# document

id: 9ce819643290c0e8a172241a9a4260ae
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
index: climate_gpt_only_giec
vector_id: 22222
content:
" However, if only individual events are simulated, no \r\nimmediate conclusions can be derived for changes to the occurrence \r\nprobability of these events (F.E.L. Otto et al., 2016; Shepherd, 2016a)."


id: 40521a108539dff781519caa2a962722
created_at: 2023-03-16 14:02:06
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vector_id: 7502
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"In contrast, time-slice experiments are designed to represent only \r\na  specific period of time (typically 30 years). They are often run \r\nusing global and regional models in atmosphere-only mode, forced \r\nby SSTs derived either from observations, as AMIP experiments, or \r\nfrom historical simulations and future projections of coupled global \r\nmodels. Compared to transient simulations, they offer advantages in \r\nbeing computationally cheaper (due to the lack of coupled ocean and \r\nshort duration), which allows for the number of ensemble members \r\n(T. Zhang et  al., 2016), and/or the resolution (Haarsma et  al., \r\n2013b; Davini et  al., 2017) to be increased. Convection-permitting \r\nsimulations, both covering the globe or particular regions, are \r\ncurrently conducted for short time slices only (Kendon et al., 2017; \r\nHewitt and Lowe, 2018; Coppola et al., 2020; Pichelli et al., 2021). \r\nAnother high-resolution time-slice data base is d4PDF (Mizuta et al., \r\n2017; Ishii and Mori, 2020)"


id: a6f5994a131713595c4cfe251a3ac02a
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
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vector_id: 23779
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" Experiments covering a limited integration \r\nperiod have been carried out for coupled ocean-atmosphere RCMs \r\n(Sein et al., 2015; Zou and Zhou, 2016b, 2017). However, long spin-up \r\nperiods are required to reach a stable stationary state in the deep \r\nocean that otherwise might lead to invalid projections (Planton et al., \r\n2012; Soto-Navarro et al., 2020)."


id: 14b6c021b8e1edbb6f0e1978946a002e
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
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vector_id: 769
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"In recent years, the role of internal variability in the interpretation of \r\nclimate projections has become clearer, particularly at the regional \r\nscale (Section 10.3.4.3). A considerable fraction of CMIP5 and CMIP6 \r\nresources has been invested in generating an ensemble of centennial \r\nor multi-centennial control simulations with constant external \r\nforcings (Pedro et  al., 2016; Rackow et  al., 2018). As part of the \r\nCMIP6 DECK (Eyring et al., 2016a) pre-industrial control (piControl) \r\nsimulations have been conducted (Menary et  al., 2018). Similarly, \r\ncontrol simulations with present-day conditions (pdControl) have \r\nbeen performed to represent internal variability under more recent \r\nforcing conditions (Pedro et al., 2016; Williams et al., 2018). Control \r\nsimulations have been used to study the role of internal variability, \r\nteleconnections and many other fundamental aspects of climate \r\nmodels (Z. Wang et  al., 2015; Krishnamurthy and Krishnamurthy, \r\n2016)"


id: 48c200c1d18949c236e63cb1845bdaa3
created_at: 2023-03-16 14:02:06
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vector_id: 8824
content:
" Control simulations are also used along with large ensembles \r\nof historical or scenario simulations to assess the characteristics of \r\nthe regional internal climate variability (Olonscheck and Notz, 2017)."


id: 9465c410f2472561fbf7d5aa54bf0ee2
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
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vector_id: 20861
content:
"While the ability of global models to simulate large-scale indicators \r\nof climate change has improved since AR5 (Chapter 3), the simulation \r\nof regional climate and climate change poses an additional challenge. \r\nUsers demand regional climate projections for decision-making and \r\nhave high expectations regarding accuracy and resolution (Rossler \r\net  al., 2019a), but some scientists consider such projections still \r\na  matter of basic research (Hewitson et al., 2014a). For instance, \r\nlarge-scale circulation biases or the misrepresentation of regional \r\ntopography as well as regional phenomena and feedbacks are very \r\nrelevant (Hall, 2014; Maraun and Widmann, 2018b). New global \r\nmodel ensembles such as CMIP6 (Eyring et al., 2016a), HighResMIP \r\n(Haarsma et  al., 2016) or, at the regional scale, the convection \r\npermitting simulations from the CORDEX Flagship Pilot Study (FPS) \r\non convective phenomena (Coppola et al"


id: e4611ffc0749e1615845d1edeb9cc1fb
created_at: 2023-03-16 14:02:06
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", 2020) have the potential \r\nto substantially improve the basis for generating regional climate \r\ninformation, yet uncertainties and (often unresolved) contradictions \r\nbetween model projections at the regional scale can be substantial \r\n(Fernandez et al., 2019)."


id: e434e26277e786e9ca23b84003633faf
created_at: 2023-03-16 14:02:06
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vector_id: 33383
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"Figure 10.6 (continued): (a) Top row: Mean summer (June to August) near-surface air temperature (in degC) over the Mediterranean area in Berkeley Earth and respective mean \r\nbias for five multi-model historical experiments with global models (CMIP5, CMIP6 and HighResMIP) and regional climate models (CORDEX EUR-44 and EUR-11) averaged \r\nbetween 1986-2005. Bottom row: Box-and-whisker plot shows spread of the 20 annual mean summer surface air temperature averaged over land areas in the western \r\nMediterranean region (33degN-45degN, 10degW-10degE, black quadrilateral in the first panel of the top row) for a set of references and single model runs of the five multi-model \r\nexperiments (one simulation per model) between 1986-2005. Additional observation and reanalysis data included in the bottom row are CRU TS, HadCRUT4, HadCRUT5, \r\nE-OBS, WFDE5, ERA5, ERA-Interim, CERA-20C, JRA-25, JRA-55, CFSR, MERRA2, MERRA. Berkeley Earth is shown in the first box to the left"


id: 7eb6748239addbe77db1bdb7d70847c6
created_at: 2023-03-16 14:02:06
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" (b) As (a) but for precipitation \r\nrate (mm day-1) and showing CRU TS in the first panel of the top row. Biases of the five multi-model experiments are shown with respect to CRU TS. Additional observation \r\nand reanalysis data included in the bottom row are GPCC, REGEN, E-OBS, GHCN, WFDE5, CFSR, ERA-Interim, ERA5, JRA-55, MERRA2, MERRA. CRU TS is shown in the first \r\nbox to the left. All box-and-whisker plots show the median (line), and the interquartile range (IQR = Q3-Q1, box), with top whiskers extending to the last data less than \r\nQ3 + 1.5 x IQR and analogously for bottom whiskers. Data outside the whiskers range appear as flyers (circles). Further details on data sources and processing are available \r\nin the chapter data table (Table 10.SM.11)."


id: 395fa5acb7739bd03147e215bbfb5a4f
created_at: 2023-03-16 14:02:06
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content:
"Recent studies highlight the importance of user-defined or user-relevant \r\ndiagnostics for model evaluation (Maraun et al., 2015; Rhoades et al., \r\n2018; Rossler et  al., 2019b; Nissan et  al., 2020). Diagnostics have \r\nbeen used to assess the performance of climate models to produce \r\nuseful input data for impact models as in the comparison between \r\nRCMs and convection-permitting models to capture flood-generating \r\nprecipitation events in the Alps (Reszler et al., 2018). Alternatively, \r\nthe observed impact can be compared to that simulated by an impact \r\nmodel that uses input from both observations and climate models. \r\nThis approach has been used to evaluate the influence of statistical \r\ndownscaling and bias adjustment on hydrological (Rojas et al., 2011; \r\nH. Chen et  al., 2012; Gutierrez et  al., 2019; Rossler et  al., 2019b), \r\nagricultural (Ruiz-Ramos et al., 2016; Galmarini et al., 2019), forest \r\nand wildfire (Abatzoglou and Brown, 2012; Migliavacca et al"


id: 4086f2ffc53caa6a1323d9a8daa42a0b
created_at: 2023-03-16 14:02:06
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", 2013) \r\n(Bedia et al., 2013), snow depth (Verfaillie et al., 2017), and regional \r\nocean modelling (e.g., Macias et al., 2018)."


id: 974252a02807145860d42627985aee0
created_at: 2023-03-16 14:02:06
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content:
"Since AR5, model evaluation has made use of a broad combination \r\nof diagnostics (Colette et  al., 2012; Kotlarski et  al., 2014; Eyring \r\net al., 2016b; Gleckler et al., 2016; Ivanov et al., 2017, 2018; Vautard \r\net al., 2021), ranging from long-term means to indices of extreme \r\nevents (Zhang et al., 2011; Sillmann et al., 2013) or a combination \r\nof these (Dittus et al., 2016). This evaluation has shown that global \r\nmodels have pervasive biases in some aspects of their large-scale \r\nbehaviour (Section 1.5.3.1, Chapter 3). More complex diagnostics are \r\nused to characterize specific meteorological phenomena (Sprenger \r\net al., 2017), such as feedbacks in the El Nino-Southern Oscillation \r\n(ENSO; Bellenger et  al., 2014), Madden-Julian Oscillation (MJO) \r\ncharacteristics (Benedict et  al., 2014; Jiang et  al., 2015; D.  Kim \r\net  al., 2015; Ahn et  al., 2017), extratropical modes of variability \r\n(Lee et al., 2019), cyclone tracking (Neu et al., 2013; Flaounas et al"


id: 438defd6eb2909d097a98b55a62f3aa8
created_at: 2023-03-16 14:02:06
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", \r\n2018), front detection (Hope et  al., 2014; Schemm et  al., 2015), \r\nthunderstorm environment parameters (Bukovsky et  al., 2017), \r\nAfrican easterly waves (McCrary et al., 2014; Martin and Thorncroft, \r\n2015), land-atmosphere coupling (Spennemann and Saulo, 2015; \r\nSantanello et  al., 2018), and sea-atmosphere coupling (Bellenger \r\net al., 2014; Mayer et al., 2017)."


id: c1847de1a18ceab687ff2e39abe2f15d
created_at: 2023-03-16 14:02:06
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vector_id: 28035
content:
"Downscaling is expected to improve the representation of a region's \r\nclimate compared to the driving global model (Di Luca et al., 2015). \r\nArguably, there should be a clear physical reason for the improvement, \r\nwhich is applicable to the evaluation of added value in downscaled \r\nprojections (Giorgi et  al., 2016). The added value depends on the \r\nregion, season, and governing physical processes (Lenz et al., 2017; \r\nSchaaf and Feser, 2018). Thus, added value of downscaling global \r\nmodel simulations is most likely where regional- and local-scale \r\nprocesses play an important role in a region's climate, for example \r\nin complex or heterogeneous terrain such as mountains (Lee and \r\nHong, 2014; Prein et al., 2016b), urban areas (Argueso et al., 2014), \r\nalong coastlines (Feser et al., 2011; Herrmann et al., 2011; Bozkurt \r\net  al., 2019), or where convective processes are important (Prein \r\net al., 2015)"


id: f3118119f9309b8a95e808652456aea8
created_at: 2023-03-16 14:02:06
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" Examples of model improvements and added value are \r\ngiven in the following subsections and the Atlas."


id: 2fb3bec558282046dce82088f466f66b
created_at: 2023-03-16 14:02:06
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vector_id: 4925
content:
"Major large-scale meteorological phenomena for mid- to high \r\nlatitude mean and extreme climate include atmospheric blocking \r\nand extratropical cyclones (Section 2.3.1.4). Atmospheric blocking is \r\ncharacterized by a  quasi-stationary, long-lasting, high-pressure \r\nsystem that blocks and diverts the movement of synoptic cyclones \r\n(Woollings et al., 2018). In regions where blocking occurs, it is known \r\nto lead to cold conditions in winter and warmth and drought during \r\nsummer, defining the seasonal regional climate in certain years \r\n(Sousa et al., 2017, 2018b). Extratropical cyclones are storm systems \r\nthat propagate preferentially in confined storm-track regions, \r\ncharacterized by large eddy kinetic energy, heat and momentum \r\ntransports that shape regional weather at mid- to high latitudes \r\n(Shaw et  al., 2016). Given their importance in shaping mean and \r\nextreme regional climate (Sections 3.3.3.3, 11.3 and 11"


id: 441af4f618eadbad3b09a02c4a76d7be
created_at: 2023-03-16 14:02:06
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"4), an \r\naccurate representation of blocking and extratropical cyclones in \r\nglobal and regional climate models is needed to better understand \r\nregional climate variability and extremes as well as to project future \r\nchanges (Section 11.7.2; Grotjahn et al., 2016; Mitchell et al., 2017; \r\nRohrer et al., 2018; Huguenin et al., 2020). An overview of CMIP5 and \r\nCMIP6 model performance in simulating blocking and extratropical \r\ncyclones is given in Section 3.3.3.3. CMIP6 models still suffer from \r\nlong-standing blocking biases identified in previous generations \r\nof models. However, blocking location has improved compared to \r\nCMIP5, while comparable performance is seen for blocking frequency \r\nand persistence (Figure 10.7). Increasing horizontal model resolution \r\nto about 20 km in the HighResMIP experiments improves the \r\nrepresentation of blocking frequency and its spatial pattern in most \r\nmodels, but no clear effect could be shown for blocking persistence"


id: e7ec0392272d6e9b6d7d0d81ea8a0c8e
created_at: 2023-03-16 14:02:06
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vector_id: 33984
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" \r\nBiases associated with these two phenomena are highly region- and \r\nseason-dependent and their amplitudes vary among CMIP models \r\n(Drouard and Woollings, 2018; Schaller et al., 2018; Woollings et al., \r\n2018; Harvey et al., 2020; Schiemann et al., 2020)."


id: 3d03d38bf2654580002dfa799dcc93a9
created_at: 2023-03-16 14:02:06
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"the connection between large-scale and regional biases is obvious, \r\ngiven the strong control of regional climate by large-scale phenomena, \r\nresearch on this connection is still limited. Munday and Washington \r\n(2018) relate CMIP5 model rainfall biases over South Africa to \r\nanomalous low-level moisture transport across high topography \r\ndue to upstream wind biases and inaccurate representation of \r\nunresolved orographic drag effects. Addor et  al. (2016) show that \r\nthe overestimated frequency of westerly synoptic situations was \r\na significant contributor to the wet bias in several RCMs in winter over \r\nSwitzerland. Pepler et al. (2014, 2016) suggest that better capturing \r\nwesterly-driven synoptic systems such as cold fronts and cut-off lows \r\nin climate models could be key in simulating the observed pattern \r\ncorrelation between rainfall and zonal wind in southern south-east \r\nAustralia"


id: c7382a7c71d2e8e94d3b04e7b88700c3
created_at: 2023-03-16 14:02:06
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vector_id: 28934
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" Cannon (2020) shows global improvement in performance \r\ngoing from CMIP5 to CMIP6 for both frequency and persistence of \r\ncirculation types."


id: 42ecc281328589803a4304fa2f6c9fad
created_at: 2023-03-16 14:02:06
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vector_id: 7922
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"driving reanalysis near regions with complex orography and/or large \r\nwater masses (Poan et al., 2018). However, this is not necessarily true \r\nif the domain is large enough because the RCM and its biases will then \r\ncontrol the circulation leading to a biased performance with regard \r\nto storm characteristics (Pontoppidan et al., 2019). An ensemble of \r\n12 RCMs with and without air-sea coupling reasonably reproduced \r\nthe climatology of Mediterranean cyclones, and air-sea coupling had \r\na rather weak impact (Flaounas et al., 2018). Over the Gulf Stream, \r\nhowever, air-sea coupling played an important role in representing \r\ncyclone development (Vries et al., 2019). Sanchez-Gomez and Somot \r\n(2018) showed that the effect of RCM internal variability on density \r\nof cyclone tracks is very significant and larger than for other variables \r\nsuch as precipitation"


id: 6eb8bb4076d0718c37648c26c5d84ce7
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
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" It is larger in summer than in winter, in \r\nparticular over the Iberian Peninsula, northern Africa and the eastern \r\nMediterranean, which are regions of enhanced cyclogenesis during \r\nthe warm season."


id: b4718f814945c7d81e03122474763546
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
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index: climate_gpt_only_giec
vector_id: 25912
content:
"ENSO teleconnection patterns for model experiments with fewer \r\nobservational constraints. They also show that ENSO warm phase \r\n(El Nino) teleconnections are better represented than those for \r\nthe cold phase (La Nina). Individual CMIP5 and CMIP6 models \r\nshow a  good ability to represent the observed teleconnections at \r\naggregated spatial scales (Power and Delage, 2018; Section  3.7.3 \r\nand Figure  3.38). The evaluation of the atmospheric dynamical \r\nlinkages is also an important part of the assessment. Hurwitz et al. \r\n(2014) showed that CMIP5 models broadly simulate the expected \r\n(as seen in the MERRA reanalysis) upper-tropospheric responses to \r\ncentral equatorial Pacific or eastern equatorial Pacific ENSO events in \r\nboreal autumn and winter. CMIP5 models also simulate the correct \r\nsign of the Arctic stratospheric response, consisting of polar vortex \r\nweakening during eastern and central Pacific Nino events and \r\nvortex strengthening during both types of La Nina events"


id: 8810fde0f5365a7a300c315d2172f6b9
created_at: 2023-03-16 14:02:06
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" In contrast, \r\nmost CMIP5 models do not capture the observed weakening of the \r\nSouthern Hemisphere polar vortex in response to central Pacific \r\nENSO events (Brown et al., 2013)."


id: e7c414d2cd055a06763c349b2a0fac5c
created_at: 2023-03-16 14:02:06
updated_at: 2023-03-16 14:02:06
content_type: text
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vector_id: 33963
content:
"Convection is the process of vertical mixing due to atmospheric \r\ninstability. Deep moist convection is associated with thunderstorms \r\nand severe weather such as heavy precipitation and strong wind \r\ngusts. Convection may occur in single locations, in spatially extended \r\nsevere events such as supercells, and organized into larger mesoscale \r\nconvective systems such as squall lines or tropical cyclones, and \r\nembedded in fronts (see below). Shallow and deep convection are \r\nnot explicitly simulated but parametrized in standard global and \r\nregional models. In consequence, these models suffer from several \r\nbiases. AR5 has stated that many CMIP3 and CMIP5 models simulate \r\nthe peak in the diurnal cycle of precipitation too early, but increasing \r\nresolution and better parametrizations help to mitigate this problem \r\n(Flato et al., 2014). Similar issues arise for RCMs with parametrized \r\ndeep convection (Prein et al"


id: ba5d6da0621f72b9880f04868dbece14
created_at: 2023-03-16 14:02:06
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", 2015), which also tend to overestimate \r\nhigh cloud cover (Langhans et al., 2013; Keller et al., 2016)."


id: 700a70eeca043e2ccad1925d67a853c3
created_at: 2023-03-16 14:02:06
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vector_id: 15245
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"Non-hydrostatic RCMs at convection-permitting resolution (4 km \r\nand finer) improve features such as the initiation and diurnal cycle \r\nof convection (Zhu et al., 2012; Prein et al., 2013a, b; Fosser et al., \r\n2015; Stratton et al., 2018; Sugimoto et al., 2018; Finney et al., 2019; \r\nBerthou et  al., 2020; Ban et  al., 2021; Pichelli et  al., 2021), the \r\ntriggering of convection by orographic lifting (Langhans et al., 2013; \r\nFosser et al., 2015), and maximum vertical wind speeds in convective \r\ncells (Meredith et  al., 2015a). Also spatial patterns of precipitation \r\n(Prein et al., 2013a, b; Stratton et al., 2018), precipitation intensities \r\n(Prein et al., 2015; Fumiere et al., 2020; Ban et al., 2021; Pichelli et al., \r\n2021), the scaling of precipitation with temperature (Ban et al., 2014), \r\ncloud cover (Bohme et al., 2011; Langhans et al., 2013) and its resultant \r\nradiative effects (Stratton et al., 2018), as well as the annual cycle \r\nof tropical convection (Hart et al"


id: 2e633f2db7647ff24d1e8ac884bffd8f
created_at: 2023-03-16 14:02:06
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", 2018) are improved. Phenomena \r\nsuch as supercells, mesoscale convective systems, or the local weather \r\nassociated with squall lines are not captured by global models and \r\nstandard RCMs. Convection-permitting RCM simulations, however, \r\nhave been shown to realistically simulate supercells (Trapp et  al., \r\n2011), mesoscale convective systems, their life cycle and motion (Prein \r\net al., 2017; Crook et al., 2019), and heavy precipitation associated \r\nwith a squall line (Kendon et al., 2014). There is high confidence that \r\nsimulations at convection-permitting resolution add value to the \r\nrepresentation of deep convection and related phenomena."


id: 65617bfe0881167edd3fd0ec27e8229c
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
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vector_id: 13526
content:
"Synoptically-forced winds may be channelled and accelerated in long \r\nvalleys. For instance, the Tramontana, Mistral and Bora are northerly \r\nwinds blowing down-valley from central France and the Balkans into \r\nthe Mediterranean (Flaounas et  al., 2013). In winter, these winds \r\nmay cause severe cold air outbreaks along the coast. Flaounas \r\net al. (2013) have shown that a GCM with a horizontal resolution of \r\nroughly 3.75deg longitude/1.875deg latitude (roughly 400 km x 200 km \r\ndepending on latitude) is unable to reproduce these winds because \r\nof the coarse representation of orography. Fifty-kilometre RCM \r\nsimulations did not realistically represent the Mistral (Obermann \r\net al., 2018) and Bora winds (Belusic et al., 2018), but simulations \r\nat 12 km added substantial value. Similarly, Cholette et  al"


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created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
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vector_id: 21002
content:
" (2015) \r\nfound that a 30 km RCM resolution was not sufficient to adequately \r\nsimulate the channelling of winds in the St Lawrence River Valley in \r\neastern Canada, whereas a 10 km resolution was."


id: 1d2147b19639fa1be3f595b98f4ebe87
created_at: 2023-03-16 14:02:07
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vector_id: 2025
content:
"Convection is the key ingredient of tropical cyclones. An \r\nintercomparison of high-resolution AGCM simulations (Shaevitz \r\net al., 2014) showed that tropical cyclone intensities appeared to be \r\nbetter represented with increasing model resolution. Takayabu et al. \r\n(2015) have compared simulations of typhoon Haiyan at different \r\nresolutions ranging from 20  km to 1 km (Figure  10.8). While the \r\neyewall structure in the precipitation pattern was strongly smoothed \r\nin the coarse resolution simulations, it was well-resolved at the highest \r\nresolution. Gentry and Lackmann (2010) found similar improvements \r\nin simulating hurricane Ivan for horizontal resolutions between 8 km \r\nand 1 km. High-resolution coupled ocean-atmosphere simulations \r\nimprove the representation of the radial structure of core convection \r\nand thereby the rapid intensification of the cyclone (Kanada \r\net  al., 2017b)"


id: 869f87b361a5e7e9ce4d45ad9418bb57
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
content_type: text
index: climate_gpt_only_giec
vector_id: 18717
content:
" There is high confidence that convection-permitting \r\nresolution is required to realistically simulate the three-dimensional \r\nstructure of tropical cyclones."


id: 2896db521a166c1c081c89892e7c5356
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
content_type: text
index: climate_gpt_only_giec
vector_id: 3810
content:
"A particularly relevant coastal phenomenon is the sea breeze, which \r\nis caused by the differential heating of water and land during the \r\ndiurnal cycle and typically reaches several tens of kilometres inland. \r\nReanalyses and global models have too coarse a  resolution to \r\nrealistically represent this phenomenon, such that they typically \r\nunderestimate precipitation over islands and misrepresent its diurnal \r\ncycle (Lucas-Picher et al., 2017). RCMs improve the representation of \r\nsea breezes and thereby precipitation in coastal areas and islands. \r\nOver Cuba and Florida only a  12 km-resolution RCM is able to \r\nrealistically simulate the inland propagation of precipitation during \r\nthe course of the day (Lucas-Picher et al., 2017). RCM simulations \r\nat 20 km horizontal resolution realistically represented the sea \r\nbreeze circulation in the Mediterranean Gulf of Lions including the \r\nintensity, direction and inward propagation (Drobinski et al., 2018)"


id: c17242b6deb1e6d2cb9462fe1d31b597
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
content_type: text
index: climate_gpt_only_giec
vector_id: 28022
content:
" \r\nEven though a coupled ocean-atmosphere simulation improved the \r\nrepresentation of diurnal SST variations, the sea breeze representation \r\nitself was not improved."


id: 5f48a8a4dc60ec1a606ee3e90381c753
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
content_type: text
index: climate_gpt_only_giec
vector_id: 12560
content:
"Weather fronts are two-dimensional surfaces separating air masses \r\nof different characteristics and are a  key element of mid-latitude \r\ncyclones. In particular cold fronts are regions of relatively strong \r\nuplift and hence often associated with severe weather (e.g., Schemm \r\net al., 2016). Stationary or slowly moving fronts may cause extended \r\nheavy precipitation. The evaluation of how climate models represent \r\nfronts, however, remains limited. Catto et  al. (2014) found in both \r\nERA-Interim and CMIP5 models that frontal frequency and strength \r\nwere realistically simulated, albeit with some biases in the location. \r\nFollow-up investigations, for boreal and austral winter (Catto et al., \r\n2015) found frontal precipitation frequency to be too high and the \r\nintensity too low, but these compensating biases resulted in only \r\na small total precipitation bias"


id: bbbd9783dff28d4d9dd75f5fa411db1a
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
content_type: text
index: climate_gpt_only_giec
vector_id: 27093
content:
" Blazquez and Solman (2018) found \r\nsimilar results for Southern Hemisphere (SH) winter, and also showed \r\nthat CMIP5 models typically overestimate the fraction of frontal \r\nprecipitation compared to total precipitation. As for the reference, the \r\nERA-Interim reanalysis misrepresents conditional symmetric instability \r\nassociated with fronts, and the corresponding precipitation (Glinton \r\net  al., 2017). Only a  few studies evaluating fronts in RCMs have \r\nbeen conducted. Kawazoe and Gutowski (2013) diagnosed strong \r\ntemperature gradients associated with extreme winter precipitation \r\nin the North American Regional Climate Change Assessment Program \r\n(NARCCAP) RCM ensemble (Mearns et  al., 2012) and found the \r\nmodels agreed well with gradients in a  reanalysis. De Jesus et  al"


id: 7f91c56b24107f8c80ceff4d952721f1
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
content_type: text
index: climate_gpt_only_giec
vector_id: 17617
content:
" \r\n(2016) diagnosed the representations of cold fronts over southern \r\nBrazil in two RCMs, finding that they were only underestimated \r\nby about 5% across the year, but in one RCM, summer cold fronts \r\nwere underestimated by 17%. An RCM-based reanalysis suggests \r\nthat high-resolution RCM simulations improve the representation of \r\norographic influences on fronts (Jenkner et al., 2009)."


id: 65344fabe2d49030ab81119ebb966ed0
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
content_type: text
index: climate_gpt_only_giec
vector_id: 13489
content:
"feedbacks like tropical cyclones in the Indian Ocean (Samson \r\net al., 2014), Indian summer monsoon (Samanta et al., 2018), East \r\nAsian summer monsoon (Zou et  al., 2016), near coastline intense \r\nprecipitation in the Mediterranean (Berthou et  al., 2015, 2018), \r\nair-sea fluxes influencing heat and humidity advection over land \r\n(Sevault et al., 2014; Lebeaupin Brossier et al., 2015; Akhtar et al., \r\n2018) or snow bands in the Baltic region (Pham et al., 2017). The \r\npositive impact of ocean-coupling on the simulation of strongly \r\nconvective phenomena such as Medicanes, a  class of severe \r\ncyclones in the Mediterranean, can only be diagnosed when using \r\nrelatively fine atmospheric resolution of about 10 km (Akhtar et al., \r\n2014; Flaounas et al., 2018; Gaertner et al., 2018). A positive impact \r\nof ocean coupling has been quantified in marginal sea regions with \r\nreduced large-scale influence (e.g"


id: defa55cae0b48e94b0ec642e600fedeb
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
content_type: text
index: climate_gpt_only_giec
vector_id: 32579
content:
",  in the Baltic Sea area during \r\nweak phases of the NAO and thus weak influence of Atlantic \r\nwesterlies (Kjellstrom et al., 2005; Pham et al., 2018). There is some \r\nevidence that coupled ocean components also positively impact \r\nRCM simulations of inland climates such as precipitation extremes \r\nin central Europe (Ho-Hagemann et al., 2017; Akhtar et al., 2019). \r\nThere is high confidence that coupled ocean-atmosphere RCMs \r\nimprove the representation of ocean-atmosphere feedbacks and \r\nrelated phenomena."


id: ea4b489911341fe4075d547c76d3b732
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
content_type: text
index: climate_gpt_only_giec
vector_id: 34369
content:
"The snow-albedo feedback contributes to enhanced warming at \r\nhigh elevations (Section  8.5; Pepin et  al., 2015). Global models \r\noften do not simulate it realistically due to their misrepresentation \r\nof orography in complex terrain (Hall, 2014; Walton et  al., 2015). \r\nThe elevation dependence of historical warming, which is partly \r\ncaused by the snow-albedo effect, is realistically represented across \r\nEurope by the ENSEMBLES RCMs (Kotlarski et  al., 2015). Some \r\nEURO-CORDEX RCMs simulate a  spring snow-albedo feedback \r\nclose to that observed, whereas others considerably overestimate it \r\n(Winter et al., 2017). In a multi-physics ensemble RCM experiment, \r\nthe cold bias in north-eastern Europe is amplified by the albedo \r\nfeedback (Garcia-Diez et al., 2015). For the Rocky Mountains, RCM \r\nsimulations generally reproduce the observed spatial and seasonal \r\nvariability in snow cover, but strongly overestimate the snow albedo \r\n(Minder et al., 2016)"


id: 2cbcdd20d6c78e9cd9e94fb234457f83
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
content_type: text
index: climate_gpt_only_giec
vector_id: 4470
content:
" There is high confidence (medium evidence and \r\nhigh agreement) that RCMs considerably improve the representation \r\nof the snow-albedo effect in complex terrain."


id: df27a4766e9c1db1b53d15a9a0281957
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
content_type: text
index: climate_gpt_only_giec
vector_id: 32609
content:
"Soil-moisture feedbacks influence changes in both temperature \r\nand precipitation. More than 30% of CMIP5 models overestimate \r\nthe influence of preceding precipitation (a proxy for soil moisture) \r\non temperature extremes in Europe and the USA (Donat et  al., \r\n2018), and many CMIP5 models simulate an unrealistic influence \r\nof evaporation on temperature extremes for wet regions in Europe \r\nand the US (Ukkola et al., 2018). RCMs were found to realistically \r\nsimulate the correlation between latent and sensible heat fluxes and \r\ntemperature (coupling strength) over Africa (Knist et al., 2017; Careto \r\net al., 2018) and in northern and southern Europe, but to overestimate \r\nit in central Europe (Knist et al., 2017). Land surface models driven \r\nby global reanalysis agreed relatively well with observations"


id: 29a1325208adb8d9be593654c8a15ad2
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
content_type: text
index: climate_gpt_only_giec
vector_id: 3984
content:
" \r\nHowever, the coupling strength varied strongly across models at the \r\nregional scale, and a realistic partitioning of the incoming radiation \r\ninto latent and sensible heat fluxes did not necessarily result in \r\na realistic soil moisture-temperature coupling (Gevaert et al., 2018; \r\nBoe et al., 2020a)."


id: eaff385d8ce303cf47e3cbe3ef096915
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
content_type: text
index: climate_gpt_only_giec
vector_id: 34478
content:
"Dust, with its regional character in both emissions and climatic \r\ninfluences, has traditionally been specified in climate simulations \r\nwith a  climatological estimate. In CMIP5 models, the influence of \r\nvegetation changes on mineral dust is largely underestimated while \r\nthe influence of surface wind and precipitation are overestimated, \r\nresulting in a low bias of dust load (Pu and Ginoux, 2018). Interactive \r\ndust emission modules that simulate the dust optical depth in most of \r\nthe key emission regions have only been recently introduced (Pu and \r\nGinoux, 2018). However, coarse dust is underestimated in global \r\nmodels (Adebiyi and Kok, 2020). Simulations of future changes in \r\ndust are hindered by the uncertainties in future regional wind and \r\nprecipitation as the climate warms (Evan et al., 2016), in the effect \r\nof CO2 fertilization on source extent (Huang et al., 2017), in the dust \r\nfeedbacks (Evans et al"


id: f56598327bfdbeca5efccbf8d470e8a2
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
content_type: text
index: climate_gpt_only_giec
vector_id: 36129
content:
", 2019), and in the effect of human activities \r\nthat change land use and disturb the soil, including cropping and \r\nlivestock grazing, recreation and urbanization, and water diversion \r\nfor irrigation (Ginoux et al., 2012)."


id: a08577ec9be328ca5bd642daf25f3a4a
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
content_type: text
index: climate_gpt_only_giec
vector_id: 22791
content:
"Evaluating the representation of soil-moisture-precipitation feedbacks \r\nin climate models is challenging as different processes may induce \r\nfeedbacks including moisture recycling, boundary-layer dynamics \r\nand mesoscale circulation. Moreover, the effects of soil moisture \r\non precipitation may be region and scale dependent and may even \r\nchange sign depending on the strength of the background flow (Taylor \r\net al., 2013; Froidevaux et al., 2014; Guillod et al., 2015; Larsen et al., \r\n2016; Tuttle and Salvucci, 2016). On seasonal-to-interannual time \r\nscales, CMIP5 models showed a stronger soil-moisture-precipitation \r\nfeedback than estimated by satellite data (Levine et al., 2016). Taylor \r\net  al"


id: c01fe441249eab13a09bf98bc9107016
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
content_type: text
index: climate_gpt_only_giec
vector_id: 27778
content:
" (2013) found that convection-permitting RCMs perform well \r\nat simulating surface-induced mesoscale circulations in daytime \r\nconvection and the observed negative soil moisture feedback, \r\nwhereas an RCM with parametrized convection, even when run at \r\nthe same resolution, simulated an unrealistic positive feedback. \r\nThere is medium evidence and high agreement that simulations at \r\nconvection-permitting resolution are required to realistically represent \r\nsoil-moisture-precipitation feedbacks."


id: 4e04f70334a370d36ec99bd6a155d441
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
content_type: text
index: climate_gpt_only_giec
vector_id: 9696
content:
"eruptions (larger than the one from Mount Pinatubo in 1991) may \r\nlead to a positive phase of the winter NAO in the following few years \r\n(with an uncertainty on the exact years affected), but this influence \r\nis not well-reproduced in climate models and requires very large \r\nensembles (Driscoll et  al., 2012; Toohey et  al., 2014; Swingedouw \r\net  al., 2017; Menegoz et  al., 2018b). The ability to simulate the \r\neffect of volcanic aerosol in global models is evaluated in VolMIP \r\n(Zanchettin et  al., 2016). Given the relevance of volcanic aerosol, \r\na  good knowledge of the initial conditions is important because \r\nthe response has proven to be sensitive to them (Menegoz et  al., \r\n2018a; Zanchettin et  al., 2019). A  few decadal prediction systems \r\nhave illustrated that current systems can predict some aspects of \r\nregional climate a few years in advance (Swingedouw et al., 2017; \r\nIlling et al., 2018; Menegoz et al., 2018a; Hermanson et al., 2020)"


id: 81bad4a89649fba75cd4c13ef6ae442b
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
content_type: text
index: climate_gpt_only_giec
vector_id: 17941
content:
" \r\nHowever, a better performance requires information about volcanic \r\nlocation (Haywood et al., 2013; Pausata et al., 2015; Stevenson et al., \r\n2016; F. Liu et al., 2018a), strength (Emile-Geay et al., 2008; H.-G. Lim \r\net al., 2016; F. Liu et al., 2018b), and seasonality (Stevenson et al., \r\n2017; Sun et al., 2019a, b)."


id: dafc8b129240faeb3fe2140b80278c03
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
content_type: text
index: climate_gpt_only_giec
vector_id: 31973
content:
"Some recent regional climate changes can only be simulated by \r\nclimate models if anthropogenic aerosols are correctly included \r\n(Sections 10.4.2.1, 10.6.3 and 10.6.4; Chapters 6 and 8). Examples \r\nof the importance of correctly representing anthropogenic aerosols \r\nare the recent enhanced warming over Europe (Nabat et al., 2014; \r\nDong et al., 2017), the cooling over the East Asian monsoon region, \r\nleading to a weakening of the monsoon (Section 8.3.2.4; Song et al., \r\n2014; Q. Wang et  al., 2017), as well as changes in the monsoons \r\nof West Africa (Sections 8.3.2.4 and 10.4.2.1) and South Asia \r\n(Sections 8.3.2.4 and 10.6.3; Undorf et al., 2018). The relevance of \r\nappropriately representing anthropogenic aerosols has been widely \r\nstudied in regional models (Boe et al., 2020a; Gutierrez et al., 2020), \r\nwith an advantage for models with interactive aerosol schemes \r\n(Druge et  al., 2019; Nabat et  al., 2020)"


id: ebcf4e6e1e6468f46cace87b337b7322
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
content_type: text
index: climate_gpt_only_giec
vector_id: 34605
content:
" Without a  fully coupled \r\nchemistry module, radiative forcing can be simulated by including \r\nsimple models of sulphate chemistry or specifying the optical \r\nproperties from observations and prescribing the effect of aerosols \r\non the cloud-droplet number (Fiedler et  al., 2017, 2019; Stevens \r\net al., 2017). In all cases, the specification of the aerosol load limits \r\nthe trustworthiness of the simulations at the regional scale when \r\nenough detail is not provided (Samset et al., 2019; Shonk et al., 2020; \r\nZ. Wang et al., 2021)."


id: 53d331c878425057cb503911729a0489
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
content_type: text
index: climate_gpt_only_giec
vector_id: 10655
content:
"Bias adjustment methods, if driven by reanalysis predictors, in \r\nprinciple adjust well all the aspects that they intend to address \r\n(Maraun and Widmann, 2018b). For temperature, all univariate \r\nmethods are good for adjusting means, variance, and high quantiles \r\n(Gutierrez et  al., 2019; Hertig et  al., 2019). For precipitation, \r\nmeans, intensities, wet-day frequencies, and wet-dry and dry-wet \r\ntransitions are well-adjusted (Gutierrez et al., 2019; Maraun et al., \r\n2019b). The representation of high quantiles depends on the \r\nchosen method, although flexible quantile mapping performs best \r\n(Hertig et  al., 2019). Empirical (non-parametric) methods perform \r\nbetter than parametric methods over the observed range, but it is \r\nunclear how this translates into extrapolation to unobserved values \r\n(IPCC, 2015; Hertig et al., 2019). Many quantile mapping methods \r\noverestimate interannual variability (Maraun et al., 2019b)"


id: ea98e0edb88ba9023dc9ce4f7af131df
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
content_type: text
index: climate_gpt_only_giec
vector_id: 34418
content:
" Temporal \r\nand spatial dependence are usually not adjusted and thus inherited \r\nfrom the driving model (Maraun et al., 2019b; Widmann et al., 2019). \r\nSpatial fields are thus typically too smooth in space, even after bias \r\nadjustment (Widmann et al., 2019)."


id: 639879c20ef738f1f2b44d9996b6aa91
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
content_type: text
index: climate_gpt_only_giec
vector_id: 13240
content:
"Weather generators represent well most aspects that are explicitly \r\ncalibrated. This typically includes mean, variance, high quantiles \r\n(for precipitation, if explicitly modelled), and short-term temporal \r\nvariability for both temperature and precipitation, whereas \r\ninterannual variability is strongly underestimated (Frost et al., 2011; \r\nHu et al., 2013a; Keller et al., 2015; Dubrovsky et al., 2019; Gutierrez \r\net  al., 2019; Hertig et  al., 2019; Maraun et  al., 2019b; Widmann \r\net al., 2019). There is growing evidence that some spatial weather \r\ngenerators fairly realistically capture the spatial dependence of \r\ntemperature and precipitation (Frost et al., 2011; Hu et al., 2013a; \r\nKeller et al., 2015; Evin et al., 2018; Dubrovsky et al., 2019)"


id: c927aec0c0a0678a04d8f54be55a00b1
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
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index: climate_gpt_only_giec
vector_id: 29210
content:
" There is \r\nhigh confidence that weather generators can realistically simulate \r\na  wide range of local weather characteristics at single locations, \r\nbut there is limited evidence and low agreement of the ability of \r\nweather generators to realistically simulate the spatial dependence \r\nof atmospheric variables across multiple sites."


id: 95e0445e7e130730e844e0364e99a2a2
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
content_type: text
index: climate_gpt_only_giec
vector_id: 21080
content:
"RCM experiments are often set up such that changes in forcing agents \r\nare included only via the boundary conditions, but not explicitly \r\nincluded inside the domain. Jerez et al. (2018) demonstrated that not \r\nincluding time-varying GHG concentrations within the RCM domain \r\nmay misrepresent temperature trends by 1-2degC per century. Including \r\nthe past trend in anthropogenic sulphate aerosols in reanalysis-driven \r\nRCM simulations substantially improved the representation of recent \r\nbrightening and warming trends in Europe (Nabat et al., 2014; see \r\nSections 10.3.3.6 and 10.6.4, and Atlas.8.4). Similarly, Bukovsky (2012) \r\nargued that RCMs may not capture observed summer temperature \r\ntrends in the USA because changes in land cover are not taken \r\ninto account. Barlage et al. (2015) have revealed that including the \r\nbehaviour of groundwater in land schemes increases the performance \r\nof an RCM model to represent climate variability in the central USA. \r\nHamdi et al"


id: 226b15c0c1a088e9f26c1f63f4564026
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
content_type: text
index: climate_gpt_only_giec
vector_id: 2863
content:
" (2014) found that an RCM that did not incorporate the \r\nhistorical urbanization in the land-use, land-cover scheme is not able \r\nto reproduce the warming trend observed in urban stations, with \r\na larger bias for the minimum temperature trend."


id: 32312ce40f9f36d9b25d7ef101658472
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
content_type: text
index: climate_gpt_only_giec
vector_id: 5308
content:
"Increasing resolution (Haarsma et al., 2016) or performing downscaling \r\nmay be particularly important when it modifies the climate change \r\nsignal of a  lower resolution model in a  physically plausible way \r\n(Hall, 2014). Improvements may result from a better representation \r\nof regional processes, upscale effects, as well as the possibility of \r\na  region-specific model tuning (Sorland et  al., 2018). For instance, \r\nGula and Peltier (2012) showed that a higher resolution allows for \r\na more realistic simulation of lake-induced precipitation, resulting in \r\na more credible projection of changes in the snow belts of the North \r\nAmerican Great Lakes. Similarly, Giorgi et al. (2016) demonstrated \r\nthat an ensemble of RCMs better represents high-elevation surface \r\nheating and in turn increased convective instability. As a result, the \r\nsummer convective precipitation response was opposite to that \r\nsimulated by the driving global models (Figure  10.9). Similarly, \r\nWalton et  al"


id: 51d9f50d1730bf28e7871b0c66fc7da5
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
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index: climate_gpt_only_giec
vector_id: 10338
content:
" (2015) showed that a  kilometre-scale RCM enables \r\na  more realistic representation of the snow-albedo feedback in \r\nmountainous terrain compared to standard resolution global models, \r\nleading to a  more plausible simulation of elevation-dependent \r\nwarming. Bukovsky et al. (2017) argue that strong seasonal changes \r\nin warm-season precipitation in the Southern Great Plains of the USA, \r\nprojected by RCMs, are more credible than the weaker global model \r\nchanges because precipitation is better simulated in the RCMs."


id: 4773126531ed4aec119cfddf746824b5
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
content_type: text
index: climate_gpt_only_giec
vector_id: 8628
content:
"AR5 stated that confidence in climate model projections is based \r\non the physical understanding of the climate system and its \r\nrepresentation in climate models. A  climate model's credibility for \r\nfuture projections may be increased if the model is able to simulate \r\npast variations in climate (Sections 10.3.3.8, 10.4.1 and 10.6; Flato \r\net al., 2014). In particular, the credibility of downscaled information \r\ndepends on the quality of both the downscaling method and of the \r\nglobal model providing the large-scale boundary conditions (Flato \r\net al., 2014). Credibility is closely linked to the concept of adequacy \r\nor fitness-for-purpose (Section 1.5.4.1; Parker, 2009). From a regional \r\nperspective, one may ask about the fitness of a climate model for \r\nsimulating future changes of specific aspects of a specific regional \r\nclimate. The required level of model fitness may depend on the user \r\ncontext (Section  10.5)"


id: 91ffd8c61fcd7d60b6305c0b72acfa27
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
content_type: text
index: climate_gpt_only_giec
vector_id: 20493
content:
" A  key challenge is to link performance at \r\nrepresenting present and past climate (Sections 10.3.3.3-10.3.3.8) \r\nto the confidence in future projections (Section 1.3.5; Baumberger \r\net al., 2017) and it is addressed in this subsection."


id: 1927ad1c16ed5bf5c87eda737d7db5d9
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updated_at: 2023-03-16 14:02:07
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vector_id: 1451
content:
"A general idea of model fitness for a  given application may be \r\nobtained by checking whether relevant large- (Section 10.3.3.4) and \r\nregional-scale (Sections 10.3.3.5 and 10.3.3.6) processes are explicitly \r\nresolved (Figure 10.3). The basis for confidence in climate projection \r\nis a solid process understanding (Flato et al., 2014; Baumberger et al., \r\n2017). Thus, the key to assessing the fitness-for-purpose of a model is \r\nthe evaluation of how relevant processes controlling regional climate \r\nare represented (Collins et al., 2018). A process-based evaluation may \r\nbe more appropriate than an evaluation of the variables of interest \r\n(e.g.,  temperature, precipitation), because biases in the latter may \r\nin principle be reduced if the underlying processes are realistically \r\nsimulated (Cross-Chapter Box 10.2), while individual variables may \r\nappear as well-represented because of compensating errors (Flato \r\net al., 2014; Baumberger et al., 2017)"


id: 8855433be8a26fd2170e14695c912521
created_at: 2023-03-16 14:02:07
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content:
" Combining a process-based \r\nevaluation with a  mechanistic explanation of projected changes \r\nfurther increases confidence in projections (Bukovsky et al., 2017). \r\nFitness-for-purpose can also be assessed by comparing the simulated \r\nresponse of a  model with simulations of higher resolution models \r\nthat better represent relevant processes (Baumberger et al., 2017). \r\nFor instance, Giorgi et al. (2016) have corroborated their findings on \r\nprecipitation changes comparing standard RCM simulations with \r\nconvection-permitting simulations."


id: 98a76834e2209f17aa65e5d5c64cf93b
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
content_type: text
index: climate_gpt_only_giec
vector_id: 21534
content:
"Global models reproduce many of the features of observed climate \r\nand its variability at regional scales. However, global models \r\ncan show a  variety of biases in, for instance, precipitation and \r\ntemperature at scales ranging from continental (Prasanna, 2016) to \r\nsub-continental scales (Lovino et al., 2018), both in the mean and \r\nin higher order moments of the climatological distribution of the \r\nvariable (Figure  10.6; Ren et  al., 2019; Xin et  al., 2020). Regional \r\nbiases could occur even if all the relevant large-scale processes are \r\ncorrectly represented, but not their interaction with regional features \r\nsuch as orography or land-sea contrasts (Section  10.3.3.4). These \r\nbiases have been considered an important limiting factor in model \r\nusability, especially at the regional scale (Palmer, 2016)"


id: b7bbb31b771eb6098e6e900037c196bd
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
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vector_id: 26441
content:
" In  spite \r\nof this, global model simulations have been extensively used to \r\ncreate regional estimates of climate change (Chapters 11, 12 and \r\nAtlas), taking into account the result of a performance assessment \r\n(Chapter  11, Sections 10.3.3.3-10.3.3.8, and Atlas; Jiang et  al., \r\n2020). However, their application is limited in part by the effective \r\nresolution of these models (Klaver et al., 2020)."


id: bbe6d71830581e613ac6fe512f1eb9b3
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
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index: climate_gpt_only_giec
vector_id: 27122
content:
"Global model performance at the regional scale is assessed in \r\nterms of the time or spatial averages of key variables (see Atlas; \r\nBrunner et  al., 2019), the ability to reproduce their seasonal \r\ncycle (Hasson et  al.,  2013) or a  set of extreme climate indicators \r\n(Chapter  11; Luo et  al., 2020) and the representation of regional \r\nprocesses and phenomena, feedbacks, drivers and forcing impacts \r\n(Sections  10.3.3.4-10.3.3.6). In many cases, the performance \r\nestimates have been used to select models for either an application \r\nor a more in-depth study (Lovino et al., 2018), to select the models \r\nthat provide boundary conditions to perform RCM simulations \r\n(McSweeney et al., 2015) or to weight the results of the global model \r\nsimulations (Sanderson et al., 2015; Brunner et al., 2020)"


id: a15f104f87d4237573b9d089f85067d0
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
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vector_id: 22950
content:
" While some \r\nlarge-scale metrics are improved between the CMIP5 and CMIP6 \r\nexperiments (Chapter 3; Cannon, 2020), there is not yet concluding \r\nevidence of a systematic improvement for surface variables at the \r\nregional scale."


id: 14e6ea089ea1e58b607edee04f0c1487
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
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index: climate_gpt_only_giec
vector_id: 797
content:
"Bias adjustment assumes that model biases are time invariant \r\n(or more precisely, independent of the climate state), such that the \r\nadjustment made to present climate simulations is still applicable \r\nto future climate simulations. Many findings challenge the validity \r\nof this assumption, as already assessed in AR5 (Flato et al., 2014). \r\nFurther research has addressed this issue by means of perfect \r\nmodel experiments (Section  10.3.2.5) and process understanding. \r\nPerfect-model studies with GCMs found that circulation, energy, \r\nand water-cycle biases are roughly state-independent (Krinner \r\nand Flanner, 2018), whereas temperature biases depend linearly \r\non temperature (Kerkhoff et  al., 2014). Others show that regional \r\ntemperature biases may depend on soil moisture and albedo, \r\nand may thus be state-dependent (Maraun, 2012; Bellprat et  al., \r\n2013; Maraun et al., 2017; see Cross-Chapter Box 10.2 for further \r\nlimitations of bias adjustment)"


id: f8d9fca7b07cde86fd532049a73edcc5
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
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index: climate_gpt_only_giec
vector_id: 36663
content:
" The fitness of weather generators for \r\nfuture projections depends on whether they account for all relevant \r\nchanges in their parameters, either by predictors or change factors \r\n(Maraun and Widmann, 2018b)."


id: f05e697d1d27a78fb05676b16f3fcc01
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
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index: climate_gpt_only_giec
vector_id: 35316
content:
"There is very high confidence (robust evidence and high agreement) \r\nthat RCMs add value to global simulations in representing many \r\nregional weather and climate phenomena, especially over regions \r\nof complex orography or with heterogeneous surface characteristics \r\nand for local-scale phenomena. Realistically representing local-scale \r\nphenomena such as land-sea breezes requires simulations at \r\na  resolution of the order of 10 km (high confidence). Simulations \r\nat  kilometre-scale resolution add value in particular to the \r\nrepresentation of convection, sub-daily summer precipitation extremes \r\n(high confidence) and soil-moisture-precipitation feedbacks (medium \r\nconfidence). Resolving regional processes may be required to correctly \r\nrepresent the sign of regional climate change (medium confidence)"


id: 4bede0c789528238188e9493e0e1a103
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
content_type: text
index: climate_gpt_only_giec
vector_id: 9368
content:
" \r\nHowever, the performance of RCMs and their fitness for future \r\nprojections depend on their representation of relevant processes, \r\nforcings and drivers in the specific context (Sections 10.3.3.4-10.3.3.8)."


id: dc9d444aeb048e06530f81341c87e55e
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
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index: climate_gpt_only_giec
vector_id: 32213
content:
"van Haren et al., 2015; Feng et al., 2017; Fabiano et al., 2020), although \r\nthe resolution increase is not a  guarantee of overall improvement \r\n(Section 8.5.1; Fabiano et al., 2020; Hertwig et al., 2021). For instance, \r\nincreasing resolution in global models has been shown to improve \r\nAsian monsoon rainfall anchored to orography and the monsoon \r\ncirculation (Johnson et al., 2016), but fails to solve the major dry bias. \r\nIt is also difficult to disentangle the role of resolution increase and \r\nmodel tuning on the performance of the GCM (Anand et al., 2018). \r\nSome efforts have been undertaken to complement the performance \r\nimprovements of resolution by using stochastic parametrizations \r\n(Palmer, 2019), which explicitly acknowledge the multi-scale nature \r\nof the climate system, in standard resolution global models with some \r\nsuccess (Dawson and Palmer, 2015; MacLeod et al., 2016; Zanna et al., \r\n2017, 2019)"


id: a4415c95802c24fa2d00b0bcd12a2225
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
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index: climate_gpt_only_giec
vector_id: 23398
content:
" The expectation is to achieve a similar performance to \r\nthe increase in resolution at a reduced computational cost."


id: 502cff7f22c9eba5501dd88c895ee0e7
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
content_type: text
index: climate_gpt_only_giec
vector_id: 10054
content:
"Statistical downscaling, bias adjustment and weather generators \r\noutperform uncorrected output of global and regional models \r\nfor a  range of statistical aspects at single locations due to their \r\ncalibration (Casanueva et  al., 2016), but RCMs are superior when \r\nspatial fields are relevant (Mehrotra et  al., 2014; Vaittinada Ayar \r\net al., 2016; Maraun et al., 2019a). Similarly, there is some evidence \r\nthat bias adjustment is comparable in performance when applied to \r\nglobal models and dynamically downscaled global models only for \r\nsingle locations, but dynamical downscaling prior to bias adjustment \r\nclearly adds value once spatial dependence is relevant (Maraun \r\net al., 2019a). These results may explain why dynamical downscaling \r\ndoes not add value to global model simulations for (single-site) \r\nagricultural modelling, when both global and regional models are \r\nbias adjusted (Glotter et al"


id: 8de8fd56af7fc464cdd91fb4c589b529
created_at: 2023-03-16 14:02:07
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vector_id: 19859
content:
", 2014), but dynamical downscaling adds \r\nvalue compared to bias-adjusted global model output for spatially \r\ndistributed hydrological models (Qiao et al., 2014)."


id: fcf133a3d8ca9bb89d2fbada2cda3399
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
content_type: text
index: climate_gpt_only_giec
vector_id: 37308
content:
"Since AR5, physical climate storyline approaches (see also \r\nChapter  1, Section  10.5.3, Box  10.2, and Atlas.2.5.2) have been \r\ndeveloped to better characterize and communicate uncertainties \r\nin regional climate projections (Shepherd, 2019). A  special class \r\nof such storylines attempts to attribute regional uncertainties \r\nto uncertainties in remote drivers. For instance, the Dutch \r\nMeteorological Service has presented climate projections for the \r\nNetherlands for different plausible changes of the mid-latitude \r\natmospheric circulation and different levels of European warming \r\n(van den Hurk et al., 2014). Manzini et al. (2014) have quantified \r\nthe impact of uncertainties in tropical upper troposphere warming, \r\npolar amplification, and stratospheric wind change on Northern \r\nHemisphere winter climate change"


id: b10bca6d35860b15c89f8659a20495eb
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
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index: climate_gpt_only_giec
vector_id: 25360
content:
" Based on these results, Zappa \r\nand Shepherd (2017) separated the multi-model ensemble into \r\nphysically consistent sub-groups or storylines of qualitatively \r\ndifferent projections in relevant remote drivers of the atmospheric \r\ncirculation. In a similar vein, (Ose et al., 2020) trace uncertainties in \r\nprojections of the East Asian summer monsoon and Mindlin et al. \r\n(2020) conditioned the response of Southern Hemisphere mid\u0002latitude circulation and precipitation to greenhouse gas forcing on \r\nlarge-scale climate indicators (Section 8.4.2.9.2)."


id: 1ce4ca0ae3f35fc058f4abae7fecd9ad
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
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index: climate_gpt_only_giec
vector_id: 1996
content:
"Modelling chains for generating regional climate information range \r\nfrom the definition of forcing scenarios to the global modelling, \r\nand potentially to dynamical or statistical downscaling and bias \r\nadjustment (Section  10.3.1). The propagation and potential \r\naccumulation of uncertainties along the chain has been termed the \r\ncascade of uncertainty (Wilby and Dessai, 2010). Even within one \r\nmodel, like a global model, uncertainty propagates across scales. From \r\na process point of view, these uncertainties are related to forcings \r\nand global climate sensitivity, and errors in the representation of the \r\nlarge-scale circulation (Section  10.3.3.3; McNeall et  al., 2016) and \r\nregional processes (Section  10.3.3.4), feedbacks (Section  10.3.3.5) \r\nand drivers (Section 10.3.3.6). From a modelling point of view, these \r\nuncertainties are related to the choice of dynamical and statistical \r\nmodels (Section  10.3.1) and experimental design (Section  10.3.2)"


id: a84da98c6392f32e337781e3c5bc6fb7
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
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vector_id: 24008
content:
" \r\nThe overall uncertainty can be statistically decomposed into the \r\nindividual sources (Evin et al., 2019; Christensen and Kjellstrom, 2020), \r\nalthough there might be non-linear dependencies between them."


id: 23284f08606773d3fbb79bf8edf1f06b
created_at: 2023-03-16 14:02:07
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index: climate_gpt_only_giec
vector_id: 2976
content:
"Climate response uncertainties (Chapter  1) can be represented by \r\nmulti-model ensembles, although the sampled uncertainty typically \r\nunderestimates the full range of uncertainty (Collins et al., 2013b; \r\nShepherd et al., 2018; Almazroui et al., 2021). Traditionally, climate \r\nresponse uncertainty has been characterized by the ensemble spread \r\naround the multi-model mean change. The change has then further \r\nbeen qualified in terms of the agreement across models and compared \r\nto estimates of internal climate variability (Collins et  al., 2013b). \r\nSince AR5, several limitations of this approach have been identified \r\n(Madsen et  al., 2017) such as the failure to address physically \r\nplausible, but low-likelihood, high-impact scenarios (Chapters 1, 4, 8 \r\nand 9; Sutton, 2018) or that qualitatively different or even opposite \r\nchanges may be equally plausible at the regional scale (Shepherd, \r\n2014)"


id: f788c5d809cd25b82a624e862a34a625
created_at: 2023-03-16 14:02:07
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vector_id: 36457
content:
" In a  multi-model mean these different responses would be \r\nlumped together, strongly dampened, and qualified as non-robust, \r\nwhereas in fact high impacts might occur. Further, the multi-model \r\nmean itself is often implausible because it is a statistical construct \r\n(Zappa and Shepherd, 2017). Overall, there is high confidence that "


id: d3160a7851d3828f4e118af9900e9953
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
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vector_id: 30732
content:
"Analysis of multi-model archives such as CMIP or CORDEX simulation \r\nresults cannot easily disentangle model uncertainty and uncertainty \r\nrelated to internal variability. Since AR5, the development of \r\nsingle-model (global model and/or RCM) initial-condition large \r\nensembles (SMILEs) has emerged as a  promising way to robustly \r\nassess the regional-scale forced response to external forcings and the \r\nrespective contribution of internal variability and model uncertainty \r\nto future regional climate changes (Section 4.2.5; Deser et al., 2014, \r\n2020; Kay et al., 2015; Sigmond and Fyfe, 2016; Aalbers et al., 2018; \r\nBengtsson and Hodges, 2019; Dai and Bloecker, 2019; Leduc et al., \r\n2019; Maher et al., 2019; von Trentini et al., 2019; Lehner et al., 2020)"


id: c09e072a7881088804e827030c7f12dd
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
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vector_id: 27868
content:
" \r\nThe recent development of a multi-model archive of SMILE simulations \r\nfacilitates the quantification and comparison of  the influence of \r\ninternal variability on global model-based regional climate projections \r\nbetween different models (Deser et  al., 2020; Lehner et  al., 2020). \r\nAnother related development is the more frequent use of observation\u0002based statistical models to assess the influence of internal variability \r\non regional-scale global and regional model projections (Thompson \r\net  al., 2015; Salazar et  al., 2016). However, these methods often \r\nimplicitly assume that regional-scale internal variability does not \r\nchange under anthropogenic forcing, which is a strong assumption \r\nthat does not seem to hold at regional and local scales (LaJoie and \r\nDelSole, 2016; Pendergrass et  al., 2017; W. Cai et  al., 2018; Dai \r\nand Bloecker, 2019; Mankin et al., 2020; Milinski et al., 2020)."


id: c8b4611dcd0363721d994e2f08ab957a
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
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vector_id: 29133
content:
"likely range (Chapter 4), or some other relevant measure of sensitivity, \r\nsuch as the projected range of tropical SSTs (Suzuki-Parker et al., 2018). \r\nA further choice is to emphasize models that do not have the same \r\norigins or that do not use similar parametrizations and thus might be \r\nviewed as independent, a criterion that could be applied to both global \r\nmodels (Chapter 4) and RCMs (Evans et al., 2014). Global models and \r\nRCMs could also be discarded that unrealistically represent processes \r\ncontrolling the regional climate of interest (McSweeney et al., 2015; \r\nMaraun et  al., 2017; Bukovsky et  al., 2019; Eyring et  al., 2019). \r\nBox 4.1 offers a more detailed discussion of the issues surrounding \r\nthese approaches. Finally, global models may be selected to represent \r\ndifferent physically self-consistent changes in regional climate (Zappa \r\nand Shepherd, 2017)"


id: 789c17150cbcc90bf685db9e0cbbdee9
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
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content:
" Statistical methods can provide estimates of \r\noutcomes from missing global model-RCM combinations in a  large \r\nmatrix (Deque et al., 2012; Heinrich et al., 2014; Evin et al., 2019)."


id: e227df68f7aea82698ae74a934a55829
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
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index: climate_gpt_only_giec
vector_id: 33078
content:
"However, even using a relatively small set of global models can still \r\ninvolve substantial computation that strains available resources, \r\nboth for performing the simulations and for using all simulations \r\nin the ensemble for further impacts assessment. The NARCCAP \r\nprogramme (Mearns et al., 2012) used only a subset of its possible \r\nglobal model-RCM combinations that balanced comprehensiveness \r\nof sampling the matrix with economy of computation demand, while \r\nstill allowing discrimination, via ANOVA methods, of global model \r\nand RCM influences on regional climate change (Mearns et  al., \r\n2013). An advantage of the sparse, but balanced matrix for those \r\nusing the downscaling output for further studies, is that they have \r\na smaller, yet comprehensive set of global model-RCM combinations \r\nto work with"


id: 1b97c86458e8111af1102ba001097990
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
content_type: text
index: climate_gpt_only_giec
vector_id: 1801
content:
" Alternatively, data-clustering methods can clump \r\ntogether downscaling simulations featuring similar climate-change \r\ncharacteristics, so that only one representative simulation from \r\neach cluster may be needed for further impacts analysis, again \r\nsystematically reducing the necessary number of simulations to work \r\nwith (Mendlik and Gobiet, 2016; Wilcke and Barring, 2016)."


id: d27b849e8d6f1a95d6a1a1bc6023777c
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
content_type: text
index: climate_gpt_only_giec
vector_id: 30642
content:
"Ensembles of climate simulations play an important role in quantifying \r\nuncertainties in the simulation output (Sections 10.3.4.2 and \r\n10.3.4.3). In addition to providing information on internal variability, \r\nensembles of simulations can estimate scenario uncertainty and \r\nmodel (structural) uncertainty. Chapter  4, especially Box  4.1, \r\ndiscusses issues involved with evaluating ensembles of global \r\nmodel simulations and their uncertainties. In a downscaling context, \r\nfurther considerations are necessary, such as the selection of global \r\nmodel-RCM combinations when performing dynamical downscaling. \r\nThis is a  relevant issue when resources are limited. The structural \r\nuncertainty of both the global model and the downscaling method \r\ncan be important (e.g., Mearns et al., 2012; Dosio, 2017), as well as \r\nfurther potential uncertainty created by inconsistencies between the \r\nglobal model and the downscaling method (e.g., Dosio et al"


id: b5c61994c92046ec28a3c0ee502bb63e
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
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index: climate_gpt_only_giec
vector_id: 26128
content:
", 2019), \r\nwhich could include, for example, differences in topography or the \r\nway to model precipitation processes (Mearns et al., 2013)."


id: 94a7b78bfd70f0c4160c5648bbe5fbdf
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
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index: climate_gpt_only_giec
vector_id: 20897
content:
"Independently of the resources, participation of multiple models in \r\na  simulation programme such as CORDEX for RCMs or CMIP for \r\nglobal models creates ensembles of opportunity, which are ensembles \r\npopulated by models that participants chose to use without there \r\nnecessarily being an overarching guiding principle for an optimum \r\nchoice. As discussed in Chapter 4, these ensembles are likely suboptimal \r\nfor assessing sources of uncertainty. An important contributor to the \r\nsuboptimal character of such an ensemble is that the models are \r\nnot independent. Some may also have larger biases than others. Yet \r\noften, the output from models in these ensembles has received equal \r\nweight when viewed collectively, as was the case in much of the AR5 \r\nassessment (e.g., Collins et al., 2013b; Knutti et al., 2013; Flato et al., \r\n2014; Kirtman et al., 2014)"


id: 237960a5c85b5d71ce5dc7092970e5f9
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
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index: climate_gpt_only_giec
vector_id: 3030
content:
" A number of emerging methodologies aim \r\nat optimizing the ensembles available by weighting the simulation \r\nresults according to a  number of criteria relevant at the regional \r\nscale that aim at obtaining more realistic estimates of the uncertainty \r\n(Sanderson et al., 2015; Brunner et al., 2020)."


id: 15abef9af699f225ee8b573a16b7d29f
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
content_type: text
index: climate_gpt_only_giec
vector_id: 924
content:
"Bias adjustment methods like quantile mapping can modify simulated climate trends, with impacts on changes to climate indices, \r\nin particular, extremes (Haerter et al., 2011; Dosio et al., 2012; Ahmed et al., 2013; Hempel et al., 2013; Maurer and Pierce, 2014; \r\nCannon et al., 2015; Dosio, 2016; Casanueva et al., 2020). Some argue that these trend modifications are implicit corrections of \r\nstate-dependent biases (Boberg and Christensen, 2012; Gobiet et al., 2015). However, others argue that the modification is generally \r\ninvalid because the modification is linked to the representation of day-to-day rather than long-term variability (Pierce et al., 2015; \r\nMaraun et al., 2017); a given temperature value does not necessarily belong to the same weather state in present and future climate \r\n(Maraun et al., 2017); the modification affects the models climate sensitivity (Hempel et al., 2013); and is affected by random internal \r\nclimate variability (Switanek et al., 2017)"


id: f60a78aa78851ed087b22b072ea9900a
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
content_type: text
index: climate_gpt_only_giec
vector_id: 36220
content:
" Thus, trend preserving quantile mapping methods have been developed (Section 10.3.1.3.2), \r\nalthough some authors found no clear advantage of these methods (Maurer and Pierce, 2014). Further research is required to fully \r\nunderstand the validity of trend modifications by quantile-mapping."


id: abbd9aeb569100c1721213eb9a0e743a
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content:
"The large-scale circulation has a strong impact on regional climate, thus circulation errors will cause regional climate biases (Section 10.3.3.3). \r\nAs bias adjustment in general does not account for circulation errors, it is therefore important to understand the impact of these errors on the \r\noutcome of the bias adjustment (Addor et al., 2016; Photiadou et al., 2016; Maraun et al., 2017). If the frequency of precipitation-relevant \r\nweather types is biased, a standard bias adjustment (not accounting for this frequency bias) would remove the overall climatological bias, \r\nbut the precipitation falling in a given weather type could still be substantially biased (Addor et al., 2016). Adjusting the number of wet \r\ndays can artificially deteriorate the spell-length distribution (Maraun et al., 2017). In the presence of location biases of circulation patterns, \r\nbias adjustment may introduce physically implausible solutions (Maraun et al., 2017)"


id: d75587b7e33899a3952640f77865de81
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content:
" Bias adjusting the location of circulation features \r\n(Levy et al., 2013) may introduce inconsistencies with the model orography, land-sea contrasts, and SSTs (Maraun et al., 2017)."


id: cfb61f8d5adcec0920f7d37cf07575ed
created_at: 2023-03-16 14:02:07
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content:
"adjustment for downscaling has been shown to artificially modify long-term trends, misrepresent the spatial characteristics of extreme \r\nevents, and misrepresent local weather phenomena such as temperature inversions (Maraun, 2013a; Gutmann et al., 2014; Maraun \r\net al., 2017). Crucially, sub-grid influences on the local climate change signal are not represented. For instance, if a mountain chain is \r\nnot resolved in the driving model, the snow-albedo feedback is not represented by the bias adjustment such that local temperature \r\ntrends in high altitudes are under-represented (Cross-Chapter Box 10.2, Figure 1; Maraun et al., 2017). It has therefore been suggested \r\nto account for local random variability by combining bias adjustment with stochastic downscaling (Volosciuk et al., 2017; Lange, \r\n2019), although this approach still does not account for local modifications of the climate change signal"


id: 7786df474b3908caf7b485ea287b0237
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
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vector_id: 16357
content:
" Two approaches have been \r\nproposed to represent these local changes: dynamical downscaling with high-resolution RCMs (Maraun et al., 2017) or statistical \r\nemulators of such (Walton et al., 2015). Sections 10.3.3.4-10.3.3.6 and 10.3.3.9 discuss other examples where RCMs improve the \r\nrepresentation of regional phenomena and regional climate change."


id: 337f2946dba51b6134e9d4fda199e152
created_at: 2023-03-16 14:02:07
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content:
"Impact models, as well as indices of climatic impact-drivers, often require input of several meteorological variables (Chapter 12). In several \r\nsituations, for example, if the dependence between the variables is not well-simulated, univariate bias adjustment of the individual \r\nvariables may increase biases in the resulting indicator (Zscheischler et al., 2019). A simple alternative would be a bias adjustment of \r\nthe indicator, but such a procedure may substantially alter the climate change signal, in particular for extreme events (Casanueva et al., \r\n2018). In principle, multivariate bias adjustment methods are good to adjust all statistical aspects of the multivariate distribution that they \r\nintend to adjust. Depending on the method, this includes the correlation structure or even broader aspects of the dependence (Cannon, \r\n2016, 2018; Vrac, 2018; Francois et al., 2020)"


id: f6165bb7bfcbf29e7fdcf4652428df04
created_at: 2023-03-16 14:02:07
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content:
" If multivariate adjustment includes a spatial dimension, then spatial dependence is adjusted \r\nwell (Vrac, 2018), but care is needed when applied across large areas (Francois et al., 2020). Adjustment of multivariate dependence \r\nnecessarily modifies the temporal sequencing of the driving model (Cannon, 2016; Maraun, 2016). The extent of the modification depends \r\non the chosen method and the number of variables to adjust (Vrac and Friederichs, 2015; Cannon, 2016; Vrac, 2018; Francois et al., 2020)."


id: a4774030349478fd2fb14d53f90c6611
created_at: 2023-03-16 14:02:07
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content:
"In the light of these issues, several authors dismiss the use of bias adjustment for climate change studies (Vannitsem, 2011; Ehret et al., 2012). \r\nEhret et al. (2012) and IPCC (2015) propose to at least provide the raw model output alongside the adjusted data. Maraun et al. (2017) \r\nargue that the target resolution should be similar to the model resolution to avoid downscaling issues. IPCC (2015) and Maraun et al. \r\n(2017) highlighted the relevance of understanding model biases and the misrepresentations of the underlying physical processes prior to \r\nany adjustment. Together with Galmarini et al. (2019), they point out the need for collaboration between bias adjustment users, experts in \r\nclimate modelling and experts in the considered regional climate. As new research avenues, development of process-oriented bias adjustment \r\nmethods (Addor et al., 2016; Verfaillie et al"


id: 519962fc4256d68bc031122cc7c9e1bc
created_at: 2023-03-16 14:02:07
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vector_id: 10294
content:
", 2017; Manzanas and Gutierrez, 2019) or run-time bias adjustment integrated into the climate \r\nsimulation, for example, to reduce circulation errors (Guldberg et al., 2005; Kharin et al., 2012; Krinner et al., 2019, 2020) are proposed."


id: d5b65e0c1732408a6dff586152220fe6
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
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vector_id: 31144
content:
"Detection of mean precipitation changes caused by human influence is \r\nmuch more difficult, due to a larger role of internal variability at regional \r\nto local scales, as well as substantial modelling and observational \r\nuncertainty (Wan et al., 2015; Sarojini et al., 2016; C. Li et al., 2017). \r\nHowever, multi-decadal precipitation changes due to anthropogenic \r\nforcing have been detected for several regions. Ma et al. (2017b) show \r\nthat anthropogenic forcing has strongly contributed to the observed \r\nshift of China daily precipitation towards heavy precipitation. The \r\nobserved weakening of the East Asia summer monsoon, also known \r\nas the southern flooding and northern drought pattern has been \r\npartially linked to anthropogenic forcing (Section  8.3.2.4.2; Song \r\net  al., 2014; Zhou et  al., 2017; Tian et  al., 2018)"


id: 6a38f97f2e6583396f740e40ea0f3245
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
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vector_id: 14311
content:
" Changes in GHGs \r\nlead to increasing precipitation over southern China, while changes \r\nin anthropogenic aerosols over East Asia are the dominant factors \r\ndetermining drought conditions over northern China (Song et al., 2014; \r\nTian et al., 2018). Based on all-forcing and single-forcing simulation \r\nensembles with a high-resolution model, Delworth and Zeng (2014) \r\nfound that the observed long-term regional austral autumn and winter \r\nrainfall decline over southern and particularly south-west Australia is \r\npartially reproduced in response to anthropogenic changes in GHGs \r\nand ozone in the atmosphere, whereas anthropogenic aerosols do \r\nnot contribute to the simulated precipitation decline. In contrast, the \r\nobserved increase of north-west Australian summer rainfall since 1950 \r\nhas been partially attributed to anthropogenic aerosol based on CMIP5 \r\ndetection and attribution single-forcing simulations (Section 8.3.2.4.6; \r\nDey et al., 2019a, b)."


id: cd6008b79246edb4f8c283a6239536a4
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
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vector_id: 29857
content:
"Attribution at sub-continental and regional scales is usually more \r\ncomplicated than at the global scale due to various factors: a larger \r\ncontribution from internal variability, an increased similarity among \r\nthe responses to different external forcings leading to a more difficult \r\ndiscrimination of their effects, the importance at regional scale of \r\nsome omitted forcings in global model simulations, and model \r\nbiases related to the representation of small-scale phenomena \r\n(Zhai et  al., 2018). Since AR5 and in addition to standard optimal \r\nfingerprint regression-based approaches (Section  3.2.1 and Zhai \r\net al. 2018), several emerging methodologies have been increasingly \r\nused for regional-scale climate change attribution. These include \r\nseveral statistical approaches that differ in their use or omission of \r\nspatiotemporal co-variance information"


id: b1cabbe399eeaa4e720290de423577f
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
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vector_id: 25474
content:
" Dynamical adjustment and \r\npattern recognition techniques fall into the category of spatiotemporal \r\nmethods while univariate detection and attribution methods rely on \r\nsingle grid-point analysis. Finally, the development, evaluation and use \r\nof all these methodologies rely upon the availability of multiple and \r\nhigh-quality observational datasets (Section 10.2) as well as multi\u0002model simulations of the historical period constrained by different \r\nexternal forcing combinations, including single-forcing experiments \r\nand single-model initial-condition large ensembles (SMILEs)."


id: 85ae124eb19b562b6287242454b04d58
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
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vector_id: 18571
content:
"and precipitation trends over Europe. Similarly, O'Reilly et al. (2017) \r\napplied dynamical adjustment techniques to more carefully \r\ndetermine the influence of the Atlantic Multi-decadal Variability \r\n(AMV; Annex IV.2.7) on continental climates. Over Europe, summer \r\ntemperature anomalies induced thermodynamically by the warm \r\nphase of the AMV are further reinforced by circulation anomalies; \r\nmeanwhile, precipitation signals are largely controlled by dynamical \r\nresponses to the AMV. Based on a  partial least-squares approach, \r\nGong et al. (2019) showed that recent winter temperature 30-year \r\ntrends over northern East Asia are strongly influenced by internal \r\nvariability linked to decadal changes of the Arctic Oscillation. Using \r\ndynamical adjustment purely on precipitation observations, R. Guo \r\net  al"


id: 4510770e55a3c26cf852921e36d9555b
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
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index: climate_gpt_only_giec
vector_id: 8290
content:
" (2019) showed that human influence has led to increased \r\nwinter precipitation across north-eastern North America, as well as \r\na small region of north-western North America, and to an increase in \r\nprecipitation across much of north-western and north central Eurasia. \r\nThe latter results confirm previous findings obtained by standard \r\noptimal fingerprinting methods (Wan et al., 2015)."


id: b3328e958dcb61c958dafd2243c52c1c
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
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vector_id: 25708
content:
"The typical ensemble size of CMIP historical climate simulations for \r\na  given model traditionally range between one and ten members, \r\nwith three often being the default choice. At the regional scale, \r\na simple ensemble average with such sample sizes does not provide \r\nrobust estimates of the response patterns to external forcing (Maher \r\net al., 2019; Deser et al., 2020). Since AR5, pattern filtering methods \r\nsuch as signal-to-noise maximizing empirical orthogonal functions \r\n(Ting et  al., 2009) have been shown to improve the identification \r\nof forced response patterns when few model members are available \r\n(Wills et al., 2020). Using SMILEs as a test bed, it has been shown that \r\npattern filtering strongly reduces the number of ensemble members \r\nneeded to estimate the forced response pattern compared to simple \r\nensemble averaging"


id: 192a657b09c10f17955e4d514103263d
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vector_id: 1452
content:
" Pattern filtering allows the identification of low \r\nsignal-to-noise signals such as the El Nino-like response to volcanic \r\neruptions (Khodri et al., 2017; Wills et al., 2020)."


id: fffb80266f39573d97ca57c4f80aebc2
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
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index: climate_gpt_only_giec
vector_id: 37808
content:
"Time scale separation methods such as the low-frequency \r\ncomponent analysis and ensemble empirical mode decomposition \r\nmethods take advantage of the longer time scale associated with \r\nanthropogenic external forcing compared to that of most internal \r\nmodes of variability. The low-frequency component analysis method \r\ntries to find low-frequency variability patterns by searching for linear \r\ncombinations of a moderate number of empirical orthogonal functions \r\nthat maximize the ratio of low-frequency to total variance. It has first \r\nbeen used to separate internal modes of interannual and decadal \r\nvariability from slowly varying and externally-forced variability \r\nin the Pacific and Atlantic oceans (Wills et  al., 2018, 2019). The \r\nmethodology has also been applied to patterns of observed surface \r\nair temperature to isolate the slow components of observed changes \r\nthat are consistent with the expected response to anthropogenic \r\ngreenhouse gas and aerosol forcing (Wills et al"


id: b11ab9cb12e4e27341182ea96ccd16f7
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
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vector_id: 25371
content:
"Methods to extract the response to external forcing in an observed or \r\nsimulated single realization include dynamical adjustment (Smoliak \r\net al., 2015; Deser et al., 2016; Sippel et al., 2019) and time scale \r\nseparation methods (DelSole et al., 2011; Wills et al., 2018, 2020). \r\nDynamical adjustment seeks to isolate changes in surface air \r\ntemperature or precipitation that are due purely to atmospheric \r\ncirculation changes. The residual can then be analysed and attributed \r\nto internal changes in both land or ocean surface conditions and \r\nthe thermodynamical response to external forcing. Smoliak et  al. \r\n(2015) performed their dynamical adjustment using partial least \r\nsquares regression of temperature to remove variations arising from \r\nsea level pressure changes. Deser et  al. (2016) used constructed \r\natmospheric circulation analogues and resampling to estimate the \r\ndynamical contribution to changes in temperature. Sippel et  al"


id: 8cedd8c1b437176c58d4e1b51388a5c5
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content:
" \r\n(2019) used machine learning techniques known as regularized linear \r\nregression to provide estimates of circulation-induced components \r\nof precipitation and temperature variability from global to local \r\nscales. It is noteworthy that the dynamical adjustment method by \r\nitself cannot account for the component of the forced response \r\nassociated with circulation changes that project onto atmospheric \r\ninternal variability. However, this component can be estimated within \r\na model framework by averaging the dynamical contribution across \r\nmultiple members of a SMILE (Deser et al., 2016)."


id: b37ad47fad70012789c079ddf7a7360a
created_at: 2023-03-16 14:02:07
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vector_id: 25745
content:
"and all-forcing are compared with distributions of trends purely due \r\nto internal variability and derived from long simulations with constant \r\npre-industrial external forcing. Consistency between observed and \r\nsimulated historical trends is also assessed with statistical tests that \r\ncan be applied independently over a large number of grid points. The \r\nfraction of area over a given region where the change is classified \r\nas detectable, attributable, or consistent/inconsistent, is then finally \r\nestimated. The method can be viewed as a  simple consistency \r\ntest for both amplitude and pattern of observed versus simulated \r\ntrends. Its application to CMIP3 and CMIP5 models suggests that \r\n80% of the Earth's surface has a detectable anthropogenic warming \r\nsignal (Knutson et al., 2013)"


id: 85c11368f5f55faf4555fb497a5b42eb
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vector_id: 18585
content:
" Regarding regional land precipitation \r\nchanges over the 1901-2010 and 1951-2010 periods, application of \r\nthe univariate detection method based on CMIP5 models suggests \r\nattributable anthropogenic changes at several locations such as \r\nincreases over regions of the north-central USA, southern Canada, \r\nEurope, and southern South America and decreases over parts of the \r\nMediterranean region, northern tropical Africa and south-western \r\nAustralia (Delworth and Zeng, 2014; Knutson and Zeng, 2018)."


id: 83e091032ee7552244b8e0583cc824be
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
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vector_id: 18281
content:
"Another regional attribution technique is based on the similarity of \r\npast changes between observations and one or several simulations \r\nof a large ensemble that share the same time evolution for a suggested \r\ndriver of these changes. Huang et al. (2020b) used a perturbed physics \r\nensemble to attribute the drying trend of the Indian monsoon over \r\nthe latter half of the 20th century to decadal forcing from the Pacific \r\nDecadal Variability (PDV; Annex IV.2.6). The ensemble members \r\npredicted different trends in PDV behaviour across the 20th century \r\nand the negative precipitation trend was only replicated in those \r\nmembers with a  strong negative-to-positive PDV transition across \r\nthe 1970s, consistent with the observed PDV behaviour (see also the \r\ndetailed case study in Section 10.6.3). In a similar manner, Cvijanovic \r\net al"


id: 88e0bb6a5e3a9905ab82962445afb828
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
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index: climate_gpt_only_giec
vector_id: 19070
content:
" (2017) addressed the possible influence of Arctic sea ice loss on \r\nthe North Pacific pressure ridge and, consequently, on south-western \r\nUSA precipitation. They sampled the uncertainties in selected sea \r\nice physics parameters to achieve a 'low Arctic sea ice' state in their \r\nperturbed simulations. They then compared the latter with control \r\nsimulations representative of sea ice conditions at the end of the \r\n20th century to assess changes purely due to sea ice loss."


id: eae88bda23bc18314d5e1fd49721f2f0
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
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index: climate_gpt_only_giec
vector_id: 34466
content:
"The Sahel, fed by the West African monsoon, has experienced severe \r\ndecadal rainfall variations (Figure 10.11a). Abundant rainfall in the \r\n1950s-1960s was followed by a large negative trend (Figure 10.11b) \r\nuntil at least the 1980s, over which annual rainfall fell by 20-30% \r\n(Hulme, 2001). The subsequent partial recovery (B. Wang et al., 2021) \r\nis more uncertain: rain-gauge studies suggest a return to long-term \r\npositive anomalies in the western Sahel in the early 2000s (Panthou \r\net  al., 2018), while CHIRPS merged satellite/gauge data show \r\na wetter western Sahel since 1981 (Bichet and Diedhiou, 2018a, b). \r\nThe recovery has been more significant over the central rather than \r\nthe western Sahel (Lebel and Ali, 2009; Maidment et  al., 2015; \r\nSanogo et al., 2015) and a multiple-gauge record supports a greater \r\nrecovery to the eastern side (Nicholson et al., 2018)"


id: ae064a2e46a13d51f3d38e93ee699c59
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
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index: climate_gpt_only_giec
vector_id: 24914
content:
" In this attribution \r\nexample, drivers of the long-term drought and subsequent partial \r\nrecovery are discussed, including anthropogenic GHG and aerosol \r\nemissions, and sea surface temperature (SST) variations that, in part, \r\nrelate to internal variability. The reader is also referred to assessment \r\nin Section 8.3.2.4. We define the Sahel within 10degN-20degN across to \r\n30degE, consistent with the eastern boundary used in Chapter 8, and \r\nthe rainy season as spanning June to September."


id: 4b1e74fcfc16b356da72ed6828277917
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
content_type: text
index: climate_gpt_only_giec
vector_id: 9240
content:
"Sahel drought, while North Atlantic warming promotes increased \r\nrainfall (Rodriguez-Fonseca et al., 2015). The SST influence has been \r\nformalized in an AMV framework (Giannini et al., 2013; Martin and \r\nThorncroft, 2014; Martin et al., 2014; Park et al., 2015), suggesting \r\nthat relative North Atlantic SST warming increases the Northern \r\nHemisphere differential warming, enhancing Sahel rainfall. The AMV \r\ninfluence is supported by CMIP5 initialized decadal hindcasts (Gaetani \r\nand Mohino, 2013; Mohino et al., 2016; Sheen et al., 2017), which \r\noutperform empirical predictions based on persistence. Some caution \r\nis needed since the full magnitude of internal variability is not captured \r\nin most CMIP5 models, as poor resolution prevents reproduction \r\nof AMV teleconnection responses (Vellinga et  al., 2016), and the \r\nmagnitude of AMV-related SST variation may be underestimated \r\nin CMIP5 (Section 3.7.7, which also assesses that the AMV may be \r\npartially forced)"


id: d97014e182beb550d2fc79961744318a
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
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index: climate_gpt_only_giec
vector_id: 31720
content:
" The influence of PDV has been studied to a lesser \r\nextent, with the PDV positive phase having a  negative impact on \r\nSahel rainfall in combined observational/CMIP5 analysis (Villamayor \r\nand Mohino, 2015). The closer match between the observed rainfall \r\ndeclining trend and those in an atmosphere-only SMILE, in which SSTs \r\nare matched to observations, compared to three coupled SMILEs in "


id: d1c3975851d80603eb9fa077b6f267be
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
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index: climate_gpt_only_giec
vector_id: 30532
content:
"In terms of anthropogenic emissions, regional aerosol emissions \r\nfrom Europe, and to a  lesser extent from Asia, have been shown \r\nin a  global model to weaken Sahel precipitation either through \r\na weakened Saharan heat low or via the Walker circulation (Dong \r\net al., 2014). Greenhouse gases (GHGs) and anthropogenic aerosol \r\ncan be considered together to control ITCZ position based on \r\ntemperature asymmetry at the hemispheric scale. GHGs increase \r\nSahel precipitation, while aerosol reduces it (in coupled slab-ocean \r\nmodel experiments by Ackerley et al. (2011) following Biasutti and \r\nGiannini (2006)). This effect is stronger when models account for \r\naerosol-cloud interactions (Allen et al., 2015). Perturbed physics GCM \r\nensembles suggests that aerosol emissions were the main driver of \r\nobserved drying over 1950-1980 (Ackerley et al., 2011), supported \r\nby CMIP5 single-forcing experiments (Polson et al., 2014)"


id: c0583e2d37e1122bb8302acf37979b73
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
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index: climate_gpt_only_giec
vector_id: 27822
content:
" A coherent \r\ndrying signal in CMIP5 over the extended 1901-2010 period has \r\nalso been found, although smaller than the observed trend (Knutson \r\nand Zeng, 2018). By applying aerosol scaling factors to the historical "


id: 3e281f40b59fb87f8dea643134e695b
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
content_type: text
index: climate_gpt_only_giec
vector_id: 7147
content:
"For the partial recovery in West African monsoon and Sahel rainfall \r\nsince the late 1980s, a  detection study using three reanalyses \r\n(Cook and Vizy, 2015) shows a  connection to increasing Saharan \r\ntemperatures at a rate two to four times greater than the tropical \r\nmean, also confirmed by multiple observational and satellite-based \r\ndata (Zhou and Wang, 2016; Vizy and Cook, 2017) and the review \r\nof Cook and Vizy (2019). Reanalyses are also noted to significantly \r\nunderestimate the Saharan warming (Zhou and Wang, 2016). \r\nSaharan warming causes a stronger thermal low and more intense \r\nmonsoon flow, providing more moisture to the central and eastern \r\nSahel, supported by CMIP5 models (Lavaysse et al., 2016), although \r\nnot all models capture the observed rainfall-heat-low relationship. \r\nSahel rainfall is also incorrectly located in prototype versions of \r\na  few CMIP6 models, related to tropospheric temperature biases \r\n(Martin et  al., 2017)"


id: 9d53a5944c9705b942167002b07f69e8
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
content_type: text
index: climate_gpt_only_giec
vector_id: 22278
content:
" Amplified Saharan warming has increased \r\nthe wind shear, leading to a tripling of extreme storms since 1982, \r\nwhich may partially explain the recovery (Taylor et al., 2017). Instead, \r\nobservations, multiple models and SST-sensitivity experiments with \r\nAGCMs have suggested that stronger Mediterranean Sea evaporation \r\nenhances low-level moisture convergence to the Sahel, increasing \r\nrainfall (Park et  al., 2016). Meanwhile, an AGCM study suggested \r\nthat GHGs alone (in the absence of SST warming) could cause \r\nSahel rainfall recovery, with an additional role for anthropogenic \r\naerosol (Dong and Sutton, 2015); recent changes in North Atlantic \r\nSSTs, although substantial, did not exert a  significant impact on \r\nthe recovery. Large spread in the recovery in a five-member AGCM \r\nensemble suggests that atmospheric internal variability cannot be \r\ndiscounted (Roehrig et al., 2013)."


id: 3a4bffee184d0f7ed379c66817dd1fd7
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
content_type: text
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vector_id: 6540
content:
"A positive trend in summer (December to February) precipitation has \r\nbeen detected in multiple observational sources in south-eastern \r\nSouth America since the beginning of the 20th  century (Gonzalez \r\net al., 2013; Vera and Diaz, 2015; Wu et al., 2016; H. Zhang et al., \r\n2016; Diaz and Vera, 2017; Saurral et  al., 2017). Sedimentary \r\nrecords from the Mar Chiquita lake indicate that the last quarter \r\nof the 20th  century was wetter than any period during the last \r\n200  years (Piovano et  al., 2004). In this attribution example the \r\ndrivers contributing to the positive trend for the period 1951-2014 \r\nare discussed (Figure  10.12a). Precipitation anomalies of Climatic \r\nResearch Unit Time Series (CRU TS) as well as for the two members \r\nof a SMILE with the most negative and positive trends for 1951-2014 \r\nare displayed in Figure 10.12b. The trend for 1951-2014 using CRU TS \r\nand GPCC is illustrated in Figure 10"


id: 1b1f4ffad3aa0387a9562cfa9b86115f
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
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index: climate_gpt_only_giec
vector_id: 1737
content:
"12c, and for the region defined by \r\nthe black quadrilateral, it amounts to 2.8 (CRU TS) - 3.5 (GPCC) mm \r\nper month and decade (see black crosses in Figure  10.12d) while \r\nthe mean summer monthly precipitation for the same period is \r\n104 (CRU TS) -109 (GPCC) mm. The trend is also detectable in daily \r\nand monthly extremes (Re and Barros, 2009; Marengo et al., 2010; \r\nPenalba and Robledo, 2010; Doyle et al., 2012; Donat et al., 2013; \r\nLorenz et al., 2016)."


id: b8aa9e2c54d3b48975a5a68a8e8460aa
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
content_type: text
index: climate_gpt_only_giec
vector_id: 26579
content:
"Consistent timing of the southward ITCZ shift during the decline \r\nperiod in CMIP3 and CMIP5 historical simulations supports the role \r\nof external forcing, chiefly anthropogenic aerosol (Hwang et  al., \r\n2013). The evolution of the observed decline and recovery is largely \r\nfollowed by the CMIP5 multi-model mean, further supporting the \r\nrole of external drivers (Giannini and Kaplan, 2019). Updated results \r\nfrom CMIP6 for historical simulations with all and single forcings \r\nare represented in Figure  10.11d,e showing smaller trends than \r\nthose observed. Giannini and Kaplan (2019) attempted to unify the \r\ndriving mechanisms for decline and recovery based on singular-value \r\ndecomposition of observed and modelled SSTs. Since the 1950s, \r\ntropical warming arising from GHGs and North Atlantic cooling \r\nfrom aerosol led to regional stabilization, suppressing Sahel rainfall"


id: 570d335efc203c8ec4ae456792e15613
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
content_type: text
index: climate_gpt_only_giec
vector_id: 11179
content:
" \r\nThe subsequent reduction in aerosol emissions then led to North \r\nAtlantic warming and recovery of Sahel rainfall. Such mechanisms \r\ncontinue into the near-term future in idealized and modified RCP \r\nexperiments, with scenarios featuring more aggressive reductions in \r\naerosol emissions, or including aerosol-cloud interactions, favouring \r\na greater northward shift of rainfall (Allen, 2015; Westervelt et al., \r\n2017, 2018; Scannell et al., 2019). There is paleoclimate evidence of \r\nchanges to Sahel rainfall in the past, in particular with enhancement \r\nof the West African monsoon during the mid-Holocene. However, the "


id: 20443f5a5e4588d9eb175340414fb982
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
content_type: text
index: climate_gpt_only_giec
vector_id: 2527
content:
"than those observed (e.g.,  CMIP6 trends in red open circles in \r\nFigure  10.12d). Diaz  et  al. (2021) showed that to capture the \r\nobserved trend a multi-model ensemble of SMILEs is needed. Out of \r\nthe 12 large ensembles examined (with ensemble size varying in the \r\n16-100  range), only seven simulated the observed trend within \r\ntheir range. This could partly be explained by model biases in mean \r\nprecipitation and its interannual variability. In the sub-ensemble of six \r\nmodels that reproduce reasonably well the observed spatial patterns \r\nof mean precipitation and interannual variability, the ensemble mean \r\nspread is lower, and the forced response, taken as the multi-model \r\nensemble mean, is slightly more positive than that of the six poorly \r\nperforming models"


id: 250825be6425d8a0fa087fe476b14f66
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
content_type: text
index: climate_gpt_only_giec
vector_id: 3270
content:
" The signal-to-noise ratio, estimated as the ratio \r\nof the forced response to the spread due to internal variability, is \r\nalso slightly higher for the best-performing models, suggesting that \r\nselecting the best-performing models may have an influence on \r\nboth attribution of the observed trend and emergence of the forced \r\nresponse in future (Section 10.4.3)."


id: abee245d597b28ac75c4cc123daf5fa4
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
content_type: text
index: climate_gpt_only_giec
vector_id: 24571
content:
"The positive trend of precipitation has also been attributed to \r\nanthropogenic GHG emissions and stratospheric ozone depletion. \r\nCMIP5 models only show a positive trend when including anthropogenic \r\nforcings (Vera and Diaz, 2015). These results were supported by \r\nKnutson and Zeng (2018) based on univariate detection/attribution \r\nanalysis of annual mean trends for the 1901-2010 and 1951-2010 \r\nperiods. However, the main features of summer mean precipitation \r\nand variability of South America are still not well-represented in all \r\nCMIP5 and CMIP6 models (Gulizia and Camilloni, 2015; Diaz and Vera, \r\n2017; Diaz et al., 2021). This motivates the construction of ensembles \r\nthat exclude the worst performing models (Section  10.3.3.4)"


id: 6dbc114e8bf0cb1d156c07f17750cf9d
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
content_type: text
index: climate_gpt_only_giec
vector_id: 14873
content:
" The \r\nconstruction of ensembles of CMIP5 historical simulations with \r\nrealistic representation of precipitation anomalies with opposite sign \r\nover south-eastern South America and eastern Brazil showed that the \r\ntrend since the 1950s could be related to changes in precipitation \r\ncharacteristics only when simulations included anthropogenic \r\nforcings (Diaz and Vera, 2017). GHG emissions have been related \r\nto increased precipitation in south-eastern South America through \r\nthree different mechanisms (Figure  10.12a). First, GHG warming \r\ninduces a non-zonally uniform pattern of SST warming that includes \r\na  warming pattern over the Indian and Pacific oceans that excites \r\nwave responses over South America (Junquas et al., 2013). Zonally \r\nuniform SST patterns of warming alone lead to precipitation signals \r\nopposite to those observed in an AGCM (Junquas et al., 2013)"


id: b08aeaf4881c47237c5b410c577eaf3c
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
content_type: text
index: climate_gpt_only_giec
vector_id: 25296
content:
" Second, \r\nGHG radiative forcing drives an expansion of the Hadley cell so that \r\nits descending branch moves poleward from the region, generating \r\nanomalous ascending motion and precipitation (H. Zhang et al., 2016; \r\nSaurral et al., 2019). The third mechanism by which increased GHG \r\ncan contribute to increased precipitation in the region is through \r\na  delay of the stratospheric polar vortex breakdown. As depicted \r\nin Figure 10.12a, both stratospheric ozone depletion and increased \r\nGHGs have contributed to the later breakdown of the polar vortex \r\nin recent decades (McLandress et  al., 2010; Wu and Polvani, 2017; \r\nCeppi and Shepherd, 2019). Mindlin et  al. (2020) developed future \r\natmospheric circulation storylines (Section  10.3.4.2, Box  10.2) for \r\nSouthern Hemisphere mid-latitudes with the CMIP5 models and found \r\nthat for south-eastern South America summer precipitation, increases \r\nare related to the late-spring breakdown of the stratospheric polar \r\nvortex"


id: b6b24e6d469c35fca9c50b179e0946eb
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
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vector_id: 26263
content:
" The connecting mechanism is through a  lagged southward \r\nshift of the jet stream (Saggioro and Shepherd, 2019), which enhances \r\ncyclonic activity over the region (Wu and Polvani, 2017).\r\nA common feature among the above discussed studies is that even \r\nif global models simulate positive trends when forced with GHG \r\nand/or stratospheric ozone, these trends are in general smaller "


id: 1fbb9b3e54855d16dba51cb5f0c4908f
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
content_type: text
index: climate_gpt_only_giec
vector_id: 2425
content:
"Persistent hydroclimatic drought in south-western North America \r\nremains a much-studied event. Drought is a regular feature of the \r\nsouth-western North America's climate regime, as can be seen in \r\nboth the modern record, and through paleoclimate reconstructions \r\n(Cook et al., 2010; Woodhouse et al., 2010; Williams et al., 2020), as \r\nwell as in future climate model projections (Cook et al., 2015a). Since \r\nthe early 1980s, which were relatively wet in terms of precipitation \r\nand streamflow, the region has experienced major multi-year \r\ndroughts such as the turn-of-the-century drought that lasted \r\nfrom 1999 to 2005, and the most recent and extreme 2012-2014 \r\ndrought that in certain locations is perhaps unprecedented in the \r\nlast millennium (Section  8.3.1.6; Griffin and Anchukaitis, 2014; \r\nRobeson, 2015). Shorter dry spells also happened between these \r\nmulti-year droughts making 1980 to present a  period with an \r\nexceptionally steep trend from wet to dry (Figure 10"


id: 3cc92c196a8681daa89e8c181d0ac47e
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
content_type: text
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vector_id: 6930
content:
"13a), leading \r\nto strong declines in Rio Grande and Colorado river flows (Lehner \r\net al., 2017b; Udall and Overpeck, 2017). While robust attribution \r\nof this trend is complicated by the large natural variability in this \r\nregion, the 20th century warming has been suggested to increase \r\nthe chances for hydrological drought periods by lowering runoff \r\nefficiency (Woodhouse et al., 2016; Lehner et al., 2017b; Woodhouse \r\nand Pederson, 2018) and affecting evapotranspiration (Williams \r\net al., 2020). There is some evidence suggesting that the Last Glacial \r\nMaximum, a period of low atmospheric CO2, about 21 ka ago, has \r\na  thermodynamically-driven zonal mean precipitation response \r\nsimilar to that of the current state with relatively high CO2 levels "


id: 18039ef0a416a0ac2b695657ed844e10
created_at: 2023-03-16 14:02:07
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vector_id: 1269
content:
"when compared with the pre-industrial period. Pluvial conditions \r\nat that time and a reduction in precipitation from the Last Glacial \r\nMaximum to the pre-industrial period are consistent with drying \r\ntrends for the region in models with GHG concentrations exceeding \r\npre-industrial levels. However, the dominant large-scale drivers \r\nresponsible for the precipitation changes observed during these \r\ntwo transitions are markedly different: mainly ice-sheet retreat and \r\nincreasing insolation on one hand, increasing GHGs on the other \r\nhand. This suggests that the Last Glacial Maximum correspondence \r\nis fortuitous which strongly limits its use to capture future \r\nhydrological cycle changes (Section 8.3.2.4.4; Morrill et al., 2018; \r\nLowry and Morrill, 2019). Furthermore, the conclusion of the Last \r\nGlacial Maximum drying versus wetting seems to strongly depend \r\non the physical property of interest, hydrologic or vegetation \r\nindicators (Scheff et al., 2017)"


id: 409a24e481f75365fc4e56689e45515b
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
content_type: text
index: climate_gpt_only_giec
vector_id: 7543
content:
" Droughts are characterized by deficits \r\nin total soil moisture content that can be caused by a combination \r\nof decreasing precipitation and warming temperature, which \r\npromotes greater evapotranspiration. Regional-scale attribution \r\nof the prevalence of south-western North America drought since \r\n1980 then mostly focuses on the attribution of change in these \r\ntwo variables."


id: 5dd4bd6135cd1d0b3c0cdd46e59aee12
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
content_type: text
index: climate_gpt_only_giec
vector_id: 12296
content:
"It has also been suggested that the ocean-controlled influence \r\nis limited and internal atmospheric variability has to be invoked \r\nto fully explain the observed history of drought on decadal time \r\nscales (Seager and Hoerling, 2014; Seager and Ting, 2017). From \r\nroughly 1980 to the present, the regional climate signals show an \r\ninteresting mix between forced and internal variability. Lehner et al. \r\n(2018) used a dynamical adjustment method and large ensembles \r\nof coupled and SST-forced atmospheric experiments to suggest \r\nthat the observed south-western North America rainfall decline \r\nmainly results from the effects of atmospheric internal variability, \r\nwhich is in part driven by a PDV-related phase shift in Pacific SST \r\naround 2000 (Figure 10.13b,c). Based upon four SMILEs (three using \r\na  GCM and another one an AGCM constrained by observed SSTs) \r\nand a  CMIP6 multi-model suite constrained by observed external \r\nforcings, Figure 10.13 shows, in agreement with Lehner et al"


id: 4c66f7416a7187d896bde552f710b93f
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
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vector_id: 9446
content:
" (2018), \r\nthat observed SSTs with their associated atmospheric response are \r\nthe main drivers of the south-western North America precipitation \r\ndecrease during the 1983-2014 period. Once aspects of the internal \r\nvariability are removed by dynamical adjustment, the observed \r\nprecipitation change signal and simulated anthropogenically-forced \r\ncomponents look more similar (Lehner et al., 2018)."


id: b23f8ecf74fb9eedeb9946d9dd2be66c
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
content_type: text
index: climate_gpt_only_giec
vector_id: 25549
content:
"The observed south-western North America drying fits the narrative \r\nof what might happen in response to increasing GHG concentrations \r\ndue to a  poleward expansion of the subtropics, that is conducive \r\nto drying trends over subtropical to mid-latitude regions (Hu et al., \r\n2013b; Birner et  al., 2014; Lucas et  al., 2014). However, several \r\nstudies based on modern reanalyses and CMIP5 models have \r\nrecently shown that the current contribution of GHGs to Northern \r\nHemisphere tropical expansion is much smaller than in the Southern \r\nHemisphere and will remain difficult to detect due to large internal \r\nvariability, even by the end of the 21st  century (Section  3.3.3.1; \r\nGarfinkel et al., 2015; Allen and Kovilakam, 2017; Grise et al., 2018, \r\n2019). In addition, the widening of the Northern Hemisphere tropical \r\nbelt exhibits strong seasonality and zonal asymmetry, particularly \r\nin autumn and the North Atlantic (Amaya et al., 2018; Grise et al., \r\n2018)"


id: c2f49411cd96a0f039161e0f729c0953
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
content_type: text
index: climate_gpt_only_giec
vector_id: 28253
content:
" Therefore, it seems that the recent Northern Hemisphere \r\ntropical expansion results from the interplay of internal and forced \r\nmodes of tropical width variations and that the forced response \r\nhas not robustly emerged from internal variability (Sections 3.3.3.1 \r\nand 10.4.3)."


id: cbdb058b7c1d5cdca8af92549ce64643
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
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index: climate_gpt_only_giec
vector_id: 29611
content:
"variables and space-time co-variance are fully preserved in the \r\ntime-shift approach, which is not the case for pattern-scaling (Herger \r\net  al., 2015). Importantly, pattern scaling cannot account for the \r\nnon-linearity arising from either interacting quasi-linear processes \r\n(Chadwick and Good, 2013) and purely non-linear mechanisms, \r\nwhich have been shown to be present in CMIP5 models for high \r\nGWL (4degC) and affect precipitation more than temperature at the \r\nregional-scale (Section 8.5.3.1; Good et al., 2015, 2016). The time-shift \r\napproach can also be used to test whether regional climate change \r\npatterns depend on the rate of global mean warming and external \r\nforcing pathways, in addition to global warming magnitude. A global \r\nevaluation of both approaches in projecting the forced temperature \r\nand precipitation response for a highly mitigated scenario based on \r\na moderately mitigated one has been performed using a perfect-model \r\nframework (Tebaldi and Knutti, 2018)"


id: 8adebd461f270bd468c25bd1177218b2
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
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vector_id: 19386
content:
" The amplitude of errors for \r\nboth approaches appears to be substantially smaller than model \r\nuncertainty approximated by the CMIP5 multi-model spread."


id: 1a68d6915e88ec3226ca6601fe9c975b
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
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vector_id: 1631
content:
"Based on large and coordinated modelling exercises such as CMIP5 \r\nand CORDEX, the time-shift approach has been largely used to \r\nassess differences in regional climate impacts for different GWLs, \r\nwith a  strong focus on 1.5degC versus 2degC (Karmalkar and Bradley, \r\n2017; Dosio and Fischer, 2018; Karnauskas et al., 2018; W. Liu et al., \r\n2018; Taylor et  al., 2018; Weber et  al., 2018; Chapter  3, SR1.5, \r\nHoegh-Guldberg et al., 2018). Comparisons between pattern-scaling \r\nand time-shift approaches allow assessment of the scalability \r\nof the regional climate change signal and the extent to which \r\npattern-scaling assumptions still hold at regional scale for a  wide \r\nrange of GWL. This was the approach followed by Matte et al. (2019) \r\nin their assessment of the scalability of European regional climate \r\nprojections"


id: 6a605e0296c56d6869f6443190b0d473
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
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vector_id: 14336
content:
" Based on EURO-CORDEX projections, they performed \r\na  detailed comparison between the pattern scaling and the GWL \r\nspatial patterns (GWL range: 1degC, 2degC and 3degC) for different seasons, \r\nregional model resolutions, and both temperature and precipitation. \r\nHigh pattern correlation values (greater than 0.9) are found between \r\nthe scaled pattern and all GWL patterns for temperature. In the case \r\nof precipitation, the correspondence is slightly lower, especially in \r\nsummer, for high GWLs (2degC and 3degC) and much lower for 1degC."


id: 9c417f6cb8570d125b9699afd5f8fa8d
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
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index: climate_gpt_only_giec
vector_id: 22138
content:
"Robust assessments and comparisons of past emergence between \r\nobservations and models are strengthened by the use of consistent \r\ndefinitions of signal and noise (Lehner et al., 2017a; Hawkins et al., \r\n2020). In the case of future emergence under increasing greenhouse \r\ngas emissions, two main approaches have been followed to assess \r\nemergence. The first is based on estimating the signal and noise (and \r\nsometimes the signal-to-noise ratio as well) in individual models \r\nbefore using the resulting distribution median or mean to construct \r\nthe final emergence metric (Hawkins and Sutton, 2012; Maraun, \r\n2013b; Sui et  al., 2014; Barrow and Sauchyn, 2019). The second \r\nmethod first estimates the signal as a  multi-model mean change \r\nand the noise variance as a combination of internal variability and \r\nmodel structural differences (Giorgi and Bi, 2009; Mariotti et  al., \r\n2015; Nguyen et al., 2018)"


id: d9ca65b0764f341530daf983d3aaded1
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vector_id: 31782
content:
" The first approach allows the definition \r\nof emergence of the signal relative to internal variability only and \r\ntreats model error as source of uncertainty (Maraun, 2013b; Lehner \r\net al., 2017a). The second assumes that the multi-model mean is the \r\noptimal estimate of the signal and confounds internal variability and \r\nmodel structural differences in the noise estimate. It is noteworthy \r\nthat most emergence studies implicitly assume model independence \r\n(Annan and Hargreaves, 2017; Boe, 2018; Box  4.1) and therefore \r\nsensitivity of emergence results to model selection or weighting is \r\nrarely performed (Akhter et al., 2018)."


id: 4dc28a158d2aa8cf37e41e454d688f6f
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
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index: climate_gpt_only_giec
vector_id: 9662
content:
"Precise definitions of signal and noise as well as a  metric to \r\nmeasure the relative importance of the signal are key ingredients \r\nof the emergence framework and depend on the framing question. \r\nIn particular, emergence study results can depend on the specific \r\ndefinitions of signal and noise such as the level of spatial and \r\ntemporal aggregation (McSweeney and Jones, 2013). For instance, \r\ngrid-point scale emergence will likely be delayed compared with \r\nregion-average emergence (Section  11.2.4 and Cross-Chapter \r\nBox Atlas.1, Figure 2; Fischer et al., 2013; Maraun, 2013b; Lehner et al., \r\n2017a). The signal is often estimated by a running mean multi-decadal \r\naverage or probability distribution function of the physical variable \r\nunder scrutiny in order to avoid false emergence due to manifestation \r\nof multi-decadal internal variability (King et al., 2015)"


id: 42c9e9b7cc6cd4e2f60bcd7e86385a30
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vector_id: 7901
content:
" In the case of \r\nextremes such as climate records, a notion of multi-year persistence \r\nor recurrence can also be used to fully characterize the anthropogenic \r\nsignal and its emergence (Christiansen, 2013; Bador et al., 2016)."


id: 1a8c75ba7fef8761a4e645f528a91244
created_at: 2023-03-16 14:02:07
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vector_id: 1657
content:
"This section assesses approaches and challenges for producing \r\nclimate information for climate risk assessments as well as for \r\nadaptation and policy decisions at regional scales (Section 10.1.2.1). \r\nAn overview of the different sources used for developing regional \r\nclimate information is given in Section 10.5.1. The role of the user \r\ncontext in the construction of climate information is assessed in \r\nSection 10.5.2. The distillation to combine multiple lines of evidence \r\nis assessed in Section 10.5.3. Finally, climate services in the context \r\nof regional climate information are assessed in Section 10.5.4. The \r\nrole of storylines in constructing climate information is assessed in \r\nBox  10.2. The assessment of how regional climate information is \r\ndistilled in the report is treated in Cross-Chapter Box 10.3, whereas \r\nthe assessment of information on regional, physical climate processes \r\nthat impact society or ecosystems, termed climatic impact-drivers \r\n(Section 10"


id: df6232c40645d3593f6c53ff7b2eaf4f
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vector_id: 32643
content:
"1), appears in Chapter 12, as well as more information \r\non climate services in Cross-Chapter Box 12.2."


id: e45bba18fba379906c08159ae7a260dd
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"We now illustrate the potential influence of internal variability on \r\nlate or lack of emergence for a few AR6 land regions (Figure 10.15b). \r\nFor each of these AR6 regions, the time evolution of the percentage \r\narea with robust annual mean precipitation change is estimated \r\nfor both the CMIP6 multi-model ensemble and the three coupled \r\nSMILEs used throughout Chapter 10. Similarity in percentage area \r\ntime evolution between CMIP6 and the three coupled SMILEs \r\nsuggests that internal variability can substantially influence the \r\ntiming of emergence. For example, internal variability could explain \r\nthe mid-21st  century emergence (percentage area greater than \r\n50%) of the drying and wetting signal over the Mediterranean and \r\nSouth Asia (see also Section  10.6.3) regions, respectively. Internal \r\nvariability can also contribute to the late and moderate emergence \r\nover South-Eastern South America (see also Section 10.4.2) and West \r\nSouth Africa (see also Section 10.6.2)"


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" In contrast, it cannot explain \r\nthe lack of robust changes (percentage area less than 30%) over \r\nWestern Africa at the end of the 21st century, suggesting that model \r\ndifferences are also contributing to the lack of emergence (Monerie \r\net al., 2017a, b). In addition to different forced signals, the differences \r\nof time evolution between the three SMILEs, in particular for African \r\nregions, point to the issue of global model performance in accurately \r\nrepresenting internal variability and its future changes. While \r\noverestimation and underestimation of internal variability in current \r\nmodels have been reported (Eade et al., 2014; Laepple and Huybers, \r\n2014), methodological challenges to assess the magnitude and \r\nspatial pattern of model biases in simulating internal variability, still \r\nremain (Section 10.3.4.3)"


id: 2bfcefc9532a7b019564f4b4b87d5321
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" Therefore, the existence of model biases \r\nand the limited knowledge of their characteristics lead to limitations \r\nabout a  precise quantification of internal variability influence on \r\ndelayed regional-scale emergence."


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"The rise in demand for relevant regional climate information \r\n(Hewitt et  al., 2012, 2020; Lourenco et  al., 2016) has resulted in \r\ndiverse approaches to produce it. Historically, the construction of \r\nclimate information has been embedded in a  linear supply chain: \r\nextracting the source data, processing into maps or derived data \r\nproducts, preparing the material for communication, and delivering \r\nto users (Section  10.1.4). Typical products are open-access, \r\nweb-portal delivery services of data (Hewitson et al., 2017), which \r\nmay also be implemented as commercialized climate services \r\n(Webber and  Donner, 2017). Such a  chain, although it is intended \r\nto meet a demand for regional climate information, contains many \r\nassumptions that are not obvious to the recipients and that may \r\nintroduce possible misunderstandings in the handover from one \r\ncommunity to the next (Meinke et al., 2006; Lemos et al., 2012)"


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" In \r\nrecognition that data is not necessarily relevant information, a new \r\npathway towards a  tailored distillation of climate information has \r\nemerged. The construction of information assessed in this section \r\ndraws on multiple sources (Figure  10.16), whereby the context \r\nframing for an application is addressed through co-design with \r\nusers. The constructed information is then translated into the context \r\nof the user taking into account the values of all actors involved \r\n(Sections 10.5.2 and 10.5.3, and Figure 10.1)."


id: 59c27d2967e7c9a226d272726749bf45
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"Figure  10.16 | Illustration of how using different sources can result in different and potentially conflicting information. Change in daily precipitation \r\n(2071-2100 RCP8.5 relative to 1981-2010) over Western Africa as simulated by an ensemble of regional climate models (RCMs) driven by global climate models (GCMs). \r\n(a) Change in daily precipitation (mm) for April to September, as mean of 17 CORDEX models (Dosio et al., 2020) (b-e) Time-latitude diagram of daily precipitation change for \r\nfour selected RCM-GCM combinations. For each month and latitude, model results are zonally averaged between 10degW-10degE (blue box in a). Different GCM-RCM combinations \r\ncan produce substantially different and contrasting results, when the same RCM is used to downscale different GCMs (b, d), or the same GCM is downscaled by different \r\nRCMs (d, e). GCM1=IPSL-IPSL-CM5A, GCM2=ICHEC-EC-EARTH, RCM1=RCA4, RCM2=REMO2009. Adapted from Dosio et al. (2020), CCBY4.0 https://creativecommons"


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"org/\r\nlicenses/by/4.0/.  Further details on data sources and processing are available in the chapter data table (Table 10.SM.11)."


id: e8b00aa9c524c4468ed18bee9f7e5d4c
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"Different sources of information may be more appropriate for \r\nsome purposes than others, as they may provide information better \r\naligned to the spatial and temporal scales of interest, in different \r\nformats, and tailored to different types of application. In some cases, \r\na  purpose may be best served using several types of information \r\ntogether. For example, when model data is the primary source, it can \r\nbe advantageous to employ data from multiple models or even from \r\na range of different experiment types (Section 10.3.2) supported by \r\nassessing how the models reflect changes in driving processes. In this \r\nmanner a purpose may be best served by seeking the congruence of \r\nseveral types of information together, though one needs to recognize \r\nhow well the attributes of each source align with the specific need \r\nfor information"


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" Depending on resources, one may even design model \r\nexperiments specifically for a given use, such as constructing physical \r\nclimate storylines of individual events (Section 10.3.2 and Box 10.2). \r\nSuch analyses may be complemented by event attribution studies \r\n(Section 11.1.4)."


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"The importance of a  co-production process does not preclude the \r\nclimate-research community from taking steps to develop and convey \r\nrelevant information on its own. Indeed, communicating expert \r\nconsensus about contested scientific issues is beneficial (Goldberg \r\net  al., 2019). Climate services (Section  10.5.4), in particular, can \r\nbecome an effective means for using sources from the climate \r\ncommunity and crafting these to be consistent with the needs, \r\ninterests and values of stakeholder communities. However, simply \r\npresenting more information without recognizing user values and \r\nthe contextual elements listed in Section 10.5.2.1 may be ineffective \r\n(Kahan, 2013). An aversion to climate information discordant with \r\none's pre-existing beliefs can actually become stronger for people \r\nwho are more scientifically literate: they feel more confident sifting \r\nthrough all sources of information to find support for their positions \r\n(Kahan, 2012)"


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" A  challenge is that if climate information is not \r\nframed carefully, recognizing context and user values, it may make \r\nthe sceptical person less receptive to further information about \r\nclimate change (Corner et al., 2012; Hart and Nisbet, 2012; Shalev, \r\n2015). A  further complication is that audiences may view climate \r\nchange as a problem distant in time and space (Spence et al., 2012), \r\ntoo threatening to acknowledge (Brugger et  al., 2015; McDonald \r\net  al.,  2015), or too economically challenging to accept (Bessette \r\net al., 2017). Identifying positive outcomes that align with user values, \r\ninstead of adaptation and mitigation efforts, appears to promote the \r\ninterest in and the success of climate information (Bain et al., 2012)."


id: f95c43ff1078adccc9bea873a0cb1a1c
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"relevant for decisions at the regional scale (Section  10.5.2; Kruk \r\net  al., 2017; Vizy and Cook, 2017; Djenontin and Meadow, 2018; \r\nParker and Lusk, 2019; Norstrom et al., 2020; Turnhout et al., 2020). \r\nAs a response, regional climate change information is increasingly \r\nbeing developed through participatory and context-specific dialogues \r\nthat bring together producers and users across disciplines and define \r\nclimate impacts as one of the many stressors shaping user decisions \r\n(Brown and Wilby, 2012; Lemos et  al., 2012). Although there are \r\nmultiple practical issues involving communication (Rossler et  al., \r\n2019a), such as providing data in a format that users can interpret, \r\nbeing mindful of the contextual issues raised in Section  10.5"


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"2 \r\nallows non-scientists to be involved in decisions about approaches \r\nand assumptions for the distillation and thus to take ownership of \r\nthe resultant information and to make informed decisions based on \r\nthe distilled information (Pettenger, 2016; Verrax, 2017). Importantly, \r\nthe application of transdisciplinary engagement processes that \r\nemphasize the role of non-scientists in the learning and knowledge \r\nproduction process builds relationships and trust between information \r\nusers and producers, which is arguably as important for the uptake \r\nof climate science into decision-making as the nature of the climate \r\ninformation itself (Section 10.5.2)."


id: f8ee16942ce73b1ddf35e70f460f6af6
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"Nevertheless, the scale of regional climate information does not \r\nhave to be the same as the decision scale. Physical-climate storylines \r\n(Box 10.2) valid at large scales can be used to develop understanding \r\nthat is relevant to local decisions. For example, global climate change \r\naffecting Antarctic ice-mass loss is relevant to formulating Dutch \r\nresponses to sea level rise (Haasnoot et  al., 2020). On the other \r\nhand, extreme precipitation processes can occur on scales of tens \r\nof kilometres and smaller and thus require high resolution climate \r\ninformation when projecting future changes (e.g., Xie et al., 2015). \r\nAn important factor for developing effective climate information \r\nusing the distillation process is aligning the vulnerabilities of the \r\nsocial and economic systems under consideration ranging from, for \r\nexample, those important to a farmer to those important to a national \r\nagricultural ministry (Andreassen et al., 2018; O'Higgins et al., 2019)"


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" \r\nThus, more sophisticated matching of spatial and temporal resolution \r\nof climate information with decision scales requires engagement \r\nacross a hierarchy of governance structures at national, regional and \r\nlocal level (e.g., Lagabrielle et al., 2018)."


id: 9881a978d906b2a5803c78a7c917459f
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"The preceding sections laid out the diversity of sources of climate \r\ninformation (Section  10.5.1) and important elements for its use \r\nin a  decision context (Section  10.5.2). Here, it is assessed how \r\ncontext-relevant climate information can be distilled from these \r\nsources of information. Although the term distillation lacks a clear \r\ndefinition in the literature, it has, in principle, two aspects: the \r\nconstruction of (potentially user-targeted) information that is \r\ndefensible and evidence-based (Giorgi, 2020), and the translation \r\nof this information into a  specific context, targeting a  specific \r\npurpose and set of values. The former typically involves data from \r\nmultiple sources, including expert knowledge, and comprehensively \r\nconsiders relevant uncertainties to give physically plausible climate \r\ninformation"


id: c0b83648aa27d1dcfdd7dc7b83a6d267
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" The latter translates the information explicitly into the \r\nuser context, such as by linking it to experience, by formulating \r\na  narrative, by highlighting the relevance for the user context, or \r\nby putting the climate information into the context of the relevant \r\nnon-climatic stressors."


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"The transdisciplinary interaction with stakeholders has been \r\ncategorized into top-down, bottom-up and interactive approaches \r\n(Berkhout et al., 2013). Traditional top-down approaches frame the \r\nresearch from the perspective of global climate change as a driver \r\nof regional climate risk. Bottom-up approaches, also referred to as \r\nscenario-neutral impact studies (Prudhomme et al., 2010; A. Brown \r\net al., 2012; C. Brown et al., 2012; Culley et al., 2016) begin with \r\nthe user's articulation of vulnerability in the context of climatic \r\nand non-climatic stressors, follow with the definition of key system \r\nthresholds of climatic variables, and only incorporate climate data \r\nto assess the likelihood of threshold exceedances. Bottom-up \r\napproaches are special cases of robust decision-making (Lempert \r\net al., 2006; Lempert and Collins, 2007; Walker et al., 2013; Weaver \r\net  al"


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", 2013), which are designed to account for uncertainties not \r\nrepresented by climate models as well as non-climatic stressors. \r\nInteractive approaches combine aspects of top-down and bottom-up \r\napproaches. The choice of approach depends on the context. While \r\nbottom-up approaches might be optimal in a local context, where \r\ncase-specific risks are addressed, top-down approaches provide \r\ngeneric information that may serve a  range of different purposes, \r\nfor example, at the national scale (Berkhout et al., 2013). All these \r\napproaches benefit from the integration of fully distilled climate \r\ninformation (Berkhout et al., 2013; Maraun and Widmann, 2018b)."


id: 76732ee1759667b4c484197e8ab10e5a
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"Awareness and understanding of the users' decision-making context \r\nis a central and key aspect of developing tailored, context-appropriate \r\ninformation (Briley et al., 2015), as clearly evidenced by the climate \r\nservices' experiences (e.g.,  Vincent et  al., 2018). Understanding \r\nthe context, however, is not trivial and requires understanding of \r\nboth the user and provider (Guido et  al., 2020) if the information \r\nis to be robust, reliable and relevant (Giorgi, 2020). Translating the \r\ninformation into context requires consideration of terminology and \r\nexpectations (Briley et al., 2015), issues of user interpretation (Daron \r\net al., 2015), and hence necessitating engagement in co-production \r\nwith all attendant challenges (Vincent et  al., 2021). The actual \r\nprovision of climate information may be conducted at different levels \r\nof sophistication, ranging from generic data provision via web portals \r\n(Hewitson et al"


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", 2017), potentially including impact-relevant climate \r\nindicators, region-specific factsheets and stakeholder reports, social \r\nmedia (Pearce et  al., 2019), to a  close engagement with specific \r\nstakeholders in co-exploring the research (Steynor et al., 2016)."


id: 7eda3348ea5d0ef134f12f207bbbb6b
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"Climate services have been defined as the provision of climate \r\ninformation to assist decision-making (Sections 1.2.3, and 12.6, \r\nand Cross-Chapter Box 12.2). Services are expected to be based on \r\nscientifically credible information and expertise, have appropriate \r\nengagement from users and providers, have an effective access \r\nmechanism and aim at meeting the users' needs (Hewitt et  al., \r\n2020). To achieve this, climate services synthesize context-relevant \r\nclimate information addressing questions for a wide range of climate \r\ntime scales. From this point of view, climate services are instruments \r\nfor the production, translation and transfer of climate information \r\nand knowledge for their use in climate-informed decision-making \r\nand climate-smart policy and planning (Hewitt et  al., 2012)"


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" The \r\nappropriate provision of climate services considers the diagnosis of \r\nclimate information needs, the service itself and a number of good \r\npractices still under development (Vaughan et al., 2018)."


id: 7fc6d24975e03ae84b801090efe488ef
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"There is high confidence that distilling climate information for \r\na  specific purpose benefits from a  co-production process that \r\ninvolves users of the information, considers the specific user context \r\nand the values of relevant actors such as users and scientists, and \r\ntranslates the resultant information into the broader user context. \r\nThis process allows users to take ownership of the information, builds \r\nrelationships and trust between information users and producers \r\nand helps to overcome barriers in the information construction. This \r\nprocess enhances trust in the information as well its usefulness, \r\nrelevance, and uptake, especially when the communication involves \r\ncomplex, contextual details (high confidence)"


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" The optimal approach \r\nfor the transdisciplinary collaboration with users depends on the \r\nspecific context conditioned by the sources available and the actors \r\ninvolved, which together are dependent on the regions considered \r\nand the framing by the question being addressed."


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"Physical climate storylines are self-consistent and plausible unfolding of a physical trajectory of the climate system, or a weather or \r\nclimate event, on time scales from hours to multiple decades (Section 1.4.4.2). Storylines that condition climatic features and processes \r\non a set of plausible but distinct large-scale climatic changes enables the exploration of uncertainties in regional climate projections \r\n(Box 10.2, Figure 1 and Section 10.3.4.2). For instance, Zappa and Shepherd (2017) condition projected changes in European surface \r\nwind speeds on different plausible projections of tropical upper tropospheric warming and the polar vortex strength in the CMIP5 \r\nmulti-model ensemble. Storylines of specific events are generated to explore the unfolding and impacts of comparable events in \r\ncounterfactual climates (Lackmann, 2015; Meredith et al., 2015b; Takayabu et al., 2015; Hegdahl et al., 2020; Sillmann et al., 2021)"


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" Those \r\nevent storylines can be based on pseudo-global warming studies (Lackmann, 2015; Meredith et al., 2015b; Takayabu et al., 2015; see \r\nSection 10.3.2.2), selected and possibly downscaled events from long-term climate projections (Hegdahl et al., 2020; Huang et al., 2020a),"


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"Storylines can also be used to communicate climate information by narrative elements describing the main climatological features and \r\nthe relevant consequences in the user context (Flottum and Gjerstad, 2017; Moezzi et al., 2017; Dessai et al., 2018; Scott et al., 2018; \r\nJack et al., 2020). Co-produced narratives have been demonstrated to enhance knowledge integration in decision-making contexts \r\n(e.g., de Bruijn et al., 2016). Narrative elements have also been employed to convey information from climate models (Corballis, 2019). \r\nJack et al. (2020) introduced the concept of climate risk narratives and developed a set of principles, such as using present tense in \r\ntheir presentation to avoid the effects of future discounting and writing individual narratives without uncertainty language to assume \r\nan imagined observer perspective"


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" From this point of view, event storylines are particularly useful for communication purposes as they \r\nlink to the experience and episodic memory of stakeholders (Schacter et al., 2007; Steynor et al., 2016; Shepherd et al., 2018)."


id: 4ea1fb6a1dff975f78e04b639b6e644a
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"Box 10.2, Figure 1 | Schematic of two types of physical climate storylines with a particular climate impact of concern (red). The storylines are \r\ndefined by specified elements (dark blue). Variable elements (light blue) are simulated conditional on the specified elements. The white elements are 'blocked' since \r\ntheir state does not need to be known to determine the light blue elements. Other types of storylines could be defined by specifying other elements (e.g., storylines \r\nof different climate sensitivities or different representative concentration pathways). (a) Event storyline, where the particular dynamical conditions during the event \r\nas well as the regional warming are specified and control the hazard arising from the event. (b) Dynamical storyline, where the global warming level and remote \r\ndrivers are specified and control the long-term changes in atmospheric dynamics and regional warming"


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" In both storylines, the impact is also conditioned on specified \r\nexposure and vulnerability. Figure adapted from Shepherd (2019)."


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"Multiple, sometimes contrasting, lines of evidence are derived from the various data sources, methodologies and approaches \r\nthat can be used to construct climate information (Section 10.5 and Figure 10.1). Such data sources and methodologies include \r\ntheoretical understanding of relevant processes, drivers and feedbacks of climate at regional scale, observed data from multiple \r\ndatasets (e.g., ground station networks, satellite products, reanalysis, etc.), simulations from different model types (including general \r\ncirculation models (GCMs), regional climate models (RCMs), statistical downscaling methods, etc.) and experiments (e.g"


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", Coupled \r\nModel Intercomparison Project Phases 5 and 6 (CMIP5 and 6), Coordinated Regional Climate Downscaling Experiment (CORDEX), and \r\nsingle-model initial-condition large ensembles), methodologies to attribute observed changes or events to large- and regional-scale \r\nanthropogenic and natural drivers and forcings as well as other relevant local knowledge (e.g., indigenous knowledge)."


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"The information is then evaluated against its fitness-for-purpose, for example, whether it is adequate to provide robust evidence to \r\nderive an assessment. In the case of observed data, issues to be considered include (but are not limited to): spatial and temporal \r\nresolution, accuracy, gaps in the recorded data, homogeneity in the station network, uncertainty treatment, etc. (Sections 10.2, 11.2, \r\n11.9, 12.4; Atlas.1.4). In the case of modelled data, an assessment of the fitness-for-purpose typically includes an evaluation of \r\nnumerical or statistical methods adopted, adequate representation of the physical processes, forcings and feedbacks relevant for the \r\nregion and the change under consideration, the availability of adequate ensembles to assess the interplay between forced response \r\nand internal variability and the uncertainty in future projections (Sections 10.3, 10.4, 11.2, 11.9, 12.4 and Chapter Atlas)"


id: e6f4cb3a79dd2b159a35d0c391998e1d
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" Attribution \r\nassessments are usually based on models and observations for which the fitness-for-purpose is assessed with similar criteria as \r\nthose described above (Cross-Working Group Box: Attribution in Chapter 1). The assessment is made either directly or indirectly by \r\nscrutinizing the data and methods of the relevant literature against the criteria listed above."


id: f3e6592f963301aca41e10cf3dcd035c
created_at: 2023-03-16 14:02:07
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"This section presents three comprehensive examples of steps for \r\ndistilling regional climate information from the multiple sources \r\nof regional climate information presented in this chapter. These \r\nexamples build on the general framework presented in Section 10.5, \r\nexamining in particular the strengths and challenges in linking the \r\ndifferent sources, while also exposing the assumptions behind and \r\nconsequences of decisions made in the process. The examples are \r\nframed taking into account societal perspectives that provide context \r\nfor their regional climate statements. Although the nature of an \r\nIPCC Working Group I assessment precludes engaging with users of \r\nclimate information (Section 10.5), we do cite relevant national and \r\nregional reports that give user perspectives to set a foundation from \r\nwhich one could distil climate information for users"


id: b7fb48d9aa2957ce1255f825101ec3c1
created_at: 2023-03-16 14:02:07
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content:
"Cape Town's crisis resulted from a  combination of a  strong, rare \r\nmulti-year meteorological drought (Figure  10.18), estimated at 1 in \r\n300 years (Wolski, 2018), and factors related to the nature of the water \r\nsupply system, operational water management and water resource \r\npolicies. Cape Town was very successful in implementing water-saving \r\nactions after the previous drought of 2000-2003, reducing water losses \r\nfrom over 22% to 15% (Frame and Killick, 2007; DWA, 2013), breaking \r\nthe previous coupling of growth in water demand with growth in \r\npopulation. As a  consequence, Cape Town won a Water Smart City \r\naward from the C40 Cities program only three years prior to the crisis. \r\nHowever, the water-saving actions, together with changing priorities \r\nin water resource provision from infrastructure-oriented towards \r\nresource and demand management, may well have led to delays \r\nin implementation of the expansion of water supply infrastructure \r\n(Muller, 2018)"


id: 8cdf486de45fcd91503135f0c44e5e02
created_at: 2023-03-16 14:02:07
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content:
" The expansion plan, formulated a decade prior to the \r\ncrisis, included an expectation of long-term climate-change drying in \r\nthe region (DWAF, 2007). The crisis also exposed structural deficiencies \r\nof water management and inadequacy of a policy process in which \r\ndecisions about local water resources are taken at a  national level, \r\nparticularly in a situation of political tension (Visser, 2018). The crisis \r\nwas widely seen as a harbinger of future problems to be faced by the \r\ncity, and a highlight of vulnerability of many cities in the world resulting \r\nfrom the interplay of three factors: (i) the fast urban-population growth, \r\n(ii) the economic, policy, infrastructural and water resource paradigms \r\nand constraints, and (iii) anthropogenic climate change."


id: edd5248b3279a80181af92c87062a451
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content:
"Cape Town's 'Day Zero' water crisis in 2018 threatened a shut-down \r\nof water supply to 3.4 million inhabitants of the city and resulted in \r\ndomestic water use restriction of 50 litres per person per day lasting for \r\nnine months (pre-drought unconstrained water use was about 170 litres \r\nper person per day, DWA, 2013), punitive water tariffs, and temporary \r\nclosure of irrigation systems. Problems with water supply in many \r\nlarge cities in developing countries are endemic and rarely reported \r\ninternationally. The water crisis in Cape Town attracted considerable \r\ninternational attention to a city with functional government structures, \r\nwell-developed services (compared to other urban centres in Africa), \r\na centre of international tourism, and an economic hub with GDP of \r\n22 billion USD (about 7,500 USD per capita, Gallie et al., 2018). Economic \r\nand social impacts of the crisis were significant"


id: f21b7e23415dd596c090d6455a298c77
created_at: 2023-03-16 14:02:07
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" Loss of revenue for \r\ncompanies of all sizes resulted not only from the scaling down of water\u0002dependent activities, but also from the need to invest in water-efficient \r\ntechnologies and processes. Tourism was affected through reduced \r\narrivals and  bookings, although only temporarily (CTT, 2018). In the \r\nagricultural sector, 30,000 people were laid-off and production dropped \r\nby 20% (Piennaar and Boonzaaier, 2018). The crisis initially polarized \r\nsociety, with conflict emerging between various water users and erosion \r\nof trust in the government, but eventually social cohesion and an acute \r\nawareness of limited water resources emerged (Robins, 2019)."


id: bd68b3ad52e18112b1a869e03cfd3702
created_at: 2023-03-16 14:02:07
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content:
"Figure 10.18 | Historical and projected rainfall and Southern Annular Mode (SAM) over the Cape Town region. (a) Yearly accumulation of rainfall (in mm) \r\nobtained by summing monthly totals between January and December, with the drought years 2015 (orange), 2016 (red), and 2017 (purple) highlighted in colour. (b) Monthly \r\nrainfall for the drought years (in colour) compared with the 1981-2014 climatology (grey line). Rainfall in (a) and (b) is the average of 20 quality controlled and gap-filled \r\nseries from stations within the Cape Town region (31degS-35degS, 18degW-20.5degW). (c) Time series of the SAM index and of historical and projected rainfall anomalies (%, baseline \r\n1980-2010) over the Cape Town region. Observed data presented as 30-year running means of relative total annual rainfall over the Cape Town region for station-based \r\ndata (black line, average of 20 stations as in (a) and (b), and gridded data (average of all gridcells falling within 31degS-35degS, 18degW-20"


id: cd24b44a304d81cb0dfac878d4c5639d
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content:
"5degW), GPCC (green line) and CRU TS \r\n(olive line). Model ensemble results presented as the 90th-percentile range of relative 30-year running means of rainfall and the SAM index from 35 CMIP5 (blue shading) and \r\n35 CMIP6 (red shading) simulations, 6 CORDEX simulations driven by 1 to 10 GCMs (cyan shading), 6 CCAM (purple shading) simulations from individual ensemble members, \r\nand 50 members from the MIROC6 SMILE simulations (orange shading). The light blue, dark red and yellow lines correspond to NCEP/NCAR, ERA20C and 20CR, respectively. \r\nThe SAM index is calculated from sea level pressure reanalysis and GCM data as per Gong and Wang (1999) and averaged over the aforementioned bounding box. CMIP5, \r\nCORDEX and CCAM projections use RCP8.5, and CMIP6 and MIROC6 SMILE projections use SSP5-8.5. (d) Historical and projected trends in rainfall over the Cape Town region \r\nand in the SAM index. Observations and gridded data processed as in (c)"


id: 413dfc41a4e15b3e5f6d251ad76e8d36
created_at: 2023-03-16 14:02:07
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content:
" Trends calculated as Theil-Sen trend with block-bootstrap confidence interval estimate. Markers show \r\nmedian trend, bars 95% confidence interval. Global models in each CMIP group were ordered according to the magnitude of trend in rainfall, and the same order is maintained \r\nin panels showing trends in the SAM. Further details on data sources and processing are available in the chapter data table (Table 10.SM.11)."


id: 29138afa0f91c77a7a3b43060eb52e4d
created_at: 2023-03-16 14:02:07
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vector_id: 3889
content:
"The Cape Town regional rainfall is also potentially linked to other \r\nhemispheric phenomena, such as the expansion of the tropics and, \r\nspecifically, the South Atlantic high-pressure system and the position \r\nof the subtropical jet, which share some variability with the SAM. \r\nThe relationships between these phenomena and Cape Town rainfall \r\nhave not been thoroughly investigated outside of the context of \r\nthe 2015-2017 drought, but the drought itself was associated \r\nwith poleward expansion of the subtropical anticyclones in the \r\nSouth Atlantic and South Indian oceans and (a resulting) poleward \r\ndisplacement of the moisture corridor across the South Atlantic \r\n(Sousa et al., 2018a), as well as a weaker subtropical jet (Mahlalela \r\net al., 2019). Burls et al. (2019) also link the decline in the number of \r\nrainy days to the increase in sea level pressure along the poleward \r\nflank of the South Atlantic high-pressure system and the intensity \r\nof the post-frontal ridging high"


id: 2bddbbbd7ef2ae114acfa3a40395b36e
created_at: 2023-03-16 14:02:07
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content:
" Additionally, there is a  possible \r\nlinkage between Cape Town rainfall and near-shore cold sea surface \r\ntemperature (SST) anomalies arising from Ekman upwelling due to \r\nreduced westerly and increased south-easterly winds. These might \r\nlead to suppression of convection and reduction of rainfall over \r\nland (Rouault et  al., 2010). All these phenomena are conceptually \r\nconsistent with the poleward migration of the westerlies and \r\nexpansion of the tropics."


id: 3dfe894d75d90193f18d56680e00a963
created_at: 2023-03-16 14:02:07
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content:
"Because the primary rainfall mechanism is frontal rain, the most \r\nrelevant large-scale drivers are those that affect cyclogenesis, \r\nfrontogenesis and the mid-latitude westerlies' latitudinal position \r\nand moisture supply. These drivers and, thus, the region's rainfall \r\nare linked to the Antarctic Oscillation (AAO; Reason and Rouault, \r\n2005) or Southern Annular Mode (SAM), the dominant monthly and \r\ninterannual mode of Southern Hemisphere atmospheric variability, \r\nand a  measure of the pressure gradient between mid- and high \r\nlatitudes. (See Sections 3.3, 3.7, 4.3 and Annex IV.2.2 for more general \r\ndiscussion of the SAM.) While in the post-1930 period, the SAM \r\ndisplays a long-term positive trend, the Cape Town region's rainfall \r\ndoes not, and only the post-1979 trends of rainfall and SAM  are \r\nconceptually consistent. For example, a  positive trend in the SAM \r\nis associated with a negative trend in rainfall (Section 10.6.2.5 and \r\nFigure 10.18)"


id: 44cf86659e7d4c7a5834835a62ac45c4
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content:
" There is also good agreement between the seasonality \r\nof the SAM and rainfall trends in the post-1979 period: a drying trend \r\nappears strongly in December to February and March to May, but not \r\nin June to August and September to November (Wolski et al., 2021), \r\nand trends in the SAM have similar seasonal dependence (E.-P. Lim \r\net al., 2016; Section 3.7.2). Additionally, there is a similar seasonal \r\npattern in the post-1979 trends in indices capturing the southern \r\nedge of the Hadley circulation (Grise et al., 2018)."


id: 2f6774af6ccc550c838d0b12599858f6
created_at: 2023-03-16 14:02:07
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content:
"simulations (Figure  10.18; Almazroui et  al., 2020c). The consistency \r\nacross the models is a robust signal compared to the rest of southern \r\nAfrica, where the climate change signal varies spatially: stronger drying \r\nin the west and moderate drying or weak wetting in the east (DEA, \r\n2013, 2018; see Atlas.4.4 for further discussion of southern Africa \r\nprecipitation projections). Rainfall changes projected for the Cape \r\nTown region are consistent with projected changes in hemispheric\u0002scale processes and regional-scale dynamics that point toward reduced \r\nfrequency of frontal systems affecting that  region. These changes \r\ninclude robust signals in CMIP5 models for the Southern Hemisphere \r\nfor a poleward expansion of the tropics (Hu et al., 2013b), poleward \r\ndisplacement of mid-latitude storm tracks (Chang et al., 2012), increased \r\nstrength and poleward shift of the westerly winds (Bracegirdle et al., \r\n2018) and subtropical jet-streams (Chenoli et  al"


id: bf61cef0dc56d9f503f80caf3cb1853b
created_at: 2023-03-16 14:02:07
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vector_id: 27666
content:
", 2017), and a  shift \r\ntoward a  more positive phase of the SAM (E.-P. Lim et  al., 2016). \r\nHowever, despite the consistency in circulation changes, the emergence \r\nof anthropogenic rainfall change above unforced variability in West \r\nSouthern Africa remains uncertain for annual rainfall throughout most \r\nof the 21st century, even under SSP5-8.5 (Figure 10.15)."


id: bb7aac96dd847bcd70693c705fa2b9ba
created_at: 2023-03-16 14:02:07
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vector_id: 27057
content:
"Paleoclimate studies reveal that long-term variability in the winter \r\nrainfall region of South Africa (including Cape Town) is consistent with \r\na general framework of warming/cooling-induced latitudinal migration \r\nof the westerlies and transformation of the subtropical high-pressure \r\nbelt and associated hemispherical processes (see  section  10.2.3.2 \r\nfor assessment of paleoclimate analysis). The synchronicity of winter \r\nrainfall with Antarctic ice-core-derived polar temperature anomalies is \r\nconsistently revealed in studies using different paleoclimate proxies and \r\ntime scales of 1400 years (Stager et al., 2012), about 3000 years (Hahn \r\net al., 2016) and 12,000 years (Weldeab et al., 2013)"


id: 397a61cc2eba17f19d4a32a0ca003c5e
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vector_id: 6418
content:
" Changes in rainfall \r\nregimes at shorter (decadal) time scales appear to reflect influence \r\nof local processes such as the Agulhas current's interaction with the \r\nAtlantic, resulting in changes in SST and coastal upwelling, as well as \r\nmodification of the wind tracks by topography (Stager et al., 2012)."


id: 106b4ef827d68a36bbc90b3b97aa1326
created_at: 2023-03-16 14:02:07
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content:
"Due to the small scale of the Cape Town region, robust comparison \r\nof CMIP simulations to observations is difficult. However, in general, \r\nCMIP5 models capture the seasonality well, such as the dominance \r\nof austral winter rains, although they overestimate the peak and \r\nunderestimate the shoulder season rainfall (Mahlalela et  al., \r\n2019). Trends in rainfall are particularly difficult to assess as they \r\nare generally weak and depend strongly on the time period and \r\ndataset adopted for the analyses (Section 10.6.2.3). A multi-method \r\nattribution study (Otto et al., 2018) estimates the probability of the \r\n2015-2017 drought to have increased by a  factor of three since \r\npre-industrial times (with a wide 95% confidence interval of 1.5 to 6)"


id: 66cc2b81efc2e316e8b5e66feb4f9c46
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content:
" \r\nHowever, throughout the 20th century, a substantial portion of the \r\nglobal models (about 36% of CMIP5 and 44% of CMIP6 models, as \r\nwell as many of the MIROC SMILE members) simulate a statistically \r\nsignificant (95% level) decline in total annual rainfall, while there is \r\nno robust long-term trend in observations (Figure 10.18). Section 10.4 \r\noffers a more detailed assessment of attribution challenges."


id: 55119dd7f9580335139b84c8cbb8a21d
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content:
"Dynamical downscaling studies implemented with a stretched-grid \r\nmodel (Engelbrecht et al., 2009) revealed a signal compatible with \r\nthe driving CMIP5 ensemble, that is, consistent drying throughout \r\nthe region, amplifying in time, irrespective of the considered \r\nemissions scenario and the generation of global models (DEA, 2013, \r\n2018). A  multi-model CORDEX ensemble indicates a  robust signal \r\nof reduction of total annual rainfall in the future, although there is \r\nless agreement on how changes in rainfall occurrence may evolve \r\nin the region, such as through fewer consecutive rain days or longer \r\ndry spells (Abiodun et al., 2017; Maure et al., 2018). For the end of \r\nthe century under RCP8.5, Dosio et  al. (2019) also found drying. \r\nMoreover, in their analysis, the drying is associated with an increase \r\nin the number of consecutive dry days and a reduction in number of \r\nrainy days"


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created_at: 2023-03-16 14:02:07
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content:
" Their results are consistent with the driving global models \r\nfor all the precipitation indices, and they are robust independent of \r\nthe choice of the regional climate model (RCM) or global model. \r\nHowever, collectively, these analyses indicate that uncertainty \r\nremains in the characteristics of the precipitation decrease."


id: d407cdec1816cdbf7e52afd7b1004279
created_at: 2023-03-16 14:02:07
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"Evidence from paleoclimate records (Sections 8.3.2.4.1) shows high \r\nconfidence in a weakened Indian monsoon during cold epochs of the past \r\nsuch as the Younger Dryas (12,800-11,600 years ago) as measured by \r\nspeleothem oxygen isotopes (Kathayat et al., 2016). There is a pressing \r\nneed to understand if the monsoon will change in the future under \r\nanthropogenic forcing and to quantify such changes. Multiple datasets \r\nhave shown robust negative trends since the 1950s until the turn of the \r\ncentury (Bollasina et al., 2011) followed by a recovery (Jin and Wang, \r\n2017), yet repeated assessments project the monsoon to increase in \r\nstrength under enhanced GHG forcing (Christensen et al., 2007, 2013; \r\nSections 8.3.2.4.1 and 8.4.2.4.1). The apparent contradiction between \r\nfuture projections and observed historical trends makes the region an \r\nideal choice for an in-depth assessment"


id: 3113a278f106e40cb147586c7b8a71a4
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" The reader is also referred to \r\nthe South Asia (SAS) regional assessment of precipitation extremes \r\n(Section 11.9), which is not discussed here for brevity."


id: 6963f13945421c4faca8cee24b990052
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content:
"Stronger Northern Hemisphere aerosol emissions cool it relative to \r\nthe Southern Hemisphere, increasing northward energy transport \r\nat the expense of moisture transport towards India (Bollasina et al., \r\n2011). The attribution to anthropogenic aerosols is supported in CMIP5 \r\nsingle-forcing experiments, including some testing the sensitivity to \r\nlocal and remote emissions (Guo et al., 2015, 2016; Shawki et al., 2018), \r\ncomparing CMIP5 GCMs forced by both aerosol and GHG to GHG \r\nonly (Salzmann et al., 2014) and reducing emissions to pre-industrial \r\nlevels (Takahashi et al., 2018). The large spread between individual \r\nmodel realisations of comparable magnitude to the aerosol-induced \r\nsignal suggested to Salzmann et  al. (2014) that internal variability \r\nmay also play a  role over regions such as northern-central India. \r\nFurther uncertainty surrounds the level of radiative forcing. Dittus \r\net al. (2020) forced a GCM with historical aerosol emissions scaled \r\nbetween 0.2  and 1"


id: 46d641d0f86a30d2bd328dad6c3af079
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content:
"5 times their observed values, representing the \r\nspread in CMIP5 effective radiative forcing. The strongest forcing led \r\nto around 0.5 mm day-1 less late-20th century Indian monsoon rainfall \r\nthan the weakest (Shonk et  al., 2020). Meanwhile, the uncertainty \r\nsurrounding aerosol-cloud interactions could change the sign of \r\nlong-term precipitation trends (Takahashi et al., 2018)."


id: 56c00edfbd7466a99e6f8354b18dacd6
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content:
"before a rainfall recovery (Krishnan et al., 2016). By holding aerosol \r\nemissions at 2005 levels, lower monsoon rainfall is found throughout \r\nthe 21st  century than in a  standard RCP8.5 scenario (Zhao et  al., \r\n2019), suggesting that the timing of the recovery will be partially \r\ncontrolled by the rate at which aerosol emissions decline. The spread \r\nin spatial distribution of aerosol emissions in SSPs may also play \r\na role in near-term projections (Samset et al., 2019). Under divergent \r\nair-quality policies, SSP3 features a  dipole of declining sulphate \r\nemissions for China but increases over India, leading to suppression \r\nof GHG-related precipitation increases there (Wilcox et  al., 2020). \r\nFor the near-term future around the mid-21st century, the interplay \r\nbetween internal variability and external forcing must be considered \r\n(Singh and AchutaRao, 2019). Huang et al"


id: 856155636ae213a30badfb17c72f5718
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content:
" (2020a) used two SMILEs \r\nto show that internal variability related to PDV could potentially \r\novercome the GHG-forced upward trend in Indian monsoon rainfall, \r\nconsistent with assessments of the global monsoon for the near \r\nterm (Section  4.4.1.4). Emergence of the anthropogenic signal for \r\nSouth Asian precipitation is shown from the 2050s onwards in CMIP6 \r\n(Figure 10.15b)."


id: b18dcfa398e22852e2eeb9349777115b
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content:
"Figure 10.19 (continued): (a) Observational uncertainty demonstrated by a snapshot of rain-gauge density (% of 0.05deg subgrid boxes containing at least one gauge) \r\nin the APHRO-MA 0.5deg daily precipitation dataset for June to September 1956. (b) Multi-model ensemble (MME) mean bias of 34 CMIP6 models for June to September \r\nprecipitation (mm day-1) compared to CRU TS observations for the 1985-2010 period. (c) Maps of rainfall trends (mm day-1 per decade) in CRU TS observations (1950-2000), \r\nthe CMIP6 MME-mean of SSP5-8.5 future projections for 2015-2100 (34 models), the CMIP6 hist-GHG and hist-aer runs, both measured over 1950 to 2000. (d) Low-pass \r\nfiltered time series of June to September precipitation anomalies (%, relative to 1995-2014 baseline) averaged over the central India box shown in panel (b). The averaging \r\nregion (20degN-28degN, 76degE-87degE) follows other works (Bollasina et al., 2011; Jin and Wang, 2017; Huang et al., 2020b)"


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content:
" Time series are shown for CRU TS (brown), GPCC (dark \r\nblue), REGEN (green), APHRO-MA (light brown) observational estimates and the IITM all-India rainfall product (light blue) in comparison with the CMIP6 mean of 13 models \r\nfor the all-forcings historical (pink) the aerosol-only (hist-aer, grey) and greenhouse gas-only (hist-GHG, blue). Dark red and blue lines show low-pass filtered MME-mean \r\nchange in the CMIP6 historical/SSP5-8.5 (34 models) and CMIP5 historical/RCP8.5 (41 models) experiments for future projections to 2100. The filter is the same as that \r\nused in Figure 10.11 (d). To the right, box-and-whisker plots show the 2081-2100 change averaged over the CMIP5 (blue) and CMIP6 (dark red) ensembles. Note that some \r\nmodels exceed the plotting range (CMIP5: GISS-E2-R-CC, GISS-E2-R, IPSL-CM5B-LRl and CMIP6: CanESM5-CanOE, CanESM5 and GISS-E2-1-G)"


id: cac600c7f82bad63fb492a47f70d17f0
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
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vector_id: 29464
content:
" (e) Precipitation linear trend \r\n(% per decade) over Central India for historical 1950-2000 (left) and future 2015-2100 (right) periods in Indian Monsoon rainfall in observed estimates (black crosses), the \r\nCMIP5 historical-RCP8.5 simulations (blue), the CMIP6 ensemble (dark red) for historical all-forcings experiment and SSP5-8.5 future projection, the CMIP6 hist-GHG (light blue \r\ntriangles), hist-aer (grey triangles) and historical all-forcings (same sample as for hist-aer and hist-GHG, pink circles). Ensemble means are also shown. Box-and-whisker plots \r\nshow the trend distribution of the three coupled and the d4PDF atmosphere-only (for past period only) SMILEs used throughout Chapter 10 and follow the methodology used \r\nin Figure 10.6. (f) Example spread of trends (mm day-1 per decade) for the period 2016-2045 in RCP8.5 SMILE experiments of the MPI-ESM model, showing the difference \r\nbetween the three driest and three wettest trends among ensemble members over central India"


id: 9a033c9e595b80b56eb2f0578662c7d4
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
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vector_id: 21757
content:
" All trends are estimated using ordinary least-squares regression. Further details \r\non data sources and processing are available in the chapter data table (Table 10.SM.11)."


id: 36a227b0d610ffb24e53c937f8d4823f
created_at: 2023-03-16 14:02:07
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vector_id: 5984
content:
"consistent improvement other than for spatial patterns (e.g., rainfall \r\nclose to better-resolved orography); some characteristics were made \r\nworse. Both the rainfall pattern and its bias were worsened in CORDEX \r\ncompared to CMIP5 in Mishra et al. (2018). In contrast, Varikoden \r\net  al. (2018) found improved representation of historical rainfall \r\npatterns, such as over the Western Ghats mountains (consistent with \r\nSingh et  al., 2017), reducing the dry bias; improvements were not \r\nfound over the northern plains, which are dominated by synoptic \r\nvariability known as monsoon depressions. Similarly, Sabin et  al. \r\n(2013) compared a  uniform 1deg resolution model ensemble with \r\nanother zoomed to about 35 km over South Asia. Local zooming \r\nimproved simulation of orographic precipitation and the monsoon \r\ntrough. For the future, a  surrogate approach (like pseudo-global \r\nwarming, see Section  10.3.2"


id: 30cdaf83d24462bcdc3b7efb5a7c9027
created_at: 2023-03-16 14:02:07
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vector_id: 5101
content:
"2) was used in an RCM to test the \r\nimpacts of warming or moistening on monsoon depressions (Sorland \r\nand Sorteberg, 2016; Sorland et  al., 2016). The depressions  are \r\nfound to give more rainfall in future, dominated by strengthened \r\nsynoptic circulation from the warming perturbation. By forcing an \r\nRCM with a  perturbed parameter ensemble of a  GCM, Bal et  al. \r\n(2016) made projections under SRES A1B for the 2020s, 2050s and \r\n2080s. They noted increases in rainfall of 15-24% for India. Finally, \r\nevidence from a single CORDEX South Asia RCM showed a mixed \r\nsignal for changes in peak season rainfall under RCP2.6 and RCP8.5 \r\n(Ashfaq et al., 2021)."


id: 9442d51f8d4c44605dac2a1a2ccdf28e
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
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vector_id: 20837
content:
"While several of the above studies selected model subsets to \r\nconstrain future projections based on standard performance metrics \r\nof the historical period, such as pattern correlation and root-mean\u0002square error, Latif et al. (2018) included a performance measure based \r\non agreement with historical rainfall trends. This is an unproven \r\nconstraint for regional projections (Section  10.3.3.9), since the \r\n20th-century rainfall trend over India is assessed to have been driven \r\nchiefly by aerosol and other factors such as PDV (Sections 8.3.2.4.1 \r\nand 10.6.3.5), while the dominant late-21st century forcing is GHG \r\nemissions. Modern emergent-constraint techniques (Section 10.3.4.2) \r\nare being applied to the Indian monsoon such as G. Li et al. (2017), \r\nwho found that models with excessive tropical western Pacific rainfall \r\ntend to project a greater Indian monsoon rainfall change in future, \r\ndue to an exaggerated cloud-radiation feedback"


id: 9f36b3b59f0ce5ef6c10b1bfe2167a8c
created_at: 2023-03-16 14:02:07
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content:
"Statistical downscaling and other post-processing require calibration \r\nin historical conditions (e.g., Akhter et  al., 2019) and assessment \r\nof fitness-for-purpose (Section  10.3.3.9) before use for future \r\nprojections. Given the noted biases in GCM monsoon simulation \r\n(Section  10.6.3.5), Vigaud et  al. (2013) used a  variant of quantile \r\nmapping to bias adjust (Section  10.3.1.3.2 and Cross-Chapter \r\nBox 10.2) GCM outputs. For the historical period, the pattern, mean \r\nand seasonal cycle of rainfall versus the input GCMs were improved. \r\nIncreased future monsoon rain, albeit in older SRES A2 projections, \r\nwas found for southern India. Salvi et  al. (2013) used regression\u0002based perfect prognosis (Section 10.3.1.3.1) for the whole country at \r\n0.5deg resolution based on five ensemble members of a GCM in SRES \r\nscenarios. They noted increases over rainy regions of west coast and \r\nnorth-east India, but decreases in the north, west and south-east. \r\nMadhusoodhanan et  al"


id: 42fa5e35c64b03fce3ceb5be79edba36
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
content_type: text
index: climate_gpt_only_giec
vector_id: 7934
content:
" (2018) statistically downscaled 20 CMIP5 \r\nmodels to 0.05deg resolution. While the global models projected "


id: 4c52105d10704eebea973a7469ed818d
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
content_type: text
index: climate_gpt_only_giec
vector_id: 9436
content:
"The storylines terminology could be used to loosely describe \r\nthe interplay between internal variability and forced change \r\n(see Section 10.6.3.6), such as considering the difference between \r\ngroups of wettest and driest ensemble members of a SMILE for the \r\nnear-term future in Figure  10.19f. However, given the interest in \r\nlow-likelihood high-impact scenarios (Sutton, 2018), we can also \r\nconsider possible storylines for the Indian monsoon constructed \r\nfrom evidence in paleoclimate records and modelling. For example, \r\na  future AMOC collapse could cause reduced monsoon rainfall \r\n(Section  8.6.1; Liu et  al., 2017), offsetting increases expected \r\ndue to GHG. Large tropical volcanic eruptions are also known to \r\nweaken the Asian summer monsoon, in observations and model \r\nsimulations over the last millennium (Section 8.5.2.3; Zambri et al"


id: b36e9cdcc19e11804e6d57049795be6b
created_at: 2023-03-16 14:02:07
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vector_id: 25738
content:
", \r\n2017), although a hemispheric dependence is found, with Southern \r\nHemisphere eruptions even strengthening the monsoon around \r\nIndia (Zuo et al., 2019). Typically, future climate projections do not \r\nconsider plausible eruption scenarios and their mitigating effects \r\non greenhouse warming (see also Cross-Chapter Box 4.1). A single\u0002model ensemble (Bethke et al., 2017) demonstrates a future drier \r\nIndian monsoon relative to conditions in which volcanic eruptions \r\nare not considered, although the effects of GHG warming dominate \r\nbeyond the mid-term."


id: 92454b1611866eab7ca817ecae37b676
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
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index: climate_gpt_only_giec
vector_id: 20535
content:
"There is very high confidence (robust evidence, high agreement) of \r\na negative trend of summer monsoon rainfall over the second half \r\nof the 20th  century averaged over all of India. There is medium \r\nagreement over trends at the regional level owing to uncertainty \r\namong observational products, which hinders model evaluation, \r\ndownscaling and assessment of changes to extremes. There is high \r\nconfidence (robust evidence, medium agreement) that anthropogenic \r\naerosol emissions over the Northern Hemisphere and internal \r\nvariability have contributed to the negative trend, while there is \r\nhigh confidence (robust evidence, medium agreement) that Indian \r\nsummer monsoon rainfall will increase at the end of the 21st century \r\nin response to increased GHG forcing, due to the dominance of \r\nthermodynamic mechanisms. No contradictory evidence is found from \r\ndownscaling methods"


id: 51708ba53b7b15abfd8b988ebe584d9f
created_at: 2023-03-16 14:02:07
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vector_id: 10271
content:
" The contrast between declining rainfall in the \r\nobservational record and long-term future increases can be explained \r\nusing multiple lines of evidence. They are not contradictory since they \r\nare attributable to different mechanisms (primarily aerosols and \r\ngreenhouse gases, respectively). The long-term future changes are \r\ngenerally consistent across global (including at high resolution) and \r\nregional climate models, and supported by theoretical arguments. \r\nFurthermore, while there are subtle differences found in past periods \r\nwith a  climate similar to the future climate (the mid-Holocene), \r\ndifferent physical mechanisms at play suggest that paleoclimate \r\nevidence does not reduce confidence in the future projections. \r\nIn the near term, there is high confidence that internal variability \r\nwill dominate."


id: 5f270e7688043e72e32eeb939aa94729
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
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index: climate_gpt_only_giec
vector_id: 12526
content:
"The Mediterranean summer climate is affected by large-scale modes \r\nof natural variability, the most dominant being the NAO (Annex IV) \r\nin winter and the summer NAO in summer (Folland et al., 2009; Blade \r\net al., 2012), although regional differences exist. The influence of those \r\nmodes of variability over the eastern Mediterranean is recognized by \r\nsome studies (Chronis et al., 2011; Kahya, 2011; Black, 2012; Blade \r\net al., 2012), but disputed by others (Ben-Gai et al., 2001; Ziv et al., 2006; \r\nDonat et al., 2014; Turki et al., 2016; Zamrane et al., 2016; Han et al., \r\n2019). During positive summer NAO phase, associated with an upper\u0002level trough over the Balkans, the Mediterranean is anomalously wet \r\n(Blade et al., 2012). Drivers of Mediterranean climate variability include \r\nmodes of variability such as the AMV (Sutton and Dong, 2012) and the \r\nAsian monsoon (Rodwell and Hoskins, 1996; Logothetis et al., 2020). \r\nIn addition, the increase of GHGs (e.g., Zittis et al"


id: d3d9c375450e1d72d09f5df782d9772a
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", 2019), the decrease \r\nof anthropogenic aerosols over Europe and the Mediterranean since \r\nthe 1980s resulting from air pollution policies (Turnock et al., 2016), \r\nand anthropogenic land-use change (Millan, 2014; MedECC 2020) \r\nhave been shown to be linked to the regional warming. The role of the \r\nzonal averaged circulation as a driver for the Mediterranean climate \r\nhas been stressed by (Garfinkel et al., 2020)."


id: 5980e95ae360412b0d98eae8d8e38ba6
created_at: 2023-03-16 14:02:07
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index: climate_gpt_only_giec
vector_id: 11576
content:
"The Mediterranean has a heterogeneous climate that is partly semi-arid, \r\nespecially along the southern coast of the Mediterranean Sea (Lionello \r\net al., 2012). It is characterized by mild humid winters and dry warm or \r\nhot summers, which are associated with large scale subsidence that is \r\npartly related to the downward branch of the Hadley circulation. Other \r\nfactors affecting the Mediterranean circulation include the monsoon \r\nheating over Asia (Rodwell and Hoskins, 1996; Cherchi et al., 2014; Osso \r\net al., 2019) and circulation anomalies induced by topography (Simpson \r\net al., 2015). Seasonal and interannual variability is strongly linked to \r\nnatural modes of variability (Section  10.6.4.4). The Mediterranean \r\nSea acts as an evaporation source that dominates the  regional \r\nhydrological cycle, which is characterized by local cyclogenesis and \r\na separate branch of the mid-latitude storm track (Lionello et al., 2016)"


id: f333396bdd67477ba2a33d0fac62c5f
created_at: 2023-03-16 14:02:07
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content:
" \r\nIt also affects remote locations such as the Sahel (Park et al., 2016; \r\nSection 10.4.2.1). Strong storms can develop over the Mediterranean. \r\nAmong these, Medicanes are particularly destructive and exhibit \r\nseveral similarities with tropical cyclones (Cavicchia et  al., 2014; \r\nKouroutzoglou et al., 2015; Gaertner et al., 2018). The Mediterranean \r\nregion is also characterized by strong land-atmosphere coupling and \r\nfeedbacks (Seneviratne et  al., 2006) generating prolonged droughts \r\nand intense heatwaves, which can also affect continental Europe \r\n(Zampieri et al., 2009). Other aspects of Mediterranean climate include \r\nregional winds, which can be very strong due to the channelling effect \r\n(Obermann et al., 2018) and extreme rainfall during autumn (Ducrocq \r\net al., 2014; Ribes et al., 2019)."


id: 5ce503c71bf59cbf552e21d690657d67
created_at: 2023-03-16 14:02:07
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content:
"Over the period 1960-2014, observed trends over land are consistent \r\nwith those of most of the multi-model or SMILEs ensembles \r\n(Figure 10.20f), although large differences exist for individual models \r\nand ensemble members. The modelled ensemble-mean trends show \r\nlarge geographical variations. Generally, both global and regional \r\nmodels often underestimate the observed trend especially over parts \r\nof North Africa, Italy, the Balkans and Turkey. The cold bias in global \r\nmodels is related to simulated SLP trends that are anti-correlated \r\nto the observed trend, which is probably due to systematic model \r\nerrors (Boe et al., 2020b). Biases in the simulation of soil-moisture \r\nand cloud-cover might also have contributed to the underestimation \r\nof the warming trend in GCMs (van Oldenborgh et  al., 2009). \r\nThe CORDEX results (at both 0.44deg and 0.11deg resolution) show \r\nconsistently smaller values than those in global models and the \r\navailable datasets (Figure 10"


id: aee6a7444d7f9625d882c0ae6dccfaa3
created_at: 2023-03-16 14:02:07
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vector_id: 25054
content:
"20g; Vautard et al., 2021). This is partly \r\ndue to the overestimation in the temperature evolution before 1990 \r\n(Figure 10.20e), possibly because of differences in the aerosol forcing \r\n(Boe et  al., 2020a), although the driving global models also have \r\na cold bias (Vautard et al., 2021). Cold biases for recent decades are \r\nalso found in Med-CORDEX simulations (Dell'Aquila et al., 2018) and \r\nby RCM simulations over the southern part of the Mediterranean, \r\nMiddle East and North Africa region (Almazroui, 2016; Almazroui \r\net al., 2016a, b; Zittis and Hadjinicolaou, 2017; Ozturk et al., 2018), \r\nalthough higher resolution, new bare soil albedo and modified aerosol \r\nparametrization significantly improve the results (Bucchignani et al., \r\n2016a, b, 2018). Despite large differences in the multi-model mean \r\ntrend (Figure  10.20g), in most of the land points the observed \r\ntrend lies within the model range in all ensembles"


id: a91609d74c8218716ab43a75fef954be
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
content_type: text
index: climate_gpt_only_giec
vector_id: 24123
content:
" For the SST bias \r\nexhibited by coupled RCMs the choice of driving global model has \r\nthe largest impact (Darmaraki et al., 2019; Soto-Navarro et al., 2020)."


id: 58a2491013d435a88d9690f30661cfd2
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
content_type: text
index: climate_gpt_only_giec
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content:
"The Mediterranean is expected to be one of the most prominent \r\nand vulnerable climate change hotspots (Diffenbaugh and Giorgi, \r\n2012). CMIP5, CMIP6, HighResMIP and CORDEX (Section 10.6.4.7) \r\nsimulations all project a  future warming for the 21st  century that \r\nranges between 3.5degC and 8.75degC for RCP8.5 at the end of this \r\ncentury for those ending at 2100 (Figure 10.21a, b). CMIP6 results \r\nproject more pronounced warming than CMIP5 for a given emissions \r\nscenario and time period (Figure  10.21c; Coppola et  al., 2020). \r\nHowever, when analysing the Mediterranean warming in terms \r\nof mean global warming levels, the two ensembles largely agree, \r\nshowing that summer warming is projected to reach values up to \r\n40-50% larger than the global annual warming, largely independent \r\nof models and emissions scenarios (Figure 10.21d)"


id: 341dbb94a8bd20c9234e6d76844d1a0d
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content:
" Large regional \r\ndifferences exist, with enhanced warming projected over Turkey, \r\nthe Balkans, the Iberian Peninsula and North African regions \r\n(Figures 10.14a, 10.21c; Almazroui et al., 2020a) and reaching, locally, \r\nvalues of up to double the global mean (Lionello and Scarascia, \r\n2018). The enhanced summer warming also increases the amplitude \r\nof the seasonal cycle (Yettella and England, 2018)."


id: d3780671f3dcaaa6b757d271e69896b4
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
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index: climate_gpt_only_giec
vector_id: 30795
content:
"atmospheric circulation depend, among other factors, on how these \r\ndrivers have changed and will change. Zappa and Shepherd (2017) \r\nhave analysed this for the Mediterranean region and developed \r\na set of storylines based on different plausible evolutions of those \r\ndrivers and their impact on the Mediterranean winter climate. \r\nImportant identified drivers during winter are tropical and polar \r\namplification of global warming and the polar stratospheric vortex \r\n(Manzini et  al., 2014; Simpson et  al., 2018), with implications \r\nfor precipitation. Zappa (2019) discusses the relative amplitude \r\nof tropical and Arctic warming, response of the AMOC, patterns of \r\nPacific SST change, and changes in stratospheric vortex strength as \r\npossible drivers of the Mediterranean summer climate and stresses \r\nthat given the present state of knowledge, alternative storylines \r\nbased on these drivers should be considered as equally plausible \r\nfuture manifestations of regional climate change"


id: 957e6f43dfe053b80867356c721096ac
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
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index: climate_gpt_only_giec
vector_id: 21019
content:
" Brogli et  al. \r\n(2019a, b) and Kroner et al. (2017) have revealed thermodynamic \r\nprocesses, lapse rate, and circulation as important drivers for \r\nMediterranean summer climate."


id: 59a00d05b953e9df51fdb4711f07278
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
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content:
"Despite the large efforts of these regional downscaling projects, the \r\nglobal model-RCM matrix is still sparse and lacking a systematic \r\ndesign to explore the uncertainty sources (e.g.,  global model, \r\nRCM, scenario, resolution) (Section 10.3). Focusing on the Iberian \r\npeninsula, Fernandez et  al. (2019) argued that the driving global \r\nmodel is the main contributor to uncertainty in the ensemble. \r\nPhysically consistent but implausible temperature changes in \r\nRCMs can occur. An example is a strong temperature increase over \r\nthe Pyrenees due to excessive snow cover in the present climate \r\n(Fernandez et al., 2019). Based on an older set of RCM simulations \r\n(ENSEMBLES), Deque et al. (2012) also argued that the largest source \r\nof uncertainty in the temperature response over southern Europe \r\nis the choice of the driving global model (whereas for summer \r\nprecipitation the choice of the RCM dominates the uncertainty). \r\nFinally, Boe et al"


id: e5d5dfb7cc8df13511585a43d7403d48
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index: climate_gpt_only_giec
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content:
" (2020a) found that over a large area of Europe, \r\nincluding parts of the Mediterranean, RCMs project a  summer \r\nwarming 1.5degC-2degC colder than in their driving global models for \r\nthe end of the 21st century. This is caused by differences in solar \r\nradiation related to the absence of time-varying anthropogenic \r\naerosols in RCMs (Boe et al., 2020a; Gutierrez et al., 2020), which \r\nalso affects the noted differences in cloud cover between global \r\nmodels and RCMs (Bartok et al., 2017)."


id: 97ddf51d94db6e674c2ede48197348df
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
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index: climate_gpt_only_giec
vector_id: 21410
content:
"Traditionally, the distillation process to produce contextualized, \r\npolicy relevant information has taken place at regional or national \r\nlevel. For example, the potential effects of climate change on \r\npublic health are discussed in several national climate change \r\nand adaptation reports (Bruci et  al., 2016; MoARE, 2016; MoE, \r\n2016; MoEP, 2018; MoEU, 2018). Although these reports are \r\nextremely helpful and widely used for the development of national \r\nadaptation policies, they are often based on non-comprehensive \r\nand heterogeneous sources of climate information (e.g.,  MEEN, \r\n2018; MoE/UNDP/GEF, 2019). For instance, future climate change \r\nprojections are based on a  limited number of socio-economic \r\nscenarios and climate model simulations, which are also often \r\nnot evaluated comprehensively (e.g.,  Bruci et  al., 2016; MoARE, \r\n2016; MoEU, 2018)"


id: 73d860808005b356472a50cf9cc825bf
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content:
" In addition, these reports are often not peer\u0002reviewed, not available in English, and mainly limited to the country \r\nlevel, thus making it difficult to compare the details of the climate \r\ninformation across them."


id: b36d747d5660b090e14a611fb1f6c275
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
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index: climate_gpt_only_giec
vector_id: 25736
content:
"During nighttime, urban centres are often several degrees warmer than the surrounding rural area, a phenomenon known as the \r\nnighttime canopy urban heat island effect (Bader et al., 2018; Kuang, 2019; Li et al., 2019; Y. Li et al., 2020a). While green and blue \r\ninfrastructures can mitigate the urban heat island effect, three main factors contribute to its development (Hamdi et al., 2020; Masson \r\net al., 2020): (i) three-dimensional urban geometry including building density and plan area, street aspect ratio and building height; \r\n(ii) thermal characteristics of impervious surfaces; and (iii) anthropogenic heat release, either from building energy consumption, \r\nespecially waste heat from air conditioning systems, or as direct emissions from industry, traffic, or human metabolism (Ichinose et al., \r\n1999; Sailor, 2011; de Munck et al., 2013; Bohnenstengel et al., 2014; Chow et al., 2014; Salamanca et al., 2014; Dou and Miao, 2017; \r\nMa et al., 2017a; Chrysoulakis et al"


id: 9357b2552803d9f2397707ea60c4bfc6
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updated_at: 2023-03-16 14:02:07
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vector_id: 20697
content:
", 2018; Takane et al., 2019). Urban heat island magnitude is also affected by aerosols due to air \r\npollution in urban areas (Cheng et al., 2020; Han et al., 2020) and by local background climate (Zhao et al., 2014; Ward et al., 2016)."


id: b9015192177baaed8e196bfcbb88e92f
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"Long-term climate datasets (a year or more) at the small spatial scales required to resolve processes of interest for cities (<1 km) \r\nare scarce (Bader et al., 2018; Caluwaerts et al., 2020). Moreover, urban observation sites often represent only parts of the urban \r\nenvironment and are suboptimal for detecting urban effects (e.g., sites in city parks). Recently, city-scale climate monitoring networks \r\nas well as satellite and ground-based remote sensing are being used (though still missing in Global South cities; Technical Annex I), \r\nenhancing our understanding of the urban microclimate and its interaction with climate change, and providing key information \r\nfor users (F. Chen et al., 2012; Barlow et al., 2017; Bader et al., 2018). It has been found that harmonization of collection practices, \r\ninstrumentation, station locations, and quality control methodologies across urban environments needs improvement to facilitate \r\ncollaborative research (Muller et al., 2013; Barlow et al"


id: a67462ebde384f7c83d0f75b47201d8d
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", 2017). Real time crowdsourcing data is becoming available (Section 10.2.4). \r\nThe urban climate community is making efforts to understand how these methods can complement traditional datasets (Meier et al., \r\n2017; Zheng et al., 2018; Langendijk et al., 2019b; Venter et al., 2020)."


id: c5b0cf3e51897e7b53b221543aa0dad0
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"Exchanges of heat, water and momentum between the urban surface and its overlying atmosphere are calculated using specific surface\u0002atmosphere exchange schemes. Three different schemes, here in order of increasing complexity, can be distinguished (Masson, 2006; \r\nGrimmond et al., 2010, 2011; Chen et al., 2011; Best and Grimmond, 2015): (i) in the slab or bulk approach, the three-dimensional city \r\nstructure is not resolved but cities are represented by modifying soil and vegetation parameters within land surface models, increasing \r\nroughness length and displacement height (e.g., Seaman et al., 1989; Dandou et al., 2005; Best et al., 2006; Liu et al., 2006). The energy \r\nbalance is often modified to account for the radiation trapped by the urban canopy, heat storage, evaporation and anthropogenic \r\nheat fluxes"


id: 5cdd2322a3df0e79fbd10d016845b643
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" (ii) Single-layer urban canopy modules use a simplified geometry (urban canyon, with three surface types: roof, road and \r\nwall) that approximately capture the three-dimensional dynamical and thermal physical processes influencing radiative and energy \r\nfluxes (Masson, 2000; Kusaka et al., 2001). (iii) Multi-layer urban canopy modules compute urban effects vertically, allowing a direct \r\ninteraction with the planetary boundary layer (Brown, 2000; Martilli et al., 2002; Hagishima et al., 2005; Dupont and Mestayer, 2006; \r\nHamdi and Masson, 2008; Schubert et al., 2012). Building-energy models that estimate anthropogenic heat from a building for given \r\natmospheric conditions can be incorporated. Recent model development has focused on improving the representation of urban \r\nvegetation (Lee et al., 2016; Redon et al., 2017; Mussetti et al., 2020)."


id: 2c950c02f56df40ecbb13682a8a36e5a
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
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vector_id: 4443
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"Global (McCarthy et al., 2010; Oleson et al., 2011; Zhang et al., 2013; H. Chen et al., 2016; Katzfey et al., 2020; Sharma et al., 2020; \r\nHertwig et al., 2021) and regional modelling groups (Oleson et al., 2011; Kusaka et al., 2012a; McCarthy et al., 2012; Hamdi et al., \r\n2014; Trusilova et al., 2016; Daniel et al., 2019; Halenka et al., 2019; Langendijk et al., 2019a) are beginning to implement these \r\nurban parametrizations within the land surface component of their models. There is very high confidence (robust evidence and \r\nhigh agreement) that while all types of urban parametrizations generally simulate radiation exchanges in a realistic way, they have \r\nstrong biases when simulating latent heat fluxes, though recent research incorporating in-canyon vegetation processes improved their \r\nperformance. There is medium confidence (medium evidence, high agreement) (Kusaka et al., 2012b; McCarthy et al., 2012; Hamdi \r\net al., 2014; Trusilova et al., 2016; Janicke et al"


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content:
", 2017; Daniel et al., 2019) that a simple single-layer parametrization, is sufficient for \r\nthe correct simulation of the urban heat island magnitude and its interplay with regional climate change."


id: 9f78ada01e09e83dded134e7b87e6b7
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content:
"Pepin et al., 2015), leading to warming of 2degC-2.5degC at 5000 m over 1961-2006, but only 0.5degC at sea level (Xu et al., 2016). However, \r\nEDW behaviour appears to depend on region, time period and elevation (D. Guo et al., 2019; b. Li et al., 2020) and understanding is limited \r\nby the sparse observational network (You et al., 2020). Observational and model analyses have attributed EDW to GHG and black carbon \r\nemissions, accelerating warming by snow-albedo feedback (Ming et al., 2012; Gautam et al., 2013; Xu et al., 2016; Yan et al., 2016; Lau \r\nand Kim, 2018; Y. Zhang et al., 2018), or the more pronounced cooling effect of scattering aerosols at low elevations and stratospheric \r\nozone depletion (Guo and Wang, 2012; Zeng et al., 2015). There is high confidence that the eastern and central HKH has exhibited rising \r\ntemperatures (Cross-Chapter Box 10.4, Figure 1), with warming dependent on season and elevation"


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" There is high confidence that much of \r\nthe warming can be attributed to GHGs, but the effect of albedo has only medium confidence. There is high confidence in more frequent \r\nextreme warm events and fewer extreme cold events over the eastern Himalayas in the last five decades."


id: 577c1037fb533a2e4248505935ccc19
created_at: 2023-03-16 14:02:07
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"Cross-chapter Box 10.4, Figure 1 | Historical annual-mean surface air temperature linear trend (degC per decade) and its attribution over the \r\nHindu Kush Himalaya (HKH) region. (a) Observed trends from Berkeley Earth (also showing the HKH outline), CRU TS (also showing the AR6 Tibetan Plateau (TIB) \r\noutline, for ease of comparison to the Interactive Atlas), APHRO-MA and JRA-55 datasets over 1961-2014. (b) Models showing the coldest, median and warmest \r\nHKH temperature linear trends among the CMIP6 historical ensemble over 1961-2014. (c) Low-pass-filtered time series of annual-mean surface air temperature \r\nanomalies (degC, baseline 1961-1980) over the HKH region as outlined in panel (a), showing means of CMIP6 hist all-forcings (red), and the CMIP6 hist all-forcings \r\nsample corresponding to DAMIP experiments (pink), for hist-aer (grey) and hist-GHG (pale blue). Observed datasets are Berkeley Earth (dark blue), CRU (brown), \r\nAPHRO-MA (light green) and JRA-55 (dark green)"


id: e7248069f308ef9934874d3bb9f5cbf5
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" The filter is the same as that used in Figure 10.10. (d) Distribution of annual mean surface air temperature trends \r\n(degC per decade) over the HKH region from 1961 to 2014 for ensemble means, the aforementioned observed and reanalysis data (black crosses), individual members \r\nof CMIP6 hist all-forcings (red circles), CMIP6 hist-GHG (blue triangles), CMIP6 hist-aer (grey triangles), and box-and-whisker plots for the SMILEs used throughout \r\nChapter 10 (grey shading). Ensemble means are also shown. All trends are estimated using ordinary least-squares regression and box-and-whisker plots follow the \r\nmethodology used in Figure 10.6. Further details on data sources and processing are available in the chapter data table (Table 10.SM.11)."


id: c91848a31cf25b5e16546686f605ee66
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"Annual and summer precipitation over the central-eastern HKH show decreasing trends over 1979-2010 in multiple observed datasets, \r\nattributable to a weakening South Asian monsoon (Yao et al., 2012a; Palazzi et al., 2013; Roxy et al., 2015). There are contradictory \r\ntrends in the western HKH (Azmat et al., 2017; Yadav et al., 2017; H. Li et al., 2018; Meher et al., 2018), where most precipitation is \r\nassociated with western disturbances on the subtropical westerly jet, but trends in western disturbance activity are unclear (Kumar \r\net al., 2015; Hunt et al., 2019; Krishnan et al., 2019a). There has been an increased frequency and intensity of extreme precipitation \r\nover the central-western HKH but contrasting evidence in the east (Sheikh et al., 2015; Talchabhadel et al., 2018). The number of \r\nconsecutive wet days has increased over 1961-2012, but with no uniform trend in consecutive dry days (Zhan et al., 2017)"


id: dac1b7bc2b82b5262b3463b46d7a4485
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" There is \r\nmedium confidence that the eastern-central HKH has experienced decreased summer precipitation (Section 10.6.3). There is medium \r\nconfidence in the increase of summer extreme precipitation over the western HKH."


id: 20c037f35b30a6a87cc99f5b2e878097
created_at: 2023-03-16 14:02:07
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vector_id: 2590
content:
"The SROCC assessed that snow cover has declined in duration, depth and accumulated mass at lower elevations in mountain regions, \r\nincluding the HKH (high confidence). Glaciers are losing mass (very high confidence) and permafrost is warming (high confidence) \r\nover high mountains in recent decades, and it is very likely that atmospheric warming is the main driver. A significant reduction in \r\nHKH glacier area has been observed since the 1970s, with smaller glaciers generally shrinking faster (e.g., Bolch et al., 2019). HKH \r\nglacier mass loss took place at the lowest rate among high mountain areas in the last 20 years, although with one of the largest \r\ntotal losses (Section 9.5.1.1 and Figure 9.20; Shean et al., 2020). The highest mass-loss rates occurred in the eastern and northern \r\nHKH, while gains occurred in the west (e.g., Shean et al., 2020). Glacier mass gain has been coined as the 'Karakoram anomaly' \r\n(Sections 8.3.1.7.1 and 9.5"


id: 89cfc29108f9b06cd69229dce82e8bca
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"1), explained by a combination of low temperature sensitivity of debris-covered glaciers, a decrease in \r\nsummer air temperatures, and increased snowfall possibly linked to evapotranspiration from irrigated agriculture (You et al., 2017; \r\nBolch et al., 2019; de Kok et al., 2020a; Farinotti et al., 2020). Meanwhile, increased air temperature and decreased snowfall explain \r\nthe glacier mass decrease elsewhere (Bonekamp et al., 2019; de Kok et al., 2020b; Farinotti et al., 2020; Shean et al., 2020). There is \r\nhigh confidence that glaciers in most HKH regions have thinned, retreated and lost mass since the 1970s."


id: bb20b16b8501faa3512d1d6c3ed5f17a
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"In AR5, the HKH was projected to continue warming over the 21st century, faster than the likely ranges for the global mean and \r\nSouth Asia. New CMIP5 results show temperature increases across mountainous HKH by about 1degC-2degC (in some places in summer \r\n4degC-5degC) during 2021-2050 compared to 1961-1990 (Shrestha et al., 2015). Projected warming differs by up to 1degC between east and \r\nwest, with higher values in winter (Sanjay et al., 2017; see Interactive Atlas). Statistically significant mean warming (0.30degC-0.90degC \r\nper decade until the end of the 21st century) across all RCPs has been projected by CORDEX South Asia (Dimri et al., 2018). CMIP6 \r\nmodels report that north-western South Asia, including the western Himalayas, is projected to experience temperature increases \r\nexceeding 6degC by the end of the 21st century under SSP5-8.5 relative to 1995-2014 (Almazroui et al., 2020b)"


id: c1e49c960c07b8412e8095d276abde4f
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" Results from CMIP5, \r\nCMIP6 and CORDEX ensembles for different warming levels are shown in the Interactive Atlas and summarized in Figure Atlas.20. \r\nThe HKH will likely continue warming in the coming decades."


id: a0915aa9752201e78565254c1e9b7238
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"The SR1.5 (IPCC, 2018b) stated that heavy precipitation risk in high-elevation regions is projected to be higher at 2degC compared to \r\n1.5degC of global warming (medium confidence). CMIP5 models project increased annual or summer monsoon precipitation over the \r\nHKH in the 21st century (Palazzi et al., 2015; Kitoh and Arakawa, 2016), intensifying by about 22% in the hilly south-eastern Himalaya \r\nand TP for the long term in RCP8.5, but with no trends in the western HKH (Rajbhandari et al., 2015; Krishnan et al., 2019a). CMIP6 \r\nprojects an increase of winter precipitation over the western Himalayas, with a corresponding decrease in the east (Almazroui et al., \r\n2020b). HKH projections are subject to large uncertainties in CMIP5 and CORDEX (Hasson et al., 2013, 2017; Mishra, 2015; Sanjay \r\net al., 2017). CORDEX, in particular, has inherent limitations at reproducing the characteristics of summer monsoon rainfall variability \r\n(Singh et al., 2017)"


id: 818132f816776e20b6fb987fc46c02e2
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"The SROCC assessed that glaciers will lose substantial mass (high confidence) and permafrost will undergo increasing thaw and \r\ndegradation (very high confidence) over high mountain regions (including the HKH), with stronger changes for higher emissions \r\nscenarios. Regional differences in warming and precipitation projections and glacier properties cause considerable differences in \r\nglacier response within High Mountain Asia (Kraaijenbrink et al., 2017). Glacier mass loss will accelerate through the 21st century, \r\nincreasing with RCP after 2030 (Section 9.5.1.3; Marzeion et al., 2014). Loss of between 40 +- 25% to 69 +- 21 % of 2015 glacier \r\nvolume is expected by 2100 in RCP 2.6 and RCP 8.5, respectively (Section 9.5.1.3 and Figure 9.21). Glacier mass loss is expected due to \r\ndecreased snowfall, increased snowline elevations and longer melt seasons"


id: 9ebdacc30c54e9fbc58224a6a028e60e
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content:
" However, due to projection uncertainties, simplicity of the \r\nmodels, and limited observations, there is medium confidence in the magnitude and timing of glacier mass changes (Section 9.5.1.3). \r\nGlacier mass in HKH will decline through the 21st century (high confidence), more so under high-emissions scenarios."


id: 29dd9155ab01339f29511981951c6aeb
created_at: 2023-03-16 14:02:07
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vector_id: 4016
content:
"The development, delivery, and use of climate change information requires engaging all parties involved: \r\nthose producing the climate data and related knowledge, those communicating it, and those who combine \r\nthat information with their knowledge of the community, region or activity that climate change may impact. \r\nTo be successful, these parties need to work together to explore the climate data and thus co-develop the \r\nclimate information needed to make decisions or solve problems, distilling output from the various sources \r\nof climate knowledge into relevant climate information. Effective partnerships recognize and respond to the \r\ndiversity of all parties involved (including their values, beliefs and interests), especially when they involve \r\nculturally diverse communities and their indigenous and local knowledge of weather, climate and their society. \r\nThis is particularly true for climate change - a global issue posing challenges that vary by region"


id: 17fd7075400e10e3f28564d657d7ff02
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content:
" By recognizing \r\nthis diversity, climate information can be relevant and credible, most notably when conveying the complexity of \r\nrisks for human systems and ecosystems and for building resilience."


id: 8a45d4c99e16bc3af00ef86ad3d45f4f
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vector_id: 19290
content:
"Constructing useful climate information requires considering all available sources in order to capture the fullest \r\npossible representation of projected changes and distilling the information in a way that meets the needs of the \r\nstakeholders and communities impacted by the changes. For example, climate scientists can provide information \r\non future changes by using simulations of global and/or regional climate and inferring changes in the weather \r\nbehaviour influencing a region. An effective distillation process (FAQ 10.1, Figure 1) engages with the intended \r\nrecipients of the information, especially stakeholders whose work involves non-climatic factors, such as human \r\nhealth, agriculture or water resources"


id: b64d190d4b93e45d8c7c94126cc3ddea
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content:
" The distillation evaluates the accuracy of all information sources \r\n(observations, simulations, expert judgement), weighs the credibility of possible conflicting information, and \r\narrives at climate information that includes estimating the confidence a user should have in it. Producers of \r\nclimate data should further recognize that the geographic regions and time periods governing stakeholders' \r\ninterest (for example, the growing season of an agricultural zone) may not align well with the time and space \r\nresolution of available climate data; thus additional model development or data processing may be required to \r\nextract useful climate information."


id: f89d816145a94040ca32b0dec9020e97
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"One way to distil complex information for stakeholder applications is to connect this information to experiences \r\nstakeholders have already had through storylines as plausible unfoldings of weather and climate events related \r\nto stakeholders' experiences. Dialogue between stakeholders and climate scientists can determine the most \r\nrelevant experiences to evaluate for possible future behaviour. The development of storylines uses the experience \r\nand expertise of stakeholders, such as water-resource managers and health professionals, who seek to develop \r\nappropriate response measures. Storylines are thus a pathway through the distillation process that can make \r\nclimate information more accessible and physically comprehensible. For example, a storyline may take a common \r\nexperience like an extended drought, with depleted water availability and damaged crops, and show how \r\ndroughts may change in the future, perhaps with even greater precipitation deficits or longer duration"


id: 4d4aa4c65154d4ee5025f479fde7f128
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" With \r\nappropriate choices, storylines can engage nuances of the climate information in a meaningful way by building \r\non common experiences, thus enhancing the information's usefulness."


id: 49d8f0a702781deb43c43f3da0591b4b
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content:
"Mathias Hauser (Switzerland), Megan Kirchmeier-Young (Canada/United States of America), \r\nLisa  V.  Alexander (Australia), Richard P. Allan (United Kingdom), Mansour  Almazroui \r\n(Saudi  Arabia), Lincoln M. Alvez (Brazil), Margot Bador (France, Australia/France), \r\nRondrotiana  Barimalala (South  Africa/Madagascar), Richard A. Betts (United Kingdom), \r\nSuzana J. Camargo (United States of America/Brazil, United States of America), Pep G. Canadell \r\n(Australia), Erika Coppola (Italy), Markus G. Donat (Spain/Germany, Australia), Herve Douville \r\n(France), Robert J. H. Dunn (United  Kingdom/Germany,United Kingdom), Erich Fischer \r\n(Switzerland), Hayley J. Fowler (United  Kingdom), Nathan  P.  Gillett (Canada), Peter  Greve \r\n(Austria/Germany), Michael Grose (Australia), Lukas  Gudmundsson (Switzerland/\r\nGermany, Iceland), Jose Manuel Guttierez (Spain), Lofti Halimi (Algeria), Zhenyu  Han \r\n(China), Kevin  Hennessy (Australia), Richard  G"


id: f51d4a9af7c3f9658baab1bb0542e4d2
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updated_at: 2023-03-16 14:02:07
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content:
"  Jones (United  Kingdom), Yeon-Hee Kim \r\n(Republic of Korea), Thomas Knutson (United States of America), June-Yi Lee (Republic of Korea), \r\nChao Li (China), Georges-Noel T. Longandjo (South Africa/Democratic Republic of the Congo), \r\nKathleen L.  McInnes (Australia), Tim  R.  McVicar (Australia), Malte Meinshausen (Australia/\r\nGermany), Seung-Ki Min (Republic of Korea), Ryan  S.  Padron  Flasher (Switzerland/Ecuador, \r\nUnited States of America), Christina M. Patricola (United States of America), Roshanka Ranasinghe \r\n(The  Netherlands/Sri Lanka, Australia), Johan  Reyns (The Netherlands/Belgium), Joeri  Rogelj \r\n(United Kingdom/Belgium), Alex C. Ruane (United States of America), Daniel  Ruiz Carrascal \r\n(United States of America/Colombia), Bjorn  H.  Samset (Norway), Jonathan  Spinoni (Italy), "


id: bb301cccf9a817ea0d3ce827c8ca52af
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updated_at: 2023-03-16 14:02:07
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vector_id: 26994
content:
"This chapter assesses changes in weather and climate extremes \r\non regional and global scales, including observed changes and \r\ntheir attribution, as well as projected changes. The extremes \r\nconsidered include temperature extremes, heavy precipitation \r\nand pluvial floods, river floods, droughts, storms (including \r\ntropical cyclones), as well as compound events (multivariate and \r\nconcurrent extremes). The assessment focuses on land regions \r\nexcluding Antarctica. Changes in marine extremes are addressed \r\nin Chapter  9  and Cross-Chapter Box  9.1. Assessments of past \r\nchanges and their drivers are from 1950 onward, unless indicated \r\notherwise. Projections for changes in extremes are presented for \r\ndifferent levels of global warming, supplemented with information \r\nfor the conversion to emissions scenario-based projections (Cross\u0002Chapter Box  11.1 and Table  4.2)"


id: 8676cab65aaaf50b5e0ff7384db03327
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content:
" Since the IPCC Fifth Assessment \r\nReport (AR5), there have been important new developments and \r\nknowledge advances on changes in weather and climate extremes, in \r\nparticular regarding human influence on individual extreme events, \r\non changes in droughts, tropical cyclones, and compound events, and \r\non projections at different global warming levels (1.5degC-4degC). These, \r\ntogether with new evidence at regional scales, provide a  stronger \r\nbasis and more regional information for the AR6 assessment on \r\nweather and climate extremes."


id: bc91519773af69a346ebc68b0c7994be
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vector_id: 27247
content:
"Since AR5, the confidence about past and future changes \r\nin weather and climate extremes has increased due to \r\nbetter physical understanding of processes, an increasing \r\nproportion of the scientific literature combining different \r\nlines of evidence, and improved accessibility to different \r\ntypes of climate models (high confidence). There have \r\nbeen improvements in some observation-based datasets, \r\nincluding reanalysis data (high confidence). Climate models \r\ncan reproduce the sign (direction) of changes in temperature \r\nextremes observed globally and in most regions, although the \r\nmagnitude of the trends may differ (high confidence). Models \r\nare able to capture the large-scale spatial distribution of precipitation \r\nextremes over land (high confidence). The intensity and frequency of \r\nextreme precipitation simulated by Coupled Model Intercomparison \r\nProject Phase 6 (CMIP6) models are similar to those simulated by \r\nCMIP5 models (high confidence)"


id: 619673c27b155505db21206f1cbe6971
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
content_type: text
index: climate_gpt_only_giec
vector_id: 12922
content:
" Higher horizontal model resolution \r\nimproves the spatial representation of some extreme events \r\n(e.g., heavy precipitation events), in particular in regions with highly \r\nvarying topography (high confidence). {11.2, 11.3, 11.4}"


id: 227a40c9ef54d82f5c55f218877f3b0d
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
content_type: text
index: climate_gpt_only_giec
vector_id: 2872
content:
"The frequency and intensity of hot extremes will continue to \r\nincrease and those of cold extremes will continue to decrease, \r\nat global and continental scales and in nearly all inhabited \r\nregions1 with increasing global warming levels. This will be the \r\ncase even if global warming is stabilized at 1.5degC. Relative to present\u0002day conditions, changes in the intensity of extremes would be at least \r\ndouble at 2degC, and quadruple at 3degC of global warming, compared \r\nto changes at 1.5degC of global warming. The number of hot days and \r\nhot nights and the length, frequency, and/or intensity of warm spells \r\nor heatwaves will increase over most land areas (virtually certain). In \r\nmost regions, future changes in the intensity of temperature extremes \r\nwill very likely be proportional to changes in global warming, and up \r\nto two to three times larger (high confidence)"


id: 1e310bf470ee48bd3a36f8f36a35f892
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
content_type: text
index: climate_gpt_only_giec
vector_id: 2188
content:
" The highest increase \r\nof temperature of hottest days is projected in some mid-latitude and \r\nsemi-arid regions and in the South American Monsoon region, at \r\nabout 1.5 times to twice the rate of global warming (high confidence). \r\nThe highest increase of temperature of coldest days is projected \r\nin Arctic regions, at about three times the rate of global warming \r\n(high confidence). The frequency of hot temperature extreme events \r\nwill very likely increase nonlinearly with increasing global warming, \r\nwith larger percentage increases for rarer events. {11.2, 11.3, 11.9; \r\nTable 11.1; Figure 11.3}"


id: 9dcce98cfadd5e4f0b9a6d306cbf7bc3
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
content_type: text
index: climate_gpt_only_giec
vector_id: 22351
content:
"More regions are affected by increases in agricultural and \r\necological droughts with increasing global warming (high \r\nconfidence). Several regions will be affected by more severe \r\nagricultural and ecological droughts even if global warming \r\nis stabilised at 2degC, including MED, WSAF, SAM and SSA (high \r\nconfidence), and ESAF, MDG, EAU, SAU, SCA, CAR, NSA, NES, SWS, \r\nWCE, NCA, WNA and CNA (medium confidence). Some regions \r\nare also projected to be affected by more severe agricultural and \r\necological droughts at 1.5degC (MED, WSAF, ESAF, SAU, NSA, SAM, \r\nSSA, CNA, medium confidence)"


id: 56d00314f3750d8fae1fcc6be964f4f8
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
content_type: text
index: climate_gpt_only_giec
vector_id: 11134
content:
" At 4degC of global warming, about \r\n50% of all inhabited AR6 regions would be affected by increases \r\nin agricultural and ecological droughts (WCE, MED, CAU, EAU, SAU, \r\nWCA, EAS, SCA, CAR, NSA, NES, SAM, SWS, SSA, NCA, CNA, ENA, \r\nWNA, WSAF, ESAF, MDG: medium confidence or higher), and only two \r\nregions (NEAF, SAS) would experience decreases in agricultural and \r\necological drought (medium confidence). There is high confidence\r\nthat the projected increases in agricultural and ecological droughts \r\nare strongly affected by evapotranspiration increases associated with \r\nenhanced atmospheric evaporative demand. Several regions are \r\nprojected to be more strongly affected by hydrological droughts with \r\nincreasing global warming (at 4degC of global warming: NEU, WCE, \r\nEEU, MED, SAU, WCA, SCA, NSA, SAM, SWS, SSA, WNA, WSAF, ESAF, \r\nMDG: medium confidence or higher)"


id: 1a2721371410baa027fa7f135bbe6478
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
content_type: text
index: climate_gpt_only_giec
vector_id: 1579
content:
" There is low confidence that \r\neffects of enhanced atmospheric carbon dioxide (CO2) concentrations \r\non plant water-use efficiency alleviate extreme  agricultural and \r\necological droughts in conditions  characterized by  limited soil \r\nmoisture and enhanced atmospheric evaporative demand. There is \r\nalso low confidence that these effects will substantially reduce global \r\nplant transpiration and the severity of hydrological droughts. There is \r\nhigh confidence that the land carbon sink will become less efficient \r\ndue to soil moisture limitations and associated drought conditions \r\nin some regions in higher-emissions scenarios, in particular under \r\nglobal warming levels above 4degC. {11.6, 11.9, Cross-Chapter Box 5.1}"


id: 9570ab0d6220b261a312b154d3b4000d
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
content_type: text
index: climate_gpt_only_giec
vector_id: 21012
content:
"SREX report, IPCC, 2012) provided a comprehensive assessment on \r\nchanges in extremes and how exposure and vulnerability to extremes \r\ndetermine the impacts and likelihood of disasters. Chapter  3  of \r\nthat report (Seneviratne et al., 2012, hereafter also referred to as \r\nSREX Chapter  3) assessed physical aspects of extremes, and laid \r\na foundation for the follow-up IPCC assessments. Several chapters \r\nof the IPCC Fifth Assessment Report (AR5) (IPCC, 2013) addressed \r\nclimate extremes with respect to observed changes (Hartmann et al., \r\n2013), model evaluation (Flato et al., 2013), attribution (Bindoff \r\net al., 2013), and projected long-term changes (Collins et al., 2013). \r\nAssessments were also provided in the IPCC Special Report on Global \r\nWarming of 1.5degC (SR1.5) (IPCC, 2018; Hoegh-Guldberg et al., 2018), \r\non climate change and land (SRCCL; (IPCC, 2019a), and on oceans \r\nand the cryosphere (SROCC; IPCC, 2019b)"


id: 1e1cbc40a31cd1e6c400f746d8e96bdc
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
content_type: text
index: climate_gpt_only_giec
vector_id: 2176
content:
"The assessment of projected future changes in extremes is presented \r\nas function of different global warming levels (Section  11.2.4 and \r\nCross-Chapter Box  11.1). This provides traceability and comparison \r\nto the SR1.5 assessment (Hoegh-Guldberg et al., 2018, hereafter \r\nreferred to as SR1.5 Chapter 3). Also, this is useful for decision makers \r\nas actionable information, as much of the mitigation policy discussion \r\nand adaptation planning can be tied to the level of global warming. \r\nFor example, regional changes in extremes, and thus their impacts, can \r\nbe linked to global mitigation efforts. There is also the advantage of \r\nseparating uncertainty in future projections due to regional responses \r\nas a  function of global warming levels from other factors such as \r\ndifferences in global climate sensitivity and emissions scenarios (Cross\u0002Chapter Box  11.1)"


id: 295420912eae806d2b014217bafc2e65
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
content_type: text
index: climate_gpt_only_giec
vector_id: 3940
content:
" Information is also provided on the translation \r\nbetween information provided at global warming levels and for single \r\nemissions scenarios (Cross-Chapter Box  11.1). This facilitates easier \r\ncomparison with the AR5 assessment and with some analyses provided \r\nin other chapters as function of emissions scenarios."


id: 6cd53e1e6909fc5808c662d77c57492a
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
content_type: text
index: climate_gpt_only_giec
vector_id: 14742
content:
"At the global scale, and also at the regional scale to some extent, \r\nmany of the changes in extremes are a  direct consequence of \r\nenhanced radiative forcing, and the associated global warming and/\r\nor resultant increase in the water-holding capacity of the atmosphere, \r\nas well as changes in vertical stability and meridional temperature \r\ngradients that affect climate dynamics (see Box 11.1). Widespread \r\nobserved and projected increases in the intensity and frequency of \r\nhot extremes, together with decreases in the intensity and frequency \r\nof cold extremes, are consistent with global and regional warming \r\n(Section 11.3 and Figure 11.2). Extreme temperatures on land tend \r\nto increase more than the global mean temperature (Figure 11.2), \r\ndue in large part to the land-sea warming contrast, and additionally \r\nto regional feedbacks in some regions (Section 11.1.6)"


id: 56997a17414eba5982c0e77d21b4f48b
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
content_type: text
index: climate_gpt_only_giec
vector_id: 11098
content:
" Increases in \r\nthe intensity of temperature extremes scale robustly, and in general \r\nlinearly, with global warming across different geographical regions \r\nin projections up to 2100, with minimal dependence on emissions \r\nscenarios (Section 11.2.4, Figure 11.3, and Cross-Chapter Box 11.1; \r\nSeneviratne et al., 2016; Wartenburger et al., 2017; Kharin et al., \r\n2018). The frequency of hot temperature extremes (see Figure 11.6), \r\nthe number of heatwave days and the length of heatwave seasons \r\nin various regions also scale well, but nonlinearly (because of \r\nthreshold effects, Section  11.2.1), with global mean temperatures \r\n(Wartenburger et al., 2017; Y. Sun et al., 2018a). "


id: ac0844899548f87460b794763a7bf840
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
content_type: text
index: climate_gpt_only_giec
vector_id: 24588
content:
"Since AR5, the attribution of extreme weather events, or the \r\ninvestigation of changes in the frequency and/or magnitude of \r\nindividual and local- and regional-scale extreme weather events due \r\nto various drivers (Section 11.2.3 and Cross-Working Group Box 1.1) \r\nhas provided evidence that greenhouse gases and other external \r\nforcings have affected individual extreme weather events. The events \r\nthat have been studied are geographically uneven. For example, \r\nextreme rainfall events in the UK (Schaller et al., 2016; Vautard et al., \r\n2016; Otto et al., 2018b) or heatwaves in Australia (King et al., 2014; \r\nPerkins-Kirkpatrick et al., 2016; Lewis et al., 2017b) have spurred more \r\nstudies than other events. Many highly impactful extreme weather \r\nevents have not been studied in the event attribution framework. \r\nStudies in the developing world are also generally lacking. This is due \r\nto various reasons (Section 11"


id: 8d3a54841e5b8654bbf7b2ce44d8f51b
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
content_type: text
index: climate_gpt_only_giec
vector_id: 19744
content:
"2) including lack of observational data, \r\nlack of reliable climate models and other problems (Otto et al., 2020). \r\nWhile the events that have been studied are not representative of all \r\nextreme events that occurred, and results from these studies may also \r\nbe subject to selection bias, the large number of event attribution \r\nstudies provide evidence that changes in the properties of these \r\nlocal and individual events are in line with expected consequences \r\nof human influence on the climate and can be attributed to external \r\ndrivers (Section  11.9). Figure  11.4 summarizes assessments of \r\nobserved changes in temperature extremes, in heavy precipitation \r\nand in droughts, and their attribution in a map form. "


id: a4e652b6439b1f350905a069dc680557
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
content_type: text
index: climate_gpt_only_giec
vector_id: 23511
content:
"An increase in the concentration of greenhouse gases in the atmosphere leads to the warming of tropospheric air and the Earth's \r\nsurface. This direct thermodynamic effect leads to warmer temperatures everywhere, with an increase in the frequency and intensity \r\nof warm extremes, and a decrease in the frequency and intensity of cold extremes. The initial increase in temperature leads to other \r\nthermodynamic responses and feedbacks affecting the atmosphere and the surface. These include an increase in the water vapour \r\ncontent of the atmosphere (water vapour feedback, see Section 7.4.2.2) and a change in the vertical profile of temperature (lapse rate \r\nfeedback, see Section 7.4.2.2)"


id: 222e8daf665253faa8a4d66bff1c71e5
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
content_type: text
index: climate_gpt_only_giec
vector_id: 2825
content:
" While the water vapour feedback always amplifies the initial temperature increases (positive feedback), \r\nthe lapse rate feedback amplifies near-surface temperature increases (positive feedback) in mid- and high latitudes but reduces \r\ntemperature increases (negative feedback) in tropical regions (Pithan and Mauritsen, 2014). "


id: 50e44d397f9605de09e4cae5da56a0c7
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
content_type: text
index: climate_gpt_only_giec
vector_id: 10175
content:
"Changes in temperature also control changes in water vapour through increases in evaporation and in the water-holding capacity of \r\nthe atmosphere (Section 8.2.1). At the global scale, column-integrated water vapour content increases roughly following the Clausius-\r\nClapeyron (C-C) relation, with an increase of approximately 7% per 1degC of global-mean surface warming (Section 8.2.1). Nonetheless, \r\nat regional scales, water vapour increases differ from this C-C rate due to several reasons (Section 8.2.2), including a change in \r\nweather regimes and limitations in moisture transport from the ocean, which warms more slowly than land (Byrne and O'Gorman, \r\n2018). Observational studies (Fischer and Knutti, 2016; Sun et al., 2021) have shown that the observed rate of increased precipitation \r\nextremes is similar to the C-C rate at the global scale"


id: d9aaaab2d09ea62543b1ae62d4dbd614
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
content_type: text
index: climate_gpt_only_giec
vector_id: 31760
content:
" Climate model projections show that the increase in water vapour leads to \r\nrobust increases in precipitation extremes everywhere, with a magnitude that varies between 4% and 8%per 1degC of surface warming \r\n(thermodynamic contribution, Box 11.1, Figure 1b). At regional scales, climate models show that the dynamic contribution (Box 11.1, \r\nFigure 1c) can be substantial and strongly modify the projected rate of change of extreme precipitation (Box 11.1, Figure 1a) with large \r\nregions in the subtropics showing robust reductions and other areas (e.g., equatorial Pacific) showing robust amplifications (Box 11.1, \r\nFigure 1c). However, the dynamic contributions show large differences across models and are more uncertain than thermodynamic \r\ncontributions (Box 11.1, Figure 1c; Shepherd, 2014; Trenberth et al., 2015; Pfahl et al., 2017). "


id: 8eccb16a8513743b3074be21da5f956f
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
content_type: text
index: climate_gpt_only_giec
vector_id: 19991
content:
"Box 11.1, Figure 1: Multi-model Coupled Model Intercomparison Project Phase 5 (CMIP5) mean fractional changes (in % per degree of warming). \r\n(a) changes in annual maximum precipitation (Rx1day); (b) changes in Rx1day due to the thermodynamic contribution; and (c) changes in Rx1day due to the dynamic \r\ncontribution estimated as the difference between the total changes and the thermodynamic contribution. Changes were derived from a linear regression for the period \r\n1950-2100. Uncertainty is represented using the simple approach: no overlay indicates regions with high model agreement, where >=80% of models (n=22) agree \r\non the sign of change; diagonal lines indicate regions with low model agreement, where <80% of models agree on the sign of change. For more information on the \r\nsimple approach, please refer to the Cross-Chapter Box Atlas 1. A detailed description of the estimation of dynamic and thermodynamic contributions is given in Pfahl \r\net al. (2017)"


id: 9624ac793f3bbff5100d764002306887
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
content_type: text
index: climate_gpt_only_giec
vector_id: 21130
content:
" Figure adapted from Pfahl et al. (2017), originally published in Nature Climate Change/Springer Nature. Further details on data sources and processing \r\nare available in the chapter data table (Table 11.SM.9). "


id: 1be7d41102959b5683bf04e2bc91844f
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
content_type: text
index: climate_gpt_only_giec
vector_id: 1855
content:
"Extreme precipitation can also be enhanced by dynamic responses and feedbacks occurring within storms that result from the extra \r\nlatent heat released from the thermodynamic increases in moisture (Lackmann, 2013; Willison et al., 2013; Marciano et al., 2015; \r\nNie et al., 2018; Mizuta and Endo, 2020). The extra latent heat released within storms has been shown to increase precipitation \r\nextremes by strengthening convective updrafts and the intensity of the cyclonic circulation (e.g., Molnar et al., 2015; Nie et al., 2018), \r\nalthough weakening effects have also been found in mid-latitude cyclones (e.g., Kirshbaum et al., 2017). Additionally, the increase \r\nin latent heat can also suppress convection at larger scales due to atmospheric stabilization (Nie et al., 2018; Tandon et al., 2018; \r\nKendon et al., 2019)"


id: 71adf96b03c48f505b14a7ace9819d2f
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
content_type: text
index: climate_gpt_only_giec
vector_id: 15516
content:
" As these dynamic effects result from feedback processes within storms where convective processes are crucial, \r\ntheir proper representation might require improving the horizontal/vertical resolution, the formulation of parametrizations, or both, \r\nin current climate models (i.e., Kendon et al., 2014; Westra et al., 2014; Ban et al., 2015; Meredith et al., 2015; Prein et al., 2015; \r\nNie et al., 2018)."


id: de454dc0348c1fdbc8d86217a7de6ac2
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
content_type: text
index: climate_gpt_only_giec
vector_id: 32459
content:
"Atmospheric large-scale circulation patterns and associated \r\natmospheric dynamics are important determinants of the regional \r\nclimate (Chapter 10). As a result, they are also important to the magnitude, \r\nfrequency, and duration of extremes (Box 11.4). Aspects of changes in \r\nlarge-scale circulation patterns are assessed in Chapters 2, 3, 4 and 8, \r\nand representative atmospheric and oceanic modes are described in \r\nAnnex IV. This subsection provides some general concepts, through \r\na couple of examples, on why the uncertainty in the response of large\u0002scale circulation patterns to external forcing can cascade to uncertainty \r\nin the response of extremes to external forcings. Details for specific \r\ntypes of extremes are covered in the relevant subsections. For example, \r\nthe occurrence of the El Nino-Southern Oscillation (ENSO) influences \r\nprecipitation regimes in many areas, favouring droughts in some regions \r\nand heavy rains in others (Box 11.4)"


id: f2527509d9ea3e556065ab9a1e8ea07b
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
content_type: text
index: climate_gpt_only_giec
vector_id: 35653
content:
" The extent and strength of the Hadley \r\ncirculation influences regions where tropical and extratropical cyclones \r\noccur, with important consequences for the characteristics of extreme \r\nprecipitation, drought, and winds (Section 11.7). Changes in circulation \r\npatterns associated with land-ocean heat contrast, which affect the \r\nmonsoon circulations (Section 8.4.2.4), lead to heavy precipitation along \r\nthe coastal regions in East Asia (Freychet et al., 2015). As a result, changes \r\nin the spatial and/or temporal variability of the atmospheric circulation in \r\nresponse to warming affect characteristics of weather systems such \r\nas tropical cyclones (Sharmila and Walsh, 2018), storm tracks (Shaw \r\net al., 2016), and atmospheric rivers (Section 11.7; Waliser and Guan, \r\n2017)"


id: 42815ba4594194fa56f5eb9540139e44
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
content_type: text
index: climate_gpt_only_giec
vector_id: 7854
content:
" Changes in weather systems come with changes in the frequency \r\nand intensity of extreme winds, extreme temperatures, and extreme \r\nprecipitation, on the backdrop of thermodynamic responses of extremes \r\nto warming (Box 11.1). Floods are also affected by large-scale circulation \r\nmodes, including ENSO, the North Atlantic Oscillation (NAO), the Atlantic \r\nMulti-decadal Variability (AMV), and the Pacific Decadal Variability (PDV) \r\n(Kundzewicz et al., 2018; Annex IV). Aerosol forcing, through changes \r\nin patterns of sea surface temperatures (SSTs), also affects circulation \r\npatterns and tropical cyclone activities (Takahashi et al., 2017)."


id: a02f257bce85d8b7d8ebadf4b586d32a
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
content_type: text
index: climate_gpt_only_giec
vector_id: 22737
content:
"Land use can affect regional extremes, in particular hot extremes, \r\nin several ways (high confidence). This includes effects of land \r\nmanagement (e.g.,  cropland intensification, irrigation, double \r\ncropping) as well as of land cover changes (deforestation; Sections \r\n11.3.2 and 11.6). Some of these processes are not well represented \r\n(e.g.,  effects of forest cover on diurnal temperature cycle) or not \r\nintegrated (e.g.,  irrigation) in climate models (Sections 11.3.2 and \r\n11.3.3). Overall, the effects of land-use forcing may be particularly \r\nrelevant in the context of low-emissions scenarios, which include \r\nlarge land-use modifications, for instance those associated with the \r\nexpansion of biofuels, bioenergy with carbon capture and storage, \r\nor re-/afforestation to ensure negative emissions, as well as with the \r\nexpansion of food production (e.g., SR1.5, Chapter 3; Cross-Chapter \r\nBox 5.1 in this Report; van Vuuren et al., 2011; Hirsch et al., 2018)"


id: 9e2d2f5b4e55a2d90cefbc3620f36f01
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
content_type: text
index: climate_gpt_only_giec
vector_id: 22410
content:
" \r\nThere are also effects on the water cycle through freshwater use \r\n(Section 11.6 and Cross-Chapter Box 5.1)."


id: 45da279094fd531212ba2f6b4c6cced
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
content_type: text
index: climate_gpt_only_giec
vector_id: 8377
content:
"Aerosol forcing also has a strong regional footprint associated with \r\nregional emissions, which affects temperature and precipitation \r\nextremes (high confidence) (Sections 11.3 and 11.4). From around the \r\n1950s to 1980s, enhanced aerosol loadings led to regional cooling \r\ndue to decreased global solar radiation ('global dimming') which \r\nwas followed by a phase of 'global brightening' due to a reduction \r\nin aerosol loadings (Chapters 3 and 7; Wild et al., 2005). King et al. \r\n(2016b) show that aerosol-induced cooling delayed the timing of \r\na significant human contribution to record-breaking heat extremes in \r\nsome regions. However, the decreased aerosol loading since the 1990s \r\nhas led to an accelerated warming of hot extremes in some regions. \r\nBased on Earth system model (ESM) simulations, Dong et al"


id: b86c4c3a90238c5dea19b5c2d8f15b2d
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
content_type: text
index: climate_gpt_only_giec
vector_id: 26545
content:
" (2017) \r\nsuggest that a  substantial fraction of the warming of the annual \r\nhottest days in Western Europe since the mid-1990s has been due to \r\ndecreases in aerosol concentrations in the region. Dong et al. (2016b) \r\nalso identify non-local effects of decreases in aerosol concentrations \r\nin Western Europe, which they estimate played a dominant role in \r\nthe warming of the hottest daytime temperatures in north-east Asia \r\nsince the mid-1990s, via induced coupled atmosphere-land surface \r\nand cloud feedbacks, rather than a direct impact of anthropogenic \r\naerosol changes on cloud condensation nuclei. "


id: c2016a948014e69fedc99341e8c16b08
created_at: 2023-03-16 14:02:07
updated_at: 2023-03-16 14:02:07
content_type: text
index: climate_gpt_only_giec
vector_id: 28105
content:
"In general, changes in atmospheric large-scale circulation due to \r\nexternal forcing are uncertain, but there are some robust changes \r\n(Sections 2.3.1.4 and 8.2.2.2). Among them, there has been a very likely \r\nwidening of the Hadley circulation since the 1980s and the extratropical \r\njets and cyclone tracks have likely been shifting poleward since the \r\n1980s (Section  2.3.1.4). The poleward expansion affects drought \r\noccurrence in some regions (Section  11.6), and results in poleward \r\nshifts of tropical cyclones and storm tracks (Sections 11.7.1 and \r\n11.7.2). Although it is very likely that the amplitude of ENSO variability \r\nwill not robustly change over the 21st century (Section 4.3.3.2), the \r\nfrequency of extreme ENSO events (Box 11.4), defined by precipitation \r\nthreshold, is projected to increase with global warming (Section 6.5 \r\nof SROCC)"


id: f66e8bd3211b18394a9bae886a0cafe7
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" This would have implications for projected changes in \r\nextreme events affected by ENSO, including droughts over wide areas \r\n(Section 11.6; Box 11.4) and tropical cyclones (Section 11.7.1). A case \r\nstudy is provided for extreme ENSO events in 2015-2016 in Box 11.4 \r\nto highlight the influence of ENSO on extremes. "


id: 891d0e4dd7b92d21254af731bd7512f9
created_at: 2023-03-16 14:02:07
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"In addition to regional forcings, regional feedback mechanisms can \r\nalso substantially affect extremes (high confidence) (Sections 11.3, \r\n11.4 and 11.6). In particular, soil moisture feedbacks play an important \r\nrole for extremes in several mid-latitude regions, leading to a marked \r\nadditional warming of hot extremes compared to mean global warming \r\n(Seneviratne et al., 2016; Bathiany et al., 2018; Miralles et al., 2019), \r\nwhich is superimposed on the known land-sea contrast in mean \r\nwarming (Vogel et al., 2017). Soil moisture-atmosphere feedbacks \r\nalso affect drought development (Section 11.6). Additionally, effects of \r\nland surface conditions on circulation patterns have also been reported \r\n(Koster et al., 2016; Sato and Nakamura, 2019). These regional feedbacks \r\nare also associated with substantial spread in models (Section 11"


id: 57d5c0406a1f651dd5134702cebaddaf
created_at: 2023-03-16 14:02:07
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"3), \r\nand contribute to the identified higher spread of regional projections of \r\ntemperature extremes as a function of global warming, compared with \r\nthe spread resulting from the differences in projected global warming \r\n(global transient climate responses) in climate models (Seneviratne \r\nand Hauser, 2020). In addition, there are also feedbacks between soil \r\nmoisture content and precipitation occurrence, generally characterized \r\nby negative spatial feedbacks and positive local feedbacks (Taylor \r\net al., 2012; Guillod et al., 2015). Climate model projections suggest \r\nthat these feedbacks are relevant for projected changes in heavy \r\nprecipitation (Seneviratne et al., 2013). However, there is evidence that \r\nclimate models do not capture the correct sign of the soil moisture-\r\nprecipitation feedbacks in several regions, in particular spatially, and/\r\nor in some cases also temporally (Taylor et al., 2012; Moon et al., 2019)"


id: 87178617d347d99c039744d2cb213788
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" \r\nIn the Northern Hemisphere high latitudes, the snow- and ice-albedo \r\nfeedback, along with other factors, is projected to largely amplify \r\ntemperature increases (e.g., Pithan and Mauritsen, 2014), although the \r\neffect on temperature extremes is still unclear. It also remains unclear \r\nwhether snow-albedo feedbacks in mountainous regions might have \r\nan effect on temperature and precipitation extremes(e.g., Gobiet et al., "


id: 75c5e3322d8128c65f04d1b2132645ce
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"The SREX (Chapter 3) assigned low confidence to changes in low-likelihood, high-impact (LLHI) events (termed 'low-probability high\u0002impact scenarios'). Such events are often not anticipated and thus sometimes referred to as 'surprises'. There are several types of LLHI \r\nevents. Abrupt changes in mean climate are addressed in Chapter 4. Unanticipated LLHI events can either result from tipping points \r\nin the climate system (Section 1.4.4.3), such as the shutdown of the Atlantic thermohaline circulation (SROCC Chapter 6; Collins \r\net al., 2019) or the drydown of the Amazonian rainforest (SR1.5 Chapter 3, Hoegh-Guldberg et al., 2018; Drijfhout et al., 2015), or \r\nfrom uncertainties in climate processes, including climate feedbacks, that may enhance or damp extremes either related to global or \r\nregional climate responses (Seneviratne et al., 2018a; Sutton, 2018)"


id: 3367f8920e39f1dffee2261f0dcc4d66
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" The low confidence does not by itself exclude the possibility of \r\nsuch events occuring, rather it indicates a poor state of knowledge. Such outcomes, while improbable, could be associated with very \r\nhigh impacts, and are thus highly relevant from a risk perspective (see Section 1.4.3 and Box 11.4; Sutton, 2018, 2019). Alternatively, \r\nhigh impacts can occur when different extremes occur at the same time, or in short succession at the same location, or in several \r\nregions with shared vulnerability (e.g., food-basket regions Gaupp et al., 2019). These 'compound events' are assessed in Section 11.8, \r\nand Box 11.4 provides a case study example. "


id: 584073f33e658089e0748bde929d5aac
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"Difficulties persist in determining the likelihood of occurrence and time frame of potential tipping points and LLHI events. However, \r\nnew literature has emerged on unanticipated and LLHI events. There are some events that are sufficiently rare that they have not been \r\nobserved in meteorological records, but whose occurrence is nonetheless plausible within the current state of the climate system - \r\nsee examples below and in McCollum et al. (2020). The rare nature of such events and the limited availability of relevant data makes \r\nit difficult to estimate their occurrence probability and thus gives little evidence on whether to include such hypothetical events in \r\nplanning decisions and risk assessments. The estimation of such potential surprises is often limited to events that have historical \r\nanalogues (including before the instrumental records began, Wetter et al., 2014), albeit the magnitude of the event may differ"


id: 13f83393f7feecb66a954b0188aa237f
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" \r\nAdditionally, there is also a limitation of available resources to exhaust all plausible trajectories of the climate system. As a result, there \r\nwill still be events that cannot be anticipated. These events can be surprises to many in that the events have not been experienced, \r\nalthough their occurrence could be inferred by statistical means or physical modelling approaches (Chen et al., 2017; van Oldenborgh \r\net al., 2017; Harrington and Otto, 2018a). Another approach focusing on the estimation of low-probability events and of events \r\nwhose likelihood of occurrence is unknown consists in using physical climate models to create a physically self-consistent storyline of \r\nplausible extreme events and assessing their impacts and driving factors in past (Section 11.2.3) or future conditions (Section 11.2.4) \r\n(Hazeleger et al., 2015; Shepherd, 2016; Zappa and Shepherd, 2017; Cheng et al., 2018; Shepherd et al., 2018; Sutton, 2018; Schaller \r\net al., 2020; Wehrli et al., 2020). "


id: a988784d1b69d6d48ee1296b144e7206
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"One factor making such events hard to anticipate is the fact that we now live in a non-stationary climate, and that the framework of \r\nreference for adaptation is continuously moving. As an example, the concurrent heatwaves that occurred across the Northern Hemisphere \r\nin the summer of 2018 were considered very unusual and were unprecedented given the total area that was concurrently affected (Drouard \r\net al., 2019; Kornhuber et al., 2019; Toreti et al., 2019; Vogel et al., 2019); however, the probability of this event under 1degC global warming \r\nwas found to be about 16% (Vogel et al., 2019), which is not particularly low. Similarly, the 2013 summer temperature over eastern \r\nChina was the hottest on record at the time, but it had an estimated recurrence interval of about four years in the climate of 2013 \r\n(Sun et al., 2014). Furthermore, when other aspects of the risk, vulnerability, and exposure are historically high or have recently increased \r\n(see WGII, Chapter 16, Section 16"


id: 3da94f8c32bab52a53759faa22f5e66d
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"4), relatively moderate extremes can have very high impacts (Otto et al., 2015b; Philip et al., 2018b). \r\nAs warming continues, the climate moves further away from its historical state we are familiar with, resulting in an increased likelihood of \r\nunprecedented events and surprises. This is particularly the case under high global warming levels - for example, the climate of the late \r\n21st century under high-emissions scenarios, above 4degC of global warming (Cross-Chapter Box 11.1)."


id: 4ebe54d20203723aaffcaf7134150283
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"patterns with amplification of resulting responses to human-induced global warming. Also multivariate extremes at single locations \r\npose specific challenges to anticipation (Section 11.8), with low likelihoods in the current climate but the probability of occurrence of \r\nsuch compound events strongly increasing with increasing global warming levels (Vogel et al., 2020a). Therefore, in order to estimate \r\nwhether, and at what level of global warming, very high impacts arising from extremes would occur, the spatial extent of extremes \r\nand the potential of compounding extremes need to be assessed. Sections 11.3, 11.4, 11.7 and 11.8 highlight increasing evidence that \r\ntemperature extremes, higher intensity precipitation accompanying tropical cyclones, and compound events such as dry/hot conditions \r\nconducive to wildfire or storm surges resulting from sea level rise and heavy precipitation events, pose widespread threats to societies \r\nalready at relatively low warming levels"


id: 46396feb7754a126fe3422552c3c983a
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" Studies have already shown that the probability for some recent extreme events is so small in \r\nthe undisturbed world that these events were extremely unlikely to occur without human influence (Section 11.2.4). Box 11.2, Table 1, \r\nprovides examples of projected changes in LLHI extremes (single extremes, compound events) of potential relevance for impact and \r\nadaptation assessments showing that today's very rare events can become commonplace in a warmer future."


id: 21e4f6a24769099203363335c0c4ffef
created_at: 2023-03-16 14:02:08
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"Changes in extremes have also been examined from two \r\nperspectives:  changes in the frequency for a  given magnitude of \r\nextremes; or changes in the magnitude for a particular return period \r\n(frequency). Changes in the probability of extremes (e.g., temperature \r\nextremes) depend on the rarity of the extreme event that is assessed, \r\nwith a  larger change in probability associated with a  rarer event \r\n(e.g.,  Kharin et al., 2018). However, changes in the magnitude \r\nrepresented by the return levels of the extreme events may not be \r\nas sensitive to the rarity of the event. While the answers to the two \r\ndifferent questions are related, their relevance may differ for distinct \r\naudiences. Conclusions regarding the respective contribution of \r\ngreenhouse gas forcing to changes in magnitude versus frequency \r\nof extremes may also differ (Otto et al., 2012)"


id: 649526226166478e0db4ab1e9c2e0c0e
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" Correspondingly, the \r\nsensitivity of changes in extremes to increasing global warming is \r\nalso dependent on the definition of the considered extremes. In the \r\ncase of temperature extremes, changes in magnitude have been \r\nshown to often depend linearly on global surface temperature \r\n(Seneviratne et al., 2016; Wartenburger et al., 2017), while changes in \r\nfrequency tend to be nonlinear and can, for example, be exponential \r\nfor increasing global warming levels (Fischer and Knutti, 2015; Kharin \r\net al., 2018). When similar damage occurs once a  fixed threshold \r\nis exceeded, it is more important to ask a  question regarding \r\nchanges in the frequency. But when the exceedance of this fixed \r\nthreshold becomes a normal occurrence in the future, this can lead \r\nto a  saturation in the change of probability (Harrington and Otto, \r\n2018a). Also, if the impact of an event increases with the intensity \r\nof the event, it would be more relevant to examine changes in the \r\nmagnitude"


id: 51f9245603edd4b1569e2b2df579e0ad
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"In the literature, an event is generally considered extreme if the value \r\nof a variable exceeds (or lies below) a threshold. The thresholds have \r\nbeen defined in different ways, leading to differences in the meaning \r\nof extremes that may share the same name. For example, two sets \r\nof metrics for the frequency of hot/warm days have been used in \r\nthe literature. One set counts the number of days when maximum \r\ndaily temperature is above a relative threshold defined as the 90th or \r\nhigher percentile of maximum daily temperature for the calendar day \r\nover a base period. An event based on such a definition can occur at \r\nany time of the year, and the impact of such an event would differ \r\ndepending on the season"


id: 654e00fe7664eeb5b65a3e4651547ef
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" The other set counts the number of days \r\nin which maximum daily temperature is above an absolute threshold \r\nsuch as 35degC, because exceeding this temperature can sometimes \r\ncause health impacts (however, these impacts may depend on \r\nlocation and whether ecosystems and the population are adapted \r\nto such temperatures). While both types of hot extreme indices \r\nhave been used to analyse changes in the frequency of hot/warm \r\nevents, they represent different events that occur at different times "


id: c0752695b78cfbd142532e3eb0e0de40
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"In general, changes in temperature extremes from various reanalyses \r\nwere most consistent with gridded observations after about 1980, \r\nbut larger differences were found during the pre-satellite era (Donat \r\net al., 2014b). Overall, lower agreement across reanalysis datasets \r\nwas found for extreme precipitation changes, although temporal \r\nand spatial correlations against observations were found to be still \r\nsignificant. In regions with sparse observations (e.g., Africa and parts \r\nof South America), there is generally less agreement for extreme \r\nprecipitation between different reanalysis products, indicating \r\na  consequence of the lack of an observational constraint in these \r\nregions (Donat et al., 2014b, 2016a). More recent reanalyses, such as \r\nERA5 (Hersbach et al., 2020), seem to have improved over previous \r\nproducts, at least over some regions (e.g., Mahto and Mishra, 2019; \r\nGleixner et al., 2020; Sheridan et al., 2020)"


id: 167e8fd61d8b53defbb9987c023beed3
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" Caution is needed when \r\nreanalysis data products are used to provide additional information \r\nabout past changes in these extremes in regions where observations \r\nare generally lacking. "


id: 43ddac8650c21173e8bc15cdb63635b0
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"Extreme weather and climate events occur on time scales of \r\nhours (e.g.,  convective storms that produce heavy precipitation) \r\nto days (e.g.,  tropical cyclones, heatwaves), to seasons and years \r\n(e.g., droughts). A robust determination of long-term changes in these \r\nevents can have different requirements for the spatial and temporal \r\nscales and sample size of the data. In general, it is more difficult \r\nto determine long-term changes for events of fairly large temporal \r\nduration, such as 'megadroughts' that last several years or longer \r\n(e.g., Ault et al., 2014), because of the limitations of the observational \r\nsample size. Literature that studies changes in extreme precipitation \r\nand temperature often uses indices representing specifics of extremes \r\nthat are derived from daily precipitation and temperature values. \r\nStation-based indices would have the same issues as those for the \r\nmean climate regarding the quality, availability, and homogeneity of \r\nthe data"


id: 15aee7239e513e58a39b080b16d10ceb
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" For the purpose of constructing regional information and/\r\nor for comparison with model outputs, such as model evaluation, \r\nand detection and attribution, these station-based indices are often \r\ninterpolated onto regular grids. Two different approaches, involving \r\ntwo different orders of operation, have been used in producing such \r\ngridded datasets. "


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"Many factors affect confidence in information on pre-instrumental extremes. First, the geographical coverage of paleoclimate \r\nreconstructions of extremes is not spatially uniform (Smerdon and Pollack, 2016) and depends on both the availability of archives and \r\nrecords, which are environmentally dependent, and also the differing attention and focus from the scientific community. In Australia, \r\nfor example, the paleoclimate network is sparser than for other regions, such as Asia, Europe and North America, and synthesized \r\nproducts rely on remote proxies and assumptions about the spatial coherence of precipitation between remote climates (Cook et al., \r\n2016c; Freund et al., 2017). Second, pre-instrumental evidence of extremes may be focused on understanding archetypal extreme \r\nevents, such as the climatic consequences of the 1815 eruption of Mount Tambora, Indonesia (Veale and Endfield, 2016). These studies \r\nprovide narrow evidence of extremes in response to specific forcings (M"


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" Li et al., 2017) for specific epochs. Third, natural archives may \r\nprovide information about extremes in one season only and may not represent all extremes of the same types. "


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"Evidence of shorter duration extreme event types, such as floods and tropical storms, is further restricted by the comparatively low \r\nchronological controls and temporal resolution (e.g., monthly, seasonal, yearly, multiple years) of most archives compared to the \r\nevents (e.g., minutes to days). Natural archives may be sensitive only to intense environmental disturbances, and so only sporadically \r\nrecord short-duration or small spatial-scale extremes. Interpreting sedimentary records as evidence of past short-duration extremes is \r\nalso complex and requires a clear understanding of natural processes (Wilhelm et al., 2019). For example, paleoflood reconstructions \r\nof flood recurrence and intensity produced from geological evidence (e.g., river and lake sediments), speleothems (Denniston and \r\nLuetscher, 2017), botanical evidence (e.g.,  flood damage to trees, or tree ring reconstructions), and floral and faunal evidence \r\n(e.g"


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",  diatom fossil assemblages) require understanding of sediment sources and flood mechanisms. Pre-instrumental records of \r\ntropical storm intensity and frequency (also called paleotempest records) derived from overwash deposits of coastal lake and marsh \r\nsediments are difficult to interpret. Many factors have an impact on whether disturbances are deposited in archives (Muller et al., \r\n2017) and deposits may provide sporadic and incomplete preservation histories (e.g., Tamura et al., 2018). "


id: a57638cd39620bdb74622b8c0f231b9b
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"There is high confidence in the occurrence of long-duration and severe drought events during the Common Era for many locations, \r\nalthough their severity compared to recent drought events differs between locations and the lengths of reconstruction provided. \r\nRecent observed drought extremes in some regions - such as the eastern Mediterranean Levant (Cook et al., 2016a), California in the \r\nUSA (Cook et al., 2014b; Griffin and Anchukaitis, 2014), and in the Andes (Garreaud et al., 2017; Dominguez-Castro et al., 2018) - do \r\nnot have precedents within the multi-century periods reconstructed in these studies, in terms of duration and/or severity. In some \r\nregions (in south-western North America (Asmerom et al., 2013; Cook et al., 2015), the Great Plains region (Cook et al., 2004), the \r\nMiddle East (Kaniewski et al., 2012), and China (Gou et al., 2015)), recent drought extremes may have been exceeded in the Common \r\nEra"


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"There is high confidence that the magnitude of floods over the Common Era exceeded observed records in some locations, including \r\nCentral Europe and eastern Asia. Recent literature supports the AR5 assessments of floods (Masson-Delmotte et al., 2013). For example, \r\nhigh temporally resolved records provide evidence of Common Era floods exceeding the probable maximum flood levels in the Upper \r\nColorado River, USA (Greenbaum et al., 2014) and peak discharges that are double gauge levels along the middle Yellow River, China \r\n(Liu et al., 2014). Further studies demonstrate pre-instrumental or early instrumental differences in flood frequency compared to \r\nthe instrumental period, including reconstructions of high and low flood frequency in the European Alps (e.g., Swierczynski et al., \r\n2013; Amann et al., 2015) and Himalayas (Ballesteros Canovas et al., 2017)"


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" The combination of extreme historical flood episodes \r\ndetermined from documentary evidence also increases confidence in the determination of flood frequency and magnitude, compared \r\nto using geomorphological archives alone (Kjeldsen et al., 2014). In regions, such as Europe and China, that have rich historical flood \r\ndocuments, there is strong evidence of high-magnitude flood events over pre-instrumental periods (Kjeldsen et al., 2014; Benito et al., \r\n2015; Macdonald and Sangster, 2017). A key feature of paleoflood records is variability in flood recurrence at centennial timescales \r\n(Wilhelm et al., 2019), although constraining climate-flood relationships remains challenging. Pre-instrumental floods often occurred \r\nin considerably different contexts in terms of land use, irrigation, and infrastructure, and may not provide direct insight into modern \r\nriver systems, which further prevents long-term assessments of flood changes being made based on these sources. "


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"are considered and often utilize fully coupled climate models (Sun \r\net al., 2014). Other more conditional approaches involve prescribing \r\ncertain aspects of the climate system. These range from prescribing \r\nthe pattern of the surface ocean change at the time of the event \r\n(e.g.,  Hoerling et al., 2013, 2014), often using Atmospheric Model \r\nIntercomparison Project (AMIP) style global models, where the \r\nchoice of sea surface temperature and ice patterns influences the \r\nattribution results (Sparrow et al., 2018), to prescribing the large\u0002scale circulation of the atmosphere and using weather forecasting \r\nmodels or methods (e.g., Pall et al., 2017; Patricola and Wehner, 2018; \r\nWehner et al., 2018a). These highly conditional approaches have also \r\nbeen called 'storylines' (Cross-Working Group Box  1.1; Shepherd, \r\n2016) and can be useful when applied to extreme events that are \r\ntoo rare to otherwise analyse, or where the specific atmospheric \r\nconditions were central to the impact"


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" These methods are also used \r\nto enable the use of very high-resolution simulations in cases were \r\nlower-resolution models do not simulate the regional atmospheric \r\ndynamics well (Shepherd, 2016; Shepherd et al., 2018). However, the \r\nimposed conditions limit an overall assessment of the anthropogenic \r\ninfluence on an event, as the fixed aspects of the analysis may also \r\nhave been affected by climate change. For instance, the specified \r\ninitial conditions in the highly conditional hindcast attribution \r\napproach often applied to tropical cyclones (e.g.,  Takayabu \r\net al., 2015; Patricola and Wehner, 2018) permit only a conditional \r\nstatement about the magnitude of the storm if similar large-scale \r\nmeteorological patterns could have occurred in a  world without \r\nclimate change, thus precluding any attribution statement about \r\nthe change in frequency if used in isolation"


id: 486394413852b7b438f7163a9d3fac1
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" Combining conditional \r\nassessments of changes in the intensity with a multi-model approach \r\ndoes allow for the latter as well (Shepherd, 2016)."


id: 43b05e74b84166e1fd22c481f22e6076
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"Attribution science concerns the identification of causes for changes \r\nin characteristics of the climate system (e.g., trends, single extreme \r\nevents). A general overview and summary of methods of attribution \r\nscience is provided in  the Cross-Working Group Box  1.1. Trend \r\ndetection using optimal fingerprinting methods is a well-established \r\nfield, and has been assessed in AR5 (Chapter 10, Bindoff et al., 2013), \r\nand Section 3.2.1 of this Report. There are specific challenges when \r\napplying optimal fingerprinting to the detection and attribution of \r\ntrends in extremes and on regional scales where the lower signal\u0002to-noise ratio is a  challenge. In particular, the method generally \r\nrequires the data to follow a Normal (Gaussian) distribution, which \r\nis often not the case for extremes"


id: 198b0118a1e0201e598e5e1b9dc28d70
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" However, recent studies showed \r\nthat extremes can be transformed to a  Gaussian distribution, for \r\nexample, by averaging over space, so that optimal fingerprinting \r\ntechniques can still be used (Wen et al., 2013; Zhang et al., 2013; \r\nWan et al., 2019). Non-stationary extreme value distributions, which \r\nallow for the detailed detection and attribution of regional trends in \r\ntemperature extremes, have also been used (Z. Wang et al., 2017a). "


id: 97b215c9b6d1c64d621c0c6074700eb1
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"The outcome of event attribution is dependent on the definition of \r\nthe event (Leach et al., 2020), as well as the framing (Otto et al., \r\n2016; Christidis et al., 2018; Jezequel et al., 2018) and uncertainties \r\nin observations and modelling. Observational uncertainties arise in \r\nestimating the magnitude of an event as well as its rarity (Angelil \r\net al., 2017). Results of attribution studies can also be very sensitive \r\nto the choice of climate variables (Sippel and Otto, 2014; Wehner \r\net al., 2016). Attribution statements are also dependent on the spatial \r\n(Uhe et al., 2016; Cattiaux and Ribes, 2018; Kirchmeier-Young et al., \r\n2019) and temporal (Harrington, 2017; Leach et al., 2020) extent \r\nof event definitions, as events of different scales involve different \r\nprocesses (W. Zhang et al., 2020) and large-scale averages generally \r\nyield higher attributable changes in magnitude or probability due \r\nto the smoothing out of noise"


id: a0585792dcbf6845c7a7634eec6cc1b6
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" In general, confidence in attribution \r\nstatements for large-scale heat and lengthy extreme precipitation \r\nevents have higher confidence than shorter and more localized events, \r\nsuch as extreme storms, an aspect also relevant for determining the \r\nemergence of signals in extremes or the confidence in projections \r\n(see also Cross-Chapter Box Atlas.1). "


id: 6f2222f8cc20855cdc83a415c56e2155
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"However, since AR5, the attribution of extreme weather events has \r\nemerged as a growing field of climate research with an increasing \r\nbody of literature (see series of supplements to the annual State \r\nof the Climate report (Peterson et al., 2012, 2013a; Herring et al., \r\n2014, 2015, 2016, 2018), including the number of approaches to \r\nexamining extreme events (described in Easterling et al., 2016; \r\nOtto, 2017; Stott et al., 2016)). A commonly used approach - often \r\ncalled the risk-based approach in the literature, and referred to \r\nhere as the 'probability-based approach'  - produces statements \r\nsuch as 'anthropogenic climate change made this event type twice \r\nas likely' or 'anthropogenic climate change made this event 15% \r\nmore intense'. This is done by estimating probability distributions of \r\nthe index characterizing the event in today's climate, as well as in \r\na counterfactual climate, and either comparing intensities for a given \r\noccurrence probability (e.g"


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",  1-in-100-year event) or probabilities \r\nfor a  given magnitude (see FAQ 11.3). There are a  number of \r\ndifferent analytical methods encompassed in the probability-based \r\napproach, building on observations and statistical analyses (e.g., van \r\nOldenborgh et al., 2012), optimal fingerprint methods (Sun et al., \r\n2014), regional climate and weather forecast models (e.g., Schaller \r\net al., 2016), global climate models (GCMs) (e.g., Lewis and Karoly, \r\n2013), and large ensembles of atmosphere-only GCMs (e.g.,  Lott \r\net al., 2013). A key component in any event attribution analysis is \r\nthe level of conditioning on the state of the climate system. In the \r\nleast conditional approach, the combined effect of the overall \r\nwarming and changes in the large-scale atmospheric circulation "


id: cf66c7f3d938d41f86fc027e202310f8
created_at: 2023-03-16 14:02:08
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"The lack of model evaluation, in particular in early event attribution \r\nstudies, has led to criticism of the emerging field of attribution science \r\nas a whole (Trenberth et al., 2015) and of individual studies (Angelil \r\net al., 2017). In this regard, the storyline approach (Shepherd, 2016) \r\nprovides an alternative option that does not depend on the model's \r\nability to represent the circulation reliably. In addition, several ways \r\nof quantifying statistical uncertainty (Paciorek et al., 2018) and model \r\nevaluation (Lott and Stott, 2016; Philip et al., 2018b, 2020) have been \r\nemployed to evaluate the robustness of event attribution results. \r\nFor the unconditional probability-based approach, multi-model and \r\nmulti-approach (e.g.,  combining observational analyses and model \r\nexperiments) methods have been used to improve the robustness of \r\nevent attribution (Hauser et al., 2017; Otto et al., 2018a; Philip et al., \r\n2018b, 2019, 2020; van Oldenborgh et al., 2018; Kew et al"


id: fee7d2139cf4769468d5837d12fd6f71
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"While regional changes in many types of extremes do scale robustly \r\nwith global surface temperature, generally irrespective of emissions \r\nscenarios (Section  11.1.4, Figures 11.3, 11.6 and 11.7 and Cross\u0002Chapter Box 11.1), effects of local forcing can distort this relation. \r\nFor example, emissions scenarios with the same radiative forcing \r\ncan have different regional extreme precipitation responses resulting \r\nfrom different aerosol forcing (Z. Wang et al., 2017b). Another \r\nexample is related to forcing from land-use and land cover changes \r\n(Section  11.1.6). Climate models often either overestimate or \r\nunderestimate observed changes in annual maximum daily maximum \r\ntemperature, depending on the region and considered models (Donat \r\net al., 2017; Vautard et al., 2020). Part of the discrepancies may be \r\ndue to the lack of representation of some land forcings, in particular \r\ncrop intensification and irrigation (N.D. Mueller et al., 2016; Findell \r\net al., 2017; Thiery et al"


id: 25b3004a743d85ab27059eaf56c59f17
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", 2017, 2020). Since these local forcings are \r\nnot represented, and their future changes are difficult to project, \r\nthese can be important caveats when using GWL scaling to project \r\nfuture changes for these regions. However, these caveats also apply \r\nto the use of scenario-based projections."


id: 22b352dbcc48f572591a1e6a3508b9ac
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"The most important quantity used to characterize past and future \r\nclimate change is global warming relative to its pre-industrial \r\nlevel. Changes in global warming are linked quasi-linearly to \r\nglobal cumulative carbon dioxide (CO2) emissions (IPCC, 2013), \r\nand for their part, changes in regional climate, including many \r\ntypes of extremes, scale quasi-linearly with changes in global \r\nwarming, often independently of the underlying emissions scenarios \r\n(SR1.5 Chapter  3; Seneviratne et al., 2016; Matthews et al., 2017; \r\nWartenburger et al., 2017; Kharin et al., 2018; Y. Sun et al., 2018a; \r\nTebaldi and Knutti, 2018; Beusch et al., 2020; Li et al., 2021). \r\nIn addition, the use of global warming levels in the context of global \r\npolicy documents - in particular the 2015 Paris Agreement (UNFCCC, \r\n2016) implies that information on changes in the climate system, \r\nand specifically extremes, as a  function of global warming are of \r\nparticular policy relevance. Cross-Chapter Box  11"


id: 434133c601f81f594cc9e484932bb58b
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"1 provides an \r\noverview on the translation between information at global warming \r\nlevels (GWLs) and scenarios."


id: 5ebb3cf9e2973d5bc52887ac8efb8424
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"Wartenburger et al., 2017; Betts et al., 2018; Dosio and Fischer, 2018; Samset et al., 2019; Tebaldi et al., 2020; see Sections 4.6.1, 8.5.3, \r\n9.3.1, 9.5, 9.6.3, 10.4.3 and 11.2.4 for further details). The regional response patterns at given GWLs have been found to be consistent \r\nacross different scenarios for many climate variables (Cross-Chapter Box 11.1 Figure 2; Pendergrass et al., 2015; Seneviratne et al., \r\n2016; Wartenburger et al., 2017; Seneviratne and Hauser, 2020). The consistency tends to be higher for temperature-related variables \r\nthan for variables in the hydrological cycle or variables characterizing atmospheric dynamics, and for intermediate to high-emissions \r\nscenarios than for low-emissions scenarios (e.g.,  for mean precipitation in the Representative Concentration Pathway (RCP) 2.6 \r\nscenario: Pendergrass et al., 2015; Wartenburger et al., 2017). Nonetheless, Cross-Chapter Box 11"


id: 50d71e287b308d6b6a56ad0fce638722
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"1 Figure 2 illustrates that, even for \r\nmean precipitation, which is known to be forcing dependent (Sections 4.6.1 and 8.5.3), scenario differences in the response pattern at \r\na given GWL are smaller than model uncertainty and internal variability in many regions (Herger et al., 2015). The response pattern is \r\nfurther found to be broadly consistent between models that reach a GWL relatively early, and those that reach it later under a given \r\nShared Socio-economic Pathway (SSP; see Cross-Chapter Box 11.1, Figure 2g,h)."


id: d244d95f4cc5c25cfea8a32ae1a2883c
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"The limited scenario dependence of the GWL-based response for many variables implies that the regional response to emissions \r\nscenarios can be split in almost independent contributions of: (i) the transient global warming response to scenarios (see Chapter 4); \r\nand (ii) the regional response as function of a given GWL, which has also been referred to as 'regional climate sensitivity' (Seneviratne \r\nand Hauser, 2020). This property has also been used to develop regionally resolved emulators for global climate models, using global \r\nsurface temperature as input (Beusch et al., 2020; Tebaldi et al., 2020). Analyses of the CMIP6 and CMIP5 multi-model ensembles \r\nshows that the GWL-based responses are very similar for temperature and precipitation extremes across the ensembles (Seneviratne \r\nand Hauser, 2020; Wehner, 2020; Li et al., 2021)"


id: 6e06122efe9b2c617615493e2ed523f4
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" This is despite their difference in global warming response (Chapter 4), confirming \r\na substantial decoupling between the two responses (global warming vs GWL-based regional response) for these variables. Thus, the \r\nGWL approach isolates the uncertainty in the regional climate response from the global warming uncertainty induced by scenario, \r\nglobal mean model response and internal variability (Cross-Chapter Box, Figure 1)."


id: e1f5274ec42ce3ac458567ebc27b52b1
created_at: 2023-03-16 14:02:08
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"Cross-Chapter Box 11.1, Figure 2 | (a-c) Coupled Model Intercomparison Project Phase 6 (CMIP6) multi-model mean precipitation change at 2degC \r\nglobal warming level (GWL) (20-year mean) in three different Shared Socio-economic Pathway (SSP) scenarios relative to 1850-1900. All models \r\nreaching the corresponding GWL in the corresponding scenario are averaged. The number of models averaged across is shown at the top right of the panel. The maps \r\nfor the other two SSP scenarios SSP1-1.9 (five models only) and SSP3-7.0 (not shown) are consistent. (d-f) Same as (a-c) but for annual mean temperature. (g)\r\nAnnual mean temperature change at 2degC in CMIP6 models with high warming rate reaching the GWL in the corresponding scenario before the earliest year of the \r\nassessed very likely range (Section 4.3.4). (h) Climate response at 2degC GWL across all SSP1-1.9, SSP2-2.6, SSP2-4.5. SSP3-7.0 and SSP5-8.5 in all other models not \r\nshown in (g)"


id: 805ec26eef8fe0ceafd95fce47778a12
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" The close agreement of (g) and (h) demonstrates that the mean temperature response at 2degC is not sensitive to the rate of warming, and thereby the \r\nglobal mean surface air temperature (GSAT) warming of the respective models in 2081-2100. Uncertainty is represented using the advanced approach: No overlay \r\nindicates regions with robust signal, where >=66% of models show change greater than the variability threshold and >=80% of all models agree on the sign of change; \r\ndiagonal lines indicate regions with no change or no robust signal, where <66% of models show a change greater than the variability threshold; crossed lines indicate \r\nregions with conflicting signal, where >=66% of models show change greater than the variability threshold and <80% of all models agree on the sign of change. For \r\nmore information on the advanced approach, please refer to the Cross-Chapter Box Atlas.1."


id: b05cf7edfeebb5bf9d8ed91a5c2fca57
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"In the model-based asssessment of Chapters 4, 8, 10, 11, 12 and the Atlas, the estimation of changes at GWLs are generally defined as \r\nthe 20-year time period in which the mean global surface air temperature (GSAT; Cross-Chapter Box 2.3) first exceeds a certain anomaly \r\nrelative to 1850-1900 - for simulations that start after 1850, relative to all years up to 1900 (Cross-Chapter Box Figure 3). The years \r\nwhen each individual model reaches a given GWL for CMIP6 and CMIP5 can be found in Hauser et al. (2021). The changes at given GWLs \r\nare identified for each ensemble member (for all scenarios) individually. Thereby, a given GWL is potentially reached a few years earlier \r\nor later in different realizations of the same model due to internal variability, but the temperature averaged across the 20-year period \r\nanalysed in any simulation is consistent with the GWL"


id: d07099128a50876b2932eec5f39ee0b1
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" Instead of blending the information from the different scenarios, the Interactive \r\nAtlas can be used to compare the GWL spatial patterns and timings across the different scenarios (see Section Atlas 1.3.1). "


id: a30af0b58985b7ae4f02e61a2fa08489
created_at: 2023-03-16 14:02:08
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index: climate_gpt_only_giec
vector_id: 23216
content:
"2019). Soil-moisture-temperature feedbacks affect past and present\u0002day heatwaves in observations and model simulations, both locally \r\n(Miralles et al., 2014a; Cowan et al., 2016, 2020; Hauser et al., 2016; \r\nMeehl et al., 2016; Wehrli et al., 2019) and beyond the regions of \r\nfeedback occurrence through changes in regional circulation patterns \r\n(Stefanon et al., 2014; Koster et al., 2016; Sato and Nakamura, 2019). \r\nThe uncertainty due to the representation of land-atmosphere \r\nfeedbacks in ESMs is a cause of discrepancy between observations \r\nand simulations (Clark et al., 2006; Mueller and Seneviratne, 2014; \r\nMeehl et al., 2016). The decrease of plant transpiration or the \r\nincrease of stomata resistance under enhanced CO2 concentrations \r\nis a direct CO2 forcing of land temperatures (warming due to reduced \r\nevaporative cooling), which contributes to higher warming on land \r\n(Lemordant et al., 2016; Vicente-Serrano et al., 2020b)"


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" The snow/ice\u0002albedo feedback plays an important role in amplifying temperature \r\nvariability in the high latitudes (Diro et al., 2018) and can be the \r\nlargest contributor to the rapid warming of cold extremes in the mid\u0002and high latitudes of the Northern Hemisphere (Gross et al., 2020). "


id: 924aac28aef660190e39161923c351eb
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"Extreme temperature events are associated with large-scale \r\nmeteorological patterns (Grotjahn et al., 2016). Quasi-stationary \r\nanticyclonic circulation anomalies or atmospheric blocking events are \r\nlinked to temperature extremes in many regions, such as in Australia \r\n(Parker et al., 2014; Perkins-Kirkpatrick et al., 2016), Europe (Brunner \r\net al., 2017, 2018; Schaller et al., 2018), Eurasia (Yao et al., 2017), \r\nAsia (Chen et al., 2016; Ratnam et al., 2016; Rohini et al., 2016), \r\nand North America (Yu et al., 2018, 2019; Zhang and Luo, 2019). \r\nMid-latitude planetary wave modulations affect short-duration \r\ntemperature extremes such as heatwaves (Perkins, 2015; Kornhuber \r\net al., 2020). The large-scale modes of variability (Annex IV) affect \r\nthe strength, frequency and persistence of these meteorological \r\npatterns and, hence, temperature extremes"


id: eb05ffc4d26a1d12c0edcc7579cc750d
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" For example, cold \r\nand warm extremes in the mid-latitudes are associated with \r\natmospheric circulation patterns such as the Pacific-North American \r\n(PNA) pattern, as well as atmosphere-ocean coupled modes such \r\nas Pacific Decadal Variability (PDV), the North Atlantic Oscillation \r\n(NAO), and Atlantic Multi-decadal Variability (AMV) (Section 11.1.5; \r\nKamae et al., 2014; Johnson et al., 2018; Ruprich-Robert et al., 2018; \r\nYu et al., 2018, 2020; Muller et al., 2020; Qasmi et al., 2021). Changes \r\nin the modes of variability in response to warming would therefore \r\naffect temperature extremes (Clark and Brown, 2013; Horton \r\net al., 2015). The level of confidence in those changes varies, both \r\nin the observations and in future projections, affecting the level of \r\nconfidence in changes in temperature extremes in different regions"


id: 4d17343ee65a8b1a8e4786bd40b868b9
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" \r\nAs highlighted in Chapters 2  to 4  of this Report, it is likely that \r\nthere have been observational changes in the extratropical jets and \r\nmid-latitude jet meandering (Section  2.3.1.4.3 and Cross-Chapter \r\nBox  10.1). There is low confidence in possible effects of Arctic \r\nwarming on mid-latitude temperature extremes (Cross-Chapter \r\nBox 10.1). A large portion of the multi-decadal changes in extreme \r\ntemperature remains after the removal of the effect of these modes \r\nof variability, and can be attributed to human influence (Kamae et al., \r\n2017b; Wan et al., 2019). Thus, global warming dominates changes in \r\ntemperature extremes at the regional scale and it is very unlikely that \r\ndynamic responses to greenhouse-gas induced warming would alter \r\nthe direction of these changes. "


id: dc81062b4d55b06fccb0ebf72c6f0bd4
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"Regional external forcings, including land-use changes and \r\nemissions of anthropogenic aerosols, play an important role in the \r\nchanges of temperature extremes in some regions (high confidence) \r\n(Section  11.1.6). Deforestation may have contributed to about \r\none third of the warming of hot extremes in some mid-latitude \r\nregions since the pre-industrial time (Lejeune et al., 2018). Aspects \r\nof agricultural practice, including no-till farming, irrigation, and \r\noverall cropland intensification, may cool hot temperature extremes \r\n(Davin et al., 2014; N.D. Mueller et al., 2016). For instance, cropland \r\nintensification has been suggested to be responsible for a cooling of \r\nthe highest temperature percentiles in Midwest USA (N.D. Mueller \r\net al., 2016). Irrigation has been shown to be responsible for \r\na cooling of hot temperature extremes of up to 1degC-2degC in many \r\nmid-latitude regions in the present climate (Thiery et al"


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", 2017, 2020), \r\na process not represented in most of state-of-the-art ESMs (CMIP5, \r\nCMIP6). Double cropping may have led to increased hot extremes \r\nin the inter-cropping season in part of China (Jeong et al., 2014). \r\nRapid increases in summer warming in western Europe and north\u0002east Asia since the 1980s are linked to a reduction in anthropogenic \r\naerosol precursor emissions over Europe (Nabat et al., 2014; Dong \r\net al., 2016b, 2017), in addition to the effect of increased greenhouse \r\ngas forcing (see also Section  10.1.3.1). This effect of aerosols on \r\ntemperature-related extremes is also noted for declines in short-lived \r\nanthropogenic aerosol emissions over North America (Mascioli et al., \r\n2016). On the local scale, the urban heat island (UHI) effect results \r\nin higher temperatures in urban areas than in their surrounding \r\nregions, and contributes to warming in regions of rapid urbanization, \r\nin particular for nighttime temperature extremes (Box 10.3; Phelan \r\net al"


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content:
", 2015; Chapman et al., 2017; Y. Sun et al., 2019). But these local \r\nand regional forcings are generally not or not well represented in the \r\nCMIP5 and CMIP6 simulations (see also Section 11.3.3), contributing \r\nto uncertainty in model simulated changes. \r\nIn summary, greenhouse gas forcing is the dominant driver leading \r\nto the warming of temperature extremes. At regional scales, changes \r\nin temperature extremes are modulated by changes in large-scale \r\npatterns and modes of variability, feedbacks including soil-moisture-\r\nevapotranspiration-temperature or snow/ice-albedo-temperature "


id: 37e7f064107f1cea0b92ae13fce14fb2
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"warming of TXx in Europe and north-western South America, and \r\na particularly high warming of TNn in the Arctic. Consistent with the \r\nobserved warming in global surface temperature (Section 2.3.1.2) \r\nand the observed trends in TXx and TNn, the frequency of TX90p \r\nhas increased, while that of cold nights (TN10p) has decreased since \r\nthe 1950s: Nearly all land regions showed statistically significant \r\ndecreases in TN10p (Alexander, 2016; Dunn et al., 2020), though \r\ntrends in TX90p are variable with some decreases in Southern South \r\nAmerica, mainly during austral summer (Rusticucci et al., 2017). \r\nA decrease in the number of cold spell days is also observed over \r\nnearly all land surface areas (Easterling et al., 2016) and in the \r\nnorthern mid-latitudes in particular (van Oldenborgh et al., 2019). \r\nThese observed changes are also consistent when a  new global \r\nland surface daily air temperature dataset is analysed (P. Zhang \r\net al., 2019)"


id: e256467a67ba1fd780815a4e3718ef2b
created_at: 2023-03-16 14:02:08
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" Warming trends in temperature extremes globally, \r\nand in most land areas, over the path  century are also found to \r\nbe consistent in a  range of observation-based datasets (Fischer \r\nand Knutti, 2014; Donat et al., 2016a; Dunn et al., 2020), with the \r\nextremes related to daily minimum temperatures changing faster \r\nthan those related to daily maximum temperatures (Dunn et al., \r\n2020; see Figure 11.2). Seasonal variations in trends in temperature\u0002related extremes have been demonstrated. A  warming in warm\u0002season temperature extremes is detected, even during the 'slower \r\nsurface global warming' period from the late 1990s to early 2010s \r\n(Cross-Chapter Box 3.1; Kamae et al., 2014; Seneviratne et al., 2014; \r\nImada et al., 2017). Many studies of past changes in temperature \r\nextremes for particular regions or countries show trends consistent \r\nwith this global picture, as summarized below and in Tables 11.4, \r\n11.7, 11.10, 11.13, 11.16 and 11.19. "


id: fd57d13f30873a9cf267fed88b2cd98c
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"Since SREX (IPCC, 2012) and AR5 (IPCC, 2014), many regional\u0002scale studies have examined trends in temperature extremes using \r\ndifferent metrics that are based on daily temperatures, such as the \r\nCommission for Climatology/World Climate Research Program/\r\nCommission for Oceanography and Marine Meteorology joint Expert \r\nTeam on Climate Change Detection and Indices (ETCCDI) indices \r\n(Dunn et al., 2020). The additional observational records, along with \r\na stronger warming signal, show very clearly that changes observed \r\nat the time of AR5 (IPCC, 2014) continued, providing strengthened \r\nevidence of an increase in the intensity and frequency of hot extremes \r\nand decrease in the intensity and frequency of cold extremes. While \r\nthe magnitude of the observed trends in temperature-related \r\nextremes varies depending on the region, spatial and temporal \r\nscales, and metric assessed, evidence of a  warming effect is \r\noverwhelming, robust, and consistent"


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" In particular, an increase \r\nin the intensity and frequency of hot extremes is almost always \r\nassociated with an increase in the hottest temperatures and in the \r\nnumber of heatwave days. It is also the case for changes (decreases) \r\nin cold extremes. For this reason, and to simplify the presentation, \r\nthe phrase 'increase in the intensity and frequency of hot extremes' \r\nis used to represent, collectively, an increase in the magnitude of \r\nextreme day and/or night temperatures, in the number of warm days \r\nand/or nights, and in the number of heatwave days. Changes in cold \r\nextremes are assessed similarly. "


id: 1acd5ebacaec57ad3827540d3faa8c98
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"In Africa (Table  11.4), while it is difficult to assess changes in \r\ntemperature extremes in parts of the continent because of a  lack \r\nof data, evidence of an increase in the intensity and frequency of \r\nhot extremes and decrease in the intensity and frequency of cold \r\nextremes is clear and robust in regions where data are available. \r\nThese include an increase in the frequency of warm days and nights \r\nand a decrease in the frequency of cold days and nights with high \r\nconfidence (Donat et al., 2013a, 2014b; Kruger and Sekele, 2013; \r\nChaney et al., 2014; Filahi et al., 2016; Moron et al., 2016; Ringard \r\net al., 2016; Barry et al., 2018; Gebrechorkos et al., 2018) and an \r\nincrease in heatwaves (Russo et al., 2016; Ceccherini et al., 2017). The \r\nincrease in TNn is more notable than in TXx (Figure 11.9). Cold spells \r\noccasionally strike subtropical areas, but are likely to have decreased \r\nin frequency (Barry et al., 2018)"


id: 1e42e3801244271087325f01475b2e0e
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" The frequency of cold events has likely\r\ndecreased in South Africa (Song et al., 2014; Kruger and Nxumalo, \r\n2017), North Africa (Filahi et al., 2016; Driouech et al., 2021), and \r\nthe Sahara (Donat et al., 2016a). Over the whole continent, there \r\nis medium confidence in an increase in the intensity and frequency \r\nof hot extremes and decrease in the intensity and frequency of cold \r\nextremes; it is likely that similar changes have also occurred in areas \r\nwith poor data coverage, as warming is widespread and as projected \r\nfuture changes are similar over all regions (Section 11.3.5)."


id: 30f9e559a187e8f1733eca33195f828b
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"2020), as well as in numerous regional studies (Table 11.7). The area \r\nfraction with extreme warmth in Asia increased during 1951-2016 \r\n(Imada et al., 2018). The frequency of warm extremes increased and \r\nthe frequency of cold extremes decreased in East Asia (B. Zhou et al., \r\n2016; Chen and Zhai, 2017; Yin et al., 2017; W. Lee et al., 2018; Qian \r\net al., 2019) and west Asia (Acar Deniz and Gonencgil, 2015; Erlat \r\nand Turkes, 2016; Rahimi and Hejabi, 2018; Rahimi et al., 2018) with \r\nhigh confidence. The duration of heat extremes has also lengthened \r\nin some regions, for example, in southern China (Luo and Lau, \r\n2016), but there is medium confidence of heat extremes increasing \r\nin frequency in South Asia (AlSarmi and Washington, 2014; Sheikh \r\net al., 2015; Mazdiyasni et al., 2017; Zahid et al., 2017; Nasim et al., \r\n2018; Khan et al., 2019; Sen Roy, 2019). Warming trends in daily \r\ntemperature extremes indices have also been observed in central \r\nAsia (Hu et al"


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", 2016; Feng et al., 2018), the Hindu Kush Himalaya (Sun \r\net al., 2017), and South East Asia (Supari et al., 2017; Cheong et al., \r\n2018). The intensity and frequency of cold spells in all Asian regions \r\nhave been decreasing since the beginning of the 20th century (high \r\nconfidence) (Sheikh et al., 2015; Donat et al., 2016a; Dong et al., \r\n2018; van Oldenborgh et al., 2019)."


id: 6b7fad38c0b10c7203506fa199912e12
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"In Central and South America (Table 11.13), there is high confidence\r\nthat observed hot extremes (TN90p, TX90p) have increased, and \r\ncold extremes (TN10p, TX10p) have decreased over recent decades, \r\nthough trends vary among different extremes types, datasets, and \r\nregions (Skansi et al., 2013; Dittus et al., 2016; Rusticucci et al., 2017; \r\nMeseguer-Ruiz et al., 2018; Salvador and de Brito, 2018; Dereczynski \r\net al., 2020; Dunn et al., 2020; Olmo et al., 2020). An increase in \r\nthe intensity and frequency of heatwave events was also observed \r\nbetween 1961 and 2014 in an area covering most of South America \r\n(Ceccherini et al., 2016; Geirinhas et al., 2018). However, there \r\nis medium confidence that warm extremes (TXx and TX90p) have \r\ndecreased in the last decades over the central region of South-Eastern \r\nSouth America (SES) during austral summer (Tencer and Rusticucci, \r\n2012; Skansi et al., 2013; Rusticucci et al., 2017; Wu and  Polvani, \r\n2017)"


id: c6cd6848dab1c02136a35f2ce5a1cb64
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" There is medium confidence that TNn extremes are warming \r\nfaster than TXx extremes, with the largest warming rates observed \r\nover North-East Brazil (NEB) and Northern South America (NSA) for \r\ncold nights (Skansi et al., 2013). "


id: 86cdb720754e3ced8f61644b4c3d0cc9
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"In Australasia (Table 11.10), there is very robust evidence for very \r\nlikely increases in the number of warm days and warm nights and \r\ndecreases in the number of cold days and cold nights since 1950 \r\n(Lewis and King, 2015; Jakob and Walland, 2016; Alexander and \r\nArblaster, 2017). The increase in extreme minimum temperatures \r\noccurs in all seasons over most of Australia and typically exceeds the \r\nincrease in extreme maximum temperatures (X.L. Wang et al., 2013b; \r\nJakob and Walland, 2016). However, some parts of Southern Australia \r\nhave shown stable or increased numbers of frost days since the \r\n1980s (Dittus et al., 2014) (see also Section 11.3.4). Similar positive \r\ntrends in extreme minimum and maximum temperatures have been \r\nobserved in New Zealand, in particular in the autumn and winter \r\nseasons, although they generally show higher spatial variability \r\n(Caloiero, 2017)"


id: b46b463dd055fedf714494f1ba75d2a2
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content:
"In North America (Table  11.19), there is very robust evidence for \r\na very likely increase in the intensity and frequency of hot extremes \r\nand decrease in the intensity and frequency of cold extremes for the \r\nwhole continent, though there are substantial spatial and seasonal \r\nvariations in the trends. Minimum temperatures display warming \r\nconsistently across the continent, while there are more contrasting \r\ntrends in the annual maximum daily temperatures in parts of the \r\nUSA (Figure 11.9; Lee et al., 2014; van Oldenborgh et al., 2019; Dunn \r\net al., 2020). In Canada, there is a  clear increase in the intensity \r\nand frequency of hot extremes and decrease in the intensity and \r\nfrequency of cold extremes (Vincent et al., 2018). In Mexico, a clear \r\nwarming trend in TNn was found, particularly in the northern arid \r\nregion (Montero-Martinez et al., 2018). The number of warm days has \r\nincreased and the number of cold days has decreased (Garcia-Cueto \r\net al., 2019)"


id: 17e7c966180e7cb7bde40496107b6c3
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" Cold spells have undergone a reduction in magnitude \r\nand intensity in all regions of North America (Bennett and Walsh, \r\n2015; Donat et al., 2016a; Grotjahn et al., 2016; Vose et al., 2017; \r\nGarcia-Cueto et al., 2019; van Oldenborgh et al., 2019). "


id: 6beeaa203f48f7e4c302a557a8e44fa
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content:
"In summary, it is virtually certain that there has been an increase in \r\nthe number of warm days and nights and a decrease in the number of \r\ncold days and nights on the global scale since 1950. Both the coldest \r\nextremes and hottest extremes display increasing temperatures. It is \r\nvery likely that these changes have also occurred at the regional \r\nscale in Europe, Australasia, Asia, and North America. It is virtually \r\ncertain that there has been increases in the intensity and duration \r\nof heatwaves and in the number of heatwave days at the global \r\nscale. These trends likely occur in Europe, Asia, and Australia. There \r\nis medium confidence in similar changes in temperature extremes in \r\nAfrica and high confidence in South America; the lower confidence is \r\ndue to reduced data availability and fewer studies"


id: f0f3b5485f952b995620ea6f1a53a238
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content:
" Annual minimum \r\ntemperatures on land have increased about three times more than \r\nglobal surface temperature since the 1960s, with particularly strong \r\nwarming in the Arctic (high confidence)."


id: a096911ba65994e26d1ed0b9775b2e32
created_at: 2023-03-16 14:02:08
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content:
"The AR5 assessed that CMIP3 and CMIP5 models generally captured \r\nthe observed spatial distributions of the mean state and that the \r\ninter-model range of simulated temperature extremes was similar \r\nto the spread estimated from different observational datasets; \r\nthe models generally captured trends in the second half of the \r\n20th  century for indices of extreme temperature, although they \r\ntended to overestimate trends in hot extremes and underestimate \r\ntrends in cold extremes (Flato et al., 2013). Post-AR5 studies on \r\nthe CMIP5 models' performance in simulating mean and changes \r\nin temperature extremes continue to support the AR5 assessment \r\n(Fischer and Knutti, 2014; Sillmann et al., 2014; Ringard et al., 2016; \r\nBorodina et al., 2017b; Donat et al., 2017; Di Luca et al., 2020b). Over \r\nAfrica, the observed warming in temperature extremes is captured by \r\nCMIP5 models, although it is underestimated in Western and Central \r\nAfrica (Sherwood et al., 2014; Diedhiou et al., 2018)"


id: aa013a6c9b43cf5d835e1439b5b9723b
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" Over East Asia, \r\nthe CMIP5 ensemble performs well in reproducing the observed \r\ntrend in temperature extremes averaged over China (Dong et al., \r\n2015). Over Australia, the multi-model mean performs better than \r\nindividual models in capturing observed trends in gridded station\u0002based ETCCDI temperature indices (Alexander and Arblaster, 2017). "


id: 51096b139265ddb4344346941a2171c3
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"Trends in some measures of heatwaves are also observed at the \r\nglobal scale. Globally averaged heatwave intensity, heatwave \r\nduration, and the number of heatwave days have significantly \r\nincreased from 1950-2011 (Perkins, 2015). There are some regional \r\ndifferences in trends in characteristics of heatwaves, with significant \r\nincreases reported in Europe (Russo et al., 2015; Forzieri et al., 2016; \r\nSanchez-Benitez et al., 2020) and Australia (CSIRO and BOM, \r\n2016;  Alexander and Arblaster, 2017). In Africa, there is medium \r\nconfidence that heatwaves, regardless of the definition, have been \r\nbecoming more frequent, longer-lasting, and hotter over more \r\nthan three decades (Fontaine et al., 2013; Mouhamed et al., 2013; \r\nCeccherini et al., 2016, 2017; Forzieri et al., 2016; Moron et al., 2016; \r\nRusso et al., 2016). The majority of heatwave characteristics examined \r\nin China between 1961 and 2014 show increases in heatwave days, \r\nconsistent with warming (You et al"


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", 2017; Xie et al., 2020). Increases \r\nin the frequency and duration of heatwaves are also observed in \r\nMongolia (Erdenebat and Sato, 2016) and India (Ratnam et al., 2016; \r\nRohini et al., 2016). In the UK, the lengths of short heatwaves have \r\nincreased since the 1970s, while the lengths of long heatwaves (more \r\nthan 10 days) have decreased over some stations in the south-east "


id: 1fabc7553921ee46805f2853135abda4
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"CMIP6 models better represent some of the underlying processes \r\nleading to extreme temperatures, such as seasonal and diurnal \r\nvariability and synoptic-scale variability (Di Luca et al., 2020a). Whether \r\nthese improvements are sufficient to enhance our understanding of \r\npast changes, or to reduce uncertainties in future projections, remains \r\nunclear. The relative error estimates in the simulation of various indices \r\nof temperature extremes in the available CMIP6 models show that \r\nno single model performs the best on all indices, and the multi-model \r\nensemble seems to outperform any individual model due to its reduction \r\nin systematic bias (Kim et al., 2020). Figure 11.10 show errors in the \r\n1979-2014 average annual TXx and annual TNn simulated by available \r\nCMIP6 models in comparison with HadEX3 and ERA5 (Kim et al., 2020; \r\nWehner et al., 2020; Li et al., 2021)"


id: 1765671796debaba3bbc82ecf8e6357
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" While the magnitude of the model \r\nerror depends on the reference dataset, the model evaluations drawn \r\nfrom different reference datasets are quite similar. In general, models \r\nreproduce the spatial patterns and magnitudes of both cold and hot \r\ntemperature extremes quite well. There are also systematic biases. \r\nHot extremes tend to be too cool in mountainous and high-latitude \r\nregions, but too warm in the eastern USA and South America. For cold \r\nextremes, CMIP6 models are too cool, except in north-eastern Eurasia \r\nand the southern mid-latitudes. Errors in seasonal mean temperatures \r\nare uncorrelated with errors in extreme temperatures and are often of \r\nopposite sign (Wehner et al., 2020). "


id: 9e8b206516d15194dfad9dd252d4f165
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"Several regional climate models (RCMs) have also been evaluated in \r\nterms of their performance in simulating the climatology of extremes \r\nin various regions of the Coordinated Regional Downscaling \r\nExperiment (CORDEX) (Giorgi et al., 2009), especially in East Asia \r\n(Ji and Kang, 2015; Yu et al., 2015; Park et al., 2016; Bucchignani \r\net al., 2017; Gao et al., 2017a; Niu et al., 2018; Y. Sun et al., 2018b; \r\nWang et al., 2019), Europe (Vautard et al., 2013, 2021; Smiatek \r\net al., 2016; Gaertner et al., 2018; Cardoso et al., 2019; Lorenz et al., \r\n2019; Jacob et al., 2020; Kim et al., 2020), and Africa (J. Kim et al., \r\n2014; Diallo et al., 2015; Dosio, 2017; Samouly et al., 2018; Mostafa \r\net al., 2019). Compared to GCMs, RCM simulations show an added \r\nvalue in simulating temperature-related extremes, though this \r\ndepends on topographical complexity and the parameters employed \r\n(see  Section  10.3.3). The improvement with resolution is noted in \r\nEast Asia (Park et al., 2016; W"


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" Zhou et al., 2016; Shi et al., 2017; Hui \r\net al., 2018). However, in the European CORDEX ensemble, different \r\naerosol climatologies with various degrees of complexity were used \r\nin projections (Bartok et al., 2017; Lorenz et al., 2019) and the land \r\nsurface models used in the RCMs do not account for physiological CO2\r\neffects on photosynthesis leading to enhanced water-use efficiency \r\nand decreased evapotranspiration (Schwingshackl et al., 2019), \r\nwhich could lead to biases in the representation of temperature \r\nextremes in these projections (Boe et al., 2020). In addition, there \r\nare key cold biases in temperature extremes over areas with complex \r\ntopography (Niu et al., 2018). Over North America, 12 RCMs were \r\nevaluated over the ARCTIC-CORDEX region (Diaconescu et al., 2018)"


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" \r\nModels performed well at simulating climate indices related to mean \r\nair temperature and hot extremes over most of the Canadian Arctic, \r\nwith the exception of the Yukon region where models displayed \r\nthe largest biases related to topographic effects. Two RCMs were \r\nevaluated against observed extremes indices over North America \r\nover the period 1989-2009, with a cool bias in minimum temperature "


id: f0b221bf8fedf68eec656e61f4e00f68
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content:
"in the frequency or intensity of cold extremes to human influence, \r\ndominated by anthropogenic greenhouse gas emissions, on global \r\nand continental scales, and for many AR6 regions. These include \r\nattribution of changes in the magnitude of annual TXx, TNx, TXn, and \r\nTNn, based on different observational datasets including, HadEX2 \r\nand HadEX3, CMIP5 and CMIP6 simulations, and different statistical \r\nmethods (Kim et al., 2016; Z. Wang et al., 2017a; Seong et al., 2021). \r\nAs is the case for an increase in mean temperature (Section 3.3.1), \r\nan increase in extreme temperature is mostly due to greenhouse gas \r\nforcing, offset by aerosol forcing. The aerosols' cooling effect is clearly \r\ndetectable over Europe and Asia (Seong et al., 2021). As much as 75% \r\nof the moderate daily hot extremes (above 99.9th percentile) over \r\nland are due to anthropogenic warming (Fischer and Knutti, 2015)"


id: 8602a7a27a857825b828f71ecd10def6
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" \r\nNew results are found to be more robust due to the extended period \r\nthat improves the signal-to-noise ratio. The effect of anthropogenic \r\nforcing is clearly detectable and attributable in the observed changes \r\nin these indicators of temperature extremes, even at country and \r\nsub-country scales, such as in Canada (Wan et al., 2019). Changes in \r\nthe number of warm nights, warm days, cold nights, and cold days, \r\nand other indicators such as the Warm Spell Duration Index (WSDI), \r\nare also attributed to anthropogenic influence (Christidis and Stott, \r\n2016; Hu et al., 2020). "


id: b09324eeefff04fd68101b947f2beaaf
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content:
"Some land forcings are not well represented in climate models. \r\nAs  highlighted in the Special Report on Climate Change and Land \r\n(SRCCL) Chapter 2, there is high agreement that temperate deforestation \r\nleads to summer warming and winter cooling (Anderson et al., 2011; \r\nGalos et al., 2011, 2013; Anderson-Teixeira et al., 2012; Chen et al., \r\n2012; Wickham et al., 2013; Zhao and Jackson, 2014; Ahlswede and \r\nThomas, 2017; Bright et al., 2017; Strandberg and Kjellstrom, 2019), \r\nwhich has substantially contributed to the warming of hot extremes in \r\nthe northern mid-latitudes over the course of the 20th century (Lejeune \r\net al., 2018) and in recent years (Strandberg and Kjellstrom, 2019). \r\nHowever, observed forest effects on the seasonal and diurnal cycle of \r\ntemperature are not well-captured in several ESMs: while observations \r\nshow a cooling effect of forest cover compared to non-forest vegetation \r\nduring daytime (Li et al"


id: 3738cab5e49f42193ff4063635e7f480
created_at: 2023-03-16 14:02:08
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content:
", 2015), in particular in arid, temperate, and \r\ntropical regions (Alkama and Cescatti, 2016), several ESMs simulate \r\na warming of daytime temperatures for regions with forest versus non\u0002forest cover (Lejeune et al., 2017). Also irrigation effects, which can \r\nlead to regional cooling of temperature extremes, are generally not \r\nintegrated in current generations of ESMs (Section 11.3.1)."


id: 1c2062e4844ad547fed3a0b9fbdc3dcf
created_at: 2023-03-16 14:02:08
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content:
"Regional studies, including for Asia (Dong et al., 2018; Lu et al., 2018), \r\nAustralia (Alexander and Arblaster, 2017), and Europe (Christidis and \r\nStott, 2016), found similar results. A  clear anthropogenic signal is \r\nalso found in the trends in the Combined Extreme Index (CEI) for \r\nNorth America, Asia, Australia, and Europe (Dittus et al., 2016). While \r\nvarious studies have described increasing trends in several heatwave \r\nmetrics (heatwave duration, the number of heatwave days, etc.) in \r\ndifferent regions (e.g.,  Cowan et al., 2014; Bandyopadhyay et al., \r\n2016; M. Sanderson et al., 2017), few recent studies have explicitly \r\nattributed these changes to causes; most of them stated that observed \r\ntrends are consistent with anthropogenic warming"


id: bc31ae9b9d8a2d4285b317617792b652
created_at: 2023-03-16 14:02:08
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vector_id: 27186
content:
" The detected \r\nanthropogenic signals are clearly separable from the response to \r\nnatural forcing, and the results are generally insensitive to the use \r\nof different model samples, as well as different data availability, \r\nindicating robust attribution. Studies of monthly, seasonal, and \r\nannual records in various regions (Kendon, 2014; Lewis and King, \r\n2015; Bador et al., 2016; Meehl et al., 2016; C. Zhou et al., 2019) and \r\nglobally (King, 2017) show an increase in the breaking of hot records \r\nand a decrease in the breaking of cold records (King, 2017). Changes \r\nin anthropogenically attributable record-breaking rates are noted to \r\nbe largest over the Northern Hemisphere land areas (Shiogama et al., \r\n2016). Yin and Sun (2018) found clear evidence of an anthropogenic \r\nsignal in the changes in the number of frost and ice days, when \r\nmultiple model simulations were used"


id: aa5d9fa9a33f3926035b331c6e5a543a
created_at: 2023-03-16 14:02:08
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content:
" In some key wheat-producing \r\nregions of Southern Australia, increases in frost days or frost season \r\nlength have been reported (Dittus et al., 2014; Crimp et al., 2016); \r\nthese changes are linked to decreases in rainfall, cloud-cover, and \r\nsubtropical ridge strength, despite an overall increase in regional \r\nmean temperatures (Dittus et al., 2014; Pepler et al., 2018). "


id: 322d19e64c0434175f617ec157258bf4
created_at: 2023-03-16 14:02:08
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vector_id: 5304
content:
"severity and probability of the hottest month by more than 80% of \r\nthe available observational area on the global scale. Christidis and \r\nStott (2014) provide clear evidence that warm events have become \r\nmore probable because of anthropogenic forcings. Sun et al. (2014) \r\nfound that human influence has caused a more than 60-fold increase \r\nin the probability of the extreme warm 2013 summer in eastern \r\nChina since the 1950s. Human influence is found to have increased \r\nthe probability of the historically hottest summers in many regions of \r\nthe world, both in terms of mean temperature (B. Mueller et al., 2016) \r\nand wet bulb globe temperature (WBGT; C. Li et al., 2017). In most \r\nregions of the Northern Hemisphere, changes in the probability of \r\nextreme summer average WBGT were found to be about an order \r\nof magnitude larger than changes in the probability of extreme hot \r\nsummers estimated by surface air temperature (C. Li et al., 2017)"


id: d429e37fcebf00dc47007b75f629ab12
created_at: 2023-03-16 14:02:08
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content:
" \r\nIn addition to these generalized, global-scale approaches, extreme \r\nevent studies have found an attributable increase in the probability \r\nof hot annual and seasonal temperatures in many locations, \r\nincluding Australia (Knutson et al., 2014b; Lewis and Karoly, 2014), \r\nChina (Sun et al., 2014; Sparrow et al., 2018; Zhou et al., 2020), Korea \r\n(Y.-H. Kim et al., 2018) and Europe (King et al., 2015b). "


id: 263d78240183a6435a894c382f54e1ae
created_at: 2023-03-16 14:02:08
updated_at: 2023-03-16 14:02:08
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vector_id: 3434
content:
"The relative strength of anthropogenic influences on temperature \r\nextremes is regionally variable, in part due to differences in changes \r\nin atmospheric circulation, land-surface feedbacks, and other \r\nexternal drivers such as aerosols. For example, in the Mediterranean \r\nand over western Europe, risk ratios on the order of 100 have been \r\nfound (Kew et al., 2019; Vautard et al., 2020), whereas in the USA, \r\nchanges are much less pronounced. This is probably a reflection of \r\nthe land-surface feedback enhanced extreme 1930s temperatures \r\nthat reduce the rarity of recent extremes, in addition to the definition \r\nof the events and framing of attribution analyses (e.g., spatial and \r\ntemporal scales considered). Local forcing may mask or enhance \r\nthe warming effect of greenhouse gases. In India, short-lived \r\naerosols or an increase in irrigation may be masking the warming \r\neffect of greenhouse gases (Wehner et al., 2018c)"


id: 9a1287fad51d8d35541e07a3b386cb7
created_at: 2023-03-16 14:02:08
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content:
" Irrigation and \r\ncrop intensification have been shown to lead to a cooling in some \r\nregions, in particular in North America, Europe, and India (high \r\nconfidence) (N.D. Mueller et al., 2016; Thiery et al., 2017, 2020; Chen \r\nand Dirmeyer, 2019). Deforestation has contributed about one third \r\nof the total warming of hot extremes in some mid-latitude regions \r\nsince pre-industrial times (Lejeune et al., 2018). Despite all of these \r\ndifferences, and larger uncertainties at the regional scale, nearly \r\nall studies demonstrated that human influence has contributed \r\nto an increase in the frequency or intensity of hot extremes and to \r\na decrease in the frequency or intensity of cold extremes. "


id: f63eaf80d5d81f2cd427a4136b3b08ee
created_at: 2023-03-16 14:02:08
updated_at: 2023-03-16 14:02:08
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index: climate_gpt_only_giec
vector_id: 36250
content:
"There have also been many extreme event attribution studies that \r\nexamined short-duration temperature extremes, including daily \r\ntemperatures, temperature indices, and heatwave metrics. Examples \r\nof these events from different regions are summarized in various \r\nannual Explaining Extreme Events supplements of the Bulletin of \r\nthe American Meteorological Society (Peterson et al., 2012, 2013a; \r\nHerring et al., 2014, 2015, 2016, 2018, 2019, 2020), including \r\na  number of approaches to examine extreme events (described in \r\nEasterling et al., 2016; Stott et al., 2016; Otto, 2017). Several studies \r\nof recent events from 2016 onwards have determined an infinite \r\nrisk ratio (a fraction of attributable risk, or FAR, of 1), indicating that \r\nthe occurrence probability for such events is close to zero in model \r\nsimulations without anthropogenic influences (see Herring et al., \r\n2018, 2019, 2020; Imada et al., 2019; Vogel et al., 2019)"


id: 5a963a29b8957c8f2684163c805e10a6
created_at: 2023-03-16 14:02:08
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vector_id: 11771
content:
" Though it \r\nis difficult to accurately estimate the lower bound of the uncertainty \r\nrange of the FAR in these cases (Paciorek et al., 2018), the fact that \r\nthose events are so far outside the envelop of the models with only \r\nnatural forcing indicates that it is extremely unlikely for those events \r\nto occur without human influence. "


id: d918bcfa97c68f248fe8a8860709a02c
created_at: 2023-03-16 14:02:08
updated_at: 2023-03-16 14:02:08
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vector_id: 31663
content:
"In summary, long-term changes in various aspects of long- and \r\nshort-duration extreme temperatures, including intensity, frequency, \r\nand duration have been detected in observations and attributed to \r\nhuman influence at global and continental scales. It is extremely \r\nlikely that human influence is the main contributor to the observed \r\nincrease in the intensity and frequency of hot extremes and the \r\nobserved decrease in the intensity and frequency of cold extremes \r\non the global scale. It is very likely that this applies on continental \r\nscales as well. Some specific recent hot extreme events would have \r\nbeen extremely unlikely to occur without human influence on the \r\nclimate system. Changes in aerosol concentrations have affected \r\ntrends in hot extremes in some regions, with the presence of aerosols \r\nleading to attenuated warming, in particular from 1950 to 1980"


id: d57c8e5ff918772732d84552d8fdf169
created_at: 2023-03-16 14:02:08
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vector_id: 31111
content:
" \r\nCrop intensification, irrigation and no-till farming have attenuated \r\nincreases in summer hot extremes in some regions, such as Central \r\nNorth America (medium confidence)."


id: 7792f2111afcdfee4aac6436614bf257
created_at: 2023-03-16 14:02:08
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index: climate_gpt_only_giec
vector_id: 16366
content:
"the present-day 20-year return values of TXx and the running three\u0002day average of the daily maximum temperature every other year, on \r\naverage, under the Representative Concentration Pathway 8.5 (RCP8.5) \r\nscenario, as opposed to only 34% under RCP4.5. By the end of the \r\ncentury, these area fractions increase to 92% and 62%, respectively. \r\nSuch nonlinearities in the characteristics of future regional extremes are \r\nshown, for instance, for Europe (Dosio and Fischer, 2018; Spinoni et al., \r\n2018b; Lionello and Scarascia, 2020), Asia (Guo et al., 2017; Harrington \r\nand Otto, 2018b; King et al., 2018), and Australia (Lewis et al., 2017a) \r\nunder various global warming thresholds. The nonlinear increase in \r\nfixed-threshold indices (e.g., based on a percentile for a given reference \r\nperiod, or on an absolute threshold) as a function of global warming \r\nis consistent with a  linear warming of the absolute temperature of \r\nthe temperature extremes (e.g., Whan et al., 2015)"


id: dae366e51b9849b0bcfabd6c9c52ab54
created_at: 2023-03-16 14:02:08
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vector_id: 31959
content:
" Compared to the \r\nhistorical climate, warming will result in strong increases in heatwave \r\narea, duration and magnitude (Vogel et al., 2020b). These changes are \r\nmostly due to the increase in mean seasonal temperature, rather than \r\nchanges in temperature variability, though the latter can have an effect \r\nin some regions (Brown, 2020; Di Luca et al., 2020b; Suarez-Gutierrez \r\net al., 2020a)."


id: 1bfe706985003d1f94ba7381ef2d4975
created_at: 2023-03-16 14:02:08
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vector_id: 1868
content:
"Projections of temperature-related extremes in RCMs in the CORDEX \r\nregions demonstrate robust increases under future scenarios and can \r\nprovide information on finer spatial scales than GCMs (e.g., Coppola \r\net al., 2021b). Five RCMs in the CORDEX-East Asia region project \r\nincreases in the 20-year return values of temperature extremes \r\n(summer maxima), with models that exhibit warm biases projecting \r\nstronger warming (Park and Min, 2019). Similarly, in the African \r\ndomain, future increases in TX90p and TN90p are projected (Dosio, \r\n2017; Mostafa et al., 2019). This regional-scale analysis provides \r\nfine-scale information, such as distinguishing the increase in TX90p \r\nover sub-equatorial Africa (Democratic Republic of the Congo, \r\nAngola, and Zambia) with values over the Gulf of Guinea, Central \r\nAfrican Republic, South Sudan, and Ethiopia"


id: be46c4204b6b2681c15cf01b850e39a1
created_at: 2023-03-16 14:02:08
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content:
" Empirical statistical \r\ndownscaling has also been used to produce more robust estimates \r\nfor future heatwaves compared to RCMs based on large multi-model \r\nensembles (Furrer et al., 2010; Keellings and Waylen, 2014; Wang \r\net al., 2015; Benestad et al., 2018)."


id: 9d00ba1d4dda56bfb04dbb7adfee4631
created_at: 2023-03-16 14:02:08
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content:
"In all continental regions, including Africa (Table  11.4), Asia \r\n(Table  11.7), Australasia (Table  11.10), Central and South America \r\n(Table 11.13), Europe (Table 11.16), North America (Table 11.19) and \r\nat the continental scale, it is very likely that the intensity and frequency \r\nof hot extremes will increase and the intensity and frequency of cold \r\nextremes will decrease compared with the 1995-2014 baseline, even \r\nunder 1.5degC global warming. Those changes are virtually certain\r\nto occur under 4degC global warming. At the regional scale, and for \r\nalmost all AR6 regions, it is likely that the intensity and frequency of \r\nhot extremes will increase and the intensity and frequency of cold \r\nextremes will decrease compared with the 1995-2014 baseline, \r\neven under 1.5degC global warming. Those changes are virtually \r\ncertain to occur under 4degC global warming"


id: 71bd1b2ba258ece89fb9901183a46d89
created_at: 2023-03-16 14:02:08
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" Exceptions include lower \r\nconfidence in the projected decrease in the intensity and frequency of \r\ncold extremes compared with the 1995-2014 baseline under 1.5degC \r\nof global warming (medium confidence) and 4degC of global warming \r\n(very likely) in Northern Central America, Central North America, and \r\nWestern North America."


id: 185f13a743d23de5f85f974f07239d6b
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"In Europe (Table 11.16), 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 (Lau and Nath, \r\n2014; Ozturk et al., 2015; Russo et al., 2015; Schoetter et al., 2015; \r\nVogel et al., 2017; Winter et al., 2017; Jacob et al., 2018; Lhotka et al., \r\n2018; Rasmijn et al., 2018; Suarez-Gutierrez et al., 2018; Cardoso \r\net al., 2019; Lionello and Scarascia, 2020; Molina et al., 2020; Coppola \r\net al., 2021b; Li et al., 2021). Increases in heatwaves are greater over \r\nthe southern Mediterranean and Scandinavia (Forzieri et al., 2016; \r\nAbaurrea et al., 2018; Dosio and Fischer, 2018; Rohat et al., 2019). \r\nThe biggest increases in the number of heatwave days are expected \r\nfor southern European cities (Guerreiro et al., 2018a; Junk et al"


id: 715b492221d788d1a7552797cd10ca6a
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content:
", \r\n2019), and Central European cities will see the biggest increases in \r\nmaximum heatwave temperatures (Guerreiro et al., 2018a)."


id: 9aabc8e65bdf6324e7e7a69bb4dc38e0
created_at: 2023-03-16 14:02:08
updated_at: 2023-03-16 14:02:08
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index: climate_gpt_only_giec
vector_id: 21863
content:
"In Asia (Table  11.7), evidence includes increases in the intensity \r\nand frequency of hot extremes, such as warm days, warm nights, and \r\nheatwaves, 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 (Sillmann \r\net al., 2013b; Zhou et al., 2014; R. Zhang et al., 2015; Zhao et al., \r\n2015; Pal and Eltahir, 2016; Singh and Goyal, 2016; Xu et al., 2017; \r\nGao et al., 2018; Han et al., 2018; Shin et al., 2018; Sui et al., 2018; \r\nL. Li et al., 2019; Zhu et al., 2020). More intense heatwaves of longer \r\ndurations and occurring at a  higher frequency are projected over \r\nIndia (Murari et al., 2015; Mishra et al., 2017) and Pakistan (Nasim \r\net al., 2018). Future mid-latitude warm extremes, similar to those \r\nexperienced during the 2010 event, are projected to become more \r\nextreme, with temperature extremes increasing potentially by 8.4degC \r\n(RCP8"


id: 1d9fbd20d36149a4e1d04699ebcbe06b
created_at: 2023-03-16 14:02:08
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content:
"5) over north-west Asia (van der Schrier et al., 2018). Over \r\nWest and East Siberia, and Russian Far East, an increase in extreme \r\nheat durations is expected in all scenarios (Sillmann et al., 2013b; \r\nKattsov et al., 2017; Reyer et al., 2017). In the MENA regions (Arabian \r\nPeninsula and Western Central Asia), extreme temperatures could \r\nincrease by almost 7degC by 2100 under RCP8.5 (Lelieveld et al., 2016)."


id: dc3075a0d6a4b72082bf9d625f214174
created_at: 2023-03-16 14:02:08
updated_at: 2023-03-16 14:02:08
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index: climate_gpt_only_giec
vector_id: 32152
content:
"In North America (Table 11.19), 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(Grotjahn et al., 2016; Vose et al., 2017; Alexandru, 2018; C. Li et al., \r\n2018, 2021; C. Yang et al., 2018; X. Zhang et al., 2019; Coppola \r\net al., 2021b). Projections of temperature extremes for the end of \r\nthe 21st century show that warm days and nights are very likely to \r\nincrease, and cold days and nights are very likely to decrease in all \r\nregions. There is medium confidence in large increases in warm days \r\nand warm nights in summer, particularly over the USA, and in large \r\ndecreases in cold days in Canada in autumn and winter (Grotjahn \r\net al., 2016; Vose et al., 2017; Alexandru, 2018; C. Li et al., 2018, 2021; \r\nC. Yang et al., 2018; X"


id: c251342beae32f75bbec442df880b101
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vector_id: 28158
content:
" Zhang et al., 2019; Coppola et al., 2021b). \r\nMinimum winter temperatures are projected to rise faster than mean \r\nwinter temperatures (Underwood et al., 2017). Projections for the \r\nend of the century under RCP8.5 showed the four-day cold spell that \r\nhappens on average once every five years is projected to warm by \r\nmore than 10degC. CMIP5 models do not project current 1-in-20-year \r\nannual minimum temperature extremes to recur over much of the \r\ncontinent (Wuebbles et al., 2014)."


id: 712f03fb2867282b4cf12f00d0aa5f57
created_at: 2023-03-16 14:02:08
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index: climate_gpt_only_giec
vector_id: 15442
content:
"There has been new evidence of the effect of local land-use and \r\nland-cover change on heavy precipitation. There is a growing set of \r\nliterature linking increases in heavy precipitation in urban centres \r\nto urbanization (Argueso et al., 2016; Y. Zhang et al., 2019b). \r\nUrbanization intensifies extreme precipitation, especially in the \r\nafternoon and early evening, over the urban area and its downwind \r\nregion (medium confidence) (Box  10.3). There are four possible \r\nmechanisms: (i) increases in atmospheric moisture due to horizontal \r\nconvergence of air associated with the urban heat island effect (Shastri \r\net al., 2015; Argueso et al., 2016); (ii) increases in condensation due \r\nto urban aerosol emissions (Han et al., 2011; Sarangi et al., 2017); \r\n(iii)  aerosol pollution that impacts cloud microphysics (Box  8.1; \r\nSchmid and Niyogi, 2017); and (iv) urban structures that impede \r\natmospheric motion (Shepherd, 2013; Ganeshan and Murtugudde, \r\n2015; Paul et al., 2018)"


id: a0e6db71e7ea9569c105752db51ae477
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index: climate_gpt_only_giec
vector_id: 22866
content:
" Other local forcing, including reservoirs \r\n(Woldemichael et al., 2012), irrigation (Devanand et al., 2019), or \r\nlarge-scale land-use and land-cover change (Odoulami et al., 2019), \r\ncan also affect local extreme precipitation."


id: b8589dbd0ef27ff1a68c071736c9ac70
created_at: 2023-03-16 14:02:08
updated_at: 2023-03-16 14:02:08
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index: climate_gpt_only_giec
vector_id: 26533
content:
"There is a  considerable body of literature examining scaling of \r\nsub-daily precipitation extremes, conditional on day-to-day air or \r\ndew-point temperatures (Westra et al., 2014; Fowler et al., 2021). \r\nThis scaling, also termed 'apparent scaling' (Fowler et al., 2021), is \r\nrobust when different methodologies are used in different regions, \r\nranging between the C-C and two-times the C-C rate (e.g., Formayer \r\nand Fritz, 2017; Lenderink et al., 2017; Burdanowitz et al., 2019). \r\nThis is confirmed when sub-daily precipitation data collected from \r\nmultiple continents (Lewis et al., 2019) are analysed in a consistent \r\nmanner using different methods (Ali et al., 2021). It has been \r\nhoped that apparent scaling might be used to help understand past \r\nand future changes in extreme sub-daily precipitation. However, \r\napparent scaling samples multiple synoptic weather states, mixing \r\nthermodynamic and dynamic factors that are not directly relevant for \r\nclimate change responses (Section 8"


id: 2281097d2fb78be5b7785b32206c3428
created_at: 2023-03-16 14:02:08
updated_at: 2023-03-16 14:02:08
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index: climate_gpt_only_giec
vector_id: 2877
content:
"2.3.2; Prein et al., 2016b; Bao \r\net al., 2017; X. Zhang et al., 2017; Drobinski et al., 2018; Sun et al., \r\n2020). The spatial pattern of apparent scaling is different from those \r\nof projected changes over Australia (Bao et al., 2017) and North \r\nAmerica (Sun et al., 2020) in regional climate model simulations. \r\nIt thus remains difficult to use the knowledge about apparent scaling \r\nto infer past and future changes in extreme sub-daily precipitation \r\naccording to observed and projected changes in local temperature. "


id: d432ced68e62da997c74616ff7aaa1fd
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content:
"The average annual maximum precipitation amount in a  day \r\n(Rx1day) has significantly increased since the mid-20th century over \r\nland (Du et al., 2019; Dunn et al., 2020) and in the humid and dry \r\nregions of the globe (Dunn et al., 2020). The percentage of observing \r\nstations with statistically significant increases in Rx1day is larger \r\nthan expected by chance, while the percentage of stations with \r\nstatistically significant decreases is smaller than expected by chance, \r\nover the global land as a  whole and over North America, Europe, \r\nand Asia (Figure 11.13; Sun et al., 2021) and over global monsoon \r\nregions (Zhang and Zhou, 2019) where data coverage is relatively \r\ngood. The addition of the past decade of observational data shows \r\na more robust increase in Rx1day over the global land region (Sun \r\net al., 2021). Light, moderate, and heavy daily precipitation has all \r\nintensified in a gridded daily precipitation dataset (Contractor et al., \r\n2020a)"


id: ea0d0fbd7605b9a2321361e1c6c0b081
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content:
" Daily mean precipitation intensities have increased since \r\nthe mid-20th century in a majority of land regions (high confidence) \r\n(Section 8.3.1.3). The probability of precipitation exceeding 50 mm/\r\nday increased during 1961-2018 (Benestad et al., 2019). The globally \r\naveraged annual fraction of precipitation from days in the top 5% \r\n(R95pTOT) has also significantly increased (Dunn et al., 2020). The \r\nincrease in the magnitude of Rx1day in the 20th century is estimated \r\nto be at a  rate consistent with C-C scaling with respect to global \r\nmean temperature (Fischer and Knutti, 2016; Sun et al., 2021). Studies \r\non past changes in extreme precipitation of durations longer than \r\na  day are more limited, though there are some studies examining \r\nlong-term trends in annual maximum five-day precipitation (Rx5day). \r\nOn global and continental scales, long-term changes in Rx5day are \r\nsimilar to those of Rx1day in many aspects (Zhang and Zhou 2019; \r\nSun et al., 2021)"


id: c7639f9b50f733f848bccccd1960906e
created_at: 2023-03-16 14:02:08
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vector_id: 28958
content:
" As discussed below, at the regional scale, changes in \r\nRx5day are also similar to those of Rx1day where there are analyses \r\nof changes in both Rx1day and Rx5day. "


id: 41a944dbf655b2580615924d083fbbb4
created_at: 2023-03-16 14:02:08
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vector_id: 7730
content:
"In Africa (Table 11.5), evidence shows an increase in extreme daily \r\nprecipitation for the late half of the 20th century over the continent \r\nwhere data are available; there is a  larger percentage of stations \r\nshowing significant increases in extreme daily precipitation than \r\ndecreases (Sun et al., 2021). There are increases in different metrics \r\nrelevant to extreme precipitation in various regions of the continent \r\n(Chaney et al., 2014; Harrison et al., 2019; Dunn et al., 2020; Sun \r\net al., 2021). There is an increase in extreme precipitation events in \r\nSouthern Africa (Weldon and Reason, 2014; Kruger et al., 2019) and \r\na general increase in heavy precipitation over East Africa, the Greater \r\nHorn of Africa (Omondi et al., 2014). Over sub-Saharan Africa, \r\nincreases in the frequency and intensity of extreme precipitation \r\nhave been observed over the well-gauged areas during 1950-2013; \r\nhowever, this covers only 15% of the total area of sub-Saharan Africa \r\n(Harrison et al"


id: 39a7b3d07983ae8cf9d65626a4ce9aa9
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", 2019). There is medium confidence about the increase \r\nin extreme precipitation for some regions where observations are \r\nmore abundant, but for Africa as whole, there is low confidence \r\nbecause of a general lack of continent-wide systematic analysis, the \r\nsporadic nature of available precipitation data over the continent, \r\nand spatially non-homogenous trends in places where data are \r\navailable (Donat et al., 2014a; Mathbout et al., 2018b; Alexander \r\net al., 2019; Funk et al., 2020)."


id: acc09cd45910f9fe76896606e991053d
created_at: 2023-03-16 14:02:08
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vector_id: 24726
content:
"Overall, there is a lack of systematic analysis of long-term trends in \r\nsub-daily extreme precipitation at the global scale. Often, sub-daily \r\nprecipitation data have only sporadic spatial coverage and are of \r\nlimited length. Additionally, the available data records are far shorter \r\nthan needed for a robust quantification of past changes in sub-daily \r\nextreme precipitation (C. Li et al., 2019a). Despite these limitations, \r\nthere are studies in regions of almost all continents that generally \r\nindicate intensification of sub-daily extreme precipitation, although \r\nthere remains low confidence in an overall increase at the global \r\nscale. Studies include an increase in extreme sub-daily rainfall in \r\nsummer over South Africa (Sen Roy and Rouault, 2013), annually \r\nin Australia (Guerreiro et al., 2018b), over 23 urban locations in \r\nIndia (Ali and Mishra, 2018), in Peninsular Malaysia (Syafrina et al"


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content:
", \r\n2015), and in eastern China in the summer season during 1971-2013 \r\n(Xiao  et al., 2016). In some regions in Italy (Arnone et al., 2013; \r\nLibertino et al., 2019) and in the USA during 1950-2011 (Barbero \r\net al., 2017), there is also an increase. In general, an increase in sub\u0002"


id: 3347e2371ea7ad30a45a968120a9601c
created_at: 2023-03-16 14:02:08
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content:
"precipitation and spatial and temporal coverage of the data. These \r\ninclude an increase in daily precipitation extremes over central Asia \r\n(Hu et al., 2016), most of South Asia (Zahid and Rasul, 2012; Pai et al., \r\n2015; Sheikh et al., 2015; Adnan et al., 2016; Malik et al., 2016; Dimri \r\net al., 2017; Priya et al., 2017; Roxy et al., 2017; Hunt et al., 2018; \r\nKim et al., 2019; Wester et al., 2019), the Arabian Peninsula (Rahimi \r\nand Fatemi, 2019; Almazroui and Saeed, 2020; Atif et al., 2020), \r\nSouth East Asia (Siswanto et al., 2015; Supari et al., 2017; Cheong \r\net al., 2018); the north-west Himalaya (Malik et al., 2016), parts of \r\nEast Asia (Baek et al., 2017; Nayak et al., 2017; Ye and Li, 2017), the \r\nwestern Himalayas since the 1950s (Ridley et al., 2013; Dimri et al., \r\n2015; Madhura et al., 2015), West and East Siberia, and Russian Far \r\nEast (Donat et al., 2016a). A decrease was found over the eastern \r\nHimalayas (Sheikh et al., 2015; Talchabhadel et al., 2018)"


id: 6f9d24115a6c129c11fce89cdfd9ee0
created_at: 2023-03-16 14:02:08
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vector_id: 15187
content:
" Increases \r\nhave been observed over Jakarta (Siswanto et al., 2015), but Rx1day \r\nover most parts of the Maritime Continent has decreased (Villafuerte \r\nand Matsumoto, 2015). Trends in extreme precipitation over China \r\nare mixed with increases and decreases (G. Fu et al., 2013; Jiang \r\net al., 2013; Ma et al., 2015; Yin et al., 2015; Xiao et al., 2016) and \r\nare not significant over China as whole (Jiang et al., 2013; Hu et al., \r\n2016; Ge et al., 2017; Deng et al., 2018; He and Zhai, 2018; W. Li et al., \r\n2018a; Tao et al., 2018; M. Liu et al., 2019b; Chen et al., 2021). With \r\nfew exceptions, most South East Asian countries have experienced an \r\nincrease in rainfall intensity, but with a reduced number of wet days \r\n(Donat et al., 2016a; Cheong et al., 2018; Naveendrakumar et al"


id: d971e80f7a6d80cdbdd8da1c776c8ae
created_at: 2023-03-16 14:02:08
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vector_id: 31723
content:
", \r\n2019), though large differences in trends exists if the trends are \r\nestimated from different datasets, including gauge-based, remotely \r\nsensed, and reanalysis data, over a relatively short period (Kim et al., \r\n2019). There is a significant increase in heavy rainfall (>100 mm day-\r\n1\r\n) and a significant decrease in moderate rainfall (5-100 mm day-1) \r\nin central India during the South Asian monsoon season (Deshpande \r\net al., 2016; Roxy et al., 2017). "


id: 53d990b3048289c611b9c5a8bde86a42
created_at: 2023-03-16 14:02:08
updated_at: 2023-03-16 14:02:08
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vector_id: 10659
content:
"In Central and South America (Table  11.14), evidence shows an \r\nincrease in extreme precipitation, but in general there is low\r\nconfidence; while continent-wide analyses produced wetting trends \r\nare not robust. Rx1day increased at more stations than it decreased \r\nin South America between 1950 and 2018 (Sun et al., 2021). Over \r\nthe period 1950-2010, both Rx5day and R99p increased over large \r\nregions of South America, including North-Western South America, \r\nNorthern South America, and South-Eastern South America (Skansi \r\net al., 2013). There are large regional differences. A decrease in daily \r\nextreme precipitation is observed in north-eastern Brazil (Skansi \r\net al., 2013; Bezerra et al., 2018; Dereczynski et al., 2020). Trends \r\nin extreme precipitation indices were not statistically significant \r\nover the period 1947-2012 within the Sao Francisco River basin in \r\nthe Brazilian semi-arid region (Bezerra et al., 2018)"


id: 953f427a4c451b21d4b6ebea3463049e
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" An increase in \r\nextreme rainfall is observed in the Amazon with medium confidence\r\n(Skansi et al., 2013) and in South-Eastern South America with \r\nhigh confidence (Skansi et al., 2013; Valverde and Marengo, 2014; \r\nBarros et al., 2015; Avila et al., 2016; Wu and Polvani, 2017; Lovino \r\net al., 2018; Dereczynski et al., 2020). Among all sub-regions, South\u0002Eastern South America shows the highest rate of increase for rainfall \r\nextremes, followed by the Amazon (Skansi et al., 2013). Increases \r\nin the intensity of heavy daily rainfall events have been observed in \r\nthe southern Pacific and in the Titicaca basin (Skansi et al., 2013; \r\nHuerta and Lavado-Casimiro, 2021). In Southern Central America, \r\ntrends in annual precipitation are generally not significant, although \r\nsmall (but significant) increases are found in Guatemala, El Salvador, \r\nand Panama (Hidalgo et al., 2017)"


id: ab8c1abb7046203a9e4c3a298ac6cb9a
created_at: 2023-03-16 14:02:08
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vector_id: 24509
content:
" Small positive trends were found \r\nin multiple extreme precipitation indices over the Caribbean region \r\nover a  short time period (1986-2010) (Stephenson et al., 2014; \r\nMcLean et al., 2015)."


id: f08d960e5603ad901008a19a85f03bc8
created_at: 2023-03-16 14:02:08
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vector_id: 35343
content:
"In Europe (Table 11.17), there is robust evidence that the magnitude \r\nand intensity of extreme precipitation has very likely increased since \r\nthe 1950s. There is a  significant increase in Rx1day and Rx5day \r\nduring 1950-2018 in Europe as a  whole (Sun et al., 2021, also \r\nFigure  11.13). The number of stations with increases far exceeds \r\nthose with decreases in the frequency of daily rainfall exceeding its \r\n90th or 95th percentile in century-long series (Cioffi et al., 2015). The \r\nfive-, 10-, and 20-year events of one-day and five-day precipitation \r\nduring 1951-1960 became more common since the 1950s (van \r\nden Besselaar et al., 2013). There can be large discrepancies among \r\nstudies and regions and seasons (Croitoru et al., 2013; Willems, \r\n2013; Casanueva et al., 2014; Roth et al., 2014; Fischer et al., 2015); \r\nevidence for increasing extreme precipitation is more frequently \r\nobserved for summer and winter, but not in other seasons (Madsen \r\net al., 2014; Helama et al"


id: 840c823c7c52d12c0f04cff6a5cd2f53
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content:
", 2018). An increase is observed in central \r\nEurope (Volosciuk et al., 2016; Zeder and Fischer, 2020), and in \r\nRomania (Croitoru et al., 2016). Trends in the Mediterranean region \r\nare in general not spatially consistent (Reale and Lionello, 2013), with \r\ndecreases in the western Mediterranean and some increases in the \r\neastern Mediterranean (Rajczak et al., 2013; Casanueva et al., 2014; \r\nde Lima et al., 2015; Gajic-Capka et al., 2015; Sunyer et al., 2015; \r\nPedron et al., 2017; Serrano-Notivoli et al., 2018; Ribes et al., 2019). \r\nIn the Netherlands, the total precipitation contributed from extremes \r\nhigher than the 99th percentile doubles per 1degC increase in warming \r\n(Myhre et al., 2019), though extreme rainfall trends in Northern \r\nEurope may differ in different seasons (Irannezhad et al., 2017). "


id: 37db3a61e58fadc6335853f0d4e9cabf
created_at: 2023-03-16 14:02:08
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content:
"In Australasia (Table  11.11), available evidence has not shown \r\nan increase or a  decrease in heavy precipitation over Australasia \r\nas a  whole (medium confidence), but heavy precipitation tends \r\nto increase over Northern Australia (particularly the north-west) \r\nand decrease over the eastern and southern regions (e.g., Jakob and \r\nWalland, 2016; Guerreiro et al., 2018b; Dey et al., 2019b; Dunn \r\net al., 2020; Sun et al., 2021). Available studies that used long\u0002term observations since the mid-20th  century showed nearly as \r\nmany stations with an increase as those with a decrease in heavy \r\nprecipitation (Jakob and Walland, 2016) or slightly more stations \r\nwith a decrease than with an increase in Rx1day and Rx5day (Sun \r\net al., 2021), or strong differences in Rx1day trends with increases \r\nover Northern Australia and Central Australia in general, but mostly \r\ndecreases over Southern Australia and Eastern Australia (Dunn et al., \r\n2020)"


id: 65c4e2034015d6a6152673dbbdafb9ab
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vector_id: 13593
content:
" Over New Zealand, decreases are observed for moderate-\r\nheavy precipitation events, but there are no significant trends for very \r\nheavy events (more than 64 mm in a day) for the period 1951-2012. \r\nThe number of stations with an increase in very wet days is similar \r\nto that with a decrease during 1960-2019 (MfE and Stats NZ, 2020). \r\nOverall, there is low confidence in trends in the frequency of heavy \r\nrain days, with mostly decreases over New Zealand (Harrington and \r\nRenwick, 2014; Caloiero, 2015). "


id: 6121b2c204af04e0798f28b898fa248f
created_at: 2023-03-16 14:02:08
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vector_id: 12855
content:
"In North America (Table  11.20), there is robust evidence that the \r\nmagnitude and intensity of extreme precipitation has very likely\r\nincreased since the 1950s. Both Rx1day and Rx5day have significantly \r\nincreased in North America during 1950-2018 (Sun et al., 2021, also \r\nFigure 11.13). There is, however, regional diversity. In Canada, there \r\nis a  lack of detectable trends in observed annual maximum daily \r\n(or shorter duration) precipitation (Shephard et al., 2014; Mekis et al., \r\n2015; Vincent et al., 2018). In the USA, there is an overall increase in \r\none-day heavy precipitation, both in terms of intensity and frequency \r\n(Villarini et al., 2012; Donat et al., 2013b; Wu, 2015; Easterling et al., \r\n2017; H. Huang et al., 2017; Howarth et al., 2019; Sun et al., 2021), \r\nexcept for the southern USA (Hoerling et al., 2016) where internal \r\nvariability may have played a substantial role in the lack of observed \r\nincreases"


id: 3ecfd9edf005450413b6618d4d349904
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" In Mexico, increases are observed in R10mm and R95p \r\n(Donat et al., 2016a), very wet days over the cities (Garcia-Cueto \r\net al., 2019) and in total precipitation (PRCPTOT) and Rx1day (Donat \r\net al., 2016b). "


id: 36861ea090d1896711f0b45e5296abb7
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content:
"models is generally more comparable to observations (Sillmann \r\net al., 2013b; Kusunoki, 2017, 2018b; Scher et al., 2017) as these \r\nmodels tend to produce more realistic storms compared to coarser \r\nmodels (Section 11.7.2). Higher horizontal resolution alone improves \r\nsimulation of extreme precipitation in some models (Wehner et al., \r\n2014; Kusunoki, 2017, 2018b), but this is insufficient in other models \r\n(Bador et al., 2020) as parametrization also plays a significant role \r\n(M. Wu et al., 2020). A simple comparison of climatology may not \r\nfully reflect the improvements of the new models that have more \r\ncomprehensive process formulations (Di Luca et al., 2015). Dittus et al"


id: 1b834462841de606160de531ce438d11
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" \r\n(2016) found that many of the eight CMIP5 models they evaluated \r\nreproduced the observed increase in the difference between areas \r\nexperiencing an extreme high (90%) and an extreme low (10%) \r\nproportion of the annual total precipitation from heavy precipitation \r\n(R95p/PRCPTOT) for Northern Hemisphere regions. Additionally, \r\nCMIP5 models reproduced the relation between changes in extreme \r\nand non-extreme precipitation: an increase in extreme precipitation \r\nis at the cost of a decrease in non-extreme precipitation (Thackeray \r\net al., 2018), a  characteristic found in the observational record \r\n(Gu and Adler, 2018)."


id: 1fdcdbd7c72a839ca98148c56b27ce56
created_at: 2023-03-16 14:02:08
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index: climate_gpt_only_giec
vector_id: 2447
content:
"Evaluating climate model competence in simulating heavy \r\nprecipitation extremes is challenging due to a  number of factors, \r\nincluding the lack of reliable observations and the spatial scale \r\nmismatch between simulated and observed data (Avila et al., 2015; \r\nAlexander et al., 2019). Simulated precipitation represents areal \r\nmeans, but station-based observations are conducted at point \r\nlocations and are often sparse. The areal-reduction factor, the ratio \r\nbetween pointwise station estimates of extreme precipitation and \r\nextremes of the areal mean, can be as large as 130% at CMIP6 \r\nresolutions (about 100 km) (Gervais et al., 2014). Hence, the order in \r\nwhich gridded station based extreme values are constructed (i.e"


id: f70d7450899f4f4c8176e176f44cda9c
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content:
", if \r\nthe extreme values are extracted at the station first and then gridded, \r\nor if the daily station values are gridded and then the extreme values \r\nare extracted) represents different spatial scales of extreme \r\nprecipitation and needs to be taken into account in model evaluation \r\n(Wehner et al. 2020). This aspect has been considered in some studies. \r\nReanalysis products are used in place of station observations for their \r\nspatial completeness as well as spatial-scale comparability (Sillmann \r\net al., 2013a; Kim et al., 2020; Li et al., 2021). However, reanalyses \r\nshare similar parametrizations to the models themselves, reducing \r\nthe objectivity of the comparison. "


id: 23fcee2cadf15d3ef7c72d7ffbca3e5
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index: climate_gpt_only_giec
vector_id: 3102
content:
"The CMIP6 models perform reasonably well in capturing large-scale \r\nfeatures of precipitation extremes, including intense precipitation \r\nextremes in the intertropical convergence zone (ITCZ), and weak \r\nprecipitation extremes in dry areas in the tropical regions (Li et al., \r\n2021) but a double-ITCZ bias over the equatorial central and eastern \r\nPacific that appeared in CMIP5 models remains (Section 3.3.2.3). There \r\nare also regional biases in the magnitude of precipitation extremes \r\n(Kim et al., 2020). The models also have difficulties in reproducing \r\ndetailed regional patterns of extreme precipitation, such as over the \r\nnorth-east USA (Agel and Barlow, 2020), though they performed \r\nbetter for summer extremes over the USA (Akinsanola et al., 2020)"


id: be936cecf4e98f6035c08b4ea31cac70
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content:
" The \r\ncomparison between climatologies in the observations and in model \r\nsimulations shows that the CMIP6 and CMIP5 models that have similar \r\nhorizontal resolutions also have similar model evaluation scores, \r\nand their error patterns are highly correlated (Wehner et al., 2020). "


id: 9e62f1135a7431cb89d71ab7db9beb65
created_at: 2023-03-16 14:02:08
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content:
"In general, extreme precipitation in CMIP6 models tends to be \r\nsomewhat larger than in CMIP5 models (Li et al., 2021), reflecting \r\nsmaller spatial scales of extreme precipitation represented by slightly \r\nhigher-resolution models (Gervais et al., 2014). This is confirmed by \r\nKim et al. (2020), who showed that Rx1day and Rx5day simulated \r\nby CMIP6 models tend to be closer to point estimates of HadEX3 \r\ndata (Dunn et al., 2020) than those simulated by CMIP5. Figure 11.14 \r\nshows the multi-model ensemble bias in mean Rx1day over the \r\nperiod 1979-2014 from 21 available CMIP6 models when compared \r\nwith observations and reanalyses. Measured by global land root\u0002mean-square error, the model performance is generally consistent \r\nacross different observed/reanalysis data products for the extreme \r\nprecipitation metric (Figure 11.14)"


id: c5db36c4b81a445a050af05059b4125f
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index: climate_gpt_only_giec
vector_id: 28702
content:
" The magnitude of extreme area \r\nmean precipitation simulated by the CMIP6 models is consistently \r\nsmaller than the point estimates of HadEX3, but the model values \r\nare more comparable to those of areal-mean values (Figure 11.14) \r\nof the ERA5 reanalysis or REGEN (Contractor et al., 2020b). Taylor\u0002plot-based performance metrics reveal strong similarities in the \r\npatterns of extreme precipitation errors over land regions between \r\nCMIP5 and CMIP6 (Srivastava et al., 2020; Wehner et al., 2020) and \r\nbetween annual mean precipitation errors and Rx1day errors for both \r\ngenerations of models (Wehner et al., 2020)."


id: 36da51d5cd924d29e7e2581c16dd9399
created_at: 2023-03-16 14:02:08
updated_at: 2023-03-16 14:02:08
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index: climate_gpt_only_giec
vector_id: 6018
content:
"Since AR5 there has been new and robust evidence and improved \r\nunderstanding of human influence on extreme precipitation. In \r\nparticular, detection and attribution analyses have provided consistent \r\nand robust evidence of human influence on extreme precipitation \r\nof one- and five-day durations at global to continental scales. \r\nThe observed increases in Rx1day and Rx5day over the Northern \r\nHemisphere land area during 1951-2005 can be attributed to the \r\neffect of combined anthropogenic forcing, including greenhouse \r\ngases and anthropogenic aerosols, as simulated by CMIP5 models \r\nand the rate of intensification with regard to warming is consistent \r\nwith C-C scaling (Zhang et al., 2013). This is confirmed to be robust \r\nwhen an additional nine years of observational data and the CMIP6 \r\nmodel simulations were used (Cross-Chapter Box 3.2, Figure 1; Paik \r\net al., 2020). The influence of greenhouse gases is attributed as the \r\ndominant contributor to the observed intensification"


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vector_id: 23897
content:
" The global \r\naverage of Rx1day in the observations is consistent with simulations \r\nby both CMIP5 and CMIP6 models under anthropogenic forcing, \r\nbut not under natural forcing (Cross-Chapter Box 3.2, Figure 1). The \r\nobserved increase in the fraction of annual total precipitation falling \r\ninto the top fifth or top first percentiles of daily precipitation can \r\nalso be attributed to human influence at the global scale (Dong \r\net al., 2021). The CMIP5 models were able to capture the fraction \r\nof land experiencing a strong intensification of heavy precipitation \r\nduring 1960-2010 under anthropogenic forcing, but not in unforced \r\nsimulations (Fischer et al., 2014). But the models underestimated \r\nthe observed trends (Borodina et al., 2017a). Human influence also \r\nsignificantly contributed to the historical changes in record-breaking \r\none-day precipitation (Shiogama et al., 2016). There is also limited \r\nevidence of the influences of natural forcing"


id: cc14db411ee58675d5af4a13ff27e3fb
created_at: 2023-03-16 14:02:08
updated_at: 2023-03-16 14:02:08
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index: climate_gpt_only_giec
vector_id: 29650
content:
" Substantial reductions in \r\nRx5day and Simple Daily Intensity Index (SDII) for daily precipitation \r\nintensity over the global summer monsoon regions occurred during \r\n1957-2000 after explosive volcanic eruptions (Paik and Min, 2018). \r\nThe reduction in post-volcanic eruption extreme precipitation in the \r\nsimulations is closely linked to the decrease in mean precipitation, for \r\nwhich both thermodynamic effects (moisture reduction due to surface \r\ncooling) and dynamic effects (monsoon circulation weakening) play \r\nimportant roles. \r\nThere has been new evidence of human influence on extreme \r\nprecipitation at continental scales, including the detection of "


id: d3f440d196c4041bd29271039428ec9c
created_at: 2023-03-16 14:02:08
updated_at: 2023-03-16 14:02:08
content_type: text
index: climate_gpt_only_giec
vector_id: 30863
content:
"Studies using regional climate models (RCMs), for example, CORDEX \r\n(Giorgi et al., 2009) over Africa (Dosio et al., 2015; Klutse et al., 2016; \r\nPinto et al., 2016; Gibba et al., 2019), Australia, East Asia (Park et al., \r\n2016), Europe (Prein et al., 2016a; Fantini et al., 2018), and parts \r\nof North America (Diaconescu et al., 2018) suggest that extreme \r\nrainfall events are better captured in RCMs compared to their host \r\nGCMs due to their ability to address regional characteristics, for \r\nexample, topography and coastlines. However, CORDEX simulations \r\ndo not show good skill over South Asia for heavy precipitation, and \r\ndo not add value with respect to their GCM source of boundary \r\nconditions (Mishra et al., 2014b; S. Singh et al., 2017). The evaluation \r\nof models in simulating regional processes is discussed in detail in \r\nSection  10.3.3.4. The high-resolution simulation of mid-latitude \r\nwinter extreme precipitation over land is of similar magnitude to \r\npoint observations"


id: 71556404c953f1eea62c4848ea427a9e
created_at: 2023-03-16 14:02:08
updated_at: 2023-03-16 14:02:08
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vector_id: 15468
content:
" Simulation of summer extreme precipitation has \r\na large bias when compared with observations at the same spatial \r\nscale. Simulated extreme precipitation in the tropics also appears to \r\nbe too large, indicating possible deficiencies in the parametrization \r\nof cumulus convection at this resolution. Indeed, precipitation \r\ndistributions at both daily and sub-daily time scales are much \r\nimproved with a convection-permitting model (Belusic et al., 2020) \r\nover Western Africa (Berthou et al., 2019b), East Africa (Finney et al., \r\n2019), North America and Canada (Cannon and Innocenti, 2019; \r\nInnocenti et al., 2019) and over Belgium in Europe (Vanden Broucke \r\net al., 2019). "


id: e0a2fa0fd9b8577eb04890d84f01eb1c
created_at: 2023-03-16 14:02:08
updated_at: 2023-03-16 14:02:08
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vector_id: 32846
content:
"Mediterranean (Vautard et al., 2015), parts of the USA for individual \r\nevents (Knutson et al., 2014a; Szeto et al., 2015; Eden et al., 2016; \r\nvan Oldenborgh et al., 2017), extreme rainfall in 2014 over Northland, \r\nNew Zealand (Rosier et al., 2015) or China (Burke et al., 2016; Sun \r\nand Miao, 2018; Yuan et al., 2018b; Zhou et al., 2018). However, \r\nfor other heavy rainfall events, studies identified a lack of evidence \r\nabout anthropogenic influences (Imada et al., 2013; Schaller et al., \r\n2014; Otto et al., 2015c; Siswanto et al., 2015). There are also studies \r\nwhere results are inconclusive because of limited reliable simulations \r\n(Christidis et al., 2013b; Angelil et al., 2016). Overall, both the spatial \r\nand temporal scales on which extreme precipitation events are \r\ndefined are important for attribution; events defined on larger scales \r\nhave larger signal-to-noise ratios and thus the signal is more readily \r\ndetectable"


id: d36b35efe6f87b287b56e15c3726fabc
created_at: 2023-03-16 14:02:08
updated_at: 2023-03-16 14:02:08
content_type: text
index: climate_gpt_only_giec
vector_id: 30783
content:
" At the current level of global warming, there is a strong \r\nenough signal to be detectable for large-scale extreme precipitation \r\nevents, but the chance of detecting such signals for smaller-scale \r\nevents decreases (Kirchmeier-Young et al., 2019)."


id: 21a6b2c9bb3c0fb695b13bd459ec1b80
created_at: 2023-03-16 14:02:08
updated_at: 2023-03-16 14:02:08
content_type: text
index: climate_gpt_only_giec
vector_id: 2721
content:
"Evidence of human influence on extreme precipitation at regional \r\nscales is more limited and less robust. In north-west Australia, the \r\nincrease in extreme rainfall since 1950 can be related to increased \r\nmonsoonal flow due to increased aerosol emissions, but cannot be \r\nattributed to an increase in greenhouse gases (Dey et al., 2019a). \r\nAnthropogenic influence on extreme precipitation in China was \r\ndetected in one study (H. Li et al., 2017), but not in another using \r\ndifferent detection and data-processing procedures (W. Li et al., \r\n2018a), indicating the lack of robustness in the detection results. \r\nA still weak signal-to-noise ratio seems to be the main cause for the \r\nlack of robustness, as detection would become robust 20 years in \r\nthe future (W. Li et al., 2018a). Krishnan et al"


id: 78ace07018100f083e196d976c600c61
created_at: 2023-03-16 14:02:08
updated_at: 2023-03-16 14:02:08
content_type: text
index: climate_gpt_only_giec
vector_id: 16547
content:
" (2016) attributed the \r\nobserved increase in heavy rain events (intensity >100 mm day-1) \r\nin the post-1950s over central India to the combined effects of \r\ngreenhouse gases, aerosols, land-use and land-cover changes, and \r\nrapid warming of the equatorial Indian Ocean SSTs. Roxy et al. (2017) \r\nand Devanand et al. (2019) showed that the increase in widespread \r\nextremes over the South Asian Monsoon during 1950-2015 is due \r\nto the combined impacts of the warming of the Western Indian \r\nOcean (Arabian Sea) and the intensification of irrigation water \r\nmanagement over India. "


id: f5485808dd4bbfe19154d49d009952ac
created_at: 2023-03-16 14:02:08
updated_at: 2023-03-16 14:02:08
content_type: text
index: climate_gpt_only_giec
vector_id: 36107
content:
"In summary, most of the observed intensification of heavy precipitation \r\nover land regions is likely due to anthropogenic influence, for which \r\ngreenhouse gases emissions are the main contributor. New and \r\nrobust evidence since AR5 includes attribution to human influence \r\nof the observed increases in annual maximum one-day and five\u0002day precipitation and in the fraction of annual precipitation falling \r\nin heavy events. The evidence since AR5 also includes a  larger \r\nfraction of land showing enhanced extreme precipitation and \r\na  larger probability of record-breaking one-day precipitation than \r\nexpected by chance, both of which can only be explained when \r\nanthropogenic greenhouse gas forcing is considered. Human \r\ninfluence has contributed to the intensification of heavy precipitation \r\nin three continents where observational data are more abundant \r\n(high confidence) (North America, Europe and Asia)"


id: 715d37508ebfa158ec07df45285baf62
created_at: 2023-03-16 14:02:08
updated_at: 2023-03-16 14:02:08
content_type: text
index: climate_gpt_only_giec
vector_id: 15474
content:
" On the spatial \r\nscale of AR6 regions, there is limited evidence of human influence on \r\nextreme precipitation, but new evidence is emerging; in particular, \r\nstudies attributing individual heavy precipitation events  found \r\nthat human influence was a  significant driver of the events, \r\nparticularly in the winter season. "


id: a3ba0a098a420d2ec3e4b9c7d63a1141
created_at: 2023-03-16 14:02:08
updated_at: 2023-03-16 14:02:08
content_type: text
index: climate_gpt_only_giec
vector_id: 23323
content:
"The AR5 concluded it is very likely that extreme precipitation events \r\nwill be more frequent and more intense over most of the mid\u0002latitude land masses and wet tropics in a  warmer world (Collins \r\net al., 2013). Post-AR5 studies provide more and robust evidence\r\nto support the previous assessments. These include an observed \r\nincrease in extreme precipitation (Section 11.4.3) and human causes \r\nof past changes (Section  11.4.4), as well as projections based on \r\neither GCM and/or RCM simulations. The CMIP5 models project \r\nthat the rate of increase in Rx1day with warming is independent \r\nof the forcing scenario (Section  8.5.3.1; Pendergrass et al., 2015) \r\nor forcing mechanism (Sillmann et al., 2017a). This is confirmed \r\nin CMIP6 simulations (Sillmann et al., 2019; Li et al., 2021)"


id: 6662000637afdedeae2dd5360b705e7e
created_at: 2023-03-16 14:02:08
updated_at: 2023-03-16 14:02:08
content_type: text
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vector_id: 13700
content:
"confirmed by near-linear scaling between extreme precipitation and \r\nglobal warming levels at regional scales (Seneviratne and Hauser, \r\n2020). Internal variability modulates changes in heavy rainfall (Wood \r\nand Ludwig, 2020), resulting in different changes in different regions \r\n(Seneviratne and Hauser, 2020). Extreme precipitation nearly always \r\nincreases across land areas with larger increases at higher global \r\nwarming levels, except in very few regions, such as Southern Europe \r\naround the Mediterranean Basin at low warming levels (Table 11.17). \r\nThe very likely ranges of the multi-model ensemble changes across \r\nall land grid boxes in the 50-year return values for Rx1day and \r\nRx5day between 1.5degC and 1degC warming levels are above zero for \r\nall continents except Europe, with the lower bound of the likely \r\nrange above zero over Europe (Li et al., 2021)"


id: 1d2c741810b45ed68af9333c088c4790
created_at: 2023-03-16 14:02:08
updated_at: 2023-03-16 14:02:08
content_type: text
index: climate_gpt_only_giec
vector_id: 2031
content:
" Decreases in extreme \r\nprecipitation are confined mostly to subtropical ocean areas and are \r\nhighly correlated to decreases in mean precipitation due to storm \r\ntrack shifts. These subtropical decreases can extend to nearby land \r\nareas in individual realizations. "


id: 8bde447e20c13d818262f88419b6b33c
created_at: 2023-03-16 14:02:08
updated_at: 2023-03-16 14:02:08
content_type: text
index: climate_gpt_only_giec
vector_id: 19524
content:
"Projected increases in the probability of extreme precipitation of \r\nfixed magnitudes are nonlinear and show larger increases for more \r\nrare events (Figures 11.7 and 11.15; Fischer and Knutti, 2015; Kharin \r\net al., 2018; Li et al., 2021). The CMIP5 model projected increases \r\nin the probability of high (99th and 99.9th) percentile precipitation \r\nbetween 1.5degC and 2degC warming scenarios are consistent with what \r\ncan be expected based on observed changes (Fischer and Knutti, \r\n2015), providing confidence in the projections. The CMIP5 model \r\nsimulations show that the frequency for present-day climate 20-year \r\nextreme precipitation is projected to increase by 10% at the 1.5degC \r\nglobal warming level, and by 22% at the 2.0degC global warming \r\nlevel, while the increase in the frequency for present-day climate \r\n100-year extreme precipitation is projected to increase by 20% and \r\nmore than 45% at the 1.5degC and 2.0degC warming levels, respectively \r\n(Kharin et al., 2018)"


id: 95b4e315ecbaca719ac91f8d28faecb8
created_at: 2023-03-16 14:02:08
updated_at: 2023-03-16 14:02:08
content_type: text
index: climate_gpt_only_giec
vector_id: 21049
content:
" CMIP6 simulations with SSP scenarios show that \r\nthe frequency of 10-year and 50-year events will be approximately \r\ndoubled and tripled, respectively, at a very high warming level of 4degC \r\n(Figure 11.7; Li et al., 2021). "


id: 51d50fd7e70353e25dfeb4017a93090c
created_at: 2023-03-16 14:02:08
updated_at: 2023-03-16 14:02:08
content_type: text
index: climate_gpt_only_giec
vector_id: 10336
content:
"methane (CH4), solar forcing, or sulphate (SO4) (Sillmann et al., 2019). \r\nIn some models - CESM1 in particular - the extreme precipitation \r\nresponse to warming may follow a quadratic relation (Pendergrass \r\net al., 2019). Figure  11.15 shows changes in the 10- and 50-year \r\nreturn values of Rx1day at different warming levels as simulated by \r\nthe CMIP6 models. The median value of the scaling over land, across \r\nall Shared Socio-economic Pathway (SSP) scenarios and all models, \r\nis close to 7% per 1degC of warming for the 50-year return value of \r\nRx1day. It is just slightly smaller for the 10- and 50-year return \r\nvalues of Rx5day (Li et al., 2021). The 90% ranges of the multimodel \r\nensemble changes across all land grid boxes in the 50-year return \r\nvalues for Rx1day and Rx5day do not overlap between 1.5degC and \r\n2degC warming levels (Li et al., 2021), indicating that a small increment \r\nsuch as 0"


