# document

id: 96a5bf1ea5ea818a1f899f3718c46331
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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"This section provides an assessment of the different approaches \r\nused in emergence studies as well as sensitivities to methodological \r\nchoices. The section then focuses on the possible influence of \r\ninternal variability on future emergence of the simulated mean \r\nprecipitation anthropogenic signal at regional scales with some \r\nillustrative examples."


id: 940d6efbd8000fa1baf686ff64050d8c
created_at: 2023-03-16 14:00:48
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"In climate science, emergence or distinguishability of a signal refers to \r\nthe appearance of a persistent change in the probability distribution \r\nand/or temporal properties of a  climate variable compared with \r\nthat of a  reference period (Section  1.4.2; Giorgi and Bi, 2009; \r\nMahlstein et al., 2011, 2012; Hawkins and Sutton, 2012). Similar to \r\nanthropogenic climate change detection (Cross-Working Group Box: \r\nAttribution in Chapter 1), signal emergence can be detected, at least \r\ninitially, without identifying the physical causes of the emergence \r\n(Section  1.4.2). In the context of human influence on climate, the \r\nobjective of emergence studies is the search for the appearance of \r\na signal characterizing an anthropogenically-forced change relatively \r\nto the climate variability of a reference period, defined as the noise."


id: 7c05248815557e508c635271b9b9db5c
created_at: 2023-03-16 14:00:48
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"Metrics can vary from a  simple signal-to-noise ratio to statistical \r\ndistributional tests (King et al., 2015; Gaetani et al., 2020) and give \r\nmedian estimates and uncertainty bounds for the date (or time of \r\nemergence) corresponding to the exceedance of specific thresholds by \r\nthe emergence metric. Reconciling future emergence results among \r\ndifferent studies is challenging due to their many methodological \r\ndifferences including the choice of the reference period, the selected \r\nclimate models and scenario, the precise definition of signal and noise \r\nand the choice of different signal-to-noise thresholds to characterize \r\nrobust emergence. Contrasting with binary yes/no statements, \r\nemergence can also be viewed as a continuous process characterized \r\nby an amplitude or level, for example the value of the signal-to-noise \r\nratio, that is a function of time or global warming level."


id: a66991f197b2dc59b676a2bfaf9b2fca
created_at: 2023-03-16 14:00:48
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"Emergence is also sensitive to the noise characteristics: assuming \r\na  common signal definition, larger signal-to-noise values and \r\nearlier emergence will arise if the noise is based on decadal mean \r\nvariability rather than interannual variability (Kusunoki et al., 2020). \r\nDepending on the framing question, the noise can include or omit \r\nexternal natural forcing such as volcanic and solar forcing (Zhang \r\nand Delworth, 2018; Silvy et  al., 2020). Furthermore, emergence \r\nresults are very sensitive to the choice and length of the reference "


id: 9605c8b9c67fc28e7f0ecee68e5b1f73
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"Since AR5, the development and production of SMILEs (Sections 4.2.5 \r\nand 10.3.4.3) has allowed the assessment of the influence of internal \r\nvariability on anthropogenic signal emergence. The influence of \r\ninternal variability, and specifically of the unforced atmospheric \r\ncirculation, on temperature signal emergence can delay or advance \r\nthe time of emergence by a decade or two in mid- to high-latitude \r\nregions (Lehner et al., 2017a; Koenigk et al., 2020). Internal variability \r\ncan also result in small or decreasing decadal to multi-decadal "


id: 6537f5f15410ae5d8551bd41f6506ab7
created_at: 2023-03-16 14:00:48
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"heatwave frequency trends under the historical anthropogenic forcing \r\nover most regions, thereby delaying emergence of unprecedented \r\nheatwave frequency trends relative to the pre-industrial trend \r\ndistribution (Sections 11.2-11.3; Perkins-Kirkpatrick et al., 2017)."


id: 65d7b145aa9c70d8ce9ca0371d948098
created_at: 2023-03-16 14:00:48
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"(Sarojini et al., 2016), to a much delayed emergence of the forced \r\nprecipitation response in observations (Hawkins et al., 2020). Based on \r\nthe CMIP6 multi-model ensemble forced with the scenario SSP5-8.5, \r\nwe assess the future emergence of mean precipitation forced change \r\nas a  function of GWLs for all AR6 land regions (Figure  10.15a). \r\nThe methodology is a  straightforward adaptation of the standard \r\napproach (Hawkins and Sutton, 2012). While the standard method \r\nis only based on the signal-to-noise ratio exceedance of a specified \r\nthreshold (taken as one), the approach used here assumes that \r\ngrid-point emergence occurs when the forced change is considered \r\nrobust following the AR6 WGI definition of robustness for projected "


id: 7e19768ca6af29ed5f932bcc0a09bdc6
created_at: 2023-03-16 14:00:48
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"Regional precipitation future changes are much more impacted by \r\ninternal variability than their temperature counterpart (Monerie \r\net al., 2017b; Dai and Bloecker, 2019; Singh and AchutaRao, 2019; \r\nvon Trentini et  al., 2019; Koenigk et  al., 2020). Relative to mean \r\ntemperature changes, this larger influence of internal variability \r\non mean precipitation changes contributes, among other factors "


id: 297706a1be43afa05161919e2ec80ce5
created_at: 2023-03-16 14:00:48
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"changes (Cross-Chapter Box Atlas.1). At a GWL of 1degC, emergence \r\nonly occurs in high-latitude regions (Wan et al., 2015; R. Guo et al., \r\n2019), albeit with only small (less than 30%) area fraction with \r\nrobust change. Robust changes in tropical and subtropical regions \r\nonly appear from GWLs of 1.5degC, for example in south-western South \r\nAmerica (Boisier et al., 2016), western Africa (Hawkins et al., 2020; \r\nSection 10.4.2.1) and southern Australia (Delworth and Zeng, 2014). \r\nSubstantial (taken here simply as area fraction greater than 50%) \r\nemergence only occurs in some tropical, subtropical and mid-latitude \r\nregions when high GWLs (3degC-4degC) are reached. Importantly, even \r\nat these high GWL values, there are still a  large number of these \r\nregions with robust changes covering less than 50% of their area. \r\nIn contrast, most high-latitude regions have an area fraction with \r\nrobust changes greater than 80% at GWLs of 3degC and above."


id: 16bead6863a512392af471512b4da008
created_at: 2023-03-16 14:00:48
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"There is high confidence that consistency in definitions of signal and \r\nnoise, choice of the reference period and signal-to-noise threshold, is \r\nimportant to robustly assess the future emergence of anthropogenic \r\nsignals across different types or generations of models, as well \r\nas comparing past emergence results between observations and \r\nmodels. There is high confidence that internal variability can delay the \r\nemergence of the regional-scale mean precipitation anthropogenic \r\nsignal in many regions, mainly located in the tropics, subtropics and \r\nmid-latitudes. An accurate estimation of the delay in regional-scale \r\nemergence caused by internal variability remains challenging due to \r\nglobal model biases in their representation of internal variability as "


id: 89681dcf5ee66572097a92e8832c64c2
created_at: 2023-03-16 14:00:48
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"Regional climate information may be constructed from a  diverse \r\nrange of sources, each depending on different assumptions and \r\naffected by different methodological limitations (Sections 10.2, 10.3 \r\nand 10.4). The construction of information may lead to products for \r\ndirect adoption by users, or intermediate products for further analysis \r\nby users and climate services agencies in collaboration with climate \r\nscientists. Widely used sources include:"


id: da98487b79c016aecb9f48151ebce201
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"of both local and national government legislation that can involve \r\na range of different constituencies and decision makers (Scott et al., \r\n2018; Savelli et al., 2021)."


id: 4b1d60bc6fbdc251f8542b721675ee95
created_at: 2023-03-16 14:00:48
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"Context plays a role in determining the risks that may affect human \r\nsystems and ecosystems and consequently the climate information \r\nneeds. The context may also limit access to such information. Hence, \r\nthe context imposes inherent constraints on how climate information \r\ncan be constructed and optimally aligned with its intended \r\napplication. Although contexts are unlimited in variety, some key \r\ncontextual elements include:"


id: 275c089653b25d3976ffec0e397d0680
created_at: 2023-03-16 14:00:48
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"Users of climate information may face the so-called practitioner's \r\ndilemma: a  plethora of different and potentially contrasting \r\nsources (Figure  10.16) may be available without a  comprehensive \r\nand user-relevant evaluation, and these datasets may also lack \r\na transparent and easily understandable explanation of underlying \r\nassumptions, strengths and limitations (Barsugli et  al., 2013; \r\nHewitson et  al., 2017). Often, the choice of information source is \r\ntherefore not determined by what is most relevant and informative \r\nfor the question at hand, but rather by practical constraints such as \r\naccessibility and ease of use and may be limited to the availability of \r\njust one source in extreme cases (Rossler et al., 2019a)."


id: e6fed73e64963e7a7f6fea0d419c2396
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"These and other contextual elements can frame subsequent \r\ndecisions about the construction of regional climate information for \r\napplications. For example, an engineer typically seeks quantitative \r\ninformation, while the policy community may be more responsive to \r\nstorylines and how information is positioned within a causal network \r\ndescribing regional climate risk (Section 1.4.4 and Box 10.2). Multiple \r\ncontexts can coexist and potentially result in competing approaches \r\n(for example, when urban governance contends with regional \r\nwater-resource management in the same area)."


id: e0f2e2a8cd7931b8843043dc497fdb8e
created_at: 2023-03-16 14:00:48
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"Without considering the specific context, the distillation of climate \r\ninformation relevant to users may poorly serve the goal of informing \r\nadaptation and policy (Cash et al., 2003; Lemos et al., 2012; Baztan \r\net al., 2017). Section 10.1.4 identifies three implicit framing issues \r\nof constructing and delivering user-relevant climate information: \r\npractical issues arising from the climate information sources, issues \r\nwith including the context in constructing the information, and \r\ndifficulties presented by complex networks of practitioners. The social \r\ncontext strongly influences decisions about constructing information \r\nand requires a  nuanced and holistic approach to recognize the \r\ncomplexity of a  coupled social and physical system (Daron et  al., \r\n2014). For example, urban water managers must recognize the \r\ndependency of the city on different water resources and the interplay "


id: 98437d98c1b1c56f55b7fc18ae6ef1dd
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"Developing climate information relevant to user needs can be \r\ninfluenced by the explicit and implicit values of all parties: those \r\nconstructing the information, those communicating the information, \r\nthose receiving the information, and, critically, those who construct \r\nthe problem statement being addressed. A  discussion of how "


id: daa99361fbd87d2d9ef5638f2fb8f201
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"values in the scientific community shape climate research appears \r\nin Section 1.2.3.2. The influence of values need not be a source of \r\nbias or distortion; it is sometimes appropriate and beneficial: critical \r\nscrutiny from a diverse range of value-governing perspectives may \r\nuncover and challenge biases and omissions in the information that \r\nmight otherwise go unrecognized (Longino, 2004). Dialogue among \r\nall parties in a culturally, socially, and economically heterogeneous \r\nsociety is therefore important for recognizing and reconciling value \r\ndifferences to best yield information that is salient, relevant and \r\navoids ambiguity, most notably when informing the complexity of \r\nrisks and resilience for human systems and ecosystems in developing \r\nnations (e.g., Baztan et al., 2017)."


id: 6659be0b26751eace8c846219a40aa69
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"A substantial body of evidence shows that the receptivity of \r\nindividuals to climate information is strongly conditioned by \r\nmotivated reasoning (Hart and Nisbet, 2012; Kahan, 2012, 2013), \r\nwherein a  person's reception of climate information is influenced \r\nby the values of the community with which the person identifies. \r\nAdherence to a  community's values forms part of an individual's \r\nsocial identity (Hart and Nisbet, 2012). Individuals thus frame their \r\nanalysis and understanding of climate information in the context \r\nof cultural values espoused by their community (Hart and Nisbet, \r\n2012; Kahan, 2012, 2013; Campbell and Kay, 2014; Bessette et al., \r\n2017; Tschakert et al., 2017; Vezer et al., 2018). Successful framing of \r\nclimate information products thus seeks to identify common ground \r\nwith users, taking account of their values and interests."


id: 33416879807646c2ce34794ac5c998f8
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"Thus, a  challenge with constructing climate information for users, \r\nespecially about impactful change, is that producing the information \r\nmay need to involve people with a  variety of backgrounds, who \r\nhave different sets of experiences, capabilities, and values. The \r\ninformation thus would need to accommodate and be relevant to \r\na range of different ways of viewing the problem (Sarewitz, 2004; \r\nRosenzweig and Neofotis, 2013; Gorddard et  al., 2016). Failure to \r\nrecognize the variety of people using the climate information can \r\nmake it ineffective, even if the source data on which it is based is of \r\nthe highest quality, and may create a danger of maladaptation."


id: 58b6b1a2a66fb3d3959a24ff508b0474
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"Given the relevance of both context and values, the effectiveness of \r\nclimate information can increase if developed in partnership with the \r\ntarget communities (Figure  10.17; Tschakert et  al., 2016). Such an \r\napproach can inspire trust among all parties and at the same time \r\npromote a  co-production process (Cash et  al., 2003). Recipients \r\nof information have the greatest trust when the communicator is \r\nperceived as understanding their context and sharing their values \r\nand identity (Corner et  al., 2014). As a  consequence, developing \r\nmental models informed by user values can help with understanding \r\ncomplex climate models and their outcomes (Bessette et al., 2017)."


id: c53ee0fd3e6dad219688643cdb2c7dde
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"Climate processes occur on a range of spatial and temporal scales, from \r\nglobal to local, from centuries and longer to days or less (Section 10.1.2 \r\nand Figure 10.3). Similarly, decisions by stakeholders cover a range of "


id: 1ee4b657a172d05fc05a9d9d1d429f46
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"spatial and temporal scales that can vary with the size of their region \r\nof interest and scope of activity. However, the link between decision \r\nscales and the spatial and temporal resolution of climate and related \r\nnon-climatic, natural-system information is not straightforward, and \r\nfailure to recognize mismatches between the two can undermine the \r\neffectiveness and relevance of the information (Cumming et al., 2006; \r\nSayles, 2018)."


id: 7858c782c923cc19fc27fc3d158ccd1d
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"Data, either from observations or models, is in general not inherently \r\ninformation, but may contain relevant information if interpreted \r\nappropriately (Hewitson et  al., 2017). The same applies to other \r\nsources of climate information. Relevance is controlled by the given \r\nuser context (Section 10.5.2.1) and relates to the required temporal \r\nand spatial scales (Section 10.5.2.3), the characteristics of required \r\nvariables (often referred to as indicators), and the meteorological \r\nand climatic phenomena driving these variables (Section 10.1.3). For \r\nexample, if climate information for driving impact models is sought \r\n(e.g., McSweeney et al., 2015), the impact modelling analysis in the \r\ntarget region is the specific user context."


id: 64f928b2112a1e2db125269c3717bb17
created_at: 2023-03-16 14:00:48
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"Climate risk assessment considers all plausible outcomes (Weaver \r\net al., 2017; Marchau et al., 2019; Sutton, 2019). Thus, a key element \r\nof information construction is the exploration and reconciliation of \r\ndifferent sources of information (Barsugli et al., 2013; Hewitson et \r\nal., 2014b; Maraun and Widmann, 2018b) and involves mainly two \r\nissues: first, assessing the fitness of different sources in the given \r\ncontext and thereby potentially omitting (or down-weighting) \r\nselected sources (Sections 10.3.3), and, second, integrating different \r\nsources into a broader picture within a context (Sections 10.3.4). "


id: 6e45d7b573c383956e85759231f42323
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"Distilling climate information for a  specific purpose benefits from \r\na co-production process that includes non-climate-scientists in the \r\nresearch design, analysis and the exploration and interpretation of \r\nthe  results to best place it in context of the intended application \r\n(Collins and Ison, 2009; Berkhout et  al., 2013; Wildschut, 2017; \r\nBhave et al., 2018; Dessai et al., 2018). Consideration of the specific \r\ncontexts of information requirements by the provider as well as \r\nincluding the user values in connecting the science with users is \r\nincreasingly recognized as paramount to construct information "


id: 9e5819198aee71944da7d701a948ef2
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"(Hewitson et  al., 2017). Context-specific collaboration, especially if \r\norganized in workshop, enables a close transdisciplinary co-exploration \r\nof the results as in the form of climate risk narratives (Jack et al., 2020, \r\nBox  10.2). Such approaches explicitly account for the user context, \r\nvalues and non-climatic stressors (Steynor and Pasquini, 2019)."


id: 8194a37e04ec33427038c441997587ae
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"These approaches often can be used in combination to increase \r\nconfidence in conclusions drawn (Hewitson et al., 2017)."


id: 95fccad9702ea260e9e1515bd1b54106
created_at: 2023-03-16 14:00:48
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"As implied by Section  10.5.2, meeting the needs of users can be \r\na substantial challenge for climate scientists if they misunderstand \r\nor have limited understanding of user needs and context (Porter \r\nand Dessai, 2017). Several barriers in user communities can trigger \r\nand sustain this challenge. This can include an institutional aversion to \r\nincorporating new tools into decision-making (Callahan et al., 1999). \r\nCoincident with this factor, there may be limited staff capacity, lack \r\nof management support and lack of a mandate to plan for climate \r\nchange (Lee and Whitely Binder, 2010)."


id: 425243bf30610c935c604dd2411f0b75
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"Following from those challenges, constructing and communicating \r\nregional climate information often occurs under the overarching \r\nassumption that uncertainty is a  problem and reducing uncertainty \r\nis the priority (Eisenack et al., 2014; J. Otto et al., 2016). This is both \r\na psychological (Morton et al., 2011) as well as a pragmatic barrier in \r\ncases where uncertainty appears to limit the ability to make decisions \r\n(Mukheibir and Ziervogel, 2007). However, where in-depth engagements \r\nwith decision contexts are undertaken, these initial barriers are often \r\ndismantled to reveal a more complex, nuanced and potentially more \r\nproductive intersection with climate information producers that can \r\nefficiently handle uncertainty (e.g., Rice et al., 2009; Lemos et al., 2012; \r\nMoss, 2016). Specifically, disclosure of all uncertainties in the climate "


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"Climate information products may often lack explanations of their \r\npotential use and misuse (Street, 2016; Lamb, 2017; Chimani et al., \r\n2020). This is particularly important if the information is provided \r\nas a  generic, publicly accessible product without a  specific context "


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"necessary to consider all physically plausible outcomes from multiple \r\nlines of evidence, especially in the case when they are contrasting \r\nsuch as in the examples of Cross-Chapter Box 10.1 and Section 10.6.2."


id: 49b573f1764de3f6acc4b170348bc16f
created_at: 2023-03-16 14:00:48
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"information, transparency about the sources of these uncertainties, \r\nand tailoring the uncertainty information to specific decision \r\nframeworks have the potential for reducing problems of distilling and \r\ncommunicating uncertain climate information (J. Otto et al., 2016)."


id: fe9cfbea051e2b64de7acede799610d8
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"The preceding subsections assess research on the distillation of \r\nclimate information, which is directly relevant for the development \r\nof climate services. Distillation, when implemented appropriately and \r\ninterpreted with all due caveats, leads to credible climate information \r\nwith a broader foundation of evidence to be used in climate services \r\npractice according to the recommendations of the Global Framework \r\nfor Climate Services (Hewitt et al., 2012). As stated in Chapter 12, \r\nclimate services set new scientific challenges to research. Examples \r\nof some of the challenges have been given in Chapters 1 and 12, \r\nwhich are complemented by the barriers to the distillation assessed \r\nin Section 10.5.3.3."


id: 4c71e1686fa329c9d2c773183757f482
created_at: 2023-03-16 14:00:48
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"Drawing upon multiple lines of evidence in the construction of \r\nclimate information increases the fitness of this information and \r\ncreates a  stronger foundation (high confidence). The lines of \r\nevidence can include multiple observational datasets, ensembles \r\nof different model types, process understanding, expert judgement, \r\nand indigenous knowledge, among others. Attribution studies, the \r\ncharacterization of possible outcomes associated with internal \r\nvariability and a comprehensive assessment of observational, model \r\nand forcing uncertainties and possible contradictions using different \r\nanalysis methods are important elements of distillation. To make the \r\nmost appropriate decisions and responses to changing climate it is "


id: d60204544f3b605e70410d5b954e9c9a
created_at: 2023-03-16 14:00:48
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"Communicating the full extent of available information on future climate for a region, including an uncertainty quantification, can \r\nact as a barrier to the uptake and use of such information (Lemos et al., 2012; Daron et al., 2018). To address the need to simplify \r\nand increase the relevance of information for specific contexts, recent studies have adopted storyline and narrative approaches \r\n(Section 1.4.4.2; Hazeleger et al., 2015; Shepherd et al., 2018). As such, these approaches are an important tool for the climate \r\ninformation distillation (Section  10.5.3). Here we assess these in a  regional climate information context, namely for exploring \r\nuncertainties, embedding climate information into a given user context, and communicating climate change information."


id: e3b8eb08301932a486e64d9b1f612a6
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"or based on expert judgment of plausible changes to observed events (Pisaric et al., 2011; Dessai et al., 2018). They can be used for \r\nattributing events to different causal factors (Lackmann, 2015; Meredith et al., 2015b; Takayabu et al., 2015; Trenberth et al., 2015; \r\nShepherd, 2016a; Section 11.2.4) as well as for exploring the unfolding of events in future climates."


id: 8b6f07fb412707cd69ac353864efafc5
created_at: 2023-03-16 14:00:48
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"Physical climate storylines are complementary to probabilistic or unconditional risk-based approaches, and are particularly suitable to \r\nexplore low-likelihood changes or events, which are often associated with the highest impacts (Shepherd et al., 2018; Sillmann et al., \r\n2020; Section 4.8). They also facilitate providing local context to large-scale trends and changes, by conditioning the projections on \r\nlocally relevant circumstances (Hazeleger et al., 2015). Storylines are also developed based on expert elicitation and include plausible \r\nchanges beyond those simulated by existing model projections in order to explore deep uncertainties (Dessai et al., 2018)."


id: 9fec24f8cbb98e011590e33876cf41b4
created_at: 2023-03-16 14:00:48
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"Storylines can be combined with impact modelling (Strasser et al., 2019; Hegdahl et al., 2020) and can be embedded in a user's \r\nrisk landscape (Shepherd, 2019; Box 10.2, Figure 1). In particular, this holds for event storylines, where confounding factors such as \r\nregional characteristics like land-use changes and non-climatic drivers of the event are an element of the storyline (Pisaric et al., 2011; \r\nDessai et al., 2018; Lloyd and Shepherd, 2020; Sillmann et al., 2021). In a co-production process, multidisciplinary expert knowledge \r\nas well as the values and interests of the intended audiences and stakeholders can be explicitly considered (Kok et al., 2014; Bhave \r\net al., 2018; Dessai et al., 2018; Scott et al., 2018; Hegdahl et al., 2020)."


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"Contributors: Claudine Dereczynski (Brazil), Melissa I. Gomis (France/Switzerland), Richard G. Jones (United Kingdom), Roshanka Ranasinghe \r\n(The Netherlands/Sri Lanka, Australia), Alex C. Ruane (The United States of America), Sonia I. Seneviratne (Switzerland), Anna A. Sorensson \r\n(Argentina), Bart van den Hurk (The Netherlands), Robert Vautard (France), Sergio M. Vicente-Serrano (Spain)"


id: 410aff5ab7e95ce73eeafa7ac01d1a37
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"This Cross-Chapter Box illustrates how assessments of past, present and future regional climate changes (e.g., change in an extreme \r\nevent index or climatic impact-driver, CID) are derived in the WGI report. Robust assessments can be derived when changes are \r\nsupported by multiple lines of evidence."


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"The assessment is derived following the IPCC uncertainty guidance through a distillation process of multiple lines of evidence on \r\nobserved trends, attribution of trends or events, climate model projections and physical understanding, covered in several chapters \r\nof the WGI Report."


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"In particular, this Cross-Chapter Box explains the methodology used to derive the regional assessments summarized in the Technical \r\nSummary (TS) table that are, in turn, used as a basis for the synthesis assessment in the Summary for Policymakers (SPM)."


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"The process consists of three discrete steps, listed below and schematically illustrated in Cross-Chapter Box 10.3, Figure 1:"


id: 850d4947a7e0b2c09af6a4395f5b6907
created_at: 2023-03-16 14:00:48
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"1. Collection and assessment of the fitness-for-purpose of available information\r\nAny specific climate change that is regionally relevant is assessed looking at lines of evidence, potentially across multiple indices. For \r\nexample, several definitions of 'drought' exist that refer to a variety of the underlying processes, temporal and spatial scales, as well as \r\nsectoral applications and associated impacts (Sections 11.6 and 12.3). Such diverse definitions need to be gathered from the relevant \r\nliterature, compared, and individually assessed if appropriate."


id: 9031f29ff3f44f584b45a06dae5b2b3d
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"2. Assessment of confidence of the multiple lines of evidence\r\nOnce the relevant information has been collated for a given regional change, an assessment of the confidence is first made for each \r\nline of evidence separately. The assessment of confidence is the result of expert judgment drawing around a set of questions such as:"


id: 1591331df56c801c81d958597e9cc673
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"3. Distillation of regional information and synthesis of the independent assessments\r\nTo ensure transparency, a traceback matrix is constructed (refer to 10.SM) that, for each region and index, identifies where in the \r\nchapters the relevant information can be found, together with a summary of the relevant information in the Technical Summary."


id: 8a539d125083b245fddafd19f88f1059
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"Based on assessments mainly in Chapters 8, 9 11, 12 and Atlas, the table in Technical Summary (TS.4.3.1) collates, by means of colours \r\nand symbols, the assessment of the confidence in past trend, attribution and direction of future change. This distillation process is \r\nillustrated below with two examples: (i) a relatively simple case for the assessment of extreme heat over South-Eastern South America, \r\nwhere most of the lines of evidence agree, and (ii) ecological, agricultural and hydrological drought in the Mediterranean, which is \r\nmore complex due to the different definitions of 'drought' and the sometimes conflicting information arising from different lines \r\nof evidence and the example shown here is preceded by the decision to focus on these types of drought rather than, for example, \r\nmeteorological drought."


id: 4cc043b76e281e4e0c46cb74f00c77ec
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"Mean temperature and extreme maximum and minimum temperatures have shown an increasing trend (high confidence). An increase \r\nin the intensity and in the frequency of heatwave events between 1961 and 2014 is also observed. However, there is medium \r\nconfidence that warm extremes have decreased in the last decades over the central region of SES during austral summer (Section 11.9 \r\nand Atlas.7.2.2)."


id: eddafc0556b749ac40f962f49e419ccc
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"Based on trend detection and attribution studies of maximum and minimum temperatures and event attribution of heatwaves in \r\nthe region, there is high confidence in a human contribution to the observed increase in the intensity and frequency of hot extremes \r\n(Section 11.9)."


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"The increasing heat stress over summer in much of SES has been attributed to human influence on the climate system (Section 12.4.4.1)"


id: 4c8137b0dfb5806bdeee04c8d0d960aa
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"There is high confidence that by the end of century most regions in South America will undergo extreme heat stress conditions much \r\nmore often than in the recent past, with about 50-100 more days per year under SSP1-2.6 and more than 200 additional days per year \r\nunder SSP5-8.5 (high confidence) (Section 12.4.4.1)."


id: 2c1ed1cac5c7a938bd757fa6d7b9c0a2
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"Based on different lines of evidence (GCMs, RCMs) an increase in the intensity and frequency of hot extremes is extremely likely for \r\nSES at all assessed warming levels (compared with pre-industrial) (Section 11.9)."


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"Synthesized assessment in the Technical Summary from multiple lines of evidence\r\nThere is high confidence that extreme temperatures have increased over SES over the last decades and that human influence likely\r\ncontributed to the observed changes in extreme temperatures. An increase in the frequency and intensity of heatwave events has been \r\nobserved. Most land regions will frequently undergo extreme heat stress conditions by the end of the 21st century, with an increase \r\nin the frequency of heatwaves and heat stress conditions (Technical Summary TS.4.3.2)."


id: 5b003a9be3aeedf25f1c0b7d99ea077c
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"Hydrological modelling suggests that the recent decline in soil moisture in the Mediterranean is unprecedented in the last 250 years. \r\nPaleoclimate evidence extends this view, additionally indicating that dryness in the Mediterranean is approaching an extreme \r\ncondition compared to the last millennium (Section 8.3.1.6)."


id: d771250702b9317d1685316b48bd1ee9
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"There is an increase in probability and intensity of agricultural and ecological droughts (medium confidence) and there is an increase \r\nin frequency and severity of hydrological droughts (high confidence) (Section 11.9)."


id: 7d150114c3c48da923b45f17fe1a30a1
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"Global warming has contributed to drying in dry summer climates including the Mediterranean (high confidence). Records of soil \r\nmoisture indicate that higher temperatures and increased atmospheric demand have played a strong role in driving Mediterranean \r\naridity. Multiple lines of evidence suggest that anthropogenic forcings are causing increased aridity and drought severity in the \r\nMediterranean region (high confidence) (Section 8.3.1.6)."


id: 3600851805e76a106ad8ed899d3eb59d
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"An increasing trend towards agricultural and ecological droughts has been attributed to human-induced climate change in the \r\nMediterranean (medium confidence). Model-based assessment shows with medium confidence a human fingerprint on increased \r\nhydrological drought, related to rising temperature and atmospheric demand, and frequency and intensity of recent drought events. \r\nThere is medium confidence that change in land-use and terrestrial water management contribute to trends in hydrological drought \r\n(Section 11.9)."


id: 9b4bf38dbb97bf45e33ef36561ca081
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"There is high confidence that drought severity and intensity will increase in the Mediterranean. Increased evapotranspiration due \r\nto growing atmospheric water demand will decrease soil moisture (high confidence). The seasonality of runoff and streamflow (the \r\nannual difference between the wettest and driest months of the year) is expected to increase with global warming (high confidence). \r\nAnnual runoff is very likely to decrease. Under middle or high-emissions scenarios, the likelihood of extreme droughts increases by \r\n200-300% in the Mediterranean. The paleoclimate record provides context for these future expected changes: climate change will shift \r\nsoil moisture outside the range of observed and reconstructed values spanning the last millennium (high confidence) (Sections 8.4.1.5 \r\nand 8.4.1.6)."


id: 408609f639b081b729a959d7677a129b
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"There is medium confidence in the increase of agricultural and ecological drought at +1.5degC, high confidence at +2degC and very likely\r\nat +4degC, with large decreases in soil water availability during drought events and increase in drought magnitude. There is medium \r\nconfidence in the increase in hydrological drought at +1.5degC, high confidence at +2degC and very likely at +4degC with very strong decrease \r\n(40-60%) of total runoff in the spring-summer half-year and a 50-60% increase in frequency of days under low flow (Section 11.9)."


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"There is high confidence that agricultural, ecological and hydrological droughts will increase in the Mediterranean region by mid- and \r\nend-of-century under all RCPs (except RCP2.6/SSP1-2.6), or for GWLs equal to or higher than 2degC (Section 12.4.5.2)."


id: 5fd9791394761f11322587f38317fbb
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"Synthesized assessment in the Technical Summary from multiple lines of evidence\r\nThere is high confidence that hydrological droughts have increased in the Mediterranean since the 1960s related to rising temperature \r\nand atmospheric demand, and medium confidence of a human fingerprint on this increase. There is medium confidence in the increase \r\nof ecological and agricultural droughts and in their attribution to human-induced climate change. There is high confidence of an \r\nincrease in ecological, agricultural and hydrological droughts for warming levels exceeding 2degC, and medium confidence of an increase \r\nfor lower warming levels (Technical Summary TS4.3.2)."


id: 2d4b4bf6bdccfc0cc7fc0b675d3cfa17
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"because most of the components for constructing regional climate \r\ninformation outlined in Chapter 10 are directly relevant to each case."


id: bfa89562cdab0235f5c1204af4480793
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"Following this structure, construction of the regional climate \r\ninformation presented in these examples depends on an assessment \r\nof observational uncertainty relative to the magnitude of a climate \r\nchange signal (Section 10.2), the evaluations of model performance \r\nto judge the fitness-for-purpose of a  given model (Section  10.3), \r\nand expert judgement.  These factors contribute to attribution of \r\nhistorical climate change signals (Section  10.4), recognizing that \r\nattribution must account for the interplay between externally forced "


id: 55bfa77ea5e46a9c4c94356d1a53dcdf
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"signals and unforced internal variability. This interplay is explored \r\nusing multiple model ensembles, including, when appropriate and \r\nfeasible, single-model initial-condition large ensembles (SMILEs). The \r\nmultiple lines of evidence for the climate information may conflict, \r\nthus requiring distillation of the evidence (Section  10.5) to arrive \r\nat climate-change statements. When moving from global climate \r\ninformation to climate information at the regional scale, following the \r\nstructure above provides a basis for arriving at relevant and credible \r\nclimate information. The comprehensive examples of distilling climate \r\ninformation thus show the value of working with multiple lines of \r\nevidence to develop robust climate change information for a region."


id: fe5b61e5a0547384f5f33d0666bbcf86
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"In addition to the three comprehensive examples, this section \r\ncontains two additional examples analysing multiple sources of \r\nregional climate information. Box  10.3 on urban climate assesses \r\ninformation that provides a  foundation for understanding climatic \r\nbehaviour in urban areas and its projected change. Cross-Chapter \r\nBox 10.4 on climate change over the Hindu Kush Himalaya assembles \r\ninformation rooted in several chapters and previous assessment \r\nreports to assess understanding of several climate elements \r\n(temperature, precipitation, snow and glaciers, and extreme events) \r\nfor the region and their projected changes."


id: 35960d165aecf8fc463b85057a86be89
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"As these examples will show, the distillation process of regional climate \r\ninformation from multiple lines of evidence can vary substantially \r\nfrom one case to another. Confidence in the distilled regional climate \r\ninformation is enhanced when there is agreement across multiple lines \r\nof evidence, but the outcome of distilling regional climate information \r\ncan be limited by inconsistent or contradictory sources."


id: c1ce4817ff0c4e04933b6e072efb9509
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"An evaluation of the relative role of rainfall and temperature signal \r\nin the 2015-2017 hydrological drought gives a  strong indication \r\nthat lack of rainfall was the primary driver (Otto et al., 2018) leading \r\nto the 2018 water crisis. Thus, the remainder of this section focuses \r\non rainfall. Section 11.6 offers a discussion of African drought over \r\nbroader areas, including mechanisms relevant to them."


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"Cape Town is located at the south-western tip of Africa, within an \r\napproximately 100 km x 300 km region that receives 80% of its \r\nrainfall during the austral winter (March to October), with the largest \r\nportion in June to August. In the vicinity of Cape Town, rainfall is \r\nstrongly heterogeneous, ranging from about 300 mm/year on coastal \r\nplains to >2,000 mm/year in mountain ranges. The Cape Town water \r\nsupply relies on surface water reservoirs located in a  few small \r\nmountain catchments (about 800 km2 in total). The Cape Town region \r\nreceives 85% of its rainfall from a series of cold fronts forming within \r\nmid-latitude cyclones. The remainder is brought in by infrequent \r\ncut-off lows that occur throughout the year (Favre et al., 2013). This \r\ncreates a very strong water resource dependency on a single rainfall \r\ndelivery mechanism that may be strongly affected by anthropogenic \r\nclimate change (Chapter 4 and Section 10.6.2.6)."


id: 68d8bcf64d313677bd2c3038ac8b1f12
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"The 2015-2017 drought had strong low-rainfall anomalies in shoulder \r\nseasons (March to May and September to November, though weaker \r\nin the latter), and average rainfall in June and July (Sousa et al., 2018a; \r\nMahlalela et al., 2019). The anomaly resulted from fewer rainfall events \r\nand lower average intensity of events. The anomaly was strongest in \r\nthe mountainous region where the water supply system's catchments \r\nare located (Wolski et al., 2021)."


id: ecd3405843192314643ecb4fd7e705e4
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"2015-2017 drought to the hemispheric processes expressed by the \r\nSAM (Sousa et al., 2018a; Burls et al., 2019; Mahlalela et al., 2019) \r\nfocused almost exclusively on the post-1979 period, when global \r\nreanalyses are available. Detailed understanding of the drivers of \r\nprevious (1930s and 1970s) Cape Town region droughts and the \r\nrole of hemispheric processes expressed by the SAM in the pre-1979 \r\nperiod is missing."


id: 857a2d42c3b8901d53c7561ca7cc06da
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"Although the 2015-2017 drought was unprecedented in the historical \r\nrecord, the Cape Town region has experienced other droughts of \r\nsubstantial magnitude, notably in the 1930s, 1970s and more recently \r\nin 2000-2003. Long-term (>90 years) rainfall trends are mixed in sign, \r\nlocation-dependent, and weak (Kruger and Nxumalo, 2017; Wolski \r\net al., 2021); mid-term (about 50 years) trends are similarly mixed in \r\nsign (MacKellar et al., 2014). In the south-western part of the region, \r\nrainfall is mostly decreasing in the post 1981 period, particularly in \r\nDecember-January-February and March-April-May, although there \r\nis no trend or a weak wetting in June-July-August (Sousa et al., 2018a; \r\nWolski et al., 2021). Rainfall trends of similar magnitude and duration \r\nto the post-1981 trend accompanied previous strong droughts in the \r\nregion (Wolski et al., 2021)."


id: f20addb4fa4d72db617370b4773407f7
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"South Africa and the Cape Town region have good instrumental \r\nweather data. Records start in the late 1800s, with in excess of \r\n10 gauges reporting since the 1920s, expanding to about 80 gauges \r\nin the 1980s, but the number of stations has declined since. The \r\nmountains have only a  few stations, which receive more than \r\n1000 mm per year. In view of the strong heterogeneity of rainfall, \r\nchanges in the number of stations contributing to datasets such as \r\nClimatic Research Unit (CRU) and Global Precipitation Climatology \r\nProject results in a lack of consistency between them, which limits \r\ntheir reliability in the region (Section 10.2; Wolski et al., 2021)."


id: 1c59328b8c94ad8f0e6938b0559f0638
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"Rainfall in the Cape Town region also responds to SST anomalies in \r\nthe south-east Atlantic, including the Agulhas Current retroflection \r\nregion, which may drive intensification of low-pressure systems, \r\nleading to the trailing front strengthening as it makes landfall over \r\nthe Cape Town region (Reason and Jagadheesha, 2005). There are \r\nalso linkages at the seasonal time scale between the Cape Town \r\nregional rainfall and Antarctic sea ice (Blamey and Reason, 2007)."


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"In addition to mid-latitude controls, subtropical processes also play \r\na role in the Cape Town region's rainfall variability. The 10degS-30degS \r\nregion of the subtropical Atlantic, parts of the South American \r\ncontinent and even parts of the African continent north of Cape Town \r\nare sources of moisture for atmospheric river events contributing \r\nto frontal rainfall (Blamey et  al., 2018; Ramos et  al., 2019), with \r\nimplications for the 2015-2017 drought (Sousa et  al., 2018a). \r\nAlso, the second major rainfall contributing system, cut-off-lows, is \r\nconditional on moisture supply from the subtropics (Abba Omar and \r\nAbiodun, 2020)."


id: c6922823fa10e1bd2ee6d2166f2088bd
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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content:
"Although El Nino-Southern Oscillation (ENSO) influences climate in \r\nsouthern Africa, any relationship between ENSO and Cape Town's \r\nrainfall is weak and inconsistent, showing the strongest impact in \r\nMay to June (Philippon et al., 2012). ENSO, however, does influence \r\nlarge-scale processes and phenomena relevant to the drought, \r\nthough the relationship between ENSO and the SAM is complex, with \r\neach ENSO event influencing the SAM differently in different seasons "


id: fc9fd799358ee92f7e379477129731b6
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 37261
content:
"In the longer-term, Cape Town regional rainfall is characterized by \r\na multi-decadal scale quasi-periodicity (Figure 10.18; Dieppois et al., \r\n2019; Wolski et al., 2021), with the 2015-2017 drought and previous \r\nstrong droughts (1930s and 1970s) occurring during the rainfall's \r\nperiodic low phases. However, the studies linking the Cape Town "


id: 294f8482e6366b4a1f1f81cf633c6d1c
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 3937
content:
"(Ding et al., 2012). Similarly, ENSO affects meridional circulation and \r\nthus the subtropical anticyclone as well as the polar and subtropical \r\njets (Seager et al., 2019), but only modifying, not controlling, their \r\nrole in Cape Town's rainfall."


id: abe32d8b1ca4d14b64ac10fbdd8ebc57
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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vector_id: 24564
content:
"There is also a  substantial increase in the frequency of conditions \r\nsupporting atmospheric rivers and water vapour transport towards \r\nthe south-west coast of southern Africa in the projected climate \r\n(Espinoza et  al., 2018). This behaviour has strong implications for \r\nthe region, as most topographically high locations receive rainfall \r\nfrom persistent atmospheric rivers (Blamey et al., 2018). A thorough \r\nunderstanding of the role of atmospheric rivers in the Cape Town \r\nregion under a changing climate is missing."


id: 44df4e49ec0ff8a49ef50d9731320438
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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vector_id: 8258
content:
"Global models capture the overall behaviour of the observed main \r\nhemispherical processes, such as the expansion of the tropics, \r\na positive trend in SAM and the poleward shift of the westerly jet. \r\nHowever, they fail to capture details of their observed climatology \r\nand variability (Simpson and Polvani, 2016), and the magnitudes of \r\nsimulated trends vary, though the models typically underestimate \r\nobserved trends in these processes (Purich et al., 2013; Staten et al., \r\n2018). In general, CMIP5 models do capture the SAM-regional \r\nrainfall association, although not consistently across all seasons \r\n(Purich et al., 2013; E.-P. Lim et al., 2016)."


id: 5906959b929edd2c33f89245c1bd2182
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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vector_id: 11499
content:
"There is a  consistency in rainfall projections with the projections of \r\nrainfall drivers and with the general understanding of the influence \r\nof global warming on the circulation dynamics and rainfall patterns \r\nin the region. Thus, the expansion of the South Atlantic high-pressure "


id: bd6aae19c8bec62fbe8ec606804a03f5
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 27367
content:
"Global models show strong consistency in a drying signal for the Cape \r\nTown region, with the reduction in total annual rainfall of up to 20% \r\nby the end of the 21st century in CMIP5 RCP8.5 and CMIP6 SSP5-8.5 "


id: 4db8d531be7528b798a11edf00fef3d
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
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vector_id: 9656
content:
"system, related to widespread warming of the tropics and poleward \r\nshift of the subsiding limb of the Hadley cell, is associated with the \r\nsouthward displacement of the subtropical jet, and southward \r\nmigration of mid-latitude westerlies and storm tracks, in addition to \r\nchanges in the SAM (Section 10.6.2.4). These effects are also relatively \r\nconsistent with recent (post-1980s) declines in rainfall in the Cape Town \r\nregion. The storyline of an extended drought is thus a set of events that \r\ncan yield reduced rainfall in the Cape Town region: a poleward shift \r\nof the downward branch of the Hadley cell that produces a sustained \r\nsouthward shift in mid-latitude westerlies and storm tracks. The \r\nbehaviour is potentially reinforced by changes in the SAM."


id: f0f5c737fe919eb4a122fd3f3703f62d
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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index: climate_gpt_only_giec
vector_id: 35404
content:
"There is high agreement among observational data and reanalyses \r\nthat the recent (post-1979) downward trend in the Cape Town \r\nregion's rainfall leading to the 2015-2017 drought is related to \r\nthe hemispheric processes of poleward shift in the westerlies and \r\nexpansion of the Hadley circulation. However, there is less support \r\nfor the precipitation-circulation relationship in historical CMIP5 \r\nand CMIP6 simulations. As a  consequence, there is only medium \r\nconfidence that these process changes produced the 2015-2017 \r\ndrought leading to the 2018 water crisis."


id: 2dff48a5b0d8848b3e2572fc31af0b53
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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index: climate_gpt_only_giec
vector_id: 4668
content:
"Local geography gives rise to distinct differences in societal experience \r\nof the summer monsoon. The south-westerly monsoon winds are \r\nincident upon the Western Ghats mountains on the west coast, leading \r\nto orographic enhancement and heavy rains (Shige et al., 2017), which \r\nsupply rivers with water for much of the southern peninsula, often the \r\nsubject of inter-regional water disputes. The northern plains contain \r\nthe Ganges river and also India's most intensive agriculture, both rainfed \r\nand irrigated. Synoptic systems known as monsoon depressions cross \r\nthe northern east coast, supplying much of the rain in central India \r\n(Hunt and Fletcher, 2019). Further north, the eastern Himalayas  are \r\ndominated by the summer monsoon, while the western Himalayas \r\nreceive most rainfall from western disturbances during winter (Palazzi \r\net al., 2013). Meanwhile, south-eastern India sits under a rain shadow \r\n(the only region to receive more rainfall during the winter monsoon)."


id: a391abf167e294e6f1806b057722d505
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 23298
content:
"For the water-resource planner who has to deal with potential \r\ndrought like the 2015-2017 event, several lines of evidence \r\nindicate future drying: the projected precipitation by global models \r\nand RCMs of different spatial resolutions, and the observed and \r\nprojected changes of circulation patterns consistent with drier \r\nconditions, the paleoclimatic evidence confirming a millennial-scale \r\ncirculation-rainfall link. However, the distillation is limited by a lack \r\nof information about whether or not a relationship between Cape \r\nTown precipitation and large-scale circulation processes adequately \r\nexplains droughts in the twentieth century prior to 1979."


id: c47e7a191371c4984d25fcb36f661eab
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
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vector_id: 28488
content:
"India has one of the oldest rain-gauge networks in the world, leading \r\nto the production of numerous observational products (reviewed \r\nin Khouider et  al., 2020). Gridded gauge-based products dating \r\nback to the 19th  century reveal pronounced decadal variability \r\n(Sontakke et al., 2008). Trends for India over the whole 20th century \r\nare inconclusive (Knutson and Zeng, 2018), although declining over \r\ncentral and northern areas (Roxy et al., 2015). Assessment of multiple \r\nobservational datasets covering the Indian summer monsoon reveals \r\nsignificant declining rainfall over the second half of the 20th century \r\n(Section 8.3.2.4.1 and Figure 10.19c,d). A subsequent recovery has \r\nbeen noted since the early 2000s (Jin and Wang, 2017)."


id: 6e7c543f895012c3917fdf24f926c7aa
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 15011
content:
"Thus, although a clear association appears in observations from 1979 \r\nonward between increasing GHG concentrations, drying in the Cape \r\nTown region and behaviour of a key circulation process, the SAM, \r\nfurther analysis suggests caution. Not all global models show \r\nthe historical post-1979 association among these factors, and when \r\nthe observational record is extended back further to times when the \r\nanthropogenic greenhouse forcing was weaker, there is no strong \r\nassociation between the SAM and Cape Town drought. Thus, there is \r\nonly medium confidence in the expectation of a future drier climate \r\nfor Cape Town."


id: c84e464c2084afb194d69f2502218bac
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
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vector_id: 29088
content:
"Observational products containing critical inhomogeneities in gauge \r\ndistribution and reporting over time are acknowledged as suitable for \r\nmesoscale analysis (Rajeevan and Bhate, 2009), while use for climate \r\ntrends requires consistent reporting over time from quality-controlled \r\ngauges (e.g., about 2000 gauges since the 1950s in Rajeevan et al., \r\n2006). A  newer 0.25deg-gridded product covering 1901 onwards \r\n(Pai et  al., 2014, 2015), based on Shepard's interpolation method \r\nfor irregularly-spaced stations (Shepard, 1968), shows increased \r\nintensity of daily rainfall and extremes over some regions, especially \r\nin the late-20th century. However, changes to the inputted gauges "


id: 4018c20343d6f9b3eef60b088be11792
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 7460
content:
"The Indian summer monsoon provides 80% of the country's annual \r\nrainfall from June to September, supplying the majority of water for \r\nagriculture, industry, drinking and sanitation to over a billion people. \r\nAny variations in the monsoon on time scales from days to decades can \r\nhave large impacts (Challinor et al., 2006; Gadgil and Gadgil, 2006). "


id: 30c60ca9e1906df89ddf21ecaa8cc267
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
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vector_id: 5097
content:
"may have introduced an artificial jump in extreme rainfall since 1975 \r\nover central India (Section  10.2.2.3; Lin and Huybers, 2019). They \r\nsuggest that this method may have masked declines in mean rainfall \r\nand highlight the need for availability of raw gauge data to allow \r\ntransparent assessments. Khouider et  al. (2020) have successfully \r\ntested a  probabilistic interpolation method for India to overcome \r\nproblems inherent in algorithms based on inverse-distance weighting \r\nwhen applied to data-sparse regions. An example snapshot of the \r\nuneven distribution of rain gauges in a  common observational \r\nproduct is shown in Figure 10.19a."


id: e898c0e189397e1761efc14675dc247a
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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vector_id: 34102
content:
"is not in line with expectations arising from thermodynamic \r\nconstraints on the water cycle in a  warming world (Section  8.2.2) \r\nand has been regarded as a puzzle (Goswami et al., 2006). Assessing \r\nthe attribution of 20th-century changes to Indian rainfall is the \r\nsubject of coordinated modelling under the Global Monsoon MIP \r\n(GMMIP; Zhou et al., 2016), but is complicated by long-standing dry \r\nbiases in coupled CMIP3, CMIP5 (Sperber et al., 2013) and CMIP6 \r\n(Figure  10.19b) global models. These dry biases are connected to \r\na lower tropospheric circulation that is too weak (Sperber et al., 2013) \r\nand wet biases in the equatorial Indian Ocean (Bollasina and Ming, \r\n2013). Section  8.3.2.4.1 finds high confidence that anthropogenic \r\naerosol emissions have dominated the observed declining trends \r\nof countrywide Indian summer monsoon rainfall, consistent with \r\nfindings at the global-monsoon scale (Section 3.3.3.2)."


id: 2ab33028ba9d30f8936c01676752c3d0
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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vector_id: 4142
content:
"The uncertainty among local and international observational products \r\nfor India can pose challenges when evaluating climate models \r\n(as in Section 10.2.2.6; Prakash et al., 2015). For the seasonal mean \r\nsummer monsoon rainfall, Collins et al. (2013a) found large biases \r\nseparating many CMIP5 models from the available observational \r\nproducts. However, for seasonal mean variability, the spread across \r\nobservational products was larger than across the CMIP5 ensemble."


id: 63e5ffcad6eca78dc847a334ce337a4
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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vector_id: 13284
content:
"There is some evidence that declining Indian monsoon rainfall \r\nis due to regional SST warming patterns, themselves arising due \r\nto radiative forcing from GHG (e.g.,  in the Indian Ocean, Guemas \r\net al., 2013). Roxy et al. (2015) artificially raised SST in a GCM in \r\nthe equatorial Indian Ocean (the region of strongest observed SST \r\nwarming), leading to a weakened monsoon. Annamalai et al. (2013) \r\nused a  GCM to suggest instead that preferential warming of the \r\nwestern North Pacific may force a Rossby-wave response to its west \r\nthat weakens the monsoon through dry advection and subsidence. \r\nThese hypotheses are not borne out in GHG-forced future projections \r\n(Section 10.6.3.6)."


id: 4b8b7d98e03524d8d372b58b9f01c15a
created_at: 2023-03-16 14:00:48
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vector_id: 9307
content:
"A small anthropogenic contribution may be expected from local \r\nland-use/land-cover changes and land management. India is the \r\nworld's most irrigated region with around 0.5  mm/day in places, \r\nalthough peaking higher in summer (Cook et al., 2015b; McDermid \r\net  al., 2017). Including irrigation in GCMs and RCMs slows the \r\nmonsoon circulation and diminishes rainfall (Lucas-Picher et al., 2011; \r\nGuimberteau et al., 2012; Shukla et al., 2014; Tuinenburg et al., 2014; \r\nCook et al., 2015b) due to reduced surface temperature (Thiery et al., \r\n2017), reducing the monsoon wind and moisture fluxes towards "


id: 835a611dab531504bbe79c3128549caf
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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vector_id: 18192
content:
"The interplay of these external and internal drivers is key to \r\nunderstanding past and future monsoon change."


id: 39a6d11922f41e58cbbddf52e89990ea
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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index: climate_gpt_only_giec
vector_id: 6445
content:
"The robust decline of Indian summer monsoon rainfall averaged \r\nover India in the second half of the 20th century (Section 10.6.3.3) "


id: f3bd301e5d649f5396ac546c9e5aadc4
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
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vector_id: 35862
content:
"India (Mathur and AchutaRao, 2020). However, implementation \r\nmethodologies for irrigation in climate models are simplified and \r\noften do not account for spatial heterogeneity while overestimating \r\ndemand and supply (Section  10.3.3.6; Nazemi and Wheater, 2015; \r\nPokhrel et al., 2016). Changing forest cover to agricultural land in \r\nan RCM (Paul et al., 2016) finds weakened summer monsoon rainfall \r\nespecially in central and eastern India, due to decreased local \r\nevapotranspiration. Decreased evapotranspiration from a  warmer \r\nsurface since the 1950s in the CMIP5 ensemble may also feedback \r\non the supply of moisture (Ramarao et al., 2015). Based on an AGCM \r\nstudy and literature review, Krishnan et al. (2016) support the role \r\nof land-use/land-cover change in adding to the effects of aerosol \r\nin weakening the monsoon, in addition to dynamic effects on the \r\ncirculation caused by rapid warming of the Indian Ocean."


id: c47281bdc5f9fa4c50617d04781151f1
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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index: climate_gpt_only_giec
vector_id: 28479
content:
"In addition to anthropogenic forcing, there is evidence that internal \r\nvariability in the Pacific is a significant driver. Huang et al. (2020b) \r\ncompared a  large perturbed-physics ensemble (HadCM3C) with \r\na SMILE for the historical period. Ensemble members replicating the \r\nnegative Indian rainfall trend were accompanied by a strong phase \r\nchange in the PDV from negative to positive, consistent with SST \r\nobservations. Jin and Wang (2017) have demonstrated increasing \r\nIndian monsoon rainfall since 2002 in a variety of observed datasets, \r\nsuggesting the increase is due either to a  change in dominance \r\nof a  particular forcing (for example from aerosol to GHG) or to \r\na change in phase of internal variability such as the PDV. Huang et al. \r\n(2020b) partially attribute the rainfall recovery to a  phase change \r\nin the PDV, supported by a SMILE study combined with reanalyses \r\n(Ha et al., 2020)."


id: 990a535537373405a19a73b9e46f9d0d
created_at: 2023-03-16 14:00:48
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content:
"In long-term projections, robust signals consist of a  weakened \r\nupper-tropospheric meridional temperature gradient, either due to \r\nupper-level heating over the tropical Pacific (Sooraj et al., 2015) or \r\nIndian oceans (Sabeerali and Ajayamohan, 2018) in CMIP5, and \r\nincreased seasonal mean rainfall, including in CMIP6 (Almazroui \r\net  al., 2020b; B. Wang et  al., 2021). The weakened temperature \r\ngradient combines with increased atmospheric stability to weaken \r\nthe monsoon overturning circulation, with some findings showing \r\nnorthward movement of the lower-tropospheric monsoon winds \r\nin response to a stronger land-sea temperature contrast in CMIP3 \r\nand CMIP5 (Sandeep and Ajayamohan, 2015; Endo et al., 2018). The \r\nnorthward shift was also found in the genesis of synoptic systems \r\n(monsoon depressions) in a  single high-resolution AGCM forced \r\nby an ensemble of SSTs derived from four GCMs under the RCP8.5 \r\nscenario (Sandeep et al., 2018)."


id: a44bf1366f09b9d0933af5ef6d30d890
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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index: climate_gpt_only_giec
vector_id: 23406
content:
"The drying trend of Indian summer monsoon rainfall since the \r\nmid-20th century can be attributed with high confidence to aerosol \r\nas the dominant anthropogenic forcing with a further contribution \r\nfrom internal variability, supported by the review of B. Wang et al. \r\n(2021) including CMIP6 results. Understanding the interplay \r\nbetween anthropogenic and internal drivers will be important for \r\nunderstanding future change."


id: 8a36c621fd54320b95d3d5feab34e977
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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index: climate_gpt_only_giec
vector_id: 19281
content:
"Projections can also be expressed in terms of global-mean warming \r\nlevels (GWLs) rather than time horizons (Cross-Chapter Box  11.1). \r\nAdvancing on SR1.5, amplification of mean and extreme monsoon \r\nrainfall at 2.0degC compared to 1.5degC has been found both by an \r\nAGCM forced by future SST patterns (Chevuturi et al., 2018) and by \r\nusing time slices in CMIP5 GCMs (Yaduvanshi et al., 2019; J. Zhang \r\net al., 2020). These findings are consistent with the general scaling \r\nof Indian monsoon precipitation per degree of warming in CMIP5 \r\n(Zhang et  al., 2019) and CMIP6 (B. Wang et  al., 2021). Increasing \r\nGWLs also lead to emergence of the anthropogenic signal over larger \r\nproportions of the South Asian region (Figure 10.15a)."


id: 3fb559a2dc61954e588d4f3dce6fdbc8
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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vector_id: 7409
content:
"The AR5 (Christensen et  al., 2013) concluded that Indian summer \r\nmonsoon rainfall will strengthen under all RCP future climate \r\nscenarios, while the circulation will weaken (medium confidence). \r\nSR1.5 (Hoegh-Guldberg et al., 2018) found only low confidence in \r\nprojections of monsoon change at 1.5degC and 2degC, or any difference \r\nbetween them. The AR6 assessment of Chapter 8 (Section 8.4.2.4.1) \r\nfinds more precipitation in future projections (also depicted in \r\nFigure  10.19c,d,e), supported by reviews of CMIP3, CMIP5 and \r\nCMIP6 models (Turner and Annamalai, 2012; Kitoh, 2017; Z. Chen \r\net al., 2020; B. Wang et al., 2021)."


id: 381a849edafc839f4ae973bf105c068
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
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vector_id: 6197
content:
"Decomposition of the increased rainfall signal showed that while the \r\ndynamic component led to a drying tendency, this was overcome by \r\nthe thermodynamic contribution (Sooraj et al., 2015; Z. Chen et al., \r\n2020). Alternative decomposition experiments using AGCMs and \r\ntheir coupled counterparts found increases in the lower-tropospheric \r\ntemperature gradient and monsoon rainfall to be dominated by the \r\nfast radiative response to GHG increase rather than SST changes \r\n(Li and Ting, 2017; Endo et al., 2018). The response to SST forcing \r\nfeatured a large model spread, particularly arising from the dynamic "


id: 888cce90e9131c0edb602bca0761658d
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 19012
content:
"Given the assessment for a  future wetter monsoon dominated by \r\nGHG emissions and attribution of the late-20th  century decline to \r\naerosol (Sections 8.3.2.4.1 and 10.6.3.5), the change between \r\ndominant forcings will lead, at some point, to a positive trend. For \r\nexample, RCP4.5 experiments in an AGCM forced by coupled model\u0002derived future SSTs showed continuation of 20th-century drying, "


id: c986c8d8a54499941bf6dc8517bc4114
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 29265
content:
"component (Li and Ting, 2017). Chen and Zhou (2015) found that \r\nthe Indo-Pacific SST warming pattern dominated the uncertainty in \r\nIndian monsoon rainfall change. Finally, in assessing the relative \r\nimpact of CO2 radiative forcing and plant physiological changes in \r\nquadrupled CO2 experiments in four Earth system models, Cui et al. \r\n(2020) showed little impact of plant physiology on annual rainfall for \r\nthe Indian region."


id: 9eed1b3fa995da90731e3562d5b76b9b
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 22546
content:
"In summary, long-term future scenarios dominated by GHG increases \r\n(such as the RCPs) suggest increases in Indian summer monsoon \r\nrainfall (high confidence), dominated by thermodynamic mechanisms \r\nleading to increases in the available moisture. In the near-term, \r\nthere is high confidence (medium agreement, robust evidence) that \r\nincreased rainfall trends due to GHGs could be overcome by aerosol \r\nforcing or internal variability."


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created_at: 2023-03-16 14:00:48
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content_type: text
index: climate_gpt_only_giec
vector_id: 26913
content:
"Coordinated monsoon-relevant dynamical downscaling efforts \r\nsuch as CORDEX South Asia (Gutowski Jr. et  al., 2016; Choudhary \r\net al., 2018) are relevant to the Indian summer monsoon, first with \r\nassessment of their added value (Section 10.3.3.2 and Atlas.5.3.3). \r\nSingh et  al. (2017) compared nine CORDEX-South Asia RCMs \r\nagainst their driving CMIP5 GCMs, for present-day rainfall patterns \r\nand processes related to intra-seasonal variability. They found no "


id: a8a0b483c386a1de44078bbb10e7264e
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 24060
content:
"increased rainfall, the downscaled ensemble depicted both increasing \r\nand decreasing trends, indicating uncertainty. However, key physical \r\nprocesses operating at below-GCM scale cannot be resolved nor \r\ncalibrated for, such as aspects of the flow around topography. \r\nThis is notably an issue given the resolution disparity between the \r\ndriving global models and output, and the regional challenges in \r\nobservational data used for calibration (Section 10.6.3.3)."


id: fbbabc2b4d9d83eff50d04ab38c8a13d
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 37107
content:
"Above, we presented assessments from observational and model \r\nattribution studies of the historical period, followed by future climate \r\nprojections in global and regional models, and storylines approaches \r\nincluding low-likelihood high impact events. Miscellaneous lines of \r\nevidence are considered here."


id: be72546aff3cf9e390fc866d70e976b3
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 27513
content:
"There are mixed messages as to whether downscaling adds value to \r\nclimate projections of the Indian summer monsoon; however, there is \r\nhigh confidence in projections of precipitation changes in orographic \r\nregions given the consistent improved representation in these regions \r\namong several dynamical downscaling studies."


id: 57098c133dcd5edc902cb2ef9d5eb028
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 11174
content:
"Our assessment could also be informed by attempting to constrain \r\nfuture projections of the Indian summer monsoon using paleoclimate \r\nevidence. In modelling work of the mid-Holocene (D'Agostino et al., \r\n2019), the increased obliquity (axial tilt) and altered orbital precession \r\nlead to an enhanced monsoon with a stronger dynamic component \r\n(strengthening the mean monsoon overturning) controlling the \r\nincrease in monsoon rainfall. In future climates however, the dynamic \r\ncontribution decreases (Section  10.6.3.6), yet the increased \r\nthermodynamic component (greater moisture availability) overcomes \r\nthis to cause a wetter monsoon. Monsoon changes under different \r\nepochs may not be governed by the same mechanisms (D'Agostino \r\net  al., 2019; Hill, 2019), making the mid-Holocene, in particular, \r\nunsuitable as a period to compare with."


id: d52950162dfeea8eae6a1f6ee6c26276
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 31066
content:
"Formal storyline approaches (see Box  10.2) have been used \r\ninfrequently for the Indian summer monsoon, representing \r\na  knowledge gap. In an expert-elicitation approach (Dessai et  al., \r\n2018), physically plausible futures substantiated by climate processes \r\nwere constructed, focusing on a river basin in southern India. Possible \r\noutcomes were framed based on changes in two drivers: availability \r\nof moisture from the Arabian Sea and strength of the low-level flow. \r\nThe narratives identified were able to explain 70% of the variance in \r\nmonsoon rainfall over 1979-2013, the implication being that climate \r\nuncertainties could be easily communicated to stakeholders in the \r\ncontext of present-day variability."


id: e8f1f94928f9af4897c9c30bcad8e722
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 34163
content:
"Finally, the recent national climate-change assessment for India \r\n(Krishnan et  al., 2020) has distilled multiple lines of evidence to \r\nshow declining summer monsoon rainfall over the second half \r\nof the 20th  century, attributable to emissions of anthropogenic \r\naerosols, while future projections informed by CMIP5 modelling and \r\ndominated by GHG forcing show increased mean rainfall by the end \r\nof the 21st century."


id: 7b48cf8ddd59b66b562da711ebace325
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 16982
content:
"The few studies on low-likelihood high-impact scenarios, often in \r\nsingle models, together with findings in SR1.5 (Hoegh-Guldberg et al., \r\n2018), noting the small radiative forcing in 1.5degC or 2degC scenarios, or \r\nthe absence of large aerosol emissions at the end of the 21st century \r\nin RCPs, give us low confidence in abrupt changes to the monsoon \r\non this time scale."


id: 759506e1495892d301b987a28660c304
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 16089
content:
"Consequently, basin-wide, homogeneous, quality controlled \r\nobservational datasets are lacking, especially before the advent \r\nof substantial satellite observations in the 1970s. Observational \r\nuncertainties exist also for those regions that are covered by high \r\nquality networks such as European Climate Assessment & Dataset \r\n(ECA&D; Flaounas et al., 2012)."


id: 1a9bec6125c518bdbe9f7ea93a8d28d3
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 1665
content:
"The Mediterranean region is loosely denoted as the region that \r\nsurrounds the Mediterranean Sea, and it is characterized by complex \r\norography and strong land-sea contrasts. The region contains a dense \r\nand growing human population, with large regional differences: \r\nwhereas the population of the European Mediterranean countries has \r\nbeen relatively stable or even declining during the past decades, the \r\npopulation of countries in Mediterranean areas of the Middle East and \r\nNorth Africa has quadrupled between 1960 and 2015, and the degree \r\nof urbanization has risen from 35 to 64% during the same period \r\n(Cramer et al., 2018) and during the more recent period 2000-2020 \r\nthe urban expansion rate has exceeded 5% (Kuang et al., 2021)."


id: 9b8f9bbd75cb0c43f3d4c7c68a1a84b9
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 22033
content:
"Large differences of up to 7degC between the CRU and UDEL datasets \r\nhave been found especially over mountainous areas, such as the Atlas \r\nin Morocco (Zittis and Hadjinicolaou, 2017; Strobach and Bel, 2019). \r\nBucchignani et al. (2016a, b) compared three different datasets (CRU, \r\nUDEL, and MERRA) with the available ground observations and found \r\nthat although the geographical distribution of the bias is qualitatively \r\nsimilar for the three datasets, differences exist, with the absolute \r\nbias being generally lower in Modern-Era Retrospective Analysis \r\nfor Research and Applications (MERRA) especially over North Africa \r\nduring the summer and winter season. There is high confidence that \r\nthe sparse monitoring network in parts of the Mediterranean region \r\nstrongly increases the uncertainty across different gridded datasets \r\n(Section 10.2.2.3, Figure 10.20b,c)."


id: 5b49da0eaf7f50e2790b6fa38d2e69e5
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 11869
content:
"The Mediterranean region has experienced significant climate variability \r\nover recent decades and has been affected in particular by severe \r\nheatwaves and droughts (Sections 8.3, 11.3, 11.6 and 12.4; Hoegh\u0002Guldberg et al., 2018). Increasing summer temperatures will enhance \r\nthe frequency and intensity of such extreme events and will cause \r\nadditional environmental and socio-economic pressure on the region."


id: 1250f7bd76721f3265353ee254b52d3e
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 386
content:
"The attribution of observed Mediterranean summer warming to \r\nabove drivers and implications for future projections will be discussed \r\nin Sections 10.6.4.5 and 10.6.4.6."


id: c16d16fa05afa28a9f02bad01772449f
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 28018
content:
"Observational datasets show large agreement on the historical \r\n(1960-2014) temperature evolution at basin-wide scale (Figure 10.20e), \r\nwith an enhanced warming since the 1990s, and the early decades \r\nof the 21st century being on average approximately more than 1degC \r\nwarmer than late 19th  century levels (van der Schrier et  al., 2013; \r\nCramer et  al., 2018; Lionello and Scarascia, 2018; Figure  10.20e). "


id: 9e0260bdf22ade92b3dab4fa643a91dd
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 22386
content:
"The Mediterranean region spans a  wide variety of countries \r\nand economies. This has led to large differences in the existence and \r\navailability of observational records, with the southern part of the area \r\nbeing sparsely covered by meteorological stations (Figure 10.20b). "


id: 89b32d7fc99619fb3693d358d9372ce2
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 19201
content:
"Over recent decades, the surface air temperature of the Mediterranean \r\nincluding the Mediterranean Sea has warmed by around 0.4degC per \r\ndecade (Macias et al., 2013). Observed trends over land show large \r\ngeographical heterogeneity (Figure  10.20d) and notable differences \r\nexist amongst different datasets at grid point scale (Figure  10.20c; \r\nQasmi et al., 2021)."


id: af206d1e474796856c752106a0abf807
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 25079
content:
"Boers et al. (2017) who attribute it to the direct aerosol effect. Using \r\nmodel sensitivity experiments, Nabat et al. (2014) also associated the \r\nincrease in Mediterranean SST since 1980-2012 with the decrease in \r\naerosol concentrations (Atlas.8.2, Atlas.8.3 and Atlas.8.5)."


id: 80c0eb2b63332d72844de3d74f7a38
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 17780
content:
"Several mechanisms have been proposed for the enhanced \r\nMediterranean warming, although their relative importance and the \r\npossible interplay between them are not fully understood. Circulation \r\nchanges might have contributed to this enhanced warming \r\n(Figure  10.20a). Sutton and Dong (2012) argued that the AMV \r\ninduced a shift around the 1990s towards warmer southern European \r\nsummers. This mechanism is associated with a  linear baroclinic \r\natmospheric response to the AMV-related surface heat flux. Also \r\nO'Reilly et al. (2017) related warm summer decades to the AMV, but \r\nthe connection was shown to be mainly thermodynamic. Qasmi et al. \r\n(2021) estimate an increase in Mediterranean summer temperature of \r\n0.2degC-0.8degC during a positive AMV."


id: 8a0fb631b8fc7a59e71611241761064e
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 19250
content:
"Increased warming over land compared to the sea is expected due to \r\nthe lapse-rate changes associated with tropospheric moisture contrasts \r\n(Kroner et al., 2017; Byrne and O'Gorman, 2018; Brogli et al., 2019b; \r\nFigure  10.20a). Enhanced land-sea temperature contrast leads to \r\nrelative humidity and soil moisture feedbacks (Rowell and Jones, 2006), \r\nthe latter also depending on weather regimes (Quesada et al., 2012). \r\nThe globally enhanced land-sea contrast in near surface temperature \r\nis also a robust result in CMIP5 and CMIP6 models (Section 4.5.1.1)."


id: c785da6ab3627b87ef863f56393fcf4e
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 28977
content:
"Due to its semi-arid climate, strong atmosphere-land coupling has \r\ncontributed to the larger increase of mean summer temperature \r\ncompared to the increase of annual mean temperature (Seneviratne \r\net al., 2006). In particular, during drought spells, limits to evaporation \r\ndue to low soil moisture provide a positive feedback and enhances the \r\nintensity of heatwaves (Lorenz et al., 2016; Box 11.1). By comparing \r\nreanalysis-driven RCM simulations with observations, Knist et  al. \r\n(2017) found that RCMs are able to reproduce soil moisture interannual \r\nvariability, spatial patterns, and annual cycles of surface fluxes over \r\nthe period 1990-2008, revealing a strong land-atmosphere coupling \r\nespecially in southern Europe in summer. In addition cloud feedbacks \r\ncan modulate the Mediterranean summer temperature (Mariotti and \r\nDell'Aquila, 2012)."


id: d8b4dd86f0c90411df8d1fa747f15924
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 31610
content:
"The observed trends over 1901-2010 are outside the range of internal \r\nvariability shown in CMIP5 pre-industrial control experiments and \r\nconsistent with, or greater than those simulated by experiments \r\nincluding both anthropogenic and natural forcings (Knutson et al., \r\n2013) and therefore partly attributable to anthropogenic forcing. \r\nThe decrease of anthropogenic aerosols over Europe including the \r\nMediterranean resulting from European de-industrialisation and \r\nair pollution policies (Turnock et  al., 2016) has been highlighted \r\nas an important contributor to the observed warming (Ruckstuhl \r\net al., 2008; Philipona et al., 2009; de Laat and Crok, 2013; Nabat \r\net al., 2014; Besselaar et al., 2015; Dong et al., 2017; Boe et al., 2020a). \r\nPfeifroth et al. (2018) argue that this brightening is mainly due to \r\ncloud changes caused by the indirect aerosol effect with a minor role \r\nfor the direct aerosol effect, in contrast to Nabat et al. (2014) and "


id: fc31e361a6a3a8559d185f3e3dd3e22c
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 37182
content:
"can contribute to the enhanced warming by land surface feedbacks \r\n(Whan et al., 2015; Lorenz et al., 2016; Russo et al., 2019). A negative \r\nfeedback to this dryness-induced warming might be provided by an \r\nenhanced moisture transport into the dry area associated with the \r\ndynamical response of the atmosphere (Zhou et al., 2021). Due to \r\nthe arid climate, no positive soil moisture-temperature feedback is \r\nfound over the North African regions of the Mediterranean, where \r\nthe surface energy budget is mostly governed by radiative cooling \r\n(Lelieveld et al., 2016), implying that soil moisture feedbacks are not \r\ncontributing to enhanced warming over those regions."


id: 94b6e6f1b7b3aa7eb805c1386544f2bb
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 20907
content:
"As noted in Section 10.6.4.4, the Mediterranean summer climate is \r\naffected by large-scale circulation patterns, of which the summer \r\nNAO is the most important (Folland et al., 2009; Blade et al., 2012). \r\nBarcikowska et  al. (2020) highlight the importance of correctly \r\nsimulating the summer NAO impact on the Mediterranean climate, \r\nas it partly offsets the anthropogenic warming signal in the western \r\nand central Mediterranean."


id: 1f1d4b0f5543d9d11d9376a32f5832f9
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 2335
content:
"Climate models project a reduction in precipitation in all seasons, and \r\na northward and eastward expansion of the Mediterranean climate, \r\nwith the affected areas becoming more arid with an increased \r\nsummer drying (Atlas.8.5; Alessandri et al., 2015; Mariotti et al., 2015; \r\nRajczak and Schar, 2017; Waha et  al., 2017; Barredo et  al., 2018; \r\nLionello and Scarascia, 2018; Spinoni et al., 2018, 2020). The drying "


id: 5ee9d01e7b29894d1edeb292b246bf5d
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 12477
content:
"Over the Mediterranean region, daily maximum temperature is \r\nprojected to increase more than the daily minimum. Consequently, \r\nthe difference between daytime maxima and nighttime minima is "


id: 1dbdfafa3b25eefc7f3cac28101c8268
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 2118
content:
"has been addressed in many studies since AR5. Recent examples \r\nof dynamical downscaling are EURO-CORDEX (Jacob et  al., 2014) \r\nand Med-CORDEX (Ruti et al., 2016; Somot et al., 2018), but earlier \r\nactivities have included ENSEMBLES (Deque et al., 2012; Fernandez \r\net  al., 2019), PRUDENCE (Christensen et  al., 2002), CIRCE (Gualdi \r\net al., 2013) and ESCENA (Jimenez-Guerrero et al., 2013)."


id: 2c7581dfc54be00f2387f50795a8be10
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 4427
content:
"expected to increase, particularly in summer (Lionello and Scarascia, \r\n2018). Temperature extremes will be affected as well, with a dramatic \r\nincrease in the number of warm days and reduction of cold nights \r\n(Section  11.9; Lionello and Scarascia, 2020). The Mediterranean \r\nsummer warming will also increase the frequency and intensity of \r\nheatwaves (Section 11.9)."


id: be116198179b09dfb9bd79eed24a882e
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 27458
content:
"From an analysis of CORDEX results, studies showed that southern \r\nEurope is projected to face a  robust non-linear increase in \r\ntemperature larger than the global mean (Zittis et al., 2019), EURO\u0002CORDEX projections, that are driven by CMIP5 global models, \r\nproject a less pronounced warming than that of CMIP6 (Coppola \r\net al., 2021; see Figure 10.21c). The non-linear increase is especially \r\nevident for both hot and cold extremes (Section 11.9; Maule et al., \r\n2017; Jacob et al., 2018; Kjellstrom et al., 2018). In particular, Dosio \r\nand Fischer (2018) showed that in many places in southern Europe \r\nand the Mediterranean, the increase in the number of nights "


id: 1ed724237123066d893537d5895e1ce0
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 2295
content:
"To unravel the complex interactions and feedbacks over the region on \r\na range of spatial and temporal scales, regional downscaling projects \r\nare being developed to provide more comprehensive and detailed \r\ninformation on the future of the Mediterranean. The importance \r\nof regional downscaling for investigating the subregional details \r\ncaused by the complex morphology of the Mediterranean region \r\nis a well-known issue in the literature (Planton et al., 2012), which "


id: 393fe194d518e2ee3687d6b501a1dc54
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 6380
content:
"with temperature above 20degC is more than 60% larger under \r\n2degC warming compared to 1.5degC. Over the region, the projected \r\ntemperature increase, including a  higher probability of severe \r\nheatwaves (Russo et al., 2015), is accompanied by a reduction in \r\nprecipitation (Jacob et  al., 2014; Dosio, 2016; Rajczak and Schar, \r\n2017), resulting in projected increases of drought frequency and \r\nseverity (Spinoni et al., 2018, 2020; Raymond et al., 2019). Also, the \r\nfrequency and severity of marine heatwaves of the Mediterranean \r\nSea are projected to increase (Darmaraki et  al., 2019; see \r\nSection 12.4 and Atlas.8.4)."


id: 8e911c27dd685b024054bf0da4299cc0
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 19950
content:
"Only a  limited number of RCM simulations for the MENA domain \r\nare currently available. For the southern and eastern Mediterranean, \r\nthey project a mean warming ranging from 3degC for RCP4.5 to 9degC \r\nfor RCP8.5 at the end of this century compared to its beginning \r\n(Bucchignani et  al., 2018; Ozturk et  al., 2018). The frequency and \r\nduration of heatwaves and annual number of extremely hot days \r\n(i.e., those with maximum temperature >50degC) in the southern \r\nMediterranean will increase substantially. For 2070-2099 with \r\nrespect to 1971-2000 the latter might even reach 70 days for \r\nRCP8.5 (Lelieveld et al., 2016; Almazroui, 2019; Driouech et al., 2020; \r\nVarela et al., 2020)."


id: 915790c1c9cff4bd11ef9b9e7e414aa3
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 20396
content:
"Low-likelihood high-impact events might affect future Mediterranean \r\nclimate. An example of such an event is the collapse of the AMOC \r\n(Weijer et al., 2019), that would bring widespread cooling over the \r\nNorthern Hemisphere. For the Mediterranean this is estimated to be \r\na few degrees Celsius during summer in the case of a total collapse \r\n(Jackson et al., 2015)."


id: 14c2bfe6aef70c4eb8cc403aefc4ef95
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 776
content:
"There is very high confidence (high agreement, robust evidence) that \r\nthe Mediterranean region has experienced a  summer temperature \r\nincrease in recent decades that is faster than the increase for \r\nthe Northern Hemisphere summer mean. There is also very high \r\nconfidence (high agreement, robust evidence) that the projected \r\nMediterranean summer temperature increase will be larger than the \r\nglobal warming level, with an increase in the frequency and intensity \r\nof heatwaves."


id: 578c219a4ac7e16a8cc1dc0ba8bfceba
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 11269
content:
"Statistical downscaling studies for the Mediterranean confirm the \r\nresults from global model and RCM studies, with large agreement \r\namong future projections showing lower rates of warming in winter \r\nand spring, and, in most cases, higher ones in summer and autumn \r\n(Jacobeit et al., 2014)."


id: 3450c35994590de7156448a30d239868
created_at: 2023-03-16 14:00:48
updated_at: 2023-03-16 14:00:48
content_type: text
index: climate_gpt_only_giec
vector_id: 5646
content:
"The atmospheric circulation is influenced by large-scale, often \r\nslowly varying components of the climate system, such as ocean, \r\nsea ice and soil moisture. Historical and future changes of the "


id: 425923125a6c5c3dc108c0cb575e3246
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 7839
content:
"Urban areas have special interactions with the climate system that produce heat islands. This box presents information about these \r\nprocesses, how they are parametrized in climate modules, and on the role of urban monitoring networks. A discussion on the observed \r\nclimate trends and climate change projections for urban areas follows."


id: 54f4206b48e1d417574726e3ed7901b0
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 10826
content:
"There is medium evidence but high agreement (Parker, 2010; Zhang et al., 2013; H. Chen et al., 2016) that the global annual mean \r\nsurface air temperature response to urbanization is negligible. There is very high confidence that the different observed warming trend \r\nin cities as compared to their surroundings can partly be attributed to urbanization (Box 10.3, Figure 1; Park et al., 2017)."


id: dd28519b760da8f322290b2842d1adc2
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
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content:
"There is very high confidence (robust evidence and high agreement) that the annual mean minimum temperature is more affected by \r\nurbanization than the maximum temperature (Ezber et al., 2007; Fujibe, 2009; Hamdi, 2010; Elagib, 2011; Camilloni and Barrucand, \r\n2012; Hausfather et al., 2013; Robaa, 2013; Argueso et al., 2014; Alghamdi and Moore, 2015; Alizadeh-Choobari et al., 2016; Sachindra \r\net al., 2016; Liao et al., 2017; Lokoshchenko, 2017; J. Wang et al., 2017; Arsiso et al., 2018). Beside temperature, urbanization can \r\ninduce an urban dryness island, which refers to lower relative humidity in cities than in nearby rural locations (Lokoshchenko, 2017; \r\nBian et al., 2020) and the urban wind island, where slower wind speeds are observed in cities (Wu et al., 2017; Bader et al., 2018; Peng \r\net al., 2018). There is medium confidence (medium evidence and medium agreement) (Schlunzen et al., 2010; Ganeshan et al., 2013; "


id: adae23c69bf28dcf5479fa91e92cc1a7
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
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vector_id: 24863
content:
"Ganeshan and Murtugudde, 2015; Haberlie et al., 2015; Daniels et al., 2016; Liang and Ding, 2017; McLeod et al., 2017; Y. Li et al., 2020b) \r\nthat cities induce increases in mean and extreme precipitation over and downwind of the city especially in the afternoon and early evening."


id: 3d46bce84e686269b556b002a4229277
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
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vector_id: 7004
content:
"Estimates of the urban heat island under further climate change are very uncertain because studies using different methods report \r\ncontrasting results. However, there is very high confidence (robust evidence and high agreement) that the projected change of the urban \r\nheat island under climate change conditions is one order of magnitude less than the projected warming in both urban and rural areas under \r\nsimulation constraints of no urban growth (McCarthy et al., 2010, 2012; Oleson et al., 2011; Fruh et al., 2011; Adachi et al., 2012; Kusaka et \r\nal., 2012a; Oleson, 2012; Hamdi et al., 2014; Sachindra et al., 2016; Hatchett et al., 2016; Arsiso et al., 2018; Hoffmann et al., 2018)."


id: 98052459b3f6f81821d04ab0f5aa4997
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
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vector_id: 21428
content:
"Combining climate change conditions together with urban growth scenarios, there is very high confidence (robust evidence and \r\nhigh agreement) that future urbanization will amplify the projected air temperature warming irrespective of the background climate \r\n(Georgescu et al., 2013; Argueso et al., 2014; Mahmood et al., 2014; Doan et al., 2016; Kim et al., 2016; Kusaka et al., 2016; Grossman\u0002Clarke et al., 2017; Kaplan et al., 2017; X. Li et al., 2018). Urbanization will have a strong influence on minimum temperatures that \r\ncould be locally comparable in magnitude to the global GHG-induced warming (Berckmans et al., 2019). There is very high confidence\r\n(robust evidence and high agreement) for the combination of future urban development and more frequent occurrence of extreme \r\nclimatic events, such as heatwaves (Hamdi et al., 2016; Bader et al., 2018; He et al., 2021)."


id: 23e8a2b812896dc0debbf555b064fb2a
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
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vector_id: 3090
content:
"The choice of urban planning scenarios and RCM projections shows a large sensitivity during nighttime, up to 0.6degC (Kusaka et al., 2016). \r\nThe sensitivity is significantly less than the uncertainties arising from global emissions scenarios or global model projections. However, \r\nthere is a large difference between RCM simulations with and without urban land use, indicating that this impact is comparable to \r\nthe uncertainties related to the use of different global model projections (Hamdi et al., 2014; Kusaka et al., 2016; Daniel et al., 2019). \r\nTherefore, impact assessments and adaptation plans for urban areas require high spatial resolution climate projections along with \r\nmodels that represent urban processes, ensemble dynamical and statistical downscaling, and local-impact models (Masson et al., \r\n2014; Baklanov et al., 2018, 2020; Duchene et al., 2020; Schoetter et al., 2020; Le Roy et al., 2021; Zhao et al., 2021)."


id: a5ea9911d56eeec57559ce8dcace215a
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
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vector_id: 23661
content:
"Contributors: Bhupesh Adhikary (Nepal), Muhammad Adnan (Pakistan), Muhammad Amjad (Pakistan), Subimal Ghosh (India), \r\nRafiq Hamdi (Belgium), Akm Saiful Islam (Bangladesh), Richard G. Jones (United Kingdom), Martin Jury (Austria), Asif Khan (Pakistan), \r\nAkio Kitoh (Japan), Krishnan Raghavan (India), Lucas Ruiz (Argentina), Laurent Terray (France)"


id: f5f7459e0201b9080c6b6f334e05ac3e
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
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vector_id: 36210
content:
"The Hindu Kush Himalaya (HKH) constitutes the largest glacierized region outside the poles and provides the headwaters for several \r\nmajor rivers (Sharma et al., 2019). Since the 1960s, the HKH has experienced significant trends in the mean and extremes of temperature \r\nand precipitation, accompanied by glacier mass loss and retreat, snowmelt and permafrost degradation (Yao et al., 2012a, b; Azam \r\net al., 2018; Bolch et al., 2019; Krishnan et al., 2019a, b; Chug et al., 2020; Sabin et al., 2020). Observational uncertainty and lack \r\nof consistent, high-quality datasets hamper reliable assessments of climate change and model evaluation over several mountain \r\nareas, including the HKH (Section 10.2.2). This box assesses observed and projected climate change in the extended HKH (outline in \r\nCross-Chapter Box 10.4, Figure 1a), in which we include the Tibetan Plateau (TP) and Pamir mountains."


id: 841fb081f4ac22e09219d07f9dbaf288
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 18325
content:
"Little evidence was presented in the AR5 (IPCC, 2014) other than increased minimum and maximum temperature trends in the western \r\nHimalaya (Hartmann et al., 2013). The SROCC assessed that HKH (named High Mountain Asia) surface-air temperature has warmed \r\nmore rapidly than the global mean over recent decades (high confidence). Annual mean HKH surface air temperature increased \r\nsignificantly (about 0.1degC per decade) over 1901-2014 (Ren et al., 2017), although Cross-Chapter Box 10.4, Figure 1d shows an \r\nobservational range of 0.20degC-0.25degC per decade over 1961-2014. There is a rising trend of extreme warm events and fewer extreme \r\ncold events over 1961-2015 (Krishnan et al., 2019b; Wester et al., 2019). However, summer cooling over the Karakoram (western \r\nHKH) was reported for 1960-2010 (Forsythe et al., 2017). A key relevant process is elevation-dependent warming (EDW; reviewed in"


id: d9d09d633023c6e2f7138c691f214864
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
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vector_id: 31790
content:
"The assessments in this chapter are based on a  rapidly growing \r\nbody of evidence from the peer-reviewed literature, most of which \r\nwas not previously considered by IPCC reports. Several challenges \r\nin the construction of regional climate change information have \r\nbeen identified:"


id: 66a7af84543490297a3bc8a442ade175
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 13741
content:
"Addressing these challenges could facilitate the assessment of both \r\nsources and methodologies that lead to an increased fitness and \r\nusefulness of regional climate information for a wide range of purposes."


id: affba9cec4512108d7ae6132d910c768
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
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vector_id: 25215
content:
"We acknowledge the E-OBS dataset and the data providers in the \r\nECA&D project (https://www.ecad.eu) for their help and the Japan \r\nAerospace Exploration Agency (JAXA) for delivering the  GSMaP \r\n(Global Satellite Mapping of Precipitation) data to us. The invaluable \r\ncontributions from Lisa van Aardenne (South Africa), Peng Cai \r\n(China), Joseph Ching (China), Huili He (China), Kenshi  Hibino \r\n(Japan), Yukiko  Imada (Japan), Nazrul Islam (Saudi Arabia), \r\nIsadora Christel Jimenez (Spain) and Misako Kachi (Japan) are also \r\ngreatly acknowledged. We acknowledge the World Climate Research \r\nProgramme for coordinating the modelling intercomparison projects \r\nCMIP and CORDEX and thank the climate modelling groups for \r\nproducing and making available their model output."


id: adcf021686019e464f1c8041f399ee79
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 24884
content:
"The world is physically and culturally diverse, and the challenges posed by climate change vary by region and \r\nlocation. Because climate change affects so many aspects of people's daily work and living, climate change \r\ninformation can help with decision-making, but only when the information is relevant for the people involved in \r\nmaking those decisions. Users of climate information may be highly diverse, ranging from professionals in areas \r\nsuch as human health, agriculture or water management to a broader community that experiences the impacts \r\nof changing climate. Providing information that supports response actions thus requires engaging all relevant \r\nstakeholders, their knowledge and their experiences, formulating appropriate information, and developing \r\na mutual understanding of the usefulness and limitations of the information."


id: f225a57943f32e0c41fdbc16c95e02ae
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 35628
content:
"Forging partnerships among all involved with producing, exploring and distilling climate data into climate \r\ninformation is at the centre of creating stakeholder-relevant information. These partnerships can occur through \r\ndirect interaction between climate scientists and stakeholders as well as through organizations that have \r\nemerged to facilitate this process, such as climate services, national and regional climate forums, and consulting \r\nfirms providing specialized climate information. These so-called 'boundary organizations' can serve the varied \r\nneeds of all who would fold climate information into their decision processes. All of these partnerships are vital "


id: 9154aa8fc123cb4fce00ba48df02f069
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 20394
content:
"for arriving at climate information that responds to physical and cultural diversity and to challenges posed by \r\nclimate change that can vary region-by-region around the world."


id: 6b805510f365079eaf0f125dff059ccf
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 14531
content:
"In decision-making, climate information is more useful if the physical and cultural diversity across the world is considered."


id: d4a006790337819e12c1948cb89acdaf
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 30976
content:
"FAQ 10.1, Figure 1 | Climate information for decision makers is more useful if the physical and cultural diversity across the world is \r\nconsidered. The figure illustrates schematically the broad range of knowledge that must be blended with the diversity of users to distil information that will \r\nhave relevance and credibility. This blending or distillation should engage the values and knowledge of both the stakeholders and the scientists. The bottom \r\nrow contains examples of stakeholders' interests and is not all-inclusive. As part of the distillation, the outcomes can advance the United Nations' Sustainable \r\nDevelopment Goals, covered in part by these examples."


id: ca91b07864997e8d8b355b0851fe2f33
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 29436
content:
"Urban areas experience air temperatures that can be several degrees Celsius warmer than surrounding areas, \r\nespecially during the night. This 'urban heat island' effect results from several factors, including reduced \r\nventilation and heat trapping due to the close proximity of tall buildings, heat generated directly from human \r\nactivities, the heat-absorbing properties of concrete and other urban building materials, and the limited amount \r\nof vegetation. Continuing urbanization and increasingly severe heatwaves under climate change will further \r\namplify this effect in the future."


id: a318ee23535a48b0ff9581bb16201551
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
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vector_id: 23223
content:
"Today, cities are home to 55% of the world's population. This number is increasing, and every year cities welcome \r\n67 million new residents, 90% of whom are moving to cities in developing countries. By 2030, almost 60% of \r\nthe world's population is expected to live in urban areas. Cities and their inhabitants are highly vulnerable to \r\nweather and climate extremes, particularly heatwaves, because urban areas already are local hotspots. Cities are \r\ngenerally warmer - up to several degrees Celsius at night - than their surroundings. This warming effect, called \r\nthe urban heat island, occurs because cities both receive and retain more heat than the surrounding countryside \r\nareas and because natural cooling processes are weakened in cities compared to rural areas."


id: dd4f6ba6866b0ce3c0167c0e15648821
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 32310
content:
"Three main factors contribute to amplify the warming of urban areas (orange bars in FAQ 10.2, Figure 1). The \r\nstrongest contribution comes from urban geometry, which depends on the number of buildings, their size and \r\ntheir proximity. Tall buildings close to each other absorb and store heat and also reduce natural ventilation. \r\nHuman activities, which are very concentrated in cities, also directly warm the atmosphere locally, due to heat \r\nreleased from domestic and industrial heating or cooling systems, running engines, and other sources. Finally, \r\nurban warming also results directly from the heat-retaining properties of the materials that make up cities, \r\nincluding concrete buildings, asphalt roadways, and dark rooftops. These materials are very good at absorbing \r\nand retaining heat, and then re-emitting that heat at night."


id: 49f533c513e0532d3a50b57ea572c13c
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 9042
content:
"The urban heat island effect is further amplified in cities that lack vegetation and water bodies, both of which can \r\nstrongly contribute to local cooling (green bars in FAQ 10.2, Figure 1). This means that when enough vegetation \r\nand water are included in the urban fabric, they can counterbalance the urban heat island effect, to the point of \r\neven cancelling out the urban heat island effect in some neighbourhoods."


id: 4026b2f62d8f011c802d28c79adb2c69
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 7469
content:
"The urban heat island phenomenon is well-known and understood. For instance, temperature measurements \r\nfrom thermometers located in cities are corrected for this effect when global warming trends are calculated. \r\nNevertheless, observations, including long-term measurements of the urban heat island effect are currently too \r\nlimited to allow a full understanding of how the urban heat island varies across the world and across different \r\ntypes of cities and climatic zones, or how this effect will evolve in the future."


id: fd713a655131c694c58cc7018b48a38b
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
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content:
"As a result, it is hard to assess how climate change will affect the urban heat island effect, and various studies \r\ndisagree. Two things are, however, very clear. First, future urbanization will expand the urban heat island areas, \r\nthereby amplifying future warming in many places all over the world. In some places, the nighttime warming \r\nfrom the urban heat island effect could even be on the same order of magnitude as the warming expected from \r\nhuman-induced climate change. Second, more intense, longer and more frequent heatwaves caused by climate \r\nchange will more strongly impact cities and their inhabitants, because the extra warming from the urban heat \r\nisland effect will exacerbate the impacts of climate change."


id: 70b5e81b3f7f6aa6ff0275ba2d7e4d63
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 15353
content:
"In summary, cities are currently local hotspots because their structure, material and activities trap and release \r\nheat and reduce natural cooling processes. In the future, climate change will, on average, have a limited effect \r\non the magnitude of the urban heat island itself, but ongoing urbanization together with more frequent, longer \r\nand warmer heatwaves will make cities more exposed to global warming."


id: 859ba1960a50b16532040b7d0428e439
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 18556
content:
"Cities are usually warmer than their surrounding areas due to factors that trap and release heat\r\nand a lack of natural cooling influences, such as water and vegetation."


id: a8ddfe49293af70bc551d16daa047bc6
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 24095
content:
"FAQ 10.2, Figure 1 | Efficiency of the various factors at warming up or cooling down neighbourhoods of urban areas. Overall, cities tend to be \r\nwarmer than their surroundings. This is called the 'urban heat island' effect. The hatched areas on the bars show how the strength of the warming or cooling \r\neffects of each factor varies depending on the local climate. For example, vegetation has a stronger cooling effect in temperate and warm climates. Further \r\ndetails on data sources are available in the chapter data table (Table 10.SM.11)."


id: 2882c4bd37adaed0dc71a80b2f0cf482
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 3788
content:
"Zscheischler, J. et al., 2018: Future climate risk from compound events. Nature \r\nClimate Change, 8(6), 469-477, doi:10.1038/s41558-018-0156-3."


id: 3b2bf769cf42a7597131097415f96f89
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 6665
content:
"Muhammad Adnan (Pakistan), Wafae Badi (Morocco), Claudine Dereczynski (Brazil), \r\nAlejandro  Di  Luca (Australia/Canada/Argentina), Subimal Ghosh (India), Iskhaq Iskandar \r\n(Indonesia), James Kossin (United States of America), Sophie Lewis (Australia), Friederike Otto \r\n(United Kingdom/Germany), Izidine Pinto (South Africa/Mozambique), Masaki Satoh (Japan), \r\nSergio M. Vicente-Serrano (Spain), Michael Wehner (United States of America), Botao Zhou (China)"


id: c35397fb5595230807c4cfc9d60b30fa
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 28297
content:
"Qiaohong Sun (Canada/China), Ying Sun (China), Mouhamadou Bamba Sylla (Rwanda/Senegal), \r\nClaudia Tebaldi (United States of America), Laurent Terray (France), Wim Thiery (Belgium), \r\nJessica  Tierney (United States of America), Maarten K. van Aalst (The Netherlands), \r\nBart van den Hurk (The Netherlands), Robert Vautard (France), Wen Wang (China), Seth Westra \r\n(Australia), Jakob Zscheischler (Germany) "


id: 1e6d02b41f3fc460d361302243551d0
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 2229
content:
"Seneviratne, S.I., X. Zhang, M. Adnan, W. Badi, C. Dereczynski, A. Di Luca, S. Ghosh, I. Iskandar, J. Kossin, S. Lewis, \r\nF.  Otto, I.  Pinto, M. Satoh, S.M. Vicente-Serrano, M. Wehner, and B. Zhou, 2021: Weather and Climate Extreme \r\nEvents in a Changing Climate. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group \r\nI  to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, \r\nA. Pirani, S.L. Connors, C. Pean, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, \r\nJ.B.R.  Matthews, T.K. Maycock, T. Waterfield, O. Yelekci, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, \r\nCambridge, United Kingdom and New York, NY, USA, pp. 1513-1766, doi:10.1017/9781009157896.013."


id: c01e7a9c29f26e49b80906d00329a1e1
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 27777
content:
"global warming. Projected percentage changes in frequency are \r\nhigher for the rarer extreme events (high confidence). {11.1, 11.2, \r\n11.3, 11.4, 11.6, 11.9, Cross-Chapter Box 11.1}"


id: 2039f96eb1470b10cede0ef9bd0b276f
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 2517
content:
"It is an established fact that human-induced greenhouse gas \r\nemissions have led to an increased frequency and/or intensity \r\nof some weather and climate extremes since pre-industrial \r\ntime, in particular for temperature extremes. Evidence of \r\nobserved changes in extremes and their attribution to human \r\ninfluence (including greenhouse gas and aerosol emissions and land\u0002use changes) has strengthened since AR5, in particular for extreme \r\nprecipitation, droughts, tropical cyclones and compound extremes \r\n(including dry/hot events and fire weather). Some recent hot extreme \r\nevents would have been extremely unlikely to occur without human \r\ninfluence on the climate system. {11.2, 11.3, 11.4, 11.6, 11.7, 11.8}"


id: bd619706b1bc9a4106ece3483d07a417
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 27359
content:
"The frequency and intensity of hot extremes (including \r\nheatwaves) have increased, and those of cold extremes have \r\ndecreased on the global scale since 1950 (virtually certain). \r\nThis also applies at regional scale, with more than 80% of AR6 \r\nregions1 showing similar changes assessed to be at least likely.\r\nIn a few regions, limited evidence (data or literature) prevents the \r\nreliable estimation of trends. {11.3, 11.9}"


id: 8078cea2eba4f23f88d17f20a124e19b
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 17742
content:
"Regional changes in the intensity and frequency of climate \r\nextremes generally scale with global warming. New evidence \r\nstrengthens the conclusion from the IPCC Special Report on \r\nGlobal Warming of 1.5degC (SR1.5) that even relatively small \r\nincremental increases in global warming (+0.5degC) cause \r\nstatistically significant changes in extremes on the global scale \r\nand for large regions (high confidence). In particular, this is the \r\ncase for temperature extremes (very likely), the intensification \r\nof heavy precipitation (high confidence) including that \r\nassociated with tropical cyclones (medium confidence), and \r\nthe worsening of droughts in some regions (high confidence). \r\nThe occurrence of extreme events unprecedented in the observed \r\nrecord will rise with increasing global warming, even at 1.5degC of "


id: 2faa51f4cf6b0b6136a444b865e0b40b
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 4921
content:
"Human-induced greenhouse gas forcing is the main driver \r\nof the observed changes in hot and cold extremes on the \r\nglobal scale (virtually certain) and on most continents (very \r\nlikely). The effect of enhanced greenhouse gas concentrations on \r\nextreme temperatures is moderated or amplified at the regional \r\nscale by regional processes such as soil moisture or snow/ice-albedo \r\nfeedbacks, by regional forcing from land-use and land-cover changes, \r\nor aerosol concentrations, and decadal and multi-decadal natural \r\nvariability. Changes in anthropogenic aerosol concentrations have \r\nlikely affected trends in hot extremes in some regions. Irrigation and \r\ncrop expansion have attenuated increases in summer hot extremes \r\nin some regions, such as the Midwestern USA (medium confidence). "


id: c7b170e3f2edd33192661d8eb14ace76
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 28998
content:
"Urbanization has likely exacerbated changes in temperature extremes \r\nin cities, in particular for nighttime extremes. {11.1, 11.2, 11.3}"


id: 9b46478ba2aec7b5d4e5dabee70b902e
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 21973
content:
"heavy precipitation will follow the rate of increase in the maximum \r\namount of moisture that the atmosphere can hold as it warms (high \r\nconfidence), of about 7% per 1degC of global warming. The increase in \r\nthe frequency of heavy precipitation events will be non-linear with \r\nmore warming and will be higher for rarer events (high confidence), \r\nwith a likely doubling and tripling in the frequency of 10-year and \r\n50-year events, respectively, compared to the recent past at 4degC of \r\nglobal warming. Increases in the intensity of extreme precipitation \r\nat regional scales will vary, depending on the amount of regional \r\nwarming, changes in atmospheric circulation and storm dynamics \r\n(high confidence). {11.4, Box 11.1}"


id: 5ea4579200ed2877e78d28cd54f3d737
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 12426
content:
"The projected increase in the intensity of extreme precipitation \r\ntranslates to an increase in the frequency and magnitude \r\nof pluvial floods  - surface water and flash floods  - (high \r\nconfidence), as pluvial flooding results from precipitation \r\nintensity exceeding the capacity of natural and artificial \r\ndrainage systems. {11.4}"


id: 5696e4644edb66a20951fbe349d4061a
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 11095
content:
"Significant trends in peak streamflow have been observed \r\nin some regions over the past decades (high confidence). \r\nThe  seasonality of river floods has changed in cold regions where \r\nsnow-melt is involved, with an earlier occurrence of peak streamflow \r\n(high confidence). {11.5}"


id: 570c0c6979b1b711ac7e68de47f1d52a
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 11176
content:
"Global hydrological models project a larger fraction of land \r\nareas to be affected by an increase in river floods than by \r\na  decrease in river floods (medium confidence). Regional \r\nchanges in river floods are more uncertain than changes in pluvial \r\nfloods because complex hydrological processes and forcings, \r\nincluding land cover change and human water management, are \r\ninvolved. {11.5}"


id: 77f15526ed3617d11fc652eed013ab4a
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 16432
content:
"The frequency and intensity of heavy precipitation events have \r\nlikely increased at the global scale over a  majority of land \r\nregions with good observational coverage. Heavy precipitation \r\nhas likely increased on the continental scale over three \r\ncontinents: North America, Europe, and Asia. Regional increases \r\nin the frequency and/or intensity of heavy precipitation have been \r\nobserved with at least medium confidence for nearly half of AR6 \r\nregions, including WSAF, ESAF, WSB, SAS, ESB, RFE, WCA, ECA, TIB, \r\nEAS, SEA, NAU, NEU, EEU, GIC, WCE, SES, CNA, and ENA. {11.4, 11.9}"


id: 917fed402985e2b6f89d11c2e8f85d86
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 20427
content:
"Different drought types exist, and they are associated with \r\ndifferent impacts and respond differently to increasing \r\ngreenhouse gas concentrations. Precipitation deficits and changes \r\nin evapotranspiration govern net water availability. A lack of sufficient \r\nsoil moisture, sometimes amplified by increased atmospheric \r\nevaporative demand, results in agricultural and ecological drought. \r\nLack of runoff and surface water result in hydrological drought. {11.6}"


id: 4e48adea31a817c2c507b2cbba1e687a
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 9743
content:
"Human influence, in particular greenhouse gas emissions, \r\nis likely the main driver of the observed global-scale \r\nintensification of heavy precipitation over land regions. It is \r\nlikely that human-induced climate change has contributed to the \r\nobserved intensification of heavy precipitation at the continental \r\nscale in North America, Europe and Asia. Evidence of a  human \r\ninfluence on heavy precipitation has emerged in some regions (high \r\nconfidence). {11.4, 11.9, Table 11.1}"


id: 2e094d77699c18ac98474d62daefa6bb
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 4677
content:
"Human-induced climate change has contributed to increases \r\nin agricultural and ecological droughts in some regions due to \r\nevapotranspiration increases (medium confidence). Increases \r\nin evapotranspiration have been driven by increases in atmospheric \r\nevaporative demand induced by increased temperature, decreased \r\nrelative humidity and increased net radiation (high confidence). \r\nTrends in precipitation are not a main driver in affecting global-scale \r\ntrends in drought (medium confidence), but have induced increases in \r\nmeteorological droughts in a few AR6 regions (NES: high confidence; \r\nWAF, CAF, ESAF, SAM, SWS, SSA, SAS: medium confidence). Increasing "


id: 52de86a28ddf3488db0fe5656270064d
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 10494
content:
"Heavy precipitation will generally become more frequent and \r\nmore intense with additional global warming. At a global \r\nwarming level of 4degC relative to the pre-industrial level, very \r\nrare (e.g., one in 10 or more years) heavy precipitation events \r\nwould become more frequent and more intense than in the \r\nrecent past, on the global scale (virtually certain) and in all \r\ncontinents and AR6 regions. The increase in frequency and \r\nintensity is extremely likely for most continents and very likely\r\nfor most AR6 regions. At the global scale, the intensification of "


id: 8cd27c7680af5e146589888f96a754a2
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 19673
content:
"of mean water vapour increase over oceans (about 7% per 1degC of \r\nwarming)  and in some cases exceeding this rate  due to increased \r\nlow-level moisture convergence caused by increases in TC wind \r\nintensity (medium confidence).  {11.7, 11.4, Box 11.1}"


id: feb8fbf8f5b1358ed349b4bc855b1187
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 37619
content:
"trends in agricultural and ecological droughts have been observed \r\non all continents (WAF, CAF, WSAF, ESAF, WCA, ECA, EAS, SAU, MED, \r\nWCE, WNA, NES: medium confidence), but decreases only in one AR6 \r\nregion (NAU: medium confidence). Increasing trends in hydrological \r\ndroughts have been observed in a  few AR6 regions (MED: high \r\nconfidence; WAF, EAS, SAU: medium confidence). Regional\u0002scale attribution shows that human-induced climate change has \r\ncontributed to increased agricultural and ecological droughts (MED, \r\nWNA), and increased hydrological drought (MED) in some regions \r\n(medium confidence). {11.6, 11.9}"


id: baaf26feec2d330e745d42f0713157da
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 26912
content:
"It is likely that the global proportion of Category 3-5 tropical \r\ncyclone instances2 has increased over the past four decades. \r\nThe average location where TCs reach their peak wind intensity \r\nhas very likely migrated poleward in the western North Pacific \r\nOcean since the 1940s, and TC translation speed has likely slowed \r\nover the conterminous USA since 1900. Evidence of similar trends \r\nin other regions is not robust. The global frequency of TC rapid \r\nintensification events has likely increased over the past four decades. \r\nNone of these changes can be explained by natural variability alone \r\n(medium confidence). "


id: ba8a99e6bf7d28edaa262d602cf73131
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 26882
content:
"The proportion of intense TCs, average peak TC wind speeds, \r\nand peak wind speeds of the most intense TCs will increase \r\non the global scale with increasing global warming (high \r\nconfidence). The total global frequency of TC formation will \r\ndecrease or remain unchanged with increasing global warming \r\n(medium confidence). {11.7.1} "


id: 4ad4bb2406d0a510bd18b7c3fd897e0
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 9189
content:
"There is low confidence in past changes of maximum \r\nwind speeds and other measures of dynamical intensity of \r\nextratropical cyclones. Future wind speed changes are expected \r\nto be small, although poleward shifts in the storm tracks \r\ncould lead to substantial changes in extreme wind speeds in \r\nsome regions (medium confidence). There is low confidence in \r\npast trends in characteristics of severe convective storms, such as \r\nhail and severe winds, beyond an increase in precipitation rates. The \r\nfrequency of spring severe convective storms is projected to increase \r\nin the USA, leading to a lengthening of the severe convective storm \r\nseason (medium confidence); evidence in other regions is limited. \r\n{11.7.2, 11.7.3}."


id: 72da79220906c0b60d7f03b949318d0c
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 15684
content:
"The probability of compound events has likely increased in \r\nthe past due to human-induced climate change and will likely \r\ncontinue to increase with further global warming. Concurrent \r\nheatwaves and droughts have become more frequent, and this \r\ntrend will continue with higher global warming (high confidence). \r\nFire weather conditions (compound hot, dry and windy events) \r\nhave become more probable in some regions (medium confidence) \r\nand there is high confidence that they will become more frequent \r\nin some regions at higher levels of global warming. The probability \r\nof compound flooding (storm surge, extreme rainfall and/or river \r\nflow) has increased in some locations (medium confidence), and will \r\ncontinue to increase due to sea level rise and increases in heavy \r\nprecipitation, including changes in precipitation intensity associated \r\nwith tropical cyclones (high confidence). The land area affected by \r\nconcurrent extremes has increased (high confidence). Concurrent "


id: c97ed1692688ecbb73ad9942be75952a
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 29262
content:
"The average and maximum rain rates associated with tropical \r\ncyclones (TCs), extratropical cyclones and atmospheric rivers \r\nacross the globe, and severe convective storms in some \r\nregions, increase  in a  warming world (high confidence). \r\nAvailable event attribution studies of observed strong TCs provide \r\nmedium confidence for a human contribution to extreme TC rainfall. \r\nPeak TC rain rates increase with local warming at least at the rate "


id: 85e4aef52d3e3cc3e50cff40eade5246
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
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content:
"extreme events at different locations, but possibly affecting similar \r\nsectors (e.g.,  critical crop-producing areas for global food supply) \r\nin different regions, will become more frequent with increasing \r\nglobal warming, in particular above 2degC of global warming (high \r\nconfidence). {11.8, Box 11.2, Box 11.4}. "


id: 5e685b6ee5da5824fbccdfb9d5fbda6b
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 12385
content:
"The future occurrence of low-likelihood, high-impact \r\nevents linked to climate extremes is generally associated \r\nwith low confidence, but cannot be excluded, especially at \r\nglobal warming levels above 4degC. Compound events, including \r\nconcurrent extremes, are a factor increasing the probability of low\u0002likelihood, high-impact events (high confidence). With increasing \r\nglobal warming, some compound events with low likelihood in \r\npast and current climates will become more frequent, and there is \r\na higher chance of occurrence of historically unprecedented events \r\nand surprises (high confidence). However, even extreme events that \r\ndo not have a particularly low probability in the present climate (at \r\nmore than 1degC of global warming) can be perceived as surprises \r\nbecause of the pace of global warming (high confidence). {Box 11.2}"


id: cb1ee04f2c4ac8ce0153b9953c9f9818
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 29515
content:
"This chapter provides assessments of changes in weather and climate \r\nextremes (collectively referred to as extremes) framed in terms of \r\nthe relevance to the Working Group II (WGII) assessment. It assesses \r\nobserved changes in extremes, their attribution to causes, and future \r\nprojections, at three global warming levels: 1.5degC, 2degC, and 4degC. This \r\nchapter is also one of the four 'regional chapters' of the WGI Report \r\n(along with Chapters 10 and 12 and the Atlas). Consequently, while \r\nit encompasses assessments of changes in extremes at global and \r\ncontinental scales to provide a large-scale context, it also addresses \r\nchanges in extremes at regional scales. "


id: 94a1ff238c3f40373409616867b6e3f
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 20894
content:
"Extremes are climatic impact-drivers (Annex VII: Glossary, see \r\nChapter  12 for a  comprehensive assessment). The IPCC risk \r\nframework (Chapter  1) articulates clearly that the exposure and \r\nvulnerability to climatic impact-drivers, such as extremes, modulate \r\nthe risk of adverse impacts of these drivers, and that adaptation \r\nwhich reduces exposure and vulnerability will increase resilience, \r\nresulting in a reduction in impacts. Nonetheless, changes in extremes \r\nlead to changes in impacts as a direct consequence of changes in \r\ntheir magnitude and frequency, and also through their influence on \r\nexposure and resilience. "


id: cf01f017d7618d25855633f1e0b34858
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
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vector_id: 30101
content:
"This chapter is structured as follows (Figure  11.1): This section \r\n(11.1) provides the general framing and introduction to the chapter, \r\nhighlighting key aspects that underlie the confidence and uncertainty \r\nin the assessment of changes in extremes, and introducing some main \r\nelements of the chapter. To provide readers with a quick overview \r\nof past and future changes in extremes, a synthesis of global-scale \r\nassessments for different types of extremes is included at the end \r\nof this section (Tables 11.1 and 11.2). Section  11.2 introduces \r\nmethodological aspects of research on climate extremes. Sections \r\n11.3 to 11.7 assess past changes and their attribution to causes, \r\nand projected future changes in extremes, for different types of \r\nextremes, including temperature extremes, heavy precipitation and "


id: 23790c92b5d456c66c28198f57d235b2
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 3029
content:
"pluvial floods, river floods, droughts, and storms, in separate sections. \r\nSection 11.8 addresses compound events. Section 11.9 summarizes \r\nregional assessments of changes in temperature extremes, in \r\nprecipitation extremes and in droughts by continents in tables. The \r\nchapter also includes several boxes and FAQs on more specific topics."


id: 338df08a88e5a9083b63a2f4f95de8cf
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 5531
content:
"often based on extremes derived from daily values. Studies of events \r\non longer time scales for temperature or precipitation, or on sub\u0002daily extremes, are scarcer, which generally limits the assessment for \r\nsuch events. Nevertheless, extremes on time scales different from \r\ndaily are assessed for temperature extremes and heavy precipitation, \r\nwhen possible (Sections 11.3 and 11.4). Droughts and tropical \r\ncyclones are treated as phenomena in general in the assessment, \r\nnot limited by their extreme forms, because these phenomena are \r\nrelevant to impacts (Sections 11.6 and 11.7). Both precipitation and \r\nwind extremes associated with storms are considered. "


id: 1b01e29b50ccb618010b51ccd7bde6e0
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 1720
content:
"Building on the SREX report and AR5, this Report defines an extreme \r\nweather event as 'an event that is rare at a  particular place and \r\ntime of year', and an extreme climate event as 'a pattern of extreme \r\nweather that persists for some time, such as a season' (see Glossary). \r\nThe definitions of 'rare' are wide ranging, depending on applications. \r\nSome studies consider an event as an extreme if it is unprecedented; \r\nother studies consider events that occur several times a  year as \r\nmoderate extreme events. Rarity of an event with a fixed magnitude \r\nalso changes under human-induced climate change, making events \r\nthat are unprecedented so far rather probable under present \r\nconditions, but unique in the observational record - and thus often \r\nconsidered as 'surprises' (see Box 11.2). "


id: 19561952ba2aa7f00d8380fc29687f66
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
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index: climate_gpt_only_giec
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content:
"Multiple concomitant extremes can lead to stronger impacts than \r\nthose resulting from the same extremes had they happened in \r\nisolation. For this reason, the occurrence of multiple extremes that \r\nare multivariate and/or concurrent and/or happening in succession, \r\nalso called 'compound events' (SREX Chapter 3), are assessed in this \r\nchapter based on emerging literature on this topic (Section  11.8). \r\nBox 11.2 also provides an assessment on low-likelihood, high-impact \r\nscenarios associated with extremes."


id: 9b74aa9fb5e7de58e7ce83924039212b
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 22012
content:
"Various approaches are used to define extremes. These are generally \r\nbased on the determination of relative (e.g.,  90th percentile) or \r\nabsolute (e.g., 35degC for a hot day) thresholds above which conditions \r\nare considered extremes. Changes in extremes can be examined from \r\ntwo perspectives, either focusing on changes in frequency of given \r\nextremes, or on changes in their intensity. These considerations in the \r\ndefinition of extremes are further addressed in Section 11.2.1."


id: a118447eed6aa8c280b03af56fe61f5d
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 22896
content:
"The types of extremes assessed in this chapter include temperature \r\nextremes, heavy precipitation and pluvial floods, river floods, droughts, \r\nand storms. The drought assessment addresses meteorological \r\ndroughts, agricultural and ecological droughts, and hydrological \r\ndroughts (see Glossary). The storms assessment addresses tropical \r\ncyclones, extratropical cyclones, and severe convective storms. \r\nThis chapter also assesses changes in compound events  - that \r\nis, multivariate or concurrent extreme events  - because of their \r\nrelevance to impacts as well as the emergence of new literature on \r\nthe subject. Most of the considered extremes were also assessed in \r\nSREX and AR5. Compound events were not assessed in depth in past \r\nIPCC reports (SREX Chapter 3; Section 11.8 of this Report). Marine\u0002related extremes such as marine heatwaves and extreme sea level, \r\nare assessed in Section 9.6.4 and Box 9.2 of this Report. "


id: fcfe9e869bce66b0034ba28ebf4706f5
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
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index: climate_gpt_only_giec
vector_id: 37317
content:
"A global-scale synthesis of this chapter's assessments is provided in \r\nSection 11.1.7. In particular, Tables 11.1 and 11.2 provide a synthesis \r\nfor observed and attributed changes, and projected changes in \r\nextremes, respectively, at different global warming levels (1.5degC, \r\n2degC, and 4degC). Tables on regional-scale assessments for changes \r\nin temperature extremes, heavy precipitation and droughts, are \r\nprovided in Section 11.9."


id: 66b0ca62f8c7ecef5b9bdaf248763dd8
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 13749
content:
"Extremes and related phenomena are of various spatial and \r\ntemporal scales. Tornadoes have a  spatial scale as small as less \r\nthan 100 metres and a temporal scale as short as a few minutes. \r\nIn contrast, a  drought can last for multiple years, affecting vast \r\nregions. The level of complexity of the involved processes differs from \r\none type of extreme to another, affecting our capability to detect, \r\nattribute and project changes in weather and climate extremes. \r\nTemperature and precipitation extremes studied in the literature are "


id: 7bc48d04695fbdd9daec443dd99f12e1
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 17072
content:
"The SREX, AR5, and SR1.5 assessed that there is evidence from \r\nobservations that some extremes have changed since the mid\u000220th century, that some of the changes are a result of anthropogenic \r\ninfluences, and that some observed changes are projected to continue \r\ninto the future. Additionally, other changes are projected to emerge \r\nfrom natural climate variability under enhanced global warming \r\n(SREX Chapter 3; AR5 Chapter 10). "


id: b429c8df3603a6e6ecf7257f5345dc6b
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 25861
content:
"example, associated with changes in land cover and land use or \r\naerosol emissions, can have non-local or some (non-homogeneous) \r\nglobal-scale effects. In general, there is high confidence in changes in \r\nextremes due to global-scale thermodynamic processes (i.e., global \r\nwarming, mean moistening of the air) as the processes are well \r\nunderstood, while the confidence in those related to dynamic \r\nprocesses or regional and local forcing, including regional and local \r\nthermodynamic processes, is much lower due to multiple factors (see \r\nthe following subsection and Box 11.1). "


id: 6b62d89e71d56f479b512334aa7026d9
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 14513
content:
"Changes in annual maximum one-day precipitation (Rx1day) are \r\nproportional to mean global surface temperature changes, at about \r\n7% increase per 1degC of warming, that is, following the Clausius-\r\nClapeyron relation (Box  11.1), both in observations (Westra et al., \r\n2013) and in future projections (Kharin et al., 2013) at the global \r\nscale. Extreme short-duration precipitation in North America also \r\nscales with global surface temperature (Prein et al., 2016b; C. Li et al., \r\n2019a). At the local and regional scales, changes in extremes are also \r\nstrongly modulated and controlled by regional forcings and feedback \r\nmechanisms (Section  11.1.6), whereby some regional forcings, for "


id: 503d4ef2548ba7c2d98794a03c8ba21d
created_at: 2023-03-16 14:00:49
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content:
"Figure 11.2 | Time series of observed temperature anomalies for global average annual mean temperature (black), land average annual mean temperature \r\n(green), land average annual hottest daily maximum temperature (TXx, purple), and land average annual coldest daily minimum temperature (TNn, blue).\r\nGlobal and land mean temperature anomalies are relative to their 1850-1900 means and are based on the multi-product mean annual time series assessed in Section 2.3.1.1.3 \r\n(see text for references). TXx and TNn anomalies are relative to their respective 1961-1990 means and are based on the HadEX3 dataset (Dunn et al., 2020) using values for \r\ngrid boxes with at least 90% temporal completeness over 1961-2018. Further details on data sources and processing are available in the chapter data table (Table 11.SM.9)."


id: 97646620f3a2aaa2328c9273cbd0a809
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 21347
content:
"Changes in weather and climate extremes are determined by local exchanges in heat, moisture, and other related quantities \r\n(thermodynamic changes) and those associated with atmospheric and oceanic motions (dynamic changes). While thermodynamic and \r\ndynamic processes are interconnected, considering them separately helps to disentangle the roles of different processes contributing \r\nto changes in climate extremes (e.g., Shepherd, 2014). "


id: d0d3cd451c5247cb017908aba07a5060
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
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content:
"Thermodynamic responses and feedbacks also occur through surface processes. For instance, observations and model simulations \r\nshow that temperature increases, including extreme temperatures, are amplified in areas where seasonal snow cover is reduced due \r\nto decreases in surface albedo (see Section 11.3.1). In some mid-latitude areas, temperature increases are amplified by the higher \r\natmospheric evaporative demand (Fu and Feng, 2014; Vicente-Serrano et al., 2020a) that results in a drying of soils in some regions \r\n(Section 11.6), leading to increased sensible heat fluxes (soil-moisture-temperature feedback, see Sections 11.1.6 and 11.3.1 for \r\nmore background). Other thermodynamic feedback processes include changes in the water-use efficiency of plants under enhanced \r\natmospheric carbon dioxide (CO2) concentrations that can reduce the overall transpiration, and thus also enhance temperature in \r\nprojections (Sections 8.2.3.3, 11.1.6, 11.3 and 11.6)."


id: 706068dde7a0a95670cc826126be15f9
created_at: 2023-03-16 14:00:49
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content:
"Changes in the spatial distribution of temperatures can also affect temperature extremes by modifying the characteristics of weather \r\npatterns (e.g.,  Suarez-Gutierrez et al., 2020a). For example, a  robust thermodynamic effect of polar amplification is a  weakened \r\nnorth-south temperature gradient, which amplifies the warming of cold extremes in the Northern Hemisphere mid- and high latitudes \r\nbecause of the reduction of cold air advection (Holmes et al., 2015; Schneider et al., 2015; Gross et al., 2020). Much less robust is the \r\ndynamic effect of polar amplification (Section 7.4.4.1) and the reduced low-altitude meridional temperature gradient that has been \r\nlinked to an increase in the persistence of weather patterns (e.g., heatwaves) and subsequent increases in temperature extremes \r\n(Cross-Chapter Box 10.1; Francis and Vavrus, 2012; Coumou et al., 2015, 2018; Mann et al., 2017). "


id: a2af3a00f62e786afe5213238489f799
created_at: 2023-03-16 14:00:49
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content:
"Dynamic contributions can occur in response to changes in the vertical and horizontal distribution of temperature (thermodynamics) \r\nand can affect the frequency and intensity of synoptic and subsynoptic phenomena, including tropical cyclones, extratropical cyclones, \r\nfronts, mesoscale-convective systems and thunderstorms. For example, the poleward shift and strengthening of the Southern \r\nHemisphere mid-latitude storm tracks (Section 4.5.1) can modify the frequency or intensity of extreme precipitation. However, the \r\nprecise way in which dynamic changes will affect precipitation extremes is unclear due to several competing effects (Shaw et al., 2016; \r\nAllan et al., 2020)."


id: a8394dae5dd460e866e9d3246d57c60e
created_at: 2023-03-16 14:00:49
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vector_id: 23987
content:
"Droughts are also affected by thermodynamic and dynamic processes (Sections 8.2.3.3 and 11.6). Thermodynamic processes affect \r\ndroughts by increasing atmospheric evaporative demand (Martin, 2018; Gebremeskel Haile et al., 2020; Vicente-Serrano et al., \r\n2020a) through changes in air temperature, radiation, wind speed, and relative humidity. Dynamic processes affect droughts through \r\nchanges in the occurrence, duration and intensity of weather anomalies, which are related to precipitation and the amount of sunlight \r\n(Section 11.6). While atmospheric evaporative demand increases with warming, regional changes in aridity are affected by increasing \r\nland-ocean warming contrast, vegetation feedbacks and responses to rising CO2 concentrations, and dynamic shifts in the location of \r\nthe wet and dry parts of the atmospheric circulation in response to climate change, as well as internal variability (Byrne and O'Gorman, \r\n2015; Kumar et al., 2015; Allan et al., 2020)."


id: d87c69dc2c0a6c9479658655b74f4184
created_at: 2023-03-16 14:00:49
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content:
"In summary, both thermodynamic and dynamic processes are involved in the changes of extremes in response to warming. \r\nAnthropogenic forcing (e.g., increases in greenhouse gas concentrations) directly affects thermodynamic variables, including overall \r\nincreases in high temperatures and atmospheric evaporative demand, and regional changes in atmospheric moisture, which intensify \r\nheatwaves, droughts and heavy precipitation events when they occur (high confidence). Dynamic processes are often indirect responses \r\nto thermodynamic changes, are strongly affected by internal climate variability, and are also less well understood. As such, there is low \r\nconfidence in how dynamic changes affect the location and magnitude of extreme events in a warming climate."


id: 6b0871cd370473492e3529677662653
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"spatial distribution of these patterns, which contributes to uncertainty \r\nin projected responses of extremes, especially in the near term. "


id: 9c87c9e6dc7b39815b58383875b280a7
created_at: 2023-03-16 14:00:49
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content:
"At the local and regional scales, changes in extremes are strongly \r\nmodulated by local and regional feedbacks (SRCCL, Jia et al., 2019; \r\nSeneviratne et al., 2013; Miralles et al., 2014a; Lorenz et al., 2016; \r\nVogel et al., 2017), changes in large-scale circulation patterns \r\n(Section 11.1.5), and regional forcings such as changes in land use \r\nor aerosol concentrations (Chapters 3  and 7; Findell et al., 2017; \r\nHirsch et al., 2017, 2018; Thiery et al., 2017; Z. Wang et al., 2017b). \r\nIn some cases, such responses may also include non-local effects \r\n(e.g., de Vrese et al., 2016; Persad and Caldeira, 2018; Miralles et al., \r\n2019; Schumacher et al., 2019). Regional-scale forcing and feedbacks \r\noften affect temperature distributions asymmetrically, with generally \r\nhigher effects for the hottest percentiles (Section 11.3). "


id: 4becaba8b42a01d2b267743903f9eed
created_at: 2023-03-16 14:00:49
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content:
"In summary, large-scale atmospheric circulation patterns are \r\nimportant drivers for local and regional extremes. There is overall low \r\nconfidence about future changes in the magnitude, frequency, and "


id: 381dd85f41a2f232d5d41b1f22a36d1b
created_at: 2023-03-16 14:00:49
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content:
"2014). However, these feedbacks play an important role in projected \r\nchanges in high-latitude warming (Hall and Qu, 2006), and, in \r\nparticular, in changes in cold extremes in these regions (Section 11.3)."


id: cc80ecfb82ccaf803f5b7f0db7152ad4
created_at: 2023-03-16 14:00:49
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content:
"Finally, extreme events may also regionally amplify one another. \r\nFor example, this is the case for heatwaves and droughts, with high \r\ntemperatures and stronger radiative forcing leading to drying tendencies \r\non land due to increased evapotranspiration (Section 11.6), and drier \r\nsoils then inducing decreased evapotranspiration and higher sensible \r\nheat flux and hot temperatures (Box 11.1, Section 11.8; Seneviratne \r\net al., 2013; Miralles et al., 2014a; Vogel et al., 2017; Zscheischler and \r\nSeneviratne, 2017; S. Zhou et al., 2019; Kong et al., 2020). "


id: 531529c79ea22908211f46c84252d54b
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
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vector_id: 10538
content:
"In summary, regional forcings and feedbacks  - in particular those \r\nassociated with land use and aerosol forcings - and soil-moisture-\r\ntemperature, soil moisture-precipitation, and snow/ice-albedo-\r\ntemperature feedbacks, play an important role in modulating \r\nregional changes in extremes. These can also lead to a  higher \r\nwarming of extreme temperatures compared to mean temperature \r\n(high confidence), and possibly cooling in some regions (medium \r\nconfidence). However, there is only medium confidence in the \r\nrepresentation of the associated processes in state-of-the-art ESMs."


id: 5a6c730c020c69b5b243f6e0913286fe
created_at: 2023-03-16 14:00:49
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content:
"Tables 11.1 and 11.2 provide a synthesis for observed and attributed \r\nchanges in extremes, and projected changes in extremes, respectively, \r\nat different levels of global warming. This synthesis assessment \r\nfocuses on the assessed range of observed and projected changes. \r\nIn this chapter, the assessed likely range in a  projection typically "


id: ac81c48df8737e32da060fcc0d335a4a
created_at: 2023-03-16 14:00:49
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content:
"Figure 11.5 | Confidence and likelihood of past changes and projected future changes at 2degC of global warming on the global scale. The information in this \r\nfigure is based on Tables 11.1 and 11.2. "


id: 66658ea7fc5a849a46957323f3811862
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"134\r\nN\r\n0\r\n153045\r\nW. North-A\r\n0\r\n153045\r\nN. C\r\n2.0 3.0 \r\ndegC\r\nobal land\r\near eventear event\r\ned changes in th\r\nperatures that we\r\ne AR6 regions. Fo\r\nrtainty range. The \r\ndifferent Shared S"


id: e645d21ef72ccaf83f31a5256699a035
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content:
"02468\r\nW. No\r\n1.5 2.0 3.0 \r\ndegC\r\nGlobal land\r\n0-year event0-year event\r\nojected change\r\nximum daily precip\r\n global land area \r\n extend to the 90\r\nroject Phase 6 (CM"


id: c420991eb0c06144a43c421a73c0d9f3
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"corresponds to the 90% range of the multi-model ensemble spread \r\nto take into account other sources of uncertainty, unless stated \r\notherwise. Some low-likelihood, high-impact scenarios that can be of \r\nhigh relevance are addressed in Box 11.2."


id: f63046cedc7c10e496980181a30c0798
created_at: 2023-03-16 14:00:49
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content:
"Building on the assessments from Tables 11.1 and 11.2, Figure 11.5 \r\nprovides a synthesis on the level of confidence in the attribution and \r\nprojection of changes in extremes. In the case where the signal in \r\nthe observations is still relatively weak but the physical processes \r\nunderlying the changes in extremes in response to human forcing \r\nare well understood, confidence in the projections would be higher \r\nthan in the attribution because of strengthening in the signal with \r\nwarming. But, when the observed signal is already strong and when \r\nobservational evidence is consistent with model simulated responses, "


id: f2ce6f2aab292c3eb5d58745c76b3efc
created_at: 2023-03-16 14:00:49
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content:
"Table 11.1 | Synthesis table on observed changes in extremes and contribution by human influence. Note that observed changes in marine extremes are assessed \r\nn Cross-Chapter Box 9.1. "


id: 6d071e4581f1aec711285baba899dcf1
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content:
"Further synthesis for regional assessments are provided in \r\nFigure  11.4 (event attribution), Figure  11.6 (projected change \r\nin hot temperature extremes) and Figure 11.7 (projected changes in \r\nprecipitation extremes). A  synthesis on regional assessments for \r\nobserved, attributed and projected changes in extremes is provided \r\nin Section 11.9 for all AR6 reference regions (see Section 1.4.5 and \r\nFigures 1.18 and Atlas.2 for definitions of AR6 regions)."


id: 63d36c1bcc728e53d15c77eef611249b
created_at: 2023-03-16 14:00:49
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content:
"confidence in the projection may be lower than that in attribution if \r\ncertain physical processes could be expected to behave differently \r\nin a  much warmer world and under much higher greenhouse gas \r\nforcing, and in particular if such a behaviour is poorly understood."


id: 150e40956180e60dee1e5b53d7d96f57
created_at: 2023-03-16 14:00:49
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content:
"Table 11.2 | Synthesis table on projected changes in extremes. Note that projected changes in marine extremes are assessed in Chapter 9 and Cross-Chapter Box 9.1 \r\n(marine heatwaves). Assessments are provided compared to pre-industrial conditions."


id: f83efb5e02468eb68832ac98d1e053d
created_at: 2023-03-16 14:00:49
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content:
"In many parts of the world, observational data are limited to 50-60 years. This means that the chance to observe an extreme event \r\nat a particular location that occurs once in several hundred or more years is small. Thus, when a very extreme event occurs, it becomes \r\na  surprise to many (Bao et al., 2017; McCollum et al., 2020), and very rare events are often associated with high impacts (van \r\nOldenborgh et al., 2017; Philip et al., 2018b; Tozer et al., 2020). Attributing and projecting very rare events in a particular location by \r\nassessing their likelihood of occurrence within the same larger region and climate thus provides another way to make quantitative \r\nassessments regarding events that are extremely rare locally. Some examples of such events include: "


id: 19229525cd934d59128658684d9fb6f5
created_at: 2023-03-16 14:00:49
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content:
"Another factor highlighted in Section 11.8 and Box 11.4 making events high-impact and difficult to anticipate is that several locations \r\nunder moderate warming levels could be affected simultaneously, or very repeatedly by different types of extremes (Mora et al., 2018; \r\nGaupp et al., 2019; Vogel et al., 2019). Box 11.4 shows that concurrent events at different locations, which can lead to major impacts \r\nacross the world, can also result from the combination of anomalous circulation or natural variability (e.g., El Nino-Southern Oscillation) "


id: 9e1ef2ed8da709ed4323c155237cadf2
created_at: 2023-03-16 14:00:49
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content:
"Box 11.2, Table 1 | Examples of changes in low-likelihood, high-impact extreme conditions (single extremes, compound events) at different \r\nglobal warming levels."


id: 7000e54fc1bd6f98074735c2ee44d6af
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content:
"In summary, the future occurrence of LLHI events linked to climate extremes is generally associated with low confidence, but cannot \r\nbe excluded, especially at global warming levels above 4degC. Compound events, including concurrent extremes, are a factor increasing \r\nthe probability of LLHI events (high confidence). With increasing global warming, some compound events with low likelihood in past \r\nand current climate will become more frequent, and there is a higher chance of historically unprecedented events and surprises (high \r\nconfidence). However, even extreme events that do not have a particularly low probability in the present climate (at more than 1degC of \r\nglobal warming) can be perceived as surprises because of the pace of global warming (high confidence)."


id: 1d27a28e0f65c7a95756fb73c1f7624f
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content:
"of the year, possibly affected by different types of processes and \r\nmechanisms, and possibly also associated with different impacts. "


id: 34b20846d34db358458eab171d0f3119
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
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index: climate_gpt_only_giec
vector_id: 5693
content:
"This section provides an assessment of observational data and \r\nmethods used in the analysis and attribution of climate change \r\nspecific to weather and climate extremes. It also introduces some \r\nconcepts used in presenting future projections of extremes. Later \r\nsections (Sections 11.3-11.8) also provide additional assessments \r\non relevant observational datasets and model validation specific to \r\nthe type of extremes to be assessed. General background on climate \r\nmodelling is provided in Chapters 4 and 10."


id: d9973acd47924063659dc038614c06e
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"rarely integrated in the assessment of projected changes in extremes. \r\nFraming is considered when forming the assessments of this Chapter, \r\nincluding how extremes are defined and how the questions are asked \r\nin the literature."


id: ad3f801045beca672c67ff1a03288eec
created_at: 2023-03-16 14:00:49
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"to represent areal means (Chen and Knutson, 2008; Gervais et al., \r\n2014; Avila et al., 2015; Di Luca et al., 2020b). In regions with very \r\nlimited station density (e.g., Africa), the gridded values are closer to \r\npoint estimates of extremes. The difference in spatial scales among \r\nobservational data products and model simulations needs to be \r\ncarefully accounted for when interpreting the comparison among \r\ndifferent data products. For example, the average annual maximum \r\ndaily maximum temperature (TXx) over land computed from the \r\noriginal ERA-Interim reanalysis (at 0.75deg resolution) is about 0.4degC \r\nwarmer than that computed when the ERA-Interim dataset is \r\nupscaled to the resolution of 2.5deg x 3.75deg (Di Luca et al., 2020)."


id: 9f76d4cbb2d236c4e36a22bb6ba18615
created_at: 2023-03-16 14:00:49
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content:
"Studies of past and future changes in weather and climate extremes, \r\nand in the mean state of the climate, use the same original sources \r\nof weather and climate observations, including in situ observations, \r\nremotely sensed data, and derived data products such as reanalyses. \r\nSections 2.3 and 10.2 assess various aspects of these data sources \r\nand data products from the perspective of their general use, and in \r\nthe analysis of changes in the mean state of the climate in particular. \r\nBuilding on these previous chapters, this subsection highlights \r\nparticular aspects that are related to extremes and are most relevant \r\nto the assessment of this Chapter. The SREX (Chapter 3, Seneviratne \r\net al., 2012) and AR5 (Chapter 2, Hartmann et al., 2013) addressed \r\ncritical issues regarding the quality and availability of observed data \r\nand their relevance for the assessment of changes in extremes. "


id: cd24e800afa24bff9ebeff5979a5ee6d
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"Extreme indices computed from various reanalysis data products have \r\nbeen used in some studies, but reanalysis extreme statistics have not \r\nbeen rigorously compared to observations (Donat et al., 2016a). "


id: 2855aefbbc492f7a9a74c03155f6da75
created_at: 2023-03-16 14:00:49
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"Satellite remote sensing data have been used to provide information \r\nabout precipitation extremes because several products provide \r\ndata at sub-daily resolution for precipitation, for example, Tropical \r\nRainfall Measuring Mission (TRMM; Maggioni et al., 2016) and \r\nclouds, for example, Himawari (Bessho et al., 2016; Chen et al., \r\n2019). However, satellites do not observe the primary atmospheric \r\nstate variables directly and polar orbiting satellites do not observe \r\nany given place at all times. Hence, their utility as a substitute for \r\nhigh-frequency (i.e., daily) ground-based observations is limited. For \r\ninstance, Timmermans et al. (2019) found little relationship between \r\nthe timing of extreme daily and five-day precipitation in satellite and \r\ngridded station data products over the USA. "


id: 6310f1b6af6d467fb524144ed12e0f7f
created_at: 2023-03-16 14:00:49
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"In some cases, such as for the HadEX3 dataset (Dunn et al., 2020), \r\nindices of extremes are computed using time series directly derived \r\nfrom stations first, and are then gridded over the space. As the indices \r\nare computed at the station level, the gridded data products represent \r\npoint estimates of the indices averaged over the spatial scale of the \r\ngrid box. In other instances, daily values of station observations are \r\nfirst gridded (e.g.,  Contractor et al., 2020a), and the interpolated \r\nvalues can then be used to compute various indices. Depending on the \r\nstation density, values for extremes computed from data gridded this \r\nway represent extremes of spatial scales anywhere from the size of \r\nthe grid box to a point. In regions with high station density (e.g., North \r\nAmerica, Europe), the gridded values are closer to extremes of area \r\nmeans and are thus more appropriate for comparisons with extremes \r\nestimated from climate model output, which is often considered "


id: ee5d00e7fa6511afd1d0aa85385ed470
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"Examining extremes in pre-instrumental information can help to put events occurring in the instrumental record (referred to as \r\n'observed') in a longer-term context. This box focuses on extremes in the Common Era (CE, the last 2000 years), because there is \r\ngenerally higher confidence in pre-instrumental information gathered from the more recent archives from the Common Era than from\r\nearlier evidence. It addresses evidence of extreme events in paleoreconstructions, documentary evidence (such as grape harvest data, \r\nreligious documents, newspapers, and logbooks) and model-based analyses, and whether observed extremes have or have not been \r\nexceeded in the Common Era. This box provides overviews of: (i) AR5 assessments; (ii) types of evidence assessed here; evidence of: \r\n(iii) droughts; (iv) temperature extremes; (v) paleofloods; and (vi) paleotempests; and (vii) a summary. "


id: 7c7fffb1120a6fcbe751e9e989121ebe
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"Chapter 5 of AR5 (Masson-Delmotte et al., 2013) concluded with high confidence that droughts of greater magnitude and of longer \r\nduration than those observed in the instrumental period occurred in many regions during the preceding millennium. There was high \r\nconfidence in evidence that floods during the past five centuries in northern and Central Europe, the western Mediterranean region, \r\nand eastern Asia were of a greater magnitude than those observed instrumentally, and medium confidence in evidence that floods \r\nin the Near East, India and Central North America were comparable to modern observed floods. While AR5 assessed 20th century \r\nsummer temperatures compared to those reconstructed in the Common Era, it did not assess shorter duration temperature extremes."


id: 466567e32959a73a11497acb92c1306b
created_at: 2023-03-16 14:00:49
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vector_id: 8460
content:
"Overall, the most complete pre-instrumental evidence of extremes occurs for long-duration, large spatial-scale extremes, such as for \r\nmulti-year meteorological droughts or seasonal- and regional-scale temperature extremes. Additionally, more precise insights into \r\nrecent extremes emerge where multiple studies have been undertaken, compared to the confidence in extremes reported at single sites \r\nor in single studies, which may not necessarily be representative of large-scale changes, or for reconstructions that synthesize multiple \r\nproxies over large areas (e.g., drought atlases). Multiproxy synthesis products combine paleoclimate temperature reconstructions \r\nand cover sub-continental- to hemispheric-scale regions to provide continuous records of the Common Era (e.g., Ahmed et al., 2013; \r\nNeukom et al., 2014 for temperature)."


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"depending on the length of the reconstruction and the seasonal perspective provided (see Cook et al., 2016c; Freund et al., 2017 for \r\nAustralia). There can also be differing conclusions for the severity, or even the occurrence, of specific individual pre-instrumental \r\ndroughts when different evidence is compared (e.g., Wetter et al., 2014; Buntgen et al., 2015). "


id: 9cf2fddf9ed94167fb216da0f4e90076
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
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vector_id: 22229
content:
"There is medium confidence that the magnitudes of large-scale, seasonal-scale extreme high temperatures in observed records exceed \r\nthose reconstructed over the Common Era in some locations, such as Central Europe. In one example, multiple studies have examined \r\nthe unusualness of present-day European summer temperature records in a long-term context, particularly in comparison to the \r\nexceptionally warm year of 1540 CE in Central Europe. Several studies indicate that recent extreme summers (2003 and 2010) in \r\nEurope have been unusually warm in the context of the last 500 years (Barriopedro et al., 2011; Wetter and Pfister, 2013; Wetter et al., \r\n2014; Orth et al., 2016b), or longer (Luterbacher et al., 2016). Others studies show that summer temperatures in Central Europe in \r\n1540 were warmer than the present-day (1966-2015) mean, but note that it is difficult to assess whether or not the 1540 summer \r\nwas warmer than observed record extreme temperatures (Orth et al., 2016b). "


id: 7b1fe4ca595a93d78cfb3a90c50b1311
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
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vector_id: 16965
content:
"There is medium confidence that periods of both more and less tropical cyclone activity (frequency or intensity) than observed \r\noccurred over the Common Era in many regions. Paleotempest studies cover a limited number of locations that are predominantly \r\ncoastal, and hence provide information on specific locations that cannot be extrapolated basin-wide (see Muller et al., 2017). In \r\nsome locations, such as the Gulf of Mexico and the New England, USA, coast, similarly intense storms to those observed recently \r\nhave occurred multiple times over centennial timescales (Donnelly et al., 2001; Bregy et al., 2018). Further research focused on the \r\nfrequency of tropical storm activity. Extreme storms occurred considerably more frequently in particular periods of the Common Era, \r\ncompared to the instrumental period in north-east Queensland, Australia (Nott et al., 2009; Haig et al., 2014), and the Gulf Coast \r\n(e.g., Brandon et al., 2013; Lin et al., 2014). "


id: 9b89f8364bdac490ffb96616900b423c
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
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index: climate_gpt_only_giec
vector_id: 22025
content:
"The probability of finding an unprecedented extreme event increases with a longer length of past record-keeping, in the absence \r\nof longer-term trends. Thus, as a record is extended to the past based on paleoreconstruction, there is a higher chance of very rare \r\nextreme events having occurred at some time prior to instrumental records. Such an occurrence is not, in itself, evidence of a change, \r\nor lack of a change, in the magnitude or the likelihood of extremes in the past or in the instrumental period at regional and local scales. \r\nYet, the systematic collection of paleoclimate records over wide areas may provide evidence of changes in extremes. In one study, \r\nextended evidence of the last millennium from observational data and paleoclimate reconstructions using tree rings indicates that \r\nhuman activities affected the worldwide occurrence of droughts as early as the beginning of the 20th century (Marvel et al., 2019). "


id: db688d9de72e41da498bc8c1a4372d0d
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
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index: climate_gpt_only_giec
vector_id: 32033
content:
"In summary, there is low confidence in overall changes in extremes derived from paleo-archives. There is high confidence that long\u0002duration and severe drought events occurred at many locations during the last 2000 years. There is also high confidence that high\u0002magnitude flood events occurred at some locations during the last 2000 years, but overall changes in infrastructure and human water \r\nmanagement make the comparison with present-day records difficult. But these isolated paleo-drought and paleo-flood events are \r\nnot evidence of a change, or lack of a change, in the magnitude or the likelihood of relevant extremes. "


id: 798abff0465e0207129157203248537b
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 16695
content:
"Apart from the detection and attribution of trends in extremes, new \r\napproaches have been developed to answer the question of whether, \r\nand to what extent, external drivers have altered the probability and \r\nintensity of an individual extreme event (NASEM, 2016). In AR5, there \r\nwas an emerging consensus that the role of external drivers of climate \r\nchange in specific extreme weather events could be estimated and \r\nquantified in principle, but related assessments were still confined \r\nto particular case studies, often using a single model, and typically \r\nfocusing on high-impact events with a clear attributable signal. "


id: da9c14822edfb6678e11cd78b0fef108
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
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index: climate_gpt_only_giec
vector_id: 31924
content:
"The reliability of the representation of the event in question in the \r\nclimate models used in a  study is essential (Angelil et al., 2016; \r\nHerger et al., 2018). Extreme events characterized by atmospheric \r\ndynamics that stretch the capabilities of current-generation models \r\n(Section 10.3.3.4; Shepherd, 2014; Woollings et al., 2018) limit the \r\napplicability of the probability-based approach of event attribution. "


id: f6efe2d034f000b6e52216f6fc1748c
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
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index: climate_gpt_only_giec
vector_id: 36361
content:
"These encompass a scenario compatible with the lowest limit of the \r\nParis Agreement (+1.5degC), a scenario slightly overshooting the aims \r\nof the Paris Agreement (+2degC), and a 'worst-case' scenario with no \r\nmitigation (+4degC). Cross-Chapter Box  11.1 provides a  background \r\non the GWL sampling approach used in AR6, for the computation \r\nof GWL projections from climate models contributing to Phase 6 of \r\nthe Coupled Model Intercomparison Project (CMIP6) as well as for \r\nthe mapping of existing scenario-based literature for CMIP6 and the \r\nCMIP Phase 5 (CMIP5) to assessments as function of GWLs (see also \r\nSection 11.9. and Table 11.3 for an example)."


id: fe59cd2a6f3131d4abb4f8a853ab83ed
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
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vector_id: 37551
content:
"In the regional tables provided in Section  11.9, the different lines \r\nof evidence from event attribution studies and trend attributions \r\nare assessed alongside one another to provide an assessment \r\nof the human contribution to observed changes in extremes in \r\nall AR6 regions. "


id: 866c3a33ce4b65e46f602379afa7281b
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
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content:
"The SR1.5 (Chapter  3) assessed different climate responses at \r\n+1.5degC of global warming, including transient climate responses, \r\nshort-term stabilization responses, and long-term equilibrium \r\nstabilization responses, and their implications for future projections \r\nof different extremes. Indeed, the temporal dimension  - that \r\nis, when the given GWL occurs  - also matters for projections, in \r\nparticular beyond the 21st century, and for some climate variables \r\nrelated to components of the climate system associated with large \r\ninertia (e.g., sea level rise and associated extremes). Nonetheless, \r\nfor assessments focused on conditions within the next decades, \r\nand for the main extremes considered in this chapter, derived \r\nprojections are relatively insensitive to details of climate scenarios \r\nand can be well-estimated based on transient simulations (Cross\u0002Chapter Box 11.1; see also SR1.5)."


id: 69ffd5ab4fedf705deefab4d3a5b05c9
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
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vector_id: 14279
content:
"The assessment of projections of future changes in extremes as \r\nfunction of GWL has an advantage in separating uncertainty \r\nassociated with the global warming response (see Chapter 4) from \r\nthe uncertainty resulting from the regional climate response as \r\na function of GWLs (Seneviratne and Hauser, 2020). If the interest is \r\nin the projection of regional changes at certain GWLs, such as those \r\ndefined by the Paris Agreement, projections based on time periods \r\nand emissions scenarios have unnecessarily larger uncertainty due \r\nto differences in model global transient climate responses. To take \r\nadvantage of this feature and to provide easy comparison with \r\nSR1.5, assessments of projected changes in this chapter are largely \r\nprovided in relation to future GWLs, with a  focus on changes at \r\n+1.5degC, +2degC, and +4degC of global warming above pre-industrial \r\nlevels (e.g., Tables 11.1 and 11.2 and regional tables in Section 11.9). "


id: 455cee656bfc64c0ded3fe290e2e0578
created_at: 2023-03-16 14:00:49
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content:
"An important question is the identification of the GWL at which \r\na  given change in a  climate extreme can begin to emerge from \r\nclimate noise. Figure 11.8 displays analyses of the GWLs at which \r\nemergence in hot extremes - annual maximum daily temperature \r\nrepresented by TXx and heavy precipitation represented by Rx1day \r\nis identified in AR6 regions for the whole CMIP5 and CMIP6 \r\nensembles. Overall, signals for extremes emerge very early for \r\nTXx, already below 0.2degC in many regions (Figure 11.8a,b), and at \r\naround 0.5degC in most regions. This is consistent with conclusions \r\nfrom the SR1.5 Chapter 3 for less-rare temperature extremes (TXx \r\non the yearly time scale), which shows that a difference as small as "


id: 8da833bf602a7c82498bc86f25430695
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
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content:
"temperature relative to the 1850-1900 period (see Cross-Chapter \r\nBox 2.3), are used in SR1.5 and in the assessment of Reasons for \r\nConcerns in the WGII reports (see also Cross-Chapter Box 12.1). \r\nCross-Chapter Box 11.1, Figure 1 illustrates how the assessment \r\nof the climate response at GWLs relates to the uncertainty in \r\nscenarios regarding the timing of the respective GWLs, as well as \r\nto the uncertainty in the associated regional climate responses, \r\nincluding extremes and other climatic impact-drivers (CIDs). \r\nFor many (but not all) climate variables and CIDs, the response \r\npattern for a given GWL is consistent across different scenarios \r\n(Chapters 1, 4, 9, 11 and Atlas). GWLs are defined as long-term \r\nmeans (e.g.,  20-year averages) compared to the pre-industrial \r\nperiod, are commonly used in the literature, and were also \r\nunderlying main assessments of SR1.5 (Chapter 3). "


id: 3f1b82cc6cf332034c36bbeb199950ef
created_at: 2023-03-16 14:00:49
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content:
"Numerous studies have compared the regional response to \r\nanthropogenic forcing at GWLs in annual and seasonal mean \r\nvalues and extremes of different climate and impact variables \r\nacross different multi-model ensembles and/or different \r\nscenarios (e.g., Frieler et al., 2012; Schewe et al., 2014; Herger \r\net al., 2015; Schleussner et al., 2016; Seneviratne et al., 2016; "


id: 347cecee5fc249a4692985f76f3d6bd
created_at: 2023-03-16 14:00:49
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content:
"In contrast to linear pattern scaling (Mitchell, 2003; Collins et al., 2013), the use of GWLs as a dimension of integration does not \r\nrequire linearity in the response of a climate variable. It is therefore useful even for metrics that do not show a linear response, such \r\nas the frequency of heat extremes over land and oceans (Fischer and Knutti, 2015; Frolicher et al., 2018; Kharin et al., 2018; Perkins\u0002Kirkpatrick and Gibson, 2017) if the relationship of the variable of interest to the GWL is scenario independent. The latter means that \r\nthe response is independent of the pathway and relative contribution of various radiative forcings. For some more complex indices like \r\nwarm-spell duration, or for regions with strong aerosol changes, discrepancies can be larger (Z. Wang et al., 2017b; King et al., 2018; \r\nTebaldi et al., 2020). (See also the subsection below on GWLs vs scenarios for further caveats.) "


id: 32b160f66d38cb5993f5fbff9993884b
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content:
"Mapping between GWL- and scenario-based responses in model analyses \r\nTo map scenario-based climate projections into changes at specific GWLs, first, all individual Earth system model (ESM) simulations \r\nthat reach a certain GWL are identified. Second, the climate response patterns at the respective GWL are calculated using an approach \r\ntermed here 'GWL-sampling' - sometimes also referred to as epoch analysis, time shift, or time sampling approach - taking into \r\naccount all models and scenarios (Cross-Chapter Box, Figure 3). Note that the range of years when a given GWL is reached in the\r\nCMIP6 ensemble is different from the AR6 assessed range of projected global surface temperature (Section 4.3.4; Table 4.5). The latter \r\nfurther takes into account different lines of evidence, including the assessed observed warming between pre-industrial and present "


id: e76c24ec94327d92aa7c043819c6fdc3
created_at: 2023-03-16 14:00:49
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index: climate_gpt_only_giec
vector_id: 33904
content:
"day, information from observational constraints on CMIP6, and emulators using the assessed transient climate response (TCR) \r\nand equilibrium climate sensitivity (ECS) ranges (Section 4.3.4). Hence the Chapter 4 assessed range (Table 4.5) is the reference to \r\ndetermine when a given GWL is likely reached under given scenarios, while the mapping between scenarios/time frames and GWLs is \r\nused to assess the respective regional responses happening at these time frames (which also allows accounting for the global surface \r\ntemperature assessment, rather than using scenarios analyses directly from CMIP6 output). "


id: 998527515e6cf992cf91d81707fc7379
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
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index: climate_gpt_only_giec
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content:
"A large fraction of the literature considers scenario-based analyses for given time slices. When GWL-based information is required \r\ninstead, an approximated mapping of the multi-model mean can be derived based on the known GWL in the given experiments for \r\na particular time period. As a rough approximation, CMIP6 multi-model mean projections for the near-term (2021-2040) correspond \r\nto changes at about 1.5degC, and projections for the high-end scenario (SSP5-8.5) for the long-term (2081-2100) correspond to about \r\n4degC-5degC of global warming (see Table 4.2 for changes in the CMIP6 ensemble and the Chapter 11 Supplementary Material (11.SM.1) \r\nand Hauser (2021) for details on other time periods and CMIP5). These approximated changes are used for some of the GWL-based \r\nassessments provided in the Chapter 11 regional tables (Section 11.9 and Table 11.3) when literature based on scenario projections is \r\nused to assess estimated changes at given GWLs."


id: 138a8721b327519f2c73f0b996563a39
created_at: 2023-03-16 14:00:49
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content:
"The use of scenarios remains a key element to inform mitigation decisions (Cross-Chapter Box 1.4), to assess which emissions pathways \r\nare consistent with a certain GWL (Cross-Chapter Box 1.4, Figure 1), to estimate when certain GWLs are reached (Section 4.3.4), and \r\nto assess for which variables it is meaningful to use GWLs as a dimension of integration. The use of scenarios is also essential for \r\nvariables whose climate response strongly depends on the contribution of radiative forcing (e.g., aerosols) or land-use and land \r\nmanagement changes, are time and warming rate dependent (e.g., sea level rise), or differ between transient and quasi-equilibrium \r\nstates. Furthermore, the use of concentration or emission-driven scenario simulations is required if regional climate assessments need \r\nto account for the uncertainty in GSAT changes or climate-carbon feedbacks."


id: 9099de21bee7a52ba787d29f32fc0794
created_at: 2023-03-16 14:00:49
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index: climate_gpt_only_giec
vector_id: 20283
content:
"Forcing dependence of the GWL response is found for global mean precipitation (Section 8.4.3), but less for regional patterns of mean \r\nprecipitation changes (Cross-Chapter Box 11.1, Figure 2). Limited dependence is found for extremes, as highlighted above. In the \r\ncryosphere, elements that are quick to respond to warming like sea ice area, permafrost and snow, show little scenario dependence \r\n(Sections 9.3.1.1, 9.5.2.3 and 9.5.3.3), whereas slow-responding variables such as ice volumes of glaciers and ice sheets respond with \r\na substantial delay and, due to their inertia, the response depends on when a certain GWL is reached. This also applies to some extent \r\nfor sea level rise where, for example, the contributions of melting glaciers and ice sheets depend on the pathway followed to reach \r\na given GWL (Section 9.6.3.4). "


id: 8b2552dd4648872004ed4ca805e77a05
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
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index: climate_gpt_only_giec
vector_id: 19419
content:
"In addition to the lagged effect, the climate response at a given GWL may differ before and after a period of overshoot, for example \r\nin the Atlantic Meridional Overturning Circulation (e.g., Palter et al. 2018). Finally, as assessed in IPCC SR1.5, there is a difference in \r\nthe response even for temperature-related variables if a GWL is reached in a rapidly warming transient state or in an equilibrium state \r\nwhen the land-sea warming contrast is less pronounced (e.g., King et al. 2020). However, in this Report, GWLs are used in the context \r\nof projections for the 21st century when the climate response is mostly not in equilibrium and where projections for many variables \r\nare less dependent on the pathway than for projections beyond 2100 (Section 9.6.3.4)."


id: 40bf0010c397cd803c9a247d241c9a6b
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
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index: climate_gpt_only_giec
vector_id: 7566
content:
"GWL-based projections can inform society and policymakers on how climate would change under GWLs consistent with the aims \r\nof the Paris Agreement (stabilization at 1.5degC/well below 2degC), as well as on the consequences of missing these aims and reaching \r\nGWLs of 3degC or 4degC by the end of the century. The AR6 assessment shows that every bit of global warming matters and that changes \r\nin global warming of 0.5degC lead to statistically significant changes in mean climate and climate extremes on global scale and for \r\nlarge regions (Sections 4.6.2, 11.2.4, 11.3, 11.4, 11.6 and 11.9, Figures 11.8 and 11.9, Atlas and Interactive Atlas), as also assessed \r\nin IPCC SR1.5. "


id: c60d7aebd16fd15032b71631a7742706
created_at: 2023-03-16 14:00:49
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index: climate_gpt_only_giec
vector_id: 28738
content:
"The SREX (IPCC, 2012) and AR5 (IPCC, 2014) concluded that \r\ngreenhouse gas forcing is the dominant factor for the increases in the \r\nintensity, frequency, and duration of warm extremes and the decrease \r\nin those of cold extremes. This general global-scale warming is \r\nmodulated by large-scale atmospheric circulation patterns, as well as \r\nby feedbacks such as soil moisture-evapotranspiration-temperature \r\nand snow/ice-albedo-temperature feedbacks, and local forcings \r\nsuch as land-use change or changes in aerosol concentrations at the "


id: 9181e08f952ad7251122b10fcaf636dd
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
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index: climate_gpt_only_giec
vector_id: 20430
content:
"This section assesses changes in temperature extremes at global, \r\ncontinental and regional scales. The main focus is on the changes in the \r\nmagnitude and frequency of moderate extreme temperatures (those \r\nthat occur several times a  year) to very extreme temperatures \r\n(those that occur once in 10 or more years) of time scales from a day \r\nto a season, though there is a strong emphasis on the daily scale \r\nwhere literature is most concentrated. "


id: 3bcded1df0396d1293f6e822d408848
created_at: 2023-03-16 14:00:49
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index: climate_gpt_only_giec
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content:
"regional and local scales (Sections 11.1.5 and 11.1.6, and Box 11.1). \r\nTherefore, changes in temperature extremes at regional and local \r\nscales can have heterogeneous spatial distributions. Changes in \r\nthe magnitudes (or intensities) of extreme temperatures are often \r\nlarger than changes in global surface temperature, because of larger \r\nwarming on land than on the ocean surface (Section 2.3.1.1), and \r\nbecause of feedbacks, though they are of similar magnitude to \r\nchanges in the local mean temperature (Figure 11.2). "


id: 647a01708c48f51ec48e208000e4916c
created_at: 2023-03-16 14:00:49
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content:
"Land-atmosphere feedbacks strongly modulate regional- and \r\nlocal-scale changes in temperature extremes (high confidence) \r\n(Section 11.1.6; Seneviratne et al., 2013; Lemordant et al., 2016; Donat \r\net al., 2017; Sillmann et al., 2017b; Hirsch et al., 2019). This effect is \r\nparticularly notable in mid-latitude regions where the drying of soil \r\nmoisture amplifies high temperatures, especially through increases \r\nin sensible heat flux (Whan et al., 2015; Douville et al., 2016; Vogel \r\net al., 2017). Land-atmosphere feedbacks amplifying temperature \r\nextremes also include boundary-layer feedbacks and effects on \r\natmospheric circulation (Miralles et al., 2014a; Schumacher et al., "


id: 63f571c365f381bed958a06a8a2c2809
created_at: 2023-03-16 14:00:49
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vector_id: 13294
content:
"feedbacks, and local and regional forcings such as land-use and land\u0002cover changes, or aerosol concentrations, and decadal and multi\u0002decadal natural variability. This leads to heterogeneity in regional \r\nchanges and their associated uncertainties (high confidence). \r\nChanges in anthropogenic aerosol concentrations have likely affected \r\ntrends in hot extremes in some regions. Irrigation and crop expansion \r\nhave attenuated increases in summer hot extremes in some regions, \r\nsuch as the Midwestern USA (medium confidence). Urbanization has \r\nlikely exacerbated the effects of global warming in cities, in particular \r\nfor nighttime temperature extremes."


id: b52003e98b2405b48fd3a9c9b9e733fc
created_at: 2023-03-16 14:00:49
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index: climate_gpt_only_giec
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content:
"The SREX (IPCC, 2012) reported a very likely decrease in the number \r\nof cold days and nights and increase in the number of warm days \r\nand nights at the global scale. Confidence in trends was assessed as \r\nregionally variable (low to medium confidence) due to either a lack \r\nof observations or varying signals in sub-regions. "


id: 8199a78b457b063b18f268ac2e435df4
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
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index: climate_gpt_only_giec
vector_id: 17922
content:
"On the global scale, evidence of an increase in the number of \r\nwarm days and nights and a decrease in the number of cold days \r\nand nights, and an increase in the coldest and hottest extreme \r\ntemperatures is very robust and consistent among all variables. \r\nFigure 11.2 displays time series of globally averaged TXx and TNn on \r\nland. Warming of land mean TXx is similar to the mean temperature \r\nwarming on land, which is about 45% higher than global warming \r\n(Section 2.3.1). Warming of land mean TNn is even higher, with about \r\n3degC of warming since 1960 (Figure 11.2). Figure 11.9 shows maps \r\nof linear trends over 1960-2018 in TXx, TNn, and frequency of warm \r\ndays (TX90p). The maps for TXx and TNn show trends consistent \r\nwith overall warming in most regions, with a  particularly high "


id: 13fd990ec0fbced0ca08104b9f46b4be
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index: climate_gpt_only_giec
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content:
"In Asia (Table 11.7), there is very robust evidence for a  very likely\r\nincrease in the intensity and frequency of hot extremes and decrease \r\nin the intensity and frequency of cold extremes in recent decades. \r\nThis is clear in global studies (e.g.,  Alexander, 2016; Dunn et al., "


id: 4b5327831ea28804aa8159c1ee0ad189
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content:
"extremes have been reported for the period 1951-2011 (Whan et al., \r\n2014; McGree et al., 2019). "


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"In Europe (Table  11.16), there is very robust evidence for a  very \r\nlikely increase in maximum temperatures and the frequency of \r\nheatwaves. The increase in the magnitude and frequency of high \r\nmaximum temperatures has been observed consistently across \r\nregions, including in central Europe (Twardosz and Kossowska-Cezak, \r\n2013; Christidis et al., 2015; Lorenz et al., 2019) and southern Europe \r\n(Croitoru and Piticar, 2013; El Kenawy et al., 2013; Christidis et al., \r\n2015; Nastos and Kapsomenakis, 2015; Fioravanti et al., 2016; Ruml \r\net al., 2017). In Northern Europe, a strong increase in extreme winter \r\nwarming events has been observed (Matthes et al., 2015; Vikhamar\u0002Schuler et al., 2016). Temperature observations for winter cold spells \r\nshow a  long-term decreasing frequency in Europe (Brunner et al., \r\n2018; van Oldenborgh et al., 2019), and typical cold spells, such \r\nas that observed during the 2009-2010 winter, had an occurrence "


id: 67d46b43ee817429380b3e574e5df759
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content:
"probability two times smaller currently than if climate change had \r\nnot occurred (Christiansen et al., 2018)."


id: ea8c3a0eec9ede9a806619f15e703abe
created_at: 2023-03-16 14:00:49
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"of England (M. Sanderson et al., 2017). In Central and South America, \r\nthere are increases in the frequency of heatwaves (Barros et al., \r\n2015; Bitencourt et al., 2016; Ceccherini et al., 2016; Piticar, 2018), \r\nalthough decreases in Excess Heat Factor (EHF), which is a metric for \r\nheatwave intensity, are observed in South America in data derived \r\nfrom HadGHCND (Cavanaugh and Shen, 2015). "


id: f9825bf23e7888f44218495ff8bc3e73
created_at: 2023-03-16 14:00:49
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content:
"Extreme heat events have increased around the Arctic since 1979, \r\nparticularly over Arctic North America and Greenland (Matthes \r\net al., 2015; Dobricic et al., 2020), which is consistent with summer \r\nmelt (Section 9.4.1). Observations north of 60@N show increases in \r\nwinter warm days and nights over 1979-2015, while cold days and \r\nnights declined (Sui et al., 2017). Extreme heat days are particularly \r\nstrong in winter, with observations showing the warmest mid-winter \r\ntemperatures at the North Pole rising at twice the rate of mean \r\ntemperature (Moore, 2016), as well as increases in Arctic winter \r\nwarm days (Vikhamar-Schuler et al., 2016; Graham et al., 2017). \r\nArctic annual minimum temperatures have increased at about three \r\ntimes the rate of global surface temperature since the 1960s (Figures \r\n11.2 and 11.9), consistent with the observed mean cold season \r\n(October-May) warming of 3.1degC in the region (Atlas 11.2)."


id: e1359846b5edac8c7c74bfe95ae9c6a8
created_at: 2023-03-16 14:00:49
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vector_id: 32932
content:
"Initial analyses of CMIP6 simulations (H. Chen et al., 2020; Di Luca \r\net al., 2020a; Kim et al., 2020; Thorarinsdottir et al., 2020; Wehner \r\net al., 2020; Li et al., 2021) indicate that the CMIP6 models perform \r\nsimilarly to the CMIP5 models regarding biases in hot and cold \r\nextremes. In general, CMIP5 and CMIP6 historical simulations are \r\nsimilar in their performance in simulating the observed climatology \r\nof extreme temperatures (high confidence). The general warm bias \r\nin hot extremes and cold bias in cold extremes reported for CMIP5 \r\nmodels (Kharin et al., 2013; Sillmann et al., 2013a) remain in CMIP6 \r\nmodels (Di Luca et al., 2020a). However, there is some evidence that "


id: c4229b0519ee5a301e7d36fe4ba7385d
created_at: 2023-03-16 14:00:49
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content:
"in the duration of the events does not impact on the reliability of the \r\nmodels' positive trends (Freychet et al., 2018). "


id: 474b32d42c601f19906909845f591a7e
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
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vector_id: 8602
content:
"Atmospheric Model Intercomparison Project (AMIP) simulations \r\nare often used in event attribution studies to assess the influence \r\nof global warming on observed temperature-related extremes. These \r\nsimulations typically capture the observed trends in temperature \r\nextremes, though some regional features, such as the lack of warming \r\nin daytime warm temperature extremes over South America and \r\nparts of North America, are not reproduced in the model simulations \r\n(Dittus et al., 2018), possibly due to internal variability, deficiencies \r\nin local surface processes, or forcings that are not represented in \r\nthe sea surface temperatures (SSTs). Additionally, the AMIP models \r\nassessed tend to produce overly persistent heatwave events. This bias "


id: 3fec79754a6c0b22184482b779de5c45
created_at: 2023-03-16 14:00:49
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vector_id: 7443
content:
"extremes shown in both RCMs (Whan and Zwiers, 2016). The most \r\nsignificant biases are found in TXx and TNn, with fewer differences \r\nin the simulation of annual minimum daily maximum temperature \r\n(TXn) and annual maximum daily minimum temperature (TNx) \r\nin Central and Western North America. Over Central and South \r\nAmerica, maximum temperatures from the Eta RCM are generally \r\nunderestimated, although hot days, warm nights, and heatwaves are \r\nincreasing in the period 1961-1990, in agreement with observations \r\n(Chou et al., 2014b; Tencer et al., 2016; Bozkurt et al., 2019)."


id: cbbe23d725005476b72b7ad3d9a3d68e
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
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index: climate_gpt_only_giec
vector_id: 29595
content:
"In summary, there is high confidence that climate models can \r\nreproduce the mean state and overall warming of temperature \r\nextremes observed globally and in most regions, although the \r\nmagnitude of the trends may differ. The ability of models to capture \r\nobserved trends in temperature-related extremes depends on the \r\nmetric evaluated, the way indices are calculated, and the time \r\nperiods and spatial scales considered. Regional climate models add \r\nvalue in simulating temperature-related extremes over GCMs in some \r\nregions. Some land forcings on temperature extremes are not well\u0002captured (effects of deforestation) or generally not representated \r\n(irrigation) in ESMs."


id: 7b9ab7c54966a5f14b63b00d04ff99fa
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
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vector_id: 17044
content:
"The SREX (IPCC, 2012) assessed that it is likely anthropogenic \r\ninfluences have led to the warming of extreme daily minimum and \r\nmaximum temperatures at the global scale. The AR5 concluded that \r\nhuman influence has very likely contributed to the observed changes \r\nin the intensity and frequency of daily temperature extremes on the \r\nglobal scale in the second half of the 20th  century (IPCC, 2014). \r\nWith regard to individual, or regionally or locally specific events, AR5 \r\nconcluded that it is likely human influence has substantially increased \r\nthe probability of occurrence of heatwaves in some locations."


id: 683b630380e16ca4ab00c96fa4babb85
created_at: 2023-03-16 14:00:49
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vector_id: 13983
content:
"A significant advance since AR5 has been a large number of studies \r\nfocusing on extreme temperature events at monthly and seasonal \r\nscales, using various extreme event attribution methods. Diffenbaugh \r\net al. (2017) found that anthropogenic warming has increased the "


id: 7f3b4141e3ca097e6278dd36ff516b89
created_at: 2023-03-16 14:00:49
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vector_id: 17570
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"the attributable signal (Fischer and Knutti, 2015; Kirchmeier-Young \r\net al., 2019). Large-scale, longer-duration events tend to have notably \r\nlarger attributable risk ratios (Angelil et al., 2014, 2018; Uhe et al., \r\n2016; Harrington, 2017; Kirchmeier-Young et al., 2019), as natural \r\nvariability is smaller. While uncertainty in the best estimates of the \r\nrisk ratios may be large, their lower bounds can be quite insensitive to \r\nuncertainties in observations or model descriptions, thus increasing \r\nconfidence in conservative attribution statements (Jeon et al., 2016)."


id: 2eb46e0ce8a26ad7afb4ec3fe1939db2
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
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index: climate_gpt_only_giec
vector_id: 4790
content:
"Studies that focused on the attributable signal in observed cold extreme \r\nevents show human influence reducing the probability of those events. \r\nIndividual attribution studies on the extremely cold winter of 2011 in \r\nEurope (Peterson et al., 2012), in the eastern USA during 2014 and \r\n2015 (Trenary et al., 2015, 2016; Wolter et al., 2015; Bellprat et al., \r\n2016), in the cold spring of 2013 in the United Kingdom (Christidis \r\net al., 2014), and of 2016 in eastern China (Qian et al., 2018; Y. Sun \r\net al., 2018b) all showed a reduced probability due to human influence \r\non the climate. An exception is the study of Grose et al. (2018), which \r\nfound an increase in the probability of the severe western Australian \r\nfrost of 2016 due to anthropogenically-driven changes in circulation \r\npatterns that drive cold outbreaks and frost probability. "


id: 375503b1961c8781474a46315af47dea
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
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index: climate_gpt_only_giec
vector_id: 6090
content:
"The AR5 (Chapter12, Collins et al., 2013) concluded that it is virtually \r\ncertain there will be more frequent hot extremes and fewer cold \r\nextremes at the global scale and over most land areas in a future "


id: 596d37c275bd15b8ce3f4bd1ebf02807
created_at: 2023-03-16 14:00:49
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vector_id: 11562
content:
"Different event attribution studies can produce a  wide range of \r\nchanges in the probability of event occurrence because of different \r\nframing. The temperature event definition itself plays a crucial role in "


id: b2c6a18fd1cbe863887c85f1ed64328
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
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index: climate_gpt_only_giec
vector_id: 25647
content:
"warmer climate, and it is very likely that heatwaves will occur \r\nwith a higher frequency and longer duration.The SR1.5 (Chapter 3, \r\nHoegh-Guldberg et al., 2018) assessment on projected changes in \r\nhot extremes at 1.5degC and 2degC global warming is consistent with the \r\nAR5 assessment, concluding that it is very likely a global warming \r\nof 2degC, when compared with a 1.5degC warming, would lead to more \r\nfrequent and more intense hot extremes on land, as well as to longer \r\nwarm spells, affecting many densely inhabited regions. The SR1.5 \r\nalso assessed it is very likely that the strongest increases in the \r\nfrequency of hot extremes are projected for the rarest events, while \r\ncold extremes will become less intense and less frequent, and cold \r\nspells will be shorter. "


id: aea4f12041a145a33d184fbe89f400cc
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 25002
content:
"extremes will occur, even at the 1.5degC GWL (Figure 11.11). At this \r\nlevel, widespread significant changes at the grid-box level occur for \r\ndifferent temperature indices (Aerenson et al., 2018). In agreement \r\nwith CMIP5 projections, CMIP6 simulations show that a  0.5degC \r\nincrement in global warming will significantly increase the intensity \r\nand frequency of hot extremes, and decrease the intensity and \r\nfrequency of cold extremes on the global scale (Figures 11.6, 11.8 \r\nand 11.12). It takes less than half of a degree for the changes in TXx \r\nto emerge above the level of natural variability (Figure 11.8) and the \r\n66% ranges of the land medians of the 10-year or 50-year TXx events \r\ndo not overlap between 1.0degC and 1.5degC in the CMIP6 multi-model \r\nensemble simulations (Figure 11.6, Li et al., 2021). "


id: a030280adf86d4b2f5096ab9fa6656dc
created_at: 2023-03-16 14:00:49
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index: climate_gpt_only_giec
vector_id: 22741
content:
"Projected warming is larger for TNn and exhibits strong equator-to\u0002pole amplification, similar to the warming of boreal winter mean \r\ntemperatures. The warming of TXx is more uniform over land and \r\ndoes not exhibit this behaviour (Figure  11.11). The warming of \r\ntemperature extremes on global and regional scales tends to scale \r\nlinearly with global warming (Section 11.1.4; Fischer et al., 2014; \r\nSeneviratne et al., 2016; Wartenburger et al., 2017; Li et al., 2021; \r\nsee also SR1.5, Chapter 3). In the mid-latitudes, the rate of warming \r\nof hot extremes can be as large as twice the rate of global \r\nwarming (Figure 11.11). In the Arctic winter, the rate of warming of \r\nthe temperature of the coldest nights is about three times the rate \r\nof global warming (Appendix, Figure  11.A.1). Projected changes "


id: 95c10efe937ad2e059f0a0cdb26033a
created_at: 2023-03-16 14:00:49
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vector_id: 21057
content:
"New studies since AR5 and SR1.5 confirm these assessments. New \r\nliterature since AR5 includes projections of temperature-related \r\nextremes in relation to changes in mean temperatures, projections \r\nbased on CMIP6 simulations, projections based on stabilized global \r\nwarming levels, and the use of new metrics. Constraints for the \r\nprojected changes in hot extremes were also provided (Borodina \r\net al., 2017b; Sippel et al., 2017b; Vogel et al., 2017). Overall, \r\nprojected changes in the magnitude of extreme temperatures over \r\nland are larger than changes in global mean temperature, over mid\u0002latitude land regions in particular (Figures 11.3, 11.11; Fischer et al., \r\n2014; Seneviratne et al., 2016; B.M. Sanderson et al., 2017; Wehner \r\net al., 2018b; Di Luca et al., 2020b). Large warming in hot and cold "


id: d0fb644a831d8d8bef9973b91f3b474e
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
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vector_id: 30411
content:
"in temperature extremes can deviate from projected changes in \r\nannual mean warming in the same regions (Figures 11.3, 11.A.1 and \r\n11.A.2; Di Luca et al., 2020b; Wehner, 2020) due to the additional \r\nprocesses that control the response of regional extremes, including, \r\nin particular, soil moisture-evapotranspiration-temperature \r\nfeedbacks for hot extremes in the mid-latitudes and subtropical \r\nregions, and snow/ice-albedo-temperature feedbacks in high\u0002latitude regions. "


id: 23ca372e9297a1893de92ece75277b21
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
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index: climate_gpt_only_giec
vector_id: 3077
content:
"The probability of exceeding a  certain hot extreme threshold will \r\nincrease, while those for cold extreme will decrease with global \r\nwarming (B. Mueller et al., 2016; Lewis et al., 2017b; Suarez\u0002Gutierrez et al., 2020b). The changes tend to scale nonlinearly with \r\nthe level of global warming, with larger changes for more rare \r\nevents (Section 11.2.4; Cross-Chapter Box 11.11; Figures 11.6 and \r\n11.12; Fischer and Knutti, 2015; Kharin et al., 2018; Li et al., 2021). \r\nFor  example, the CMIP5 ensemble projects the frequency of the \r\npresent-day climate 20-year hottest daily temperature to increase \r\nby 80% at the 1.5degC GWL and by 180% at the 2.0degC GWL, and \r\nthe frequency of the present-day climate 100-year hottest daily \r\ntemperature to increase by 200% and more than 700% at the 1.5degC \r\nand 2.0degC warming levels, respectively (Kharin et al., 2018). CMIP6 \r\nsimulations project similar changes (Li et al., 2021)."


id: 58dc0b34af2dc78395f975732fa1627a
created_at: 2023-03-16 14:00:49
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index: climate_gpt_only_giec
vector_id: 11475
content:
"than that projected for TX90p, consistent with observed past changes \r\n(Lopez-Franca et al., 2016). Under RCP8.5, the number of heatwave \r\ndays are projected to increase for the intra-Americas region for the \r\nend of the 21st century (Angeles-Malaspina et al., 2018). A general \r\ndecrease in the frequency of cold spells and frost days is projected, as \r\nindicated by several indices based on minimum temperature (Lopez\u0002Franca et al., 2016)."


id: 9842d56228937cbdf205308fd08629de
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 21467
content:
"In Africa (Table  11.4), evidence includes increases in the intensity \r\nand frequency of hot extremes, such as warm days, warm nights, \r\nand heatwaves, and decreases in the intensity and frequency of cold \r\nextremes, such as cold days and cold nights over the continent, as \r\nprojected by CMIP5, CMIP6, and CORDEX simulations (Giorgi et al., \r\n2014; Engelbrecht et al., 2015; Lelieveld et al., 2016; Russo et al., \r\n2016; Dosio, 2017; Bathiany et al., 2018; Mba et al., 2018; Nangombe \r\net al., 2018; Weber et al., 2018; Kruger et al., 2019; Coppola et al., \r\n2021b; Li et al., 2021). Cold spells are projected to decrease under \r\nall RCPs, and even at low warming levels in Western and Central \r\nAfrica (Diedhiou et al., 2018). The number of cold days is projected to \r\ndecrease in East Africa (Ongoma et al., 2018b)."


id: f27592e56668c58d8b04150a7e2a3022
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
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index: climate_gpt_only_giec
vector_id: 35670
content:
"In Australasia (Table  11.10), evidence includes increases in the \r\nintensity and frequency of hot extremes, such as warm days, warm \r\nnights, and heatwaves, and decreases in the intensity and frequency \r\nof cold extremes, such as cold days and cold nights over the \r\ncontinent, as projected by CMIP5, CMIP6, and CORDEX simulations \r\n(CSIRO and BOM, 2015; Alexander and Arblaster, 2017; Lewis et al., \r\n2017a; Herold et al., 2018; Coppola et al., 2021b; Evans et al., 2021). \r\nOver most of Australia, increases in the intensity and frequency of \r\nhot extremes are projected to be predominantly driven by the long\u0002term increase in mean temperatures (Di Luca et al., 2020b). Future \r\nprojections indicate a decrease in the number of frost days regardless \r\nof the region and season considered (Alexander and Arblaster, 2017; \r\nHerold et al., 2018)."


id: 455fec415a9b3961640991bf0eea1630
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
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index: climate_gpt_only_giec
vector_id: 8333
content:
"In summary, it is virtually certain that further increases in the intensity \r\nand frequency of hot extremes, and decreases in the intensity and \r\nfrequency of cold extremes, will occur throughout the 21st century \r\nand around the world. It is virtually certain that the number of hot \r\ndays and hot nights and the length, frequency, and/or intensity of \r\nwarm spells or heatwaves compared to 1995-2014 will increase \r\nover most land areas. In most regions, changes in the magnitude \r\nof temperature extremes are proportional to global warming levels \r\n(high confidence). The highest increase of temperature of hottest "


id: 727cb686673d9535a4d2220d2558893e
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 15631
content:
"In Central and South America (Table  11.13), evidence includes \r\nincreases in the intensity and frequency of hot extremes, such as warm \r\ndays, warm nights, and heatwaves, and decreases in the intensity and \r\nfrequency of cold extremes, such as cold days and cold nights over the \r\ncontinent, as projected by CMIP5, CMIP6, and CORDEX simulations \r\n(Chou et al., 2014a; Cabre et al., 2016; Lopez-Franca et al., 2016; \r\nStennett-Brown et al., 2017; Coppola et al., 2021b; Li et al., 2021; \r\nVichot-Llano et al., 2021). Over South-Eastern South America during \r\nthe austral summer, the increase in the frequency of TN90p is larger "


id: 4305acab8c48c4492e3fe3a4dec4f753
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
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index: climate_gpt_only_giec
vector_id: 7939
content:
"days is projected in some mid-latitude and semi-arid regions, \r\nat about 1.5 times to twice the rate of global warming (high \r\nconfidence). The highest increase of temperature of coldest days is \r\nprojected in Arctic regions, at about three times the rate of global \r\nwarming (high confidence). The probability of temperature extremes \r\ngenerally increases nonlinearly with increasing global warming \r\nlevels (high confidence). Confidence in assessments depends on the \r\nspatial and temporal scales of the extreme in question, with high \r\nconfidence in projections of temperature-related extremes at global \r\nand continental scales for daily to seasonal scales. There is high \r\nconfidence that, on land, the magnitude of temperature extremes \r\nincreases more strongly than global mean temperature."


id: 19e722c340eed09ee387a2fe234eae87
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
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index: climate_gpt_only_giec
vector_id: 1544
content:
"including tropical/extratropical cyclones and severe convective \r\nstorms, result in changes in extreme precipitation (Section  11.7). \r\nAlso,  changes in sea surface temperatures (SSTs) alter land-sea \r\ncontrast, leading to changes in precipitation extremes near coastal \r\nregions. For example, the projected larger SST increase near the \r\ncoasts of East Asia and India can result in heavier rainfall near these \r\ncoastal areas from tropical cyclones (Mei and Xie, 2016) or torrential \r\nrains (Manda et al., 2014). The warming in the western Indian Ocean \r\nis associated with increases in moisture surges on the low-level \r\nmonsoon westerlies towards the Indian subcontinent, which may \r\nlead to an increase in the occurrence of precipitation extremes over \r\ncentral India (Krishnan et al., 2016; Roxy et al., 2017). "


id: ce3d654edcf945c99b662b7c32a35ef8
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
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index: climate_gpt_only_giec
vector_id: 29967
content:
"Decreases in atmospheric aerosols results in warming and thus an \r\nincrease in extreme precipitation (Samset et al., 2018; Sillmann \r\net al., 2019). Changes in atmospheric aerosols also result in dynamic \r\nchanges such as in tropical cyclones (Takahashi et al., 2017; Strong \r\net al., 2018). Uncertainty in the projections of future aerosol \r\nemissions results in additional uncertainty in the heavy precipitation \r\nprojections of the 21st century (Lin et al., 2016). "


id: afda1efeaaf87d2e3705a0019fe2537f
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
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index: climate_gpt_only_giec
vector_id: 25189
content:
"This section assesses changes in heavy precipitation at global and \r\nregional scales. The main focus is on extreme precipitation at a daily \r\nscale where literature is most concentrated, though extremes of \r\nshorter (sub-daily) and longer (five-day or more) durations are also \r\nassessed to the extent the literature allows."


id: 7ba9fa9fc64ce8708c3949ce35a995f6
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
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index: climate_gpt_only_giec
vector_id: 17056
content:
"The SREX (Chapter 3, Seneviratne et al., 2012) assessed changes in \r\nheavy precipitation in the context of the effects of thermodynamic \r\nand dynamic changes. Box  11.1 assesses thermodynamic and \r\ndynamic changes in a  warming world to aid the understanding \r\nof changes in observations and projections in some extremes and \r\nthe sources of uncertainties (see also Section  8.2.3.2). In general, \r\nwarming increases the atmospheric water-holding capacity following \r\nthe Clausius-Clapeyron (C-C) relation. This thermodynamic effect \r\nresults in an increase in extreme precipitation at a similar rate at the \r\nglobal scale. On a regional scale, changes in extreme precipitation \r\nare further modulated by dynamic changes (Box 11.1). "


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vector_id: 23557
content:
"Large-scale modes of variability, such as the North Atlantic Oscillation \r\n(NAO), El Nino-Southern Oscillation (ENSO), Atlantic Multi-decadal \r\nVariability (AMV), and Pacific Decadal Variability (PDV) (Annex IV), \r\nmodulate precipitation extremes through changes in environmental \r\nconditions or embedded storms (Section 8.3.2). Latent heating can \r\ninvigorate these storms (Nie et al., 2018; Z. Zhang et al., 2019a); \r\nchanges in dynamics can increase precipitation intensity above \r\nthat expected from the C-C scaling rate (Sections 8.2.3.2 and 11.7; \r\nBox  11.1). Additionally, the efficiency of converting atmospheric \r\nmoisture into precipitation can change as a  result of cloud \r\nmicrophysical adjustment to warming, resulting in changes in the \r\ncharacteristics of extreme precipitation; but changes in precipitation \r\nefficiency in a warming world are highly uncertain (Sui et al., 2020)."


id: ea41b26cd1e1a4d3d412550b7e22265c
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 34364
content:
"In summary, precipitation extremes are controlled by both \r\nthermodynamic and dynamic processes. Warming-induced \r\nthermodynamic change results in an increase in extreme precipitation, \r\nat a  rate that closely follows the C-C relationship at the global \r\nscale (high confidence). The effects of warming-induced changes \r\nin dynamic drivers on extreme precipitation are more complicated, \r\ndifficult to quantify, and are an uncertain aspect of projections. \r\nPrecipitation extremes are also affected by forcings other than \r\nchanges in greenhouse gases, including changes in aerosols, land\u0002use and land-cover change, and urbanization (medium confidence). "


id: 2a88f24e29517a6475d47b2b8b89fe07
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 4117
content:
"It is difficult to separate the effect of global warming from internal \r\nvariability in the observed changes in the modes of variability \r\n(Section  2.4). Future projections of modes of variability are highly \r\nuncertain (Section  4.3.3), resulting in uncertainty in regional \r\nprojections of extreme precipitation. Future warming may amplify \r\nmonsoonal extreme precipitation. Changes in extreme storms, "


id: 9f13f2b2aaccfe8a21fb2c8556109066
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 22568
content:
"Both SREX (Chapter  3, Seneviratne et al., 2012) and AR5 (IPCC, \r\n2014 Chapter 2) concluded it was likely that the number of heavy \r\nprecipitation events over land had increased in more regions than "


id: f2a98d29c4dd8f9df9b413f1e2ccc9ac
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
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vector_id: 35707
content:
"it had decreased, though there were wide regional and seasonal \r\nvariations, and trends in many locations were not statistically \r\nsignificant. This assessment has been strengthened with multiple \r\nstudies finding robust evidence of the intensification of extreme \r\nprecipitation at global and continental scales, regardless of spatial \r\nand temporal coverage of observations and the methods of data \r\nprocessing and analysis."


id: 9327a15683d0e6f5b23e670ff4c444be
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 20666
content:
"daily heavy precipitation results in an increase in pluvial floods over \r\nsmaller watersheds (Ghausi and Ghosh, 2020). "


id: 5b228d63027941fe799f6a498cb45622
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 11846
content:
"In Asia (Table 11.8), there is robust evidence that extreme precipitation \r\nhas increased since the 1950s (high confidence), however, this \r\nis dominated by high spatial variability. Increases in Rx1day and \r\nRx5day during 1950-2018 are found over two-thirds of stations. \r\nThe  percentage of stations with statistically significant trends is \r\nlarger than can be expected by chance (Figure  11.13; Sun et al., \r\n2021). An increase in extreme precipitation has also been observed \r\nin various regional studies based on different metrics of extreme "


id: f1f8ee46d0cf5fdcee0d740f7b8c3af0
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 35596
content:
"In Small Islands, there is a lack of evidence showing changes in heavy \r\nprecipitation overall. There were increases in extreme precipitation \r\nin Tobago from 1985-2015 (Stephenson et al., 2014; Dookie et al., \r\n2019) and decreases in south-western French Polynesia and the \r\nsouthern subtropics (low confidence) (Table 11.5; Atlas.10). Extreme \r\nprecipitation leading to flooding in the Small Islands has been \r\nattributed in part to tropical cyclones, as well as being influenced by \r\nENSO (Box 11.5; Khouakhi et al., 2016; Hoegh-Guldberg et al., 2018)."


id: 3fd7255ae64444c3f89e288c824b9c95
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 7427
content:
"In summary, the frequency and intensity of heavy precipitation have \r\nlikely increased at the global scale over a majority of land regions \r\nwith good observational coverage. Since 1950, the annual maximum \r\namount of precipitation falling in a day, or over five consecutive days, \r\nhas likely increased over land regions with sufficient observational \r\ncoverage for assessment, with increases in more regions than \r\nthere are decreases. Heavy precipitation has likely increased on the \r\ncontinental scale over three continents (North America, Europe, and \r\nAsia) where observational data are more abundant. There is very low "


id: 61f4e89f14b34cd7129f37b316944856
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 12975
content:
"confidence about changes in sub-daily extreme precipitation due to \r\nthe limited number of studies and available data. "


id: 672221e3d76c3f4f8463093b479167f7
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 13821
content:
"Different generations of CMIP models have improved over time, \r\nthough quite modestly (Flato et al., 2013; Watterson et al., 2014). \r\nImprovements in the representation of the magnitude of the Expert \r\nTeam on Climate Change Detection and Indices (ETCCDI) in CMIP5 \r\nover CMIP3 (Sillmann et al., 2013a; Chen and Sun, 2015a) have \r\nbeen attributed to higher resolution, as higher-resolution models \r\nrepresent smaller areas at individual grid boxes. Additionally, the \r\nspatial distribution of extreme rainfall simulated by high-resolution "


id: 241e3aad3366004457385dcbdced76f7
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 3120
content:
"Both SREX (Chapter 3, Seneviratne et al., 2012) and AR5 (Chapter 10, \r\nIPCC, 2014) concluded with medium confidence that anthropogenic \r\nforcing has contributed to a  global-scale intensification of heavy \r\nprecipitation over the second half of the 20th  century. These \r\nassessments were based on the evidence of anthropogenic influence \r\non aspects of the global hydrological cycle, in particular, the human \r\ncontribution to the warming-induced observed increase in atmospheric \r\nmoisture that leads to an increase in heavy precipitation, and limited \r\nevidence of anthropogenic influence on extreme precipitation of \r\ndurations of one and five days."


id: e65a8501b542adb38f5458b90f521889
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 33729
content:
"In general, there is high confidence that historical simulations \r\nby CMIP5 and CMIP6 models of similar horizontal resolutions are \r\ninterchangeable in their performance in simulating the observed \r\nclimatology of extreme precipitation."


id: 5d4f39adb8aa48ae7060299b0685d36b
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 12195
content:
"In summary, there is  high confidence  in the ability of models to \r\ncapture the large-scale spatial distribution of precipitation extremes \r\nover land. The magnitude and frequency of extreme precipitation "


id: 220c0e701498f55529869fe57e1523b7
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 2804
content:
"the combined effect of greenhouse gases and aerosol forcing on \r\nRx1day and Rx5day over North America, Eurasia, and mid-latitude \r\nland regions (Zhang et al., 2013) and of greenhouse gas forcing \r\nin Rx1day and Rx5day in the mid-to-high latitudes, western and \r\neastern Eurasia, and the global dry regions (Paik et al., 2020). These \r\nfindings are corroborated by the detection of human influence in the \r\nfraction of extreme precipitation in the total precipitation over Asia, \r\nEurope, and North America (Dong et al., 2021). Human influence \r\nwas found to have contributed to the increase in frequency and \r\nintensity of regional precipitation extremes in North America during \r\n1961-2010, based on optimal fingerprinting and event attribution \r\napproaches (Kirchmeier-Young and Zhang, 2020). Tabari et al. (2020) \r\nfound the observed latitudinal increase in extreme precipitation \r\nover Europe to be consistent with model-simulated responses to \r\nanthropogenic forcing. "


id: 93b7d65ebfff35e5ccac8350db06e412
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 20753
content:
"Anthropogenic influence may have affected the large-scale \r\nmeteorological processes necessary for extreme precipitation \r\nand the localized thermodynamic and dynamic processes, both \r\ncontributing to changes in extreme precipitation events. Several new \r\nmethods have been proposed to disentangle these effects by either \r\nconditioning on the circulation state or attributing analogues. In \r\nparticular, the extremely wet winter of 2013-2014 in the UK can be \r\nattributed, approximately to the same degree, to both temperature\u0002induced increases in saturation vapour pressure and changes in the \r\nlarge-scale circulation (Vautard et al., 2016; Yiou et al., 2017). There \r\nare multiple cases indicating that very extreme precipitation may \r\nincrease at a rate more than the C-C rate (7% per 1degC of warming) \r\n(Pall et al., 2017; Risser and Wehner, 2017; van der Wiel et al., 2017; \r\nvan Oldenborgh et al., 2017; S.-Y.S. Wang et al., 2018). "


id: e6b8a6217b03dde9f8ade6dd4297539d
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 33784
content:
"Event attribution studies found an influence of anthropogenic \r\nactivities on the probability or magnitude of observed extreme \r\nprecipitation events, including European winters (Schaller et al., 2016; \r\nOtto et al., 2018b), extreme 2014 precipitation over the northern "


id: 9ee1ce2bc6f38d78abaa4f11ba52154c
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 22540
content:
"The spatial patterns of the projected changes across different \r\nwarming levels are quite similar, as shown in Figure  11.16, and "


id: ccdcef1e519ed41a0f042db16712edce
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 29768
content:
"In Central and South America (Table  11.14), extreme precipitation \r\nwill likely increase at global warming levels of 2degC and below, but \r\nvery likely increase at higher warming levels for the region as whole. \r\nA  larger increase in global surface temperature leads to a  larger \r\nincrease in extreme precipitation, independent of emissions scenarios \r\n(Li et al., 2021). But there are regional differences in the projection, "


id: 175ae2e077ffdedaf4b004cf127743d0
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 1181
content:
"part of Europe, such as the southern Mediterranean (Tramblay and \r\nSomot, 2018; Lionello and Scarascia, 2020; Coppola et al., 2021a), \r\nuncertain changes over central Europe (Argueso et al., 2012; Croitoru \r\net al., 2013; Rajczak et al., 2013; Casanueva et al., 2014; Patarcic \r\net al., 2014; Paxian et al., 2014; Roth et al., 2014; Fischer and Knutti, \r\n2015; Monjo et al., 2016) and a  strong increase in the remaining \r\nparts, including the Alps region (Gobiet et al., 2014; Donnelly et al., \r\n2017), particularly in winter (Fischer et al., 2015), and in northern \r\nEurope. In a 3degC warmer world, there will be a robust increase in \r\nextreme rainfall over 80% of land areas in northern Europe (Madsen \r\net al., 2014; Donnelly et al., 2017; Cardell et al., 2020). "


id: fc0693f1ec74b800f277927ef2672675
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 37145
content:
"In North America (Table 11.20), the intensity and frequency of extreme \r\nprecipitation will likely increase at the global warming levels of \r\n2degC and below, and very likely increase at higher warming  levels. \r\nAn increase is projected by CMIP6 model simulations (Li et al., \r\n2021) and by previous model generations (Wu, 2015; Easterling \r\net al., 2017; Innocenti et al., 2019), as well as by RCMs (Coppola \r\net al., 2021a). Projections of extreme precipitation over the southern \r\nportion of the continent and over Mexico are more uncertain, with \r\ndecreases possible (Sillmann et al., 2013b; Alexandru, 2018; Coppola \r\net al., 2021a). "


id: d836e1fc9cda19a6474ee0f9c38382b7
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 31543
content:
"In Europe (Table 11.17), extreme precipitation will likely increase at \r\nglobal warming levels of 2degC and below, but very likely increase for \r\nhigher warming levels for the region as whole. The CMIP6 multi\u0002model median projects an increase in the 10- and 50-year return \r\nvalues of Rx1day and Rx5day over a majority of the region at the 2degC \r\nglobal warming level, with more than 95% of the region showing \r\nan increase at higher warming levels (Figure 11.7; C. Li et al., 2021). \r\nThe most intense precipitation events observed today in Europe are \r\nprojected to almost double in occurrence for each 1degC of further global \r\nwarming (Myhre et al., 2019). Extreme precipitation is projected to \r\nincrease in both boreal winter and summer over Europe (Madsen \r\net al., 2014; Christensen et al., 2015; Nissen and Ulbrich, 2017). There \r\nare regional differences, with decreases or no change for the southern "


id: ed207e1316ac0e831f4a87cfac078b6f
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 34786
content:
"In summary, heavy precipitation will generally become more frequent \r\nand more intense with additional global warming. At global warming \r\nlevels of 4degC relative to the pre-industrial, very rare (e.g.,  one in \r\n10 or more years) heavy precipitation events would become more \r\nfrequent and more intense than in the recent past, on the global \r\nscale (virtually certain), and in all continents and AR6 regions: The \r\nincrease in frequency and intensity is extremely likely for most \r\ncontinents and very likely for most AR6 regions. The likelihood \r\nis lower at lower global warming levels and for less-rare heavy \r\nprecipitation events.  At the global scale, the intensification of \r\nheavy precipitation will follow the rate of increase in the maximum "


id: 578f6a6fc86800d832d29beaab0b3852
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 11272
content:
"amount of moisture that the atmosphere can hold as it warms (high \r\nconfidence), of about 7% per 1degC of global warming. The increase in \r\nthe frequency of heavy precipitation events will be non-linear with \r\nmore warming and will be higher for rarer events (high confidence), \r\nwith 10- and 50-year events to be approximately double and triple, \r\nrespectively, at the 4degC warming level. Increases in the intensity of \r\nextreme precipitation events at regional scales will depend on the \r\namount of regional warming as well as changes in atmospheric \r\ncirculation and storm dynamics leading to regional differences \r\nin the rate of heavy precipitation changes (high confidence)."


id: 74d1c218f12a030bb9871cc6a4928920
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 15973
content:
"flooding (Formetta and Feyen, 2019), masking effects of an increase \r\nin extreme precipitation on flood probability in some regions, even \r\nthough they do not eliminate very extreme floods (Vicente-Serrano \r\net al., 2017). This means that an increase in precipitation extremes \r\nmay not always result in an increase in river floods (Sharma et al., \r\n2018; Do et al., 2020). Yet, as very extreme precipitation can become \r\na dominant factor for river floods, there can be some correspondence \r\nin the changes in very extreme precipitation and river floods (Ivancic \r\nand Shaw, 2015; Wasko and Sharma, 2017; Wasko and Nathan, 2019). \r\nThis has been observed in the western Mediterranean (Llasat et al., \r\n2016), in China (Q. Zhang et al., 2015a) and in the USA (Peterson \r\net al., 2013b; Berghuijs et al., 2016; Slater and Villarini, 2016)."


id: ac6b62c9e5246c5c56ee02817c8a7ae2
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 24662
content:
"Since AR5, the number of studies on understanding how floods \r\nmay have changed, and will change in the future, has substantially \r\nincreased. Floods are a complex interplay of hydrology, climate, and \r\nhuman management, and the relative importance of these factors \r\nvaries for different flood types and regions. "


id: aa7b4d30381b73003c7bf6668b504db4
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 24355
content:
"In addition to the amount and intensity of precipitation, the main \r\nfactors for river floods include antecedent soil moisture (Paschalis \r\net al., 2014; Berghuijs et al., 2016; Grillakis et al., 2016; Woldemeskel \r\nand Sharma, 2016) and snow water-equivalent in cold regions \r\n(Sikorska et al., 2015; Berghuijs et al., 2016). Other factors are also \r\nimportant, including stream morphology (Borga et al., 2014; Slater \r\net al., 2015), river and catchment engineering (Pisaniello et al., 2012; \r\nNakayama and Shankman, 2013; Kim and Sanders, 2016), land-use \r\nand land-cover characteristics (Aich et al., 2016; Rogger et al., 2017) \r\nand changes (Knighton et al., 2019), and feedbacks between climate, \r\nsoil, snow, vegetation, etc. (Hall et al., 2014; Ortega et al., 2014; \r\nBerghuijs et al., 2016; Buttle et al., 2016; Teufel et al., 2019). Water \r\nregulation and management have, in general, increased resilience to "


id: 10c4526b0e61c0c520501c4e4f2e2093
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 112
content:
"(high confidence). Changes in extreme precipitation may be used as \r\na proxy to infer changes in some types of flash floods that are more \r\ndirectly related to extreme precipitation (high confidence). "


id: 8b3adf5bc07b9f29ad76eb31cbd80600
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 19432
content:
"In summary, the seasonality of floods has changed in cold regions \r\nwhere snowmelt dominates the flow regime in response to warming \r\n(high confidence). There is low confidence about peak flow trends over \r\npast decades on the global scale, but there are regions experiencing \r\nincreases, including parts of Asia, Southern South America, north\u0002east USA, north-western Europe, and the Amazon, and regions \r\nexperiencing decreases, including parts of the Mediterranean, \r\nAustralia, Africa, and south-western USA. "


id: c9df635e5024c0d4383883147063958d
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 29313
content:
"Hydrological models used to simulate floods are structurally diverse \r\n(Dankers et al., 2014; Mateo et al., 2017; Sen, 2018), often requiring \r\nextensive calibration since sub-grid processes and land-surface \r\nproperties need to be parametrized, irrespective of the spatial \r\nresolutions (Doll et al., 2016; Krysanova et al., 2017). The data used \r\nto drive and calibrate the models are usually of coarse resolution, \r\nnecessitating the use of a wide variety of downscaling techniques \r\n(Muerth et al., 2013). This adds uncertainty not only to the models \r\nbut also to the reliability of the calibrations. The quality of the flood \r\nsimulations also depends on the spatial scale, as flood processes \r\nare different for catchments of different sizes. It is more difficult to \r\nreplicate flood processes for large basins, as water management and \r\nwater use are often more complex for these basins. "


id: ffc7b8085994189f1a639cd5b87a8ac8
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 37784
content:
"Studies that use different regional hydrological models show a large \r\nspread in flood simulations (Dankers et al., 2014; Roudier et al., \r\n2016; Trigg et al., 2016; Krysanova et al., 2017). Regional models \r\nreproduce moderate and high flows reasonably well (0.02-0.1 flow \r\nannual exceedance probabilities), but there are large biases for the \r\nmost extreme flows (0-0.02 annual flow exceedance probability), \r\nindependent of the climatic and physiographic characteristics of the \r\nbasins (S. Huang et al., 2017a). Global-scale hydrological models have \r\neven more challenges, as they struggle to reproduce the magnitude \r\nof the flood hazard (Trigg et al., 2016). Also, the ensemble mean of \r\nmultiple models does not perform better than individual models \r\n(Zaherpour et al., 2018). "


id: 2d1fef19e110e65613c0812f1fba6b5a
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 4540
content:
"The use of hydrological models for assessing changes in floods, \r\nespecially for future projections, adds another dimension of \r\nuncertainty on top of uncertainty in the driving climate projections, \r\nincluding emissions scenarios, and in the driving climate models \r\n(both RCMs and GCMs) (Arnell and Gosling, 2016; Hundecha \r\net al., 2016; Krysanova et al., 2017). The differences in hydrological \r\nmodels (Roudier et al., 2016; Thober et al., 2018), as well as post\u0002processing of climate model output for the hydrological models \r\n(Muerth et al., 2013; Maier et al., 2018), add to uncertainty for \r\nflood projections. "


id: e943c1ec951a80adb125e3a085b123a3
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 34219
content:
"In summary, there is medium confidence that simulations for the most \r\nextreme flows by regional hydrological models can have large biases. \r\nGlobal-scale hydrological models still struggle with reproducing the \r\nmagnitude of floods. Projections of future floods are hampered by \r\nthese difficulties and cascading uncertainties, including uncertainties \r\nin emissions scenarios and the climate models that generate inputs. "


id: 6987c8bbc819e2a2f7bae21555856230
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 14199
content:
"there is low confidence in attributing changes in the probability or \r\nmagnitude of flood events to human influence because of a limited \r\nnumber of studies, differences in the results of these studies and \r\nlarge modelling uncertainties. "


id: 4381a1f11210a880bb9cdf076491ad31
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
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vector_id: 8030
content:
"Attributing changes in heavy precipitation to anthropogenic activities \r\n(Section 11.4.4) cannot be readily translated to attributing changes \r\nin floods to human activities, because precipitation is only one of \r\nthe multiple factors, albeit an important one, that affect floods. For \r\nexample, Teufel et al. (2017) showed that, while human influence \r\nincreased the odds of the flood-producing rainfall for the 2013 \r\nAlberta flood in Canada, it was not detected to have influenced \r\nthe probability of the flood itself. Schaller et al. (2016) showed \r\nthat human influence on the increase in the probability of heavy \r\nprecipitation translated linearly into an increase in the resulting river \r\nflow of the Thames in the UK in winter 2014, but its contribution to \r\nthe inundation was inconclusive. "


id: 3e9ff280ef8678b3a4dd848f5b8c141a
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
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content:
"Gudmundsson et al. (2021) compared the spatial pattern of the observed \r\nregional trends in high river flows (>90th percentile) over 1971-2010 \r\nwith that simulated by global hydrological models. The hydrological \r\nmodels were driven by outputs of climate model simulations under \r\nall historical forcing and pre-industrial forcing conditions. They found \r\ncomplex spatial patterns of extreme river flow trends. They also found \r\nthe observed spatial patterns of trends can be reproduced only if \r\nanthropogenic climate change is considered, and that simulated effects \r\nof water and land management cannot reproduce the observed spatial \r\npattern of trends. As there is only one study and multiple caveats \r\nassociated with the study, including relatively poor observational \r\ndata coverage, there is low confidence about human influence on the \r\nchanges in high river flows on the global scale. "


id: 9a8188deb79caf92c5ea1bf7d03e7e78
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
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content:
"At the continental and regional scales, the projected changes in \r\nfloods are uneven in different parts of the world, but there is a larger \r\nfraction of regions with an increase than with a decrease over the \r\n21st  century (Hirabayashi et al., 2013; Dankers et al., 2014; Arnell \r\nand Gosling, 2016; Doll et al., 2018). These results suggest medium \r\nconfidence in flood trends at the global scale, but low confidence in \r\nprojected regional changes. Increases in flood frequency or magnitude \r\nare identified for south-eastern and northern Asia and India (high \r\nagreement across studies), eastern and tropical Africa, and the high \r\nlatitudes of North America (medium agreement), while decreasing "


id: 960b379ee0343f0d6c9d6057edb7d5a3
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
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vector_id: 21112
content:
"In summary there is low confidence in the human influence on \r\nthe changes in high river flows on the global scale. In general, "


id: e1b67de8cdcdd4219eba37efc8e512b9
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
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content:
"frequency or magnitude is found for central and eastern Europe and \r\nthe Mediterranean (high confidence), and parts of South America, \r\nsouthern and central North America, and south-west Africa (low \r\nconfidence) (Hirabayashi et al., 2013; Dankers et al., 2014; Arnell and \r\nGosling, 2016; Doll et al., 2018). Over South America, most studies \r\nbased on global and regional hydrological models show an increase \r\nin the magnitude and frequency of high flows in the western Amazon \r\n(Guimberteau et al., 2013; Langerwisch et al., 2013; Sorribas et al., \r\n2016; Zulkafli et al., 2016) and the Andes (Hirabayashi et al., 2013; \r\nBozkurt et al., 2018). Section 12.4 provides a detailed assessment of \r\nregional flood projections. "


id: 1ebfa2b9f327ffa93cfe0f8bcec94135
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
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vector_id: 2284
content:
"This assessment is focused on changes in physical conditions and \r\nmetrics of direct relevance to droughts: (i) precipitation deficits; \r\n(ii)  excess of atmospheric evaporative demand (AED); (iii) soil \r\nmoisture deficits; (iv) hydrological deficits; and e) atmospheric\u0002based indices combining precipitation and AED (Table 11.A.1). In the \r\nregional tables (Section 11.9), the assessment is structured by drought \r\ntypes, addressing: (i) meteorological, (ii) agricultural and ecological, \r\nand (iii) hydrological droughts. Note that the latter two assessments \r\ndirectly inform the Chapter  12 assessment on projected regional \r\nchanges in these climatic impact-drivers (Section  12.4). The text \r\nrefers to AR6 region acronyms (Section 11.9, and see Section 1.4.5)."


id: 1690b9c5d5476f405f5b5f374e49a682
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
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vector_id: 1073
content:
"In summary, global hydrological models project a  larger fraction \r\nof land areas to be affected by an increase in river floods than by \r\na  decrease in river floods (medium confidence). There is medium \r\nconfidence that river floods will increase in the western Amazon, \r\nthe Andes, and south-eastern and northern Asia. Regional changes \r\nin river floods are more uncertain than changes in pluvial floods \r\nbecause complex hydrological processes and forcings are involved, \r\nincluding land cover change and human water management."


id: 1e887353f4af1d3aab957e28de10c2f
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
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content:
"some regions (SREX, Chapter 3; Koster et al., 2011; Gimeno et al., \r\n2012; Taylor et al., 2012; Guillod et al., 2015; Tuttle and Salvucci, \r\n2016; Santanello Jr. et al., 2018; Haslinger et al., 2019; Herrera\u0002Estrada et al., 2019). The sign of the feedbacks can be either positive \r\nor negative, as well as local or non-local (Taylor et al., 2012; Guillod \r\net al., 2015; Tuttle and Salvucci, 2016). Earth system models (ESMs) \r\ntend to underestimate non-local negative soil-moisture-precipitation \r\nfeedbacks (Taylor et al., 2012) and also show high variations in their \r\nrepresentation in some regions (Berg et al., 2017b). Soil-moisture-\r\nprecipitation feedbacks contribute to changes in precipitation \r\nin climate model projections in some regions, but ESMs display \r\nsubstantial uncertainties in their representation, and there is thus \r\nonly low confidence in these contributions (Berg et al., 2017b; Vogel \r\net al., 2017, 2018). "


id: fd8262b482d6e3f3fc5e61577c5a53ed
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
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vector_id: 37402
content:
"exclusively on AED metrics overestimate changes in soil moisture and \r\nrunoff deficits. Nevertheless, AED also directly impacts hydrological \r\ndrought, as ET from surface waters is not limited (Wurbs and Ayala, \r\n2014; Friedrich et al., 2018; Hogeboom et al., 2018; K. Xiao et al., 2018), \r\nand this effect increases under climate change projections (W. Wang \r\net al., 2018; Althoff et al., 2020). In addition, high AED increases crop \r\nwater consumptions in irrigated lands (Garcia-Garizabal et al., 2014), \r\ncontributing to intensifying hydrological droughts downstream (Fazel \r\net al., 2017; Vicente-Serrano et al., 2017)."


id: a92f6a743fd8e30ba6c36c7582afe94e
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
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vector_id: 24149
content:
"The influence of AED on drought depends on the drought type, \r\nbackground climate, the environmental conditions and the moisture \r\navailability (Hobbins et al., 2016, 2017; Vicente-Serrano et al., \r\n2020a). This influence also includes effects not related to increased \r\nET. Under low soil moisture conditions, increased AED increases \r\nplant stress, enhancing the severity of agricultural and ecological \r\ndroughts (Williams et al., 2013; Allen et al., 2015; McDowell et al., \r\n2016; Grossiord et al., 2020). Moreover, high VPD impacts overall \r\nplant physiology; it affects the leaf and xylem safety margins, and \r\ndecreases the sap velocity and plant hydraulic conductance (Fontes \r\net al., 2018). VPD also affects the plant metabolism of carbon and, \r\nif prolonged, it may cause plant mortality via carbon starvation \r\n(Breshears et al., 2013; Hartmann, 2015). Drought projections based "


id: c36e70ee6953e82b56c0e6959cfb327c
created_at: 2023-03-16 14:00:49
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vector_id: 28317
content:
"Soil moisture directly affects plant water stress and ET. Soil moisture \r\nis the primary factor that controls xylem hydraulic conductance  - \r\nthat is, water uptake in plants (Sperry et al., 2016; Hayat et al., 2019; \r\nX. Chen et al., 2020). For this reason, soil moisture deficits are the \r\nmain driver of xylem embolism, the primary cause of plant mortality "


id: 2d0d932e5ff59895742eaa3677023e1f
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
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vector_id: 4527
content:
"exceptions). These demand/supply indices are not intended to be \r\nmetrics of soil moisture, streamflow or vegetation water stress. \r\nBecause of their reliance on precipitation and AED, they are mostly \r\nrelated to the actual water balance in humid regions, in which ET is \r\nnot limited by soil moisture and tends towards AED. In water-limited \r\nregions and in dry  periods everywhere, they constitute an upper \r\nbound for overall  water-balance  deficits  (e.g.,  of surface waters) \r\nbut are also related to  conditions  conducive  to vegetation stress, \r\nparticularly under soil moisture limitation (Section 11.6.1.2). "


id: c78ed40973c1b00e442876fbd0501a89
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
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index: climate_gpt_only_giec
vector_id: 28984
content:
"This Chapter assesses changes in meteorological drought, \r\nagricultural and ecological droughts, and hydrological droughts. \r\nPrecipitation-based indices are used for the estimation of changes \r\nin meteorological droughts, such as the Standardized Precipitation \r\nIndex (SPI) and the number of consecutive dry days (CDD). Changes \r\nin total soil moisture and soil moisture-based drought events are \r\nused for the estimation of changes in agricultural and ecological \r\ndroughts, complemented by changes in surface soil moisture, water\u0002balance estimates (precipitation minus ET), and SPEI-PM and PDSI\u0002PM. For hydrological droughts, changes in low flows are assessed, \r\nsometimes complemented by changes in mean streamflow."


id: 714a73fb515bac6d55a8ac3ae0be1b2b
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
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index: climate_gpt_only_giec
vector_id: 15463
content:
"In summary, different drought types exist and they are associated \r\nwith different impacts and respond differently to increasing \r\ngreenhouse gas concentrations. Precipitation deficits and changes in \r\nevapotranspiration govern net water availability. A lack of sufficient \r\nsoil moisture, sometimes amplified by increased atmospheric \r\nevaporative demand, result in agricultural and ecological drought. Lack \r\nof runoff and surface water result in hydrological drought. Drought \r\nevents are the result of dynamic and/or thermodynamic processes, \r\nwith thermodynamic processes being the main driver of drought \r\nchanges under human-induced climate change (high confidence). "


id: d32ac6fee0c61a5e63bff9dc7f7d0a50
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
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vector_id: 30749
content:
"Evidence on observed drought trends was limited at the time of SREX \r\n(Chapter 3) and AR5 (Chapter 2). The SREX concluded: 'There is medium \r\nconfidence that since the 1950s some regions of the world have \r\nexperienced a trend to more intense and longer droughts, in particular \r\nin southern Europe and west Africa, but in some regions droughts have \r\nbecome less frequent, less intense, or shorter, for example, in Central \r\nNorth America and north-western Australia.' The assessment at the \r\ntime did not distinguish between different drought types. This Chapter \r\nincludes numerous updates on observed drought trends, associated \r\nwith extensive new literature and longer datasets since AR5. "


id: ea8ee5ced8b01e7fcf712dd83f4c0992
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
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vector_id: 34412
content:
"In several regions, AED increases have intensified recent drought \r\nevents (Williams et al., 2014, 2020; Seager et al., 2015b; Basara \r\net al., 2019; Garcia-Herrera et al., 2019), enhanced vegetation stress \r\n(Allen et al., 2015; Sangines de Carcer et al., 2018; Yuan et al., 2019), \r\nor contributed to the depletion of soil moisture or runoff through \r\nenhanced ET (high confidence) (Teuling et al., 2013; Padron et al., 2020). \r\nTrends in pan evaporation measurements and Penman-Monteith AED "


id: f0a7c1a646aa3553049dffb9089eeb95
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 35363
content:
"Evidence from observed or observations-derived trends in soil \r\nmoisture and precipitation minus ET, are combined with evidence \r\nfrom SPEI and PDSI-PM studies to derive regional assessments of \r\nchanges in agricultural and ecological droughts (Section 11.9). This \r\nassessment is summarized in Section 11.6.2.6."


id: 671589181340a9e71dc45105446ddf7a
created_at: 2023-03-16 14:00:49
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vector_id: 13810
content:
"Globally, trends in SPEI-PM and PDSI-PM suggest slightly higher \r\nincreases of drought frequency and severity in regions affected by \r\ndrying over the last decades in comparison to the SPI (Dai and Zhao, \r\n2017; Spinoni et al., 2019; Song et al., 2020), mainly in regions of \r\nWestern and Southern Africa, the Mediterranean and East Asia \r\n(Figure 11.17), which is consistent with observed soil moisture trends \r\n(Section 11.6.2.3). These indices suggest that AED has contributed to "


id: 5daca5feeb38bbb5720353790bc2cb1a
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
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vector_id: 12264
content:
"There is evidence based on streamflow records of increased \r\nhydrological droughts in East Asia (D. Zhang et al., 2018) and \r\nsouthern Africa (Gudmundsson et al., 2019). In areas of Western \r\nand Central Europe and Northern Europe, there is no evidence of \r\nchanges in the severity of hydrological droughts since 1950 based on "


id: ae69e7e909de258122cfd8814ca210b3
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
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vector_id: 24966
content:
"There is only limited evidence on the evaluation of AED in state\u0002of-the-art ESMs, which is performed on externally computed AED, \r\nbased on model output (Scheff and Frierson, 2015; Liu and Sun, \r\n2016, 2017). An evaluation of average AED in 17 CMIP5 ESMs for \r\n1981-1999 based on potential evaporation show that the models' \r\nspatial patterns resemble the observations, but the magnitude of \r\npotential evaporation displays strong divergence among models \r\nglobally and regionally (Scheff and Frierson, 2015). The evaluation \r\nof AED in 12  CMIP5 ESMs with pan evaporation observations in \r\nEast Asia for 1961-2000 (Liu and Sun, 2016, 2017) show that \r\nthe ESMs capture seasonal cycles well, but that regional AED \r\naverages are underestimated due to biases in the meteorological \r\nvariables controlling the aerodynamic and radiative components \r\nof AED. The CMIP5 ESMs also show a  strong underestimation of \r\natmospheric drying trends compared to reanalysis data (Douville and \r\nPlazzotta, 2017)."


id: e4bff78582dfd04c9899e73704920830
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
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vector_id: 33466
content:
"In summary, there is high confidence that AED has increased on \r\naverage on continents, contributing to increased ET and resulting \r\nwater stress during periods with precipitation deficits, in particular \r\nduring dry seasons. There is medium confidence in increases in \r\nprecipitation deficits in a few regions of Africa and South America. \r\nBased on multiple evidence, there is medium confidence that \r\nagricultural and ecological droughts have increased in several \r\nregions on all continents (WAF, CAF, WSAF, ESAF, WCA, ECA, EAS, \r\nSAU, MED, WCE, NES: medium confidence), while there is only \r\nmedium confidence in decreases in one AR6 region (NAU). More \r\nsevere hydrological droughts are found in fewer regions (MED: high \r\nconfidence; WAF, EAS, SAU: medium confidence). "


id: e95f36b0d9641fdd71474b264ea91a2a
created_at: 2023-03-16 14:00:49
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"The performance of climate models for representing soil moisture \r\ndeficits shows more uncertainty than for precipitation deficits since, \r\nin addition to the uncertainties related to cloud and precipitation \r\nprocesses, there is uncertainty related to the representation of \r\ncomplex soil hydrological and boundary-layer processes (van den \r\nHurk et al., 2011; Lu et al., 2019; Quintana-Segui et al., 2020). "


id: 7ba573d7d1fb66a4bebcb14f4e94b44e
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
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vector_id: 17052
content:
"Another limitation is the lack of observations, particularly for soil \r\nmoisture, in most regions (Section  11.6.2.3) and the paucity of \r\nland surface property data to parametrize land surface models, in \r\nparticular soil types, soil properties and depth (Xia et al., 2015). The \r\nspatial resolution of models is an additional limitation since the \r\nrepresentation of some land-atmosphere feedbacks and topographic \r\neffects requires detailed resolution (Nicolai-Shaw et al., 2015; Van \r\nDer Linden et al., 2019). In addition to climate models, land surface \r\nand hydrological models are also used to derive historical and \r\nprojected trends in soil moisture and related land water variables \r\n(Albergel et al., 2013; Cheng et al., 2015; Gu et al., 2019b; Padron \r\net al., 2020; Markonis et al., 2021; Pokhrel et al., 2021). "


id: c072ddd1e3298afe0cf523ee8e89ae84
created_at: 2023-03-16 14:00:49
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content:
"Cook et al., 2020). There is still limited evidence to assess whether \r\nthere are substantial differences in model performance in the two \r\nensembles, but improvements in modelling aspects relevant for soil \r\nmoisture have been reported for precipitation (Section 11.6.3.2), and \r\na better performance has been found in CMIP6 for the representation \r\nof long-term trends in soil moisture in continental USA (Yuan et al., \r\n2021). Despite the mentioned model limitations, the representation \r\nof soil moisture processes in ESMs uses physical and biological \r\nunderstanding of the underlying processes, which can well represent \r\nthe temporal anomalies associated with temporal variability and \r\ntrends in climate. In summary, there is medium confidence in the \r\nrepresentation of soil moisture deficits in ESMs and related land \r\nsurface and hydrological models. "


id: 9b8e27fbdef20da75d9ad25e1503f3a7
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
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index: climate_gpt_only_giec
vector_id: 22031
content:
"A number of studies have analysed the ability of models to capture \r\ndrought severity and trends based on climatic drought indices. Given \r\nthe limitations of ESMs in reproducing the dynamic of precipitation \r\ndeficits and AED (11.6.3.1, 11.6.3.2), atmospheric-based drought \r\nindices derived from ESM data for these two variables are also \r\naffected by uncertainties and biases. A  comparison of historical \r\ntrends in PDSI-PM for 1950-2014 derived from CMIP3 and CMIP5, \r\nwith respective estimates derived from observations (Dai and \r\nZhao, 2017) show a  similar behaviour at global scale (long-term \r\ndecrease), but low spatial agreement in the trends except in a few \r\nregions (Mediterranean, South Asia, north-western USA). In future \r\nprojections, there is an important spread in PDSI-PM and SPEI-PM \r\namong different models (Cook et al., 2014a). "


id: 293f0eb7ae2e43b23c21c6d6df2c4f58
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content:
"For future projections, the spread of soil moisture outputs among \r\ndifferent ESMs is more important than internal variability and \r\nscenario uncertainty, and the bias is strongly related to the sign of \r\nthe projected change (Ukkola et al., 2018; Lu et al., 2019; Selten et al., \r\n2020). The CMIP5 ESMs that project more drying and warming in mid\u0002latitude regions show a substantial bias in soil-moisture-temperature \r\ncoupling (Donat et al., 2018; Vogel et al., 2018). Although CMIP6 and \r\nCMIP5 simulations for soil moisture changes are similar overall, some \r\ndifferences are found in projections in a few regions (Section 11.9; "


id: 8976927a18c46052f92d279da990229
created_at: 2023-03-16 14:00:49
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content:
"in the attribution of observed short-term changes in meteorological \r\ndroughts in the region (Section 11.9). In North America, the human \r\ninfluence on precipitation deficits is complex (Wehner et al., 2017), \r\nwith low confidence in the attribution of long-term changes in \r\nmeteorological drought in AR6 regions (Section 11.9; Lehner et al., \r\n2018). In Africa there is low confidence that human influence has \r\ncontributed to the observed long-term meteorological drought \r\nincrease in Western Africa (Sections 11.9 and 10.6.2). There is low \r\nconfidence in the attribution of the observed increasing trends in \r\nmeteorological drought in East Southern Africa, but evidence that \r\nhuman-induced climate change has affected recent meteorological \r\ndrought events in the region (Section 11.9). "


id: 935c129a78e063cfc8d3c0529e9978d
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
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index: climate_gpt_only_giec
vector_id: 20701
content:
"The performance of ESMs used to assessed changes in variables \r\nrelated to meteorological droughts, agricultural and ecological \r\ndroughts, and hydrological droughts, shows the presence of biases \r\nand uncertainties compared to observations, but there is medium \r\nconfidence in their overall performance for assessing drought \r\nprojections given process understanding. Given the substantial inter\u0002model spread documented for all related variables, the consideration \r\nof multi-model projections increases the confidence of model-based \r\nassessments, with only low confidence in assessments based on \r\nsingle models. "


id: 61a38ab5f46f751a1d5c77be975bb3c7
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
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index: climate_gpt_only_giec
vector_id: 12927
content:
"In summary, the evaluation of ESMs, land surface and hydrological \r\nmodels for the simulation of droughts is complex, due to the regional \r\nscale of drought trends, their overall low signal-to-noise ratio, and the \r\nlack of observations in several regions, in particular for soil moisture \r\nand streamflow. There is medium confidence in the ability of ESMs \r\nto simulate trends and anomalies in precipitation deficits and AED, \r\nand also medium confidence in the ability of ESMs and hydrological \r\nmodels to simulate trends and anomalies in soil moisture and \r\nstreamflow deficits, on global and regional scales. "


id: f648b139f8e849ca28c552a65b486a1c
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
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index: climate_gpt_only_giec
vector_id: 36257
content:
"some events. For example, anthropogenic warming contributed to \r\nthe 2014 drought in North Eastern Africa by increasing east African \r\nand west Pacific temperatures, and increasing the gradient between \r\nstandardized western and central Pacific SSTs, causing reduced \r\nrainfall (Funk et al., 2015a). As different methodologies, models \r\nand data sources have been used for the attribution of precipitation \r\ndeficits, Angelil et al. (2017) re-examined several events using \r\na  single analytical approach and climate model and observational \r\ndatasets. Their results showed a  disagreement in the original \r\nanthropogenic attribution in a number of precipitation deficit events, \r\nwhich increased uncertainty in the attribution of meteorological \r\ndroughts events. "


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content:
"low, mean and high flows, while simulated effects of water and land \r\nmanagement do not suffice to reproduce the observed spatial pattern \r\nof trends (Gudmundsson et al., 2021). Regional studies also suggest \r\nthat climate trends have been dominant compared to land use and \r\nhuman water management for explaining trends in hydrological \r\ndroughts in some regions, for instance in Ethiopia (Fenta et al., 2017), \r\nChina (Xie et al., 2015), and North America for the Missouri and \r\nColorado basins, as well as in California (Shukla et al., 2015; Udall \r\nand Overpeck, 2017; Ficklin et al., 2018; K. Xiao et al., 2018; Glas \r\net al., 2019; Martin et al., 2020; Milly and Dunne, 2020)."


id: b0c6752616bc11bb95136e111fdcf48e
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 25332
content:
"It is often difficult to separate the role of climate trends from \r\nchanges in land use, water management and demand for changes \r\nin hydrological deficits, especially on a  regional scale. However, \r\na global study based on a recent multi-model experiment with global \r\nhydrological models and covering several AR6 regions suggests \r\na  dominant role of anthropogenic radiative forcing for trends in "


id: 186faa7cd6f58e6257134c763d94a83a
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 1340
content:
"2018). In particular, this effect was identified to be the main driver of \r\nthe intensification of the 2017 drought that affected south-western \r\nEurope, and was attributed to the human forcing (Garcia-Herrera \r\net al., 2019). Nangombe et al. (2020) and L. Zhang et al. (2020) \r\nconcluded from differences between precipitation and AED that \r\nanthropogenic forcing contributed to the 2018 droughts that affected \r\nsouthern Africa and south-eastern China, respectively, principally as \r\nconsequence of the high AED that characterized these two events."


id: 60fd4e3db69b8f8287e0657c4d54cd89
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 12828
content:
"The SREX (Chapter 3) asssessed with medium confidence projections \r\nof increased drought severity in some regions, including southern \r\nEurope and the Mediterranean, central Europe, central America and \r\nMexico, north-east Brazil, and southern Africa, and low confidence\r\nelsewhere given large inter-model spread. The AR5 (Chapters 11 and \r\n12) also assessed large uncertainties in drought projections at the "


id: 9359700dca00ac5c28cd551796cfc13e
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 20699
content:
"In Africa, a  strong increase in the length of dry spells (CDD) is \r\nprojected for 4degC of global warming over most of the continent, with \r\nthe exception of central and eastern Africa (Section 11.9; Sillmann \r\net al., 2013a; Giorgi et al., 2014; Han et al., 2019). In West Africa, \r\na strong reduction of precipitation is projected (Sillmann et al., 2013a; "


id: c97e11e32c4f41784d297950866ab7e4
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 29261
content:
"Diallo et al., 2016; Akinsanola and Zhou, 2019; Han et al., 2019; Todzo \r\net al., 2020) at 4degC of global warming, and CDD would increase with \r\nstronger global warming levels (Klutse et al., 2018). The regions most \r\nstrongly affected are southern Africa (ESAF, WSAF) (Nangombe et al., \r\n2018; Abiodun et al., 2019) and northern Africa (part of the MED \r\nregion), with increases in meteorological droughts already at 1.5degC \r\nof global warming, and further increases with increasing global \r\nwarming (Section  11.9). CDD is projected to increase more in the \r\nsouthern Mediterranean (northern Africa) than in the northern part of \r\nthe Mediterranean region (Lionello and Scarascia, 2020)."


id: 1062c4dea4afec19fe0fa227feae5d19
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
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vector_id: 62
content:
"The overall evidence suggests an increase in meteorological drought \r\nat 4degC in the WCE region (medium confidence) (Section 11.9). "


id: a03fa5ff23d9ebdacd07fbd7c6f7fd88
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 22746
content:
"Overall, based on global and regional studies, several hot spot \r\nregions are identified, displaying more frequent and severe \r\nmeteorological droughts with increasing global warming, including \r\nseveral AR6 regions at 1.5degC (WSAF, ESAF, SAU, MED, NES) and \r\n2degC of global warming (WSAF, ESAF, EAU, SAU, MED, NCA, SCA, \r\nNSA, NES) (Section  11.9). At 4degC of global warming, there is also \r\nconfidence in increases in meteorological droughts in further regions \r\n(WAF, WCE, ENA, CAR, NWS, SAM, SWS, SSA; Section 11.9), showing \r\na geographical expansion of meteorological drought with increasing \r\nglobal warming. Only few regions are projected to have less intense \r\nor frequent meteorological droughts (Section 11.9)."


id: 15ac4cef78ffd2abb250baf4f54f9d2a
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 926
content:
"In Asia, most AR6 regions show low confidence in projected changes \r\nin meteorological droughts at 1.5degC and 2degC of global warming, with \r\na few regions displaying a decrease in meteorological droughts at \r\n4degC of global warming (RAR, ESB, RFE, ECA; medium confidence), \r\nalthough there is a  projected increase in meteorological droughts \r\nin South East Asia at 4degC (medium confidence) (Section  11.9). In \r\nSouth East Asia, an increasing frequency of precipitation deficits is \r\nprojected as a consequence of an increasing frequency of extreme \r\nEl Nino (Cai et al., 2014b, 2015, 2018). "


id: 4b61a390d106c1131e139920f49e703b
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
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index: climate_gpt_only_giec
vector_id: 9274
content:
"Effects of AED on droughts in future projections is under debate. \r\nThe CMIP5 models project an increase in AED over the majority of \r\nthe world with increasing global warming, mostly as a consequence \r\nof strong VPD increases (Scheff and Frierson, 2015; Vicente-Serrano \r\net al., 2020a). However, ET is projected to increase less than AED in \r\nmany regions due to plant physiological responses related to: i) CO2\r\neffects on plant photosynthesis; and ii) soil moisture control on ET. "


id: f5560d1280aa7817a1a80e2162412aad
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 36122
content:
"An increase in the frequency, duration and intensity of meteorological \r\ndroughts is projected in south-west, south and east Australia (Kirono \r\net al., 2020; Shi et al., 2020). In Canada and most of the USA, based on \r\nthe SPI, Swain and Hayhoe (2015) identified drier summer conditions \r\nin projections over most of the region, and there is a consistent signal \r\ntoward an increase in duration and intensity of droughts in southern \r\nNorth America (Pascale et al., 2016; Escalante-Sandoval and Nunez\u0002Garcia, 2017). In California, more precipitation variability is projected, \r\ncharacterized by increased frequency of consecutive drought and \r\nhumid periods (Swain et al., 2018). "


id: e7a0ba68c124a0aca6fa75796a76443f
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
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vector_id: 33943
content:
"Substantial increases in meteorological drought are projected in \r\nEurope, in particular in the Mediterranean region, already at 1.5degC of \r\nglobal warming (Section 11.9). In southern Europe, model projections \r\ndisplay a consistent drying among models (Russo et al., 2013; Hertig \r\nand Tramblay, 2017; Guerreiro et al., 2018a; Raymond et al., 2019). In \r\nWestern and Central Europe there is some spread in CMIP5 projections, \r\nwith some models projecting very strong drying, and others close to \r\nno trend (Vogel et al., 2018), although CDD is projected to increase \r\nin CMIP5 projections under the RCP 8.5 scenario (Hari et al., 2020). "


id: 8d1962783e0c60e46ecad904bdfb7403
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
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vector_id: 19726
content:
"Another mechanism reducing the ET response to increased AED in \r\nprojections is the control of soil moisture limitations on ET, which \r\nleads to reduced stomatal conductance under water stress (Berg and \r\nSheffield, 2018; Stocker et al., 2018; Zhou et al., 2021). This response \r\nmay be further amplified through VPD-induced decreases in stomatal \r\nconductance (Anderegg et al., 2020). However, the decreased \r\nstomatal conductance in response to soil moisture limitation and \r\nenhanced CO2 would further enhance AED (Sherwood and Fu, 2014; \r\nBerg et al., 2016; Teuling, 2018; Miralles et al., 2019), whereby the \r\noverall effects on AED in ESMs are found to be of similar magnitude \r\nfor soil moisture limitation and CO2 physiological effects on stomatal \r\nconductance (Berg et al., 2016). Increased AED is thus both a driver \r\nand a  feedback with respect to changes in ET, complicating the \r\ninterpretation of its role on drought changes with increasing CO2\r\nconcentrations and global warming. "


id: 6c3867a136f815a71549dcdc17e51318
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
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vector_id: 14655
content:
"Areas with projected soil moisture decreases do not fully coincide \r\nwith areas that have projected precipitation decreases, although \r\nthere is substantial consistency in the respective patterns (Dirmeyer \r\net al., 2013; Berg and Sheffield, 2018). However, there are more \r\nregions affected by increased soil moisture deficits (Figure 11.19) than \r\nprecipitation deficits (Figures 2a,b,c and Cross-Chapter Box  11.1) \r\nas  a  consequence of enhanced AED and the associated increased \r\nET, as highlighted by some studies (Orlowsky and Seneviratne, 2013; \r\nDai et al., 2018; Section  8.2.2.1). Moisture in the top soil layer is \r\nprojected to decrease more than precipitation at all warming levels "


id: 3e22a32f6ba82dc0ee2677dc3cf2d93b
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
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vector_id: 7143
content:
"levels, show contrasting trends (Figure 11.19). In the boreal winter \r\nin the Northern Hemisphere, the areas affected by drying show high \r\nagreement with those characterized by an increase in meteorological \r\ndrought projections (Figures 8.14 and 12.4). On the contrary, in the \r\nboreal summer, the drought frequency increases worldwide in \r\ncomparison to meteorological drought projections, with large areas \r\nof the Northern Hemisphere displaying a high signal-to-noise ratio \r\n(low spead between models). This stresses the dominant influence \r\nof ET (as a result of increased AED) in intensifying agricultural and \r\necological droughts in the warm season in many locations, including \r\nmid- to high latitudes."


id: 925529450e70842f026984ec900e3ed6
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 20545
content:
"4degC global warming (Rhoades et al., 2018). This decline would cause \r\nless predictable hydrological droughts in snowmelt-dominated areas \r\nof North America (Livneh and Badger, 2020). The exact magnitude \r\nof the influence of higher temperatures on snow-related droughts \r\nis, however, difficult to estimate (Mote et al., 2016), since the \r\nstreamflow changes could affect the timing of peak streamflows but \r\nnot necessarily their magnitude. In addition, projected changes in \r\nhydrological droughts downstream of declining glaciers can be very \r\ncomplex to assess (Chapter 9, see also SROCC)."


id: dba9279958c6dda0f9936ba7b79f342b
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
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vector_id: 32080
content:
"Increased soil moisture limitation and associated changes in droughts \r\nare projected to lead to increased vegetation stress affecting the \r\nglobal land carbon sink in ESM projections (Green et al., 2019), with \r\nimplications for projected global warming (Cross-Chapter Box  5). \r\nThere is high confidence that the global land sink will become less \r\nefficient due to soil moisture limitations and associated agricultural \r\nand ecological drought conditions in some regions in higher-emissions \r\nscenarios, specially under global warming levels above 4degC; however, \r\nthere is low confidence in how these water cycle feedbacks will play \r\nout in lower-emissions scenarios (at 2degC global warming or lower; \r\nCross-Chapter Box 5.1)."


id: 1656f2af0bd9c30e4896f27f82c75336
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
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index: climate_gpt_only_giec
vector_id: 1040
content:
"Regions dependent on mountainous snowpack as a  temporary \r\nreservoir may be affected by severe hydrological droughts in \r\na  warmer world. In the southern European Alps, both winter and \r\nsummer low flows are projected to be more severe, with a  25% \r\ndecrease in the 2050s (Vidal et al., 2016). In western USA, a 22% \r\nreduction in winter snow water equivalent is projected at around 2degC \r\nof global warming, with a further decrease of a 70% reduction at "


id: 72447f15cfb5ba21483af7f58e7962bb
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 15602
content:
"Global tendencies towards more severe and frequent agricultural and \r\necological drought conditions are identified in future projections when \r\nfocusing on atmospheric-based drought indices such as the PDSI-PM \r\nor the SPEI-PM. They expand the spatial extent of drought conditions \r\ncompared to meteorological drought to most of North America, \r\nEurope, Africa, Central and East Asia and Southern Australia (Cook "


id: 35a4fc654cd7327ecff5c494ba48854c
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 5846
content:
"et al., 2014a; Chen and Sun, 2017a, b; Gao et al., 2017b; Lehner et al., \r\n2017; Zhao and Dai, 2017; Dai et al., 2018; Naumann et al., 2018; \r\nPotopova et al., 2018; Gu et al., 2020; Vicente-Serrano et al., \r\n2020c; Dai, 2021). Projections in PDSI-PM and SPEI-PM are used to \r\ncomplement total soil moisture projections in assessing projected \r\nchanges in agricultural and ecological drought (Section 11.9)."


id: 40ac9acb6203625ae75e5c99d8b8dc2
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
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vector_id: 7554
content:
"Extreme storms, such as tropical cyclones (TCs), extratropical cyclones \r\n(ETCs), and severe convective storms often have substantial societal \r\nimpacts. Quantifying the effect of climate change on extreme storms \r\nis challenging, partly because extreme storms are rare, short-lived, \r\nand local, and individual events are largely influenced by stochastic \r\nvariability. The high degree of random variability makes detection and \r\nattribution of extreme storm trends more uncertain than detection \r\nand attribution of trends in other aspects of the environment in \r\nwhich the storms evolve (e.g.,  larger-scale temperature trends). \r\nProjecting changes in extreme storms is also challenging because of \r\nconstraints in the models' ability to accurately represent the small\u0002scale physical processes that can drive these changes. Despite the \r\nchallenges, progress has been made since AR5."


id: 8dbfbc84c2f2589dfb6587e84a563e8f
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
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vector_id: 19827
content:
"In summary, more regions are affected by increases in agricultural \r\nand ecological droughts with increasing global warming (high \r\nconfidence). New evidence strengthens the SR1.5 conclusion that \r\neven relatively small incremental increases in global warming \r\n(+0.5degC) cause a  worsening of droughts in some regions (high \r\nconfidence). Some regions are projected to be affected by more \r\nsevere agricultural and ecological droughts at 1.5degC of global \r\nwarming (MED, WSAF, ESAF, SAU, NSA, SAM, SSA, can; medium \r\nconfidence). A larger number of regions are projected to be affected \r\nby more severe agricultural and ecological droughts at 2degC of global \r\nwarming, including MED, WSAF, SAM and SSA (high confidence), \r\nand ESAF, MDG, EAU, SAU, SCA, CAR, NSA, NES, SWS, WCE, NCA, "


id: d72cde354da20715d8a2d131c1450b62
created_at: 2023-03-16 14:00:49
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vector_id: 31368
content:
"will increase with continued greenhouse gas warming; it is likely that \r\nthe global frequency of TCs will either decrease or remain essentially \r\nunchanged, while it is more likely than not that the frequency of the \r\nmost intense storms will increase substantially in some ocean basins; \r\nthere is low confidence in projections of small-scale phenomena such \r\nas tornadoes and hail storms; and there is medium confidence that \r\nthere will be a reduced frequency and a poleward shift of mid-latitude \r\ncyclones due to future anthropogenic climate change. "


id: a872c8187ef4cb7ad39ff85c2b1034a8
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 24027
content:
"Identifying past trends in TC metrics remains a challenge due to the \r\nheterogeneous character of the historical instrumental data, which \r\nare known as 'best-track' data (Schreck et al., 2014). There is low \r\nconfidence in most reported long-term (multi-decadal to centennial) \r\ntrends in TC frequency- or intensity-based metrics due to changes in \r\nthe technology used to collect the best-track data. This should not be \r\ninterpreted as implying that no physical (real) trends exist, but rather \r\nas indicating that either the quality or the temporal length of the \r\ndata is not adequate to provide robust trend detection statements, \r\nparticularly in the presence of multi-decadal variability. "


id: 25846edf4b0001fa6d8173febcfbd3b0
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 3327
content:
"The SREX, AR5, SROCC, and SR1.5, do not provide assessments of \r\nthe atmospheric rivers, and SROCC and SR1.5 do not assess severe \r\nconvective storms and extreme winds. This section assesses the state \r\nof knowledge on the four phenomena of TCs, ETCs, severe convective \r\nstorms, and extreme winds. Atmospheric rivers are addressed \r\nin Chapter  8. In this respect, this assessment closely mirrors the \r\nSROCC assessment of TCs and ETCs, while updating SREX and AR5 \r\nassessments of severe convective storms and extreme winds."


id: 50be832a57a87fd54cb0786eeeb371ae
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
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vector_id: 10147
content:
"The genesis, development, and tracks of TCs depend on conditions \r\nof the larger-scale circulations of the atmosphere and ocean \r\n(Christensen et al., 2013). Large-scale atmospheric circulations, such \r\nas the Hadley and Walker circulations and the monsoon circulations \r\ncan significantly affect TCs, as can internal variability acting on various \r\ntime scales (Annex IV), from intra-seasonal (e.g., the Madden-Julian \r\nand Boreal Summer Intraseasonal oscillations and equatorial waves) "


id: 338239c43fd0ef0835aadb9dcfaa0f7b
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
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index: climate_gpt_only_giec
vector_id: 5524
content:
"A subset of the best-track data corresponding to hurricanes that have \r\ndirectly impacted the USA since 1900 is considered to be reliable, and \r\nshows no trend in the frequency of USA landfall events (Knutson et al., \r\n2019). However, an increasing trend in normalized USA hurricane \r\ndamage, which accounts for temporal changes in exposed wealth \r\n(Grinsted et al., 2019), and a  decreasing trend in TC translation \r\nspeed over the USA (Kossin, 2019) have also been identified in this "


id: c5c3957c9fd193e5e40950eb7464befe
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
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vector_id: 28692
content:
"change in TC frequency, or to systematic track shifts (or both). \r\nFrom an impacts perspective, however, these changes over land are \r\nhighly relevant and emphasize that large-scale modifications in TC \r\nbehaviour can have a broad spectrum of impacts on a regional scale."


id: f1fcf3e70901e5417bd98a259e8a4056
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 35600
content:
"TC metrics are used to evaluate how capable models are of simulating \r\npresent-day TC climatologies and variability (e.g., TC frequency, wind \r\nintensity, precipitation, size, tracks, and their seasonal and interannual \r\nchanges) (Walsh et al., 2015; Camargo and Wing, 2016; Knutson et al., \r\n2019, 2020). Other examples of TC climatology/variability metrics are \r\nspatial distributions of TC occurrence and genesis (Walsh et al., 2015), \r\nseasonal cycles and interannual variability of basin-wide activity (Zhao \r\net al., 2009; Shaevitz et al., 2014; Kodama et al., 2015; Murakami et al., \r\n2015; Yamada et al., 2017) or landfalling activity (Lok and Chan, 2018), \r\nas well as newly developed process-diagnostics designed specifically \r\nfor TCs in climate models (D. Kim  et al., 2018; Wing et al., 2019; \r\nMoon et al., 2020). "


id: 53564da5506fff2376f130f71930191
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 10578
content:
"2018; J. Sun et al., 2019; T.-C. Lee et al., 2020; Yamaguchi and Maeda, \r\n2020a; Kubota et al., 2021). "


id: a5812e120aa367edcca231d09a067f0f
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 23609
content:
"In summary, there is mounting evidence that a  variety of TC \r\ncharacteristics have changed over various time periods. It is likely that \r\nthe global proportion of Category 3-5 tropical cyclone instances and \r\nthe frequency of rapid intensification events have increased globally \r\nover the past 40 years. It is very likely that the average location \r\nwhere TCs reach their peak wind intensity has migrated poleward in \r\nthe western North Pacific Ocean since the 1940s. It is likely that TC \r\ntranslation speed has slowed over the USA since 1900. "


id: 468f27559d5cb221ddb5dd2aa202f9f1
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 8483
content:
"Operational forecasting models also reproduce TCs, and their use for \r\nclimate projection studies shows promise. However, there is limited \r\napplication for future projections as they are specifically developed \r\nfor operational purposes, and TC climatology is not necessarily well \r\nevaluated. Intercomparison of operational models indicates that \r\nenhancement of horizontal resolution can provide more credible \r\nprojections of TCs (Nakano et al., 2017). Likewise, high-resolution \r\nclimate models show promise as TC forecast tools (Zarzycki and \r\nJablonowski, 2015; Reed et al., 2020), further narrowing the \r\ncontinuum of weather and climate models, and increasing confidence \r\nin projections of future TC behaviour. However, higher horizontal \r\nresolution does not necessarily lead to an improved TC climatology \r\n(Camargo et al., 2020). "


id: b3c2be85cb9e92686f1944b1b204fb4e
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 25796
content:
"In summary, various types of models are useful to study how TCs \r\nchange in response to climate changes, and there is no unique \r\nsolution for choosing a model type. However, higher-resolution models \r\ngenerally capture TC properties more realistically (high confidence). \r\nIn particular, models with horizontal resolutions of 10-60 km are \r\ncapable of reproducing strong TCs with Category 4-5 and those of \r\n1-10 km are capable of the eye wall structure of TCs. Uncertainties in \r\nTC simulations come from details of the model configuration of both \r\ndynamical and physical processes. Models with realistic atmosphere-\r\nocean interactions are generally better than atmosphere-only models \r\nat reproducing realistic TC evolutions (high confidence)."


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created_at: 2023-03-16 14:00:49
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vector_id: 14223
content:
"Atmosphere-ocean interaction is an important process in TC \r\nevolution. Atmosphere-ocean coupled models are generally better \r\nthan atmosphere-only models at capturing realistic processes related \r\nto TCs (Murakami et al., 2015; Ogata et al., 2015, 2016; Zarzycki, 2016; \r\nKanada et al., 2017b; Scoccimarro et al., 2017). However, the basin\u0002scale SST biases commonly found in atmosphere-ocean models can \r\nintroduce substantial errors in the simulated TC number (Hsu et al., \r\n2019). Higher-resolution ocean models improve the simulation of TCs \r\nby reducing the SST climatology bias (Li and Sriver, 2018; Roberts \r\net al., 2020a). Coarse resolution atmospheric models may degrade \r\ncoupled model performance as well. For example, in a  case study \r\nof Hurricane Harvey, Trenberth et al. (2018) suggested that the lack \r\nof realistic hurricane frequency and intensity within coupled climate \r\nmodels hampers the models' ability to simulate SST and ocean heat \r\ncontent and their changes. "


id: d61ac2f82570fb5629ea74c332a9dd1e
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
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vector_id: 31199
content:
"Even with higher-resolution atmosphere-ocean coupled models, TC \r\nprojection studies still rely on assumptions in experimental design \r\nthat introduce uncertainties. Computational constraints often limit \r\nthe number of simulations, resulting in relatively small ensemble \r\nsizes and incomplete analyses of possible future SST magnitude \r\nand pattern changes (Zhao and Held, 2011; Knutson et al., 2013). \r\nUncertainties in aerosol forcing also are reflected in TC projection \r\nuncertainty (Wang et al., 2014)."


id: 35a5861bd5fecd43695a4631a0795b09
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
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vector_id: 5847
content:
"Regional climate models (RCM) with grid spacing around 15-50 km \r\ncan be used to study the projection of TCs. RCMs are run with \r\nlateral and surface boundary conditions, which are specified by the \r\natmospheric state and SSTs simulated by GCMs. Various combinations \r\nof the lateral and surface boundary conditions can be chosen for RCM \r\nstudies, and uncertainties in the projection can be further examined \r\nin general. They are used for studying changes in TC characteristics \r\nin a specific area, such as Vietnam (Redmond et al., 2015) and the \r\nPhilippines (Gallo et al., 2019)."


id: b6eea64ef235b226818cf083676446e3
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
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vector_id: 26308
content:
"The cause of the observed slowdown in TC translation speed is not \r\nyet clear. Yamaguchi et al. (2020) used large ensemble simulations \r\nto argue that part of the slowdown is due to actual latitudinal shifts \r\nof TC tracks, rather than data artefacts, in addition to atmospheric \r\ncirculation changes. G. Zhang et al. (2020) used large ensemble \r\nsimulations to show that anthropogenic forcing can lead to a robust \r\nslowdown, particularly outside of the tropics at higher latitudes. \r\nYamaguchi and Maeda (2020b) found a  significant slowdown in \r\nthe western North Pacific over the past 40 years and attributed the \r\nslowdown to a combination of natural variability and global warming. \r\nThe slowing trend since 1900 over the USA is robust and significant \r\nafter removing multi-decadal variability from the time series "


id: bf533fae8a2b7334e9711f937cfae757
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
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vector_id: 27654
content:
"Less computationally expensive downscaling approaches that allow \r\nlarger ensembles and long-term studies are also used in the projection \r\nof TCs (Emanuel et al., 2006; C.Y. Lee et al., 2018). A  statistical-\r\ndynamical TC downscaling method requires assumptions of the \r\nrate of seeding of random initial disturbances, which are generally \r\nassumed to not change with climate change (Emanuel et al., 2008; \r\nEmanuel, 2013). The results with the downscaling approach might "


id: 936a78d02020dd18a75d4df1b1203722
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 20709
content:
"(Kossin, 2019). Among the hypotheses discussed is the physical \r\nlinkage between warming and slowing circulation (Held and \r\nSoden, 2006; see also Section 8.2.2.2), with expectations of Arctic \r\namplification and weakening circulation patterns through weakening \r\nmeridional temperature gradients (Coumou et al., 2018; see also \r\nCross-Chapter Box  10.1), or through changes in planetary wave \r\ndynamics (Mann et al., 2017). The tropics expansion and the poleward \r\nshift of the mid-latitude westerlies associated with warming is also \r\nsuggested as the reason of the slowdown (G. Zhang et al., 2020). \r\nHowever, the connection of these mechanisms to the slowdown has \r\nnot been robustly shown. Furthermore, slowing trends have not been \r\nunambiguously observed in circulation patterns that steer TCs, such \r\nas the Walker and Hadley circulations (Section  2.3.1.4), although \r\nthese circulations generally slow down in numerical simulations \r\nunder global warming (Sections 4.5.1.6 and 8.4.2.2). "


id: 3f0462fa2588bf696febda6b5feb1d42
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
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vector_id: 7300
content:
"The observed poleward trend in western North Pacific TCs remains \r\nsignificant after accounting for the known modes of dominant \r\ninterannual to decadal variability in the region (Kossin et al., 2016a), \r\nand is also found in CMIP5 model-simulated TCs (in the recent \r\nhistorical period 1980-2005), although it is weaker than observed \r\nand is not statistically significant (Kossin et al., 2016a). However, \r\nthe trend is significant in 21st-century CMIP5 projections under the \r\nRCP8.5 scenario, with a similar spatial pattern and magnitude to the \r\npast observed changes in that basin over the period 1945-2016, \r\nsupporting a possible anthropogenic greenhouse gas contribution to \r\nthe observed trends (Kossin et al., 2016a; Knutson et al., 2019). "


id: 1dcd304e9e8d45564a61964f393510e2
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 2125
content:
"Event attribution is used in TC case studies to test whether the \r\nseverities of recent intense TCs are explained without anthropogenic \r\neffects. In a case study of Hurricane Sandy (2012), Lackmann (2015) "


id: cb7990277b1e1ff99bfcb6934bcf30c6
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 29560
content:
"In summary, it is very likely that the recent active TC seasons in \r\nthe North Atlantic, the North Pacific, and Arabian basins cannot be \r\nexplained without an anthropogenic influence. The anthropogenic "


id: 44ecfa79990de2ad3f12c3690bb91e26
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
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vector_id: 8273
content:
"influence on these changes is principally associated to aerosol forcing, \r\nwith stronger contributions to the response in the North Atlantic. It \r\nis more likely than not that the slowdown of TC translation speed \r\nover the USA has contributions from anthropogenic forcing. It is \r\nlikely that the poleward migration of TCs in the western North Pacific \r\nand the global increase in TC intensity rates cannot be explained \r\nentirely by natural variability. Event attribution studies of specific \r\nstrong TCs provide limited evidence for anthropogenic effects on TC \r\nintensifications so far, but high confidence for increases in TC heavy \r\nprecipitation. There is high confidence that anthropogenic climate \r\nchange contributed to extreme rainfall amounts during Hurricane \r\nHarvey (2017) and other intense TCs."


id: 9dee3d2394dc15d6219cc950c41ceb9e
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 22366
content:
"changes in tropical cyclogenesis (Walsh et al., 2015; Hoogewind \r\net al., 2020). Even within a single model, uncertainty in the pattern of \r\nfuture SST changes leads to large uncertainties (including the sign) \r\nin the projected change in TC frequency in individual ocean basins, \r\nalthough global TCs would appear to be less sensitive (Yoshida et al., \r\n2017; Bacmeister et al., 2018)."


id: eaee8eb13e44601d90efa652ab21f603
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
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vector_id: 34470
content:
"A summary of studies on TC projections for the late 21st  century, \r\nparticularly studies since AR5, is given by Knutson et al. (2020), which \r\nis an assessment report mandated by the World Meteorological \r\nOrganization (WMO). Studies subsequent to Knutson et al. (2020) \r\nare generally consistent, and the confidence assessments here \r\nclosely follow theirs (Cha et al., 2020), although there are some \r\ndifferences due to the varying confidence calibrations between the \r\nIPCC and WMO reports. "


id: d851b81cb8ee15301f3b7b83853678c9
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 31562
content:
"due to differences in regional SST warming and warming patterns \r\n(Sugi et al., 2017; Yoshida et al., 2017; Bacmeister et al., 2018). \r\nA  summary of projections of TC characteristics is schematically \r\nshown by Figure 11.20."


id: b3a6eaf6cf1aeddde94470c47369bd03
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
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vector_id: 25778
content:
"The increase in global TC maximum surface wind speeds is about 5% \r\nfor a 2degC global warming across a number of high-resolution multi\u0002decadal studies (Knutson et al., 2020). This indicates the deepening \r\nin global TC minimum surface pressure under the global warming \r\nconditions. A regional cloud-permitting model study shows that the \r\nstrongest TC in the western North Pacific can be as strong as 857 hPa \r\nin minimum surface pressure with a wind speed of 88 m s-1 under \r\nwarming conditions in 2074-2087 (Tsuboki et al., 2015). TCs are \r\nalso measured by quantities such as ACE and the power dissipation \r\nindex (PDI), which conflate TC intensity, frequency, and duration \r\n(Murakami et al., 2014). Several TC modelling studies (Yamada et al., \r\n2010; H.S. Kim et al., 2014; Knutson et al., 2015) project little change \r\nor decreases in the globally accumulated value of PDI or ACE, which \r\nis due to the decrease in the total number of TCs. "


id: 2e71cbbae65551667e0e2a6eda113137
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 4756
content:
"The coastal effects of TCs depend on TC intensity, size, track, and \r\ntranslation speed. Projected increases in sea level, average TC \r\nintensity, and TC rainfall rates each generally act to further elevate \r\nfuture storm surge and fresh-water flooding (see Section  9.6.4.2). \r\nChanges in TC frequency could contribute toward increasing or \r\ndecreasing future storm surge risk, depending on the net effects of \r\nchanges in weaker vs stronger storms. Several studies (McInnes et al., \r\n2014, 2016; Little et al., 2015; Garner et al., 2017; Timmermans et al., \r\n2017, 2018) have explored future projections of storm surge in the \r\ncontext of anthropogenic climate change with the influence of both \r\nsea level rise and future TC changes. Garner et al. (2017) investigated \r\nthe near-future changes in the New York City coastal flood hazard, \r\nand suggested a small change in storm-surge height because effects \r\nof TC intensification are compensated by the offshore shifts in TC "


id: 3eaa04f90105c540e1cca86e2fb4a41
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
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vector_id: 7243
content:
"section is on individual ETCs that have been identified using some \r\ndetection and tracking algorithms. Mid-latitude atmospheric rivers \r\nare assessed in Section 8.3.2.8."


id: 54e99017270f0205a7c401f03c53bcdd
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
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vector_id: 10819
content:
"tracks, but concluded that the overall effect due to the rising sea \r\nlevels would increase the flood hazard. Future projection studies \r\nof storm surge in East Asia, including China, Japan and Korea, also \r\nindicate that storm surges due to TCs become more severe (J.A. Yang \r\net al., 2018; Mori et al., 2019, 2021; J. Chen et al., 2020b). For the \r\nPacific Islands, McInnes et al. (2014) found that the future projected \r\nincrease in storm surge in Fiji is dominated by sea level rise, and \r\nprojected TC changes make only a minor contribution. Among various \r\nstorm surge factors, there is high confidence that sea level rise will \r\nlead to a higher possibility of extreme coastal water levels in most \r\nregions, with all other factors assumed equal. "


id: 58c9f8efb57ad982c659694da16bd75a
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 11464
content:
"In summary, average peak TC wind speeds and the proportion of \r\nCategory 4-5 TCs will very likely increase globally with warming. \r\nIt is likely that the frequency of Category 4-5 TCs will increase in \r\nlimited regions over the western North Pacific. It is very likely\r\nthat average TC rain rates will increase with warming, and likely that \r\nthe peak rain rates will increase at rate greater than the Clausius-\r\nClapeyron scaling rate of 7% per 1degC of warming in some regions \r\ndue to increased low-level moisture convergence caused by regional \r\nincreases in TC wind intensity. It is likely that the average location \r\nwhere TCs reach their peak wind intensity will migrate poleward in \r\nthe western North Pacific Ocean as the tropics expand with warming, \r\nand that the global frequency of TCs over all categories will decrease \r\nor remain unchanged. "


id: 1a6fc7830a38befac6d8e59d717dff38
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 1641
content:
"There is high confidence that coarse-resolution climate models \r\n(e.g.,  CMIP5 and CMIP6) underestimate the dynamical intensity \r\nof ETCs, including the strongest ETCs, as measured using a variety of \r\nmetrics, including mean pressure gradient, mean vorticity and near\u0002surface wind speeds, over most regions (Colle et al., 2013; Zappa \r\net al., 2013a; Govekar et al., 2014; Di Luca et al., 2016; Trzeciak \r\net al., 2016; Seiler et al., 2018; Priestley et al., 2020). There is also \r\nhigh confidence that most current climate models underestimate \r\nthe number of explosive systems (i.e., systems showing a decrease \r\nin mean sea level pressure of at least 24 hPa in 24 hours) over \r\nboth hemispheres (Seiler and Zwiers, 2016a; Gao et al., 2020; "


id: 9dbd86a28091a5be11d11ec11a1a9088
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 22341
content:
"This section focuses on extratropical cyclones (ETCs) that are either \r\nclassified as strong or extreme by using some measure of their \r\nintensity, or by being associated with the occurrence of extremes \r\nin variables such as precipitation or near-surface wind speed \r\n(Seneviratne et al., 2012). Since AR5, the high relevance of ETCs for \r\nextreme precipitation events has been well established (Pfahl and \r\nWernli, 2012; Catto and Pfahl, 2013; Utsumi et al., 2017), with 80% \r\nor more of hourly and daily precipitation extremes being associated \r\nwith either ETCs or fronts over oceanic mid-latitude regions, and \r\nsomewhat smaller, but still very large, proportions of events over \r\nmid-latitude land regions (Utsumi et al., 2017). The emphasis in this "


id: b3a704ce5830bf3818254dbd4833fd9e
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 25779
content:
"Section  3.3.3.3 concluded that there is low confidence in the \r\nattribution of observed changes in the number of ETCs in the Northern \r\nHemisphere and high confidence that the poleward shift of storm \r\ntracks in the Southern Hemisphere is linked to human activity, mostly \r\ndue to emissions of ozone-depleting substances. Specific studies \r\nattributing changes in the most extreme ETCs are not available. \r\nThe human influence on individual extreme ETC events has been \r\nconsidered only a few times and there is overall low confidence in \r\nthe attribution of these changes (NASEM, 2016; Vautard et al., 2019)."


id: a58b83008958b8113e3b805b0609dfc4
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 23617
content:
"In summary, there is low confidence in past changes in the dynamical \r\nintensity (e.g., maximum wind speeds) of ETCs and medium confidence\r\nthat, in the future, these changes will be small, although changes in \r\nthe location of storm tracks could lead to substantial changes in local \r\nextreme wind speeds. There is high confidence that average and \r\nmaximum ETC precipitation-rates will increase with warming, with the \r\nmagnitude of the increases associated with increases in atmospheric \r\nwater vapour. There is medium confidence that projected changes in \r\nthe intensity of ETCs, including wind speeds and precipitation, depend \r\non the resolution and formulation of climate models."


id: 4cc0ae3c71dad5c6aedbf708c04e4995
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
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vector_id: 9502
content:
"There are few studies on relations between changes in severe \r\nconvective storms and those of the large-scale circulation patterns. \r\nTornado outbreaks in the USA are usually associated with ETCs with \r\ntheir frontal systems and TCs (Fuhrmann et al., 2014; Tochimoto and \r\nNiino, 2016). In early June to late July in East Asia, associated with \r\nthe Baiu/Changma/Mei-yu, severe precipitation events are frequently \r\ncaused by MCSs. Severe precipitation events are also caused by \r\nremote effects of TCs, known as predecessor rain events (Galarneau \r\net al., 2010). Atmospheric rivers and other coherent types of \r\nenhanced water vapour flux also have the potential to induce severe \r\nconvective systems (Kamae et al., 2017a; Waliser and Guan, 2017; \r\nRalph et al., 2018; see Section 8.3.2.8.2). Combined with the above \r\ndrivers, topographic effects also enhance the intensity and duration "


id: 4286d44bf36bec252c606e2c2245f2fb
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
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vector_id: 7857
content:
"of severe convective systems and the associated precipitation \r\n(Ducrocq et al., 2008; Piaget et al., 2015). However, the changes in \r\nthese drivers are not generally significant, so their relations to severe \r\nconvective storms are unclear."


id: dd67a652907be8e289469edfb7731b7f
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 32334
content:
"particular aspects of MCSs, such as lengthening of active seasons \r\nand dependency on duration. MCSs have increased in occurrence \r\nand precipitation amounts since 1979 (Easterling et al., 2017). Feng \r\net al. (2016) analysed that the observed increases in spring total and \r\nextreme rainfall in the central USA are dominated by MCSs, with \r\nincreased frequency and intensity of long-lasting MCSs. "


id: de7c54e8681d011971f30bcaca7588fe
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 32508
content:
"In summary, severe convective storms are sometimes embedded \r\nin synoptic-scale weather systems, such as TCs, ETCs and fronts, \r\nand modulated by large-scale atmospheric circulation patterns. \r\nThe occurrence of severe convective storms and the associated \r\nsevere events, including tornadoes, hail, and lightning, is affected by \r\nenvironmental conditions of the atmosphere, such as CAPE and vertical \r\nshear. The uncertainty, however, arises from the balance between these \r\nenvironmental factors affecting severe storm occurrence."


id: 330765cde17c75d15dfb63c5facb240a
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 5454
content:
"In summary, because the definition of severe convective storms varies \r\ndepending on the literature and the region, it is not straightforward \r\nto make a synthesizing view of observed trends in severe convective \r\nstorms in different regions. In particular, observational trends in \r\ntornadoes, hail, and lightning associated with severe convective \r\nstorms are not robustly detected due to insufficient coverage of the \r\nlong-term observations. There is medium confidence that the mean \r\nannual number of tornadoes in the USA has remained relatively \r\nconstant, but their variability of occurrence has increased since the \r\n1970s, particularly over the 2000s, with a decrease in the number of \r\ndays per year, and an increase in the number of tornadoes on these \r\ndays (high confidence). Detected tornadoes have also increased in \r\nEurope, but the trend depends on the density of observations. "


id: bc2a72e2fbd3571f832c08e12f37e427
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
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index: climate_gpt_only_giec
vector_id: 27178
content:
"For the case of the heavy rain event of July 2018 in Japan (Box 11.4), \r\nKawase et al. (2020) took a  storyline approach to show that the \r\nrainfall during this event in Japan was increased by approximately \r\n7% due to recent rapid warming around Japan. For the case of the \r\nDecember 2015 extreme rainfall event in Chennai, India, the extremity \r\nof the event was equally caused by the warming trend in the Bay of \r\nBengal SSTs and the strong El Nino conditions (van Oldenborgh et al., \r\n2016; Boyaj et al., 2018). For hailstorms, such as those that caused \r\ndisasters in the USA in 2018, detection of the role of climate change \r\nin changing hail storms is more difficult, because hail storms are \r\nnot, in general, directly simulated by convection-permitting models \r\nand not adequately represented by the environmental parameters of \r\ncoarse-resolution GCMs (Mahoney, 2020). "


id: dfacf3bcb91e5ce01e650368721f920a
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 32690
content:
"In summary, it is extremely difficult to detect and attribute changes \r\nin severe convective storms. There is limited evidence that extreme \r\nprecipitation associated with severe convective storms has \r\nincreased in some cases."


id: 97836e81d219542e366526d8acef412b
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 21362
content:
"The other modelling approach is the analysis of the environmental \r\nconditions that control characteristics of severe convective storms \r\nusing the typical climate model results in CMIP5/6 (Allen, 2018). \r\nSevere convective storms are generally formed in environments \r\nwith large CAPE and tornadic storms are, in particular, formed with \r\na  combination of large CAPE and strong vertical wind shear. As \r\nthe processes associated with severe convective storms occur over \r\na  wide range of spatial and temporal scales, some of which are \r\npoorly understood and are inadequately sampled by observational \r\nnetworks, the model calibration approaches are generally difficult \r\nand insufficiently validated. Therefore, model simulations and their \r\ninterpretations should be done with much caution. "


id: d8218116d42773e626b69d223a8ebc4c
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 31529
content:
"In summary, there are typically two kinds of modelling approaches \r\nfor studying changes in severe convective storms. One is to use \r\nconvection-permitting models in wider regions or the global domain \r\nin time-sliced downscaling methods to directly simulate severe \r\nconvective storms. The other is the analysis of the environmental \r\nconditions that control characteristics of severe convective \r\nstorms by using coarse-resolution GCMs. Even in finer-resolution \r\nconvection-permitting models, it is difficult to directly simulate \r\ntornadoes, hail storms, and lightning, so modelling studies of these \r\nchanges are limited."


id: c8cd9e666dc23a8ae5e158805eb63319
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 29152
content:
"The other approach is the projection of the environmental conditions \r\nthat control characteristics of severe convective storms by analysing \r\nclimate model results. There is high confidence that CAPE, particularly \r\nsummer mean CAPE and high percentiles of the CAPE in the tropics \r\nand subtropics, increases in response to global warming in an \r\nensemble of climate models including those of CMIP5, mainly from \r\nincreased low-level specific humidity (Sobel and Camargo, 2011; \r\nM.S. Singh et al., 2017; J. Chen et al., 2020a). Convective inhibition "


id: 894a5e0d8ca757f775837ddab229f4d7
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 19138
content:
"It is extremely difficult to detect differences in time and space of \r\nsevere convective storms (Kunkel et al., 2013). Although some \r\ningredients that are favourable for severe thunderstorms have \r\nincreased over the years, others have not; thus, overall, changes in \r\nthe frequency of environments favourable for severe thunderstorms \r\nhave not been statistically significant. Event attribution studies on \r\nsevere convective events have now been undertaken for some cases. "


id: 5154774b30d3f8097ad4f66add54b0c5
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
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index: climate_gpt_only_giec
vector_id: 10251
content:
"becomes stronger over most land areas under global warming, \r\nresulting mainly from reduced low-level relative humidity over land \r\n(J. Chen et al., 2020a). However, there are large differences within \r\nthe CMIP5 ensemble for environmental conditions, which contribute \r\nto some degree of uncertainty (Allen, 2018). Because the relation \r\nbetween simulated environments in models and the occurrence of \r\nsevere convective storms are, in general, insufficiently validated, there \r\nis generally low confidence in the projection of severe convective \r\nstorms with the approach of the environmental conditions. "


id: 1574552de353a8bb93d6655ad5fa5bfc
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 889
content:
"Future changes in severe convection environments show \r\nenhancement of instability with less robust changes in the frequency \r\nof strong vertical wind shear in Europe (Pucik et al., 2017) and \r\nin Japan (Muramatsu et al., 2016). In Japan, the frequency of \r\nconditions favourable for strong tornadoes increases in spring, and \r\npartly in summer. "


id: 30e9a6bc5fe6a7cb9946ffab4db8bdc3
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 5119
content:
"In summary, the average and maximum rain rates associated \r\nwith severe convective storms increase in a  warming world in \r\nsome regions, including the USA (high confidence). There is high \r\nconfidence from climate models that CAPE increases in response \r\nto global warming in the tropics and subtropics, suggesting more \r\nfavourable environments for severe convective storms. The frequency \r\nof severe convective storms in spring is projected to increase in \r\nthe USA, leading to a  lengthening of the severe convective storm \r\nseason (medium confidence); evidence in other regions is limited. \r\nThere is significant uncertainty about projected regional changes in \r\ntornadoes, hail, and lightning due to limited analysis of simulations \r\nusing convection-permitting models (high confidence)."


id: 614a8ba0568510726e268f6029871594
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
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vector_id: 12880
content:
"positive wind speed trends over the ocean were larger during winter \r\nseasons than summer seasons."


id: 9f188c938642f36cb1f7da1ccf8d24b9
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
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vector_id: 22570
content:
"amplify the impact of the events, or (iii) combinations of events that \r\nare not themselves extremes but lead to an extreme event or impact \r\nwhen combined. "


id: 3ffedf9f065c5a1954b971aaf2ccc3ad
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
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vector_id: 7452
content:
"Extreme cyclonic windstorms that share some characteristics with \r\nboth TCs and ETCs occur regularly over the Mediterranean Sea and \r\nare often referred to as 'medicanes' (Ragone et al., 2018; Miglietta \r\nand Rotunno, 2019; Zhang et al., 2021). Medicanes pose substantial \r\nthreats to regional islands and coastal zones. A  growing body \r\nof literature consistently found that the frequency of medicanes \r\ndecreases under warming, while the strongest medicanes become \r\nstronger (Gaertner et al., 2007; Romero and Emanuel, 2013, 2017; \r\nCavicchia et al., 2014; Tous et al., 2016; Romera et al., 2017; \r\nGonzalez-Aleman et al., 2019). This is also consistent with expected \r\nglobal changes in TCs under warming (Section 11.7.1). Based on the \r\nconsistency of these studies, it is likely that medicanes will decrease \r\nin frequency, while the strongest medicanes become stronger under \r\nwarming scenario projections (medium confidence)."


id: b118e468324226137e7c85ee12e5d4f4
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
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vector_id: 25368
content:
"In summary, the observed intensity of extreme winds is becoming \r\nless severe in the low to mid-latitudes, while becoming more \r\nsevere in high latitudes poleward of 60 degrees (low confidence). \r\nProjected changes in the frequency and intensity of extreme winds \r\nare associated with projected changes in the frequency and intensity \r\nof TCs and ETCs (medium confidence)."


id: 68e17b75a21e4e6490f3d682a6d818a
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
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vector_id: 14091
content:
"warming, even between 1.5degC and 2degC of global warming (Box 11.2; \r\nGaupp et al., 2019). Concomitant extreme conditions at different \r\nlocations become more probable as changes in climate extremes \r\nare emerging over an increasing fraction of the land area (Sections \r\n11.2.3, 11.2.4, 11.8.2 and 11.8.3, and Box 11.4). "


id: a983633c6d534bdb54159feaf0c95616
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 24202
content:
"In summary, compound events include the combination of two or \r\nmore  - not necessarily extreme  - weather or climate events that \r\noccur (i) at the same time, (ii) in close succession, or (iii) concurrently \r\nin different regions. The land area affected by concurrent extremes has \r\nincreased (high confidence). Concurrent extreme events at different \r\nlocations, but possibly affecting similar sectors (e.g., breadbaskets) \r\nin different regions, will become more frequent with increasing \r\nglobal warming, in particular above +2degC of global warming \r\n(high confidence). "


id: a8202a999465d7015e2e46c11a634cc
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
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vector_id: 23966
content:
"Many coastal areas are also prone to the occurrence of compound \r\nprecipitation and wind extremes, which can cause damage, including \r\nto infrastructure and natural environments. A  high percentage of \r\nco-occurring wind and precipitation extremes are found in coastal \r\nregions and in areas with frequent tropical cyclones. Finally, the \r\ncombination of extreme wave height and duration is also shown to \r\ninfluence coastal erosion processes (Corbella and Stretch, 2012). "


id: 693db69f10c3021580d3690e5ab6fb7b
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
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vector_id: 14150
content:
"Coastal and estuarine zones are prone to a number of meteorological \r\nextreme events and also to concurrent extremes (see also \r\nSection 6.8.2 in SROCC). Floods are a major climatic impact-driver in \r\ncoastal regions around the world (Chapter 12), and flood occurrence \r\nmay be influenced by the dependence between storm surge, extreme \r\nrainfall, and river flow, but also by sea level rise, waves and tides, as \r\nwell as groundwater for estuaries. Floods with multiple drivers are \r\noften referred to as 'compound floods' (Wahl et al., 2015; Moftakhari \r\net al., 2017; Bevacqua et al., 2020c). "


id: 9d0fff04d0f0cc65f69aec3063b191bb
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 22251
content:
"At USA coasts, the probability of co-occurring storm surge and heavy \r\nprecipitation is higher for the Atlantic/Gulf coast relative to the Pacific \r\ncoast (Wahl et al., 2015). Furthermore, six studied locations on the \r\nUSA coast with long overlapping time series show an increase in the \r\ndependence between heavy precipitation and storm surge over the \r\nlast century, leading to more frequent co-occurring storm surge and \r\nheavy precipitation events at the present day (Wahl et al., 2015). Storm \r\nsurge and extreme rainfall are also dependent in most locations on the \r\nAustralian coasts (Zheng et al., 2013) and in Europe along the Dutch \r\ncoasts (Ridder et al., 2018), along the Mediterranean Sea, the Atlantic "


id: e10cecf762babfb7fa73132caf654481
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 32902
content:
"projected to lead to more frequent and intense compound coastal \r\nflooding events as sea levels continue to rise. "


id: 62591eeab5f29f62e0d5cecb278fc677
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 13032
content:
"In summary, there is medium confidence that, over the last century, \r\nthe probability of compound flooding has increased in some locations, \r\nincluding along the USA coastline. There is high confidence that the \r\noccurrence and magnitude of compound flooding in coastal regions \r\nwill increase in the future due to both sea level rise and increases in \r\nheavy precipitation."


id: 89faf0c64c36d7a5addb6c4b8686d4c8
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 19234
content:
"Concurrent droughts and heatwaves have a  number of negative \r\nimpacts on human society and natural ecosystems. Studies since \r\nSREX and AR5 show several occurrences of observed combinations \r\nof drought and heatwaves in various regions."


id: d47865d1cce1ffc1977a09ec6b543b76
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
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vector_id: 30953
content:
"Over most land regions, temperature and precipitation are strongly \r\nnegatively correlated during summer (Zscheischler and Seneviratne, \r\n2017), mostly due to land-atmosphere feedbacks (Sections 11.1.6 and \r\n11.3.2), but also because synoptic-scale weather systems favourable \r\nfor extreme heat are also unfavourable for rain (Berg et al., 2015). \r\nThis leads to a strong correlation between droughts and heatwaves \r\n(Zscheischler and Seneviratne, 2017). Drought events characterized \r\nby low precipitation and extreme high temperatures have occurred, \r\nfor example, in California (AghaKouchak et al., 2014), inland Eastern \r\nAustralia (King et al., 2014), and large parts of Europe (Orth et al., \r\n2016a). The 2018 growing season was both record-breaking dry and \r\nhot in Germany (Zscheischler and Fischer, 2020)."


id: 96b4a9540c9609648e7949b441780bac
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
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vector_id: 21230
content:
"In summary, there is high confidence that concurrent heatwaves and \r\ndroughts have increased in frequency over the last  century at the \r\nglobal scale due to human influence. There is medium confidence\r\nthat weather conditions that promote wildfires (fire weather) have \r\nbecome more probable in southern Europe, northern Eurasia, the \r\nUSA, and Australia over the last century. There is high confidence that \r\ncompound hot and dry conditions become more probable in nearly \r\nall land regions as global mean temperature increases. There is high \r\nconfidence that fire weather conditions will become more frequent at \r\nhigher levels of global warming in some regions."


id: f82f38bb606e375d597d193b819c69e9
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
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vector_id: 36563
content:
"Drought and heatwaves are also associated with fire weather, related \r\nthrough high temperatures, low soil moisture, and low humidity. \r\nFire weather refers to weather conditions conducive to triggering \r\nand sustaining wildfires, which generally include temperature, soil \r\nmoisture, humidity, and wind (Chapter 12). Concurrent hot and dry \r\nconditions amplify conditions that promote wildfires (Schubert et al., \r\n2014; Littell et al., 2016; Dowdy, 2018; Hope et al., 2019). Burnt area \r\nextent in western USA forests (Abatzoglou and Williams, 2016) and \r\nparticularly in California (Williams et al., 2019) has been linked to "


id: eb892389cb1357634bfb83560a4fb842
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
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index: climate_gpt_only_giec
vector_id: 34557
content:
"suggested that anthropogenic warming contributed to the 2015 Ethiopian and southern African droughts by increasing SSTs and local \r\nair temperatures (Funk et al., 2016, 2018b; Yuan et al., 2018a). It has also been suggested that the 2015-2016 extreme El Nino affected \r\ncirculation patterns in Europe during the 2015-2016 winter (Geng et al., 2017; Scaife et al., 2017). "


id: 7832702eba9449dc30e377a7e23b80b2
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
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vector_id: 16478
content:
"The atmospheric CO2 growth rate was particularly high in 2015, possibly related to some of the mentioned droughts, in particular in \r\nIndonesia and the Amazon region, leading to higher CO2 release in combination with less CO2 uptake from land areas (Humphrey et al., \r\n2018). The impact of the 2015-2016 extreme El Nino on vegetation systems via drought was also shown from satellite data (Kogan \r\nand Guo, 2017). Overall, tropical forests were a carbon source to the atmosphere during the 2015-2016 El Nino-related drought, with \r\nsome estimates suggesting that up to 2.3 PgC were released (Brando et al., 2019)."


id: 68f75ae84466838f1ca5a64f19206836
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 14104
content:
"The 2015-2016 extreme El Nino has induced extreme precipitation in some regions. Severe rainfall events were observed in Chennai \r\ncity in India in Devember 2015, and the Yangtze river region in China in June-July 2016, and it was shown that these rainfall events \r\nare partly attributed to the 2015-2016 extreme El Nino (van Oldenborgh et al., 2016; Boyaj et al., 2018; Sun and Miao, 2018; Yuan \r\net al., 2018b; Zhou et al., 2018). "


id: 4151dd06c5d04478c2f3c2a222f2f2e8
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 7675
content:
"The heavy precipitation event was characterized by unusually widespread and persistent rainfall and locally anomalous total \r\nprecipitation led by band-shaped precipitation systems, which are frequently associated with heavy precipitation events in East \r\nAsia (Section 11.7.3; Kato, 2020). The extreme rainfall in Japan was caused by anomalous moisture transport with a combination \r\nof abnormal jet condition (Takemi and Unuma, 2019; Takemura et al., 2019; Tsuji et al., 2020; Yokoyama et al., 2020), which can be \r\nviewed as an atmospheric river (Sections 8.2.2.8 and 11.7.2; Yatagai et al., 2019) caused by intensified inflow velocity and high SST \r\naround Japan (Sekizawa et al., 2019; Kawase et al., 2020). "


id: 55dd3e5cc904ccf6a66846f1494593f
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
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index: climate_gpt_only_giec
vector_id: 10990
content:
"This precipitation event and the subsequent heatwave are related to abnormal condition of the jet stream and North Pacific Subtropical \r\nHigh in this month (Shimpo et al., 2019; Ren et al., 2020), which caused extreme conditions from Europe, Eurasia, and North America \r\n(Box 11.4, Figure 2; Kornhuber et al., 2019). A combination of the positive anomaly of the North Atlantic Oscillation (NAO, Annex IV.2.1) \r\nand the meandering jets is necessary to explain the pattern of the observed anomalies (Drouard et al., 2019). A role of Atlantic SST \r\nanomaly on the meandering jets and the subtropical high have been suggested (B. Liu et al., 2019). These dynamic and thermodynamic \r\ncomponents generally have substantial influence on extreme rainfall in East Asia (Oh et al., 2018), but it is under investigation whether \r\nthese factors were due to anthropogenic forcing."


id: e8033f7791df57a7c43d296b0f045f39
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
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index: climate_gpt_only_giec
vector_id: 33998
content:
"Regarding the hot extremes that occurred across the Northern Hemisphere in the 2018 boreal May-July period, Vogel et al. (2019) \r\nfound that the event was unprecedented in terms of the total area affected by hot extremes (on average, about 22% of populated \r\nand agricultural areas in the Northern Hemisphere) for that period, but was consistent with a +1degC climate which was the estimated "


id: 8baaebbe45a2d5b5f2cdb6bf185de6ff
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
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content:
"Box 11.4, Figure 2 | Meteorological conditions in July 2018. The colour shading shows the monthly mean near-surface air temperature anomaly with respect \r\nto 1981-2010. Contour lines indicate the geopotential height in m, highlighted are the isolines on 12,000 m and 12,300 m, which indicate the approximate positions \r\nof the polar-front jet and subtropical jet, respectively. The light blue-green ellipse shows the approximate extent of the strong precipitation event that occurred at the \r\nbeginning of July in the region of Japan and Korea. All data is from the global European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5, \r\nHersbach et al., 2020)."


id: 8a0efe66d70e8c6c77e13a37cea66520
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
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"The case studies presented in this Box illustrate the current state of knowledge regarding the contribution of human-induced climate \r\nchange to recent concurrent extremes in the global domain. Recent years have seen a more frequent occurrence of such events. \r\nThe heatwave in Europe in the 2019 boreal summer and its coverage in the global domain is an additional example (Vautard et al., \r\n2020). However, very few studies investigate which types of concurrent extreme events could occur under increasing global warming. \r\nIt has been noted that such events could also be of particular risk for concurrent impacts in the world's breadbaskets (Zampieri et al., \r\n2017; Kornhuber et al., 2020; see also Section 11.8.1)."


id: 94660082c3b8195c0f96927050bc45d
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
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vector_id: 20863
content:
"Sections 11.9.2, 11.9.3 and 11.9.4 provide brief summaries of the \r\nunderlying evidence used to derive the regional assessments for \r\ntemperature extremes, heavy precipitation events, and droughts, \r\nrespectively. The assessments take into account evidence from \r\nstudies based on global datasets (global studies), as well as regional \r\nstudies. Global studies include analyses for all continents and AR6 \r\nregions with sufficient data coverage, and provide an important \r\nbasis for cross-region consistency, as the same data and methods \r\nare used for all regions. However, individual regional studies may \r\ninclude additional information that is missed in global studies, and \r\nthus provide an important regional calibration for the assessment."


id: f0f3c9ff0e10b89dd4c73f7328d14e28
created_at: 2023-03-16 14:00:49
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vector_id: 35401
content:
"The assessments are presented using the calibrated confidence and \r\nlikelihood language (Box  1.1). Low confidence is assessed when \r\nthere is limited evidence, either because of a lack of available data \r\nin the region and/or a  lack of relevant studies. Low confidence is \r\nalso assessed when there is a  lack of agreement on the evidence \r\nof a change, which may be due to large variability or inconsistent \r\nchanges depending on the considered sub-regions, time frame, \r\nmodels, assessed metrics, or studies. In cases when the evidence is "


id: f17d6f5d67a9d47c20454c8dc49daef8
created_at: 2023-03-16 14:00:49
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content:
"strongly contradictory, for example, with substantial regional changes \r\nof opposite sign, 'mixed signal' is indicated. With an assessment of \r\nlow confidence, the direction of change is not indicated in the tables. \r\nA  direction of change (increase or decrease) is provided with an \r\nassessment of medium confidence, high confidence, likely, or higher \r\nlikelihood levels. Likelihood assessments are only provided in the \r\ncase of high confidence. In some cases, there may be confidence in \r\na small or no change."


id: dc3712b3816c404302076c29d72343b3
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
content_type: text
index: climate_gpt_only_giec
vector_id: 32159
content:
"For projections, changes are assessed at three global warming levels \r\n(GWLs; Cross-Chapter Box 11.1): 1.5degC, 2degC and 4degC. The assessments \r\nuse literature based both on GWL projections and scenario-based \r\nprojections. In the case of literature on scenario-based projections, \r\na mapping between scenarios/time frames and GWLs was performed, \r\nas documented in Cross-Chapter Box 11.1. Projections of changes in \r\ntemperature and precipitation extremes are assessed relative to two \r\ndifferent baselines: the recent past (1995-2014) and pre-industrial \r\n(1850-1900). With smaller changes relative to the variability, in \r\nparticular because droughts happen on longer timescales compared \r\nto extremes of daily temperature and precipitation, it is more difficult \r\nto distinguish changes in drought relative to the recent past. As such, \r\nchanges in droughts are assessed relative to the pre-industrial \r\nbaseline, unless indicated otherwise. "


id: 4acaf217dff82729dea0f6809c217363
created_at: 2023-03-16 14:00:49
updated_at: 2023-03-16 14:00:49
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index: climate_gpt_only_giec
vector_id: 9185
content:
"Tables 11.6, 11.9, 11.12, 11.15, 11.18, and 11.21 provide regional \r\nassessments on past, attributed and projected changes in \r\ndroughts. The assessment is subdivided in three drought categories \r\ncorresponding to four drought types: i) meteorological droughts, \r\nii)  agricultural and ecological droughts, and iii) hydrological \r\ndroughts  (see Section  11.6). A  list of metrics and global studies \r\nused for the assessments is provided below. The evidence from \r\nglobal studies is complemented in each continent with evidence \r\nfrom regional studies. An overview of studies considered for the \r\nassessments in projections is provided in Table 11.3."


id: 55958b4125f2d8e80aff9111369c34c7
created_at: 2023-03-16 14:00:49
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index: climate_gpt_only_giec
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"Agricultural and ecological droughts are primarily assessed based \r\non observed and projected changes in total column soil moisture, \r\ncomplemented by evidence on changes in surface soil moisture, \r\nwater-balance (precipitation minus evapotranspiration (ET)) and \r\nmetrics driven by precipitation and atmospheric evaporative demand \r\n(AED) such as the SPEI and PDSI (Section 11.6). In the latter, only \r\nstudies including estimates based on the Penman-Monteith equation \r\n(SPEI-PM and PDSI-PM) are considered because of biases associated \r\nwith temperature-only approaches (Section  11.6). Medium to "


id: f6cb76d2189065b74a2f81a1eb5b1106
created_at: 2023-03-16 14:00:50
updated_at: 2023-03-16 14:00:50
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index: climate_gpt_only_giec
vector_id: 36339
content:
"high confidence in drying was assigned in the assessment for arid \r\nregions if a  signal was also identifiable in total soil moisture in \r\naddition to surface soil moisture or metrics that combine AED and \r\nprecipitation, which tend to dry more in these regions. For observed \r\nchanges, evidence is drawn from several sources: Padron et al. \r\n(2020) for changes in precipitation minus ET, as well as soil moisture \r\nfrom the multi-model Land Surface Snow and Soil Moisture Model \r\nIntercomparison Project within CMIP6 (11.SM; van Den Hurk et al., \r\n2016); Greve et al. (2014) for changes in precipitation minus ET, and \r\nprecipitation minus AED; Spinoni et al. (2019) for changes in SPEI-PM; \r\nand Dai and Zhao (2017) for changes in PDSI-PM. "


id: 5b9fa39b3065073f192e3aa301cf57dd
created_at: 2023-03-16 14:00:50
updated_at: 2023-03-16 14:00:50
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index: climate_gpt_only_giec
vector_id: 11920
content:
"of global warming, evidence is mostly drawn from: Cook et al. (2020) \r\n(SSP3-7.0, 2071-2100) and the Chapter 11 Supplementary Material \r\n(11.SM) based on CMIP6 for changes in total column and surface \r\nsoil moisture; and from Vicente-Serrano et al. (2020c) for changes \r\nin SPEI-PM based on CMIP5. No global-scale studies with regional\u0002scale information are available for the attribution of agricultural \r\nand ecological droughts, so this assessment is based on regional \r\ndetection and attribution or event attribution studies."


id: 295bde806f25a220984268ef25d05e10
created_at: 2023-03-16 14:00:50
updated_at: 2023-03-16 14:00:50
content_type: text
index: climate_gpt_only_giec
vector_id: 3947
content:
"Hydrological droughts are assessed based on observed and projected \r\nchanges in low flows, complemented by information on changes in \r\nmean runoff. For observed changes, evidence is drawn from three \r\nstudies (Dai and Zhao, 2017; Gudmundsson et al., 2019, 2021). For \r\nprojected changes at 1.5degC of global warming, evidence is drawn from \r\nTouma et al. (2015) based on analyses of the Standardized Runoff \r\nIndex (SRI) (CMIP5, based on 2010-2054 compared to 1961-2005), \r\ncomplemented with regional studies when available. For projected \r\nchanges at 2degC of global warming, evidence is also drawn from Cook \r\net al. (2020) for changes in runoff in CMIP6 (Scenario SSP1-2.6, 2071-\r\n2100), and from J. Zhai et al. (2020) for changes in low flows based \r\non simulations with a single model. For projected changes at 4degC of \r\nglobal warming, evidence is drawn from: Touma et al. (2015) based on \r\nCMIP5 analyses of SRI; Cook et al. (2020) for changes in surface and "


id: 2312845499d2c668f8bdac6fb7d888b9
created_at: 2023-03-16 14:00:50
updated_at: 2023-03-16 14:00:50
content_type: text
index: climate_gpt_only_giec
vector_id: 2959
content:
"For projections at 1.5degC of global warming, evidence is drawn \r\nfrom: L. Xu et al. (2019), based on CMIP5; 11.SM based on CMIP6 \r\nfor changes in total column and surface soil moisture; and from \r\nNaumann et al. (2018) for changes in SPEI-PM, based on EC-Earth \r\nsimulations driven with SSTs from seven CMIP5 Earth system models. \r\nFor projections at 2degC of global warming, evidence is drawn from \r\nL. Xu et al. (2019) based on CMIP5, and Cook et al. (2020) (SSP1-2.6, \r\n2071-2100 compared to pre-industrial) and the Chapter  11 \r\nSupplementary Material (11.SM) based on CMIP6, for changes in \r\ntotal column and surface soil moisture; evidence is also drawn from \r\nNaumann et al. (2018) for changes in SPEI-PM. For projections at 4degC "


id: d2229dfc54ab3ceca38f058f5137ef46
created_at: 2023-03-16 14:00:50
updated_at: 2023-03-16 14:00:50
content_type: text
index: climate_gpt_only_giec
vector_id: 30588
content:
" 11.3 | Global analyses considered for the assessments of drought projections. MET refers to meteorological droughts, AGR/ECOL to agricultural and ecological \r\nhts, and HYDR to hydrological droughts."


id: 8d0901b3dda36aaa31af8dafdb5b3695
created_at: 2023-03-16 14:00:50
updated_at: 2023-03-16 14:00:50
content_type: text
index: climate_gpt_only_giec
vector_id: 19712
content:
"a\r\n CMIP5 and CMIP6: Coupled Model Intercomparison Project Phases 5/6; CORDEX: Coordinated Regional Downscaling Experiment; GCMs: global climate models; RCMs: \r\nregional climate models; SST: sea surface temperatures; ISI-MIP: Inter-Sectoral Impact Model Intercomparison Project; GHMs: Global Hydrological Models; CDD: consecutive \r\ndry days index; SPI: Standardized Precipitation Index; SRI: Standardized Runoff Index; SPEI-PM: Penman-Monteith-based Standardized Precipitation Evapotranspiration Index. "


id: 9cfe2faafb7f67ccf36fd708417778b2
created_at: 2023-03-16 14:00:50
updated_at: 2023-03-16 14:00:50
content_type: text
index: climate_gpt_only_giec
vector_id: 22241
content:
"total runoff based on CMIP6; and Giuntoli et al. (2015) for changes in \r\nlow flows based on the Inter-Sectoral Impact Model Intercomparison \r\nProject (ISI-MIP) based on six Global Hydrological Models (GHMs) and \r\nfive GCMs, including an analysis of inter-model signal-to-noise ratio. \r\nOne global-scale study with regional-scale information is available for \r\nthe attribution of hydrological droughts (Gudmundsson et al., 2021), \r\nbut only in a few AR6 regions. This information was complemented \r\nwith evidence from regional detection and attribution, and event \r\nattribution studies when available."


id: 1b758bff59498bd3930c84ee2df5fda0
created_at: 2023-03-16 14:00:50
updated_at: 2023-03-16 14:00:50
content_type: text
index: climate_gpt_only_giec
vector_id: 1778
content:
"Alexis Berg, Tim Brodribb, Jamie Hannaford, Nate McDowell, \r\nJack Scheff, Peter Stott, Lena Tallaksen, Peter Thorne, Francis Zwiers."


id: c99712bd6555fc022acb77565678122e
created_at: 2023-03-16 14:00:50
updated_at: 2023-03-16 14:00:50
content_type: text
index: climate_gpt_only_giec
vector_id: 29271
content:
"Human-caused climate change alters the frequency and intensity of climate variables (e.g., surface temperature) \r\nand phenomena (e.g., tropical cyclones) in a variety of ways. We now know that the ways in which average and \r\nextreme conditions have changed (and will continue to change) depend on the variable and the phenomenon \r\nbeing considered. Changes in local surface temperature extremes closely follow the corresponding changes in \r\nlocal average surface temperatures. On the contrary, changes in precipitation extremes (heavy precipitation) \r\ngenerally do not follow those in average precipitation, and can even move in the opposite direction (e.g., with \r\naverage precipitation decreasing but extreme precipitation increasing)."


id: 6bba87a418439cf13b3920ee9d216b7a
created_at: 2023-03-16 14:00:50
updated_at: 2023-03-16 14:00:50
content_type: text
index: climate_gpt_only_giec
vector_id: 14574
content:
"Climate change will manifest very differently depending on which region, season and variable we are interested \r\nin. For example, over some parts of the Arctic, temperatures will warm at rates about three to four times higher \r\nduring winter compared to summer months. And in summer, most of northern Europe will experience larger \r\ntemperatures increases than most places in south-east South America and Australasia, with differences that can \r\nbe larger than 1degC, depending on the level of global warming. In general, differences across regions and seasons \r\narise because the underlying physical processes differ drastically across regions and seasons."


id: 8cff9a2967ad6aae6fe1f2d8cc417afc
created_at: 2023-03-16 14:00:50
updated_at: 2023-03-16 14:00:50
content_type: text
index: climate_gpt_only_giec
vector_id: 19704
content:
"In summary, the specific relationship between changes in average and extreme conditions strongly depends on \r\nthe variable or phenomenon being considered. At the local scale, average and extreme surface temperature \r\nchanges are strongly related, while average and extreme precipitation changes are often weakly related. For \r\nboth variables, the changes in average and extreme conditions vary strongly across different places due to the \r\neffect of local and regional processes."


id: dab6f9c0cd893dbea56d7b3b5ae2247b
created_at: 2023-03-16 14:00:50
updated_at: 2023-03-16 14:00:50
content_type: text
index: climate_gpt_only_giec
vector_id: 31939
content:
"FAQ 11.1, Figure 1 | Global maps of future changes in surface temperature (top panels) and precipitation (bottom panels) for long-term \r\naverage (left) and extreme conditions (right). All changes were estimated using the Coupled Model Intercomparison Project Phase 6 (CMIP6) ensemble \r\nmedian for a scenario with a global warming of 4degC relative to 1850-1900 temperatures. Average surface temperatures refer to the warmest three-month season \r\n(summer in mid- to high latitudes) and extreme temperatures refer to the hottest day in a year. Precipitation changes, which can include both rainfall and snowfall \r\nchanges, are normalized by 1850-1900 values and shown as a percentage; extreme precipitation refers to the largest daily precipitation in a year."


id: cffb99c0da7858ad9feeeb4ff572b461
created_at: 2023-03-16 14:00:50
updated_at: 2023-03-16 14:00:50
content_type: text
index: climate_gpt_only_giec
vector_id: 30247
content:
"Climate change has already increased the magnitude and frequency of extreme hot events and decreased \r\nthe magnitude and frequency of extreme cold events, and, in some regions, intensified extreme precipitation \r\nevents. As the climate moves away from its past and current states, we will experience extreme events that \r\nare unprecedented, either in magnitude, frequency, timing or location. The frequency of these unprecedented \r\nextreme events will rise with increasing global warming. Additionally, the combined occurrence of multiple \r\nunprecedented extremes may result in large and unprecedented impacts."


id: 28e36c74e7ca57811dee4f33b5854e7a
created_at: 2023-03-16 14:00:50
updated_at: 2023-03-16 14:00:50
content_type: text
index: climate_gpt_only_giec
vector_id: 3866
content:
"Unprecedented extremes - that is, events not experienced in the past - will occur in the future in five different ways \r\n(FAQ 11.2, Figure 1). First, events that are considered to be extreme in the current climate will occur in the future \r\nwith unprecedented magnitudes. Second, future extreme events will also occur with unprecedented frequency. \r\nThird, certain types of extremes may occur in regions that have not previously encountered those types of events. \r\nFor example, as the sea level rises, coastal flooding may occur in new locations, and wildfires are already occurring \r\nin areas, such as parts of the Arctic, where the probability of such events was previously low. Fourth, extreme events \r\nmay also be unprecedented in their timing. For example, extremely hot temperatures may occur either earlier or \r\nlater in the year than they have in the past."


id: 8ed0442e0a43921aa1812bd5d356a4e4
created_at: 2023-03-16 14:00:50
updated_at: 2023-03-16 14:00:50
content_type: text
index: climate_gpt_only_giec
vector_id: 19994
content:
"Finally, compound events  - where multiple extreme \r\nevents of either different or similar types occur \r\nsimultaneously and/or in succession  - may be more \r\nprobable or severe in the future. These compound \r\nevents can often impact ecosystems and societies more \r\nstrongly than when such events occur in isolation. \r\nFor example, a drought along with extreme heat will \r\nincrease the risk of wildfires and agriculture damages \r\nor losses. As individual extreme events become more \r\nsevere as a  result of climate change, the combined \r\noccurrence of these events will create unprecedented \r\ncompound events. This could exacerbate the intensity \r\nand associated impacts of these extreme events."


id: 10303cb0e3d1b784126a194d940adf85
created_at: 2023-03-16 14:00:50
updated_at: 2023-03-16 14:00:50
content_type: text
index: climate_gpt_only_giec
vector_id: 35
content:
"Unprecedented extremes have already occurred in \r\nrecent years, relative to the 20th century climate. Some \r\nrecent extreme hot events would have had very little \r\nchance of occurring without human influence on the \r\nclimate (see FAQ 11.3). In the future, unprecedented \r\nextremes will occur as the climate continues to warm. \r\nThose extremes will happen with larger magnitudes \r\nand at higher frequencies than previously experienced. \r\nExtreme events may also appear in new locations, at \r\nnew times of the year, or as unprecedented compound \r\nevents. Moreover, unprecedented events will become \r\nmore frequent with higher levels of warming, for \r\nexample at 3degC of global warming compared to 2degC of \r\nglobal warming."


id: 9fc258be8ba69e2493742391699a7275
created_at: 2023-03-16 14:00:50
updated_at: 2023-03-16 14:00:50
content_type: text
index: climate_gpt_only_giec
vector_id: 22675
content:
"FAQ 11.2, Figure 1 | New types of unprecedented extremes that will \r\noccur as a result of climate change."


id: d8b4a0d906b2155cbe8d37e470f14430
created_at: 2023-03-16 14:00:50
updated_at: 2023-03-16 14:00:50
content_type: text
index: climate_gpt_only_giec
vector_id: 31609
content:
"While it is difficult to identify the exact causes of a particular extreme event, the relatively new science of event \r\nattribution is able to quantify the role of climate change in altering the probability and magnitude of some \r\ntypes of weather and climate extremes. There is strong evidence that characteristics of many individual extreme \r\nevents have already changed because of human-driven changes to the climate system. Some types of highly \r\nimpactful extreme weather events have occurred more often and have become more severe due to these human \r\ninfluences. As the climate continues to warm, the observed changes in the probability and/or magnitude of some \r\nextreme weather events will continue as the human influences on these events increase. "


id: f7372519cda48fbea96a87956409a363
created_at: 2023-03-16 14:00:50
updated_at: 2023-03-16 14:00:50
content_type: text
index: climate_gpt_only_giec
vector_id: 36411
content:
"Attributable increases in probability and magnitude \r\nhave been identified consistently for many hot \r\nextremes. Attributable increases have also been found \r\nfor some extreme precipitation events, including \r\nhurricane rainfall events, but these results can vary \r\namong events. In some cases, large natural variations \r\nin the climate system prevent attributing changes in \r\nthe probability or magnitude of a specific extreme to \r\nhuman influence. Additionally, attribution of certain \r\nclasses of extreme weather (e.g., tornadoes) is beyond \r\ncurrent modelling and theoretical capabilities. As \r\nthe climate continues to warm, larger changes in "


id: b2ebec861e34322ac4ef68338520c152
created_at: 2023-03-16 14:00:50
updated_at: 2023-03-16 14:00:50
content_type: text
index: climate_gpt_only_giec
vector_id: 25661
content:
"probability and magnitude are expected and, as a result, it will be possible to attribute future temperature and \r\nprecipitation extremes in many locations to human influences. Attributable changes may emerge for other types \r\nof extremes as the warming signal increases."


id: 214ca7772d0fd3e49a8adf32553dfa93
created_at: 2023-03-16 14:00:50
updated_at: 2023-03-16 14:00:50
content_type: text
index: climate_gpt_only_giec
vector_id: 2676
content:
"In conclusion, human-caused global warming has resulted in changes in a wide variety of recent extreme weather \r\nevents. Strong increases in probability and magnitude, attributable to human influence, have been found for \r\nmany heatwaves and hot extremes around the world."


id: 49a8697095abaaf47ecdd83bc274eafc
created_at: 2023-03-16 14:00:50
updated_at: 2023-03-16 14:00:50
content_type: text
index: climate_gpt_only_giec
vector_id: 8980
content:
"Table 11.4 | Observed trends, human contribution to observed trends, and projected changes at 1.5degC, 2degC and 4degC of global warming for temperature \r\nextremes in Africa, subdivided by AR6 regions. See Sections 11.9.1 and 11.9.2 for details. CMIP6: Coupled Model Intercomparison Project Phase 6; TXx: hottest daily \r\nmaximum temperature; TNn: coldest daily minimum temperature; CORDEX: Coordinated Regional Downscaling Experiment; RCM: regional climate model."


id: 863bb7d2dc5b74f073c7c4160819ffb
created_at: 2023-03-16 14:00:50
updated_at: 2023-03-16 14:00:50
content_type: text
index: climate_gpt_only_giec
vector_id: 18661
content:
"CMIP6 models project \r\na robust increase in the \r\nintensity and frequency of \r\nTXx events and a robust \r\ndecrease in the intensity and \r\nfrequency of TNn events (Li \r\net al., 2021; 11.SM). Median \r\nincrease of more than 3.5degC \r\nin the 50-year TXx and TNn \r\nevents compared to the 1degC \r\nwarming level (Li et al., 2021) \r\nand more than 5degC in annual \r\nTXx and TNn compared \r\nto pre-industrial (11.SM)\r\nAdditional evidence from \r\nCMIP5 and RCM simulations \r\nfor an increase in the intensity \r\nand frequency of hot extremes, \r\nand decrease in the intensity and \r\nfrequency of cold extremes (Giorgi \r\net al., 2014; Tomozeiu et al., 2014; \r\nEngelbrecht et al., 2015; Nastos \r\nand Kapsomenakis, 2015; Zollo \r\net al., 2016; Cardoso et al., 2019; \r\nCardell et al., 2020; Driouech \r\net al., 2020; Coppola et al., 2021a)"


id: ad3cfc3464bdf70a5e1cf226b52e7168
created_at: 2023-03-16 14:00:50
updated_at: 2023-03-16 14:00:50
content_type: text
index: climate_gpt_only_giec
vector_id: 24795
content:
"CMIP6 models project \r\na robust increase in the \r\nintensity and frequency \r\nof TXx events and a robust \r\ndecrease in the intensity and \r\nfrequency of TNn events (Li \r\net al., 2021; 11.SM). Median \r\nincrease of more than 1degC \r\nin the 50-year TXx and TNn \r\nevents compared to the 1degC \r\nwarming level (Li et al., 2021) \r\nand more than 2.5degC in \r\nannual TXx and TNn compred \r\nto pre-industrial (11.SM)"


id: a61bed31ea9bf6afcda637c20af713c6
created_at: 2023-03-16 14:00:50
updated_at: 2023-03-16 14:00:50
content_type: text
index: climate_gpt_only_giec
vector_id: 23679
content:
"Additional evidence from \r\nCMIP5 and RCM simulations \r\nfor an increase in the \r\nintensity and frequency of hot \r\nextremes, and decrease in the \r\nintensity and frequency of cold \r\nextremes (Tomozeiu et al., 2014; \r\nNastos and Kapsomenakis, 2015; \r\nZollo et al., 2016; Abaurrea et al., \r\n2018; Weber et al., 2018; Cardoso \r\net al., 2019; Cardell et al., 2020; \r\nCoppola et al., 2021a)"


id: 53c2631052e972610459eaf4bef3d375
created_at: 2023-03-16 14:00:50
updated_at: 2023-03-16 14:00:50
content_type: text
index: climate_gpt_only_giec
vector_id: 10644
content:
"Additional evidence from \r\nCMIP5/CMIP3 and CORDEX \r\nsimulations for an increase in \r\nthe intensity and frequency \r\nof hot extremes (Giorgi et al., \r\n2014; Engelbrecht et al., 2015; \r\nCoppola et al., 2021a) "


id: 444ab5e6379a6e437c94e2045367fb3e
created_at: 2023-03-16 14:00:50
updated_at: 2023-03-16 14:00:50
content_type: text
index: climate_gpt_only_giec
vector_id: 8168
content:
"CMIP6 models project \r\na robust increase in the \r\nintensity and frequency of \r\nTXx events and a robust \r\ndecrease in the intensity and \r\nfrequency of TNn events (Li \r\net al., 2021; 11.SM). Median \r\nincrease of more than 3degC \r\nin the 50-year TXx and TNn \r\nevents compared to the 1degC \r\nwarming level (Li et al., 2021) \r\nand more than 4.5degC in annual \r\nTXx and TNn compared to \r\npre-industrial (11.SM)"


id: d3b8903f10e55c3f63d2d232aaccd2cb
created_at: 2023-03-16 14:00:50
updated_at: 2023-03-16 14:00:50
content_type: text
index: climate_gpt_only_giec
vector_id: 30832
content:
"CMIP6 models project \r\na robust increase in the \r\nintensity and frequency of TXx \r\nevents and a robust decrease \r\nin the intensity and frequency \r\nof TNn events (Li et al., 2021; \r\n11.SM). Median increase \r\nof more than 2.5degC in the \r\n50-year TXx and TNn events \r\ncompared to the 1degC warming \r\nlevel (Li et al., 2021) and \r\nmore than 4degC in annual \r\nTXx and TNn compared to \r\npre-industrial (11.SM)\r\nAdditional evidence from \r\nCMIP5/CMIP3 and CORDEX \r\nsimulations for an increase in \r\nthe intensity and frequency \r\nof hot extremes (Giorgi et al., \r\n2014; Engelbrecht et al., 2015; \r\nCoppola et al., 2021a) "


id: 9b56d011f67d54829456593cb36ddc78
created_at: 2023-03-16 14:00:50
updated_at: 2023-03-16 14:00:50
content_type: text
index: climate_gpt_only_giec
vector_id: 21986
content:
"Additional evidence from \r\nCMIP5 and CORDEX simulations \r\nfor an increase in the intensity \r\nand frequency of hot extremes \r\n(Weber et al., 2018; Coppola \r\net al., 2021a) "


id: fc6c4bbbcb6ae6d7a0244e4082e3a625
created_at: 2023-03-16 14:00:50
updated_at: 2023-03-16 14:00:50
content_type: text
index: climate_gpt_only_giec
vector_id: 37222
content:
"Additional evidence from \r\nCMIP5/CMIP3 and CORDEX \r\nsimulations for an increase in \r\nthe intensity and frequency \r\nof hot extremes (Giorgi et al., \r\n2014; Engelbrecht et al., 2015; \r\nCoppola et al., 2021a)"


id: 5a686991544031e52fd428f0985acd14
created_at: 2023-03-16 14:00:50
updated_at: 2023-03-16 14:00:50
content_type: text
index: climate_gpt_only_giec
vector_id: 11736
content:
"CMIP6 models project \r\na robust increase in the \r\nintensity and frequency of TXx \r\nevents and a robust decrease \r\nin the intensity and frequency \r\nof TNn events (Li et al., 2021; \r\n11.SM). Median increase of more \r\nthan 2.5degC in the 50-year TXx \r\nand TNn events compared to \r\nthe 1degC warming level (Li et al., \r\n2021) and more than 4degC in \r\nannual TXx and TNn compared \r\nto pre-industrial (11.SM)"


id: 923b0a790801dedf9c8f4be50e38965e
created_at: 2023-03-16 14:00:50
updated_at: 2023-03-16 14:00:50
content_type: text
index: climate_gpt_only_giec
vector_id: 20529
content:
"CMIP6 models project \r\na robust increase in the \r\nintensity and frequency of TXx \r\nevents and a robust decrease \r\nin the intensity and frequency \r\nof TNn events (Li et al., 2021; \r\n11.SM). Median increase of more \r\nthan 2.5degC in the 50-year TXx \r\nand TNn events compared to \r\nthe 1degC warming level (Li et al., \r\n2021) and more than 4.5degC in \r\nannual TXx and TNn compared \r\nto pre-industrial (11.SM)"


id: 86c4de9697f02d3bd9c283eb17f79b2e
created_at: 2023-03-16 14:00:50
updated_at: 2023-03-16 14:00:50
content_type: text
index: climate_gpt_only_giec
vector_id: 18739
content:
"CMIP6 models project \r\na robust increase in the \r\nintensity and frequency of TXx \r\nevents and a robust decrease \r\nin the intensity and frequency \r\nof TNn events (Li et al., 2021; \r\n11.SM). Median increase of \r\nmore than 2degC in the 50-year \r\nTXx and TNn events compared to \r\nthe 1degC warming level (Li et al., \r\n2021) and more than 3.5degC in \r\nannual TXx and TNn compared \r\nto pre-industrial (11.SM)"


id: 3eb6572c4531650b7ef7076c993b3e5f
created_at: 2023-03-16 14:00:50
updated_at: 2023-03-16 14:00:50
content_type: text
index: climate_gpt_only_giec
vector_id: 7247
content:
"Table 11.5 |  Observed trends, human contribution to observed trends, and projected changes at 1.5degC, 2degC and 4degC of global warming for heavy \r\nprecipitation in Africa, subdivided by AR6 regions. See Sections 11.9.1 and 11.9.3 for details."


id: 10dcbb437ede5841f61a0915e7bb8805
created_at: 2023-03-16 14:00:50
updated_at: 2023-03-16 14:00:50
content_type: text
index: climate_gpt_only_giec
vector_id: 131
content:
"CMIP6 models project a robust \r\nincrease in the intensity and \r\nfrequency of heavy precipitation \r\n(Li et al., 2021; 11.SM). Median \r\nincrease of more than 25% in \r\nthe 50-year Rx1day and Rx5day \r\nevents compared to the 1degC \r\nwarming level (Li et al., 2021), \r\nand more than 30% in annual \r\nRx1day and Rx5day, and 15% \r\nin annual Rx30day compared \r\nto pre-industrial (11.SM)"


id: f927ea38803536a3780cfa1a7536ea21
created_at: 2023-03-16 14:00:50
updated_at: 2023-03-16 14:00:50
content_type: text
index: climate_gpt_only_giec
vector_id: 36711
content:
"Additional evidence from \r\nCMIP5 and CORDEX simulations \r\nfor an increase in the intensity \r\nof heavy precipitation (Giorgi \r\net al., 2014; Akinsanola and \r\nZhou, 2019; Dosio et al., 2019; \r\nCoppola et al., 2021b)"


id: 5320e4c9a2e8f4516844e89199576e50
created_at: 2023-03-16 14:00:50
updated_at: 2023-03-16 14:00:50
content_type: text
index: climate_gpt_only_giec
vector_id: 10547
content:
"Table 11.6 | Observed trends, human contribution to observed trends, and projected changes at 1.5degC, 2degC and 4degC of global warming for meteorological \r\ndroughts (MET), agricultural and ecological droughts (AGR/ECOL), and hydrological droughts (HYDR) in Africa, subdivided by AR6 regions. See Sections \r\n1191 and 1194 for details"


id: 555f4befa0b25a1bdd8a2d5467848722
created_at: 2023-03-16 14:00:50
updated_at: 2023-03-16 14:00:50
content_type: text
index: climate_gpt_only_giec
vector_id: 10902
content:
"Table 11.7 | Observed trends, human contribution to observed trends, and projected changes at 1.5degC, 2degC and 4degC of global warming for temperature \r\nextremes in Asia, subdivided by AR6 regions. See Sections 11.9.1 and 11.9.2 for details."


id: 263498bf24a5c3011c1e7ad8961bab3e
created_at: 2023-03-16 14:00:50
updated_at: 2023-03-16 14:00:50
content_type: text
index: climate_gpt_only_giec
vector_id: 3427
content:
"CMIP6 models project \r\na robust increase in the \r\nintensity and frequency of TXx \r\nevents and a robust decrease \r\nin the intensity and frequency \r\nof TNn events (Li et al., 2021). \r\nMedian increase of more \r\nthan 4.5degC in the 50-year TXx \r\nand TNn events compared \r\nto the 1degC warming level \r\n(Li et al., 2021)"


id: abfdd2b5c0505a8d4de17403016acc73
created_at: 2023-03-16 14:00:50
updated_at: 2023-03-16 14:00:50
content_type: text
index: climate_gpt_only_giec
vector_id: 24582
content:
"CMIP6 models project \r\na robust increase in the \r\nintensity and frequency of TXx \r\nevents and a robust decrease \r\nin the intensity and frequency \r\nof TNn events (Li et al., 2021; \r\n11.SM). Median increase \r\nof more than 4.5degC in the \r\n50-year TXx and TNn events \r\ncompared to the 1degC warming \r\nlevel (Li et al., 2021) and more \r\nthan 5.5degC in annual TXx \r\nand TNn compared to pre\u0002industrial (11.SM)"


id: d7e0dfe9ca9e4cae1d9044f8214120e0
created_at: 2023-03-16 14:00:50
updated_at: 2023-03-16 14:00:50
content_type: text
index: climate_gpt_only_giec
vector_id: 31495
content:
"CMIP6 models project \r\na robust increase in the \r\nintensity and frequency of TXx \r\nevents and a robust decrease \r\nin the intensity and frequency \r\nof TNn events (Li et al., 2021; \r\n11.SM). Median increase \r\nof more than 1.5degC in the \r\n50-year TXx and TNn events \r\ncompared to the 1degC warming \r\nlevel (Li et al., 2021) and \r\nmore than 2.5degC in annual \r\nTXx and TNn compared \r\nto pre-industrial (11.SM)"


id: 983c62a9401b400ffa6418f4be7447b
created_at: 2023-03-16 14:00:50
updated_at: 2023-03-16 14:00:50
content_type: text
index: climate_gpt_only_giec
vector_id: 21461
content:
"Additional evidence from \r\nCMIP5 and RCM simulations \r\nfor an increase in the intensity \r\nand frequency of hot extremes \r\nand decrease in the intensity \r\nand frequency of cold extremes \r\n(Xu et al., 2017; Han et al., 2018; \r\nKhlebnikova et al., 2019b)"


id: 660d77b29b9904545af15a9fbc04871a
created_at: 2023-03-16 14:00:50
updated_at: 2023-03-16 14:00:50
content_type: text
index: climate_gpt_only_giec
vector_id: 13645
content:
"CMIP6 models project \r\na robust increase in the \r\nntensity and frequency of TXx \r\nevents and a robust decrease \r\nn the intensity and frequency \r\nof TNn events (Li et al., 2021; \r\n11.SM). Median increase \r\nof more than 3.5degC in the \r\n50-year TXx  and TNn events \r\ncompared to the 1degC warming \r\nevel (Li et al., 2021) and \r\nmore than 5.5degC in annual \r\nTXx and TNn compared \r\nto pre-industrial (11.SM)"


id: b1d1fca808f462791cb50f6ad304e0a
created_at: 2023-03-16 14:00:50
updated_at: 2023-03-16 14:00:50
content_type: text
index: climate_gpt_only_giec
vector_id: 25482
content:
"CMIP6 models project \r\na robust increase in the \r\nintensity and frequency of TXx \r\nevents and a robust decrease \r\nin the intensity and frequency \r\nof TNn events (Li et al., 2021; \r\n11.SM). Median increase of \r\nmore than 5degC in the 50-year \r\nTXx and TNn events compared to \r\nthe 1degC warming level (Li et al., \r\n2021) and more than 6degC in \r\nannual TXx and TNn compared \r\nto pre-industrial (11.SM)\r\nAdditional evidence from CMIP5 \r\nsimulations for an increase in \r\nthe intensity and frequency of \r\nhot extremes and decrease in \r\nthe intensity and frequency of \r\ncold extremes (Han et al., 2018)"


id: 3b01b00eaa12ccc6f60f60462c68fd1f
created_at: 2023-03-16 14:00:50
updated_at: 2023-03-16 14:00:50
content_type: text
index: climate_gpt_only_giec
vector_id: 6643
content:
"Additional evidence from \r\nCMIP5 and RCM simulations \r\nfor an increase in the \r\nintensity and frequency of hot \r\nextremes and decrease in the \r\nintensity and frequency of \r\ncold extremes (Xu et al., 2017; \r\nHan et al., 2018; Khlebnikova \r\net al., 2019b)"


id: c5e123cdfc9dfda0235b632e1889f01a
created_at: 2023-03-16 14:00:50
updated_at: 2023-03-16 14:00:50
content_type: text
index: climate_gpt_only_giec
vector_id: 28708
content:
"CMIP6 models project \r\na robust increase in the \r\nntensity and frequency of TXx \r\nevents and a robust decrease \r\nn the intensity and frequency \r\nof TNn events (Li et al., 2021; \r\n11.SM). Median increase of \r\nmore than 4.5degC in the 50-year \r\nTXx and TNn events compared to \r\nthe 1degC warming level (Li et al., \r\n2021) and more than 5.5degC \r\nn annual TXx and TNn compared \r\nto pre-industrial (11.SM)"


id: 48e539f40c4ee3412c4337f124778ac9
created_at: 2023-03-16 14:00:50
updated_at: 2023-03-16 14:00:50
content_type: text
index: climate_gpt_only_giec
vector_id: 8854
content:
"Additional evidence from \r\nCMIP5 and RCM simulations for  \r\nan increase in the intensity and \r\nfrequency of hot extremes and \r\ndecrease in the intensity and \r\nfrequency of cold extremes (Xu \r\net al., 2017; Han et al., 2018; \r\nKhlebnikova et al., 2019b) "


id: 28e1d64514b9366779b61c883785651d
created_at: 2023-03-16 14:00:50
updated_at: 2023-03-16 14:00:50
content_type: text
index: climate_gpt_only_giec
vector_id: 3864
content:
"CMIP6 models project \r\na robust increase in the \r\nintensity and frequency of TXx \r\nevents and a robust decrease \r\nin the intensity and frequency \r\nof TNn events (Li et al., 2021; \r\n11.SM). Median increase of \r\nmore than 4.5degC in the 50-year \r\nTXx and TNn events compared \r\nto the 1degC warming level (Li \r\net al., 2021) and more than \r\n5degC in annual TXx and TNn \r\ncompared \r\nto pre-industrial (11.SM)"


id: 83dc37d5bc91d23bc162f74c48ad5f8c
created_at: 2023-03-16 14:00:50
updated_at: 2023-03-16 14:00:50
content_type: text
index: climate_gpt_only_giec
vector_id: 18277
content:
"Additional evidence from CMIP5 \r\nand RCM simulations for an \r\nincrease in the intensity and \r\nfrequency of hot extremes and \r\ndecrease in the intensity and \r\nfrequency of cold extremes \r\n(Xu et al., 2017; Han et al., 2018; \r\nKhlebnikova et al., 2019b)."


id: 8356e4fe1c20a76711035d860fdcc137
created_at: 2023-03-16 14:00:50
updated_at: 2023-03-16 14:00:50
content_type: text
index: climate_gpt_only_giec
vector_id: 18189
content:
"CMIP6 models project \r\na robust increase in the \r\nintensity and frequency of TXx \r\nevents and a robust decrease \r\nin the intensity and frequency \r\nof TNn events (Li et al., 2021; \r\n11.SM). Median increase of \r\nmore than 4degC in the 50-year \r\nTXx and TNn events compared to \r\nthe 1degC warming level (Li et al., \r\n2021) and more than 4.5degC in \r\nannual TXx and TNn compared \r\nto pre-industrial (11.SM)"


id: 5c33bb2929f5a56261e4370085660150
created_at: 2023-03-16 14:00:50
updated_at: 2023-03-16 14:00:50
content_type: text
index: climate_gpt_only_giec
vector_id: 12020
content:
"Additional evidence from \r\nCMIP5 and RCM simulations for \r\nan increase in the intensity and \r\nfrequency of hot extremes and \r\ndecrease in the intensity and \r\nfrequency of cold extremes \r\n(Seo et al., 2014; Zhou \r\net al., 2014; Y. Xu et al., 2016; \r\nX.Wang et al., 2017; Y.Wang \r\net al. 2017; Guo et al., 2018; \r\nD. Li et al., 2018; Shi et al., 2018; \r\nSui et al., 2018; Imada et al., \r\n2019; C. Sun et al., 2019)"


id: 18aa3ce78c46ebd7974b3ca031ef5197
created_at: 2023-03-16 14:00:50
updated_at: 2023-03-16 14:00:50
content_type: text
index: climate_gpt_only_giec
vector_id: 1377
content:
"Additional evidence from \r\nCMIP5 and RCM simulations \r\nfor an increase in the \r\nintensity and frequency of \r\nhot extremes and decrease in \r\nthe intensity and frequency \r\nof cold extremes (Seo et al., \r\n2014; Zhou et al., 2014; Y. Xu \r\net al., 2016; X. Wang et al., 2017; \r\nY.Wang et al., 2017; Guo et al., \r\n2018; D. Li et al., 2018; Shi et al., \r\n2018; Sui et al., 2018; Imada \r\net al., 2019; C. Sun et al., 2019) "


id: 3f6f5eb1bde4c4162695ed66b9e37ddc
created_at: 2023-03-16 14:00:50
updated_at: 2023-03-16 14:00:50
content_type: text
index: climate_gpt_only_giec
vector_id: 7366
content:
"CMIP6 models project \r\na robust increase in the \r\nntensity and frequency of TXx \r\nevents and a robust decrease \r\nn the intensity and frequency \r\nof TNn events (Li et al., 2021; \r\n11.SM). Median increase of \r\nmore than 3.5degC in the 50-year \r\nTXx and TNn events compared to \r\nthe 1degC warming level (Li et al., \r\n2021) and more than 5.5degC in \r\nannual TXx and TNn compared \r\nto pre-industrial (11.SM)"


id: 658a1cbf7d379d0204bff78aba5500c4
created_at: 2023-03-16 14:00:50
updated_at: 2023-03-16 14:00:50
content_type: text
index: climate_gpt_only_giec
vector_id: 13553
content:
"Additional evidence \r\nfrom CMIP5 simulations \r\nfor an increase in the \r\nntensity and frequency of \r\nhot extremes and decrease \r\nn the intensity and \r\nfrequency of cold extremes \r\n(Han et al., 2018)"


id: bd1d0f80c089c49fbe424d28d6162386
created_at: 2023-03-16 14:00:50
updated_at: 2023-03-16 14:00:50
content_type: text
index: climate_gpt_only_giec
vector_id: 27319
content:
"Additional evidence from CMIP5 \r\nand RCM simulations for an \r\nincrease in the intensity and \r\nfrequency of hot extremes and \r\ndecrease in the intensity and \r\nfrequency of cold extremes \r\n(Zhou et al., 2014; Singh and \r\nGoyal, 2016; W. Zhang et al., \r\n2016b; Xu et al., 2017; Han \r\net al., 2018; C. Li et al., 2018)"


id: f21bc76c011ba56a63bc2c2ce13b8020
created_at: 2023-03-16 14:00:50
updated_at: 2023-03-16 14:00:50
content_type: text
index: climate_gpt_only_giec
vector_id: 35622
content:
"CMIP6 models project \r\na robust increase in the \r\nintensity and frequency of TXx \r\nevents and a robust decrease \r\nin the intensity and frequency \r\nof TNn events (Li et al., 2021; \r\n11.SM). Median increase of \r\nmore than 3.5degC in the 50-year \r\nTXx and TNn events compared to \r\nthe 1degC warming level (Li et al., \r\n2021) and more than 4degC in \r\nannual TXx and TNn compared \r\nto pre-industrial (11.SM)"


id: d0756ee70058b27bdb2492c976bd946b
created_at: 2023-03-16 14:00:50
updated_at: 2023-03-16 14:00:50
content_type: text
index: climate_gpt_only_giec
vector_id: 30324
content:
"Additional evidence from CMIP5 \r\nand RCM simulations for an \r\nincrease in the intensity and \r\nfrequency of hot extremes and \r\ndecrease in the intensity and \r\nfrequency of cold extremes \r\n(Sillmann et al., 2013b; \r\nMurari et al., 2015; Xu et al., \r\n2017; Han et al., 2018; Kharin \r\net al., 2018; Nasim et al., 2018; \r\nS. Ali et al., 2019)"


id: b1d2e1636eeb14e2c98f65972a95dfe2
created_at: 2023-03-16 14:00:50
updated_at: 2023-03-16 14:00:50
content_type: text
index: climate_gpt_only_giec
vector_id: 25484
content:
"Additional evidence \r\nfrom CMIP5 and RCM \r\nsimulations for an increase \r\nin the intensity and frequency \r\nof hot extremes and decrease \r\nin the intensity and frequency \r\nof cold extremes (Sillmann et al., \r\n2013b; Murari et al., 2015; Xu \r\net al., 2017; Han et al., 2018; \r\nKharin et al., 2018; Nasim et al., \r\n2018; S. Ali et al., 2019)"


id: 46b7cdc9c37c7e1f8e2051693456348d
created_at: 2023-03-16 14:00:50
updated_at: 2023-03-16 14:00:50
content_type: text
index: climate_gpt_only_giec
vector_id: 8511
content:
"CMIP6 models project \r\na robust increase in the \r\nntensity and frequency of TXx \r\nevents and a robust decrease \r\nn the intensity and frequency \r\nof TNn events (Li et al., 2021; \r\n11.SM). Median increase of \r\nmore than 2.5degC in the 50-year \r\nTXx and TNn events compared to \r\nthe 1degC warming level (Li et al., \r\n2021) and more than 4degC in \r\nannual TXx and TNn compared \r\nto pre-industrial (11.SM)"


id: 3cd6fcd195d8ee79bd149dfc1a53587e
created_at: 2023-03-16 14:00:50
updated_at: 2023-03-16 14:00:50
content_type: text
index: climate_gpt_only_giec
vector_id: 6938
content:
"Additional evidence \r\nfrom CMIP5 simulations \r\nfor an increase in the \r\nintensity and frequency of \r\nhot extremes and decrease in \r\nthe intensity and frequency \r\nof cold extremes (Xu et al., 2017; \r\nKharin et al., 2018)"


id: 36d8a2c61e923b4f8225b608092bc98
created_at: 2023-03-16 14:00:50
updated_at: 2023-03-16 14:00:50
content_type: text
index: climate_gpt_only_giec
vector_id: 6016
content:
"Table 11.8 |  Observed trends, human contribution to observed trends, and projected changes at 1.5degC, 2degC and 4degC of global warming for heavy \r\nprecipitation in Asia, subdivided by AR6 regions. See Sections 11.9.1 and 11.9.3 for details."


id: 701d7d409fae330d05ddda9778b5762
created_at: 2023-03-16 14:00:50
updated_at: 2023-03-16 14:00:50
content_type: text
index: climate_gpt_only_giec
vector_id: 15253
content:
"CMIP6 models project \r\na robust increase in the \r\nintensity and frequency of heavy \r\nprecipitation (Li et al., 2021; \r\n11.SM). Median increase of \r\nmore than 15% in the 50-year \r\nRx1day and Rx5day events \r\ncompared to the 1degC warming \r\nlevel (Li et al., 2021) and more \r\nthan 15% in annual Rx1day, \r\nRx5day, and Rx30day compared \r\nto pre-industrial (11.SM)"


id: 87e7d47566e0eb2925aa0f2504e1e464
created_at: 2023-03-16 14:00:50
updated_at: 2023-03-16 14:00:50
content_type: text
index: climate_gpt_only_giec
vector_id: 18905
content:
"Additional evidence from \r\nCMIP5 simulations for an \r\nncrease in the intensity of heavy \r\nprecipitation (Sillmann et al., \r\n2013b; Xu et al., 2017; Han \r\net al., 2018; Kharin et al., 2018; \r\nKhlebnikova et al., 2019a)"


id: ce78eaf53f1eac23a8e76746f99f4705
created_at: 2023-03-16 14:00:50
updated_at: 2023-03-16 14:00:50
content_type: text
index: climate_gpt_only_giec
vector_id: 30021
content:
"Additional evidence from \r\nCMIP5 simulations for an \r\nincrease in the intensity of heavy \r\nprecipitation (Sillmann et al., \r\n2013b; Xu et al., 2017; Han \r\net al., 2018; Kharin et al., 2018; \r\nKhlebnikova et al., 2019a)"


