960 and 2011 in \r northern Xinjiang (Xu et  al., 2015). Mean rainfall and the number \r of rainy days during the Meiyu-Baiu-Changma period from June to \r [...]"


id: a3e47755e4c40225ea7715a16e84e78c
created_at: 2023-04-19 13:22:48
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content_type: text
index: climate_gpt_v2
vector_id: 26026
content:
"[...] September have increased during 1973-2015 in Korea (Lee et  al., \r 2017). The precipitation trend has caused a large increase in summer \r precipitation at a rate of 40.6 +- 4.3 mm per decade, resulting in an \r increase of annual precipitation of 27.7 +- 5.5  mm per decade in \r South Korea from 1960 to 2010 (H.-S. Kim et al., 2016). Precipitation \r amounts exhibited a slight decrease at both the annual and seasonal \r scales in Japan for the period 1901-2012 (Duan et al., 2015)."


id: debed8aad41af82371cd08cab11c6f3
created_at: 2023-04-19 13:22:48
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index: climate_gpt_v2
vector_id: 36451
content:
"Recent studies using CORDEX-EA models with resolution of about \r 12-25 km showed that the RCMs produce relatively more detailed \r regional features of the temperature distribution compared with the \r driving GCMs (Tang et al., 2016). Over China, RCMs provide more \r spatial details and in general reduce the biases of their driving GCMs, \r in particular in DJF (December-January-February) and over areas with \r complex topography (Wu and Gao, 2020). However, RCMs also show \r biases in simulating East Asian precipitation and its variability (Park \r et al., 2016; Zhou et al., 2016; Zou and Zhou, 2016), and do not always \r show added value compared to the driving GCMs (Li et al., 2018b). \r For example, by comparing inter-GCM and inter-RCM differences \r around the Japan archipelago, it was found that RCM  generate \r relatively large differences in precipitation (Suzuki-Parker et  al., \r 2018). The RCM multi-model ensemble produces superior simulation \r [...]"


id: 4e7e9564d2e9529d65968b6f222b7935
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index: climate_gpt_v2
vector_id: 11086
content:
"[...] compared to that of a single model (Jin et al., 2016; D.-L. Guo et al., \r 2018). A comparative study of RCMs at different spatial resolutions \r showed that with coarse resolution they present some limitations \r and high-resolution RCMs offer added value for several evaluation \r metrics (Park et al., 2020)."


id: 3de1d039a1be1bff25cc48efd4fc406a
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 8053
content:
"The development of climate models provides a  solid basis for \r projection of future monsoon changes under different global warming \r scenarios. Coupled model simulations indicate that East Asia and the \r Tibetan Plateau will likely experience higher warming than the global \r mean conditions across all global warming levels (Figure Atlas.17) and \r with the projected warming greater in ECA and TIB than EAS. Also, \r in the CMIP6 ensemble, the multi-model mean and 90th percentile \r warming for a given period and emissions scenario are consistently \r greater than in the CMIP5 ensemble. Larger warming magnitudes are \r projected to occur in the southern, north-western, and north-eastern \r regions of China, parts of Mongolia, the Korean Peninsula, and Japan \r than in other regions (Li et al., 2018a). Projections indicate winter \r increases in SAT over the East Asian continent and in precipitation \r over the northern East Asian continent with 1.5degC and 2.0degC global \r [...]"


id: d13bdc65acbb30a2fc2dd87dcbff201
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 34163
content:
"Current climate models perform poorly in simulating the mean \r precipitation in East Asia, including the phase of the northward \r progression of the seasonal rainband (M. Zhang et al., 2018). Although \r there has been an improvement in the simulation of mean states, \r interannual variability and past climate changes in the progression \r from CMIP3 to CMIP5, some previously documented biases (such \r as the ridge position of the western North Pacific Subtropical High \r and the associated rainfall bias) are still evident in CMIP5 models \r (Sperber et al., 2013; Zhou et al., 2017). Most models capture the \r main characteristics of the winter mean circulation over East Asia \r reasonably well, but they still suffer from difficulty in predicting the \r interannual variability of the EAWM (Shin and Moon, 2018). Models \r have improved from CMIP5 to CMIP6 for climatological temperature \r and EAWM (D. Jiang et al., 2020). Some CMIP6 models also show \r [...]"


id: dfba6317bb4c0ec08873269bf87eac0b
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
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vector_id: 36609
content:
"[...] improvements in simulating the annual mean and interannual \r variation of precipitation (Sellar et  al., 2019; Tatebe et  al., 2019; "


id: fdd97a669fecc1bb63189953be5f9186
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 42022
content:
"from CMIP5 models show that compared with the current summer \r climate, both SAT and precipitation increase significantly over the \r East Asian continent during the 1.5degC warming period (L. Chen et al., \r 2019) and that the main mode of EASM precipitation changes from \r tripolar to dipolar (Wang et al., 2018). The increase in precipitable \r water in the wet EASM region is only slightly greater than the global \r average but the increase in precipitation is much greater (Z.  Li \r et  al., 2019). The monsoon circulation in the lower troposphere is \r projected to strengthen due to the enhanced thermal forcing by the \r Tibetan Plateau (He et al., 2019; He and Zhou, 2020), which causes \r the increased summer precipitation over the East Asian continent. \r Precipitation over eastern China increases for almost all months \r under global warming in projections from GCMs with different \r horizontal resolutions (Kusunoki, 2018a). Also, under RCP scenarios, \r [...]"


id: 9107dfb3416c2d348b1ae856fceec30f
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
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vector_id: 22718
content:
"[...] in the 21st century, mean precipitation is projected to increase (Kim \r et al., 2020), especially in the late afternoons (Oh and Suh, 2018), over \r the Korean Peninsula due to global warming and associated changes \r in EASM. Increase in JJA mean precipitation is projected in northern \r East Asia consistently among the CMIP models, while northward \r migration of early summer East Asian rainbands such as the Meiyu\u0002Baiu-Changma is delayed along with that of the mid-latitude westerly \r jet in the future (Horinouchi et al., 2019). However, the geographical \r distribution of precipitation change tends to depend more on the \r cumulus convection scheme (Ose, 2017) and horizontal resolution \r of models rather than on SST distributions. Under the RCP4.5 and \r the RCP8.5 scenarios, the interannual variability in EASM rainfall \r is projected by the multi-model ensemble mean to increase in the \r 21st  century (Ren et  al., 2017). Further studies show a  projected \r [...]"


id: 1e95e346f385c7a5602ba2a57e81c741
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
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content:
"[...] increase in heavy rainfall together with increases in rainfall intensity \r (Endo et al., 2017). Multi-model intercomparison indicates significant \r uncertainties in future projections of climate change in East Asia, \r although precipitation increases consistently across models (Zhou \r et al., 2017). Simulations under the RCP4.5 scenario project that the \r number of snow days will be reduced by the end of the 21st century \r relative to 1986-2005, primarily owing to the decline of light snowfall \r events. The total amount is projected to increase in north-western \r China but decrease in the other sub-regions (Zhou et al., 2018)."


id: f31fd2707518f1af524af5b96f6107d5
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
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content:
"in the southern part of WSB and ESB (Cohen et al., 2014; Ippolitov \r et al., 2014; Gruza et al., 2015; Kharyutkina et al., 2016; Overland \r et al., 2016; Perevedentsev et al., 2017; Wegmann et al., 2018). These \r temperature decreases were strongly correlated with significant \r warming over the Barents-Kara Sea (greater than 2.5degC per decade \r during 2003-2017) and sea ice loss, suggesting a causal link (Outten \r and Esau, 2012; Semenov et al., 2012; Overland et al., 2016; Semenov, \r 2016; Wegmann et al., 2018; Meleshko et al., 2019; Susskind et al., \r 2019), though recent studies (Blackport et  al., 2019; Clark and \r Lee, 2019) have shown that both phenomena result from mid-latitude \r circulation variability (see also Cross-Chapter Box 10.1). In addition, \r significant warming in the last decade has halved the cooling trend in \r southern WSB from -0.6degC per decade during 1976-2012 to -0.3degC \r per decade during 1976-2018 (high confidence) (Frolov et al., 2014; \r [...]"


id: ad0b664891fa28fd9103208e23a776b9
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 27682
content:
"The SRCCL identified aridification of the climate in southern East \r Siberia between 1976 and 2016 as causing an extension of the steppes \r polewards whilst climate change also extended the vegetation \r season, increasing forest productivity in most of boreal Siberia, \r but increasing risk of wildfire and tree mortality (Mirzabaev et al., \r 2019). The SROCC noted the warming climate has caused permafrost \r thaw and loss of ground ice, and thus land subsidence and collapse, \r disturbing ecosystems and human infrastructure. Permafrost stability, \r hydrology and vegetation were also impacted by recent extensive \r fires burning into the organic soil layer (Meredith et al., 2019). The \r SR1.5 noted that future, higher levels of warming lead to greater \r impacts in key systems such as the Siberian ecosystems, identified \r as one of the threatened systems ('Reason for Concern 1 - RFC1'; \r Hoegh-Guldberg et  al., 2018) with impacts at 2degC expected to be \r [...]"


id: 31c94512a7c9fe62a17989f3c5a32299
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
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index: climate_gpt_v2
vector_id: 6006
content:
"Annual surface air temperature and precipitation have very likely \r increased and maximum snow depth has likely increased over most \r of North Asia since the mid-1970s. The highest warming has been \r found in spring in ESB and RFE, strengthening from south to north \r with linear trends of 0.8degC-1.2degC per decade over the 1976-2014 \r period (high confidence). A  temperature decrease was identified \r just in winter in the southern part of WSB and ESB as a  result of \r natural variability, but halved from -0.6degC per decade in 1976-2012 \r to -0.3degC per decade for the longer 1976-2018 period due to \r recent warmer winters (high confidence). Over North Asia annual \r precipitation increases with estimated trends of 5-15 mm per decade \r in the 1976-2014 period have been recorded with an exception over \r the Kamchatka and the Chukchi peninsulas, where decreases of up \r to -20 mm per decade in the same period have been found (medium \r [...]"


id: b3422cf9abf2b44f3fcbf486ba350350
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vector_id: 28779
content:
"[...] confidence). Snow cover duration has very likely decreased over \r Siberia and increases in maximum snow depths of 1.8 cm, 1.1 cm \r and 4.6 cm per decade have been observed for WSB, ESB and RFE \r respectively from 1976 to 2016 (limited evidence)."


id: 34a23d6f1868afafc8e4f98323b1adf2
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 6496
content:
"Recent IPCC reports assessed that it is very likely that the mean \r annual temperature over South Asia has increased during the \r past  century (Figure  2.21 in Hartmann et  al., 2013, Figure  24-2 in \r Hijioka et al., 2014), and the frequency of cold (warm) days and nights \r have decreased (increased) across most of Asia since about 1950 \r (Figure 2.32 in Hartmann et al., 2013). The AR5 assessed that there is \r high confidence that the large-scale patterns of surface temperature \r are generally well simulated by the CMIP5 models though with \r problems in some regions, particularly at higher elevations over \r the Himalayas (Flato et  al., 2013). CMIP5 models projected for \r the 21st  century a  significant increase in temperature over South \r Asia (high confidence from robust evidence) and in projections of \r increased summer monsoon precipitation (medium confidence) \r (Collins et al., 2013). The AR5 assessed there is high confidence that \r [...]"


id: 5d9fcfdfe8e20b225dcff526aa9528d4
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
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index: climate_gpt_v2
vector_id: 13769
content:
"[...] high-resolution regional downscaling, which generate results \r complementary to those from global climate models, adds value to \r the simulation of spatial variations in climate in regions with highly \r variable topography (e.g.,  distinct orography, coastlines), and for \r mesoscale phenomena and extremes (Flato et al., 2013)."


id: 41cd03af4f6cd6dbeba9f910216c125e
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 8772
content:
"There has been a noticeable declining trend in rainfall with monsoon \r deficits occurring with higher frequency in different regions in \r South Asia (see also Section 8.3.2.4 on the South Asian monsoon). \r Concurrently, the frequency of heavy precipitation events has \r increased over India, while the frequency of moderate rain events \r has decreased since 1950 (high confidence) (Goswami et al., 2006; \r Dash et  al., 2009; Christensen et  al., 2013; Krishnan et  al., 2016; \r Kulkarni et al., 2017; Roxy et al., 2017). There is a considerable spread \r in the seasonal and annual mean precipitation climatology and \r interannual variability among the different observed precipitation \r datasets over India (Collins et  al., 2013; Prakash et  al., 2014; Kim \r et  al., 2018; Ramarao et  al., 2019). Yet, the regions of agreement \r among datasets lend high confidence that there has been a decrease \r in mean rainfall over most parts of the eastern and central north \r [...]"


