# document

id: df8f89536675c6856e31c77d9c6a661c
created_at: 2023-03-16 14:00:51
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"(Tanzania), Tugba  Ozturk (Turkey), Mohammad  Rahimi (Iran), Mehwish  Ramzan (Pakistan), \r\nRosh  Ranasinghe (The Netherlands/Sri Lanka, Australia), Johan  Reyns (The Netherlands/\r\nBelgium), Annette Rinke (Germany), Daniela  Schmidt (United  Kingdom), Stephane Senesi \r\n(France), Sonia  I.  Seneviratne (Switzerland), Chris Shaw (United Kingdom), Stefan Sobolowski \r\n(Norway/United States of America), Samuel  Somot (France), Anna A. Sorensson (Argentina), \r\nTannecia S. Stephenson (Jamaica), Mouhamadou Bamba Sylla (Rwanda/Senegal), Fredolin Tangang \r\n(Malaysia), Claas Teichmann (Germany), Peter W. Thorne (Ireland/United Kingdom), Blair Trewin \r\n(Australia), Geert-Jan  van  Oldenborgh (The Netherlands), Jan Melchior van Wessem (The \r\nNetherlands), Robert Vautard (France), Sergio M. Vicente-Serrano (Spain), Alejandro Vichot-Llano \r\n(Cuba), Etienne Vignon (France), Yu Xiaoyong (China, Germany), Xuebin Zhang (Canada)"


id: a7b31f0e789fe5bf46b21975053d014d
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"Maialen Iturbide (Spain), Jorge Bano-Medina (Spain), Joaquin Bedia (Spain), Ana Casanueva \r\n(Spain), Ezequiel Cimadevilla (Spain), Antonio S. Cofino (Spain), Javier Diez Sierra (Spain), \r\nJesus  Fernandez (Spain), Markel Garcia (Spain), Sixto Herrera (Spain), Rodrigo Manzanas \r\n(Spain), Josipa Milovac (Spain/Croatia), Juan Jose Saenz de la Torre (Spain), Daniel San Martin \r\n(Spain), Ivan Sanchez (Spain), Elena Suarez (Spain), Max Tuni (Spain)"


id: ff8b5656491a9ac02deb739626fa2adb
created_at: 2023-03-16 14:00:51
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vector_id: 37748
content:
"Maialen Iturbide (Spain), Ma. Laurice Preciado Jamero (Philippines), Emilie Vanvyve (United \r\nKingdom/Belgium)"


id: e2229b59f254d7bc78f1f5accb1bd5db
created_at: 2023-03-16 14:00:51
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"Gutierrez, J.M., R.G. Jones, G.T. Narisma, L.M. Alves, M. Amjad, I.V. Gorodetskaya, M. Grose, N.A.B. Klutse, S. \r\nKrakovska, J. Li, D. Martinez-Castro, L.O. Mearns, S.H. Mernild, T. Ngo-Duc, B. van den Hurk, and J.-H. Yoon, \r\n2021: Atlas. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth \r\nAssessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, \r\nS.L. Connors, C. Pean, S. Berger, N.  Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, \r\nJ.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekci, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, \r\nCambridge, United Kingdom and New York, NY, USA, pp. 1927-2058, doi:10.1017/9781009157896.021."


id: f84dab50df18f9ba6274cbfb65b3d5c0
created_at: 2023-03-16 14:00:51
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"The Atlas of the Working Group I Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change \r\n(IPCC), is dedicated to the memory of Gemma Teresa Narisma, one of the Atlas Coordinating Lead Authors."


id: 996e3246b732a4f8badb99011faf2439
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"Gemma was an internationally renowned scientist, Executive Director of the Manila Observatory (MO) and Professor of physics at \r\nthe Ateneo de Manila University in the Philippines. She undertook and coordinated research into land-atmosphere interactions, \r\nthe implications of land-use/biosphere changes on local and regional climate and aerosols and monsoons. She also worked in \r\nAustralia and the United States and was a key figure in regional climate research in South East Asia. In the Philippines, she undertook \r\nmultidisciplinary research involving local stakeholders and government on climate impacts and risks to support climate change policy, \r\nrisk assessment and development planning. "


id: 95f95b29b3e69b363c5ffff164d39392
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"Gemma was also an inspirational teacher, mentor and colleague. She supported and encouraged the young scientists she taught and \r\nworked with and focused on ensuring her research would help and empower those most at risk. And with her kindness and generosity, \r\nher soft, strong and positive energy, her sweet smile and personality she was an exceptional Coordinating Lead Author, building \r\nconsensus, motivating and supporting the team whilst also linking to other chapters and Working Group II. Her loss is felt deeply, and \r\nshe will always be remembered with great affection."


id: 3e7de9a6bab1305c9807746ce9864684
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"It is very likely that the Arctic has warmed at more than twice \r\nthe global rate over the past 50 years and that the Antarctic \r\nPeninsula experienced a  strong warming trend starting in \r\n1950s. It is likely that Arctic annual precipitation has increased, \r\nwith the highest increases during the cold season. Antarctic \r\nprecipitation and surface mass balance showed a significant \r\npositive trend over the 20th century, while strong interannual \r\nvariability masks any existing trend over recent decades1\r\n(medium confidence). Significant warming trends are observed \r\nin other West Antarctic regions and at selected stations in East \r\nAntarctica since the 1950s (medium confidence). Under all assessed \r\nemissions scenarios, both polar regions are very likely to have higher \r\nannual mean surface air temperatures and more precipitation, with \r\ntemperature increases higher than the global mean, most prominently \r\nin the Arctic. {Atlas.11.1.2, Atlas.11.1.4, Atlas.11.2.2, Atlas.11.2.4}"


id: 4b0934a672c8932d11e3dcf7050a62d9
created_at: 2023-03-16 14:00:51
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"Significant improvements in technical infrastructure, open \r\ntools and methodologies for accessing and analysing observed \r\nand simulated climate data, and the progressive adoption of \r\nFAIR (findability, accessibility, interoperability and reusability) \r\ndata principles have very likely broadened the ability to \r\ninteract with these data for a  wide range of activities, \r\nincluding fundamental climate research, providing inputs into \r\nassessments of impacts, building resilience and developing \r\nadaptations. Tools to analyse and assess climate information have \r\nimproved to allow development of information that goes beyond \r\naverages (e.g., on future climate thresholds and extremes) and that \r\nis relevant for regional climate risk assessments. {Atlas.2.2, Atlas.2.3}"


id: 967110a81d6679972a8d58ab72cafbbe
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"The Interactive Atlas is a new WGI product developed to take \r\nadvantage of the interactivity offered by web applications \r\nby allowing flexible and expanded exploration of some key \r\nproducts underpinning the assessment (including extreme \r\nindices and climatic impact-drivers). This provides a transparent \r\ninterface for access to authoritative IPCC results, facilitating their use \r\nin applications and climate services. The Interactive Atlas implements \r\nFAIR principles and builds on open tools and, therefore, is an \r\nimportant step towards making IPCC results more reproducible and \r\nreusable. {Atlas.2, Interactive Atlas}"


id: fbce9ac93239f3aa3fbe9815eb7a7547
created_at: 2023-03-16 14:00:51
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"Information on global and regional climate change in the form of \r\nmaps, tables, graphs and infographics has always been a key output \r\nof IPCC reports. With the consensus that climate has changed and will \r\ncontinue to do so, policymakers are focusing more on understanding \r\nits implications, which often requires an increase in regional and \r\ntemporal details of observed and future climate. The WGI contribution \r\nto AR5 included globally comprehensive coverage of land regions \r\nand some oceanic regions in the Atlas of Global and Regional \r\nClimate Projections (IPCC, 2013a), focusing on projected changes in \r\ntemperature and precipitation. In the WGII contribution, Chapter 21, "


id: cf8fe13534553872a95ff41495924e71
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"The Interactive Atlas is a  novel product of this Report that allows \r\nfor a flexible spatial and temporal analysis of the results presented \r\nin the Atlas and other chapters, the Technical Summary (TS) and the \r\nSummary for Policymakers (SPM), supporting and expanding on their \r\nassessments. The Interactive Atlas includes two components. The first "


id: da4051a7ec3c8fc20e5d8f7302d44e2c
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"Regional Context (Hewitson et al., 2014) included continental-scale \r\nmaps of observed and future temperature and precipitation changes, \r\nsub-continental changes in high percentiles of daily temperature \r\nand precipitation, and a  table of changes in extremes over sub\u0002continental regions (updating an assessment in the Special Report \r\non Managing the Risks of Extreme Events and Disasters to Advance \r\nClimate Change Adaptation; SREX). However, there was only limited \r\ncoordination between these two contributions despite the largely \r\ncommon data sources and their relevance across the two working \r\ngroups and to wider communities of climate change-related policy \r\nand practice. This resulted in inefficiencies and the potential for \r\nconfusing or inconsistent information. The Atlas, with its links with \r\nother WGI/II/III chapters, has been designed to help address this."


id: c6cc37ac4c9187149ded4a3a7bef2fec
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"the displayed products (describing both the underlying datasets \r\nand the applied post-processing) and most of the figures included \r\nin the Atlas chapter can be reproduced using the scripts and data \r\nprovided in the WGI-Atlas repository (see Iturbide et al., 2021 and \r\nhttps://github.com/IPCC-WG1/Atlas)."


id: e1deab749990c0b834f929ba5968ea27
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"Over the past decades scientists have engaged in a wide array of \r\ninvestigations aimed at quantifying and understanding the state \r\nof the components of the land surface-ocean-atmosphere system, \r\nthe complex nature of their interactions and impacts over different \r\ntemporal and spatial scales. As a result, a great deal has been learned \r\nabout the importance of an appropriate choice of these scales \r\nwhen estimating changes due to internal climate variability, trends, \r\ncharacterization of the spatio-temporal variability, and quantifying \r\nthe range of and establishing confidence in climate projections. It is \r\ntherefore important to be able to explore a whole range of spatial \r\nand temporal scales and this section presents the basic definitions of \r\nthose, and the domains of analysis, used by the Atlas accounting for \r\npotential synergies between WGI and WGII."


id: 5a01a4673990fb45f602ceb7aa86e2be
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"Given the aims of the Atlas, there are several important factors to \r\nconsider. There is a clear requirement for climate change information \r\nover a  wide range of 'regions', and classes thereof, and temporal \r\nscales. There is also often the need for integrated information \r\nrelevant for policy, practice and awareness raising. However, most \r\nother chapters in WGI are disciplinary, focusing on specific processes \r\nin the climate system or on its past or future behaviour, and have \r\nlimited space to be spatially and temporally comprehensive. The Atlas \r\nprovides an opportunity to facilitate this integration and exploration \r\nof information."


id: 5a542fd3c148a439ddaa41fdfaf9a515
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"Developing this information often requires a  broad range of data \r\nsources (various observations, global and regionally downscaled \r\nbaselines and projections) to be analysed and combined and, \r\nwhere appropriate, reconciled. This is a topic which is assessed from \r\na  methodological perspective in Chapter  10 using a  limited set of \r\nexamples (see also Cross-Chapter Box 10.3). The Atlas then builds on \r\nthis work with a more comprehensive treatment of the available results, \r\nlargely (but not exclusively) based on CMIP5, CMIP6 and CORDEX, to \r\nprovide wider coverage and to further demonstrate techniques and \r\nissues. These multiple lines of evidence are integrated in the Interactive \r\nAtlas, a new AR6 WGI product described in Atlas.2 allowing for flexible \r\nspatial and temporal analysis of this information with a  predefined \r\ngranularity (e.g.,  flexible seasons, regions and baselines, and future \r\nperiods of analysis including time slices and warming levels)."


id: 3c25bc6538d14364542c0d524ed1eb72
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"Chapter 1 has extensively explored this topic in Section 1.4.1 and \r\nCross-Chapter Box 1.2. A summary of the main points relevant to the \r\nAtlas chapter and the Interactive Atlas are provided here."


id: 4fbff7d3c0ef5ea56e8f3c377a820d31
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"There is no standard baseline in the literature although the World \r\nMeteorological Organization (WMO) recommends a 30-year baseline \r\napproach such as the climate-normal period 1981-2010. However, it \r\nretains the 1961-1990 period as the historical baseline for the sake \r\nof supporting long-term climate change assessments (WMO, 2017). \r\nUsing the WMO standards also provides sample sizes relevant to \r\ncalculating changes in statistics other than the mean. The AR6 WGI \r\nhas established the 1995-2014 period as the recent-past baseline \r\nperiod - for similar reasons to the 1986-2005 period used in AR5 WGI \r\n(IPCC, 2013b) - since 2014 (2005) is the final year of the historical \r\nsimulations of the models (more details in Cross-Chapter Box 1.2)."


id: 6b68dc498a4f770520277b958d237bbd
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"Generating information relevant to policy or practice requires \r\nunderstanding the context of the systems that they focus on. In addition \r\nto the hazards these systems face, their vulnerability and exposure, \r\nand the related socio-economic and other physical drivers, also need \r\nto be understood. To ensure this relevance, the Atlas is informed by the \r\nassessments in Chapter 12 and the regional and thematic chapters \r\nand cross-chapter papers of WGII. Therefore, it focuses on generating \r\ninformation on climatic impact-drivers and hazards applicable to \r\nassessing impacts on and risks to human and ecological systems \r\nwhilst noting the potential relevance of these to related contexts such \r\nas the United Nations (UN) Sustainable Development Goals and the \r\nUN Sendai Framework for Disaster Risk Reduction."


id: c29ed7e0dae838c0098d412e5bb560eb
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"The choice of a  baseline can significantly influence the analysis \r\nresults for future changes in mean climate (Cross-Chapter Box 1.2; \r\nHawkins and Sutton, 2016) as well as its variability and extremes. \r\nThus, assessing the sensitivity of results to the baseline period is \r\nimportant. The Interactive Atlas (Atlas.2) allows users to explore and \r\ninvestigate a wide range of different baseline periods when analysing \r\nchanges for future time slices or global warming levels:"


id: 6e7f02f0d428da1fd5161bc88ba996b8
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"Transparency and reproducibility are promoted in the Atlas \r\nchapter implementing FAIR principles for Findability, Accessibility, \r\nInteroperability, and Reusability of data (Wilkinson et  al., 2016). \r\nMore specifically, the Interactive Atlas provides full metadata of "


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"GCM are used, as in Nikulin et  al. (2018). Detailed reproducible \r\ninformation on the GWLs used in the Atlas is provided in the Atlas \r\nrepository (Iturbide et al., 2021)."


id: 4cd43e14825a2bb758b6cc9684bbb5fd
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"This promotes cross-Working Group consistency and facilitates \r\ncomparability with previous reports and across datasets. For instance, \r\nthe AR5 and long-term WMO baselines facilitate the intercomparison \r\nof CMIP5, CORDEX and CMIP6 projections since all have historical \r\nsimulations in these periods. Using more recent baselines introduces \r\ndiscontinuity for the CMIP5 and CORDEX models, since historical \r\nsimulations end in 2005. A  pragmatic approximation to deal with \r\nthis issue is to use scenario data to fill the missing segment, for \r\nexample for 2006-2014 use the first years of RCP8.5-driven transient \r\nprojections in which the emissions are close to those observed. This \r\napproach is used in the Atlas chapter and Chapter 12."


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"Climate information at many temporal scales and over a wide range of \r\ntemporal averaging periods is required for the assessment of climate \r\nchange and its implications. These range from annual to multi-decadal \r\naverages required to characterize low-frequency variability and trends \r\nin climate to hourly or instantaneous maximum or minimum values of \r\nimpactful climate variables. In between, information on, for example, \r\nseasonal rainfall is important and implies the need to include averaging \r\nperiods whose relevance are geographically dependent. As a result, \r\nthe Atlas chapter presents results over a wide range of time scales, \r\nfrom daily to decadal, and averaging periods with the Interactive Atlas \r\nallowing a choice of user-defined seasons and a range of predefined \r\ndaily to multi-day climate indices."


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"When assessing changes over the recent past, many studies analyse \r\ndatasets using a  range of climatologically significant periods \r\n(i.e., 30 years or more) with precise start and end dates depending \r\non data availability and the year of the study. To account for this, \r\nwhen generating assessments from this literature the term 'recent \r\ndecades' is used to refer to a period of approximately 30 to 40 years \r\nwhich ends within the period 2010-2020. An equivalent approximate \r\ndescription using specific years would be 'since the 1980s'."


id: 1cf45e9103fa6a9411178ba66e565a5c
created_at: 2023-03-16 14:00:51
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"Many factors influence the spatial scales and regions over which \r\nclimate information is required and can be reliably generated. Despite \r\nall efforts in researching, analysing and understanding climate and \r\nclimate change, a key factor in determining spatial scales at which \r\nanalysis can be undertaken is the availability and reliability of \r\ndata, both observational and from model simulations. In addition, \r\ninformation is required over a wide range of spatial domains defined \r\neither from a climatological or geographical perspective (e.g., a region \r\naffected by monsoon rainfall or a river basin) or from a socio-economic \r\nor political perspective (e.g.,  least-developed countries or nation \r\nstates). Chapter 1 provides an overview of these topics (Section 1.5.2). \r\nThis subsection discusses some relevant issues, summarizes recent \r\nadvances in defining domains and spatial scales used by AR6 analyses \r\nand how these can be explored with the Interactive Atlas."


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"Regarding the future reference periods, the Interactive Atlas \r\npresents projected global and regional climate changes at near-, \r\nmid- and long-term periods, respectively 2021-2040, 2041-2060 \r\nand 2081-2100, for a range of emissions scenarios (Atlas.1.4.3 and \r\nCross-Chapter Box 1.4)."


id: 726e8946f9b3ce3890eddd3a483342ed
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"Noting the approach taken in the recent IPCC Special Report on Global \r\nWarming of 1.5degC (SR1.5) above 1850-1900 levels (IPCC, 2018b), the \r\nAtlas also presents global and regional climate change information at \r\ndifferent global warming levels (GWLs, see Cross-Chapter Box 11.1). \r\nIn particular, to provide policy-relevant climate information and \r\nrepresent the range of outcomes from the emissions scenario and time \r\nperiods considered, GWLs of 1.5degC, 2degC, 3degC and 4degC are considered. \r\nThe information is computed from all available scenarios (e.g., only \r\n1.5degC and 2degC GWL information can be computed from projections \r\nunder the SSP1-2.6 scenario). The Interactive Atlas allows comparison \r\nof timings for global warming across the different scenarios and of \r\nspatial patterns of change, for example information at 2degC GWL is \r\ncalculated from SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 projections \r\n(Section 4.2.4)."


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"Recent IPCC reports - AR5 Chapter 14 (Christensen et al., 2013) and \r\nSR1.5 Chapter 3 (Hoegh-Guldberg et al., 2018) - have summarized \r\ninformation on projected future climate changes over sub-continental \r\nregions defined in the SREX report (Seneviratne et  al., 2012) and \r\nlater extended in AR5 from the 26 regions in SREX to include the \r\npolar, Caribbean, two Indian Ocean, and three Pacific Ocean regions \r\n(hereafter known as AR5 WGI reference regions; Figure Atlas.2a). In \r\nrecent literature, new sub-regions have been used, for example for \r\nNorth and South America, Africa and Central America, together with \r\nthe new definition of reference oceanic regions. Iturbide et al. (2020) \r\ndescribes an updated version of the reference regions which is used \r\nin this report (hereafter known as AR6 WGI reference regions) and \r\nis shown in Figure Atlas.2b. The goal of these subsequent revisions \r\nwas to ensure that they represented sub-continental areas of greater \r\nclimatic coherency."


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"To calculate GWL information for the datasets used in the Atlas \r\n(CMIP6 and CMIP5; see Atlas.1.4), this chapter adopted the \r\nprocedure used in Cross-Chapter Box 11.1. A model future climate \r\nsimulation reaches the defined GWL of 1.5degC, 2degC, 3degC or 4degC \r\nwhen its global near-surface air temperature change averaged over \r\nsuccessive 20-year periods first attains that level of warming relative \r\nto its simulation of the 1851-1900 climate (1851-1900 defines the \r\npre-industrial baseline period for calculating the required global \r\nsurface temperature baseline, Cross-Chapter Box 1.2). Note that this \r\nprocess is different from the one used in the SR1.5 report which used \r\n30-year future periods. If a projection stabilizes before reaching the \r\nrequired threshold it is unable to simulate climate at that GWL and \r\nis thus discarded. For CORDEX simulations, the periods of the driving "


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"The rationale followed for the definition of the reference regions was \r\nguided by two basic principles: 1) climatic consistency and better \r\nrepresentation of regional climate features, and 2) representativeness \r\nof model results (i.e., sufficient number of model grid boxes). \r\nThe finer resolution of CMIP6 models (as compared, on average, \r\nto CMIP5) yields better model representation of the reference \r\nregions allowing them to be revised for better climatic consistency "


id: 81aef87a07fb3ce38a6ae5f19c0d4535
created_at: 2023-03-16 14:00:51
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content:
"Figure Atlas.2 | WGI reference regions used in the (a) AR5 and (b) AR6 reports (Iturbide et al., 2020). Asterisks indicate regions that extend across both sides \r\nof the map."


id: cbfa2aab1d61df5fecf6b5dc9795598f
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"is an important determinant or influencer. In these cases the domains \r\nare subject to particular physical processes that are important for \r\nits climatology or that involve systems affected by the climate \r\nin a  way that observations and climate model simulations can be \r\nused to understand. Many of these are the basis of the chapters and \r\ncross-chapter papers of the AR6 WGII report, namely river basins, \r\nbiodiversity hotspots, tropical forests, cities, coastal settlements, \r\ndeserts and semi-arid areas, the Mediterranean, mountains and polar \r\nregions. It is therefore important to generate climate information \r\nrelevant to these typological domains and some examples of these \r\nused in the Interactive Atlas are shown in Figure Atlas.4."


id: 1d5186717c2b84c7580a48a2d7efb325
created_at: 2023-03-16 14:00:51
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"(e.g.,  dividing  heterogeneous  regions) while preserving the model \r\nrepresentation. Figure Atlas.3 illustrates this issue, displaying the \r\nnumber of grid boxes (over land for land regions) in the AR6 reference \r\nregions for two Interactive Atlas reference grids of horizontal \r\nresolutions of 1deg and 2deg, representative of the typical resolution of \r\nCMIP6 and CMIP5 models respectively. This figure shows that the \r\nnew reference regions are well suited for the assessment of model \r\nresults, with poorest model coverage for the New Zealand (NZ), \r\nCaribbean (CAR) and Madagascar (MAD) regions."


id: 6e82ce159b1a7aa26178b18b7aa7de7a
created_at: 2023-03-16 14:00:51
updated_at: 2023-03-16 14:00:51
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vector_id: 15015
content:
"The AR6 WGI (land and open ocean) reference regions are used in \r\nthe Interactive Atlas as the default regionalization for atmospheric \r\nvariables. However, these regions are not optimum for the analysis \r\nof oceanic variables since, for instance, the five upwelling regions \r\n(Canary, California, Peru, Benguela and Somali) are mostly included \r\nin 'land' regions. Therefore, the alternative set of oceanic regions \r\ndefined by their biological activity (Figure Atlas.4) is used in the \r\nInteractive Atlas for the regional analysis of oceanic variables (see \r\nFay and McKinley, 2014; Gregor et  al., 2019). Due to the many \r\npotential definitions of the regions relevant for WGI and WGII, some \r\nadditional typological and socio-economic regions have also been \r\nincluded in the Interactive Atlas."


id: 9906669b795924ceb2d3e8d89d992e50
created_at: 2023-03-16 14:00:51
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"This section introduces the observational data sources and \r\nreanalyses that are used in the assessment of regional climate \r\nchange and for evaluating and bias adjusting the results of models \r\n(more information on observational reference datasets is available \r\nin Annex  I). It also introduces the different global and regional \r\nclimate model outputs that are used for regional climate assessment \r\nconsidering both historical and future climate projections (Annex \r\nII). Many of these models are run as part of coordinated Model \r\nIntercomparison Projects (MIPs), including CMIP5, CMIP6 and \r\nCORDEX, described below. Combining information from these \r\nmultiple data sources is a significant challenge (see Section 10.5 for \r\nan in-depth treatment of the problem) though if they can be used "


id: 6dc34e8e3d4a2298f5009d1c801377ce
created_at: 2023-03-16 14:00:51
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content:
"to generate robust information on regional climate change it can \r\nguide policy and  support decisions responding to these changes. \r\nAn important and necessary part of this process is to check for \r\nconsistency amongst the data sources."


id: 2d916a08ce7cb46a313fcd290e710c18
created_at: 2023-03-16 14:00:51
updated_at: 2023-03-16 14:00:51
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content:
"Chapter 10 by providing the opportunity to visualize and expand on \r\nits assessment. This includes displaying maps of density of stations' \r\nobservations (including those that are used in the different datasets) and \r\nassessing observational uncertainty by using multiple datasets."


id: 732a7038977b0a55cef5ee244479f7af
created_at: 2023-03-16 14:00:51
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"Two of the most commonly used variables in climate studies are \r\ngridded surface air temperature and precipitation. There are many \r\ndatasets available (Annex I) and Chapter 2 provides an assessment \r\nof key global datasets, including blended land-air and sea surface \r\ntemperature datasets to assess global mean surface temperature \r\n(GMST). The Atlas separately analyses atmospheric and oceanic \r\nvariables, and for the former a number of common global datasets \r\nsupporting the assessment done in other chapters is used, including "


id: dbb393da49693880d8e0608002871ca9
created_at: 2023-03-16 14:00:51
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vector_id: 32083
content:
"There are various sources of observational information available for \r\nglobal and regional analysis. Observational uncertainty is a key factor \r\nwhen assessing and attributing historical trends, so assessment \r\nshould build on integrated analyses from different datasets (disparity, \r\ninadequacy and contradictions in existing datasets are assessed in \r\nSection  10.2). The Atlas chapter can supplement and complement "


id: 156f8c83d2fd9caca4affd502491ae40
created_at: 2023-03-16 14:00:51
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content:
"discussions on discrepancies/conflicts in observations presented \r\nin Chapter  10 and expanding on and replicating their results for \r\nother regions. In particular, the Interactive Atlas includes the global \r\nand regional observational products described here to assess \r\nobservational uncertainty over the different regions analysed."


id: e09f7332f43b893487821fb4fa99825d
created_at: 2023-03-16 14:00:51
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vector_id: 32844
content:
"Figure Atlas.5 shows the spatial coverage of the total number of \r\nobservation stations for different periods (1901-1910, 1971-1980, \r\nand 2001-2010) for two illustrative datasets: the CRU TS4 dataset \r\nfor precipitation and the SST data in HadSST4. The former illustrates \r\nspatially the declining trend of station observation data used in the \r\nprecipitation datasets for certain regions (South America and Africa) \r\nafter the 1990s. This demonstrates the regional inhomogeneity and \r\ntemporal change in station density, which is in part a consequence \r\nof many stations not reporting to the WMO networks and their data \r\nbeing held domestically or regionally. During early years (before 1950) \r\na  limited number of observations are available. This information is \r\nused in the Interactive Atlas to blank out regions not constrained with \r\nobservations in those datasets providing station density information."


id: c06a66e3a90f50469bc4b287a2daaa3
created_at: 2023-03-16 14:00:51
updated_at: 2023-03-16 14:00:51
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index: climate_gpt_only_giec
vector_id: 27834
content:
"The Atlas and Interactive Atlas use information from ERA5 and \r\nfrom the bias-adjusted version WFDE5 (Cucchi et  al., 2020) which \r\nis combined with ERA5 information over the ocean and used as \r\nthe Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) \r\nobservational reference dataset W5E5 (Lange, 2019b). This reference \r\nis also used in the Atlas for model evaluation (Atlas.1.4.4) and for \r\nbias-adjusting model outputs (Atlas.1.4.5)."


id: 26b90a6b7c434ce0a758c77ca4110168
created_at: 2023-03-16 14:00:51
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"In addition to surface observations, satellites have been widely \r\nused to produce rainfall estimates. The advantage of satellite-based \r\nrainfall products is their global coverage including remote areas but \r\nthere is significant uncertainty in these products over complex terrain \r\n(Rahmawati and Lubczynski, 2018; Satge et al., 2019). Another recent \r\ndevelopment has been on gridded datasets for climate extremes \r\nbased on surface stations, such as HadEX3 (Dunn et al., 2020), as \r\ndescribed in Section 11.2.2."


id: 7f9a97418e7d7cfc6cda44403297fe6d
created_at: 2023-03-16 14:00:51
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vector_id: 17624
content:
"There are some studies assessing observational datasets globally \r\n(Beck et  al., 2017; Q. Sun et  al., 2018) and regionally (Manzanas \r\net  al., 2014; Salio et  al., 2015; Prakash, 2019), reporting large \r\ndifferences among them and stressing the importance of considering \r\nobservational uncertainty in regional climate assessment studies. \r\nUncertainty in observations is also a key limitation for the evaluation \r\nof climate models, particularly over regions with low station density \r\n(Kalognomou et  al., 2013; Kotlarski et  al., 2019). More detailed \r\ninformation on these issues is provided in Section 10.2."


id: 797919cb35c0951e22bdfcba701bff0e
created_at: 2023-03-16 14:00:51
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"For regional studies, observational datasets with global coverage are \r\ncomplemented by a  range of regional observational analyses and \r\ngridded products, such as E-OBS (Cornes et al., 2018) over Europe, \r\nDaymet (Thornton et al., 2016) over North America, or APHRODITE \r\n(Yatagai et al., 2012) over Asia. These are highlighted in various other \r\nchapters and the Atlas expands on their treatment, complementing "


id: 42f3ddca85673d25cc5e4de3df4f97c3
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"analysis based on time slices for particular scenarios (Atlas.1.3.1). \r\nThis dimension allows for enhanced comparability of CMIP5 and \r\nCMIP6, since it constrains the regional patterns to the same global \r\nwarming level for both datasets."


id: bf612aa5ea69c78346cd4204c167892e
created_at: 2023-03-16 14:00:51
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"change signals compared to global models and adds any value, \r\nespecially in terms of high-resolution features and extremes."


id: 8c0a2ef4215f9b3de8176db381f30bd7
created_at: 2023-03-16 14:00:51
updated_at: 2023-03-16 14:00:51
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"A lot of progress has been made by the regional climate modelling \r\ncommunity since AR5 (Table AII.1) to produce and make available \r\nevaluation (reanalysis-driven) simulations over the different \r\nCORDEX domains along with downscaled CMIP5 historical and \r\nfuture climate projection information under a  range of emissions \r\nscenarios, mainly RCP2.6, RCP4.5 and RCP8.5 (Tables AII.3 and AII.4). \r\nHowever, these ensembles cover only a fraction of the uncertainty \r\nrange spanned by the full CMIP5 ensemble in the different domains \r\n(e.g., Figures Atlas.16, Atlas.17, Atlas.21, Atlas.22, Atlas.24, Atlas.26, "


id: 86d304f114998caad7fabd1b55659143
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content:
"Chapter 3 and Flato et al. (2013) describe the evaluation of CMIP6 \r\nand CMIP5 models, respectively, assessing surface variables and \r\nlarge-scale indicators. Section 10.3.3 assesses the general capability \r\nof GCMs to produce climate output for regions."


id: ec96803758b4aaf39bfd159d6aae028f
created_at: 2023-03-16 14:00:51
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content:
"Information from the existing CMIP5 and CMIP6 datasets is \r\nsupplemented with downscaled regional climate simulations from \r\nCORDEX. This facilitates an assessment of the effects from higher \r\nresolution, including whether this modifies the projected climate "


id: c4f042eeb903653c9b9989c6c9c3113c
created_at: 2023-03-16 14:00:51
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"Atlas.28 and Atlas.29; Ito et  al., 2020b). Therefore, comparison of \r\nCMIP5 and CORDEX results should be performed carefully, providing \r\nresults not only for the full CMIP5 ensemble but also for the \r\nsub-ensemble formed by the driving models since results can diverge \r\n(Fernandez et al., 2019; Iles et al., 2020)."


id: 8496720261b0eec1e1af6dc0ca3da58c
created_at: 2023-03-16 14:00:51
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"and also the reanalysis-driven simulations for evaluation purposes. \r\nIn the case of the European domain, the dataset considered is the \r\n0.11deg simulations (CORDEX EUR-11, the same dataset as used in \r\nChapter 12), which has been interpolated to a regular 0.25deg resolution \r\ngrid (the same used for the regional observations). In the case of the \r\nMediterranean domain, oceanic information (sea surface temperature, \r\nSST) is interpolated to a regular 0.11deg grid. In all cases, the indices are \r\ncomputed on the original grids and the interpolation process is applied \r\nto the resulting indices. Moreover, for the sake of comparability with \r\nCMIP6 results (in particular when using baseline periods beyond 2005), \r\nthe historical period of the CORDEX datasets has been extended to \r\n2006-2014 using the first years of RCP8.5-driven transient projections \r\nin which the emissions are close to those observed (see Atlas.1.3.1); \r\nnote that this procedure is also applied to CMIP5 simulations."


id: d8c3da789901f177721c370989c83f59
created_at: 2023-03-16 14:00:51
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content:
"The Atlas chapter and the Interactive Atlas use CORDEX information for \r\nthe following 11 individual CORDEX domains (out of the 14 domains \r\nshown in Figure Atlas.6): North, Central and South America; Europe; \r\nAfrica; South, East and South East Asia; Australasia; Arctic and \r\nAntarctica; in addition, oceanic information has been used from the \r\nMediterranean domain, which provides simulations from coupled \r\natmosphere-ocean regional climate models (RCMs). In order to \r\nharmonize the information across domains and to maximize the \r\nsize of the resulting ensembles, all the available simulations for each \r\nindividual CORDEX domain (including the standard 0.44deg CORDEX \r\nand the 0.22deg CORDEX-CORE) have been interpolated to a common \r\nregular 0.5deg-resolution grid to provide a grand ensemble covering the \r\nhistorical and future emissions scenarios RCP2.6, RCP4.5 and RCP8.5, "


id: b991a26e803e1d8698dd928c64f66d6
created_at: 2023-03-16 14:00:51
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content:
"For the different CORDEX domains, the full ensembles of models (GCM\u0002RCM matrix) used in the Atlas for the different scenarios and variables \r\nare described in the Supplementary Material (Tables  Atlas.SM.3-\r\nAtlas.SM.14) and in the Atlas repository (Iturbide  et  al., 2021), "


id: 996112ecadb6c3aa2742e42fb725ff1c
created_at: 2023-03-16 14:00:51
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vector_id: 21652
content:
"Figure Atlas.7 | Evaluation of annual and seasonal air temperature and precipitation for the six North America sub-regions, NWN, NEN, WNA, CNA, ENA \r\nand NCA (land only) for CORDEX-NAM RCM simulations driven by reanalysis or historical GCMs. Seasons are June-July-August (JJA) and December-January-\r\nFebruary (DJF). Rows represent sub-regions and columns correspond to the models. Magenta text indicates the driving historical CMIP5 GCMs (including ERA-Interim in the first \r\nset of slightly separated columns) and the black text to the right of the magenta text represents the driven RCMs. The colour matrices show the mean spatial biases; all biases \r\nhave been computed for the period 1985-2005 relative to the observational reference (E5W5, see Atlas.1.4.2). Further details on data sources and processing are available in \r\nthe chapter data table (Table Atlas.SM.15)."


id: 9ef30e6d2d40796e42598bb54afb2e2
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"including full metadata relative to ESGF versions used and the periods \r\nwith data available for the different simulations. In particular, the \r\nhistorical scenario information is only available from 1970 onwards \r\nfor some models and therefore the common period 1970-2005 is \r\nused for historical CORDEX data in the Atlas. As a result, the WMO \r\nbaseline period 1961-1990 is not available in the Interactive Atlas for \r\nCORDEX data."


id: 1765133df4bd6da6aafe8685bf30ecc
created_at: 2023-03-16 14:00:51
updated_at: 2023-03-16 14:00:51
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index: climate_gpt_only_giec
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content:
"biases for mean temperature and precipitation have been calculated \r\nfor the 11 CORDEX domains analysed."


id: e03104646985409d36093d4a8c5e783d
created_at: 2023-03-16 14:00:51
updated_at: 2023-03-16 14:00:51
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vector_id: 32765
content:
"Sections Atlas.4 to Atlas.11 assess research on CORDEX simulations \r\nover different regions, analysing past and present climate as well \r\nas future climate projections. They also focus on regional model \r\nevaluation in order to extend and complement the validation \r\nof global models in Chapter  3, considering the specific regional \r\nclimate and relevant large-scale and regional phenomena, drivers \r\nand feedbacks (Section  10.3.3). Besides the literature assessment, \r\nsome simple evaluation diagnostics have been computed for \r\nthe simulations used in the Atlas chapter to provide some basic \r\ninformation on model performance across regions. In particular, "


id: f4fd158f5e76d202d086a5bcd4ab7331
created_at: 2023-03-16 14:00:51
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"CORDEX-CORE spans the spread of the CMIP5 climate change signals for interquartile ranges of annual mean temperature and \r\nprecipitation for most of the reference regions covered (Box Atlas.1, Figure 1; Teichmann et al., 2021). However, it is still a small \r\nensemble and for other variables like extremes or climatic impact-drivers it has only been partially investigated in Coppola et al. (2021b) \r\nand needs further analysis."


id: 6e79f4246754f6f308bb792621cb2ca7
created_at: 2023-03-16 14:00:51
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content:
"extreme indices and climatic impact-drivers (CIDs) included in the \r\nInteractive Atlas are bias adjusted (in particular TX35 and TX40 in \r\ncoordination with Chapter 12). To facilitate integration with WGII, the \r\nAtlas uses the same bias-adjustment method as in ISIMIP3 (Lange, \r\n2019a) and the same observational reference (W5E5, see Atlas.1.4.2), \r\nupscaled to the same resolution as the model to avoid downscaling \r\nartefacts (Cross-Chapter Box  10.2). The ISIMIP3 bias-adjustment \r\nmethod is a  trend-preserving approach that is recommended for \r\ngeneral applications, as it reduces biases while preserving the \r\noriginal climate change signal (Casanueva et  al., 2020). Following \r\nthe recommendations given in Chapter 10, results in the Interactive \r\nAtlas are displayed for both the adjusted and the raw model output."


id: a70975dc8ad611432a5add3b2f0581ba
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"Bias adjustment is often applied to data from climate model \r\nsimulations to improve their applicability for assessing climate impacts \r\nand risk (e.g.,  in the Inter-Sectoral Impact Model Intercomparison \r\nProject, ISIMIP; Rosenzweig et al., 2017). Bias-adjustment approaches \r\n(Section  10.3.1.3) are particularly beneficial when threshold-based \r\nindices are used, but they can introduce other biases, in particular \r\nwhen applied directly to coarse-resolution GCMs (Cross-Chapter \r\nBox 10.2). Bias-adjustment techniques should be chosen carefully for \r\na  specific application. In the Atlas, bias adjustment is not applied \r\nsystematically (in particular, it is not applied for the variables \r\nassessed in the Atlas chapter), and only some threshold-dependent "


id: 2b231b055d07aecb6ef5e3febf6f7635
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content:
"Contributors: Alessandro Dosio (Italy), Melissa I. Gomis (France/Switzerland), Richard G. Jones (UK), Maialen Iturbide (Spain), \r\nMegan Kirchmeier-Young (Canada/USA), June-Yi Lee (Republic of Korea), Stephane Senesi (France), Sonia I. Seneviratne (Switzerland), \r\nPeter W. Thorne (Ireland/UK), Xuebin Zhang (Canada)"


id: eab57f69044cd9475fb558d4ed61fbe1
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"with more or less complexity (Retchless and Brewer, 2016), the most common implementation for maps in the climate science \r\ncommunity remains the overlay of symbols and/or masking of the primary variable. This Cross-Chapter Box reviews the approaches \r\nfollowed in previous IPCC reports and describes the methods used across this WGI report, presenting the rationale and discussing its \r\nrelative merits and limitations."


id: ea5be543b2f31917b1d704c9f3c120e
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content:
"The AR5 WGII (Hewitson et al., 2014) used hatching to represent non-significant trends in observations. For future projections, an \r\nelaborated approach with four mutually exclusive and exhaustive categories was proposed (to avoid some of the limitations of the \r\nAR5 WGI approach): very strong agreement (same as in WGI); strong agreement; divergent change; and little or no change. These \r\ndepended on the percentage of models showing change greater than the baseline variability and/or agreeing on sign of change (using \r\na 66% agreement threshold). Leaving the robust regions uncovered minimized any interference with the perception of underlying \r\ncolours that encoded the primary information of the figure."


id: 93d65c3ddac8443ced2cc8928074beb1
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content:
"The two special reports IPCC SR1.5 (Hoegh-Guldberg et al., 2018) and SROCC (IPCC, 2019a, c) adopted a simplified approach, using \r\nonly model agreement (>=66% of models agree on sign of change) to characterize robustness. However, cross-hatching was used in \r\nSR1.5 to highlight robust areas where models agree, whereas the SROCC used hatching/shading to represent regions where models \r\ndisagree. Similarly, stippling was used in SR1.5 to indicate regions with significant trends, whereas it was used in SROCC to represent \r\nregions where the trends were not significant."


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"present when aggregated at larger scale (Sections 11.2.4 and 11.2.5). Consequently, changes averaged over regions or a number of \r\ngrid boxes emerge from internal variability at a lower level of warming than at the grid-box level (e.g., Cross-Chapter Box Atlas.1, \r\nFigure 2). Hence, focus on grid-box significance enhances the risk of Type II errors for overlooking signals significant at the level of \r\nAR6 regions. The significance of signals is also affected by the interdependence of single simulations considered in a given ensemble, \r\nfor example when several come from the same modelling group and share parametrizations or model components (Knutti et al., \r\n2013; Maher et al., 2021). The risk of Type II errors increases when a model ensemble includes several related simulations showing \r\nno signal."


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"The AR6 WGI adapts the approaches applied in previous IPCC reports into a comprehensive framework based on the two general \r\nprinciples followed by AR5 WGII: 1) not obscuring (with stippling or hatching) the areas where relevant/robust information needs to \r\nbe highlighted (since stippling and hatching obstruct the visualization of the colours, which can affect the perception/interpretation \r\nof the underlying data); 2) using mutually exclusive and exhaustive categories to avoid leaving areas undefined. The three adopted \r\napproaches (one for observations and two for model projections) are described in Cross-Chapter Box Atlas.1, Table 1. This framework \r\nintegrates as much as possible the specificities of each WGI Chapter, proposing in some cases alternative thresholds."


id: 7acb02dd182f94da2816ea6e15c6098c
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"Approach A is intended for observations and consists of two categories, one for areas with significant trends (colour, no overlay) and \r\none for non-significant ones (coloured areas overlaid with 'x'), typically using a two-sided test for a significance level of 0.1; Chapter 2 \r\nand Atlas trends have been calculated using ordinary least squares regression accounting for serial correlation (Santer et al., 2008)."


id: 8eaeca2b0ef28348161feaf27441e828
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"Cross-Chapter Box Atlas.1, Figure 1 illustrates the application of this method considering the effect of the baseline period (1850-1900 \r\nversus 1995-2014) and shows that it provides similar results to related approaches proposed in the literature (Zappa et al., 2021)."


id: d55e6422c4a720972444ad8a6ed32c03
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"The two alternative approaches discussed above allow visualization of different levels of detail of information on the projected change \r\nand are intended for different communication purposes. Approach B just informs on the consistency of the sign of change independent"


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"of its significance relative to internal variability, whereas approach C puts the projected changes into the context of internal variability \r\nand allows the highlighting of areas of conflicting signals. It is important to note that different approaches can be applied to the same \r\nvariable between different chapters for different communication purposes. For example, in maps showing multi-model mean changes \r\nof precipitation, Chapter 4 adopts approach C but Chapter 8 applies approach B."


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"In terms of visual implementation, the approach follows recommendations resulting from conversations with IPCC national delegations: \r\n1) having a consistent approach across WGs would aid consistency and reduce the risk of confusion; 2) defining 'hatching' as 'diagonal \r\nlines' in the caption would aid accessibility for non-expert audiences; 3) a clear and concise legend that explains what these patterns \r\nrepresent should be included directly in the figure; 4) information about model uncertainty should be overlaid such that it does not \r\ndetract from the data underneath."


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"Since stippling is commonly used to represent statistical significance, diagonal lines were chosen to 'obscure' the problematic \r\ncategories in the above approaches; it also facilitates the visualization of uncertainty in the Interactive Atlas when zooming in. To \r\navoid confusion, methods or thresholds that were unrelated to the three approaches hereby presented were visualized with a different \r\npattern (i.e., model improvement between low- and high-resolution simulations in Chapter 3; agreement between observation-based \r\nproducts in Chapter 5; correlation between two variables in Chapter 6)."


id: 8c8db85cf1cb8584f5aea2cce53798af
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content:
"Cross-Chapter Box Atlas.1, Table 1 | Approaches for representing robustness (uncertainty) in maps of observed (approach A) and projected \r\n(approaches B and C) climate changes."


id: 5a833bea8952e37d6b5a681547aad4a3
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"Cross-Chapter Box Atlas.1, Figure 1 | Illustration of the simple, (a) and (b), and advanced, (c-f), approaches (B and C in Cross-Chapter Box Atlas.1, \r\nTable 1) for uncertainty representation in maps of future projections. Annual multi-model mean projected precipitation change (%) from CMIP6 for the \r\nperiod 2040-2060 (left) and 2080-2100 (right) relative to the baseline periods 1995-2014 (a-d) and 1850-1900 (e and f) under a high-emissions (SSP3-7.0) future. \r\nDiagonal and crossed lines follow the indications in Cross-Chapter Box Atlas.1, Table 1. Further details on data sources and processing are available in the chapter \r\ndata table (Table Atlas.SM.15)."


id: dc6dae5fe752a71110c03da62b322dc8
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"The Interactive Atlas includes two components. The first component \r\n(Regional Information) allows for flexible spatial and temporal analysis \r\n(Atlas.1.3) with a  predefined granularity (predefined climatological \r\nand typological regions, and user-defined seasons) through a  wide \r\nrange of maps, graphs and tables generated in an interactive manner \r\nbuilding on a collection of global and regional observational datasets \r\nand climate projections (including CMIP5, CMIP6 and CORDEX; \r\nAtlas.1.4). In particular, the Interactive Atlas provides trends and \r\nchanges for observations and projections in the form of interactive \r\nmaps for predefined historical and future periods of analysis, the former \r\nincluding the recent past and paleoclimate (Cross-Chapter Box 2.1) \r\nand the latter including future time slices (near, medium and long \r\nterm) across scenarios (RCPs and SSPs; see Cross-Chapter Box 1.4) \r\nand GWLs (1.5degC, 2degC, 3degC and 4degC; see Cross-Chapter  Box  11.1). "


id: 404d610404718b61fe404a57a51e63be
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"The WGI Interactive Atlas is a new AR6 product developed as part \r\nof the Atlas in consultation with other chapters to facilitate flexible \r\nsynthesis information for regions, and to support the Technical \r\nSummary (TS) and the Summary for Policymakers (SPM), as well as \r\nthe handshake with WGII. It includes multiple lines of evidence to \r\nsupport the assessment of observed and projected climate change \r\nby offering information for regions using both time slices across \r\nscenarios and GWLs. Coordination has been established with \r\nother chapters (particularly the regional chapters), adopting their \r\nmethodological recommendations (Chapter 10) and using common \r\ndatasets and agreed extreme indices and climatic impact-drivers \r\n(CIDs) to support and expand their assessment (Chapters 11 and 12)."


id: e430b6cf4217e3bf181d95d66f0a806e
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"Dimensions of analysis include time periods for \r\nscenarios and global warming levels (1.5oC, 2oC, 3oC \r\nand 4oC)."


id: 9acea70159072131ebea05b97243b04e
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"8 | Screenshots from the Interactive Atlas (regional information). (a) The main interface includes a global map and controls to define a particular choice \r\niable, period (reference and baseline), and season (in this example, annual temperature change from CMIP6 for a global warming level of 2degC under SSP3-7.0 \r\n0-1900). (b-e) Various visuals for the regionally averaged information for the selected reference regions."


id: 3e830f44c6897faf113badd83d685d02
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"It also provides regional information (aggregated spatial values) for \r\na number of predefined (reference and typological) regions in the form \r\nof time series, annual cycle plots, scatter plots (e.g., temperature versus \r\nprecipitation), table summaries, and ensemble and seasonal stripe \r\nplots. This allows for a comprehensive analysis (and intercomparison, \r\nparticularly using GWLs as a dimension of integration) of the different \r\ndatasets at a global and regional scale."


id: ab29583a9b47345f3f9e862e6ffe5c1d
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"The limited number of variables which can be treated on a printed \r\nAtlas also prevents the inclusion of relevant extreme indices and CIDs. \r\nThe development of an online Interactive Atlas for AR6 was proposed \r\nas a  solution to overcome these obstacles, facilitating the flexible \r\nexploration of key variables/indices and datasets assessed in all \r\nchapters through a wide range of maps, graphs and tables generated \r\nin an interactive manner, and thus also providing support to the TS and \r\nSPM. One of the main concerns raised by this new online interactive \r\nproduct was the potential danger of having an unmanageable number \r\nof final products impossible to assess following the IPCC review \r\nprocess. This was addressed by designing the Interactive Atlas with \r\nlimited and predefined functionality and granularity, thus facilitating \r\nthe review process and including use of open-source tools and code \r\nfor traceability and reproducibility of results."


id: 88f79f3b0e0ad60c1969b80521aa9710
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"The second component of the Interactive Atlas (Regional Synthesis) \r\nprovides synthesis information about changes in CIDs in several \r\ncategories such as heat and cold, wet and dry, or coastal and \r\noceanic, supporting exploration of the regional assessment findings \r\nsummarised in the TS and the SPM."


id: ad024e02c8f1e02f6263b957ed58136d
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"The Interactive Atlas can be consulted online at http://interactive\u0002atlas.ipcc.ch. Figure Atlas.8 illustrates the main functionalities \r\navailable: the controls at the top of the window allow the interactive \r\nselection of the dataset, variable, period (reference and baseline) and \r\nseason which define a  particular product of interest (e.g.,  annual \r\ntemperature change from CMIP6 for a  global warming level of \r\n2degC under SSP3-7.0 relative to 1850-1900 in this illustrative case). \r\nRegionally aggregated information can be obtained interactively by \r\nclicking on one or several sub-regions on the map and by selecting \r\none of the several options available for visuals (time series, annual \r\ncycle plots, scatter and stripe plots) and tables."


id: 45d0c8260a1d36e8f4ad9b2d905cbcee
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"The Interactive Atlas builds on the work done in the context of \r\nthe Spanish National Adaptation Plan (PNACC  - AdapteCCa; \r\nhttp://escenarios.adaptecca.es) to develop an interactive online \r\napplication centralizing and providing key regional climate change \r\ninformation to assist the Spanish climate change impact and \r\nadaptation community. The functionalities included in the AR6 WGI \r\nInteractive Atlas are an evolution of those implemented in AdapteCCa \r\nand have been adapted and extended to cope with the particular \r\nrequirements of the datasets and functionalities it includes. In \r\nparticular, the Interactive Atlas allows analysis of global and regional \r\ninformation on past trends and future climate changes through \r\na wide range of maps, graphs and tables generated in an interactive \r\nmanner, and building on six basic products (Figure Atlas.8): "


id: d18e597398eef5bab11e78a95687b75a
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"A major goal during the development of the Interactive Atlas has \r\nbeen ensuring transparency and reproducibility of results, and \r\npromoting open science and Findability, Accessibility, Interoperability, \r\nand Reuse (FAIR) principles (Wilkinson et  al., 2016) described in \r\nAtlas.2.3. As a result, full metadata are provided in the Interactive \r\nAtlas for each of the products, and the scripts used to generate the \r\nintermediated products (e.g., extreme indices and CIDs) and figures \r\nare available online in a  public repository (Iturbide et  al., 2021), \r\nwhich also includes simple notebooks illustrating key parts of the \r\ncode suitable for reusability. These scripts are based on the climate4R \r\nopen-source framework (Iturbide et al., 2019) and full metadata have \r\nbeen generated for all final products using the METACLIP framework \r\n(Bedia et  al., 2019), which builds on standards and describes \r\nprovenance of the datasets as well as the post-processing workflow."


id: 54d22295c41bfca307a376d48d2a7674
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"The idea of an online interactive Atlas was first discussed in the IPCC \r\nExpert Meeting on Assessing Climate Information for the  Regions \r\n(IPCC, 2018a). The meeting stressed the need for the AR6 regional \r\nAtlas to go beyond the AR5 experience in supporting and expanding \r\nthe assessment of key variables/indices and datasets conducted in \r\nall chapters, ensuring traceability, and facilitating the 'handshake' \r\nbetween WGI and WGII. One of the main limitations of previous \r\nproducts, including the AR5 WGI Atlas (IPCC, 2013a), is their static \r\nnature with inherent limited options and flexibility to provide \r\ncomprehensive regional climate information for different regions \r\nand applications. For instance, the use of standard seasons limits the \r\nassessment in many cases, such as regions affected by monsoons or \r\nseasonal rainband migrations or other phenomena-driven seasons. "


id: 2cc79d2ca35995da2cd39cd2c4d81198
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"The essential variables are computed for observations and reanalysis \r\ndatasets as described in Atlas.1.4.1 and Atlas.1.4.2 (note that the Atlas \r\ndoes not include observational datasets for extremes). Trend analyses \r\nare available for two alternative baseline periods (1961-2015 and \r\n1980-2015, selected according to data availability). This expands the \r\ninformation available in Chapter 2 for global observational datasets, \r\nincluding new periods of analysis and new regional observational \r\ndatasets which provide further insight into observational uncertainty. \r\nThe Interactive Atlas also includes paleoclimate information from the \r\nPaleoclimate Model Intercomparison Projects PMIP3/4 for temperature \r\nand precipitation for the Last Glacial Maximum, Last Interglacial, \r\nmid-Holocene and mid-Pliocene periods (see Cross-Chapter Box 2.1)."


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"information of the multi-model ensemble for particular region(s) selected \r\nby the user from a number of predefined alternatives (see Atlas.1.3.3 \r\nand Atlas.1.3.4 for reference and typological regions, respectively)."


id: 792f01ad38fe5a7cc3bcba15ac5aa2dd
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"Both essential variables and indices/CIDs are computed for CMIP5, \r\nCMIP6 and CORDEX model projections (Atlas.1.4.3 and Atlas.1.4.4). \r\nThe calculations are performed on the original model grids and results \r\nare interpolated to the reference regular grids at horizontal resolutions \r\nof 2deg (CMIP5), 1deg (CMIP6) and 0.5deg (CORDEX) (Iturbide et al., 2021). \r\nInformation is available for the historical, SSP1-2.6, SSP2-4.5, SSP3-7.0 \r\nand SSP5-8.5 scenarios for CMIP6, and historical, RCP2.6, RCP4.5 and \r\nRCP8.5 for CMIP5 and CORDEX, as documented in the supplementary \r\nmaterial Tables Atlas.SM.1-2 (for CMIP5/CMIP6) and Tables Atlas.\r\nSM.3-14 (for the different CORDEX domains). All products (maps, \r\ngraphs and tables) are available for different reference periods of \r\nanalysis, either time slices (2021-2040, 2041-2060 and 2081-"


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"2100 for near-, mid- and long-term future periods, respectively; see \r\nAtlas.1.3.1), or GWLs (1.5degC, 2degC, 3degC or 4degC; see Atlas.1.3.2), with \r\nchanges relative to a number of alternative baselines (including 1850-\r\n1900 pre-industrial, and 1995-2014 recent past; see Atlas.1.3.1). Note \r\nthat instead of blending the information from the different scenarios, \r\nthe Interactive Atlas allows comparison of the GWL spatial patterns \r\nand timings across the different scenarios (Cross-Chapter Box 11.1)."


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"(e.g.,  regridding, aggregation, index calculation, bias adjustment), \r\nevaluation and quality control (when applicable). Full metadata are \r\ngenerated for all final products using the METACLIP framework (Bedia \r\net  al., 2019), based on the Resource Description Framework (RDF) \r\nstandard to describe the datasets and data-processing workflow."


id: 534ff27f43881d038fa51176fcbb5b7e
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"Some of the above indices (in particular, TX35 and TX40) are highly \r\nsensitive to model biases and the application of bias-adjustment \r\ntechniques is recommended to alleviate this problem (see Cross-Chapter \r\nBox 10.2). Bias adjustment is performed as explained in Atlas.1.4.5."


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"The advanced approach for representing robustness includes \r\na  new category for identifying conflicting signals, where models \r\nare projecting significant changes but of opposite signs. This is \r\ndemonstrated in Figure Atlas.9 which shows a  region of central \r\nAfrica where models have significant changes in surface winds \r\nwith some projecting increases and others decreases. This is clearly \r\ndemonstrated in the time series below the map which shows these \r\nwind-speed changes aggregated over the CAF reference region for \r\neach of the CMIP6 models and the opposing signals in many of these."


id: 871e560c9f03a74d86ad1eb198a7dd5d
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"Provenance is defined as a  'record that describes the people, \r\ninstitutions, entities, and activities involved in producing, influencing, \r\nor delivering a piece of data or a thing'. This information can be used to \r\nform assessments about their quality, reliability or trustworthiness. In \r\nthe context of the outcomes of the Interactive Atlas, having an effective \r\nway of dealing with data provenance is a necessary condition to ensure \r\nnot only the reproducibility of results, but also to build trust on the \r\ninformation provided. However, the relative complexity of the data \r\nand the post-processing workflows involved may prevent a  proper \r\ncommunication of data provenance with full details for reproducibility. \r\nTherefore, a special effort was made in order to build a comprehensive \r\nprovenance metadata model for the Interactive Atlas products."


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"The accessibility and reproducibility of scientific results have become \r\na major concern in all scientific disciplines (Baker, 2016). During the \r\ndesign and development of the Interactive Atlas, special attention \r\nwas paid to these issues in order to ensure the transparency of the \r\nproducts feeding into the Interactive Atlas (which are all publicly \r\navailable). Accessibility is implemented in collaboration with the IPCC \r\nData Distribution Centre (DDC), since all products underpinning the \r\nInteractive Atlas, including the intermediate products required for \r\nthe indices and CIDs (monthly aggregated data), are curated and \r\ndistributed by the IPCC-DDC and include full provenance information \r\nas part of their metadata. Atlas products are generated using the open\u0002source climate4R framework (Iturbide et al., 2019) for data processing "


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"(Candan et al., 2001). It is an abstract model that has become a general \r\nmethod for conceptual description of information for the Web, using \r\na  variety of syntax notations and serialization formats. METACLIP \r\n(Bedia et  al., 2019) exploits RDF through specific vocabularies, \r\nwritten in the Web Ontology Language (OWL), describing different \r\naspects involved in climate product generation, from the data source \r\nto the post-processing workflow, extending international standard \r\nvocabularies such as PROV-O (Moreau et al., 2015). The METACLIP \r\nvocabularies are publicly available in the METACLIP repository (Bedia \r\nand Martin, 2021)."


id: d5ff97e15995e5c20daeb4aaef451020
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index: climate_gpt_only_giec
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content:
"The Interactive Atlas gives access to a  specific set of climate \r\nvariables from a  large number of climate model simulations, \r\nparticularly the (global) CMIP5, CMIP6 and (regional) CORDEX \r\narchives. The global model outputs generally give a relatively coarse \r\npicture of climate change, which is an important line of evidence \r\nfor the detection and attribution of climate change, but is rarely \r\ndirectly applicable for local climate change assessment or support \r\nof policy design (van den Hurk et al., 2018). To provide additional \r\ndetail, downscaling global projections with regional climate models \r\n(RCMs) or statistical downscaling can be undertaken but also \r\nadds a  source of uncertainty as it involves additional modelling \r\n(Section 10.3)."


id: f901cd2c6e94b104a34a841515b59f70
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"The primary purpose of the IPCC is to provide a  policy-relevant, \r\nnon-prescriptive assessment of the state of knowledge on climate \r\nchange and its impacts. This purpose is different from the provision \r\nof information targeted to implement climate policies, which is the \r\nfocus of climate services and national climate change assessment \r\ncommunities. IPCC assessments are based on quantitative \r\nobservational and model-generated data that are also used in many \r\nactivities supporting the development of climate policies. However, \r\nthe functionality of the Interactive Atlas is primarily aimed at \r\nsupporting the knowledge assessment."


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"The information displayed in the Interactive Atlas allows a number of \r\nsources of uncertainty to be quantified. 'Observational uncertainty' \r\nis represented by the use of multiple (albeit often not completely \r\nindependent) observational datasets. 'Uncertainty due to internal \r\nvariability' cannot be quantified directly since multiple realizations \r\nfrom historic and future projections are not accessible (the Interactive \r\nAtlas uses a  single realization of each model). The use of a  large \r\ncollection of model systems allows for an elaborate quantification of \r\n'model uncertainty'. In addition, a comparison of CMIP5 and CMIP6 \r\nsupports evidence of progress in model quality since AR5, while the \r\nevaluation of the added value of RCMs reveals model uncertainty \r\nrelated to spatial resolution (Section 10.3). Finally, the assessment \r\nof 'scenario uncertainty' is supported by the inclusion of multiple \r\nemissions scenarios for both CMIP5, CORDEX and CMIP6."


id: fc6eef0617a37108efd7cc6a80b6cd3
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"Much of the assessment in this report is based on multiple lines of \r\nevidence (Cross-Chapter Box 10.3). The Interactive Atlas facilitates \r\ncombining multiple observational and model-generated datasets and \r\nspatial and temporal analyses that combine to support statements \r\non the characteristics of the climate system. The use of predefined \r\nspatial and temporal aggregations imposes constraints on the ability \r\nto make specific or tailored assessments but does provide essential \r\nbackground and uncertainty information to generate broad findings \r\nand provide confidence statements on these. Also, the inclusion of \r\na selection of extremes and climatic impact-drivers (CIDs) is a new \r\nelement in the Interactive Atlas and facilitates broader application, \r\nincluding the handshake with WGII. Below, some guidelines on the \r\nuse, interpretation and limitations of the Interactive Atlas are given."


id: fb3bb6b6414b1aac961d9d47d0f17003
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"The communication of uncertainty has a profound influence on the \r\nperception of information that is exchanged during the communication \r\nprocess. An assessment of uncertainty communication and the \r\nbarriers to climate information construction is given in Section 10.5.4."


id: 8dc003e41fdd57c323c7c01d411202a4
created_at: 2023-03-16 14:00:52
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"tabular and graphical representation alternatives included as options \r\nin the Interactive Atlas (Figure Atlas.8) facilitate exploring the \r\ninformation interactively from different perspectives and in different \r\nlevels of detail, thus favouring communication with the large and \r\ndiverse audience of IPCC products."


id: aa0db409c8e5dc9ed15f90c0c0bc0fa6
created_at: 2023-03-16 14:00:52
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"To support the use of visuals provided in the Interactive Atlas for \r\napplication to different audiences, new insights since AR5 have \r\nemerged from a  range of scientific disciplines, including the \r\ncognitive and psychological sciences (Harold et  al., 2016). Studies \r\nhave used interviews and online surveys to assess interpretations of \r\nvisuals used to communicate climate information and uncertainties \r\n(Daron et  al., 2015; Lorenz et  al., 2015; McMahon et  al., 2015; \r\nRetchless and Brewer, 2016). They commonly find wide-ranging \r\ninterpretations and  varied understandings of climate information \r\namongst respondents due to the choice of visuals. In addition, Taylor \r\net  al. (2015) found that preferences for a  particular visualization \r\napproach do not always align with the approaches that achieve \r\ngreatest accuracy in interpretation. Choosing appropriate visuals for \r\na particular purpose and audience can be informed by testing and \r\nevaluation with target groups."


id: 523a5d137db123ecd7eb0e3c2eb3fa2
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"Communication aimed at informing the general public about assessed \r\nscientific findings on climate change have a different purpose and \r\nformat than if intended to inform a specific target audience to support \r\nadaptation or mitigation policies (Whetton et al., 2016). The growing \r\nsocietal engagement with climate change means IPCC reports are \r\nincreasingly used directly by businesses, the financial sector, health \r\npractitioners, civil society, the media, and educators at all levels. The \r\nIPCC reports could effectively be considered a tiered set of products \r\nwith information relevant to a range of audiences."


id: afea7120c76af8e7ce1e36d73878b439
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"The Interactive Atlas does provide access to a  collection of \r\nobservational and modelling datasets, presented in a  form that \r\nsupports the distillation of information on observed and projected \r\nclimate trends at the regional scale. Access to the repository of \r\nunderlying datasets enables further processing for particular purposes. \r\nAs noted above, it is not the intention nor the ambition of this IPCC \r\nassessment and the Interactive Atlas component to provide a climate \r\nservice for supporting targeted policies. For this an increasing number \r\nof dedicated climate change assessment programmes have been \r\ncarried out, aiming at mapping climate change information relevant \r\nfor adaptation and mitigation decision support."


id: 669aeb4212f750f5dce709e4e66c2b6
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"For instance, EEA (2018) provides an overview of European national \r\nclimate change scenario programmes. Most of these use CMIP5 (or \r\nearlier) global climate change ensembles driven by an agreed set of \r\ngreenhouse gas (GHG) emissions scenarios, followed by downscaling \r\nusing RCMs and/or statistical methods, in order to generate regionally \r\nrepresentative hydro-meteorological indicators of climate change. \r\nIn some cases, output of selected downscaled global and regional \r\nmodels is provided to users (Whetton et al., 2012; Daron et al., 2018). \r\nUptake by users is strongly dependent on providing justification of \r\nthe selection or for the downscaling procedure and if further steps are \r\nneeded to tailor the information to local scales (Lemos et al., 2012). "


id: 12d341469a5bcbcff41504de012ef4c7
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"The visual communication of climate information can take many \r\nforms. Besides the standard visual products typically used for \r\ncommunicating global and regional climate information to \r\npractitioners (e.g., maps, time series or scatter plots), the Interactive \r\nAtlas incorporates new visuals, for example, 'stripes' (RMetS, 2019), \r\nfacilitating the communication of key messages (e.g., warming and \r\nconsistency across models) to a less technical audience. The various "


id: 1504cb50c91af593ddc74a50cfe69481
created_at: 2023-03-16 14:00:52
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"More comprehensive programmes provide probabilistic climate \r\ninformation by careful analysis and interpretation of ensembles \r\nof model outputs (Lowe et  al., 2018). The information is generally \r\ntailored to professional practitioners with expertise to interpret \r\nand process this probabilistic data. This top-down probabilistic \r\ninformation chain is not always able to highlight the essential climate \r\nchange information for users, and alternative bottom-up approaches \r\nare encouraged (Frigg et al., 2013). Section 12.6.2 assesses climate \r\nservices including the national climate assessments and user uptake."


id: 6bd97693bc53d0ed40d69bdbee39c78d
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"regions. In the latter, emergence times are only calculated for those \r\nregions which have data available in more than 50% of the land \r\narea (unlike Berkeley Earth, CRUTEM does not include spatial \r\ninterpolation, see Section 2.3.1.1.3) and these are similar for all but \r\none of the regions indicating that observational uncertainty does not \r\nchange the main conclusion of widespread emergence of surface \r\ntemperature signals over land regions."


id: 8cc9b8da9faa217034c1ac53ba9ab072
created_at: 2023-03-16 14:00:52
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"As described earlier, information on projected future changes is \r\nrequired both at different time periods in the future under a range \r\nof emissions scenarios but also for different global warming levels. \r\nFigure Atlas.12 shows the global surface air temperature (GSAT) \r\nchange projection calculated from the CMIP6 ensemble mean for the \r\nmiddle of the century under the SSP1-2.6 and SSP3-7.0 emissions \r\nscenarios compared to the end-of-century warming under SSP3-7.0 \r\nand for a  global warming level of 2degC. The patterns of changes \r\nare similar to the observed warming and there is a  high level of \r\nconsistency with CMIP5 in terms of both patterns and magnitude \r\nof change (Interactive Atlas). However, for the long-term future, \r\nwarming in the CMIP6 ensemble is generally higher, reflecting the \r\nincrease in the top end of the range of climate sensitivities amongst \r\nthe CMIP6 GCMs (Figure Atlas.13)."


id: 75f6bae6e8848c3b82fc9252e8d97963
created_at: 2023-03-16 14:00:52
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"Most other chapters in WGI assess past or future behaviour of specific \r\naspects of the global climate system and this section introduces \r\nsome of the key results, specifically from Chapters 2, 4 and 9. This \r\nprovides a global overview on observations and information from the \r\nCMIP5 and CMIP6 ensembles to underpin the regional assessments \r\nin the rest of the Atlas Chapter and the results displayed in the \r\nInteractive Atlas. Thus, its aim is not to generate an assessment of \r\nregional climate change directly but to provide the global context \r\nfor this information derived later in the Atlas. Atlas.3.1 considers \r\nglobal atmospheric and land surface information with global ocean \r\ninformation in Atlas.3.2."


id: 74a9494c49fee46c5e10644969bd701e
created_at: 2023-03-16 14:00:52
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"The principal atmospheric quantities of interest for understanding how \r\nclimate change may impact human and ecological systems, as well as \r\nbeing key global indicators of change, are surface air temperature and \r\nprecipitation. They are therefore a significant focus of the regional \r\nclimate assessments in the following regional sections of the chapter \r\n(Atlas.4 to Atlas.11) and of the Interactive Atlas. Changes in these \r\nvariables over land during the recent past (1961-2015) are shown \r\nin Figure Atlas.11 using results from two global datasets (assessed \r\nin Chapter 2) to illustrate both where there is robust information on \r\nobserved trends and observational uncertainty."


id: 9e3a9af3c9c44bac5e93dce2dbb40fb4
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"For temperature, a clear signal of warming is seen over most land \r\nareas with an amplification at high latitudes, though all continents \r\napart from Africa also have regions where trends are not significant. \r\nSignificant changes in annual mean precipitation are seen over \r\nmuch more limited areas though with consistent increasing trends \r\nover some northern high-latitude regions and decreasing trends over \r\nsmaller regions in tropical Africa, the Americas and South West Asia. \r\nThe information conveyed in Figure Atlas.11 on both consensus in \r\nthe signal of change and on observational uncertainty is used in this \r\nchapter as a line of evidence to assess historical observed trends."


id: e906d135d8c6f3334899fd5767528afb
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content:
"Changes in annual mean precipitation present a  more complex \r\npicture with regions of decrease as well as increase, and areas where \r\nthere is model disagreement on the sign of the change, even when \r\nthe signal is strong in the long-term future period as shown in Cross\u0002Chapter Box Atlas.1, Figure  1. However, as with the temperature \r\nchanges, there is a  high level of consistency in the patterns and \r\nmagnitude of the precipitation changes, with changes in some areas \r\nbeing larger in the long-term future period. Considering changes \r\nover land, Cross-Chapter Box Atlas.1, Figure 1 also shows that at \r\nlower warming levels there are many regions, especially in the \r\nSouthern Hemisphere, where there is no robust signal of change \r\nfrom the models."


id: 9db61ab0fcbd527f0a28f0dad57de51e
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"As an alternative way of viewing and summarizing information in \r\nthe observational data, the panels (c) and (d) in Figure Atlas.11 show \r\nthe time at which any significant temperature trends from the Berkeley \r\nEarth and CRUTEM5 datasets, averaged over the reference regions, \r\nemerged from interannual variability - with a signal-to-noise ratio \r\ngreater than two (Hawkins et al., 2020). In the former, a regionally \r\naveraged warming signal has emerged over all of the land reference "


id: 62d67e708e9924dea9b0c8b3881e5a31
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"Hemisphere higher latitudes in winter. Finally, they demonstrate the \r\ngreater warming projected by the CMIP6 ensemble, as an average \r\nover the ensemble and the upper end of the range. See Chapter 4 for \r\nan in-depth assessment of these results."


id: be1a8531080b05ac7d48c330d46877a2
created_at: 2023-03-16 14:00:52
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"Global warming leads to systematic changes in regional climate \r\nvariability via various mechanisms such as thermodynamic responses \r\nvia altered lapse rates (Kroner et al., 2017; Brogli et al., 2019) and \r\nland-atmosphere feedbacks (Boe and Terray, 2014). These can modify \r\ntemporal and spatial variability of temperature and precipitation, \r\nincluding an altered seasonal and diurnal cycle and return frequency \r\nof extremes. Regional influences from and feedbacks with sea \r\nsurface, clouds, radiation and other processes also modulate the \r\nregional response to enhanced warming, both locally and, via \r\nteleconnections, remotely."


id: b92678d59e7bfae1b19f26705b8f6e5
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"Given their potential to influence extremes in temperature, \r\nprecipitation and other climatic impact-drivers and hazards, and \r\nthus risks to human and ecological systems, it is important to \r\nunderstand these links for developing adaptations in response \r\nto clear anthropogenic influences on individual hazards. This "


id: e1dae3c248f6c4a61f62eb49352f47a
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"will also support the related fields of disaster risk reduction and \r\nglobal sustainable development efforts (Steptoe et al., 2018). They \r\ndemonstrated that 15 regional hazards shared connections via \r\nthe El Nino-Southern Oscillation (ENSO), with the Indian Ocean \r\nDipole, North Atlantic Oscillation and the Southern Annular Mode \r\n(see Annex  IV) being secondary sources of significant regional \r\ninterconnectivity (Figure Atlas.14). Understanding these connections \r\nand quantifying the concurrence of resulting hazards can support \r\nadaptation planning as well as multi-hazard resilience and disaster \r\nrisk reduction goals."


id: 18d8ad0097773e1c59b842be1d093aa6
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"of the teleconnections for selected seasons for the interannual to \r\ndecadal modes and at an annual scale for the multi-decadal modes. \r\nThe columns related to the interannual to decadal modes focus on \r\nthe seasons where these connections are strongest but each mode \r\nof variability will often have influences in other seasons (for more \r\ndetails see Annex IV). The table shows that for many regions, seasonal \r\ntemperature and precipitation is substantially modulated by these \r\nmodes of variability - all regions feel some influence, and variability \r\nin ocean basins often has influence in multiple remote regions."


id: 90a43cbd766b3797cc57c4cbb92ac619
created_at: 2023-03-16 14:00:52
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content:
"The main modes of variability influencing global and regional \r\nclimate are comprehensively described in Annex IV. In the context \r\nof the assessment in the Atlas chapter, they are important because \r\nof their influence on the variability of temperature (Part A) and \r\nprecipitation (Part B) in regions around the world. This is quantified \r\nin Table Atlas.1, which lists the fraction of interannual variance in \r\nseasonal mean temperature and precipitation explained by variability \r\nin these modes. The table provides information on the influence "


id: c324573e79ef5b41439aa8c54843f078
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content:
"Figure Atlas.14 | Influence of major modes of variability (see Annex IV) on regional extreme events relevant to assessing multi-hazard resilience. Ribbon \r\ncolours define the driver from which they originate and their width is proportional to the correlation. Crossed lines represent where there is conflicting evidence for a correlation \r\nor where the driver is not directly related to the hazard; dots represent drivers that have both a positive and negative correlation with the hazard. Figure is copied from Steptoe \r\net al. (2018)/CCBY4.0."


id: 42bec25a7ce22f5fc49ced7c63d543de
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"emissions, demonstrating the much larger changes seen with the \r\nhigher-emissions scenario. The projected changes in sea level show \r\nthe significantly greater increases, of up to 1 m locally, under a high\u0002emissions future. Regional details of these projected changes under \r\na range of emissions scenarios and time periods can be explored in \r\nthe Interactive Atlas. An in-depth assessment of these changes is \r\npresented in Section 5.3 and Chapter 9."


id: 39c0bb34db9bd868e967999165ec799f
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
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index: climate_gpt_only_giec
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content:
"As with the atmosphere, there are several key ocean-related \r\nquantities which are relevant for understanding how climate change \r\nmay impact human and ecological systems and/or key global \r\nindicators of change. These include ocean surface temperature \r\nand heat content, sea surface height, sea ice cover and thickness, \r\nand certain chemical properties such as ocean acidity and oxygen \r\nconcentration. For example, there is growing awareness of the \r\nthreat presented by ocean acidification to ecosystem services and its \r\nsocio-economic consequences are becoming increasingly apparent \r\nand quantifiable (Hurd et al., 2018), and SR1.5 (IPCC, 2018c) noted \r\na  significant impact of low levels of global warming on the state \r\nof the global oceanic ecosystems and food security. For instance, \r\n70-90% of coral reefs are projected to decline at a warming level of \r\n1.5degC, with larger losses at 2degC. "


id: 27afe21c4256b0ff10a41c8b421484f5
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"Thus, because of their importance to coastal populations and \r\ninfrastructure and ocean ecosystems, the Interactive Atlas focuses on \r\nchange in sea-surface temperature, sea level and pH. Figure Atlas.15 \r\nshows projected changes to sea surface temperature (SST) and \r\nsea level at the end of the century under SSP1-2.6 and SSP5-8.5 "


id: aedee1d829b9926cd681f521498d7840
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"southern African region including the Namib and Kalahari deserts; \r\neach climate region has its local variations resulting in very high \r\nspatial and temporal variations (Peel et al., 2007). Based on the varied \r\nclimates, nine sub-regions are defined for Africa (Figure Atlas.16): \r\nthe Mediterranean region (MED) including North Africa, Sahara \r\nincluding parts of the Sahel (SAH), West Africa (WAF), Central Africa \r\n(CAF), North Eastern Africa (NEAF), South Eastern Africa (SEAF), \r\nWest Southern Africa (WSAF), East Southern Africa (ESAF) and \r\nMadagascar (MDG)."


id: 460a3e012ef9fd4f6b5b9e4978c84d38
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"The assessment in this section focuses on changes in average \r\ntemperature and precipitation (rainfall and snow), including the \r\nmost recent years of observations, updates to observed datasets, \r\nthe consideration of recent studies using CMIP5 and those using \r\nCMIP6 and CORDEX simulations. Assessment of changes in extremes \r\nis in Chapter  11 (Tables 11.4-11.6) and climatic impact-drivers in \r\nChapter 12 (Tables 12.1-12.12)."


id: 16cd3b6170ec4dd75b0116b43aa9cf8
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"The climatic features that characterize the intra-seasonal and \r\ninterannual variability of Africa are mainly the Madden-Julian \r\nOscillation (MJO), which is confined to the deep tropics during \r\nboreal winter, Pacific Decadal Variability (PDV), and the shift of the \r\nAtlantic Inter-tropical Convergence Zone in response to changes \r\nin the meridional SST gradient. A  positive phase of PDV weakens \r\nAfrican monsoons (Figure AIV.8d; Meehl and Hu, 2006), and MJO \r\nphase 4 suppresses convection over equatorial Africa (Figure AIV.10a; \r\nsee Annex IV). Other features influence specific sub-regions. For \r\ninstance, El Nino events increase precipitation in eastern Africa and "


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"decrease precipitation in southern Africa. Over southern Africa there \r\nis a strong link between ENSO and droughts (Meque and Abiodun, \r\n2015). The positive phase of the Indian Ocean Dipole (IOD) increases \r\nrainfall in eastern tropical Africa in boreal autumn to early winter \r\n(Figure AIV.5d), while the negative phase induces the reduction in \r\nrainfall. The West African Monsoon is influenced by Atlantic Zonal \r\nMode (AZM) with decreased rainfall over the Sahel and increased \r\nrainfall over Guinea (Losada et  al., 2010). Positive Atlantic Multi\u0002decadal Variability (AMV) influences positive anomalies all year \r\nround over a broad Mediterranean region, including North Africa."


id: b4784f03840b137c875a68ba0036cc4c
created_at: 2023-03-16 14:00:52
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"West Africa has also experienced an overall reduction of rainfall \r\nover the 20th century, with a recovery towards the last 20 years \r\nof the century (Christensen et  al., 2013). Over the last three \r\ndecades rainfall has decreased over East Africa, especially between \r\nMarch and May/June. Projected rainfall changes over Africa in \r\nthe mid- and late 21st century is uncertain. In regions of high or \r\ncomplex topography such as the Ethiopian Highlands, downscaled \r\nprojections indicate likely increases in rainfall and extreme rainfall \r\nby the end of the 21st  century. However, North Africa and the \r\nsouth-western parts of South Africa are likely to have a reduction \r\nin precipitation."


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"The consequence of increased temperature and evapotranspiration, \r\nand decreased precipitation amount, in interaction with climate \r\nvariability and human activities, have contributed to desertification in \r\ndryland areas in sub-Saharan Africa (medium confidence) as reported \r\nin SRCCL (Mirzabaev et al., 2019)."


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"Gaetani et al., 2017; Taylor et al., 2017; Biasutti, 2019) during the \r\nperiod 1980-2010. Southern African rainfall shows a  significant \r\ndowntrend of -0.013  mm day-1 year-1 in recent decades and \r\n-0.003 mm day-1 year -1 for longer periods during 1900-2010 (low \r\nconfidence) (Jury, 2013)."


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"there are growing efforts to boost African climate science by running \r\nand evaluating climate models over Africa (Endris et  al., 2013; \r\nKalognomou et al., 2013; Gbobaniyi et al., 2014; Engelbrecht et al., \r\n2015; Klutse et al., 2016; Gibba et al., 2019)."


id: 7d2998fe990cf9147137e6a7efd69c1f
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
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content:
"The CMIP project previously did not result in improved performance \r\nfor Africa (Flato et al., 2013; Rowell, 2013; Whittleston et al., 2017) \r\nand culling ensembles based on existing metrics for Africa fails to \r\nreduce the range of uncertainty in precipitation projections (Roehrig \r\net al., 2013; Yang et al., 2015; Rowell et al., 2016), but biases over \r\nAfrica are lower in CMIP6 compared to CMIP5 (Almazroui et  al., \r\n2020c). Nonetheless, the CMIP5 ensemble has been evaluated over \r\nAfrica to advance its application for climate research (Biasutti, 2013; \r\nRowell, 2013; Dike et al., 2015; McSweeney and Jones, 2016; Onyutha \r\net al., 2016; Wainwright et al., 2019) as has, more recently, the CMIP6 \r\nensemble (Almazroui et al., 2020c)."


id: 8217e423e9c09be155720732fed2a22f
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
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content:
"Temperature increases over Africa in the 20th  century can be \r\nattributed to the strong evidence of a continent-wide anthropogenic \r\nsignal in the warming (Figure  3.9; Hoerling et  al., 2006; Min and \r\nHense, 2007; Stott et  al., 2010, 2011; Niang et  al., 2014). More \r\nspecifically over West Africa, the clear emergence of temperature \r\nchange (Figure Atlas.11) is due to the relatively small natural climate \r\nvariability in the region which generates narrow climate bounds \r\nthat can be easily surpassed by relatively small climate changes \r\n(Niang et  al., 2014). Warming over North Africa is largely due to \r\nanthropogenic climate forcing (Knippertz et al., 2003; Barkhordarian \r\net al., 2012; Diffenbaugh et al., 2017)."


id: fd1d7f0383900de6561bef4020a9871b
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
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content:
"Model development has advanced in the world, but Africa still lags \r\nas a focus and in its contribution (James et al., 2018). None of the \r\ncurrent generation of global climate models (GCMs) was developed \r\nin Africa (Watterson et al., 2014), and the relevant processes in the \r\ncontinent have not been the priority for model development but \r\ntreated in a  one-size-fit-all approach (James et  al., 2018) except \r\nfor a  few studies that focused on convective-permitting climate \r\nprojections (Stratton et  al., 2018; Kendon et  al., 2019). However, "


id: 3172ffb527e188e22d9fe345c2774cd1
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
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vector_id: 5193
content:
"and 2degC GWLs (Klutse et al., 2018; Lennard et al., 2018; Maure et al., \r\n2018; Mba et  al., 2018; Nikulin et  al., 2018; Osima et  al., 2018). \r\nWhile a few studies addressed the whole African continent (Lennard \r\net al., 2018; Nikulin et al., 2018), some focused on specific regions of \r\nAfrica (Diedhiou et al., 2018; Klutse et al., 2018; Kumi and Abiodun, \r\n2018; Maure et  al., 2018; Mba et  al., 2018). CORDEX simulations \r\nproject robust warming over Africa in excess of the global mean "


id: 94b4d220df9e9bb64c5108d28664b1c8
created_at: 2023-03-16 14:00:52
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content:
"(Lennard et  al., 2018; Nikulin et  al., 2018), and over West Africa \r\nthe magnitude of regional warming reaches the 2080-2100 global \r\nwarming level one to two decades earlier (Mora et al., 2013; Niang \r\net  al., 2014; Sylla et  al., 2016; Klutse et  al., 2018). Temperature \r\nincreases projected under RCP8.5 over Sudan and northern Ethiopia \r\nimply that the Greater Horn of Africa would warm faster than the \r\nglobal mean relative to 1971-2000 (Osima et al., 2018). Over North "


id: a91d97aef945870d4d54084d5e389121
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
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vector_id: 24134
content:
"The Horn of Africa has experienced significantly decreased rainfall \r\nduring the long rains season from March to May (high confidence) \r\nand drying trends in this and other parts of Africa are attributable \r\nto oceanic influences (high confidence), resulting from both internal \r\nvariability and anthropogenic causes. Drying over the Sahel in the \r\nlast  century was attributed to an increase in the South Atlantic \r\nSST and more recently over southern African as a  response to \r\nanthropogenic-forced Indian Ocean warming. Drying over East Africa \r\nis associated with decadal natural variability in SSTs over the Pacific \r\nOcean. The enhanced rainfall intensity over the Sahel in the last two \r\ndecades is associated with increased greenhouse gases indicating an \r\nanthropogenic influence (medium confidence)."


id: 9beae803a095bc11914a34455c742be4
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
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vector_id: 22086
content:
"Relative to the late 20th  century, annual mean temperature over \r\nAfrica is projected to rise faster than the global average (very high \r\nconfidence) with the increase likely to exceed 4degC by the end of the \r\ncentury under RCP8.5 emissions. The central interiors of southern and \r\nnorthern Africa are likely to warm faster than equatorial and tropical \r\nregions (high confidence)."


id: 75ff5581e3c8f430be448eb6debf38d
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
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vector_id: 16165
content:
"The rate of surface temperature increase has generally been more \r\nrapid in Africa than the global average and by at least 0.1degC-0.2degC \r\nduring 1961-2015 (high confidence). Minimum temperatures have \r\nincreased more rapidly than maximum temperatures over inland \r\nsouthern Africa (medium confidence). Since 1970, mean temperature \r\nover East Africa has shown an increasing trend but showed \r\na decreasing trend in the previous 40 years (medium confidence)."


id: 2cd12d4ea587ba2084f28f088845d7b8
created_at: 2023-03-16 14:00:52
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content:
"South East Asia (SEA) and South West Asia (ARP and WCA) with the \r\nTibetan Plateau (TIB) being relevant and thus referred to in both \r\nthe East and South Asia assessments. Note also TIB forms a major \r\npart of the Hindu Kush Himalaya region, which is assessed in \r\nCross-Chapter Box 10.4, and relevant findings are summarized and \r\ncross-referenced in the East and South Asia sections below."


id: 217c2c4e15de7443c3bb145574e65118
created_at: 2023-03-16 14:00:52
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content:
"The CMIP5 model projections indicate an increase of temperature in \r\nboth boreal winter and summer over East Asia for RCP4.5. Based on \r\nCMIP5 model projections, there is medium confidence in an intensified \r\nEASM and increased summer precipitation over East Asia. More than \r\n85% of CMIP5 models show an increase in mean precipitation of the \r\nEASM, while more than 95% of models project an increase in heavy \r\nprecipitation events (Christensen et al., 2013).The SROCC states that \r\nfuture projections of annual precipitation indicate increases of the \r\norder of 5-20% over the 21st  century in many mountain regions, \r\nincluding the Himalaya and East Asia (Hock et al., 2019b). The SR1.5 \r\nreports that statistically significant changes in heavy precipitation \r\nbetween 1.5degC and 2degC of global warming are found in East Asia \r\n(Hoegh-Guldberg et al., 2018)."


id: 77fe37e797438e3de5cfd0ca49355e17
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
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content:
"Observational studies indicated significant decadal variations in the \r\nEAWM (Wang and Lu, 2016; He et al., 2017). It weakened significantly \r\naround the late 1980s, being relatively strong during 1976-1987 and \r\nweaker during 1988-2001. The EAWM has recovered in intensity \r\nafter 2004 and caused frequent and prevalent severe cold spells, as \r\nwell as a number of unusually harsh cold winters in many parts of \r\nEast Asia during the period 2004-2012 (Wang and Chen, 2014; Kug \r\net al., 2015; Ge et al., 2016; Gong et al., 2018). Negative zonal mean \r\nwinter SAT anomalies were observed over the whole of East Asia \r\nfrom 1980 to 1988, with positive anomalies observed over high and \r\nlow latitudes from 1988 to 2010 (Miao and Wang, 2020)."


id: bf7f22a87cb84ee55601283171c5dea
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"Precipitation trends over East Asia show considerable regional \r\ndifferences (medium confidence). Mean precipitation has shown \r\nnegligible sensitivity to the warming trend with consequently limited \r\noverall trends in China though summer rainfall daily frequency and \r\nintensity show respectively decreasing and increasing trends from "


id: 1848935f9c8595f7f0d8f2278483dfd8
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
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content:
"The IPCC AR5 (Christensen et al., 2013) reports a significant increase in \r\nmean temperatures in south-eastern China, associated with a decrease \r\nin the number of frost days under the SRES A2 emissions scenario. "


id: ed56802ad3592a1fc3b48093d89648bb
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
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vector_id: 34814
content:
"T. Wu et al., 2019). The performance of models is sensitive to cumulus \r\nconvection schemes and horizontal resolution (Haarsma et al., 2016; \r\nWu et  al., 2017; Kusunoki, 2018b). High-resolution atmospheric \r\nglobal climate models (AGCM) successfully reproduce the intensity \r\nand the spatial pattern of the EASM rainfall (Li et al., 2015; Yao et al., \r\n2017; Ito et al., 2020a) and improve the simulation of the diurnal \r\ncycle of precipitation rates and the probability density distributions \r\nof daily precipitation over Korea, Japan and northern China (Lin et al., \r\n2019), but increasing horizontal resolution (at the typical scales \r\nused in GCMs) is not always a  panacea for solving model biases \r\n(Roberts et al., 2018)."


id: 73977bc5b5dba9eff28f5306d747c8ec
created_at: 2023-03-16 14:00:52
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index: climate_gpt_only_giec
vector_id: 15809
content:
"Agriculture intensification through oasis expansion in Xinjiang region \r\nhas increased summer precipitation in the Tian Shan mountains (high \r\nconfidence from medium evidence with high agreement) (Zhang \r\net al., 2009, 2019b; Deng et al., 2015; Guo and Li, 2015; Yao et al., \r\n2016; Xu et al., 2018; Cai et al., 2019). However, there is very low \r\nconfidence of the effect of oasis expansion on the temperature \r\nwarming trend (Han and Yang, 2013; Li et al., 2013; Yuan et al., 2017)."


id: a153623b4494eb34e744cc85c5baeb5d
created_at: 2023-03-16 14:00:52
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content:
"In the context of climate warming, intense snowfalls have hit China \r\nfrequently in recent winters and have caused severe damages to the \r\nsustainability of society (Sun et  al., 2019). Observations generally \r\nshow a  decrease in the frequency and an increase in the mean \r\nintensity of snowfalls in north-western, north-eastern and south\u0002eastern China and the eastern Tibetan Plateau since the 1960s (Zhou \r\net al., 2018), but the results may depend on the objective criteria for \r\nidentifying winter snowfall (J. Luo et al., 2020)."


id: d6af19039253526a6082c1ae39e9d749
created_at: 2023-03-16 14:00:52
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content:
"Projected annual precipitation changes in the CMIP5 and CMIP6 \r\nensembles are positive for all warming levels in ECA and TIB and \r\nfor the higher warming levels in EAS. Changes in precipitation per \r\ndegree Celsius global warming are larger in DJF than in JJA in ECA \r\nbut show smaller seasonal difference in EAS (Figure Atlas.17). The \r\nEASM precipitation is projected to increase but with a  complex \r\nspatial structure (Kitoh, 2017; Moon and Ha, 2017). Simulations "


id: eb3b61cf7f0da27581ea206e6205bbb1
created_at: 2023-03-16 14:00:52
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vector_id: 34507
content:
"and varying seasonally and spatially (Luo et al., 2018). A decrease \r\nof precipitation was projected in the western region of Xinjiang \r\nduring summer. More extreme rainfall events were projected to occur \r\nduring summer and autumn."


id: 3e6f711c84ed6c9b6f52c5fdad395bdf
created_at: 2023-03-16 14:00:52
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index: climate_gpt_only_giec
vector_id: 7200
content:
"In East Asia annual mean temperature has been increasing since the \r\n1950s (high confidence). The linear trend of annual mean surface \r\nair temperature likely exceeded 0.1degC per decade over most of \r\nEast Asia from 1961 to 2015. Trends of annual precipitation show \r\nconsiderable regional differences with areas of both increases and \r\ndecreases (medium confidence), and with increases over north-west \r\nChina and South Korea (high confidence). Agricultural intensification \r\nthrough oasis expansion in Xinjiang region has increased summer \r\nprecipitation in the Tian Shan mountains (high confidence)."


id: 3d139e7906af215a02073320191e40b1
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
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index: climate_gpt_only_giec
vector_id: 6972
content:
"GCMs still show poor performance in simulating the mean rainfall and \r\nits variability over East Asia, especially over regions characterized by \r\ncomplex topography. The CMIP6 models have improved from CMIP5 \r\nfor climatological temperature and winter monsoon but show little \r\nimprovements for the summer monsoon. The RCMs produce relatively \r\nmore detailed regional features, but do not always produce superior \r\nsimulations compared with the driving GCMs."


id: c5607d2f40cb8d06010c93c7f7ef9e9e
created_at: 2023-03-16 14:00:52
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index: climate_gpt_only_giec
vector_id: 28635
content:
"The annual mean surface temperature over East Asia and the Tibetan \r\nPlateau will very likely increase under all emissions scenarios and \r\nGWLs. Larger warming magnitudes will likely occur in the northern \r\npart of EAS and in ECA and TIB. Precipitation is likely to increase over \r\nland in most of EAS at the end of the 21st  century under higher\u0002emissions scenarios (SSP3-7.0, RCP8.5 and SSP5-8.5) and global \r\nwarming levels, and in ECA and TIB under all emissions scenarios \r\nand global warming levels. Summer precipitation increase is likely\r\nto occur in East Asia, corresponding to the strengthened summer \r\nmonsoon circulation."


id: c1d2c5acd826250a9ff77195e9eaeb6d
created_at: 2023-03-16 14:00:52
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index: climate_gpt_only_giec
vector_id: 28075
content:
"The increasing temperature trends under RCP scenarios were \r\nconsistently reproduced in projections using CORDEX-EA models \r\n(Y. Kim et al., 2016) as reported in AR5 using GCMs. However, changes \r\nin annual and seasonal mean precipitation exhibit significant inter\u0002RCM differences with larger magnitudes and variability than in the \r\nGCMs (Ham et  al., 2016; Ozturk et  al., 2017; H. Sun et  al., 2018; \r\nD. Zhang et al., 2018). RCM simulations project that the Meiyu-Baiu\u0002Changma heavy rainfall will significantly increase in northern Japan \r\nat the end of the 21st century under the RCP8.5 scenario (Osakada \r\nand Nakakita, 2018), but projected precipitation amount and the \r\nnumber of precipitation days in summer around and over Japan differ \r\nas a result of RCM uncertainty (Suzuki-Parker et al., 2018). Annual \r\ntotal snowfall is projected to decrease in most parts of Japan except \r\nfor Japan's northern island under RCP2.6 (Kawase et al., 2021)."


id: 4e08fcee24be30ea18cbfcdbeff83235
created_at: 2023-03-16 14:00:52
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"North Asia extends from the Ural Mountains in the west to the \r\nPacific Ocean in the east and from the Russian Arctic in the north \r\nto West and East Central Asia and East Asia in the south. Its most \r\nrecognizable features are boreal forests and permafrost. In AR6 \r\nNorth Asia is divided into three reference regions (Figure Atlas.17): \r\nWest Siberia (WSB) with a continental climate, warm summers and \r\ncold winters, many waterlogged areas and several natural zones due \r\nto a large extent from south to north and heterogeneity in regional \r\nclimates; East Siberia (ESB) which is mainly highland with extensive \r\npermafrost and a more severe continental climate characterized by \r\nharsh, long winters and short, hot summers, and by less precipitation \r\nand snow cover than in neighbouring regions; and the Russian Far \r\nEast (RFE) with a monsoon-influenced climate, cold winters and wet "


id: 68dfad7dc9f148492b4715309b440607
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vector_id: 14089
content:
"Projejctions based on statistical downscaling of 37 CMIP5 GCMs \r\nfor Xinjiang, China, show pronounced temperature increases of \r\n0.27degC to 0.51degC per decade from 2021 to 2060 while precipitation \r\nchanges were projected to be between -1.7% to 6.8% per decade "


id: 90055b0b8890bcbf7fc3092fca121f0
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"summers in the south, and cold winters and cool summers almost \r\nwithout precipitation in the north. WSB and ESB are mainly influenced \r\nby NAO and NAM (Annex IV.2.1) and the Arctic Oscillation (AO) with \r\nassociated atmospheric blocking by the Siberian High (SH) that \r\nexhibits a pronounced decadal-to-multi-decadal variability (see also \r\nTable Atlas.1). RFE is under the influence of the ENSO (Annex IV.2.3) \r\nand the PDV (Annex IV.2.6) that mostly affect rainfall variability."


id: 2d4f763ef35a860f81da220f330f49a8
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content:
"In the previous IPCC assessment cycles, the three sub-regions \r\ncomprising North Asia in this section, along with Eastern Europe and \r\nthe Asian Arctic, were considered as either Northern Eurasia or Russia \r\nin AR4 and AR5. The AR5 WGI stated that for North and Central Asia \r\nCMIP5 models had difficulty in representing climatological means of \r\nboth temperature and precipitation, which is partly related to the \r\nscarceness of observational data in northern parts of the region and \r\nto issues related to the estimation of biases with coarse-resolution \r\nmodels (Christensen et  al., 2013). In CMIP5 projections under \r\ndifferent RCP scenarios, North Asian temperatures increase more \r\nin winter (DJF) than summer (JJA; Seneviratne et  al., 2012). With \r\nmost models projecting increased precipitation significantly above \r\nthe 20-year natural variability, it was concluded that precipitation in \r\nNorth Asia will very likely increase (Christensen et al., 2013)."


id: 724d6ef0453ee85b235242fe5187afee
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content:
"Annual precipitation totals very likely increased over North Asia in the \r\nlast half century along with more heavy and less light precipitation, \r\nmore freezing rain and less freezing drizzle (Figure Atlas.11 and the \r\nInteractive Atlas; Wen et al., 2014; Groisman et al., 2016; Ye et al., \r\n2017; Chernokulsky et al., 2019). The highest increase was observed \r\nover regions of Siberia and RFE with estimated trends of 10-25 mm \r\nper decade for the 1976-2014 period (Kokorev and Sherstiukov, 2015) \r\nor 5% per decade for the 1976-2018 period (Roshydromet, 2019). \r\nIncreases over southern RFE are the largest (over 50 mm per decade) "


id: 4a2fa5913efce3c22fa3cab322b20c72
created_at: 2023-03-16 14:00:52
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content:
"Increases in surface air temperature (SAT) have been observed since \r\nthe mid-1970s over the whole of North Asia (Frolov et  al., 2014), \r\nand particularly over the north-eastern part (Figure Atlas.11; Gruza \r\net al., 2015). Trends of annual SAT in the northern part of the region \r\nduring the last decades were very likely twice as strong as the \r\nglobal average (Figure Atlas.11; Frolov et al., 2014; Mokhov, 2015; \r\nSherstyukov, 2016) with trends in RFE of 0.8degC-1.2degC per decade \r\nfor the 1976-2014 period and more intense warming strengthening \r\nfrom south to north observed in spring in ESB (Frolov et al., 2014; \r\nIppolitov et al., 2014; Kokorev and Sherstiukov, 2015)."


id: f9786501cde35dc369ed68f22f5f6df2
created_at: 2023-03-16 14:00:52
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content:
"Figure Atlas.18 | Linear trends for the 1980-2015 period based on \r\nstation data from the World Data Centre of the Russian Institute for \r\nHydrometeorological Information (RIHMI-WDC; Bulygina et  al., 2014). \r\n(a) Snow-season duration from 1 July to 31 December (days per decade); (b) snow\u0002season duration from 1 January to 30 June (days per decade); (c) maximum annual \r\nheight of snow cover (mm per decade). Trends have been calculated using ordinary \r\nleast squares regression and the crosses indicate non-significant trend values (at the \r\n0.1 level) following the method of Santer et al. (2008) to account for serial correlation. \r\nFurther details on data sources and processing are available in the chapter data table \r\n(Table Atlas.SM.15)."


id: 7adffbdef33abe9687bcdeae8f0a8b17
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vector_id: 16924
content:
"Recent strong warming in polar regions (Section Atlas.11.2) was \r\naccompanied by cooling in winter in mid-latitude regions particularly "


id: 537c2375f81f0a69876fc5cf3c0b3d1e
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
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content:
"and are mostly due to positive changes in convective precipitation \r\nintensity in the region in the summer season (JJA) during 1966-2016 \r\n(medium confidence) (Chernokulsky et  al., 2019). A  decreasing \r\ntrend was observed in central WSB, northern ESB, the Baikal and \r\nTransbaikal regions, the Amur River region, and Primorie territories of \r\nRFE (the Kamchatka and Chukchi peninsulas) with up to -20 mm per \r\ndecade for the 1976-2014 period (Kokorev and Sherstiukov, 2015) or \r\n15-20% per decade for the 1976-2018 period (Roshydromet, 2019). \r\nOverall, solid precipitation predominantly decreased in North Asia \r\nand very likely caused both less snow cover extent (SCE) and snow \r\nwater equivalent (SWE), attributable to the anthropogenic influence \r\nwith high confidence (Sections 2.3.2.2 and 3.4.2)."


id: 8af2fff9712a419d70c57a584a27c74e
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content:
"seasons (Miao et al., 2014; Peng et al., 2019). Most GCMs capture \r\nthe main decadal SAT trend (Miao et  al., 2014), though CMIP5 \r\nGCMs fail to capture the decreasing temperature trend over East \r\nSiberia (Fei and Yong-Qi, 2015). Possible causes of GCMs' inability \r\nto represent the recent slowdown of warming is further discussed in \r\nCross-Chapter Box 3.1. For CMIP5, models with higher resolution do \r\nnot always perform better than those with lower resolutions (medium \r\nconfidence) (Miao et al., 2014)."


id: cabf37638cf9cfae05f80f5799f483a4
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 29460
content:
"Sixteen CMIP5 model simulations of SAT variability over Eurasia were \r\nevaluated against CRU observations for permafrost sub-regions (Peng \r\net al., 2019), showing a warm bias in north-west Eurasia, capturing \r\nthe climate warming over the 20th century and its acceleration during \r\nthe late 20th  century. CMIP5 GCMs generally underestimate daily \r\ntemperature range compared with observations over north-eastern \r\nRussia (Sillmann et al., 2013; Lindvall and Svensson, 2015). Currently \r\nthere is no literature on the CMIP6 ensemble over the region though \r\na  few single-model studies are available (Voldoire et  al., 2019; \r\nT. Wu et al., 2019)."


id: d31fbbc12105950d6e75603c5da5e566
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 30740
content:
"Snow characteristics depend on both temperature and precipitation, \r\nand observed trends over North Asia show large spatial heterogeneity \r\nand interannual variability (Figure Atlas.18) leading to medium \r\nconfidence that maximum snow depth has increased over Siberia, the \r\nOkhotsk Sea coast and in southern RFE since the 1960s (Callaghan \r\net al., 2011; Loginov et al., 2014), with trends during 1976-2016 of \r\n1.8 cm (in WBS), 1.1 cm (in ESB), and 4.6 cm (in RFE) per decade \r\n(Bulygina et  al., 2017). Snow cover duration increased in Yakutia, \r\nSakhalin Island and some other coastal areas of the Pacific Ocean \r\nin RFE during 1980-2009 (Callaghan et al., 2011), and decreased in \r\nWSB and ESB (Bulygina et al., 2017; Roshydromet, 2019). However, \r\nGorbatenko et al. (2019) reported that in south-eastern WSB maximal \r\nsnow depth has increased by 5-20 cm and duration of steady snow \r\ncover by between 4 and 10 days during 1989-2016 (Figure Atlas.18)."


id: 96ba82e87ac6442a5552891111fdbe85
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 21235
content:
"There is very limited use of RCMs for North Asia. CORDEX-CAS \r\ncovers North Asia, except parts of RFE, and ARCTIC-CORDEX \r\ncovers the northern regions (Figure Atlas.6). For CORDEX-CAS three \r\nRCMs (REMO, ALARO-0 and CLMcom) have been used and have \r\nwarm biases for maximum temperatures, cold biases for minimum \r\ntemperatures and a wet bias in the north during the winter (Top et al., \r\n2021). Rain gauges, however, are known to have problems in terms \r\nof measuring properly solid precipitation (e.g., due to drifting snow) \r\nwhich can greatly affect the accuracy of precipitation observations \r\nover North Asia (Harris et al., 2014)."


id: 797ead22b44c9e217c2cf15c9135bbcc
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 16688
content:
"Temperature trends and means derived from reanalysis datasets \r\n(JRA-25 and MERRA) correctly represented temperature variability \r\nshown in observational data over the Asian territory of Russia \r\nfor the 1976-2010 period (Loginov et  al., 2014). Assessment of \r\nCRU TS 3.22, CRUTEMP4, ERA-Interim and NCEP2 datasets against \r\nstation data over North Asia for annual and seasonal air temperature \r\nhas shown that the ERA-Interim reanalysis outperforms others for \r\nthe 1981-2005 period (Kokorev and Sherstiukov, 2015). The latter \r\nreanalysis also underestimates summer precipitation and shows large \r\nwet biases over north-east Asia during spring and underestimates \r\nmean seasonal temperature over north-east Asia in spring (MAM), \r\nautumn (SON), and winter (DJF), but overestimates it in summer (JJA) \r\ncompared with the CRU dataset (medium confidence) (Ozturk et al., \r\n2017; Top et al., 2021)."


id: 7993db853d6fa4a4ece4f69dbe3c7702
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 16700
content:
"CMIP5 and CMIP6 projections are consistent in the direction and \r\nranges of surface temperature change which are higher than the \r\nglobal average and with ensemble-mean warming of around 6degC \r\nfor the 4degC GWL. Projected precipitation changes are also consistent \r\nwith significant increases in winter, of up to 40% in the ensemble \r\nmean for the highest warming levels, and lower increases in summer \r\nexcept for WSB where changes are small and suggest drying at the \r\n4degC GWL (Figure Atlas.17 and the Interactive Atlas)."


id: 4d9ea821018a4f4a8838f06e745dde42
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 9644
content:
"The CMIP5 ensemble projects a warming of the annual mean SAT over \r\nnorthern Eurasia in the 21st century, likely in the range of 0.8degC-1.0degC \r\n(RCP2.6), 2.3degC-3.1degC (RCP4.5) and up to 7.2degC (RCP8.5) (Miao \r\net al., 2014; Peng et al., 2019). Mid-latitude permafrost sub-regions \r\nin Eurasia are projected to warm more than the global mean and \r\nnon-permafrost territories, with ensemble area-averaged changes \r\nof 1.7degC (RCP2.6), 3.2degC (RCP4.5) or 6.4degC (RCP8.5) in 2081-2100 \r\nrelative to 1986-2005 (Peng et al., 2019)."


id: e98dae289b69a72a14eddc9360fad4ff
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 34254
content:
"GCMs capture the main synoptic processes affecting North Asia \r\nand the CMIP5 ensemble simulates the temporal evolution of the \r\nmagnitude and position of the Siberian High (SH) over the period \r\n1872-2005 (Fei and Yong-Qi, 2015). CMIP5 models simulate \r\na  weakened intensity of the winter SH and a  strengthened \r\ninterannual variability compared to observations (Fei and Yong-Qi, \r\n2015). The characteristics of blocking events over the region (number, \r\nduration, intensity and frequency) were reasonably well reproduced \r\nby GCMs (Mokhov et al., 2014), and most overestimate the annual \r\nmean temperature over northern Eurasia (Interactive Atlas). Biases \r\nin simulated annual surface air temperature simulation primarily \r\ncome from the winter (DJF) season and are relatively smaller in other "


id: c06ee6cfe061acc929f6118a53d9dcd3
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 27838
content:
"Over the Central Asia CORDEX domain, RegCM4.3.5 simulations \r\ndriven by two different CMIP5 GCMs (HadGEM2-ES and MPI-ESM-MR) \r\nproject SAT warming for 2071-2100 relative to 1971-2000 of about \r\n3degC-4degC during the summer for RCP4.5 to over 7degC for all seasons for \r\nRCP8.5. Projected warming is most evident on the large continental \r\nSiberian Plateau with boreal and sub-boreal climates and biomes "


id: 231db3e8955b61093c67178c847509ca
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 2969
content:
"(i.e., taiga forests and tundra) during the winter season (Ozturk et al., \r\n2017). The Voeikov Main Geophysical Observatory (MGO) RCM, \r\ndriven by five CMIP5 GCMs for the RCP8.5 scenario, projects a faster \r\nincrease in annual minimum temperature as compared with maximum \r\ntemperature over the whole territory of Russia (Kattsov et al., 2017), \r\nand the smallest change in growing season lengths (i.e., periods with \r\ndaily temperatures over 5degC, 10degC and 15degC) in the area of northern \r\ntaiga in WSB and ESB comparable with other territories of Russia \r\nduring the 21st century (Torzhkov et al., 2019)."


id: cab0b3410db1850267935b0e9673ea78
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 29451
content:
"Surface air temperature and precipitation in North Asia are projected \r\nto increase further (high confidence) with warming higher than \r\nthe global average and around 6degC at the 4degC GWL. Temperature \r\nchange in 2080-2099 relative to 1981-2000 is likely in the range \r\nof 3degC in summer to 4.9degC in winter under the RCP4.5 scenario, and \r\n5.6degC in  summer to 9.7degC in winter under the RCP8.5 scenario. \r\nPrecipitation is projected to increase with ensemble-mean changes \r\nof 9% in summer under both RCP4.5 and RCP8.5, and of 22% and \r\n56% in winter respectively."


id: f5fc414bb85eeb51e7da0f01df9d3351
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 36214
content:
"For precipitation, MGO RCM projects for the Arctic-CORDEX domain \r\nunder the RCP8.5 scenario increases in annual totals for northern \r\nNorth Asia, a decrease in summer over ESB for 2006-2100 relative \r\nto 1951-2005 and significant increases in the upper limit of intense \r\nprecipitation over most of the region in winter (Kattsov et al., 2017; \r\nKhlebnikova et al., 2018). Other RCM projections show that in most \r\nseasons and for all future periods, precipitation in Siberia is not \r\nprojected to change with respect to the 1971-2000 period, except \r\nunder the RCP8.5 scenario for the winter and autumn (Ozturk et al., \r\n2017). This very limited and controversial evidence leads to low \r\nconfidence in RCM precipitation projections for North Asia and since \r\nthe projections of GCMs and ESMs are more physically consistent, \r\nassessment of future precipitation changes is based on CMIP5/\r\nCMIP6 presented in Figure Atlas.17 and the Interactive Atlas."


id: 87b73dea500715dcacd15e2b10fb4c28
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 18881
content:
"The countries in this region are mostly semi-arid to arid and therefore \r\ndepend heavily on the summer monsoon (June-September, JJAS) \r\nwhich is when most of the precipitation falls over the South Asia \r\nregion (SAS; Figure Atlas.17). The topographic mechanical effect of the \r\nTibetan Plateau (TIB) promotes moisture convergence downstream \r\nwhich triggers the early summer monsoon onset particularly over the \r\nBay of Bengal and south China. In winter, westerly disturbances (WD) \r\noriginating over the Atlantic Ocean bring moisture. The interaction \r\nbetween the WD and the Himalayas causes precipitation over \r\nnorthern and western parts of South Asia that is crucial to maintain \r\nthe glacier mass balance. The observed teleconnection patterns over \r\nSAS for temperature show cooling effects during NAM and warming \r\neffects when in positive phase with ENSO, IOB, AMM and AMV \r\n(Annex IV). IOD also influences South Asian precipitation (Annex IV)."


id: 7b96e27db0f00117d9830ab85b65ba4c
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 17041
content:
"Most of the CMIP5 and some CMIP6 GCMs overestimate the annual \r\nmean air temperature and precipitation over the North Asia region \r\n(medium confidence). GCMs generally represent the observed \r\ndecadal temperature trend (medium confidence) and biases primarily \r\ncome from the winter (DJF) season (high confidence). Results of \r\na very limited number of RCMs applied over the whole region show \r\nthat they have warmer biases for maximum and colder biases for \r\nminimum temperatures (limited evidence, medium agreement). \r\nSparsity of observational data particularly in the northern part of ESB \r\nand the whole of the RFE results in low confidence in the assessments \r\nof model performance in North Asia."


id: ea64bc4f7480e7b298adcba7e5b1a7b5
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 34385
content:
"1960-1990 as the baseline period. Similarly, SREX (Table  3-3 in \r\nSeneviratne et  al., 2012) reported low confidence (due to lack of \r\nliterature) in trends in climate indices related to extreme precipitation \r\nevents. The Indian summer monsoon circulation was found to have \r\nweakened, but this was compensated by increased local atmospheric \r\nmoisture content leading to more rainfall (medium confidence). It is \r\nlikely that the occurrence of snowfall events is decreasing in South Asia \r\nalong with other regions due to an increase in winter temperatures \r\n(Hock et  al., 2019b). Based on satellite- and surface-based remote \r\nsensing it is very likely that aerosol optical depth has increased over \r\nsouthern Asia since 2000."


id: 4ef1c746733e21b896134dbd79c79dbd
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 9859
content:
"meridional moisture transport over the Arabian Sea (Bay of Bengal) \r\n(low  confidence) (Latif et  al., 2017). Positive annual precipitation \r\ntrends are observed in global and regional datasets (Figure Atlas.11 \r\nand the Interactive Atlas) during 1961-2015 and over arid provinces \r\nof Pakistan (for rabi and kharif cropping seasons) during 1951-2015 \r\nof 2.8-34.8 mm per decade (Khan et al., 2020) imply high confidence\r\nfor increased precipitation in Pakistan. Observations located in \r\nthe monsoon-dominated strip in Pakistan indicate that the mean \r\nmonsoon onset became earlier during 1971-2010 (Ali et al., 2020)."


id: 915a620c4ffd42633c66ceb64c82c68f
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 20398
content:
"Snow and glaciers are major water resources of all countries in South \r\nAsia. Glacier melting is mainly controlled by natural phenomena \r\nbut anthropogenic emissions of black carbon (BC) are now making \r\na significant contributing to total glacial melting in the Hindu Kush \r\nHimalaya (HKH) region (Menon, 2002; Ramanathan et  al., 2007; \r\nRamanathan and Carmichael, 2008). BC concentration is seven to \r\n10 times higher in mid-altitudes (1000-4000 metres above sea \r\nlevel) than at high altitudes (>4000 metres above sea level). The \r\nconcentration of BC sampled from the surface of snow/ice samples \r\nas well as ice-core records shows decreasing ice albedo and an \r\nacceleration in glacier melting (Cross-Chapter Box  10.4; Wester \r\net al., 2019). Karakoram and western HKH snow cover is increasing, \r\na  phenomena known as the 'Karakoram anomaly', and partially \r\nattributed to an increase in the strength of westerly disturbances \r\n(Wester et al., 2019)."


id: 4d2d28ae9a5dcb1ed74e365db21f3ee3
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 9572
content:
"Recent studies show that annual mean land temperatures over India \r\nwarmed at a rate of around 0.6degC per century during 1901-2018, \r\nwhich was primarily contributed by a  significant increase in \r\nannual maximum temperature of 1.0degC per century, while the \r\nannual minimum temperature showed a lesser increasing trend of \r\n0.18degC per century during this period, with a  significant rise only \r\nin the recent few decades (1981-2010) at a  rate of 0.17degC per \r\ndecade (Srivastava et al., 2017, 2019). The annual average of daily \r\nmaximum and minimum temperatures has increased over almost all \r\nPakistan with a faster increasing trend in the south (high confidence). \r\nMinimum temperatures have increased faster (0.17degC-0.37degC per \r\ndecade) than maximum temperatures (0.17degC-0.29degC per decade) \r\nwith the diurnal temperature range reduced (-0.15degC to -0.08degC \r\nper decade) in some regions (Khan et al., 2019)."


id: 96209ac1dbad21c0841500849860900d
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
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vector_id: 21125
content:
"Significant glacier retreat has been observed since 1960 in TIB \r\nwith lower rates in the interior of the region (Yao et  al., 2007). \r\nA  large inter-decadal variation in snow cover is also observed \r\nfrom 1960 to 2010. Observations and model simulations showed \r\nthat the increasing temperature of frozen grounds is leading to \r\nthawing and reduced depth of permafrost, with further significant \r\nreductions projected under future global warming scenarios (medium \r\nconfidence) (Yang et al., 2019)."


id: 67fa9ae672bc25a583a2c0e9e3bd98f8
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
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index: climate_gpt_only_giec
vector_id: 13945
content:
"ranges, are projected to warm more (over 6degC under SSP5-8.5, with \r\nhigher warming in winters than in summer; Interactive Atlas) and this \r\nwill accelerate glacier melting in the region. The warming pattern of \r\nmaximum and minimum temperatures are projected to intensify in \r\nhigher latitudes compared with mid-latitudes of South Asia in CMIP5 \r\nsimulations for all RCP scenarios (Ullah et al., 2020)."


id: 347edeee0017f85bb7240119274ab026
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 5670
content:
"Seasonal precipitation projections show increased winter precipitation \r\nover the western Himalayas and decreased precipitation over the \r\neastern Himalayas. On the other hand, summer precipitation projections \r\nshow a  robust increase over most of South Asia, with the largest \r\nover the arid region of southern Pakistan and in adjacent areas of \r\nIndia, under SSP5-8.5 (Almazroui et al., 2020b). Daily bias-adjusted \r\nprojections from 13 CMIP6 GCMs using all emissions scenarios project \r\na warmer (3degC-5degC) and wetter (13-30%) climate in South Asia in the \r\n21st century (Mishra et al., 2020)."


id: 7608a2779ce6cccaa0486acbaa180ba3
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 16173
content:
"Summer precipitation changes in South Asia are consistent between \r\nCMIP3 and CMIP5 projections, but the model spread is large for winter \r\nprecipitation changes. Changes in summer monsoon rainfall will \r\ndominate annual changes over South Asia (Woo et al., 2019). CMIP3 \r\nGCMs project a gradual increase in annual precipitation over monsoon\u0002dominated areas of Pakistan throughout the 21st Century and increases \r\nin humid and semi-arid climate areas (Saeed and Athar, 2018)."


id: 218c37e683bee361bf4516fc46720b1b
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
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index: climate_gpt_only_giec
vector_id: 2702
content:
"Warming of 2.5degC-5degC is projected over northern Pakistan and India \r\n(Syed et al., 2014). CORDEX-South Asia projections over north-east \r\nIndia under RCP4.5 for the period 2011-2060, show increasing \r\ntrends for both seasonal maximum and minimum temperature \r\nover north-east India (Interactive Atlas). The future projections of \r\nSouth Asian monsoon from the CORDEX-CORE exhibit a  spatially \r\nrobust delay in the monsoon onset, an increase in seasonality, and \r\na reduction in the rainy season length over parts of South Asia at \r\nhigher levels of radiative forcing (Ashfaq et al., 2021)."


id: 4af03650a7080a75a2e5706e41a8efc9
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 9204
content:
"CMIP5 and CMIP6 surface temperature projections for South Asia \r\nare consistent across the range of GWLs with increases greater than \r\nthe global average, more so over TIB (Figure Atlas.17). CMIP6 models \r\nshow higher sensitivity to greenhouse gas emissions, projecting higher \r\nwarming for a given emissions scenario. The north-western parts of \r\nSouth Asia, mainly covering the Karakorum and Himalayan mountain "


id: faa83259136c4a6d95fc5dbb9a5dad20
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 36957
content:
"With TIB continuing to warm, snow cover and snow water equivalent \r\nare projected to decrease but with regional differences due to synoptic \r\ninfluences (Cross-Chapter Box  10.4; Wester et  al., 2019). There is \r\nlimited evidence on whether the 'Karakoram Anomaly' will persist \r\nin coming decades, but its long-term persistence is unlikely with "


id: 38f9a2e35ca9304df7fae4a8bde89536
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 6335
content:
"continued projected warming (high confidence) (Section 9.5.1.1). It \r\nis projected that peak river flow at higher altitudes will commence \r\nearlier, due to warming influences on snow cover area and snow/\r\nglacier melt rates and with more precipitation falling as rain rather \r\nthan snow, and the magnitude and seasonality of flow will change \r\nover South Asia (Charles et al., 2016)."


id: 5769430e6fb6e991d8a5eac880cd0677
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 11244
content:
"interactions, including those that are part of the Maritime Continent \r\nand the Philippines. Its climate is mainly tropical (i.e., hot and humid \r\nwith abundant rainfall). Rainfall seasonal variability in the region \r\nis mainly affected by the synoptic-scale monsoon systems, the \r\nnorth-south migration of the Inter-tropical Convergence Zone (ITCZ) \r\nand tropical cyclones (mainly for the Philippines and Indochina), while \r\nintra-seasonal variability can be influenced by the MJO (Annex IV). \r\nTemperature and especially rainfall are also interannually affected by \r\nENSO and Indian Ocean basin and Dipole (IOB/IOD) modes (Annex IV \r\nand Table Atlas.1)."


id: 6a4e5107a9b0010715bf8e984ee129a9
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 14325
content:
"Mean, minimum and maximum daily temperatures in South Asia \r\nare increasing and winters are getting warmer faster than summers \r\n(high confidence). The South Asian monsoon has shown contrasting \r\nbehaviour over India and Pakistan. There is high confidence that there \r\nhas been a decrease in mean rainfall over most parts of the eastern \r\nand central north regions of India and an increase in precipitation \r\nin Pakistan."


id: 44476648fdb8cfb8cd57d24332352eca
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 8166
content:
"The AR5 WGI showed that the mean annual temperature of South \r\nEast Asia has been increasing at a rate of 0.14degC-0.20degC per decade \r\nsince the 1960s, along with an increasing number of warm days and \r\nnights, and a decreasing number of cold days and nights (Christensen \r\net al., 2013). The AR5 also reported the lack of sufficient observational \r\nrecords to allow for a full understanding of past precipitation trends \r\nin most of the Asian region, including South East Asia, and that \r\nprecipitation trends that were available differed considerably across \r\nthe region and between seasons (Christensen et al., 2013)."


id: 132b0fa89823d873824003572e21a695
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 529
content:
"Global model performance over the region has improved from CMIP3 \r\nto CMIP5 to CMIP6 in the multi-model ensemble-mean simulation \r\nof the amplitude and phase of the seasonal cycles of temperature \r\nand precipitation. However, there was no appreciable improvement \r\nin regions with steep orography, and there has remained substantial \r\ninter-model spread in seasonal and annual mean temperatures \r\nover South Asia with generally cold biases which are largest in the \r\ncomplex Karakorum and Himalayan mountain ranges. CMIP6 GCMs \r\nalso show a dry bias (15-20%) in mean annual precipitation in the \r\nmajority of the South Asia region with a wet bias in Nepal, Pakistan \r\nand northern India."


id: 18888985dd9c1df8e5755b861dfa382a
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 1354
content:
"On projected changes, findings from AR5 showed that warming is \r\nvery likely to continue with substantial sub-regional variations over \r\nSouth East Asia (Christensen et  al., 2013). The median increase in \r\ntemperature over land projected by the CMIP5 ensemble mean \r\nranges from 0.8degC in RCP2.6 to 3.2degC in RCP8.5 by the end of the \r\n21st  century. Moderate future increases in precipitation are very \r\nlikely, with projected ensemble mean increases of 1% in RCP2.6 \r\nto 8% in RCP8.5 by 2100. In SR1.5, there is a projected increase in \r\nflooding and runoff over South East Asia for a 1.5degC to 2degC global \r\nwarming, and these will increase even more for a greater than 2degC \r\nlevel of warming (Hoegh-Guldberg et al., 2018)."


id: caa97a63b8c506bbe0104c1672723fe6
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
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content:
"It is likely that surface temperatures over South Asia will increase \r\nmore than the global average and more so over TIB, with projected \r\nincreases of 4.6degC (3.4degC-6.0degC) during 2081-2100 compared with \r\n1995-2014 under SSP5-8.5 and 1.3degC (0.7degC- 2.0degC) under SSP1-2.6 \r\n(Interactive Atlas). Summer monsoon precipitation in South Asia is \r\nlikely to increase by the end of the 21st century while winter monsoons \r\nare projected to be drier. Over the same time periods CMIP6 models \r\nproject an increase in annual precipitation in the range 14-36% \r\nunder SSP5-8.5 and 0.4-16% under SSP1-2.6 (medium confidence)."


id: 58e59d04717546863af5d02345bcf39d
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
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vector_id: 11479
content:
"With continued warming, TIB snow cover and snow water equivalent \r\nare likely to decrease and with more precipitation falling as rain \r\nrather than snow in SAS. It is projected that the peak river flow at \r\nhigher altitudes will commence earlier due to the effect of warming \r\non snow cover and snow/glacier melt rates, causing changes in \r\nmagnitude and seasonality of flow."


id: d975472ff5d4d7d7cdfa158fd729c76f
created_at: 2023-03-16 14:00:52
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content:
"Changes in mean precipitation are less spatially coherent over \r\nSouth East Asia. Over Thailand, the average number of rain days has "


id: 8f85e9374df3fafe8c7bd70e2048614e
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
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"decreased by 1.3 to 5.9 days per decade while average daily rainfall \r\nintensity has increased by 0.24-0.73 mm day-1 per decade (Limsakul \r\nand Singhruck, 2016). Precipitation is also affected by ENSO events \r\n(Tangang et al., 2017; Supari et al., 2018). Over South East Asia, there \r\nhas been a  significant increase in the amount of precipitation and \r\nits extremes with La Nina episodes in the past decades, especially \r\nduring the winter monsoon period (high confidence) (Villafuerte and \r\nMatsumoto, 2015; Limsakul and Singhruck, 2016; Cheong et al., 2018)."


id: 4e504f179474ff9d3dba5297e6592855
created_at: 2023-03-16 14:00:52
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content:
"Figure Atlas.11 shows trends in mean temperature and precipitation \r\nduring 1961-2015 for two global datasets, indicating a significant \r\noverall warming over South East Asia (high confidence), with \r\nhigher rates of warming in Malaysia, Indonesia, and the southern \r\nareas of mainland South East Asia (low confidence). Annual mean \r\nprecipitation trends (Atlas.1.4.1 and the Interactive Atlas, which \r\nincludes the regional dataset Aphrodite) over the region are mostly \r\nnot significant except for increases over parts of Malaysia, Vietnam \r\nand the southern Philippines (medium confidence)."


id: c8dbffd96a5786b6fafd9b14fd154846
created_at: 2023-03-16 14:00:52
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"It is important to note that the availability, quality, and temporal \r\nand spatial density of observation data may lead to uncertainties \r\nand varying results in South East Asia (Juneng et al., 2016). Some \r\nefforts have been made to produce better observationally-based \r\ngridded datasets for the region (e.g., Nguyen-Xuan et al., 2016; van \r\nden Besselaar et al., 2017; Yatagai et al., 2020)."


id: 87271238ce7121000068b6d4e1f8f095
created_at: 2023-03-16 14:00:52
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content:
"Performance in simulating rainfall over South East Asia varies among \r\nCMIP5 GCMs (high confidence). Only some are capable of reasonably \r\nsimulating the rainfall seasonal cycle and spatial pattern (Siew et al., \r\n2013; Raghavan et  al., 2018). Over mainland South East Asia, the \r\nperformance of CMIP5 GCMs in simulating rainfall during the wet \r\nseason was superior to that for annual and dry-season precipitation \r\n(J. Li et al., 2019)."


id: 41307e91b6c799061af569397b2983e7
created_at: 2023-03-16 14:00:52
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content:
"Some RCMs showed a systematic cold bias (Manomaiphiboon et al., \r\n2013; Kwan et al., 2014; Ngo-Duc et al., 2014; Loh et al., 2016; Cruz \r\nand Sasaki, 2017; Cruz et al., 2017) that was mainly due to model "


id: 94cb254091beb4e7b865f3fe5d450ec1
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
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content:
"Projections show continued warming over South East Asia, but \r\nlikely by a  slightly smaller amount than the global average. \r\nProjected changes in rainfall over South East Asia vary, depending \r\non model, sub-region and season (high confidence), with consistent \r\nprojections of increases in annual mean rainfall from CMIP5 and \r\nCMIP6 over most land areas (medium confidence) and decreases in \r\nsummer rainfall from CORDEX projections over much of Indonesia \r\n(medium confidence)."


id: b3d2340d52d6ba3bd1d7405ba98c920f
created_at: 2023-03-16 14:00:52
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content:
"Laos, and of 5-10% over the eastern Philippines and northern \r\nVietnam. During JJA, significantly drier conditions are projected \r\nover almost the entire South East Asia region except over Myanmar \r\nand northern Borneo. Over the Indonesian region, especially Java, \r\nSumatra and Kalimantan, as much as a 20-30% decrease in mean \r\nrainfall is projected during JJA by the end of the 21st century. The \r\nprojected drier condition over Indonesia from CORDEX is consistent \r\nwith that of Kusunoki (2017), Giorgi et al. (2019), Kang et al. (2019) \r\nand Supari et al. (2020) and is associated with enhanced subsidence \r\nover the region (Kang et al., 2019; Tangang et al., 2020)."


id: 78255def9f2ea27b90bba3aa8f95b97
created_at: 2023-03-16 14:00:52
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vector_id: 16471
content:
"The IPCC AR5 established it is very likely that temperatures will \r\ncontinue to increase over WCA in all seasons whilst projections \r\nof decreased annual mean precipitation had medium confidence\r\ndue to medium agreement resulting from model-dependent \r\nsub-regional and seasonal changes (Christensen et  al., 2013). The \r\nAR5 also concluded that for a better understanding of the climate \r\nof the region, results of high-resolution regional climate models also \r\nneed to be assessed and CMIP5 models generally had difficulties \r\nsimulating the mean temperature and precipitation climatology for \r\nSouth West Asia. This is partly related to the poor spatial resolution \r\nof the models not resolving the complex mountainous terrain and \r\nthe influence of different drivers of the European, Asian and African "


id: cde74090a2f1853523407adfdf9b9fc9
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
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vector_id: 29917
content:
"It is virtually certain that annual mean temperature has been \r\nincreasing in South East Asia in the past decades while changes in \r\nannual mean precipitation are less spatially coherent though with \r\nsome increasing trends over parts of Malaysia, Vietnam and the \r\nsouthern Philippines (medium confidence)."


id: f356eb1150875603f2e750cdbcb51f8d
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
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"Although various biases still exist, there is high confidence that \r\nthe models can reproduce seasonal climate patterns well over the \r\ndifferent sub-regions of South East Asia. There is medium confidence\r\nthat the RCMs show added value compared to their host GCMs over \r\nthe region."


id: 433fb9579fb69ed140f48746a6e8ef6
created_at: 2023-03-16 14:00:52
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"climates. However, observational data scarcity and issues related to \r\nthe comparison of observations with coarse-resolution models added \r\nto the uncertainty and remained poorly analysed in peer-reviewed \r\nliterature on climate model performance (Christensen et al., 2013)."


id: 419e813a4b6337c318cfede999968796
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
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vector_id: 7721
content:
"trend in winter (Khaydarov and Gerlitz, 2019). There is very high \r\nconfidence (robust evidence, high agreement) that the shrinking \r\nof the Aral Sea has induced an increase in surface air temperature \r\naround the Aral Sea region in the range of 2degC-6degC (Baidya Roy \r\net al., 2014; McDermid and Winter, 2017; Sharma et al., 2018). The \r\nplateau of Iran has experienced significant increases in the average \r\nmonthly values of daily maximum and minimum temperatures \r\nwith spatially varying rates of 0.1degC-0.3degC up to 0.3degC-0.4degC per \r\ndecade and greater spatial variation in minimum temperatures (high \r\nconfidence) (Mahmoudi et al., 2019; Fathian et al., 2020; Sharafi and \r\nMir Karim, 2020)."


id: 2b3924597625088e51d49839eb50d0ef
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
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vector_id: 4235
content:
"Observed warming over northern ARP is higher than over the south, \r\nwhere minimum temperatures are increasing faster than maximum \r\ntemperatures (Almazroui, 2020a). The rate of mean temperature \r\nincrease is estimated at 0.10degC per decade over 1901-2010 (Attada \r\net  al., 2019), while it has reached 0.63degC (likely in the range of \r\n0.24degC-0.81degC) per decade for the more recent period of 1978-2019 \r\n(Almazroui, 2020a)."


id: 2b07aa68256f5999d6ce8d0ebde05ef9
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
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vector_id: 4202
content:
"Since AR5, there has been an increasing number of studies on past \r\nclimate change in South West Asia though meteorological stations are \r\nsparsely scattered in the region. They are mainly located in the plains \r\nbelow 2 km of altitude, very scarce in mountainous areas and have \r\ndeclined in number in WCA since the end of the Soviet Union in 1991. \r\nThis increases the uncertainty in both temperature and precipitation \r\ntrends, particularly for elevated areas (high confidence) (Christensen \r\net al., 2013; Huang et al., 2014). So researchers use other sources of \r\nclimate data in the region, particularly freely available gridded data \r\n(Annex I)."


id: e82ee6ac10de24ea42e8e0197c9b1c14
created_at: 2023-03-16 14:00:52
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content:
"Globally, drylands showed an enhanced warming over the past \r\ncentury of 1.2degC-1.3degC, significantly higher than the warming over \r\nhumid lands (0.8degC-1.0degC) (J. Huang et al., 2017). A strong increase \r\nin annual surface air temperature of 0.27degC-0.47degC per decade has \r\nbeen found over WCA between 1960 and 2013 (very high confidence) \r\n(Han and Yang, 2013; Li et al., 2013; Hu et al., 2014, 2017; Huang \r\net al., 2014; Deng and Chen, 2017; Zhang et al., 2017, 2019a; H. Guo \r\net  al., 2018; Haag et  al., 2019; Yu et  al., 2019). Warming is most \r\nprominent in the spring based on the CRU dataset with rates likely\r\nranging from 0.64degC-0.82degC per decade (Hu et al., 2014). Analysis of \r\nseasonal temperature trends based on high-resolution 1 km x 1 km \r\ndownscaled dataset CHELSA and 20 stations in Uzbekistan has \r\nconfirmed the maximum significant trend in temperature from 0.6degC \r\nup to 1degC per decade in spring from 1979 to 2013 and no significant "


id: 5a1c72c2d9c9475902e769970452bcfd
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
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vector_id: 11690
content:
"A decreasing trend of precipitation is reported for ARP with the mean \r\nvalue of -6.3  mm per decade (range of -30  mm-16  mm) for the \r\nperiod 1978-2019 (low confidence) with large interannual variability \r\nover Saudi Arabia, which covers 80% of the region (AlSarmi and \r\nWashington, 2011; Almazroui et al., 2012; Donat et al., 2014). The \r\nsame decreasing trend in precipitation totals and an increasing \r\ntrend in the number of consecutive dry days are found for most of \r\nthe Iranian Plateau (medium confidence) (Rahimi and Fatemi, 2019; \r\nFathian et al., 2020; Sharafi and Mir Karim, 2020). January-to-March \r\nmean snow cover and depth over mountainous areas decreased \r\nbetween 2000 and 2019 (low to medium confidence due to limited \r\nevidence) (Safarianzengir et al., 2020)."


id: 1d8eabbf2659c7f02e4487f588f1720e
created_at: 2023-03-16 14:00:52
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vector_id: 2091
content:
"and baselines are shown in Figure Atlas.17 and further details can be \r\nexplored in the Interactive Atlas."


id: a5e74ae76c9fd99d64c2d4fc02250198
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
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content:
"There is limited evidence about the performance of GCMs and RCMs \r\nin representing the current climate of South West Asia due to very \r\nfew studies evaluating models over this region, but literature is now \r\nemerging particularly on CMIP5/CMIP6 and CORDEX simulations."


id: ece9be6e5c584e206d989d85f535187b
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
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"Over ARP, surface temperature biases for 18 of 30 CMIP5 models are \r\nwithin one standard deviation of the observed variability (Almazroui \r\net al., 2017). A warm bias in summer and a cold bias for other months \r\nalong with an underestimation of wet-season precipitation and an \r\noverestimation in the dry season have been reported in 26 CMIP5 \r\nmodels (Lelieveld et al., 2016). Thirty CMIP6 GCMs have limited skill \r\nin simulating annual precipitation patterns, annual cycle statistics \r\nand long-term precipitation trends over Central Asia partially due to \r\nconsiderable wet biases of up to 100% in the southern Xinjiang and \r\nHexi Corridor regions (Guo et al., 2021). Also, CMIP6 models display \r\na wide range of performance in reproducing ENSO teleconnections \r\nthat influence the region (Barlow et al., 2021)."


id: 2ac6ad117f0813fa189144df29c5c573
created_at: 2023-03-16 14:00:52
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"In ARP, the projected change in ensemble mean annual temperature \r\nfrom 30 CMIP6 models is from 1.6degC (SSP1-2.6) to 5.3degC (SSP5-8.5) \r\nby 2070-2099 compared to 1981-2010 (Almazroui et al., 2020a). \r\nThe projected warming is the highest in the north, reaching 5.9degC \r\nand lowest in the south (4.7degC). COSMO-CLM projections over the \r\nCORDEX-MENA domain show for ARP and WCA a strong warming \r\nwith marked seasonality for the end of the 21st  century, ranging \r\nfrom 2.5degC in winter under RCP4.5 to 8degC in summer under RCP8.5 \r\nand with large increases found over high-altitude areas in winter \r\nand spring (Bucchignani et al., 2018; Ozturk et al., 2018). The CMIP5 \r\nmulti-model mean warming in boreal summer in 2070-2099, \r\ncompared with 1951-1980, is projected to be about 2.5degC and \r\n6.5degC at the 2degC and 4degC global warming levels respectively \r\n(Huang et al., 2014)."


id: 9b7d08a4e53244ab07e459e7846eb369
created_at: 2023-03-16 14:00:52
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"Four RCMs (REMO, RegCM4.3.5, ALARO-0, and COSMO-CLM5.0) \r\ndriven by ERA-Interim, NCEP2 reanalyses and two different GCMs \r\nreproduced reasonably well the spatio-temporal patterns for \r\ntemperature and precipitation though underestimated diurnal \r\ntemperature range and had cold biases over mountainous and high \r\nplateau regions in all seasons. There is low confidence in this result \r\nbecause of low station density and a lack of high-elevation stations, \r\nand with biases dependent on the choice of the observational \r\ndataset. However, the performance of both GCMs and RCMs is better \r\nthan reanalyses when compared to available observations (Mannig \r\net al., 2013; Ozturk et al., 2017; Russo et al., 2019; Top et al., 2021)."


id: d7adef24e55bb00529b812353fe0db38
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"by 26.5% in Central Asia (Yang et al., 2017). However, regardless of \r\nthe sign of the precipitation change in the high-mountain regions \r\nof Central Asia, the influence of the warming on the snowpack will \r\nvery likely cause important changes in the timing and amount of \r\nthe spring melt (Diffenbaugh et al., 2013)."


id: 949f0eaa6407b8589c6632bce975c146
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
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"The assessment in this section focuses on changes in average \r\ntemperature and precipitation (rainfall and snow), including the \r\nmost recent years of observations, updates to observed datasets, \r\nthe consideration of recent studies using CMIP5 and those using \r\nCMIP6 and CORDEX simulations. Assessment of changes in extremes \r\nis in Chapter  11 (Tables 11.10-12) and climatic impact-drivers in \r\nChapter 12 (Table 12.5)."


id: a3c5b5581b5f02f5aa833b6a8566cb70
created_at: 2023-03-16 14:00:52
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content:
"In ARP, the projected change in ensemble mean annual precipitation \r\nfrom 30 CMIP6 models ranges from 3.8% (-2.6 to 28.8%) to 31.8% \r\n(12.0-106.5%) under SSP1-2.6 and SSP5-8.5 emissions for the \r\nperiod 2080-2100 compared with 1995-2014 (Almazroui et  al., \r\n2020a). North-west ARP precipitation is projected to decrease \r\nbetween -6 to -27% per decade and in the south precipitation \r\nto increase by up to 8.6% per decade. CMIP6 projections are in \r\nline with those from CMIP3 and CMIP5, however they are less \r\nvariable in the central area in CMIP6. The uncertainty associated \r\nwith precipitation over ARP is large because of very low annual \r\namounts and high variability."


id: 2d7daf9fb5f052344e319e81003412d9
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
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"Increases in annual surface air temperature over South West Asia \r\nare very likely in the range of 0.24degC-0.81degC per decade over the \r\nlast 50-60 years. Annual precipitation change over ARP since 1970 is \r\nestimated at -6.3 mm per decade (and in the range of -30 to 16 mm \r\nper decade) and over WCA is generally not significant except over \r\nthe elevated part of eastern WCA where increases between 1.3 mm \r\nand 4.8 mm per decade during 1960-2013 have been observed (very \r\nhigh confidence). In mountainous areas, the scarcity and decline of \r\nthe number of observation sites since the end of the former Soviet \r\nUnion in 1991 increase the uncertainty of the long-term temperature \r\nand precipitation estimates (high confidence)."


id: 513e562ccbbc3f0944759c868f543f64
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
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"Mean temperature biases in RCMs are within +-3degC in South West \r\nAsia, and annual precipitation biases are positive in almost all \r\nparts of the region, except over the ARP where they are negative \r\nin the wet season (November to April) and over WCA in winter and \r\nspring (from December to May) (medium confidence). Since regional \r\nmodel evaluation literature has only recently emerged there is \r\nmedium evidence about the performance of RCMs in South West \r\nAsia though with medium to high agreement on mean temperature \r\nand precipitation biases. RCMs simulate colder temperatures than \r\nobserved over mountainous and high plateau regions (limited \r\nevidence, high agreement)."


id: 731bdd3987fe5b98951696b65640a1b4
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
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content:
"Further warming over South West Asia is projected in the 21st century \r\nto be greater than the global average, with rates varying from 0.25degC \r\nto 0.8degC per decade depending on the season and scenario, and the \r\nmaximum rates found in the northern part of the region in summer \r\n(high confidence). The influence of the warming on the snowpack \r\nwill very likely cause changes in the timing and amount of the spring \r\nmelt. CMIP6 projected changes in annual precipitation totals are \r\nin the range of -3 to 29% (SSP1-2.6) and 12-107% (SSP5-8.5) in \r\nARP (medium confidence). Strong spatio-temporal differences with \r\noverall precipitation decreases are projected in the central and \r\nnorthern parts of WCA in summer (JJA) with increases in winter (DJF) \r\n(medium confidence)."


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"period events) and of short duration (sub-daily) extremes (medium \r\nconfidence). Likewise, however, there is a projected increase in the \r\nfrequency of drought in southern Australia (medium confidence) \r\nand in many parts of New Zealand (medium confidence). Owing \r\nto hotter and drier conditions there is high confidence that the \r\noccurrence of fire weather will increase in most of southern \r\nAustralia, and medium confidence that the fire danger index will \r\nincrease in many parts of New Zealand."


id: 54098119acd37bf0dc6aba34f452cdf7
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content:
"regions of New Zealand during the shorter 1980-2015 period, \r\nthis is in fact the reverse of the longer-term trends in 1961-2015 \r\n(Interactive Atlas)."


id: da773c0007e2bd815aa65725fc97724b
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
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content:
"The SROCC (Hock et  al., 2019b) reports on the observed and \r\nprojected decline in snow cover in Australasia, as well as the retreat \r\nof New Zealand glaciers following an advance in 1983-2008 due \r\nto enhanced snowfall. It also reports on the vulnerability of some \r\nAustralian communities and ecosystems to sea level rise, increases \r\nin the intensity and duration of marine heatwaves driven by human \r\ninfluence (high confidence), the decrease in frequency of tropical \r\ncyclones' landfall on eastern Australia since the late 1800s (low \r\nconfidence in an anthropogenic signal), and presents a case study \r\non the multiple hazards, compound risk and cascading impacts \r\nfrom climate extremes in Tasmania in 2015-2016 (including an \r\nattributable human influence on some events). The SRCCL (Mirzabaev \r\net al., 2019) found widespread vegetation 'greening' has occurred in \r\nparts of Australia, and an increase in the desertification and drought \r\nrisk in future in southern Australia."


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content:
"Reliable station observations are available from around 1900 in \r\nAustralasia, but in some regions the coverage was and remains \r\npoor. Australia and New Zealand have continued to warm, and \r\nmany rainfall trends have continued since AR5. Changes and trends \r\nin temperature and precipitation from 1961 to 2015 from three \r\ndifferent global datasets are displayed in Figure Atlas.11 and the \r\nInteractive Atlas, and show significant (at 0.1 significance level) \r\nwarming trends over southern and eastern Australia. Most of the \r\nobserved changes in precipitation over the region are not significant \r\nover this period. Although observed datasets (e.g.,  GPCC and \r\nGPCP) generally agree on a significant drying trend in the southern "


id: b44dbd53b7e0e663261cd89bc163d7e0
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
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vector_id: 25879
content:
"In Australia, there has been a decrease in snow depth and area since \r\nthe late 1950s, especially in spring (BOM and CSIRO, 2018). Based on \r\na reconstructed snow cover record, the recent rapid decrease in the \r\npast five decades has been shown to be larger by more than an order \r\nof magnitude than the maximum loss for any five-decade period \r\nover the past 2000 years (McGowan et al., 2018). In New Zealand, \r\nfrom 1977 to 2018, glacier ice volume decreased from 26.6 km3 to \r\n17.9 km3 (a loss of 33%; Salinger et al., 2019)."


id: f08b54f083d659f706eb0fe1c5e92093
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
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vector_id: 35342
content:
"overall showed added value in the simulation of the current climate \r\n(Di Virgilio et al., 2019; Evans et al., 2021)."


id: 192c7540f390b63ee00818c18342607c
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 1455
content:
"Most studies assessed in AR5 WGII were based on Coupled Model \r\nIntercomparison Project Phase 3 (CMIP3) models and Special \r\nReport on Emissions Scenarios (SRES) scenarios and CMIP5 models \r\nwhenever available. The AR5 WGI reported that model biases in \r\nannual temperature and rainfall are similar to or lower than other \r\ncontinental regions outside the tropics, with temperature biases \r\ngenerally <1degC in the multi-model mean and <2degC in most models \r\nover Australia compared to reanalysis, and with a wet bias over the \r\nAustralian inland region but a  dry bias near coasts and mountain \r\nregions of both Australia and New Zealand."


id: 8b438b5b90a9f4ff8534eeaaeea2c8a1
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
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vector_id: 19437
content:
"Similar to the global average (Chapter  4), mean temperature in \r\nAustralasia is projected to continue to rise through the 21st century \r\nat a  magnitude proportional to the cumulative greenhouse gas \r\nemissions (virtually certain, very high confidence, robust evidence), \r\nCMIP5 and CMIP6 results are shown in Figure Atlas.21. A higher end \r\nto the range of temperature projections is found in CMIP6 compared \r\nto CMIP5 (Grose et al., 2020), produced by a group of models with \r\nhigh climate sensitivity (Forster et al., 2020), and this creates a higher \r\nmulti-model-mean change. For example, projections for Australasia \r\nincluding ocean between 1995-2014 and 2081-2100 are 1.4degC \r\n(1.1degC-1.8degC, 10th-90th percentile range) in CMIP5 under RCP4.5, \r\nbut 1.8degC (1.3degC-2.5degC) in CMIP6 under SSP2-4.5."


id: d2b72376739070814e1045e91247646f
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
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index: climate_gpt_only_giec
vector_id: 30672
content:
"Early results from CMIP6 suggest incremental improvements \r\ncompared to CMIP5 in the simulation of the mean annual \r\nclimatology of temperature and precipitation of the Indo-Pacific \r\nregion surrounding Australasia, the teleconnection between ENSO \r\nand IOD and Australian rainfall and other relevant climate features \r\n(Grose et al., 2020). These assessments suggest that confidence in \r\nprojections is similar to AR5 or incrementally improved. The CORDEX \r\nAustralasia simulations are found to have cold biases in daily \r\nmaximum temperature and an overestimation of precipitation but "


id: 14419a0710745c2981fac2a17f3ab34a
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 698
content:
"Using warming levels, the results can be directly compared, \r\naccounting for the different distribution of climate sensitivities in the \r\ntwo ensembles. In this framework, Australasia (land only) is projected "


id: 3e5d92232c669632fe1fec4cb33e9374
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 7182
content:
"of change in JJA, but significant changes are projected by some \r\nmodels. Other seasonal and regional rainfall changes in Australia are \r\nreviewed in Dey et al. (2019)."


id: bef5ea007fa915b1dd6a6c4ede784d99
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 27601
content:
"to warm by a similar amount to the global average: 1.4degC-1.8degC for \r\nthe 1.5degC warming level, through to 3.9degC-4.8degC for the 4degC warming \r\nlevel from the 1850-1900 baseline in CMIP6 using SSP5-8.5 (results \r\nusing other SSPs and from CMIP5 are similar). Projected warming \r\nis greater over land than ocean, greater in Australia than in New \r\nZealand, and greater over inland Australia than in coastal regions. Due \r\nto historical warming, projected temperature change from the AR6 \r\nbaseline of 1995-2014 is lower: 0.3degC-1.0degC for the 1.5degC warming \r\nlevel, through to 2.9degC-4.0degC for the 4degC warming level. Changes \r\nfor other warming levels, sub-regions and emissions pathways are \r\nshown in Figure Atlas.21 and can be explored in the Interactive \r\nAtlas. Regional modelling suggests projected temperature increase is \r\nhigher in mountainous areas than surrounding low-elevation areas in \r\nNew Zealand and Australia (Olson et al., 2016; MfE, 2018)."


id: be2b379caf408ba96ece03fb315b5b90
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 27475
content:
"For the NZ reference region, precipitation is projected to increase \r\nin winter and annual rainfall, with some differences in magnitude \r\nbetween CMIP5, CMIP6 and CORDEX (Figure Atlas.21). This \r\nprojection of rainfall increase is a function of changes in the southern \r\nextent of the region, and notable regional differences are expected. \r\nRegional modelling suggests precipitation increases in the west and \r\nsouth of New Zealand and decreases in the north and east (MfE, \r\n2018), with medium confidence and notable differences by season. \r\nLiu et al. (2018) project that the North Island will be drier, while the \r\nSouth Island will be wetter under both 1.5degC and 2degC warming levels. \r\nThe projected increase in precipitation in the far future (2081-2100) \r\nfor the southern regions of NZ has high agreement (Interactive Atlas). \r\nOther seasonal and regional rainfall changes in Australia can be \r\nexplored in the Interactive Atlas."


id: 6a98d65bf792ea21e507d39bb9036a05
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 14385
content:
"Under future warming, the snowpack in Australia is projected \r\nto decrease by approximately 15% and 60% by 2030 and 2070 \r\nrespectively under the SRES A2 scenario (Di Luca et al., 2018), while \r\nin New Zealand the number of annual snow days is projected to \r\ndecrease by 30 days or more by 2090 under RCP8.5 (MfE, 2018). New \r\nZealand is also projected to lose up to 88 +- 5% of its glacier volume \r\nby the end of the 21st century (Chinn et al., 2012; Hock et al., 2019a)."


id: e74377b3e307d9c7d824d104010a98eb
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 33880
content:
"There is very high confidence that the climate of Australia warmed \r\nby around 1.4degC and New Zealand by around 1.1degC since reliable \r\nrecords began in 1910 and 1909 respectively, with human influence \r\nthe dominant driver. Warming is virtually certain to continue, with \r\na  magnitude roughly equal to the global average temperature. \r\nA  significant decrease in April to October rainfall in the south-west \r\nof the state of Western Australia observed from 1910 to 2019 is \r\nattributable to human influence with high confidence and is very \r\nlikely to continue in future, noting consistent projections in CMIP5 \r\nand CMIP6. Other observed and projected rainfall trends are less \r\nsignificant or less certain. Model representation of the climatology "


id: 6608a16d1264bb0573cc3218ed79eb67
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 13642
content:
"Projections for EAU vary by season, with moderate model agreement \r\non a  decrease in rainfall in winter and spring, but with lower \r\nagreement in CMIP6 compared to CMIP5, and low model agreement \r\non the direction of change in summer (Figure Atlas.21). CAU shows \r\na  similar range of change as EAU, with low model agreement on \r\nthe direction of change in DJF, moderate agreement on direction "


id: 9b62e2ffa08fe6a94d474c94dd5400eb
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 21998
content:
"of Australasian temperature and rainfall has improved since AR5, \r\nthrough an incremental improvement between CMIP5 and CMIP6, and \r\nthe development of coordinated regional modelling through CORDEX\u0002Australasia. Snow cover is likely to decrease throughout the region at \r\nhigh altitudes in both Australia and New Zealand (high confidence)."


id: 4500b33ed1cc979e880351807e7533ec
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 8283
content:
"The assessment in this section focuses on changes in average surface \r\ntemperature and precipitation (rainfall and snow), including the \r\nmost recent years of observations, updates to observed datasets, \r\nthe consideration of recent studies using CMIP5 and those using \r\nCMIP6 and CORDEX simulations. Assessment of changes in extremes \r\nis in Chapter  11 (Tables 11.13-15) and climatic impact-drivers in \r\nChapter 12 (Table 12.6). It considers climate change over the regions \r\nshown in Figure Atlas.22, extending to all territories from Mexico to \r\nSouth America, including the Caribbean islands. This figure supports \r\nthe assessment of regional mean changes over the region which, due \r\nto the high climatological and geographical heterogeneity, has been \r\nsplit into two sub-regions: Central America and the Caribbean, and \r\nSouth America."


id: d3bd07bef8177b0bb390a1df9b22b705
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 30839
content:
"One of the most prominent features of the regional climate is the \r\nincidence of tropical cyclones (TCs), which represent an important \r\nhazard for almost all the countries of the region between June and \r\nNovember. A detailed assessment is given in Chapter 11."


id: ce8257d136e63d126fa6f5aed826b092
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
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vector_id: 30029
content:
"According to AR5 (Christensen et  al., 2013), significant positive \r\ntrends of temperature have been observed in Central America (high \r\nconfidence), while significant precipitation trends are regionally \r\ndependent, especially during the summer. In addition, changes in \r\nclimate variability and in extreme events have severely affected \r\nthe region (medium confidence). A decrease in mean precipitation \r\nis projected in SCA and NCA. El Nino and La Nina teleconnections \r\nare projected to move eastwards in the future (medium confidence), \r\nwhile changes in their effects on other regions, including Central \r\nAmerica and the Caribbean is uncertain (medium confidence). There \r\nis medium confidence in projections showing an increase in seasonal \r\nmean precipitation on the equatorial flank of the ITCZ affecting parts \r\nof Central America and the Caribbean."


id: 5add7943d9f826ad7d9306d19e208a6e
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
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vector_id: 11812
content:
"The Central America and Caribbean region is assessed considering \r\nthree reference regions Southern Central America (SCA), including \r\nthe isthmus and the Yucatan Peninsula; Northern Central America \r\n(NCA), including Mexico (centre and north); and the Caribbean \r\n(CAR), including the Greater Antilles, the Lesser Antilles, the Bahamas \r\nand other small islands (see Figure Atlas.22); NCA is also covered in \r\nSection Atlas.9 North America."


id: bc815191c8befa3fe77485465d9137b9
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
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index: climate_gpt_only_giec
vector_id: 27238
content:
"In relation to the 1986-2005 baseline period, temperatures are very \r\nlikely to increase by the end of the century, even for the RCP2.6 \r\nscenario, with changes of more than 5degC in some regions for the \r\nRCP8.5 scenario. Precipitation change is projected to vary between \r\n+10% and -25% (medium confidence) (Christensen et al., 2013). The \r\nSR1.5 (Hoegh-Guldberg et al., 2018) states there is a high agreement\r\nand robust evidence that at the 1.5degC global warming level the \r\nCaribbean region will experience a 0.5degC-1.5degC warming compared \r\nto the 1971-2000 baseline period, with greatest warming over larger \r\nland masses."


id: 7d38922d2d7b5c1e12f9eaaa6cdd460b
created_at: 2023-03-16 14:00:52
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content:
"Significant warming trends between 0.2degC and 0.3degC per decade \r\nhave been observed in the three reference regions of Central America \r\nin the last 30 years (Planos Gutierrez et al., 2012; P.D. Jones et al., \r\n2016a; Hidalgo et al., 2017), with the largest increases in the North \r\nAmerican Monsoon region (high confidence) (Figure Atlas.11 and the \r\nInteractive Atlas; Cavazos et al., 2020). There is high confidence of "


id: 82ff73565b82cc85755997663d611a6b
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
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vector_id: 18139
content:
"increasing temperature over parts of NCA, reaching 0.5degC per decade \r\nin Mexico and southern Baja California, with a lower rate (0.2degC per \r\ndecade) in the Yucatan Peninsula and the Guatemala Pacific coastal \r\nregion (Cueto et al., 2010; Garcia Cueto et al., 2013; Martinez-Austria \r\net al., 2016; Martinez-Austria and Bandala, 2017; Navarro-Estupinan \r\net al., 2018; Cavazos et al., 2020) and CAR (McLean et al., 2015) over \r\nthe last 30 to 40 years. Cooling trends have been detected in limited \r\nareas of Honduras and northern Panama (Hidalgo et al., 2017)."


id: d1938f3183d80c166f9767faf0e31187
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 30504
content:
"Changes in mean precipitation rates are less consistent and long-term \r\ntrends are generally weak. Different databases show significant \r\ndifferences depending mainly on the type and resolution of data \r\n(Centella-Artola et  al., 2020). Small positive trends were observed \r\nin the total annual precipitation (Stephenson et  al., 2014). In SCA \r\nand CAR, trends in annual precipitation are generally non-significant, \r\nwith the exception of small significant positive trends for sub-regions \r\nor limited periods (Planos Gutierrez et al., 2012; Hidalgo et al., 2017), \r\nand the 1970-1999 trends in precipitation in SCA are generally \r\nnon-significant (J.M. Jones et al., 2016; Hidalgo et al., 2017). Positive \r\ntrends in the duration of the MSD have been found in this region over \r\nthe past four decades (low confidence) (Anderson et al., 2019). For \r\nCAR see also Atlas.10 Small Islands."


id: 96f8bab8f940980356b4f90cdd05753
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
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index: climate_gpt_only_giec
vector_id: 21268
content:
"Global and regional models consistently project warming in the \r\nwhole region for the end of the century, under RCP4.5 and RCP8.5 for \r\nCMIP5 projections with greater warming for continental compared to \r\ninsular territories, likely reaching values between 2degC and 4degC (high \r\nconfidence) (Campbell et al., 2011; Karmalkar et al., 2011; Cavazos \r\nand Arriaga-Ramirez, 2012; Cantet et  al., 2014; Chou et  al.,  2014; \r\nCoppola et  al., 2014; Hidalgo et  al., 2017; Colorado-Ruiz et  al., \r\n2018; Imbach et al., 2018). The greatest warming of 5.8degC for the \r\nend of the century was projected for northern Mexico under RCP8.5 \r\n(Colorado-Ruiz et  al., 2018), using an ensemble of CMIP5 GCMs \r\n(Interactive Atlas)."


id: d6b5f37b328eef0b8cf24eedafac1c9e
created_at: 2023-03-16 14:00:52
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vector_id: 31284
content:
"The ability of climate models to simulate the climate in this region \r\nhas improved in many key aspects (Karmalkar et al., 2013; Fuentes\u0002Franco et al., 2014, 2015, 2017; Vichot-Llano et al., 2014; Vichot-Llano \r\nand Martinez-Castro, 2017; Martinez-Castro et al., 2018). Particularly \r\nrelevant for this region are increased model resolution and a better \r\nrepresentation of the land surface processes (high confidence)."


id: 287c8731d300d78d86181f9ae2d66d3d
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 3785
content:
"Figure Atlas.22 and the Interactive Atlas synthesize regional mean \r\nchanges in annual mean surface air temperature and precipitation \r\nfor the Central American reference regions for CMIP6, CMIP5 and \r\nCORDEX for different warming levels and time periods. At the \r\n1.5degC GWL, it is very likely that average annual temperature in \r\nCentral America over land surpasses 1.3degC (CAR), 1.7degC (NCA) and "


id: 455a07d0e3865ae9236b4517cee6cfa2
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 8330
content:
"Colorado-Ruiz et al. (2018) assessed an ensemble of 14 GCMs from \r\nCMIP5 for a  1971-2000 baseline period, projecting precipitation \r\ndecreases of between 5% and 10% by the end of the century for the "


id: 51c817d4574084dfb7c03ae121bc781a
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 10327
content:
"Projected change in mean annual precipitation shows a large spatial \r\nvariability across Central America and the Caribbean. Under moderate \r\nfuture emissions overall negative but non-significant precipitation \r\ntrends are projected for the 21st  century (low confidence). Under \r\nhigher-emissions scenarios and at higher GWLs, average precipitation \r\nis likely to decrease in most of the region, particularly in the north\u0002western and central Caribbean and part of continental Central \r\nAmerica, especially in SCA."


id: 3d2e478d2120670a2f6ba1855c089bea
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 6990
content:
"Regional synthesis of observed and modelled climate in South \r\nAmerica is challenging due to the latitudinal extent of the continent, \r\nthe Andes Mountains, and local-to-regional climatic features, \r\nwhich are influenced by multiple drivers. The main large-scale \r\ndrivers include many modes of natural variability (Annex IV.2): the \r\ninter-decadal  modes, Atlantic Multi-decadal Variability (AMV) and \r\nPacific Decadal Variability (PDV); the interannual-to-annual modes, \r\nEl Nino-Southern Oscillation (ENSO), the Indian Ocean Dipole (IOD), \r\nthe Southern Annular Mode (SAM) and the North Atlantic Oscillation \r\n(NAO); seasonal variability driven by the meridional migration of the \r\nInter-tropical Convergence Zone (ITCZ) and the timing and intensity \r\nof the South American Monsoon System (SAmerM, Section 8.3.2.4.5), \r\nthe Madden-Julian Oscillation sub-seasonal mode of natural \r\nvariability (MJO) and the behaviour at finer scales of the tropical \r\neasterly waves."


id: 369f826af6594a4ee10e5267f8c59ed3
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
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index: climate_gpt_only_giec
vector_id: 5982
content:
"There is high agreement and high confidence in the projected decrease \r\nof precipitation by the end of the century for most of the region, \r\nparticularly for annual and summer precipitation, but there is low \r\nconfidence on the magnitude of this decrease which varies between \r\n5% and 50% for different projections and different sub-regions \r\n(see extended information in the Interactive Atlas)."


id: 9efb75c56d7a78f9971960e891d6c268
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 22556
content:
"The status of climate extreme trends and projections for the \r\nregion has been assessed in Chapter  11 and the main findings \r\nare synthesized here. There is high confidence in the projections \r\nof significant heatwave events at the end of the century in SCA \r\n(Angeles-Malaspina et al., 2018) and an increase in warm days and \r\nwarm nights over this region and CAR (Stennett-Brown et al., 2017). \r\nFor CAR islands, using dynamically downscaled CMIP3 models, \r\nKarmalkar et  al. (2013) projected an increase in drought severity \r\nat the end of the century, mainly due to a  precipitation decrease \r\nduring the early wet season. In SCA, projections suggest an increase \r\nin the MSD (Imbach et al., 2018) and an increase in consecutive dry \r\ndays (Chou et al., 2014), consistent with the projections of Stennett\u0002Brown et al. (2017)."


id: d90388257af732393b9d42546ae91214
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
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index: climate_gpt_only_giec
vector_id: 31652
content:
"Significant warming trends between 0.2degC and 0.3degC per decade \r\nhave been observed in the three reference regions of Central \r\nAmerica in the last 30 years, with the largest increases in the North \r\nAmerican  Monsoon region (high confidence). Changes in mean \r\nprecipitation rates are less consistent and long-term trends are \r\ngenerally weak. Small positive trends were observed in the total \r\nannual precipitation in part of the region."


id: 12231bb7166e949f2bb7847972b0ca74
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
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index: climate_gpt_only_giec
vector_id: 355
content:
"Warming in the continental part of the region is projected to increase \r\nin the range of the mean global values for GWL of 1.5degC and 3degC, \r\nbut in the Caribbean regional warming will be lower. Precipitation \r\nis projected to decrease with increasing GWLs, especially for CAR \r\nand SCA."


id: c93367c909ea911e0d6240c9f26d6955
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
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index: climate_gpt_only_giec
vector_id: 29216
content:
"maximum temperature generally increasing faster than the minimum \r\ntemperature, while a negative trend of up to -0.5degC per decade was \r\nobserved over the tropics."


id: ac2297a3c2ff14f9a142791e9f9ca3e4
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
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index: climate_gpt_only_giec
vector_id: 24615
content:
"According to AR5 WGII Chapter 27 (Magrin et al., 2014), during the \r\nlast decades of the 20th  century, observational studies identified \r\nsignificant trends in precipitation and temperature in South America \r\n(high confidence). Increasing trends in annual rainfall in South\u0002Eastern South America contrast with decreasing trends in central \r\nsouthern Chile and some regions of Brazil. Warming has been \r\ndetected throughout South America (near 0.7degC-1degC in the 40 years \r\nsince the mid-1970s), except for a cooling off the Chilean coast of \r\nabout -1degC over the same period."


id: b3e9ee797a9cc4e9fdc72faa634e8b58
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vector_id: 25822
content:
"The AR5 WGI (Flato et al., 2013) noted that climate simulations from \r\nCMIP3 and CMIP5 models were able to represent well the main \r\nclimatological features, such as seasonal mean and annual cycle \r\n(high confidence), although some biases remained over the Andes, \r\nthe Amazonian basin and for the South America Monsoon. On the \r\nother hand, climate models from CMIP5 showed better results when \r\ncompared to CMIP3."


id: d97d16b84584b5bfc3b93f5c61cf8734
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 31733
content:
"The SR1.5 (Hoegh-Guldberg et  al., 2018) assessed that a  further \r\nincrease of 0.5degC or 1degC is likely to have detectable effects on \r\nmean temperature and precipitation in South America, particularly \r\nin tropical regions (NWS, NAS, SAM and NES), as well as in SES, \r\ngiven that changes in mean temperatures and precipitation have \r\nalready been attributed in the last decades for global warming of \r\nless than 1degC."


id: c1faed6cb13f863c9af6d26f957a88e9
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 28099
content:
"Studies on climatic trends in South America indicate that mean \r\ntemperature and extremely warm maximum and minimum \r\ntemperatures have shown an increasing trend (high confidence), \r\nparticularly for a  large region in Northern South America and the \r\nsouth-western Andes (NSA, SAM, NES, SWS and the north of SES; \r\nSkansi et  al., 2013; de Barros Soares et  al., 2017). Also, the trend \r\nof the difference between the annual mean of the daily maximum \r\ntemperature and the annual mean of the daily minimum temperature \r\nwas positive - up to 1degC per decade - over the extratropics with the "


id: 25245e5e33e2671b4db584e841303e23
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 3282
content:
"Since AR5, there has been limited attribution literature in the South \r\nAmerica. Recent publications based on observational and modelling \r\nevidence assessed that anthropogenic forcing in CMIP5 models \r\nexplains the overall warming (high confidence) over the entire \r\nSouth American continent, including the increase in the frequency of \r\nextreme temperature events (Hannart et al., 2015). It has a detectable \r\ninfluence in explaining positive and negative precipitation trends \r\nobserved in regions such as SES and the southern Andes (Vera \r\nand Diaz, 2015; de Barros Soares et al., 2017; Boisier et al., 2018; \r\nde Abreu et al., 2019). Despite that, there is limited evidence that \r\nhuman-induced greenhouse gas emissions had an influence on the \r\n2014/2015 water shortage in south-east Brazil (Otto et  al., 2015). \r\nExtreme event attribution on sub-continental scales is assessed in \r\nChapter 11 and continental-scale attribution in Chapter 3."


id: a53cf44bebbc43cbd5b5483aa98c2ad7
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 23564
content:
"In summary, analyses of historical temperature time series point \r\nstrongly to an increased warming trend (high confidence) across \r\nmany South American regions, except for a cooling off the Chilean \r\ncoast. Annual rainfall has increased over South-Eastern South \r\nAmerica and decreased in most tropical land regions, particularly in \r\ncentral Chile (high confidence). The number and strength of extreme \r\nevents, such as extreme temperatures, droughts and floods, have \r\nalready increased (medium confidence) (Table 11.7)."


id: e808030f8fdac8d8e2296443f43d7427
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 34004
content:
"It is noted that the major barrier to the study of climate change in \r\nmany regions of South America is still the absence or insufficiency \r\nof long time series of observational data (Carvalho, 2020; Condom \r\net al., 2020). Most national datasets were created in the 1970s and \r\n1980s, preventing a more comprehensive long-term trend analysis. \r\nTo fulfil the users' demand for climatological and meteorological \r\ndata products covering the whole region, several interpolation \r\ntechniques have been used with reanalysis and gridded gauge\u0002analysis products to add the necessary spatial detail to the \r\nclimate analyses over land and for climate variability and trend \r\nstudies, but these are subject to uncertainties (Skansi et al., 2013; \r\nRozante et al., 2020)."


id: aa28f0e64c580215e74d1f5caa58a4b5
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 24310
content:
"Since AR5 the number of publications on climate model performance \r\nand their projections in South America has increased, particularly for \r\nregional climate modelling studies (Giorgi et  al., 2009; Boulanger \r\net  al., 2016; Ambrizzi et  al., 2019) and the understanding of their \r\nstrengths and weaknesses (high confidence)."


id: 7efdeb95d07dc0482d4020977e1e3bab
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 17534
content:
"Most global and regional climate models can simulate reasonably \r\nwell the current climatological features of South America, such as \r\nseasonal mean and annual cycles. However, significant biases persist \r\nmainly at regional scales (high confidence) (Blazquez and Nunez, \r\n2013b; Gulizia et al., 2013; Joetzjer et al., 2013; Jones and Carvalho, \r\n2013; Torres and Marengo, 2013; Gulizia and Camilloni, 2015; \r\nZazulie et  al., 2017; Abadi et  al., 2018; Barros and Doyle, 2018; \r\nSolman and Blazquez, 2019; Fan et  al., 2020; Rivera and Arnould, \r\n2020; Teichmann et al., 2021). During the dry season, precipitation \r\nis underestimated in most models over Amazonia (medium evidence, \r\nhigh agreement) (Torres and Marengo, 2013; Yin et al., 2013; Solman \r\nand Blazquez, 2019). Over regions with complex orography, such as "


id: 6efd932597b5cc86f8600fa293c3ff9e
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 15086
content:
"The evaluation of statistical downscaling models (ESD) in representing \r\nregional climate features in South America has increased since AR5, \r\nhowever there are still few ESD studies over the different sub-regions. \r\nPrecipitation simulations based on ESD models are able to reproduce \r\nmean precipitation over tropical and subtropical South American \r\nregions, especially over maximum precipitation areas in western \r\nColombia, south-eastern Peru, central Bolivia, Chile and the La Plata \r\nbasin (medium confidence) (Souvignet et  al., 2010; Mendes et  al., \r\n2014; Palomino-Lemus et al., 2015, 2017, 2018; Soares dos Santos \r\net al., 2016; Troin et al., 2016; Borges et al., 2017; Bettolli and Penalba, \r\n2018; Araya-Osses et  al., 2020; Bettolli et  al., 2021). Temperature \r\nsimulations are fewer but show added value to GCM simulations \r\n(medium evidence, high agreement) (Souvignet et al., 2010; Borges \r\net al., 2017; Bettolli and Penalba, 2018; Araya-Osses et al., 2020)."


id: 293a4c7473a220c9aaad995bb333eed4
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 3921
content:
"Overall, climate modelling has made some progress in the past \r\ndecade but there is no model that performs well in simulating all \r\naspects of the present climate over South America (high confidence). \r\nThe performance of the models varies according to the region, time \r\nscale and variables analysed (Abadi et al., 2018). There is also a fairly \r\nnarrow spread in the representation of temperature and precipitation \r\nover South America by the CMIP5 GCMs and also the RCMs, with \r\nbiases that can be associated with the parametrizations and schemes \r\nof surface, boundary layer, microphysics and radiation used by the \r\nmodels. Finally, observational reference datasets, such as reanalysis \r\nproducts, used in the calibration and validation of climate models can \r\nalso be quite uncertain and may explain part of the apparent biases \r\npresent in climate models (high confidence)."


id: c60d620261ba58ba305a44cbd474ff3
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 28737
content:
"Projected changes in seasonal precipitation and their uncertainties \r\ngenerally agree with the annual changes, particularly for the decreases \r\nin SWS (Figure Atlas.22). DJF precipitation changes in NSA and SAM \r\nare largely uncertain, with weak agreements in the projections, \r\nparticularly for CMIP5 and CMIP6 ensembles, which project almost \r\nno change, and decreasing precipitation for NSA and a narrow range \r\nfrom slight increases to no change respectively for SAM."


id: 4730fcab8257b092259854ca319ae9a
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 8578
content:
"It is very likely that annual mean temperature will increase over South \r\nAmerica, with a  wide range of projected changes of 1.0degC-6.0degC \r\nby the end of the 21st  century (from RCP2.6/SSP1-2.6 to RCP8.5/\r\nSSP5-8.5 emissions, Figure Atlas.22). Overall, GCMs project higher \r\ntemperature change than RCMs in austral summer and winter \r\nover all sub-regions and in winter mainly over the central part of \r\nthe continent (Interactive Atlas; Coppola et al., 2014; Llopart et al., \r\n2021; Teichmann et al., 2021). The largest warmings over the South \r\nAmerican continent are projected for the Amazon basin (SAM and \r\nNSA) and the central Andes range (southern SAM, northern SWS \r\nand south-eastern NWS; Figure Atlas.22), especially during the dry \r\nand dry-to-wet transition seasons (austral winter and spring) (high \r\nconfidence) (Blazquez and Nunez, 2013a; Coppola et  al., 2014; \r\nPabon-Caicedo et al., 2020; Teichmann et al., 2021)."


id: 30f6fa3d922738d22717c659a6e1ed04
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 5128
content:
"In summary, it is virtually certain that the climate of South America \r\nhas warmed. Studies on climate trends in South America indicate that \r\nmean temperature and maximum and minimum temperatures have \r\nincreased over the last 40 years. Long-term observed precipitation \r\ntrends show an increase over South-Eastern South America and \r\ndecreases in most tropical land regions (high confidence)."


id: ea43d4963a5c2a98bae39710d80c1891
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 34366
content:
"Evaluation of global and regional climate model simulations have \r\nincreased over South America in the past decade and shown \r\nimproved performance. However, the results reveal that no model \r\nperforms well in simulating all aspects of the present climate (very \r\nlikely). On the other hand, there is still a lack of high-quality and high\u0002resolution observational data that may explain part of the important \r\nbiases present in climate models (high confidence)."


id: b5fc6039cd2561552651a78181c0fc40
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 26162
content:
"Using warming levels (Figure Atlas.22), the temperature is projected \r\nto increase at or above the level of global warming in all regions \r\napart from SSA with additional warming (compared to a 1995-2014 \r\nbaseline) of over 4degC for the 4degC warming level in NSA and SAM. \r\nChanges for other warming levels, sub-regions and emissions \r\npathways are shown in Figure Atlas.22 and can be explored with the \r\nInteractive Atlas."


id: eea9d796170f160772b0abe85decda81
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 35020
content:
"Climate model projections show a general increase in annual mean \r\nsurface temperature over the coming century for all emissions \r\nscenarios (RCPs and SSPs) (high confidence), consistent with the \r\nobserved warming, and with all regions except SSA warming faster \r\nthan the global average. Unlike temperature, annual precipitation \r\nhas patterns of decrease in North-Eastern South America (NES) \r\nand South-Western South America (SWS), and increase in Southern \r\nSouth America (SES) and North-Western South America (NWS) "


id: bf24ab28f0b1d9370064a211038a4e74
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 27626
content:
"(high  confidence), with small changes projected under a  low\u0002emissions scenario. However, there is low confidence in the magnitude \r\nbecause of the large spread among models, both GCMs and RCMs."


id: 79bb7ef2a90c25f2052c787bf31b1f56
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 16721
content:
"warming being greatest in high latitudes in Northern Europe. Annual \r\nprecipitation has increased in Northern Europe and decreased in \r\nparts of Southern Europe. The SROCC (Hock et  al., 2019b) reports \r\nwith high confidence that a reduction in snow cover at low elevation \r\nand glacier extent is observed in recent decades, with consequent \r\nchanges in annual and seasonal runoff patterns. According to the \r\nSRCCL report (IPCC, 2019b) there is high agreement that observed \r\nvegetation greening and forestation in the last 30 years cools \r\nsummer surface temperature and warms winter temperature due \r\nto decreased snow cover and increased snow shading in forested \r\nareas. It is very likely that aerosol column amounts have declined \r\nover Europe since the mid-1980s."


id: 9af2edc5485ee2e597d93c95b1a00d9
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 21912
content:
"The assessment in this section focuses on changes in average \r\ntemperature and precipitation (rainfall and snow), including the most \r\nrecent years of observations, updates to observed datasets, the \r\nconsideration of recent studies using CMIP5 and those using CMIP6 \r\nand CORDEX simulations. Assessment of changes in extremes is \r\nin Chapter  11 (Tables 11.16-11.18) and climatic impact-drivers in \r\nChapter 12 (Table 12.7)."


id: a70de4edb14c4ddf82c80e80292452f9
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 23795
content:
"Westerly winds and the accompanying Atlantic storm track with \r\ncyclones and anticyclones travelling from the Atlantic towards \r\ninland Europe are the main climatic features that characterize daily \r\nto interannual variability in the European region. The Siberian High \r\nin winter determines cold weather in Eastern Europe and can affect \r\nother regions with cold outbreaks. Intra-seasonal and interannual \r\nvariations are driven by modes of climate variability such as the \r\nNorth Atlantic Oscillation (NAO; Table Atlas.1 and Annex IV.2). \r\nGlobal warming can lead to systematic changes in regional climate \r\nvariability via thermodynamic responses such as altered lapse \r\nrates (Kroner et al., 2017; Brogli et al., 2019) and land-atmosphere \r\nfeedbacks (Zampieri and Lionello, 2011; Boe and Terray, 2014). \r\nRegional feedbacks involving the land-sea contrast, sea surface, land \r\nsurface, clouds, aerosols, radiation and other processes modulate the \r\nregional response to enhanced warming."


id: 38a49fcdb57c9fb99d8425c0636d0c40
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 6281
content:
"Four climatic regions are defined for Europe (Figure Atlas.24). The \r\nMediterranean region (MED) in the south is characterized by mild winters \r\nand hot and dry summers (Mediterranean climate; Section 10.6.4.2). It \r\ncovers both Europe and Africa, and MED assessments in this section \r\ngenerally imply the entire MED domain unless stated otherwise. The \r\nWestern and Central Europe region (WCE) has distinct summer and \r\nwinter seasons with increasing continentality of climate eastwards. \r\nThe Northern Europe region (NEU), close to the Atlantic Ocean, is \r\ncharacterized by high humidity and relatively mild winters, and strong \r\nexposure to the Atlantic storm track. Eastern Europe (EEU) covers the \r\nwestern part of Russia and neighbouring territories and has continental \r\ncharacteristics. Many regional datasets and model projections assessed \r\nhere do not sufficiently cover the EEU region."


id: 7c36ec1093e7820018888655a0c31abc
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 17144
content:
"The AR5 WGII (Kovats et  al., 2014) reports with high confidence\r\nthat observed climate trends show regionally varying changes \r\nin temperature and rainfall in Europe. The average temperature \r\nin Europe has continued to increase, with seasonally different rates of "


id: 5d6984d99d1aaa02ea565d06360ad07c
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 12223
content:
"is shown to add accuracy and reliability in comparison to global \r\nreanalysis products, but also introduces additional uncertainties, \r\nespecially for threshold-based climate indices (Kaiser-Weiss et  al., \r\n2019). However, gridded European datasets are unreliable over data\u0002sparse regions. Also, many datasets employ different approaches \r\nto interpolation and gridding, which adds to their uncertainty and \r\ncomplicates comparative evaluations (Fantini et al., 2018; Kotlarski \r\net  al., 2019; Berthou et  al., 2020). For some sub-regions and \r\nperformance metrics, differences between datasets have been shown \r\nto be of the same magnitude as errors in regional climate models \r\n(Prein et al., 2016; Prein and Gobiet, 2017; Fantini et al., 2018), but \r\nobservational uncertainty is substantially reduced when datasets of \r\nsimilar nature and representativeness are used (Kotlarski et al., 2019)."


id: cdde100c43fcc7aff0872c302e94e18e
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 29906
content:
"more frequent and severe meteorological droughts have occurred \r\nbetween 1960 and 2000 (Spinoni et al., 2015a; Gudmundsson and \r\nSeneviratne, 2016), and in some regions cannot be explained without \r\nanthropogenic forcing (Section 10.4.1.2; Knutson and Zeng, 2018). \r\nOther studies suggest that this trend can be seen as an expression of \r\nmulti-decadal internal variability driven mainly by the North Atlantic \r\nOscillation (Table Atlas.1; Kelley et  al., 2012; Zittis, 2018). Global \r\ndimming and brightening also are reported to affect precipitation \r\ntrends in the Mediterranean region (Section 8.3.1.6 and Figure 8.7)."


id: e12d9b8a89a77c6b0870d0bb337e6e4d
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 32925
content:
"In addition to the global display of observed temperature and \r\nprecipitation trends in Figure Atlas.11, annual mean temperature \r\nand precipitation trends between 1980 and 2015 calculated from \r\nthe gridded ensemble E-OBS dataset (Cornes et al., 2018) are shown \r\nin Figure Atlas.23, together with time series of temperature and \r\nprecipitation anomalies relative to the 1980-2015 mean value from \r\nE-OBS, CRU, EWEMBI and Berkeley for temperature, and E-OBS, CRU, \r\nGPCC and GPCP for precipitation (see also Figure  2.11 for global \r\nmean values, and Atlas.1.4.1 for description of global datasets)."


id: e098688d76880f0b8dc9f225166052b7
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 32836
content:
"Multi-decadal trends in mean precipitation are generally small and \r\nnon-significant. Apart from difficulties related to observational \r\ncoverage (Prein and Gobiet, 2017), gauge undercatch (e.g., Murphy \r\net  al., 2020) and data inhomogeneity (e.g.,  Camuffo et  al., 2013), \r\nstrong interannual and multi-decadal variability is dominant over \r\nat least the last two centuries. However, significant precipitation \r\ntrends have been recorded for recent periods, for example in south\u0002western Europe between 1960 and 2000 (Pena-Angulo et al., 2020), \r\nand between 1961 and 2015 in NEU (Interactive Atlas). Also, some \r\nstudies suggest that in the MED precipitation has declined and "


id: 721a2f1232f103ea56093f235928a12b
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 15580
content:
"The increasing trend in surface shortwave radiation, documented in \r\nAR5 (Hartmann et al., 2013) to have occurred since the 1980s and \r\nreferred to as a  brightening effect, is substantiated over Europe \r\nand the Mediterranean region (Nabat et al., 2014; Sanchez-Lorenzo \r\net  al., 2015; Cherif et  al., 2020). This increasing trend has been \r\nattributed to the decrease in anthropogenic sulphate aerosols over \r\nthe 1980-2012 period (Nabat et  al., 2014). In model sensitivity \r\nexperiments, the aerosol trend has been quantified to explain \r\n81 +- 16% of the European surface shortwave trend and 23 +- 5% "


id: bb99562d2e4edef9fcb74d50bfe35a5c
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 27072
content:
"Assessments of observed European trends in meteorological extremes \r\nand CIDs are reported elsewhere in this report. Section  11.3.5 \r\ndocuments and attributes an increase in the frequency and extent \r\nof heatwaves and daily maximum temperatures, and Section 11.6.2 "


id: e328455d05fefca084fb064df40918db
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 33233
content:
"of the European surface temperature warming. It is likely that \r\ntrends in anthropogenic aerosols in Europe have generated positive \r\ntrends  in shortwave radiation and surface temperature since the \r\n1980s (Sections 6.3.3.1, 8.3.1.6 and 10.6.4)."


id: 8115101a041c278247a3e330244bd002
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 17833
content:
"discusses the uncertainty concerning the detection of trends in \r\nmeteorological droughts, and the role of increasing atmospheric \r\nevaporative demand on hydrological and ecological/agricultural \r\ndroughts. Section  8.3.1.8 reports on increasing aridity trends in \r\nthe Mediterranean related to soil moisture declines and increases \r\nin atmospheric water vapor demand. Section 11.4.2 reports on the \r\nincreased likelihood and intensity of daily precipitation extremes, \r\nwhile Sections 11.5.2 and 12.4.5.2 discuss implications for peak \r\nstreamflow. Section  12.4.5.5 discusses the increased likelihood of \r\nwildfires, while Section  12.4.5.3 discusses the substantial decadal \r\nvariability in mean wind speed and the trends in wind storms and \r\ngusts. The acceleration of sea level rise in the Atlantic and European \r\nseas has been discussed in Section 12.4.5.5."


id: 5566cc4540f03c46142e9b753c139364
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 10906
content:
"Nabat et al. (2014) argued that including realistic aerosol variations \r\nenables climate models to correctly reproduce the summer warming \r\ntrend (as is required for attributing continental annual temperature \r\ntrends, Section  3.3.1.1). However, other studies showed models to \r\nbe sensitive also to local effects, such as land surface processes, \r\nconvection, microphysics and snow albedo (Vautard et  al., 2013; \r\nDavin et al., 2016). In Euro-CORDEX the warm and dry summer bias \r\nover southern and south-eastern Europe is reduced compared to \r\nthe previous ENSEMBLES simulations (Katragkou et al., 2015; Giot \r\net al., 2016; Prein and Gobiet, 2017; Dell'Aquila et al., 2018). Natural \r\nvariability has strongly affected the historical warming and large \r\nensembles are necessary for a correct estimation of the forced signal \r\nversus natural variability (Aalbers et al., 2018; Lehner et al., 2020)."


id: 8a7c16e0bdf415a0159f7ea76ee185a6
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
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vector_id: 19325
content:
"Specific assessments of convection-permitting RCMs (CPRCMs, \r\nrunning at a resolution of typically 1 to 3 km and designed for extreme \r\nprecipitation characteristics) is undertaken in Section  10.3.3.4.1. \r\nA  unique CPRCM ensemble has been applied over the great \r\nAlpine domain and improves representation of mean and extreme \r\nprecipitation compared to coarser resolution models (Ban et  al., \r\n2021; Pichelli et al., 2021). The role of aerosol forcing is increasingly \r\nanalysed as new and more realistic aerosol datasets become available \r\n(Nabat et  al., 2013; Pavlidis et  al., 2020), and as RCMs begin to \r\ninclude interactive aerosols (Nabat et  al., 2012, 2015, 2020; Druge \r\net al., 2019). Explicitly accounting for aerosol effects in RCMs leads to \r\nimproved representation of the surface shortwave radiation at various \r\nscales: long-term means (Gutierrez et al., 2018), day-to-day variability \r\n(Nabat et al., 2015), and long-term trends (Nabat et al., 2014)."


id: 45a442f1fa1062d9e92af44bc1535fd7
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 8361
content:
"A global evaluation of annual mean temperature and precipitation \r\nfrom the CMIP6 ensemble is presented in Sections 3.3.1 and \r\n3.3.2 respectively. In general, annual mean temperature is slightly \r\nunderestimated at high latitudes and overestimated in the MED area. \r\nTemporal evolution of decadal temperature oscillations in Europe \r\nsimulated by the CMIP6 historical simulations is well reproduced \r\n(Fan et al., 2020). Fernandez-Granja et al. (2021) report an overall \r\nimprovement of CMIP6 compared to CMIP5 to reproduce atmospheric \r\nweather patterns over Europe."


id: f279469372b4112e74745e61253a6db3
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 35672
content:
"Statistical downscaling methods are assessed in Section  10.3.3.7, \r\nincluding the intercomparison and evaluation activities performed in \r\nthe framework of VALUE and Euro-CORDEX over Europe."


id: d9dc138ee59a64e78d651225be13e776
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 31792
content:
"In line with findings reported in Section  10.3.3.8, several studies \r\nargue that both GCMs and RCMs underestimate the observed \r\ntrend in European summer temperature (Dosio, 2016; Boe et  al., \r\n2020b), indicating that essential processes are missing or that the \r\nnatural variability is not correctly sampled (Dell'Aquila et al., 2018). "


id: 689d950dc4189cf89a6778d5e7581ad0
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 14051
content:
"Simulations from CMIP5 and CMIP6 indicate pronounced \r\ngeographical patterns and scenario dependence of the projections of \r\nmean temperature and precipitation. Global warming projected under "


id: aec7d653df58c1aacdf34191b7fcd791
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 25030
content:
"low confidence in the CORDEX simulations. Precipitation in JJA in \r\nEEU is reduced in CMIP6, while little change is shown in CMIP5. \r\nQuantitative estimations of climate change features from regional \r\nclimate projections in Eastern Europe (Partasenok et  al., 2015; \r\nKattsov et al., 2017) have low confidence due to the use of relatively \r\nsmall ensembles of GCMs and/or RCMs, and limited evaluation of \r\nmodel performance in the region."


id: 4b6ac89b5cdd61eeb0969f64aaed9776
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 9279
content:
"Over specific geographic features such as high mountains, RCMs \r\nfurther modify the climate change signal of precipitation simulated \r\nby the low-resolution GCMs (Giorgi et al., 2016; Torma and Giorgi, \r\n2020). This is especially true for summer precipitation over the Alps \r\nwhere opposite signs of changes in mean and extreme precipitation \r\nare generated by the CMIP5 GCM ensemble and the 12-km \r\nMed-CORDEX and EURO-CORDEX RCM ensembles (Section 10.6.4.7; \r\nGiorgi et al., 2016)."


id: 79a37270372bd637545a047f02936386
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 16706
content:
"Regional ocean warming in projections with RCSMs for the Baltic and \r\nNorth seas (Groger et al., 2015) and for the Mediterranean (Darmaraki \r\net al., 2019) is associated with increased intensity and frequency of \r\nmarine heatwaves in the Mediterranean (Section  12.4.5.5), strong \r\nfreshening in the Baltic, and, for some simulations, changes in the \r\ncirculation in response to non-uniform changes in air-sea interaction \r\n(Dieterich et al., 2019). Med-CORDEX RCSM and CMIP5 GCM results \r\nagree well on the Mediterranean SST warming rate (Mariotti et al., \r\n2015; Darmaraki et al., 2019); see also the Interactive Atlas."


id: d19ba603011889c8390f727c21ca96b9
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 30509
content:
"Assessments of projected changes in meteorological extremes and \r\nCIDs are reported elsewhere in this report. Extreme precipitation \r\nand  temperature often exhibit a  different response to global \r\nwarming than mean values. Increased intensity and frequency of \r\nextreme temperatures and heatwaves is assessed in Sections 11.3.5 \r\nand 12.4.5.1. Changes in the hydrological cycle include enhanced \r\nsoil moisture decline in southern Europe, drying in summer and \r\nautumn in Central Europe, and spring drought due to early snowmelt \r\nin Northern Europe (Sections 8.4.1, 11.6.5 and 12.4.5.2). Changes in \r\nmean and extreme wind are very uncertain (Section 12.4.5.3), while \r\nsea level rise will increase the frequency of occurrence of extreme sea \r\nlevel at most European coasts (Section 12.4.5.5)."


id: 6237675dbf41e7ce3ed287bba5896c7a
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 13006
content:
"Changes in precipitation clearly show a  seasonal signature and \r\na  meridional gradient over Europe. Mean precipitation increases \r\nby 4-5% per degC of global warming in NEU, EEU and WCE in DJF, \r\nand decreases in summer in WCE and MED (Figure Atlas.24; Jacob \r\net al., 2018). CMIP5 projections of precipitation change in MED are \r\nstrongest in DJF in the south, while changes in JJA are dominant in \r\nthe northern (European) part of MED (Lionello and Scarascia, 2018). \r\nThe European north-south gradient in precipitation response is \r\nconfirmed by the EURO-CORDEX experiment (Coppola et al., 2021a), \r\nbut Figure Atlas.24 shows that the JJA precipitation reduction in \r\nWCE projected by CMIP5 and CMIP6 at higher warming levels has "


id: 3e7eb2a271332f0b71d0227c1f071144
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 7212
content:
"It is virtually certain that annual mean temperature continues to \r\nincrease in each European region. There is medium confidence that \r\nannual mean precipitation in NEU, WCE and EEU has increased since \r\nthe early 20th  century. In the European Mediterranean trends in \r\nannual mean precipitation contain substantial spatial and temporal \r\nvariability (medium confidence). It is very likely that since the early \r\n1980s in snow-dominated areas in NEU and EEU the length of the \r\nsnowfall season is reduced with regional warming, and the melt \r\nonset dates have advanced. It is likely that decreasing trends in \r\nanthropogenic aerosols in Europe have generated positive trends \r\nin shortwave radiation and surface temperature since the 1980s."


id: 4a4eb01342a92af9d52fb66d28135a6c
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 9097
content:
"An assessment of recent literature largely confirms the findings of \r\nprevious IPCC reports but with additional detail and (in some cases) \r\nhigher confidence due to improvements in observations, reanalyses \r\nand methods. Observational datasets with global coverage are \r\ncomplemented by the E-OBS gridded ensemble temperature and \r\nprecipitation dataset, a  range of regional observational analyses, \r\nand regional reanalysis products. New RCM experiments, including \r\nCPRCMs and regional coupled climate system models, mostly \r\ncoordinated under the umbrella of CORDEX, have generated many \r\nnew projections and process studies."


id: 770085c587a3f99d870dd533e3345cc0
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 16296
content:
"At increasing levels of global warming, there is very high confidence \r\nthat temperature will increase in all European areas at a  rate \r\nexceeding global mean temperature increases, while increased \r\nmean precipitation amounts at high latitudes in DJF and reduced JJA \r\nprecipitation in southern Europe will occur with medium confidence\r\nfor global warming levels below 2degC, and with high confidence for \r\nhigher warming levels. At high latitudes and low-altitude mountain \r\nareas in Europe strong declines in snow accumulation are virtually \r\ncertain to occur with further increasing regional temperatures (very \r\nhigh confidence)."


id: 2789da946b67ada61d10ae5c1142b6ba
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 3625
content:
"The representation of mean European climate features by GCMs and \r\nRCMs is improved compared to previous IPCC assessments (medium \r\nconfidence), in spite of persisting biases in annual mean and seasonal \r\ntemperature and precipitation characteristics. The added value of \r\nregional downscaling of GCMs by RCM projections for summer mean \r\ntemperature, precipitation and shortwave radiation is constrained \r\nby the representation of processes that lead to a  systematic \r\ndifference between RCM and driving GCM, such as aerosol forcing \r\n(medium confidence)."


id: e8914465160d84048f55aab275751ca9
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 34098
content:
"causes (high confidence), in particular with primarily increasing \r\nannual precipitation and annual temperature (very high confidence). \r\nAssessment of CMIP5 ensemble projections concluded that mean \r\nannual temperature over North America and annual precipitation \r\nnorth of 45degN will very likely continue to increase in the future. Also, \r\nCMIP5 projects increases in winter precipitation over Canada and \r\nAlaska and decreases in winter precipitation over the south-western \r\nUSA and much of Mexico."


id: 8cc72376c64cbb800854460028850c13
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 19666
content:
"The assessment in this section focuses on changes in average \r\ntemperature and precipitation (rainfall and snow) for North America, \r\nincluding the most recent years of observations, updates to observed \r\ndatasets, the consideration of recent studies using CMIP5 and those \r\nusing CMIP6 and CORDEX simulations. Assessment of changes in \r\nextremes is in Chapter  11 (Tables 11.19-21) and climatic impact\u0002drivers in Chapter 12 (Table 12.8)."


id: 8dbcbc020e5299b1359f8207c69ed3ce
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 19825
content:
"The CMIP5 multi-model ensemble generally reproduces the observed \r\nspatial patterns but somewhat underestimates the extent and \r\nintensity of the North American Monsoon, and also underestimates \r\nwetting over Central North America over the period of 1950-2012 \r\nduring the winter season according to AR5 (Flato et  al., 2013). In \r\nthe long term (2081-2100), the largest changes of precipitation over \r\nNorth America are projected to occur in the mid- and high latitudes \r\nand during winter (Kirtman et al., 2013)."


id: 5202f68c7830dbd4925600b98cf10c13
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 10356
content:
"The SR1.5 (Hoegh-Guldberg et al., 2018) reported a stronger warming \r\ncompared to the global mean over Central and Eastern North \r\nAmerica, and a weakening of storm activity over North America under \r\n1.5degC of global warming. The SROCC (Hock et al., 2019b) reported \r\nthat snow depth or mass is projected to decline by 25% mainly at \r\nlower elevations over the high mountains in Western North America. \r\nThe SRCCL (Mirzabaev et al., 2019) observed vegetation greening in \r\nCentral North America with high confidence."


id: 8922595d6a21077b11b19ec41ccdfa05
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 19115
content:
"Compared to temperature, trends in annual precipitation over \r\n1961-2015 are generally non-significant though there are consistent \r\npositive trends over parts of ENA and CNA (Figure Atlas.11 and Daymet, \r\nInteractive Atlas) (high confidence). The global and regional datasets \r\nin Figure Atlas.11 and the Interactive Atlas also indicate significant \r\ndecreases in precipitation in parts of south-western USA and north\u0002western Mexico (Figure 2.15) though these are not all spatially coherent \r\nso there is only medium confidence in a drying trend over this region."


id: 7712259c041ecc8283e1cfc7955976f0
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 16301
content:
"The IPCC AR5 (Bindoff et al., 2013; Hartmann et al., 2013) found that \r\nthe climate of North America has changed due to anthropogenic "


id: 734b5afb064aaf4e17e24741a2a8a35e
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 15757
content:
"spring snowmelt in northern Canada was driven by warming-induced \r\nhigh-latitude changes such as atmospheric moisture, cloud cover, and \r\nenergy advection (Mioduszewski et al., 2014). "


id: ca04c277bc540bda0ec678a069cde093
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 29339
content:
"Several factors account for the differences in temperature and \r\nprecipitation trend significance. Observed trends in precipitation are \r\nrelatively modest compared to the very large natural interannual \r\nvariability of precipitation. Furthermore, the precipitation observing \r\nnetwork is spatially inadequate (Section  10.2.2.3) and temporally \r\ninconsistent (Section 10.2.2.2) over some regions of North America, \r\nparticularly over the Arctic and mountainous areas. So detection of \r\nmulti-decadal trends is difficult, especially for regions with summer \r\nconvective precipitation maxima that may be spatially patchy \r\n(Easterling et  al., 2017). See Section  2.3 for further discussion of \r\nprecipitation trends."


id: db53e041a22473e281bdd9b2278d855
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 32013
content:
"In an analysis of drivers of the record low snow water equivalent \r\n(SWE) values of spring 2015 in the western USA, it was found that the \r\nrelative importance of greenhouse gases varies spatially (Mote et al., \r\n2016). See also Section 3.4.2 for further discussion of anthropogenic \r\ninfluences on snow extent."


id: 9c0d902ef81032ca7516f802bd2b92d6
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 22110
content:
"Rupp et al. (2013) applied a standard fingerprinting approach to CMIP5 \r\nmodels and determined that the decline in Northern Hemisphere \r\nspring snow cover extent could only be explained by simulations \r\nthat included natural and anthropogenic forcing. In an attribution \r\nstudy focusing on direct physical causes, it was found that increased "


id: 48feaf45bd95ee0d497fc203dd9271c3
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 8868
content:
"North America has been extensively used as a test bed for regional \r\nclimate model (RCM) experiments, such as the North American \r\nRegional Climate Change Assessment Program (NARCCAP; \r\nMearns et al., 2009), the MultiRCM Ensemble Downscaling (MRED; "


id: 40668ad3e931496c9baeb779aa52e428
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 7517
content:
"However, deficiencies have also been reported. For example, excessive \r\nstorm occurrence over the east coast of North America was found \r\n(Poan et  al., 2018), and amplitude in the simulated annual cycle \r\nwas generally excessive in NA-CORDEX simulations. RCMs tend to \r\nproduce more (less) precipitation over mountains (the coastal plains; \r\nCerezo-Mota et  al., 2016) and winter precipitation in the western \r\nUSA had large positive biases in all RegCM simulations, regardless of \r\nthe driving GCM (Mahoney et al., 2021)."


id: 5d30e31e8ae028f21b7f20b3cd99698e
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 12178
content:
"Recently, convective-permitting RCMs have been used to simulate \r\nNorth American climate features and generated better simulations \r\nof precipitation. For example, summer precipitation over the \r\nsouth-western USA was improved due to better representation \r\nof organized mesoscale convective systems at the sub-daily scale \r\n(Castro et al., 2012; Liu et al., 2017; Prein et al., 2017a; Pal et al., \r\n2019), the diurnal cycle of convection (Nesbitt et  al., 2008), and \r\nin terms of means (and extremes) for the north-eastern USA \r\n(Komurcu et al., 2018)."


id: b86bc81af3a6ae8683b704ae289a9aba
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 26544
content:
"projecting increases. Projections from the NA-CORDEX ensemble are \r\nconsistent with those from the GCMs whilst providing greater detail \r\nof precipitation changes over the mountains and along the coasts \r\n(Interactive Atlas; Bukovsky and Mearns, 2020). Similar results are \r\nfound in other analyses of RCM projections (Wang and Kotamarthi, \r\n2015; Ashfaq et  al., 2016; Teichmann et  al., 2021). Also, further \r\nanalysis of the NA-CORDEX projections showed substantial changes \r\nin weather types related to increased monsoonal flow frequency and \r\ndrying of the northern Great Plains in summer (Prein et al., 2019)."


id: b586648e11c1598010d960d95be92274
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 26090
content:
"There are striking contrasts in the seasonal results for precipitation \r\nfor the sub-regions (Figure Atlas.26). The northern regions and \r\nENA all show steady increases with the global warming levels \r\n(very high confidence). For example, the projected increases in the \r\nNEN region range from 7% in the near term to 40% at the end of \r\nthe 21st  century for the SSP5-8.5 scenario. In contrast, projected \r\nchanges for NCA are for significant decreases both on an annual \r\nbasis (Interactive Atlas) and in winter, and which become greater as \r\nwarming increases (Akinsanola et al., 2020b; Almazroui et al., 2021). \r\nThe other two regions (WNA and CNA) exhibit mainly increases in \r\nwinter. In summer, distributions are in general less uniform except \r\nfor NWN and NEN, which display steady increases with global \r\nwarming levels (but smaller than in winter). WNA and CNA mainly \r\nshow decreases (based on the median values) but with some models "


id: ddf3e9b1f3d774ce5bc6d442b91c4749
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 32418
content:
"In summary, NEN, NWN and most of ENA will very likely experience \r\nincreased annual mean precipitation, with greater increases at \r\nhigher levels of warming (very high confidence). In NCA decreases \r\npredominate on an annual basis and particularly in winter "


id: d47521b63cbe950767854d1aa5d0c05c
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 30949
content:
"for example in the western USA (Fyfe et al., 2017). It is, however, likely\r\nthat some high-latitude regions will rather experience an increase in \r\ncertain winter snow cover properties (Mudryk et al., 2018; McCrary \r\nand Mearns, 2019), due to snowfall increase (Krasting et al., 2013) \r\nprevailing over the warming effect. Discussion of changes in snow \r\nin the future is also covered in Section 9.5.3, but for larger regions."


id: d8661c8e967fdf326e797adee6cc6064
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 31571
content:
"Across North America it is very likely that positive surface temperature \r\ntrends are persistent. Across near-Arctic latitudes of North America, \r\nincreases are exceptionally pronounced, greater than 0.5degC per \r\ndecade (high confidence). In parts of Eastern and Central North \r\nAmerica it is likely that annual precipitation has increased over the \r\nperiod 1961-2015 but with no clear trends in other regions except \r\nfor parts of the south-western USA and north-western Mexico where \r\nthere is medium confidence in drying."


id: 15d0813cb482d27b813215d17edcf81e
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 954
content:
"(high confidence). Projected changes in summer are highly uncertain \r\nthroughout other regions apart from the far northern parts of NEN \r\nand NWN which will likely experience increases (high confidence)."


id: f34f32e56a196b2613a4f4440c6c6f48
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 35789
content:
"Model representation of the climatology of mean temperature \r\nand precipitation has likely improved compared to AR5 over North \r\nAmerica. This is aided by continuous model development, and \r\nthe existence of new coordinated modelling initiatives such as \r\nNA-CORDEX. There is high confidence that downscaling a  subset \r\nof CMIP models that spans the range of climate sensitivities in the \r\nfull ensemble is critical for producing a  representative range of \r\ndynamically downscaled projections."


id: d2a2bdcd539af9b4a3f9572a7e3bb374
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 30660
content:
"It is virtually certain that annual and seasonal surface temperatures \r\nover all of North America will continue to increase at a rate greater \r\nthan the global average, with greater increases in the far north. It is \r\nvery likely, based on global and regional model future projections, \r\nthat on an annual time scale precipitation will increase over most of \r\nNorth America north of about 45degN and in Eastern North America, \r\nand it is likely that it will decrease in the south-western USA and \r\nnorthern Mexico, particularly in winter. Elsewhere the direction of \r\nchange of precipitation is uncertain. It is virtually certain that snow \r\ncover will experience a decline over most regions of North America \r\nduring the 21st  century, in terms of water equivalent, extent and "


id: 5c0d17039abf15a68ee87b808189ec4a
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 11989
content:
"It is virtually certain that snow cover will experience a general decline \r\nacross North America during the 21st  century, in terms of extent, \r\nannual duration and SWE, based on CMIP5 (Maloney et al., 2014), \r\nCMIP6 (Mudryk et al., 2020), NA-CORDEX (Mahoney et al., 2021) and \r\nNARCCAP (e.g., McCrary and Mearns, 2019) simulations. For some \r\nregions the decline could be discernible over the next few decades, "


id: e881238b39eac0785fc515e0d6e9b27f
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 34086
content:
"annual duration. It is, however, likely that some high-latitude regions \r\nwill rather experience an increase in winter SWE, due to the snowfall \r\nincrease prevailing over the warming effect."


id: 9540968d73de58fd5ca5cc022dc47c0c
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
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content:
"The assessment in this section focuses on changes in average \r\ntemperature and precipitation for the main Small Islands regions, \r\nincluding the most recent years of observations, updates to observed \r\ndatasets, the consideration of recent studies using CMIP5 and those \r\nusing CMIP6 and CORDEX simulations. Assessment of changes in \r\nextremes is in Chapter 11 (Sections 11.3.2, 11.4.2, 11.7.1.5 and, for \r\nthe Caribbean, Tables 11.13-15) and of changes in climatic impact\u0002drivers in Chapter 12 (Section, 12.4.7 and Table 12.9)."


id: af4cf4e04c36ff4415cee0d01f994ea2
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
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content:
"Significant positive trends in temperature ranging from 0.15degC per \r\ndecade (over the period 1953-2010) to 0.18degC per decade (over \r\nthe period 1961-2011) are noted in the tropical western Pacific, \r\nwhere the significant increasing and decreasing trends in warm and \r\ncool extremes, respectively, are also spatially homogeneous (Jones \r\net al., 2013; Whan et al., 2014; Wang et al., 2016). Similarly, much of \r\nthe Caribbean region showed statistically significant warming (at the \r\n95% level) over the period 1901-2010 (P.D. Jones et  al., 2016b). \r\nObservation records in the Caribbean region indicate a  significant \r\nwarming trend of 0.19degC per decade and 0.28degC per decade in daily \r\nmaximum and minimum temperatures, respectively, with statistically \r\nsignificant increases (at the 5% level) in the number of warm \r\ndays and warm nights during 1961-2010 (M.A. Taylor et al., 2012; \r\nStephenson et al., 2014; Beharry et al., 2015)."


id: 3cbefd8163acbd185a6bc9ed34481bc1
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
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vector_id: 6924
content:
"A weather station-based annual precipitation trend analysis over \r\n1901-2010 in the Caribbean region indicated some locations with \r\ndetectable decreasing trends (Knutson and Zeng, 2018), which \r\nwere attributable in part to anthropogenic forcing. These include \r\nsouthern Cuba, the northern Bahamas, and the Windward Islands, \r\nalthough significant trends were not found over the shorter periods \r\nof 1951-2010 and 1981-2010. In the Caribbean islands, a dataset \r\nof the Palmer Drought Severity Index (PDSI) from 1950 to 2016 \r\nshowed a clear drying trend in the region (Herrera and Ault, 2017). \r\nThe 2013-2016 period showed the most severe drought during the "


id: 2b4f129dfb6a000634c7dec5e04a4292
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 4252
content:
"The AR5 noted observed temperature increases of 0.1degC-0.2degC \r\nper decade in the Pacific Islands and that warming was very likely\r\nto continue across all Small Islands regions (Christensen et  al., \r\n2013; IPCC, 2013a). It also reported decreased rainfall over the \r\nCaribbean, increases over the Seychelles, streamflow reductions over \r\nthe Hawaiian Islands and projections of reduced rainfall over the \r\nCaribbean and drier rainy season for many of the south-west Pacific \r\nIslands (Christensen et al., 2013; IPCC, 2013a; Nurse et al., 2014). \r\nThe remaining findings are derived from the SROCC (IPCC, 2019a). "


id: f59c8f6037f411c7c3bf3210dad304c8
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
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vector_id: 36164
content:
"topography and land use may result in changes in climatic conditions \r\nover small distances (Foley, 2018), making the observational density \r\nparticularly relevant."


id: 97328b05077543d6d2038fc688a2a432
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
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vector_id: 21301
content:
"Moreover, many stations have little metadata available, including \r\nthose in Vanuatu, the Solomon Islands and Papua New Guinea \r\n(Whan et al., 2014). Compared to earlier decades, few metadata are \r\ncurrently being documented in the western Pacific islands (McGree \r\net  al., 2014), which will challenge the homogenization of long\u0002term observational records. Challenges in the Caribbean include \r\nmaintaining continuous daily time series with metadata, converting \r\nclimatological data into digital formats and making them freely \r\navailable (Stephenson et  al., 2014; Beharry et  al., 2015; P.D. Jones \r\net al., 2016a). This is also an issue in the Pacific as many data are \r\nkept in national (local) databases, with only a fraction having been \r\nincorporated into global datasets (Whan et al., 2014)."


id: 7567c69288a8e24fc9f5e92ba9776ecb
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
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vector_id: 16056
content:
"Because of the small number of stations used for interpolation \r\nand the complex mountainous topography, gridded product for \r\nthese small islands should be interpreted with caution (Frazier and \r\nGiambelluca, 2017). For the Antilles, the error in estimating CRU2.0 \r\nmonthly precipitation can stand locally between 20% and 40%. \r\nOver the Caribbean, Cavazos et  al. (2020) found a  discrepancy \r\nacross gridded observational datasets (CRU, CHIRPS and GPCP) \r\nin detecting orographic precipitation, especially during boreal \r\nsummer, making their use in climate model evaluation challenging \r\n(Herrera and Ault, 2017). Furthermore, some reanalysis products \r\nsuch as the 0.7deg x 0.7deg ERA-Interim reanalysis are not adequate as \r\nmany of the smaller Caribbean islands are not represented as land \r\n(P.D. Jones et al., 2016a)."


id: 40b133216b4a23536a5618c61ccad760
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
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content:
"In Hawaii, between 1920 and 2012, over 90% of the islands showed \r\nreduced rainfall and streamflow, an increase in the frequency of days \r\nwith zero flow (Strauch et al., 2015; Frazier and Giambelluca, 2017), \r\nand robust positive trends in drought frequency and severity (McGree \r\net  al., 2016). Over the western Pacific, interannual and decadal \r\nvariabilities also drive long-term trends in rainfall. Recent analysis \r\nof station data showed spatial variations in the mostly decreasing \r\nbut non-significant trends in annual and extreme rainfall over the \r\nwestern Pacific from 1961 to 2011 (low confidence) (McGree et al., \r\n2014). Over the southern subtropical Pacific, decreases in annual, JJA, \r\nSON and extreme rainfall, and increasing drought frequency in the \r\nwestern region, has been observed since 1951 (Jovanovic et al., 2013; \r\nMcGree et al., 2016, 2019)."


id: 2963eece678cb85ba5e20280916538fb
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
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content:
"An assessment of model performance for the Caribbean region is \r\ncontained in Atlas.7.1 on Central America. In summary, the ability of \r\nclimate models to simulate the climate over the region has improved \r\nin many key respects with the application of increased model \r\nresolution and a better representation of the land surface processes \r\nof particular importance in these advances (high confidence). \r\nRegional climate models (RCMs) simulate realistically seasonal \r\nsurface temperature and precipitation patterns including the bimodal \r\nrainfall in the precipitation annual cycle although with some timing \r\nbiases in some regions (high confidence). The important regional \r\ncirculation and precipitation features, the Caribbean low-level jet and \r\nthe midsummer drought (MSD), are well represented over a variety of \r\nRCM domains covering the region (high confidence)."


id: d79bef3a1ea67389437c49188b52d6a2
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
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vector_id: 31446
content:
"Over the western Indian Ocean significant warming trends have been \r\nreported for Mauritius (1.2degC during 1951-2016; MESDDBM, 2016), \r\nLa Reunion (0.18degC per decade over 1968-2019; Meteo-France, 2020) \r\nand Maldives (MEE, 2016). Both Mauritius and La Reunion have \r\nexperienced rainfall decreases of 8% during 1951-2016 and 1.2% \r\nper decade during 1961-2019 with generally weak, non-significant \r\nrainfall trends during 1967-2012."


id: f28f9a30481b49a4f434bdeb6ad56481
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
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vector_id: 35689
content:
"Assessing observed climate change for Small Islands is often \r\nconstrained by low station density (Ryu and Hayhoe, 2014; \r\nP.D.  Jones et  al., 2016a), digitization requirements or data-sharing \r\nlimitations (P.D. Jones et  al., 2016a). Station data typically have \r\nlonger temporal coverage relative to satellite products but are \r\nlimited in spatial coverage (Cavazos et al., 2020). For Small Island \r\nnations, spatial  gaps between observations can be very large due \r\nto the isolation of the islands (Wright et  al., 2016). Additionally, \r\nover past decades, the number of station observations has declined \r\nsubstantially in Mauritius (Dhurmea et al., 2019), Hawai'i (Bassiouni \r\nand Oki, 2013; Frazier and Giambelluca, 2017) and most Pacific \r\nIsland countries since the 1980s (Jones et al., 2013; McGree et al., \r\n2014, 2016). In Fiji, meteorological stations were located on or by \r\nthe coast and are sparse in the interior (Kumar et al., 2013). Notable "


id: 645f30177dde0f0cd70ad012a91fc88e
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
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vector_id: 13361
content:
"Over the tropical Pacific, surface temperature biases in CMIP6 models \r\nremain similar to those in CMIP5, although are reduced in the higher\u0002resolution models in the HiResMIP ensemble. CMIP6 models generally \r\nrepresent trends in sea surface temperatures better than CMIP5 \r\n(see Section 9.2.1 for more details). For precipitation, the persistent \r\ntropical Pacific bias of the double ITCZ (erroneous bands of excessive \r\nrainfall both sides of the equatorial Pacific) is still present in CMIP6 \r\nmodels although is slightly improved compared to those in CMIP3 \r\nand CMIP5 models (Section  3.3.2.3). Application of downscaling "


id: 22c68a65d04e6b6a360f67383edf7384
created_at: 2023-03-16 14:00:52
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content:
"techniques (RCMs and stretched-grid GCMs) using resolutions finer \r\nthan 10  km over the Pacific can capture topographic influences \r\non wind and rainfall to generate realistic simulations of island \r\nclimates - for example over Fiji and New Caledonia (Chattopadhyay \r\nand Katzfey, 2015; Dutheil et  al., 2019). In both cases applying \r\nbias adjustment to the sea surface temperatures used as a  lower \r\nboundary condition for the downscaling models was important to \r\ngenerate realistic simulations."


id: 84d4579ef3c437a36da79cffa7c9c209
created_at: 2023-03-16 14:00:52
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vector_id: 18424
content:
"Projected median temperature increases for Small Islands from the \r\nCMIP5 ensemble range from 1degC (RCP4.5) to 1.5degC (RCP8.5) in \r\nthe period 2046-2065, and from 1.3degC (RCP4.5) to 2.8degC (RCP8.5) \r\nby 2081-2100 relative to 1986-2005 (Harter et  al., 2015). Spatial \r\nvariations in the warming trend are projected to increase by the \r\nend of the 21st century, with relatively higher increases in the Arctic "


id: 2d9a5d6d3c52b6c38fb2dc45fdce9b09
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
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content:
"and sub-Arctic islands, and in the equatorial regions compared with \r\nislands in the Southern Ocean (Harter et al., 2015). In the western \r\nPacific, temperatures are projected to increase by 2.0degC-4.5degC by \r\nthe end of the 21st  century relative to 1961-1990 (Wang et  al., \r\n2016). The warming over land in the Lesser Antilles is estimated \r\nto be about 1.6degC (3.0degC) by 2071-2100 for the RCP4.5 (RCP8.5) \r\nscenario, relative to 1971-2000 (Cantet et  al., 2014). Projections \r\nfrom the CMIP6 ensemble support these findings (Figure Atlas.28) \r\nand across global warming levels from 1.5degC to 4degC CMIP5 and \r\nCMIP6 consistently project lower levels of warming for Small Islands \r\nthan the global average (Interactive Atlas)."


id: 68cf5ff1bff37d8b120e97ec2f776b53
created_at: 2023-03-16 14:00:52
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vector_id: 14079
content:
"It is very likely that all Small Island regions have warmed with significant \r\ntrends recorded from at least the 1960s in all territories or nations. \r\nTrends include increases of 0.15degC-0.18degC per decade in the tropical \r\nwestern Pacific (1953-2011), significant warming over the Caribbean \r\n(1901-2010) with trends of 0.19degC (0.28degC) per decade in daily \r\nmaximum (minimum temperatures) (1961-2010) and in La Reunion \r\nof 0.18degC per decade (1968-2019). There are fewer significant trends \r\nin precipitation in these regions though several locations in the \r\nCaribbean have detectable decreasing trends (high confidence), in \r\npart attributable to anthropogenic forcing (limited evidence). Also, it \r\nis likely that drying has occurred since the mid-20th century in some \r\nparts of the western Indian Ocean, and in the Pacific poleward of 20deg \r\nlatitude in both the northern and southern hemispheres."


id: f5311c18f6ae6fa5b5af22d2d582574d
created_at: 2023-03-16 14:00:52
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content:
"It is very likely that Small Island regions will continue to warm in the \r\ncoming decades at a level slightly lower than the global mean. Small \r\nIsland regions in the western and Equatorial Pacific, north Indian and \r\nSouthern oceans are likely to be wetter in the future; and those in \r\nthe Caribbean, parts of the Atlantic and west Indian oceans, and the \r\nsouthern subtropical and eastern Pacific Ocean drier."


id: d1fd2ed99a4faf255879a39052b5832d
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
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content:
"Contributors: Donovan Campbell (Jamaica), Subimal Ghosh (India), Rafiq Hamdi (Belgium), Mark Hemer (Australia), Richard G. Jones \r\n(United Kingdom), James Kossin (United States of America), Simon McGree (Australia/Fiji), Blair Trewin (Australia), Sergio M. Vicente\u0002Serrano (Spain)"


id: 4352546831eccb7172d3c3c8f8d4632e
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
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content:
"Constructing regional climate information for Small Islands involves synthesis from multiple sources. This cross-chapter box presents \r\ninformation relevant to water resources, drawing on several chapters in AR6 and Atlas.10. It introduces the context and current \r\nevidence base followed by an assessment of trends and projections in rainfall, temperature and sea levels across Small Islands and it \r\nhighlights key findings."


id: 8d59dfce084419299b74ba1bda8a5e5b
created_at: 2023-03-16 14:00:52
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content:
"Cross-Chapter Box Atlas.2: presents an overview of observed sub-regional trends relevant to water resources in some Small Islands \r\nand island regions largely from 1951. Some general observed climate trends include higher magnitude and frequency of temperatures \r\nincluding warm extremes (high confidence) (Section 12.4.7.1, Table 11.13 and Atlas.10.2), declines in high-intensity rainfall events (low\r\nto medium confidence) (Table 11.14), regional sea level rises with strong storm surges and waves resulting in increased coastal flood \r\nintensity (high confidence) (Section 12.4.7.4 and Atlas.10.2), and increased intensity and intensification rates of tropical cyclones at global \r\nscale (medium confidence) (Sections 11.7.1.2 and 12.4.7.3) and ocean acidification (virtually certain) (Chapters 2, 6 and 9, and Atlas.3.2)."


id: 14115278058d4b79d163b518d69c198c
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 668
content:
"No significant long-term trends are observed for annual Caribbean rainfall over the 20th century (low confidence) (Atlas.10.2). Over the \r\nwestern Pacific, generally decreasing but non-significant trends are noted in annual total rainfall from 1961 to 2011 (low confidence) \r\n(Atlas.10.2). June-July-August (JJA) rainfall over the Caribbean shows some drying tendencies that may be linked to the combined \r\neffect of warm ENSO events and a positive NAO phase (Giannini et al., 2000; Mendez-Lazaro et al., 2014; Fernandes et al., 2015), or to \r\nwarm ENSO events and a positive PDV (Maldonado et al., 2016). However, the work of Herrera et al. (2018) suggests that anthropogenic \r\ninfluences may also be possible, although mechanisms proposed to date have not decoupled the influence of anthropogenic trends \r\nfrom natural decadal variability (Vecchi et al., 2006; Vecchi and Soden, 2007; DiNezio et al., 2009)."


id: 781f3cdf8e338db7f85d24c0b49d5948
created_at: 2023-03-16 14:00:52
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content:
"Cross-Chapter Box Atlas.2, Table 1 | Summary of observed trends for Small Island regions. SLR = sea level rise; TC = tropical cyclone; SPCZ = South \r\nPacific Convergence Zone."


id: bb5f22fe9765281a63d6f8d5b20873bd
created_at: 2023-03-16 14:00:52
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"Southern Hemisphere subtropical Pacific June-November drying has been associated with intensification of the subtropical ridge and \r\nassociated declines in baroclinicity (Whan et al., 2014). Austral summer drying in the south-west French Polynesia sub-region has been \r\nlinked with increased greenhouse gas and ozone changes (Fyfe et al., 2012). The Southern Hemisphere jet stream has likely shifted \r\npolewards (Section 2.3.1.4.3) which is attributed largely to a trend in the Southern Annular Mode (Section 3.7.2)."


id: c757a91852db333967a394d91e48e134
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
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"These assessments are constrained by limited availability of observational datasets and of scientific studies. Assessment of observed \r\nclimate change for Small Islands is often constrained by low station density (Ryu and Hayhoe, 2014; P.D. Jones et al., 2016a), short \r\nperiods of record, digitization requirements or data-sharing limitations (P.D. Jones et al., 2016a), availability of metadata (McGree \r\net al., 2014; Stephenson et al., 2014; P.D. Jones et al., 2016b), challenges in some gridded product representations of variability, for \r\nexample, for complex topography (Frazier and Giambelluca, 2017), and challenges characterizing the impact of vertical land motion \r\non sea level rise (Atlas.10.2; Woppelmann and Marcos, 2016)."


id: 3ec6ac92fa2d044a33c0e499a6d86011
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
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content:
"Small Islands will very likely continue to warm this century, though at a rate less than the global average (Figure Atlas.28), with \r\nconsequent increased frequency of warm extremes for the Caribbean and western Pacific islands, and heatwave events for the \r\nCaribbean (high confidence) (Table 11.13). Annual and JJA rainfall declines are likely for some Indian and southern Pacific ocean \r\nregions with drying over southern French Polynesia (attributed partially to greenhouse gas increases) and farther east clearly evident \r\nin CMIP5 and CMIP6 projections (high confidence) (Figure Atlas.28). See also Section Atlas.10.4."


id: ba0f3ccc488f02c32e5a24898fbde824
created_at: 2023-03-16 14:00:52
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content:
"Sea level rise is very likely to continue in all Small Island regions (Sections 9.6.3.3 and 12.4.7.4, and Figure Atlas.28) and its effects \r\nwill be compounded by TC surge events. In general, the most intense TCs are likely to intensify and produce more flood rains with \r\nwarming, however detailed effects of climate change on TCs will vary by region (Section 11.7.1; Knutson et al., 2019). Bailey et al. \r\n(2016) projected a 20% decline in groundwater availability by 2050 in coral atoll islands of the Federated States of Micronesia and \r\nstressed that under higher sea level rises the decrease could be higher than 50% due to marine water intrusion into aquifers, as well \r\nas drought events."


id: a6af5f7f5e5dd7d332debbd300156d3f
created_at: 2023-03-16 14:00:52
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index: climate_gpt_only_giec
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content:
"Whilst this assessment demonstrates that the climate of Small Islands has and will continue to change in diverse ways, constructing \r\nclimate information for Small Islands is challenging. This is due to observational issues, incomplete understanding of some modes of \r\nvariability and their representation by climate models and the lack of availability of large ensembles of regional climate model \r\nsimulations and limited studies to decouple internal variability and anthropogenic influences."


id: 349dd59439601a4549a24a837c697064
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
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content:
"an overall assessment of the ice-sheet processes and changes, as part \r\nof the cryosphere, ocean and sea level change assessment."


id: 891265e4d5b4f3e74ab117058a338013
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
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vector_id: 19107
content:
"The Antarctic region, covered by an ice sheet and surrounded by the \r\nSouthern Ocean, is characterized by polar climate. It is the coldest, \r\nwindiest and driest continent on Earth and plays a  pivotal role in \r\nregulating the global climate and hydrological cycle. Antarctica has \r\na mean temperature of -35degC (Lenaerts et al., 2016) and receives \r\n171 mm yr-1 water equivalent of snowfall (north of 82degS, estimate \r\nbased on satellite measurements during 2006-2011; Palerme \r\net  al., 2014). Precipitation in Antarctica occurs mostly in the form "


id: 62de0dd8307b644dcae9e9fc745eab19
created_at: 2023-03-16 14:00:52
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vector_id: 13127
content:
"Snowfall increased over the Antarctic Ice Sheet over AP and \r\nWAN, offsetting some of the 20th-century sea level rise (medium \r\nconfidence). Longer records suggest either a  decrease in snowfall \r\nover the Antarctic Ice Sheet over the last 1000 years or a statistically \r\nnegligible change over the last 800 years (low confidence)."


id: f99404939b3f86bb4b46a735247edda1
created_at: 2023-03-16 14:00:52
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"Recent warming in the AP and consequent ice-shelf collapse are likely\r\nlinked to anthropogenic ozone and greenhouse gas forcing via the \r\nSAM and anthropogenically driven Atlantic sea surface. Also, there is \r\nhigh confidence in the influence of tropical sea surface temperature \r\non the Antarctic temperature and Southern Hemisphere mid-latitude \r\ncirculation, as well as the SAM. There is medium agreement but \r\nlimited evidence of an anthropogenic forcing effect on Antarctic \r\nice-sheet mass balance (low confidence) and partitioning between \r\nnatural and human drivers of atmospheric and ocean circulation \r\nchanges remains very uncertain."


id: 1b7367fd3d243bd060f4c9a4cc5f4660
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
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index: climate_gpt_only_giec
vector_id: 1777
content:
"In AR5, Church et  al. (2013) gave medium confidence in model \r\nprojections of a future Antarctic SMB increase, implying a negative \r\ncontribution to global mean sea level rise, consistent with a projection \r\nof significant Antarctic warming. Church et al. (2013) also gave high \r\nconfidence to the relationship between future temperature and \r\nprecipitation increases in Antarctica on physical grounds and from \r\nice-core evidence. In Meredith et al. (2019), the total mass balance \r\nprojections derived from ice-sheet models were reported without \r\nseparating the SMB, though projections were reported of increased \r\nprecipitation and continued strengthening of the westerly winds in \r\nthe Southern Ocean."


id: 2104057bfc962db238b5f68eddf21794
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vector_id: 2632
content:
"Antarctic climate variability is influenced by the Southern Annular \r\nMode (SAM) and regionally by other modes, including ENSO, Pacific-\r\nSouth American pattern, Pacific Decadal Variability (PDV), Indian Ocean \r\nDipole and Zonal Wave 3 (Annex IV). Climate change in Antarctica \r\nand the Southern Ocean is influenced by interactions between the \r\nice sheet, ocean, sea ice and atmosphere (Sections  9.2.3.2, 9.3.2 \r\nand 9.4.2; Meredith et al., 2019). In addition to Chapter 9, Antarctica \r\nis discussed across the report: global climate links (Chapters 2 and \r\n10), attribution (Chapter 3), global water cycle (Chapter 8), extremes \r\n(Chapter 11), and climatic impact-drivers (Chapter 12)."


id: 52b896f3bb100ed76f51148e09ee0b1d
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
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vector_id: 10473
content:
"The AR5 (Vaughan et  al., 2013) reported warming over Antarctica \r\nsince the 1950s, mostly over the AP and WAN, attributed to the \r\npositive trend in the SAM. These trends in the Antarctic temperature \r\nwere given low confidence due to substantial multi-annual to multi\u0002decadal variability, as well as uncertainties in magnitude and spatial \r\ntrend structure. The AR5 reported low confidence that anthropogenic \r\nforcing has contributed to the temperature change in Antarctica. The \r\nAR5 highlighted a large interannual variability in snow accumulation \r\nwith no significant trend since 1979 around Antarctica, and high \r\nconfidence in the overall mass loss from Antarctica, accelerated since \r\nthe 1990s."


id: 5f2fc34bbb1681b4864dea490fe0fe68
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 12539
content:
"In this and the following paragraphs, findings are from SROCC \r\n(Meredith et  al., 2019) unless otherwise stated. Warming trends \r\nwere reported over parts of WAN with record surface warmth \r\nover WAN during the 1990s compared to the past 200 years, and \r\nAP surface melting intensifying since the mid-20th  century. No \r\nsignificant temperature trends were reported over EAN and there \r\nwas low confidence in both WAN and EAN trend estimates due \r\nto sparse in situ records and large interannual to inter-decadal \r\nvariability. In the AP, concomitant increase in temperature and \r\nfoehn winds due to positive SAM caused increased surface melting \r\nover the Larsen ice shelves (medium confidence). Strong warming \r\nbetween the mid-1950s and the late 1990s led to the collapse of the \r\nLarsen B ice shelf in 2002, which had been intact for 11,000 years \r\n(medium confidence)."


id: 478c001d7b31768fde818ae31c3c3f42
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
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vector_id: 8642
content:
"Significant warming at the Byrd station (0.29degC +- 0.19degC per \r\ndecade during 1957-2016) confirms and extends earlier trend \r\nestimates (0.42degC +- 0.24degC per decade during 1958-2010) and is \r\nrepresentative of the entire WAN warming (0.22degC +- 0.12degC per \r\ndecade from 1958 to 2012 averaged over WAN excluding AP, medium \r\nconfidence due to lack of observations) (Bromwich et al., 2013, 2014; "


id: 55180730589d4ea878f5a2085054f949
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 10849
content:
"Jones et al., 2019). WAN and AP show statistically significant warming \r\nin the HadCRUTv5 observational dataset (Figure 2.11b). There is high \r\nconfidence in the long-term warming trend at the AP and WAN, and \r\nalso at the century scale based on reconstructions (Zagorodnov \r\net  al., 2012; Stenni et  al., 2017; Lyu et  al., 2020), confirming the \r\ntrends estimated by earlier studies assessed in the SROCC (Meredith \r\net al., 2019). The century-scale warming trend in the AP is very likely\r\nan emerging signal compared to natural variability, while the WAN \r\nwarming trend falls in the high end of century-scale trends over the \r\nlast 2000 years (medium confidence) (Stenni et al., 2017)."


id: 3787e0da8a14fa7e8c3a83268fec6f60
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 6117
content:
"inferred since 1979 (IMBIE team et al., 2018; Medley and Thomas, \r\n2019). While ice-core reconstructions show a significant increase in \r\nthe western AP SMB since the 1950s (Thomas et al., 2017; Medley \r\nand Thomas, 2019; Wang et al., 2019), this trend is not reproduced \r\nby regional climate models or the reanalyses used to drive them \r\n(Figure Atlas.30; van Wessem et al., 2016; Wang et al., 2019)."


id: bd79feffcfc198272abc7e3fd103145d
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 27378
content:
"According to the ice-core reconstructions, SMB over WAN (including \r\nAP) has likely increased during the 20th  century with trends of \r\n5.4 +- 2.9 Gt yr-1 per decade (1900-2010; Wang et al., 2019) mitigating \r\nglobal mean sea level rise by, respectively, 0.28 +-  0.17  mm per \r\ndecade (WAN excluding AP, during 1901-2000) and 0.62 +- 0.17 mm \r\nper decade (AP, during 1979-2000; Medley and Thomas, 2019). \r\nSignificant spatial heterogeneity in SMB trends has been observed \r\nover AP and WAN:"


id: ad4940fad05d6abaaf81daa211228da6
created_at: 2023-03-16 14:00:52
updated_at: 2023-03-16 14:00:52
content_type: text
index: climate_gpt_only_giec
vector_id: 24805
content:
"In EAN, during 1957-2016, three stations showed significant \r\nwarming (Scott 0.22degC +- 0.15degC, Novolazarevskaya 0.13degC +- 0.09degC, \r\nand Vostok 0.15degC +- 0.13degC per decade), while other stations with \r\nlong-term observations indicated no statistically significant trends \r\n(Figure Atlas.30). During 1979-2016, three coastal stations showed \r\ncooling, while at the South Pole a  warming trend was detected, \r\nincreasing to 0.61degC +- 0.34degC per decade during 1989-2018 \r\n(Figure Atlas.30; Jones et al., 2019; Clem et al., 2020; Turner et al., \r\n2020). The century-scale warming in Queen Maud Land coast based \r\non ice-core reconstructions is within the range of centennial internal \r\nvariability (Stenni et al., 2017)."


id: 9166b79d99931c09bce7b0f8741ce9ae
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
index: climate_gpt_only_giec
vector_id: 20411
content:
"The SMB of EAN increased during the 20th century which mitigated \r\nglobal mean sea level rise by 0.77 +- 0.40  mm per decade during \r\n1901-2000 (medium confidence) (Medley and Thomas, 2019). \r\nEAN SMB has been increasing at a much lower rate since 1979 as \r\nshown by observations, while regional climate models show strong \r\ninterannual variability masking any trend (low confidence due to \r\nlimited observations) (Figure Atlas.30; Medley and Thomas, 2019; \r\nRignot et al., 2019). EAN SMB changes during the 20th century and \r\nrecent decades showed large spatial heterogeneity:"


id: 19cb1c470491155c372297c5df7329f5
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
index: climate_gpt_only_giec
vector_id: 1529
content:
"Direct observations of snowfall in Antarctica using traditional \r\ngauges are highly uncertain and records from precipitation radars \r\n(Gorodetskaya et al., 2015; Grazioli et al., 2017; Scarchilli et al., 2020) \r\nare not long enough to assess trends. Estimates of precipitation and \r\nSMB are largely model-based due to the paucity of in situ observations \r\nin Antarctica (Lenaerts et al., 2019; Hanna et al., 2020). Antarctic SMB \r\nis dominated by precipitation and removal by sublimation with very \r\nsmall amounts of melt mostly important only on the ice shelves. \r\nClimate models and satellite records (IMBIE team et al., 2018; Rignot \r\net al., 2019; Mottram et al., 2021) suggest that strong interannual \r\nvariability of Antarctic-wide SMB over the satellite period currently \r\nmasks any existing trend (Figure Atlas.30) in spite of a possible ozone \r\ndepletion-related precipitation increase over the 1991-2005 period \r\n(Lenaerts et  al., 2018). No significant Antarctic-wide SMB trend is "


id: dfa523072c62593f4355a55567bd352b
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
index: climate_gpt_only_giec
vector_id: 32686
content:
"processes are either of variable quality, with extremely simple \r\nrepresentatons, or non-existent (Agosta et  al., 2015; Trusel et  al., \r\n2015). Though most meltwater refreezes in the snowpack in current \r\nclimate simulations, this may be an issue in the future climate \r\nsimulations under global warming as runoff is projected to increase \r\n(Kittel et al., 2021). Since CMIP5, representation of snow (Lenaerts \r\net al., 2016) and stable surface boundary layers (Vignon et al., 2018) \r\nhas improved in some atmospheric GCMs. In one example, the \r\nCMIP6 model CESM2 simulation of cloud and precipitation showed \r\nsubstantial improvements (Schneider et  al., 2020), though surface \r\nmelting is still considerably overestimated compared to RCMs and \r\nsatellite products (Trusel et al., 2015; Lenaerts et al., 2016)."


id: 7574ef3bd09957efe3903522f5826bca
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
index: climate_gpt_only_giec
vector_id: 16067
content:
"Regional trends during recent 50 year (1961-2010) and 100 year \r\n(1911-2010) periods are within the centennial variability of the \r\npast 1000 years, except for coastal QML (unusual 100-year increase \r\nin accumulation) and for coastal Victoria Land (unusual 100-year \r\ndecrease in accumulation) (Thomas et al., 2017). Nevertheless, the \r\ncurrent EAN SMB is not unusual compared to the past 800 years \r\n(Frezzotti et al., 2013)."


id: 9a2f1d9eeab1c454d2ecfccb1c0786f4
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
index: climate_gpt_only_giec
vector_id: 21781
content:
"Assimilation of observations in reanalysis products yields realistic \r\ntemperature patterns and seasonal variations, with the recent ERA5 \r\nreanalysis showing improved performance compared to others for \r\nmean and extreme temperature, wind and humidity, though a warm \r\nbias in near-surface air temperatures remains (Retamales-Munoz \r\net  al., 2019; Tetzner et  al., 2019; Dong et  al., 2020; Gorodetskaya \r\net al., 2020). The ability of the reanalyses to simulate precipitation and \r\nSMB is more variable; they generally overestimate the latter (Gossart \r\net  al., 2019; Roussel et  al., 2020), but are well suited to provide \r\natmospheric and sea surface boundary conditions to drive RCMs."


id: 6ddbccf6b839d8c7aa4a59b889359231
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
index: climate_gpt_only_giec
vector_id: 14900
content:
"This section provides evaluation of atmospheric global and regional \r\nclimate models, including reanalyses. Evaluation of the ice-sheet \r\nmodels and relevant processes, including selection of the atmospheric \r\nmodels used to drive ice-sheet models, is given in Section 9.4.2.2."


id: e06eaf3494ac8de7598623aee296677d
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
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vector_id: 32810
content:
"One of the major systematic biases in CMIP5 and earlier GCMs was \r\nan equatorward bias in the latitude of the Southern Hemisphere \r\nmid-latitude westerly jet, which is significantly reduced in the \r\nCMIP6 ensemble (Bracegirdle et al., 2020a). GCM Southern Ocean \r\nsea ice biases are also of importance as they influence 21st-century \r\ntemperature projections in Antarctica and simulations of present\u0002day temperatures are highly sensitive to these biases (Agosta \r\net al., 2015; Bracegirdle et al., 2015). A positive bias in near-surface \r\ntemperature over the Antarctic plateau is seen in CMIP5 models \r\n(Lenaerts et al., 2016)."


id: 8e9404acf90ffa2003e214225631fb7a
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
index: climate_gpt_only_giec
vector_id: 19953
content:
"CMIP6 GCMs showed an improved representation of the Antarctic \r\nnear-surface temperature compared to CMIP5 but little improvement \r\n(maintaining positive bias) in Antarctic precipitation estimates \r\n(Palerme et  al., 2017; Roussel et  al., 2020). An analysis of the \r\n1850-2000 SMB mean, trends, and interannual and spatial variability \r\nsuggests slightly worse agreement with ice-core-based reanalyses \r\nin CMIP6 than CMIP5 (Gorte et  al., 2020). Comparison of CMIP5 \r\nmodels with CloudSat satellite products and an ice-core-based SMB \r\nreconstruction showed almost all the models overestimate current \r\nAntarctic precipitation, some by more than 100% (Palerme et  al., \r\n2017; Gorte et  al., 2020). GCM simulations of surface snow-melt "


id: 9e870ddeb4dade382111d6c07c7a0709
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
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vector_id: 22477
content:
"Finer-resolution RCM studies demonstrate improved representation \r\nof precipitation and temperature gradients (van Wessem et  al., \r\n2018; Bozkurt et  al., 2020; Donat-Magnin et  al., 2020; Elvidge \r\net al., 2020), and strength of katabatic winds (Bintanja et al., 2014; "


id: 7b0a0354e5f25f174e89cef1c26172eb
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
index: climate_gpt_only_giec
vector_id: 16953
content:
"Souverijns et al., 2019) in coastal and mountainous regions. Adequate \r\nrepresentation of some processes is still lacking, including drifting \r\nsnow, sublimation of falling snow or the spectral dependency of \r\nsnow albedo (Lenaerts et al., 2019). Non-hydrostatic regional models, \r\nfor example Polar-WRF, MetUM or HARMONIE-AROME at spatial \r\nresolutions up to 2  km further improve regional RCM simulations, \r\nbut are still often unable to resolve relevant feedbacks and foehn \r\nprocesses (Grosvenor et al., 2014; Elvidge et al., 2015, 2020; Elvidge \r\nand Renfrew, 2016; King et al., 2017; Turton et al., 2017; Bozkurt et al., \r\n2018b; Hines et al., 2019; Vignon et al., 2019; Gilbert et al., 2020)."


id: 63489241677177dd1eb149fc1abd6353
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
index: climate_gpt_only_giec
vector_id: 13190
content:
"Existing uncertainties in the Antarctic climate representation by both \r\nGCMs and RCMs cause significant spread in the future Antarctic \r\nclimate and SMB projections (Gorte et al., 2020; Kittel et al., 2021). \r\nRun-time bias adjustment in atmospheric GCMs (Cross-Chapter \r\nBox 10.2; Krinner et al., 2019, 2020) has been proposed to provide \r\nlow-bias present and consistently corrected future RCM forcing \r\n(reducing the need for coupled model selection), which could be used \r\ndirectly for Antarctic climate projections (Krinner et al., 2019)."


id: 7555b4b6456789d8c32c6467a6491cc4
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
index: climate_gpt_only_giec
vector_id: 16044
content:
"This section provides an assessment of projections in temperature, \r\nprecipitation and SMB. See Section  9.4.2 for projected changes in \r\nthe ice-sheet total mass balance and relevant processes, and see \r\nSection 4.3.1 (Table 4.2) and Section 4.5.1 for Antarctic temperature \r\nprojections relative to other regions."


id: ea356d417df265d47fd14cbeeef654fb
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
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vector_id: 34353
content:
"Polar-CORDEX RCMs show higher variability in precipitation \r\nprojections compared to CMIP5 models with a similar spatial pattern \r\nof the areas with precipitation increase over continental Antarctica \r\nbut with higher local magnitude, and also showing a larger increase \r\nover the Weddell Sea ice shelves (Interactive Atlas). CMIP5 and CMIP6 "


id: 7db3a2f7edfa2d66358e66dfe68b4efd
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
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index: climate_gpt_only_giec
vector_id: 17353
content:
"models, bias adjusted based on regional climate model simulations, \r\nshowed that the projected warming is expected to result in increased \r\nsurface melting over the Antarctic ice shelves, with meltwater runoff \r\nunder RCP8.5 and SSP5-8.5 becoming larger than precipitation over \r\nice shelves over the period 2045-2050, surpassing intensities that \r\nwere linked with the collapse of Larsen A and B ice shelves (Trusel \r\net al., 2015; Kittel et al., 2021). Given the existing uncertainty in the \r\npresent precipitation and SMB simulations and the significant range in \r\nthe projected precipitation increase under various emissions scenarios \r\nin CMIP5, CMIP6 and CORDEX models, there is medium confidence\r\nthat the future Antarctic SMB will have a  negative contribution \r\nto sea level during the 21st  century under all emissions scenarios \r\n(see Section 9.4.2.3 for assessment of the drivers of future Antarctic \r\nice-sheet change and Section 9.4.2.6 for longer time scales)."


id: 6a6f6697ded92aa020aa2c09094e9c
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
index: climate_gpt_only_giec
vector_id: 14353
content:
"Observations show a  very likely widespread, strong warming \r\ntrend starting in the 1950s in the Antarctic Peninsula. Significant \r\nwarming trends are observed in other West Antarctic regions and at \r\nselected stations in East Antarctica (medium confidence). Antarctic \r\nprecipitation and SMB showed a significant positive trend over the \r\n20th  century according to the ice cores, while large interannual \r\nvariability masks any existing trend over the satellite period since the \r\nend of the 1970s (medium confidence)."


id: 9ce604b53d8102b85c46e8f669e96681
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
index: climate_gpt_only_giec
vector_id: 22218
content:
"An assessment of model performance for the present day shows \r\nthat high-resolution regional climate models with polar-optimized \r\nphysics are important for estimating SMB and generating climate \r\ninformation, and show improved realizations compared to reanalyses \r\nand GCMs when evaluated against observations. At the same time, \r\nCMIP6 GCMs showed an improved representation of the Antarctic \r\nnear-surface temperature compared to CMIP5, though still struggle \r\nwith the representation of precipitation. There is therefore medium \r\nconfidence in the capacity of climate models to simulate Antarctic \r\nclimate and SMB changes."


id: 1020b40513d2c1ac31d950580513178
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
index: climate_gpt_only_giec
vector_id: 26
content:
"The following summary from previous IPCC reports is derived from \r\nthe SROCC (IPCC, 2019a) unless otherwise stated. Arctic surface air \r\ntemperatures have increased from the mid-1950s, with feedbacks \r\nfrom loss of sea ice and snow cover contributing to the amplified \r\nwarming (high confidence) (IPCC, 2018c), and have likely increased \r\nby more than double the global average over the last two decades \r\n(high confidence). Arctic snow cover in June has declined from 1967 \r\nto 2018 (high confidence). Arctic glaciers are losing mass (very \r\nhigh confidence) and this along with changes in high-mountain \r\nsnowmelt have caused changes in hydrology, including river runoff, \r\nthat are projected to continue in the near term (high confidence). \r\nThe rate of ice loss from the Greenland Ice Sheet has increased; \r\nduring 2006-2015 the loss was 278 +- 11 Gt yr -1 with the rate for \r\n2012-2016 higher than for 2002-2011 and several times higher than \r\nduring 1992-2001 (high confidence)."


id: 1ddf7333ff039d876a607433aa652d3f
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
index: climate_gpt_only_giec
vector_id: 2134
content:
"Under all assessed emissions scenarios, both West and East Antarctica \r\nare very likely to have higher annual mean surface air temperatures \r\nand more precipitation, which will have a  dominant influence on \r\ndetermining future changes in the SMB (high confidence). However, \r\ndue to the challenges of model evaluation over the region and the \r\npossibility of increased meltwater runoff described above, there is \r\nonly medium confidence that the future contribution of the Antarctic \r\nSMB to sea level this century will be negative under all greenhouse \r\ngas emissions scenarios."


id: 953fa3dfdfc6c49f68d1bc523afa33a2
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
index: climate_gpt_only_giec
vector_id: 20980
content:
"The Arctic sea ice area is declining in all months of the year (very \r\nhigh confidence) with the September sea ice minimum very likely\r\nhaving reduced by 12.8 +- 2.3% per decade during the satellite \r\nera (1979-2018) to levels unprecedented for at least 1000 years \r\n(medium confidence)."


id: bc0a54a1f05fc8bd1efa8025818e5666
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
index: climate_gpt_only_giec
vector_id: 27148
content:
"The high latitudes are likely to experience an increase in annual \r\nmean precipitation under RCP8.5 (IPCC, 2013c). Further, changes \r\nin precipitation will not be uniform. Autumn and spring snow \r\ncover duration are projected to decrease by a further 5-10% from \r\ncurrent conditions in the near term (2031-2050). No further losses \r\nare projected under RCP2.6 whereas a further 15-25% reduction in \r\nsnow cover duration is projected by the end of century under RCP8.5 \r\n(high confidence)."


id: 4b899ab1c5485664cc1d9b5060fa106b
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
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index: climate_gpt_only_giec
vector_id: 9304
content:
"increase in the cold season (October-May) (medium confidence) \r\n(AMAP, 2019). Also, for neighbouring Arctic regions for example NEU, \r\nEEU and North Asia, mean annual precipitation has increased since \r\nthe early 20th century (Atlas.8 and Atlas.5.2). Estimated trends for \r\nprecipitation and snowfall fraction are mixed for the Arctic, with \r\nincreases and decreases for different regions and seasons (Vihma et al., \r\n2016). However, annual precipitation trends derived from different \r\nreanalyses do not agree, differ in sign and have low significance \r\n(Lindsay et  al., 2014; Boisvert et  al., 2018). Direct precipitation \r\nmeasurements are difficult and include uncertainties (among others \r\nmeasuring frozen precipitation), therefore precipitation estimates in \r\nthe Arctic rely on climate models and reanalyses."


id: 4db7c36e72e260167c0a5c3d739c919e
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
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index: climate_gpt_only_giec
vector_id: 9654
content:
"An average of five reanalyses for 2000-2010 suggests around 40% \r\nof Arctic Ocean precipitation falls as snow, though there is large \r\nuncertainty in this estimate (Boisvert et al., 2018). Rainfall frequency \r\nis estimated to have increased over the Arctic by 2.7-5.4% over \r\n2000-2016 (Boisvert et al., 2018) with more frequent rainfall events \r\nreported for NEU and ARO (Svalbard; Maturilli et al., 2015; AMAP, \r\n2019), and winter rain totals and frequency have increased in Svalbard \r\nsince 2000 (medium confidence) (Lupikasza et al., 2019). Rain-free \r\nwinters have rarely occurred since 1998 (Peeters et al., 2019)."


id: e2127c7975afd9163683dda2ec7811c7
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
index: climate_gpt_only_giec
vector_id: 33064
content:
"Observational records (1966-2010) for the RAR region show \r\nchanging precipitation characteristics (Ye et al., 2016), with higher \r\nprecipitation intensity but lower frequency and little change in \r\nannual precipitation total. Precipitation intensity is reported to have \r\nincreased in all seasons, strongest in winter and spring, weakest \r\nin summer, and at a rate of about 1-3% per degree Celsius of air \r\ntemperature increase."


id: 8d7604cacf5ec1649ed164409345f824
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
index: climate_gpt_only_giec
vector_id: 19782
content:
"Evaluating simulated temperature and precipitation is problematic \r\nin the Arctic due to sparse weather station observations. The lack of \r\nreliable observed precipitation datasets for the Arctic thus makes it \r\nvery unlikely to be able to evaluate objectively the skill of models to \r\nreproduce precipitation patterns (Takhsha et al., 2018)."


id: 4fff2eead357306f6f847a7a5ced07ba
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
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vector_id: 10030
content:
"A multi-dataset analysis for NEN shows a  consistent warming \r\n(Rapaic et  al., 2015), with the largest annual temperature trend \r\ngreater than 0.3degC per decade during 1981-2010 over eastern NEN \r\nand also significant warming over northern Quebec and most of \r\nthe Canadian Arctic north of the treeline. For the longer 1950-2010 \r\nperiod, a consistent warming is found over central and western NEN, \r\nbut no trend or no consensus is found over the Labrador coast. The \r\nlatter is related with cooling of the North Atlantic region during \r\nthe 1970s. For western Greenland, however, summer temperatures \r\nincreased (2.2degC in June, 1.1degC in July) from 1994 to 2015 (Saros \r\net al., 2019). For neighbouring Arctic regions of NEU, WSE and ESB, \r\ndatasets show a  consistent warming of annual mean temperature \r\nsince the mid-1970s and 1980 (Atlas.8 and Atlas.5.2)."


id: f67e4f2f845f2a26ee0254a2440cba5c
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
index: climate_gpt_only_giec
vector_id: 36293
content:
"Along with the amplified warming, the Arctic has become moister \r\n(Rinke et  al., 2019; Nygard et  al., 2020). AMAP reported Arctic \r\nprecipitation increases of 1.5-2.0% per decade, with the strongest "


id: 692891b5c3f2dc3d852a2b28af048a3c
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
index: climate_gpt_only_giec
vector_id: 14138
content:
"which have a  warm or weak bias in their Arctic temperature \r\nsimulations, closely relate the Arctic warming to changes in the large\u0002scale atmospheric circulation. In other models, which show large cold \r\nbiases, the albedo feedback effect plays a more important role for \r\nthe temperature trend magnitude. This implies that the dominant \r\nsimulated Arctic warming mechanism and trend may be dependent \r\non the bias of the model mean state (Franzke et al., 2017). Compared \r\nto CMIP5 models, Davy and Outten (2020) found lower biases in \r\nCMIP6 models' representation of sea ice extent and volume with \r\nimproved extents linked to a better seasonal cycle in the Barents Sea."


id: f0294a72400f9c4c34ba4ed18ac2d0b8
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
index: climate_gpt_only_giec
vector_id: 35288
content:
"Rapid temperature changes, such as the pronounced increase of \r\n2degC yr -1 during 2003-2012 over the Kara and Barents seas in March \r\nis well captured in Arctic CORDEX simulations (Kohnemann et  al., \r\n2017). The models show adequate skill in capturing the general \r\ntemperature patterns (Koenigk et  al., 2015; Matthes et  al., 2015; \r\nHamman et  al., 2016; Cassano et  al., 2017; Brunke et  al., 2018; \r\nDiaconescu et al., 2018; Takhsha et al., 2018), but tend to show a cold \r\ntemperature bias which is largest in winter and depends on the \r\nreference dataset. Cassano et al. (2017) showed a large sensitivity \r\nof the simulated surface climate to changes in atmospheric model \r\nphysics. In particular, large changes in radiative flux biases, driven by \r\nchanges in simulated clouds, lead to large differences in temperature \r\nand precipitation biases."


id: d00b25f292b917b89e5bf9cdce889710
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
index: climate_gpt_only_giec
vector_id: 30266
content:
"The CMIP5 models perform well in simulating 20th-century snowfall \r\nfor the Northern Hemisphere, although there is a  positive bias in \r\nthe multi-model ensemble relative to the observed data in many \r\nregions (Krasting et  al., 2013). Lack of sufficient spatial resolution \r\nin the model topography has a serious impact on the simulation of \r\nsnowfall. The patterns of relative maxima and minima of snowfall, \r\nhowever, are captured reasonably well by the models."


id: 6f51c80554f92454a402619f1b29bc1f
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
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vector_id: 15144
content:
"Mean temperature in the Arctic is projected to continue to rise \r\nthrough the 21st century significantly higher than the global average \r\n(Figure Atlas.29 and the Interactive Atlas). For the regions NWN and \r\nNEN, see Atlas.9. The Arctic is projected to reach a 2degC annual mean \r\nwarming above the 1981-2005 baseline about 25 to 50 years before \r\nthe globe as a whole under RCP8.5 and RCP4.5 (Post et al., 2019). \r\nThe Arctic warming may be as much as 4degC in the annual mean and \r\n7degC in late autumn under 2degC global warming, regardless of which \r\nscenario is considered (high confidence) (Post et al., 2019)."


id: 3aa3403c06566256c152288200e18571
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
index: climate_gpt_only_giec
vector_id: 6595
content:
"Arctic CORDEX RCMs reproduce the dominant features of regional \r\nprecipitation patterns and extremes (e.g., Glisan and Gutowski, 2014; \r\nHamman et al., 2016). Due to their higher spatial resolution, RCMs \r\nsimulates larger amounts of orographic precipitation compared \r\nto reanalyses. Overall, the simulated precipitation is within the \r\nreanalysis and global model ensemble spread, but the Arctic river \r\nbasin precipitation is closer to observations (Brunke et  al., 2018). \r\nHowever, Takhsha et  al. (2018) show that the RCMs' precipitation \r\nbias highly depends on the observational reference dataset used."


id: 7b93129b050d54676df3025dbbe99c94
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
index: climate_gpt_only_giec
vector_id: 17038
content:
"Projections from 40 CMIP5 models of the 2014-2100 Arctic annual \r\nwarming under RCP4.5 vary from 0.9degC to 6.7degC, with a multi-model \r\nmean of 3.7degC (Chylek et al., 2016). All models agree on a projected \r\nArctic amplification (of at least 1.5 times), but they disagree on the \r\nmagnitude and spatial patterns. Arctic warming trends projected \r\nby models that include a  full direct and indirect aerosol effect \r\n('fully aerosol-cloud interactive') are significantly higher than \r\nthose projected by models without a  full indirect aerosol effect \r\n(Chylek et al., 2016)."


id: a8095be8d103c1d550829558711a1f18
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
index: climate_gpt_only_giec
vector_id: 23952
content:
"The annual mean precipitation pattern of ensemble global \r\natmospheric simulations with a  high horizontal resolution agrees \r\nwell with the observations, with precipitation maxima over the \r\nGreenland and Norwegian seas (Kusunoki et  al., 2015). However, \r\nthe simulated Arctic average annual precipitation shows a positive \r\nbias with excessive precipitation over Alaska and the western Arctic \r\n(Kattsov et al., 2017)."


id: 96ec5841726297889f69d8631ff0d31f
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
index: climate_gpt_only_giec
vector_id: 21261
content:
"Projected Arctic warming exhibits a very pronounced seasonal cycle, \r\nwith exceptionally strong warming in the winter. In projections \r\nfrom 30 CMIP5 models, winter warming over ARO varies regionally \r\nfrom 3degC to 5degC by mid-century and 5degC to 9degC by late-century under \r\nRCP4.5 (high confidence) (AMAP, 2017). Averaged over the Arctic \r\nand based on 36 CMIP5 models, winter warming is 5.8degC +- 1.5degC \r\nby mid-century and 7.1degC +- 2.3degC by 2100 under RCP4.5 "


id: 58879944e24e8af82674ed5b42231cee
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
index: climate_gpt_only_giec
vector_id: 11429
content:
"Regarding the Greenland Ice Sheet (region GIC), modelled surface \r\nmass balance (SMB) has decreased since the end of the 1990s \r\n(Fettweis et al., 2020). A multi-model intercomparison study (Fettweis \r\net al., 2020) emphasized a simulated positive mean annual SMB of \r\n338 +- 68 Gt yr-1 between 1980 and 2012, with a decreasing average \r\nrate of 7.3 +- 2.0 Gt yr-2, mainly driven by an increase in meltwater "


id: 625e44d73762f3b996bc6dd034acde93
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
index: climate_gpt_only_giec
vector_id: 13038
content:
"The CMIP6 projections confirm precipitation will likely increase \r\nalmost everywhere in the Arctic (Interactive Atlas). The largest \r\nincrease is simulated over the Barents Sea, Kara Sea and East \r\nSiberian Sea regions, and over north-east Greenland. A pronounced \r\nuncertainty in the projection exists over the Arctic North Atlantic and \r\nsouth Greenland. There, the precipitation signal is not significant even \r\nby the end of the 21st century and under high-emissions scenarios \r\n(RCP8.5, SSP5-8.5). Consistent with the generally higher warming in \r\nCMIP6, compared to CMIP5, the projected precipitation increase is \r\nalso higher (high confidence) (Figure Atlas.29)."


id: e77576056fcba0b94befe49bf5d40b9
created_at: 2023-03-16 14:00:53
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content_type: text
index: climate_gpt_only_giec
vector_id: 33909
content:
"(Overland et al., 2019), and an exceptionally strong warming of up to \r\n14.1degC +- 2.9degC is projected in December under RCP8.5 (Bintanja and \r\nKrikken, 2016). Bintanja and Van Der Linden (2013) estimated the \r\nArctic winter warming over the 21st century to exceed the summer \r\nwarming by at least a factor of four, irrespective of the magnitude of \r\nthe climate forcing."


id: f7c71b3c8954bf0ce497b5990590e09a
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
index: climate_gpt_only_giec
vector_id: 36504
content:
"The Arctic mean annual precipitation sensitivity has been estimated \r\nat a 4.5% increase per degree Celsius of temperature rise, compared \r\nto a global average of 1.6-1.9% per degree Celsius of temperature \r\nrise (Bintanja and Selten, 2014) based on a set of 37 CMIP5 GCMs. \r\nKoenigk et al. (2015) stress the different precipitation sensitivity in \r\nwinter (0.8 mm per month per degree Celsius of temperature rise) and \r\nsummer (2 mm per month per degree Celsius of temperature rise). \r\nThe pattern and amplitude of precipitation changes agree in CORDEX \r\nsimulations with their driving CMIP5 models (high confidence) \r\n(Koenigk et  al., 2015; Spinoni et  al., 2020). However, more small\u0002scale variations over land and coastlines, and significantly larger \r\nprecipitation changes in summer are obvious in the downscaling."


id: 8724d269f334bfb590df5e16cbd05292
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
index: climate_gpt_only_giec
vector_id: 18794
content:
"Rain is projected to become the dominant form of precipitation in \r\nthe Arctic region by the end of the 21st century (Bintanja, 2018). The \r\nCMIP5 models show a decrease in annual Arctic snowfall under both \r\nRCP4.5 and RCP8.5 (high confidence) (Krasting et al., 2013; Bintanja \r\nand Andry, 2017). In the central Arctic, the snowfall fraction barely \r\nremains larger than 50%, with only Greenland still having snowfall \r\nfractions larger than 80% (Bintanja and Andry, 2017). The most \r\ndramatic reductions in snowfall fraction are projected to occur over \r\nthe North Atlantic and, especially, the Barents Sea."


id: a451f60f47d8677e7e2c5743c33b5994
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
index: climate_gpt_only_giec
vector_id: 23417
content:
"The ensemble of CMIP6 shows likely greater warming compared to \r\nCMIP5 (Figure Atlas.29). There is weak agreement among the models \r\nin projected temperature change over the Arctic North Atlantic under \r\nSSPs until the mid-century, but a  robust warming signal clearly \r\nemerges even there by 2100 (Interactive Atlas). Generally, the largest \r\nannual warming is simulated over the Arctic Ocean, particularly \r\nover the Barents Sea and the Kara Sea. Future warming in CORDEX \r\nRCMs and the CMIP5 models are similar (Spinoni et al., 2020). The \r\nRCM warming over the AO is smaller, while the warming over land \r\nis larger in winter and spring but smaller in summer, compared with \r\nCMIP5 (Koenigk et al., 2015)."


id: 75ea61fe4dc090cf0e941ae3c5670a34
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
index: climate_gpt_only_giec
vector_id: 16143
content:
"With ongoing warming and polar amplification in the Arctic, the \r\nGreenland Ice Sheet SMB will inevitably continue to change (high \r\nconfidence) (Lenaerts et al., 2019). For the ice sheet, despite large \r\ndifferences between model scenarios, future projections and regions \r\nagree that increasing temperatures will increase runoff which will \r\nin turn dominate the future decrease of SMB (Rae et  al., 2012; \r\nvan Angelen et al., 2014; Mottram et al., 2017; Hofer et al., 2020), \r\nconfirming the high sensitivity of the SMB to atmospheric warming. \r\nChanges in SMB will continue to dominate future mass loss from \r\nthe ice sheet, and likely even more when marine-terminating glaciers \r\nretreat onto land, and solid ice discharge is reduced (Vizcaino, 2014; \r\nLenaerts et al., 2019)."


id: 8c3300eaf4a7438b8afba127b4408489
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
index: climate_gpt_only_giec
vector_id: 19563
content:
"It is very likely that the Arctic has warmed at more than twice the \r\nglobal rate over the past 50 years and likely that annual precipitation \r\nhas increased with the highest increases during the cold season. \r\nThis is based on various Arctic amplification processes, in particular, \r\na  combination of several feedback-related processes such as sea \r\nice and snow-cover albedo, poleward energy transports, and water\u0002vapour-cloud-radiation feedbacks. The frequency of rainfall increased \r\nover the Arctic by 2.7-5.4% over the 2000-2016 period with more "


id: dc6929999b4451f1729e9ee6f381b903
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
index: climate_gpt_only_giec
vector_id: 32179
content:
"can remain larger than model uncertainty or scenario uncertainty \r\nthroughout the whole century. The Atlas (similarly to other regional \r\nchapters) uses a single realization per model (CMIP6 models provide \r\nmultiple realizations, but it is not the case for CORDEX and less so \r\nfor CMIP5), which allows for the comparison of the different lines \r\nof evidence but at the expense of internal variability having a larger \r\ninfluence on the ability to detect or quantify changes."


id: 50c1ddbb409325e9800f682999a4c2af
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
index: climate_gpt_only_giec
vector_id: 10151
content:
"The CMIP5 models reproduce the observed Arctic warming over \r\nthe past century but overestimate the amplified Arctic warming in \r\nthe recent decades (medium confidence). Arctic CORDEX simulations \r\nshow adequate skill in capturing regional temperature and \r\nprecipitation patterns and precipitation extremes (high confidence). \r\nSMB models have improved due to increased availability and quality \r\nof remotely sensed and in situ observations, and an ensemble mean \r\nof SMB model simulations provides the best estimate of the present\u0002day SMB (medium confidence)."


id: 5125f04f893fe2620416d5e28ffc32f2
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
index: climate_gpt_only_giec
vector_id: 10224
content:
"The assessment produced in the Atlas is based on the individual \r\nresults from the different lines of global and regional evidence \r\nand the consistency amongst them, as there is a lack of literature \r\non methodologies that combine multiple lines of evidence to distil \r\nregional climate change information."


id: b950869945e7b40d321cadb636cbaa6
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
index: climate_gpt_only_giec
vector_id: 26697
content:
"It is very likely that the Arctic annual mean temperature \r\nand precipitation will continue to increase, reaching around \r\n11.5degC  +-  3.4degC and 49 +- 19% over the 2081-2100 period (with \r\nrespect to a  1995-2014 baseline) under the SSP5-8.5 scenario or \r\n4.0degC +- 2.5degC and 17 +- 11% under the SSP1-2.6 scenario. These \r\nCMIP6 results show likely higher Arctic annual mean temperatures \r\ncompared to CMIP5 for a given time-period and emissions scenario, \r\nthough the projections are consistent for global warming levels."


id: 761ea96ba810f735c5d03b3e3682ef04
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
index: climate_gpt_only_giec
vector_id: 16189
content:
"Developing from the AR5 WGI Atlas Annex (IPCC, 2013a), the Atlas is \r\nan innovation in the WGI contribution to the AR6, providing a region\u0002by-region assessment of new knowledge on changes in mean climate \r\nand an online interactive tool, the Interactive Atlas. It demonstrates \r\nthe diversity in the climate changes across these regions, in the \r\nevidence base for generating information on what changes have \r\nalready occurred and why, and what further changes each region is \r\nprojected to face in the future based on different emissions scenarios \r\nand global warming levels. Finally, the Interactive Atlas allows for \r\nfurther exploration of the data underpinning the assessment material \r\ngenerated by many of the other chapters."


id: d6d1a7ce8e10b1c195a046b490b8571c
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
index: climate_gpt_only_giec
vector_id: 31307
content:
"The foundation of the regional information generated by the Atlas \r\nchapter is an assessment of the significant body of new literature \r\non regional climate change though noting substantial heterogeneity \r\nin both its availability and the involvement of regional expertise. In \r\nmany regions this allows for an in-depth assessment though in some \r\nthe range of information that can be provided and/or the level of \r\nconfidence in the information is limited. There is similar heterogeneity \r\nin the availability of observations to assess recent trends and evaluate \r\nmodel performance, with a lack of curated regional datasets in the \r\npolar regions, Northern South America, Africa and the Small Islands."


id: 543375c52e2b496730ad76b72742acf1
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
index: climate_gpt_only_giec
vector_id: 10711
content:
"Internal variability is a large contributor to the climate uncertainty at \r\nregional scales. Recent work has combined outputs of single model \r\ninitial-conditions large ensembles (SMILEs) with CMIP5 and CMIP6 \r\nto partition and gain insights on the modelled range and uncertainty \r\narising from internal variability and from model-response \r\nuncertainty for a given emissions scenario (Deser et al., 2020; Lehner \r\net al., 2020; Maher et al., 2021). The work highlights the notable role \r\nfor internal variability at regional scales, particularly for precipitation \r\nin regions with weaker forced response, where internal variability "


id: 155644408ebdaf81bc14621464bac2df
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
index: climate_gpt_only_giec
vector_id: 876
content:
"Mansour Almazroui (Saudi Arabia), Lisa Bock (Germany), Josep G. Canadell (Australia), Rafiq Hamdi \r\n(Belgium), Masao Ishii (Japan), Pedro M. S. Monteiro (South Africa), Prabir K. Patra (Japan/India), \r\nShilong Piao (China), Jin-Ho Yoon (Republic of Korea), Yongqiang Yu (China), Prodromos Zanis \r\n(Greece), Olga Zolina (Russian Federation/France)"


id: 9aeab451ebc30690bd10930f6244aae5
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
index: climate_gpt_only_giec
vector_id: 21904
content:
"IPCC, 2021: Annex I: Observational Products [Trewin, B. (ed.)]. In Climate Change 2021: The Physical Science Basis. \r\nContribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change\r\n[Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Pean, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, \r\nK. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekci, R. Yu, and B. Zhou (eds.)]. Cambridge \r\nUniversity Press, Cambridge, United Kingdom and New York, NY, USA, pp. 2061-2086, doi:10.1017/9781009157896.015."


id: ce78597999c241989ada4c92ce118486
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
index: climate_gpt_only_giec
vector_id: 30019
content:
"Reanalyses are within the scope of this annex, but historical climate \r\nmodel simulations are not. Proxy datasets are also outside the scope \r\nof this annex."


id: 47efa0d8c3d4075b851cf8dcce166d78
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
index: climate_gpt_only_giec
vector_id: 8691
content:
"The purpose of this annex is to document observational datasets used \r\nby Working Group I in the Sixth Assessment Report. This includes \r\ndetails of the types and versions of datasets, the time period they \r\ncover, the chapters in which they appear, and citations and (where \r\navailable) web links to the data."


id: ac61a484d1f306f53db73543a3dbcd7c
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
index: climate_gpt_only_giec
vector_id: 24654
content:
"Datasets which are updated regularly on an operational basis are \r\nshown as ending in 2020, even if no 2020 data have yet been \r\npublished at the time of writing."


id: 24138d8c3e53babfff115c7f8fe0cdec
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
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vector_id: 3119
content:
"Datasets are sorted alphabetically according to the dataset name or, \r\nif there is no formal name, the name of the responsible institution \r\nor lead author."


id: 9aa015632e1c2a4b7d0cf5fed03ea7f1
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
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vector_id: 21857
content:
"This list includes those observational datasets that contribute \r\nto values reported in the text or in figures, unless they are citing \r\na specific result from a paper (as opposed to an ongoing dataset for \r\nwhich that paper is a reference)."


id: a6f9831f65cbbadd00a3f77531c81acb
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
index: climate_gpt_only_giec
vector_id: 23783
content:
"Paul J. Durack (United States of America/Australia), Seita Emori (Japan), Malte Meinhausen \r\n(Australia/Germany), Sophie Nowicki (United States of America/France), Lucas Ruiz (Argentina)"


id: fe89b4d424abe9fb148c85146dcb8151
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
index: climate_gpt_only_giec
vector_id: 37587
content:
"(Taylor  et  al., 2012). This table illustrates the heterogeneity of \r\ninformation available across the different domains which limits the \r\nassessment of some scenarios in some regions."


id: fb98aad0727ac8cb4cdf9e5b77dab0dc
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
index: climate_gpt_only_giec
vector_id: 37085
content:
"The RCMs contributing to CORDEX (as available from ESGF) are listed \r\nin Table AII.2, including the main references and details on model \r\ncomponents relevant for the AR6 WGI assessment. "


id: 716dc9a3d6f7948c7b11e22b55323c54
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
index: climate_gpt_only_giec
vector_id: 15482
content:
"The Coordinated Regional Climate Downscaling Experiment (CORDEX; \r\nGutowski Jr. et al., 2016) coordinates regional downscaling activities \r\nworldwide over a number of defined domains. Regional downscaling \r\nis performed using regional climate models (RCMs) run over limited \r\ngeographical regions, driven at the boundaries by the output from \r\nthe Coupled Model Intercomparison Project (CMIP) global model \r\nsimulations. CORDEX relies on the same infrastructure as CMIP to \r\nmake the multi-model output publicly available in a  standardized \r\nformat: the data is disseminated via the Earth System Grid Federation \r\n(ESGF; Williams et al., 2016). "


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updated_at: 2023-03-16 14:00:53
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vector_id: 6695
content:
"Finally, Tables AII.3 and AII.4 provide information on the CMIP5 and \r\nRCM models used in the different CORDEX domains, respectively (the \r\nnumbers in each cell indicate the number of available simulations for \r\neach scenario). Note that CORDEX information is complex to describe \r\nsince each particular simulation is produced by a single combination \r\nof a CMIP5 boundary forcing, or 'driving model' (or reanalysis for the \r\nevaluation experiment) and an RCM model from Table AII.2. These \r\ntwo tables together provide comprehensive information on the GCM/\r\nRCM composition of the ensembles available in each domain, which \r\nis key to understanding the assessment done in WGI chapters (in \r\nparticular the regional Chapters 10, 11 and 12, and the Atlas). "


id: aba7d86b4e22dae3f52aa6398da502aa
created_at: 2023-03-16 14:00:53
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content:
"Table AII.1 lists the current CORDEX domains, displaying the different \r\nresolutions (from the lowest - 0.44deg, to the highest - 0.11deg, typically \r\nin rotated coordinates), with data available through the ESGF. \r\nFigure Atlas.6 provides a geographical map of the domains. Note that \r\n0.44deg and 0.22deg are the prioritized resolution in the CORDEX and \r\nCORDEX-CORE experimental designs, respectively, and only some \r\ndomains provide information for higher resolution (0.11deg); see Atlas, \r\nAtlas.1.4.4 and https://cordex.org for further details. Table AII.1 \r\nalso displays the number of simulations available for the following \r\nexperiments: 'evaluation' (ERA-Interim driven simulations), and the \r\n'historical', 'RCP2.6', 'RCP4.5' and 'RCP8.5' CMIP5-driven simulations "


id: 2b69e0146ae6af55ffc0f0a081304c83
created_at: 2023-03-16 14:00:53
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vector_id: 4267
content:
"Table AII.1 | CORDEX regional domains. Column 1: name of the domain. Column 2: domain code (as in ESGF specification). Column 3: horizontal grid resolutions (11, \r\n22 and 44 for 0.11deg, 0.22deg and 0.44deg resolution in the native projections, and the suffix 'i' indicating regular interpolated domains). Columns 4-8 indicate the number of \r\nsimulations available at each resolution, for the evaluation, historical, RCP2.6, RCP4.5 and RCP8.5 experiments, respectively, as archived in the ESGF as of 31 January 2021. Note \r\nthat MED-CORDEX data is stored on a dedicated server (details at www.medcordex.eu) and includes experiments with only atmosphere (the standard for other domains) and \r\natmosphere-ocean coupled regional climate models (denoted by MED and OMED, respectively). See Atlas.1.4.4 for more details on CORDEX and CORDEX-CORE experiments. "


id: 79b088b1e7286e7effa9b950916033a0
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
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content:
"provided by the one-stop-shop 'further_info_url' global attribute \r\nin each CMIP6 netCDF data file. ES-DOC also includes the CMIP6 \r\nerrata system (https://errata.es-doc.org), which tracks issues with \r\nthe model data and the potential corrections made. ES-DOC includes \r\ninformation at the model level and the experiment level. "


id: 9415243db87f11fa3ca55f89bd8df6c
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
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vector_id: 20804
content:
"Model datasets shared on ESGF are characterized by their institution, \r\nmodel, experiment, variable and ensemble member (the different \r\ntypes of ensemble strategies are introduced in Chapter 1, Section 1.4). \r\nEach ensemble member is designated by a label of four letters, each \r\nassociated with a number: 'r' for realization, 'i' for initialization, 'p' \r\nfor physics, and 'f' for forcing. For example, Table Atlas.SM.2 lists the \r\nensemble member label of each CMIP6 dataset used in the Atlas. In \r\nthe future, ES-DOC will document in more detail how each individual \r\nmember differs from the other members of a given ensemble. "


id: f809ae37175b803b3f51e6b434adb3a3
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
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vector_id: 36537
content:
"A new online service, ES-DOC, provides information about all aspects \r\nof CMIP6. Building on the Common Information Model concepts \r\nand standards (Lawrence et al., 2012), a number of documents are \r\ncreated for the CMIP6 Project, as illustrated at https://es-doc.org/\r\ncmip6/. These include documents to describe experiments, ensemble \r\nsimulations, models, conformance to the numerical requirements of \r\nthe CMIP6 protocol (see Pascoe et al., 2020 for CMIP6 experiments) \r\nand other important aspects of the CMIP6 model archive. These \r\ndifferent documents are either produced automatically or provided \r\nin a  standard way by modelling groups. Hundreds of clearly \r\nstructured properties are harvested and stored on a  database to \r\nbe used by clients and portals (e.g.,  https://search.es-doc.org/ and \r\nhttps://explore.es-doc.org/). Another entry point to the database is "


id: 9ec6fcb7aad32f94d456fd417e9bb9ef
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
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vector_id: 22519
content:
"features of the coupled gene\r\nons. Column 3: main referen\r\number of vertical levels, grid \r\ndivided by the number of grid \r\nm assuming standard atmosp\r\n active nitrogen cycle (N cyc\r\nained in the table is consiste\r\n \r\n \r\nCryosphere1) Sea Ice 2) Land Ice\r\nLa1) N2) \r\n1) CICE4 1) \r\nwso2) \r\n1) FESOM1.4 1) 1) SIS1 1) \r\n2) \r\n1) SIS1 1) \r\n2) \r\n1) SIS1 1) "


id: 48ffb45046923e31c14c82c7251fcae4
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
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vector_id: 8870
content:
"Table AII.6 | High-resolution coupled models participating in HighResMIP. Full names of the institutions are found in Table AII.5. The horizontal resolution (rounded to \r\n10 km when larger than 10 km) is the square root of the number of grid points divided by the surface area of the Earth, or the number of surface ocean grid points divided by \r\nthe area of the ocean surface, for the atmosphere and ocean respectively. When reported in hPa, the atmosphere top heights are converted into kilometres assuming standard \r\natmosphere (ISO 2533:1975, 1975)."


id: 7203db96c8caf40d004a926c33a5a9bc
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
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vector_id: 15563
content:
"More specific information on the model capabilities and parameter \r\nchoices used for each ice sheet and glacier MIP are presented in the \r\nfollowing papers: ISMIP6 initMIP-Greenland (Goelzer et  al., 2018), \r\nISMIP6 projection-Greenland (Goelzer et al., 2020), ISMIP6 initMIP\u0002Antarctica (Seroussi et  al., 2019), ISMIP6 Antarctica projection \r\n(Seroussi et al., 2020), LARMIP-2 Antarctica projections (Levermann \r\net al., 2020) and GlacierMIP (Hock et al., 2019; Marzeion et al., 2020)."


id: d709fc65d15104a7a5983709d3ba48bc
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
index: climate_gpt_only_giec
vector_id: 31339
content:
"Ice sheet and glacier models are used to assess the contribution \r\nof ice sheets and glaciers to future sea level rise as described in \r\nSection  9.6.3. New to AR6, the projections of the future sea level \r\ncontribution from ice sheets and glaciers comes from the ensemble of \r\nmodel intercomparison studies (Sections 9.4.1.2, 9.4.2.2 and 9.5.1.3, \r\nand Box 9.3). The tables here describe the models used for Greenland \r\nISMIP6 (Table AII.7), Antarctica ISMIP6 and LARMIP-2 (Table AII.8) \r\nand GlacierMIP (Table AII.9)."


id: d0eb08825a984a71cbcb279653e78ed8
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
index: climate_gpt_only_giec
vector_id: 30403
content:
"Table AII.7 | Models used in Greenland model intercomparison studies (initMIP and/or ISMIP6 projections)."


id: 285ae66e648f38e6b0c5d689e34dd2cd
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
index: climate_gpt_only_giec
vector_id: 3763
content:
"Table AII.8 | Models used in Antarctica model intercomparison studies (initMIP and/or ISMIP6 projections and/or LARMIP-2 projections)."


id: 70675a1aad70fca5a4828760e7c6f15a
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
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vector_id: 15304
content:
"Coordinating Lead Authors: \r\nFrank J. Dentener (EU/The Netherlands), Bradley Hall (United States of America), Chris Smith \r\n(United Kingdom)"


id: 4e430f54a1cc8051cb408225be204476
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
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vector_id: 9739
content:
"Jinho Ahn (Republic of Korea), William Collins (United Kingdom), Christopher D. Jones \r\n(United Kingdom), Malte Meinshausen (Australia/Germany) "


id: 9d6352d2891139055985a86cbf0af901
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
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vector_id: 22292
content:
"Ed J. Dlugokencky (United States of America), Ralph Keeling (United States of America), \r\nPaul B. Krummel (Australia), Jens Muhle (United States of America/Germany), Zebedee R. J. Nicholls \r\n(Australia), Isobel J. Simpson (Canada)"


id: 98f1c63944683deadcf6f5936f01eef3
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
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vector_id: 21577
content:
"IPCC, 2021: Annex III: Tables of historical and projected well-mixed greenhouse gas mixing ratios and effective radiative \r\nforcing of all climate forcers [Dentener F.J., B. Hall, C. Smith (eds.)]. In Climate Change 2021: The Physical Science Basis. \r\nContribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change\r\n[Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Pean, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, \r\nK. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekci, R. Yu, and B. Zhou (eds.)]. Cambridge University \r\nPress, Cambridge, United Kingdom and New York, NY, USA, pp. 2139-2152, doi:10.1017/9781009157896.017."


id: 24d302a41bfa74e284b8b6ba4b9eb5e9
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
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vector_id: 3232
content:
"Montzka et al. (2009); Dlugokencky et al. (2011); Hall et al. (2011); \r\nRigby et al. (2014); Laube et al. (2016); Simmonds et al. (2017); Adcock \r\net al. (2018); Prinn et al. (2018); Leedham Elvidge et al. (2018); Muhle \r\net al. (2019); Naus et al. (2019); Droste et al. (2020). "


id: fe636f9a89c50c036dfab958e1add2b
created_at: 2023-03-16 14:00:53
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vector_id: 37561
content:
"Annex III presents, in tabulated form, data related to historical \r\nand projected changes in greenhouse gas (GHG) mixing ratios \r\nand effective radiative forcing (ERF) of all climate forcers as \r\nassessed and used throughout Chapters 1-7. Where available 90% \r\nconfidence level uncertainties on observed GHG mixing ratios are \r\ngiven. For each species, the abundance is given as dry air mole \r\nfraction: ppm = micromoles per mole (10-6); ppb = nanomoles per \r\nmole (10-9); and ppt = picomoles per mole (10-12). ERF is given \r\nin W m-2 for CO2, N2O, and CH4 and in 10-3 Wm-2 (mW m-2) for \r\nother components. "


id: bbcec0f7c150ab91b9f180e224fef53f
created_at: 2023-03-16 14:00:53
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content_type: text
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vector_id: 27103
content:
"Projected concentrations for the five core scenarios discussed in the \r\nreport (Section 1.6.1) are from Gidden et al. (2019), Meinshausen \r\net al. (2017, 2020) and Velders et al. (2015). These scenarios \r\nspan a  wide range of plausible societal and climatic futures from \r\npotentially below 1.5degC best-estimate warming to over 4degC warming \r\nby 2100 (Section 4.3.4). Computational methods and assumptions \r\nto calculate historical and projected ERF are described in Chapter 7 \r\nand detailed information can be found in Chapter 7 Supplementary \r\nMaterial 7.SM.1.3 and 7.SM.1.4. "


id: 7dadfbde6605b0f5621d09b53a64df2f
created_at: 2023-03-16 14:00:53
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vector_id: 17349
content:
"Pre-instrumental mixing ratios are estimated from ice-core and firn \r\nair records that are described in Machida et al. (1995); Fluckiger et al. \r\n(1999); Sowers (2001); Siegenthaler et al. (2005); Ahn et al. (2012); \r\nMitchell et al. (2013); Bauska et al. (2015); Meinshausen et al. (2017); \r\nRubino et al. (2020); Ryu et al. (2020)."


id: ff02847a9d1f3956bfc0773ec05c063f
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
index: climate_gpt_only_giec
vector_id: 37669
content:
"Extended datasets and further auxiliary data are made available via \r\nhttps://doi.org/10.5281/zenodo.5705391 (Smith et al., 2021). "


id: 373533051930018445a868b11697d958
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
index: climate_gpt_only_giec
vector_id: 6074
content:
"Tables AIII.Ia-f provide historical abundances (mixing ratios) and \r\neffective radiative forcing (ERF) values for greenhouse gases assessed \r\nin this report."


id: 21886356ec254bb492a48ffabf979749
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
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vector_id: 2699
content:
"Observed (instrumental) mixing ratios are described in Masarie \r\nand Tans (1995); Trudinger et al. (2004); Worton et al. (2006); "


id: 78a3201507743ebf9aa314fa7432f6dc
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
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vector_id: 16543
content:
"Notes: 1750 from AR5; 1850-1970 from CMIP6 dataset in Meinshausen et al. (2017). 1980-2019 AGAGE or merged AGAGE and NOAA networks, depending on the date of \r\navailability. ERF (2019-1750) from Chapter 7."


id: 704329b0f3efc91fbf1aa6e3b2565831
created_at: 2023-03-16 14:00:53
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content:
"Note: aObserved from Table AIII.1a. SSP GHG concentrations (Meinshausen et al., 2017, 2020) available at greenhousegases.science.unimelb.edu.au. Concentrations of \r\nhalogenated compounds in electronic supplement. Major scenarios used in this report are selected."


id: b0bdfb01724aaf761af8fb665b08ec28
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
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content:
"Notes: a Tables AII.6.8 and 6.10 in Annex II of the IPCC AR5 WG1 report (IPCC, 2013), for which total ERF is derived from CMIP5 models and anthropogenic ERF from a simple \r\nclimate model emulator. b RCPs calculated for ERF as described in Chapter 7 Supplementary Material 7.SM.1.4. Similar to AR5, an emulator was used in AR6 to derive \r\nanthropogenic ERF (Cross-Chapter Box 7.1 and Chapter 7 Supplementary Material 7.SM.1.4). Further discussion on the difference between AR5 and AR6 radiative forcing \r\nis in Section 4.6.2. c ScenarioMIP (O'Neill et al., 2016), d AerChemMIP (Collins et al., 2017), e Allen et al. (2021)."


id: 8839342b4b54fd2d687dfdc1791d15c3
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
index: climate_gpt_only_giec
vector_id: 18956
content:
"Susanna Corti (Italy), Francois Engelbrecht (South Africa), June-Yi Lee (Republic of Korea), \r\nAmanda Maycock (United Kingdom), Shayne McGregor (Australia), Olaf Morgenstern (New \r\nZealand/Germany), Hyacinth C. Nnamchi (Nigeria, Germany/Nigeria), Juan A. Rivera (Argentina), \r\nBlair Trewin (Australia)"


id: a6e18c1856c8acf44075aff1e3c4cfaf
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
index: climate_gpt_only_giec
vector_id: 23772
content:
"IPCC, 2021: Annex IV: Modes of Variability [Cassou, C., A. Cherchi, Y. Kosaka (eds.)]. In Climate Change 2021: The Physical \r\nScience Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate \r\nChange [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Pean, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. \r\nHuang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekci, R. Yu, and B. Zhou (eds.)]. Cambridge \r\nUniversity Press, Cambridge, United Kingdom and New York, NY, USA, pp. 2153-2192, doi:10.1017/9781009157896.018."


id: fae45a5e1e6fb4efa711978c95d919d9
created_at: 2023-03-16 14:00:53
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content_type: text
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vector_id: 36995
content:
"Many modes of variability are driven by internal climate processes \r\nand provide a substantial potential source of climate predictability \r\non sub-seasonal to decadal time scales. It is essential to understand \r\nthe physical processes behind the past evolution of the modes of \r\nclimate variability in order to assess, with confidence, their future \r\nchanges. External forcing may affect their temporal (occurrence, \r\nvariance, seasonality and persistence, etc.) or spatial properties and \r\nassociated teleconnections."


id: 122e46d3bbdf8695eecb41641cd0fac6
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
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index: climate_gpt_only_giec
vector_id: 362
content:
"There are several ways to document and describe the modes of climate \r\nvariability based on more or less sophisticated statistical techniques \r\nand the dynamical nature, interpretation and understanding of \r\nthe variability (i.e., Feldstein and Franzke, 2017; Hannachi et  al., \r\n2017; Franzke et  al., 2020; Ghil and Lucarini, 2020). The simplest \r\nmethod to evaluate the temporal evolution of a spatially coherent \r\nphenomenon is to take one or more spatial averages of a climate \r\nvariable such as sea surface temperature (SST) or sea level pressure \r\n(SLP), as examples, within a  latitude-longitude box. Another \r\nclassical technique relies on principal component analysis  (PCA), \r\nalso known as empirical orthogonal function (EOF) analysis, to \r\nextract the patterns in the chosen climate variable with maximal \r\nspatial covariance. The method also extracts an associated climate "


id: bab13a356b0e3ae970cc3c5e268a4a47
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
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index: climate_gpt_only_giec
vector_id: 26914
content:
"The annex summarizes the fundamental features of spatial structure, \r\nseasonality and teleconnections for a selection of modes of climate \r\nvariability corresponding to those addressed in the report, namely: the \r\nNAO and Northern Annular Mode, the Southern Annular Mode, ENSO, \r\nthe Indian Ocean Basin and Dipole modes, the Atlantic Meridional \r\nand Zonal modes, the Pacific Decadal Variability, the Atlantic \r\nMulti-decadal Variability, and the Madden-Julian Oscillation. For \r\neach mode, the description follows a common structure, starting with \r\na short definition of the mode (based on simple box-averaged indices \r\nand EOF) and related key physical mechanisms, then a description \r\nof the associated teleconnections (based on regression techniques), \r\nand when relevant, finishes with a short summary of paleoclimate \r\nreconstruction of the mode based on proxy-based records. An \r\nillustrative figure including patterns and time series is also provided \r\nfor each mode."


id: dc16dbc7282d6fc70fc013271698b92a
created_at: 2023-03-16 14:00:53
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index: climate_gpt_only_giec
vector_id: 32140
content:
"The North Atlantic Oscillation (NAO) is the leading mode of large-scale \r\natmospheric variability in the North Atlantic basin. It is characterized \r\nby alternating (seesaw) variations in SLP between the Azores High \r\nin the subtropics and the Icelandic Low in the mid- to high latitudes, \r\nwith some northward extension deep into the Arctic (Hurrell et al., \r\n2003). The positive phase of the NAO corresponds to an anomalously \r\nstrong Azores High and concomitant deepening of the Icelandic Low \r\nwith some penetration in the sub-Arctic basin (Greenland/Norwegian/\r\nBarents Sea), accompanied by a  reinforced and northward-shifted \r\njet and storm track over a  broad North Atlantic-European sector \r\n(Figure  AIV.1a). The opposite, notwithstanding marginal spatial \r\nasymmetries, occurs for the negative phase."


id: a709babf7d4d7caba8cbfaa9683e1f39
created_at: 2023-03-16 14:00:53
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content:
"The temporal correlation between the EOF and fixed latitude-based \r\nzonal NAM estimations is equal to 0.91 for the December-January-\r\nFebruary (DJF) average and both are strongly correlated with the NAO \r\nindices (Figure AIV.1c). Correlations between all the NAO estimates \r\nare spatially and temporally very high in all the observational \r\nproducts over the modern reanalyses period (Gerber and Martineau, \r\n2018); for instance, the correlation between the station-based and \r\nEOF-based NAO indices over 1959-2018 in boreal winter using \r\nJRA-55 is equal to 0.89 (Figure AIV.1c). EOF-based NAO and NAM \r\nindices are used in Section  3.7.1 for model evaluation. The fixed\u0002latitude NAM index is used in Sections 2.4.1.1, 4.3.3.1, 4.4.3.1 and \r\n4.5.3.1, and in Section 3.7.1 for attribution of past changes."


id: e0c74264a5a6bb775cd78345a9f836f9
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index: climate_gpt_only_giec
vector_id: 32867
content:
"Given their equivalent barotropic structure, the NAO/NAM is usually \r\ndefined from SLP fields or geopotential height anomalies at various \r\nlevels (Fu et al., 2016). The NAO/NAM can be defined using pattern\u0002based statistical techniques such as EOF or clustering techniques \r\napplied to seasonal gridded atmospheric circulation fields (SLP or \r\ngeopotential height anomalies) from observations or reanalysis \r\ndata. The NAM pattern is traditionally defined as the leading EOF \r\nof pressure fields computed over the entire hemisphere, northward \r\nof 20degN (Figure AIV.1b) and its related PC defines the NAM index \r\n(Figure AIV.1c). Similarly the NAO index can be defined as the PC of \r\nthe leading EOF of the pressure field over the North Atlantic sector \r\n(Figure AIV.1a) or the temporal occurrence of the seasonal weather \r\nregimes extracted from clustering (Corti et al., 1999; Cassou et al., \r\n2011; Hannachi et al., 2017)."


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vector_id: 26198
content:
"shorter time scale (interannual), the NAO is dominated by variations \r\nin the latitude of the North Atlantic jet and storm track, whereas \r\non the longer time scale (multi-decadal and beyond) it represents \r\nchanges in their strength. The two time scales also exhibit different \r\nregional impacts on temperature and precipitation, and different \r\nrelationships to SSTs, which makes proxy reconstructions of the NAO \r\nproblematic."


id: 284b298af8b82c72b57b24893ae58afb
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
index: climate_gpt_only_giec
vector_id: 3750
content:
"There is a strong seasonal asymmetry in observed NAO teleconnection \r\npatterns and NAO temporal evolution. Impacts over Europe are \r\nopposite between summer and winter months with dominant \r\nwarmer and drier weather conditions over northwestern Europe in \r\nsummer concurrently with wetter conditions over southern Europe \r\nduring positive NAO phases (Folland et al., 2009; Blade et al., 2012). \r\nExtreme events (heatwave occurrence, droughts, etc.) are also linked \r\nto NAO phases (Cassou et  al., 2005; Drouard et  al., 2019) due to \r\nthe related latitudinal shift of the North Atlantic storm tracks (Dong \r\net al., 2013). Summer NAO trends also affect the Arctic climate as \r\nassessed in Sections 3.4.1 and 9.4.1. The NAO is associated with \r\nstorms or periods of persistent mild winds and dry/sunny spells \r\naffecting availability and power consumption (Jerez et  al., 2013; \r\nZubiate et al., 2017)."


id: c6a257464c997c503a2f6612622b24fc
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
index: climate_gpt_only_giec
vector_id: 28817
content:
"Most of the reconstructed NAO/NAM indices describe the boreal \r\nwinter patterns of the last 500 years because the variability is the \r\nmost pronounced and is on a larger scale for that season (Luterbacher, \r\n2001; Dezileau et al., 2011). Tree rings can however allow NAO boreal \r\nsummer reconstruction back to the 16th century (Folland et al., 2009; \r\nLinderholm et al., 2009). Recent efforts to develop reconstructions of \r\nthe NAO phases using pollen data (Mauri et al., 2014), speleothems \r\n(Ait Brahim et  al., 2019) and lake sediments (Olsen et  al., 2012) \r\nprovide new evidence for multi-centennial NAO-type variability \r\nalong the Holocene (Hernandez et al., 2020). A detailed description \r\nof the latest proxy-based reconstructions of the NAO and related \r\nconclusions placing the observations since the pre-industrial period \r\nin a broader context of variability are given in Section 2.4.1.1."


id: ab82ee7a03a330984a81f0623378b389
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
index: climate_gpt_only_giec
vector_id: 24500
content:
"NAO fluctuations generate ocean responses due to altered buoyancy \r\nfluxes and wind stress anomalies (Barrier et al., 2014). Signatures of \r\nthe NAO can be found in the gyre circulations (Marshall et al. 2001) \r\nand in the Atlantic Meridional Overturning Circulation (AMOC; Yeager \r\nand Danabasoglu, 2014), with a  sea level imprint in observations \r\n(Han et al., 2017). The oceanic response is dependent on the time \r\nscale of the NAO variability (Delworth and Zeng, 2016), especially \r\nfor AMOC in connection with the so-called Atlantic Multi-decadal \r\nVariability (AMV; Section AIV.2.7). This is assessed in Section 9.2.3.1."


id: 2b5ac923ed66ebe80c5053cc8041c08f
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
index: climate_gpt_only_giec
vector_id: 4256
content:
"Implications of NAO/NAM variability in terms of observed and \r\nprojected precipitation at a  regional scale (Europe, Mediterranean \r\nbasin, etc.) is comprehensively assessed in Sections 8.3.2.9, \r\n8.4.2.9 and 8.5.2. NAO implications on extremes are assessed in \r\nSections  11.3.1 and 11.4.1, and regional manifestations of NAM/\r\nNAO are further assessed in Atlas.5, Atlas.7, Atlas.8 and Atlas.9, and \r\nCross-Chapter Box Atlas.2."


id: b9bb3c33130483015d0146dd22a4145a
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
index: climate_gpt_only_giec
vector_id: 26762
content:
"Given its equivalent barotropic structure, the SAM is commonly \r\nquantified as the first EOF mode of extratropical geopotential \r\nheight anomalies at various tropospheric levels or of SLP anomalies. \r\nHowever, it extends into the stratosphere, coupling lower\u0002and middle-atmosphere dynamics. As it essentially represents \r\na meridional atmospheric mass transfer, an alternative definition is \r\nthe difference in normalized zonal mean SLP anomalies at middle \r\n(40degS) and high (65degS) southern latitudes (Gong and Wang, 1999; \r\nMarshall, 2003). The polarity of the SAM is defined such that it is "


id: 2ab15eaa9b5aa345ef8cf8e5d1189f0a
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
index: climate_gpt_only_giec
vector_id: 4140
content:
"SAM. In austral summer, during a positive phase of the SAM, drier \r\nconditions affect most of the southern Patagonia region and the \r\nsouthernmost tip of South America, while wetter conditions are \r\nfound in South Africa and Madagascar, as well as in south-eastern \r\nAustralia where colder temperatures also prevail (Figure AIV.2c, d). \r\nPositive temperature anomalies are found in New Zealand, Tasmania \r\nand the southern tip of South America. These anomalies appear as \r\nthe continental fingerprint of a  warmer belt located around 40degS \r\nthat is characterized by significant hemispheric-scale SST changes \r\nconsistent with the southern shift of the summer storm track."


id: b382dc3be1458efdac47727ac1e65722
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
index: climate_gpt_only_giec
vector_id: 25752
content:
"positive when pressure over Antarctica is anomalously low, and \r\nvice versa. While the SAM pattern is more zonally symmetric than \r\nits Northern Hemisphere counterpart (NAM; Section AIV.2.1), it \r\ndoes exhibit some asymmetries particularly in the mid-latitude node \r\nover the Pacific Ocean and Indian Ocean sectors (Fogt et al., 2012). \r\nAlternative interpretations of the SAM as a regional variability pattern \r\nin the South Pacific are also considered (Spensberger et al., 2020). The \r\nzonal asymmetries are present for all seasons, and illustrated in Figure \r\nAIV.2a for austral summer, to support the assessment in Section 3.7.2. \r\nA number of different SAM indices have been produced, and several \r\nare compared by Ho et al. (2012) and Barrucand et al. (2018), showing \r\ngood agreement after the second half of the 20th century. Over the \r\n1979-2019 period, the correlation between the EOF-based and zonal \r\nSLP-based indices for the summer SAM reaches 0.94 (Figure AIV.2b)."


id: 6fd24221864ac0d3a3c6ac584867fde0
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
index: climate_gpt_only_giec
vector_id: 15218
content:
"Moreover, several studies have described equatorward teleconnections \r\nlinking the SAM to Asian precipitation across the equator (e.g., Prabhu \r\net  al., 2017; Liu et  al., 2018). Coupling of seasonal stratospheric \r\nanomalies to the SAM can influence spring heat extremes in eastern \r\nAustralia (Lim et al., 2019). Marshall et al. (2013) described a reversal \r\nin the relationship between the SAM and East Antarctic temperatures \r\nduring austral summer/autumn in the first decade of the 21st century, \r\ndue to regional variability of the pressure pattern associated with \r\nthe zonal wavenumber 3. Other studies have analysed the temporal \r\nvariability in the interactions between the SAM and ENSO at different \r\ntime scales (e.g., Yu et al., 2015; Clem et al., 2016), being positive and \r\nsignificant during 1986-2014 (Vera and Osman, 2018)."


id: df3579dfeeb5c1a62088579a4641d45a
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
index: climate_gpt_only_giec
vector_id: 32618
content:
"During the positive phase of the SAM, the Antarctic air mass is isolated \r\nby an enhanced westerly jet that is located further south than during \r\nthe negative phase. This leads to colder conditions over the entire \r\ncontinent and negative SST anomalies along the surrounding ocean \r\n(Figure AIV.2). During the opposite, negative phase, an equatorward \r\ndisplacement of the jet and related storm track brings low-pressure \r\nsystems toward the north with a strong effect on the southern tip of \r\nSouth America (Mariani and Fletcher, 2016). The SAM is often used \r\nas a  proxy for the location of the mid-latitude westerly wind belt \r\nat seasonal to interannual time scales. However, Swart et al. (2015) \r\nnoted that trends in the SAM and the Southern Hemisphere surface \r\nwesterly jet are not directly interchangeable because multiple factors \r\nother than SAM can affect trends in the position, width and strength \r\nof the jet and surface wind."


id: 92b998e9066ad2caee445e862f8ac2b6
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
index: climate_gpt_only_giec
vector_id: 20600
content:
"Implications of SAM on observed and projected changes in the water \r\ncycle and specific regional influences are assessed in Sections 8.3.2.9 \r\nand 8.4.2.9, and in Sections 12.4, Atlas.7 and Atlas.11, respectively."


id: 8a1f0747038e7c8eb1f1e33ff5652f03
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
index: climate_gpt_only_giec
vector_id: 19259
content:
"The SAM is mainly driven by extratropical atmospheric dynamics, \r\nbut its phase is affected by remote SST anomalies such as ENSO \r\n(Section AIV.2.3) and anthropogenic forcing as assessed in Section 3.7.2."


id: 3522c3c41ec5c9986a1a9c199cbafd70
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
index: climate_gpt_only_giec
vector_id: 5768
content:
"The EOF-based SAM index is used for model evaluation in Section 3.7.2. \r\nThe fixed-latitude SAM index is used for the attribution assessment \r\nof the observed positive trend of the SAM that is maximum over \r\nthe second half of the 20th century but reduced since about 2000 \r\n(Section 2.4.1.2) due to slackened ozone influence (Section 3.7.2). \r\nThe fixed-latitude SAM index is also used in Sections 4.3.3.1, 4.4.3.1 \r\nand 4.5.3.1 to assess the future changes in Southern Hemispheric \r\nzonal circulation as a function of emissions scenarios and lead times."


id: b5c6dc3b05332dadbf7636428d3efc18
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
index: climate_gpt_only_giec
vector_id: 26129
content:
"Recent observational studies have enriched our understanding of \r\nthe widespread influence of SAM variability on mean climate and \r\nrelated environmental parameters of the entire Southern Hemisphere \r\nextratropics (e.g.,  Manatsa et  al., 2013; Waugh et  al., 2013; Raut \r\net al., 2014; Jones et al., 2016; Holz et al., 2017; Kostov et al., 2017; \r\nMarshall et  al., 2017). The influence of the SAM extends over the \r\nmid-latitude continental land masses with precipitation anomalies \r\nover southern Australia (Cai and Watterson, 2002) and South-Eastern \r\nSouth America (Silvestri and Vera, 2003) in connection with the \r\nlatitudinal migration and strength of the extratropical storm and mean \r\nwesterly circulation. Consistently with NAM/NAO (Section AIV.2.1), \r\nthere is a strong seasonality in the teleconnection associated with "


id: 6e61977f0f1dac1e40323803d42299eb
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
index: climate_gpt_only_giec
vector_id: 14991
content:
"which are all different in the magnitude, spatial structure and/or \r\nseasonal timing. As a direct consequence, it is difficult to accurately \r\nrepresent ENSO with one single index. Over time, further indices have \r\nbeen introduced to monitor ENSO event evolution (i.e., Trans Nino \r\nIndex  - TNI, Trenberth and Stepaniak, 2001) and spatial structure \r\n(i.e., El Nino Modoki Index - EMI, G. Li et al., 2010). The limited length \r\nand quality of the observational records may further affect the choice \r\nof the index, whose relevance can depend on the specific application. \r\nThe variety in ENSO indices also explains the non-universality in the \r\nqualification of ENSO status delivered by meteorological agencies \r\nresponsible for operational monitoring, operational forecast and \r\nclimate services for users."


id: fdae801923cbd2d908bbe2ff4e74289d
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
index: climate_gpt_only_giec
vector_id: 37429
content:
"SST anomalies from the NINO3 and NINO3.4 regions provide a direct \r\nmeasure of the events via its surface ocean fingerprint, while the SOI \r\nprovides a measure in terms of atmospheric perturbation. Data are \r\navailable back to 1876 for the SOI computation, whilst the uncertainty \r\nin SST-based indices, particularly those associated with the central \r\nequatorial Pacific, is large prior to the 1950s when the number of SST \r\nobservations was much lower (Kennedy et al., 2019). The complexity \r\nof ENSO is at least partly demonstrated by the observed events, "


id: df90bca1dcd3e434607c9fabdb265634
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
index: climate_gpt_only_giec
vector_id: 32677
content:
"The description of ENSO in this Annex supports the assessments of \r\nSections 2.4.2, 3.7.3, 4.3.3.2, 4.4.3.2 and 4.5.3.2. ENSO's influence \r\non ocean circulation, heat transport and sea levels are assessed in \r\nSections 9.2 and 9.6."


id: 9ffaf88aa1266fc21022edaa19045e10
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
index: climate_gpt_only_giec
vector_id: 22709
content:
"modulated by the inherent strong internal variability (Deser et al., 2017; \r\nDrouard and Cassou, 2019). In the Atlantic, a  relationship between \r\nENSO and Atlantic Nino (Section AIV.2.5) is found in boreal spring \r\n(Tokinaga et al., 2019) while the South Atlantic Subtropical Dipole mode \r\nis mostly connected to ENSO in austral summer (Rodrigues et al., 2015)."


id: 1553ef49df573c6c185078d5d61b94d1
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
index: climate_gpt_only_giec
vector_id: 875
content:
"Implications of ENSO in water cycle changes are further assessed in \r\nSections 8.3.2.9 and 8.4.2.9. The association of ENSO with extremes \r\nand specific regional climate anomalies is assessed in Sections 11.4, \r\n11.6, 11.7 and 12.4, in Box  11.4, and in Atlas.4, Atlas.5, Atlas.6, \r\nAtlas.7 and Atlas.9."


id: b671d3770553afac355b15d58740c5b6
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
index: climate_gpt_only_giec
vector_id: 26227
content:
"Being the dominant actor of variability at interannual time scales, \r\nENSO is linked to many other modes of variability both in the tropics \r\nand mid-latitudes, either in phase or with some seasonal lag. For \r\ninstance, connection has been documented between EP ENSO events \r\nand SAM during austral summer (Ciasto et al., 2015). A relationship \r\nexists between ENSO and the NAO/NAM in boreal winter through \r\nvarious mechanisms such as stratosphere-troposphere coupling \r\n(Domeisen et  al., 2015) and tropospheric pathways, including the \r\neastward extension of the PNA during El Nino events and also through \r\nthe alteration of the propagation of tropical Rossby wave trains \r\nfrom the  Pacific to the Atlantic (Drouard et  al., 2015). The tropical\u0002extratropical teleconnection is not symmetrical with the phase of ENSO \r\nand non-stationary with considerable dependence on epochs being "


id: 8fd5deb02a83d960d2d02a8dc0de0e58
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
index: climate_gpt_only_giec
vector_id: 20164
content:
"The Indian Ocean Basin (IOB) and Indian Ocean Dipole (IOD) modes \r\nare the two dominant modes of interannual climate variability in the \r\nIndian Ocean. The IOB mode features the temporal alternation of \r\nbasin-wide warming and cooling of sea surface in the positive and \r\nnegative phases, respectively (Figure AIV.4), while the IOD mode is "


id: 9de4052627a67e6a1fdd87678ac42ab2
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
index: climate_gpt_only_giec
vector_id: 22361
content:
"Paleo fluctuations of IOB and IOD as well as the changes over the \r\ninstrumental record period are assessed in Section 2.4.3, while their \r\nmodel representations are assessed in Section 3.7.4. Future variations \r\nunder global warming are assessed in Sections 4.4.3.3 (near term) \r\nand 4.5.3.3 (mid- to long terms)."


id: 25f59e38dcbd40a5440e0842b4f2c5cc
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
index: climate_gpt_only_giec
vector_id: 3396
content:
"characterized by a zonal dipole of SST anomalies in the tropics, with \r\none lobe centred off Sumatra and Java and the other covering a large \r\nportion of the western basin (Figure AIV.5). The two modes mostly \r\nvary on interannual time scales and are both correlated positively \r\nwith ENSO. In particular, the IOD tends to emerge during ENSO \r\ndevelopment from boreal summer to autumn (Stuecker et al., 2017), \r\nfollowed by the IOB which grows in ENSO peak season and persists \r\nfor a few subsequent seasons (Tokinaga and Tanimoto, 2004)."


id: d105c1111aabe69efea7e8f7e6efbb22
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
index: climate_gpt_only_giec
vector_id: 30422
content:
"Since the IOB mode is tightly associated with ENSO, related climate \r\nanomalies can be viewed as part of ENSO's influence from boreal \r\nwinter to subsequent spring. Still, the IOB mode plays an important \r\nrole in modulating the ENSO influence on several regions. The positive \r\nphase of the IOB plays a major role in suppressing precipitation in the \r\nMaritime Continent from boreal winter to spring (Watanabe and Jin, \r\n2002; Annamalai et al., 2005) and in Northern Australia in austral \r\nsummer (Taschetto et al., 2011) in conjunction with El Nino events \r\n(Figure AIV.3d). An anomalous surface anticyclone then forms over \r\nthe Philippine Sea linked to the precipitation reduction in the tropical \r\nwestern Pacific and brings more precipitation to East Asia during \r\nEl Nino winter and spring (Figure AIV.3d; Wang et al., 2000). "


id: 55bdd68d2eea0f400e9620041cab2f32
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
index: climate_gpt_only_giec
vector_id: 10970
content:
"The IOB mode is considered primarily as a  response to ENSO \r\nassociated with large-scale alteration of the Walker circulation, \r\nand thus matures in boreal spring, a season after the typical ENSO \r\npeak (Klein et al., 1999; Lau and Nath, 2000, 2003). By contrast, the \r\nBjerknes feedback is key to the formation of the IOD. This feedback \r\nrequires background surface easterlies and thermocline shallowing \r\nin the eastern part of the basin along the equator, and this condition \r\nsets in boreal spring and persists until autumn, then dissipating by \r\nearly winter in association with climatological monsoon wind swing. \r\nThe IOD mode therefore develops in boreal summer and reaches \r\nits maximum amplitude in autumn, then rapidly terminates before \r\nwinter (Schott et al., 2009). Differently from the IOB, strong internal \r\nfeedbacks enable the IOD mode to develop also without influence \r\nfrom ENSO, as noted for some specific events (Saji and Yamagata, \r\n2003b; Meyers et al., 2007)."


id: 5bcc4d1ce6364c4e0571e08941022555
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
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index: climate_gpt_only_giec
vector_id: 11957
content:
"The IOD is associated with climate anomalies in broad regions \r\n(Figure  AIV.5c,d). However, the related anomalies are affected by \r\ninfluences from concomitant ENSO. The IOD affects rainfall in eastern \r\nAfrica, South Asia, Indonesia and Australia (Figure AIV.5c,d). The IOD \r\nin the positive phase increases rainfall in eastern tropical Africa in \r\nboreal autumn to early winter (Figure AIV.5d), while the negative \r\nphase induces the opposite anomalies. These anomalies are also found \r\nin association with ENSO (Figure AIV.3d), but the explained variance \r\nby IOD is larger (Saji and Yamagata, 2003a). Both positive IOD and \r\nEl Nino events reduce precipitation and temperature over the entire \r\nMaritime Continent (Figures AIV.5c,d and AIV.3c,d) because of strong \r\nlow-level divergence coupled with upper-level convergence related \r\nto diminished convection, while the opposite anomalies are induced \r\nby negative IOD and La Nina episodes (Saji and Yamagata, 2003a). "


id: 666128938f49a9e152d5f47d6fd3ae25
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
index: climate_gpt_only_giec
vector_id: 13699
content:
"The IOB-like (Han et al., 2014) and IOD-like (Ashok et al., 2004a; Tozuka \r\net al., 2007) variability can also be identified at decadal time scales. \r\nThe decadal IOB mode is defined as the leading EOF mode of linearly \r\ndetrended monthly SST after decadal time filtering (Han et al., 2014; \r\nDong and McPhaden, 2017). Ten-year low-pass filtered Indian Ocean \r\nbasin-mean SST is an alternative index for the decadal IOB mode (Dong \r\net al., 2016), with the global-mean SST subtracted in some cases as \r\nan estimation of the response component to radiative forcing (Huang \r\net al., 2019). The decadal IOD mode can be extracted as the second EOF \r\nmode of 10-year low-pass filtered Indian Ocean SST in boreal autumn \r\n(Yang et al., 2017), while other studies use the 10-year low-pass filtered \r\nSST anomaly difference between the western and eastern equatorial \r\nIndian Ocean (Ashok et al., 2004a; Tozuka et al., 2007)."


id: 446105aaf02b3cf97ccdfb602acb2f16
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
index: climate_gpt_only_giec
vector_id: 8180
content:
"the northern and southern portion of the Atlantic. Both modes are \r\nassociated with changes in the Inter-tropical Convergence Zone \r\n(ITCZ) and related winds, and exert a strong influence on the climate \r\nin adjacent and remote regions."


id: 249114eb3a9ef04b1dfa9c71d4eb7f99
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
content_type: text
index: climate_gpt_only_giec
vector_id: 3197
content:
"Implications of these modes on water cycle changes and specific \r\nregional influences are further assessed in Sections 8.3.2.9, \r\n8.4.2.9 and 12.4, and Atlas.5, Atlas.6, Atlas.7 and Cross-Chapter \r\nBox Atlas.2, respectively."


id: 9a09801dc0b71ae41e8020c298221a5f
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
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index: climate_gpt_only_giec
vector_id: 21760
content:
"The AZM and AMM represent the first and second EOF patterns of \r\nthe tropical Atlantic variability identified using SST anomalies in this \r\nregion (Figures AIV.6a and AIV.7a). These modes may also be defined \r\nusing box averages, with the AZM defined as SST anomalies averaged \r\nover the equatorial Atlantic region called ATL3 (3degS-3degN, 0deg-20degW), \r\nand the AMM defined as the difference between the normalized SST \r\nanomalies averaged over the tropical North Atlantic and tropical \r\nSouth Atlantic (i.e., 5degN-30degN, 20degW-60degW minus 5degN-20degS, \r\n5degE-25degW). In boreal summer, the ATL3 and the EOF-AZM PC are \r\ncorrelated at 0.87 during 1958-2019 (Figure AIV.6b). The correlation \r\nbetween the AMM-box index and the EOF-AMM PC is lower at 0.39 \r\n(Figure AIV.7b) but increases to 0.67 over 1900-2019."


id: 80af91edf42196ca4cf8ed26fe118fde
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
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index: climate_gpt_only_giec
vector_id: 17768
content:
"For the AMM, the latitudinal polarity in SST anomalies is associated \r\nwith wind gradient and flow from the cooler to the warmer \r\nhemisphere (Figure AIV.7c; Ruiz-Barradas et  al., 2000; Chiang and \r\nVimont, 2004; Lubbecke et al., 2018; Foltz et al., 2019). For the AZM, \r\nsimilarly to ENSO in the Pacific Ocean, anomalous westerlies lead to \r\nlow-level convergence over warmer SST along the climatological cold \r\ntongue (Figure AIV.6c)."


id: 1271d671bf33d0825ef995d978633190
created_at: 2023-03-16 14:00:53
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content:
"The Atlantic Zonal Mode (AZM), also commonly referred to as \r\nthe Atlantic Nino, and the Atlantic Meridional Mode (AMM) are \r\nthe two dominant modes of tropical Atlantic climate variability on \r\ninterannual to decadal time scales. The AZM peaks during the boreal \r\nsummer upwelling season with maximum SST anomalies in the \r\neastern equatorial Atlantic and is commonly regarded as the Atlantic \r\ncounterpart of the Pacific El Nino (Zebiak, 1993; Keenlyside and Latif, \r\n2007; Lubbecke et al., 2018; Foltz et al., 2019). The AMM, which is \r\nmore active on longer time scales, peaks during boreal spring and \r\nexhibits a  cross-equatorial gradient of SST with opposite signs in "


id: e5598418dc64f1d12c5c10898fa46e
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
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content:
"The temporal evolution of the Atlantic modes of variability and \r\ntheir model representation are assessed in Sections 2.4.4 and 3.7.5, \r\nrespectively. Their near-term and long-term evolution is assessed in \r\nSections 4.4.3.4 and 4.5.3.4, respectively."


id: 758453625e75cf4743be5c4b1bcc735f
created_at: 2023-03-16 14:00:53
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content:
"teleconnection pattern (Haarsma and Hazeleger, 2007; Garcia-Serrano \r\net al., 2011), and can remotely affect the NAO (Dreevillon et al., 2003; \r\nPeng et  al., 2005). The AZM can also increase precipitation over \r\nsouthern Europe and the Mediterranean Sea during boreal summer \r\nthrough extratropical Rossby waves (Losada et al., 2012)."


id: 67c595e58f8df5f5951cd87b6a2ada4f
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
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vector_id: 13915
content:
"The tropical Atlantic variability has robust teleconnections with \r\nclimate variability across the global tropics and beyond. There is \r\nevidence for two-way teleconnections between the tropical Atlantic \r\nand Pacific on interannual to decadal time scales, such that the tropical \r\nAtlantic variability responds and feeds back to the Pacific ENSO \r\n(Section AIV.2.3) and Pacific Decadal Variability (PDV; Section AIV.2.6). \r\nA number of studies suggest the existence of a possible connection \r\nbetween the tropical North Atlantic and tropical Pacific variability \r\n(Wu  et  al., 2007; Ham et  al., 2013a, b; L.  Wang et  al., 2017) at \r\ninterannual time scales. This teleconnection appears to be modulated \r\nat a decadal time scale by the AMV phases (L. Wang et al., 2017) and \r\nit is also affected by global warming (Dong and Zhou, 2014)."


id: 4eccdc3ccf124a3fd40c99388b922296
created_at: 2023-03-16 14:00:53
updated_at: 2023-03-16 14:00:53
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content:
"The fluctuation of the meridional SST gradient in the tropical Atlantic \r\nmodulates the seasonal march of the ITCZ, thus influencing regional \r\nrainfall over north-eastern Brazil and the Sahel (Hastenrath and \r\nHeller, 1977; Folland et al., 1986; Foltz et al., 2012). During a negative \r\nphase of the AMM, northerly wind anomalies are associated with \r\na  southward shift of the ITCZ (Cabos et  al., 2019). During boreal \r\nspring, warmer SST over the tropical North Atlantic modulates winds \r\nover the western Indian Ocean and may strengthen the monsoon \r\ncirculation over India, affecting the continental rainfall (Vittal et al., \r\n2020). The AMM is also well known to affect the Atlantic hurricane \r\nactivity (Vimont and Kossin, 2007; Patricola et al., 2014) and tropical \r\ncyclones over the western North Pacific (Zhang et al., 2017)."


id: 8a74a58743f52c44d3580cbd6d8d0a0b
created_at: 2023-03-16 14:00:53
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content:
"The AZM-related boreal summer variability is linked with ENSO in \r\nthe following boreal winter (Polo et  al., 2008; Rodriguez-Fonseca \r\net  al., 2009; Ding et  al., 2012; Keenlyside et  al., 2013; Martin-Rey \r\net  al.,  2015; Cai et  al., 2019). This relationship is strongest during \r\nnegative AMV phases due to enhanced equatorial Atlantic variability \r\n(Martin-Rey et al., 2014, 2018; Polo et al., 2015). On the other hand, \r\nthe persistence of SST anomalies during multi-year ENSO events \r\naffects the equatorial Atlantic variability through modifications of \r\nthe Walker circulation (Tokinaga et al., 2019). An exhaustive analysis \r\nof the AZM characteristics and related teleconnections is given in \r\nLubbecke et al. (2018) and by Cabos et al. (2019)."


id: 38b4e9861ce54916643de281e7f637af
created_at: 2023-03-16 14:00:53
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content:
"Only a  few paleo-reconstructions of AMM and AZM are available. \r\nInter-hemispheric cross-equatorial SST gradients linked to changes \r\nin ITCZ locations characteristic of the AMM have been found during \r\nthe Last Glacial Maximum, Heinrich Stadial 1 and the mid-Holocene \r\n(McGee et al., 2014). Similarly, the dipole-like SST pattern in the South \r\nAtlantic subtropics, which is related to the AZM (Lubbecke et  al., \r\n2018; Foltz et al., 2019), has been reconstructed with SST proxies from \r\nmarine sediment cores during the past 12 kyr (Wainer et al., 2014)."


id: c62788b2250ab8ea5fe4ab09bac07bd
created_at: 2023-03-16 14:00:53
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vector_id: 28750
content:
"The Pacific Decadal Variability (PDV) describes the large-scale \r\nfluctuations typically observed beyond ENSO time scales in a variety \r\nof instrumental records and proxy reconstructions over the entire \r\nPacific Ocean and surrounding continents."


id: ae6d46a816d87087cc934e3a2863fde9
created_at: 2023-03-16 14:00:53
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vector_id: 24971
content:
"Historically, climate variability of the Pacific Ocean on time scales \r\nlonger than 8-10 years has been described in terms of statistical \r\nmodes, usually assessed through the decomposition into EOFs \r\nof the SST over oceanic sub-basins. The leading EOF mode of SST \r\ndecadal variability in the extratropical North Pacific is called the \r\nPacific Decadal Oscillation (PDO; Mantua et al., 1997; Mantua and \r\nHare, 2002). The PDO is recognized to correspond to a collection of \r\nmultiple processes (Newman et al., 2016). On interannual-to-decadal \r\ntime scales, the PDO is thought to be driven by atmospheric forcing \r\nlinked to stochastic changes in the Aleutian Low integrated by ocean \r\nmixed layer dynamics and related re-emergence processes, as well \r\nas to ENSO-forced teleconnections (Schneider and Cornuelle, 2005; \r\nNidheesh et  al., 2017). On decadal-to-inter-decadal time scales, \r\nsubpolar-subtropical gyre dynamics and related zonal advection "


id: 7cd6e27442ca6d63ee5d1782c9601d4f
created_at: 2023-03-16 14:00:53
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content:
"On the western side of the basin, a warm equatorial Atlantic delays \r\nthe northward migration of the ITCZ and can thus influence rainfall \r\nover North-Eastern South America (Nobre and Srukla, 1996), even \r\nbefore the peak of the AZM (Losada et  al., 2010; Mohino et  al., \r\n2011; Martin-Rey et al., 2018). When the Atlantic and Pacific basins \r\nact together, the response over North-Eastern South America \r\nis found to be strongly enhanced (Torralba et  al., 2015). In the \r\nextratropics, the decaying phase of the AZM in boreal autumn can \r\nmodify the atmospheric circulation over Europe via a circum-global "


id: 42fb77ba9e8353ad309c688caccd5bd3
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content:
"of temperature anomalies in the Kuroshio-Oyashio extension and \r\nwestward-propagating oceanic Rossby waves are thought to be key \r\nmechanisms (Qiu et al., 2007; Taguchi et al., 2007; Wills et al., 2019)."


id: c4ed390c08bb3b32d3b2345a87e3423a
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content:
"The PDO's South Pacific counterpart, called the South Pacific Decadal \r\nOscillation (SPDO; Chen and Wallace, 2015) is defined as the leading \r\nEOF mode of SST in the extratropical South Pacific. As for the PDO, the \r\nSPDO is thought to be driven by internal climate variability associated \r\nwith extratropical atmospheric modes (e.g.,  SAM; Section AIV.2.2), \r\nENSO teleconnections (Section AIV.2.3) and ocean dynamics through \r\ngyre circulation (Shakun and Shaman, 2009; Zhang et al., 2018)."


id: b9dc5f79d8f3247ba0a4b5b7eed9f814
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content:
"In the tropical Pacific, while ENSO has a clear interannual spectral \r\npeak, decadal variability is also present with a similar spatial structure \r\n(Zhang et al., 1997), called tropical Pacific Decadal Variability (TPDV) \r\nor decadal ENSO. Relative occurrence of El Nino and La Nina events \r\nin a given decade correlates with the phase of the TPDV (Power and \r\nColman, 2006)."


id: dbca8fcd93c5c9cc59fcd01da093eff
created_at: 2023-03-16 14:00:53
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content:
"In the following paragraphs and in Section  3.7.6 evaluating the \r\nmodel performance in reproducing PDV and assessing the human \r\ninfluence on the mode evolution, the TPI index is preferred. The latter \r\ndefinition is found to be less sensitive than classical EOF analysis to \r\nthe possible interplay between internal processes that intrinsically \r\ndrive the PDV (Wills et  al., 2018) and the externally-forced SST \r\nresponse simulated in historical simulations. There is no unique way \r\nto remove the impact of the external forcing in the observations \r\nat decadal to multi-decadal time scales, and any chosen method \r\nmay have significant implications for the interpretation of the PDV \r\nexpression during the instrumental era."


id: e22483eec550fb2b7e2da661d44955e2
created_at: 2023-03-16 14:00:53
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content:
"Whether the IPO is a  distinct mode of climate variability (Henley \r\net al., 2017) or not (Tung et al., 2019) remains to be further assessed \r\nand clarified. However, since the PDO, SPDO, TPDV and IPO modes \r\nare highly correlated both spatially and temporally, they can be \r\nreferred to collectively as the Pacific Decadal Variability mode (PDV; \r\nHenley, 2017; Liu and Di Lorenzo 2018), similarly to the Atlantic \r\n(Section AIV.2.7). Besides, it is noteworthy that none of these modes \r\nare actually oscillations with clear spectral peaks in the instrumental \r\nperiod (Mann et al., 2020), which advocates for the use of the PDV \r\nacronym across the report."


id: 34cbd799932301720769135d32bff25e
created_at: 2023-03-16 14:00:53
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content:
"De facto, there is no unique way to assess the PDV. It can be \r\nextracted from the IPO, defined as the leading EOF mode of decadal \r\nlow-pass filtered SST variability over the entire Pacific to remove the \r\noverwhelming influence of ENSO at interannual time scales. Henley \r\net al. (2015) introduced the tripole index (TPI) as the difference of \r\nSST anomalies between an equatorial Pacific domain (10degS-10degN, \r\n170degE-90degW) and an average of mid-latitude North (25degN-45degN, \r\n140degE-145degW) and South (50deg-15degS, 150degE-160degW) Pacific domains \r\n(Figure AIV.8a). Section 2.4.5 describes the changes in the PDV phases \r\nover the instrumental record using a variety of SST products."


id: d7f014a4fbb7a5fc139070a21ae5a71
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content:
"decadal IOB and IOD modes (Section AIV.2.4), while influence from \r\nthe Indian Ocean SST on PDV has been also suggested (Luo et al., \r\n2012; Mochizuki et al., 2016)."


id: f86ceffc50a2d3c8ad2593c36a1c9705
created_at: 2023-03-16 14:00:53
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content:
"Atlantic and western South Pacific (Grant and Walsh, 2001; Liu and \r\nChan, 2008; Li et al., 2015; Zhao et al., 2018, 2020). PDV, together with \r\nENSO, have been shown to control part of the interannual to multi\u0002decadal wildfire variability, through PDV-related persisting droughts \r\nand severe heatwaves acting as preconditioning in many regions \r\n(see for instance, Kitzberger et al., 2007; Ward et al., 2016)."


id: b38680429091e7a8b8e9f78b0bc5e8dd
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"The temporal evolution of PDV in the instrumental records and its \r\nmodel representation are assessed in Sections 2.4.5 and 3.7.6, \r\nrespectively. Skill in predicting near-term temporal evolution of the PDV \r\nand mid- to long-term PDV changes are assessed in Sections 4.4.3.5 \r\nand 4.5.3.5, respectively."


id: 39a7887dab5a7c7ddd385a593eeb9f73
created_at: 2023-03-16 14:00:53
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content:
"Implications of PDV on water cycle changes are further assessed in \r\nSections 8.3.2.4 and 8.4.2.4. Section 10.4 highlights the importance \r\nof PDV in regional climate change attribution, with an example in \r\nSection  10.6. The influence of PDV climate anomalies in specific \r\nregions, including extremes, are assessed in Sections 11.3, 11.4, \r\n11.6 and 11.7, and in Atlas.4, Atlas.5, Atlas.7 and Cross-Chapter \r\nBox Atlas.2. Its implications for ocean circulation and sea levels are \r\nassessed in Sections 9.2 and 9.6."


id: b8cd118c39b580c1f66252bb199148b9
created_at: 2023-03-16 14:00:53
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content:
"Due to the similarity of the SST-anomaly pattern with ENSO in the \r\ntropical Pacific, atmospheric teleconnections associated with the PDV \r\nare rather similar to those of ENSO but with a  longer temporal \r\nexpression. This includes the long-term alteration of the Walker \r\ncirculation, the modification of the extratropical background driven \r\nby tropically forced Rossby waves then favouring particular phases \r\nof the PNA and PSA patterns of atmospheric internal variability \r\nthat are responsible for persistent influences over the continents \r\n(e.g.,  persistent drought over California and Australia, etc.). Those \r\natmospheric patterns imprint PDO-like and SPDO-like SST patterns \r\nwith a reddening by the extratropical oceans, forming the pan-Pacific \r\nSST anomaly pattern of the PDV as described above (Newman et al., \r\n2016; Henley, 2017; Liu and Di Lorenzo, 2018)."


id: c0378e2aa641cd4f1ffac5c948eed411
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"The Atlantic Multi-decadal Variability (AMV) describes the large-scale \r\nslow fluctuations observed from one decade to the next in a variety \r\nof instrumental records and proxy reconstructions over the entire \r\nNorth Atlantic Ocean and surrounding continents. Fingerprints of the \r\nAMV can be found at the surface ocean, which is characterized by \r\nswings in basin-scale SST anomalies reflecting the interaction with \r\nthe atmosphere, with a nominal period in the order of 70 years (Deser \r\net al., 2010).AMV markers can be also tracked in the subsurface ocean \r\nin terms of heat content and density anomalies. The latter have been \r\ntraditionally linked to the low-frequency fluctuations in the Atlantic \r\nMeridional Overturning Circulation (AMOC) and related oceanic \r\nmeridional heat/salinity transport and water masses formation \r\nthrough deep convection in the sub-Arctic Seas (Zhang, 2017)."


id: 4d70e869db33b333cbc2c6078f4501a8
created_at: 2023-03-16 14:00:53
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content:
"In the tropics, a  positive PDV is associated with warm and dry \r\nanomalies in the Maritime Continent and Amazon (Meehl and Teng, \r\n2014; Figure AIV.8c, d). The Indian and African monsoons also tend \r\nto weaken (Meehl and Hu, 2006; Figure AIV.8d). Warm and dry \r\nanomalies also cover most of Australia while wet anomalies dominate \r\nin South America south of 15degS. In particular, a positive PDV increases \r\ndrought risk in Australia (Power et  al., 1999; Vance et  al., 2015). \r\nIn the oceans, a  positive PDV increases the occurrence of marine \r\nheatwaves in the Bering Sea and subtropical eastern Pacific, while \r\na negative PDV increases ocean heatwaves in the Kuroshio-Oyashio \r\nextension, Melanesia and over the tropical Indian Ocean (Holbrook \r\net al., 2019). PDV also modulates tropical cyclone activity: a positive \r\nPDV increases tropical cyclone genesis over the Philippine Sea and the \r\ntropical eastern North Pacific and decreases it over the tropical North "


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"The positive phase of the AMV is characterized by anomalous \r\nwarming over the entire North Atlantic, with the strongest amplitude \r\nin the subpolar gyre and along sea ice margin zones in the Labrador \r\nSea and Greenland/Barents Sea (approximately +0.5degC) and in the "


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"furthering a better understanding of the teleconnections associated \r\nwith the AMV. The two latter indices are shown in Figure AIV.9b and \r\ncompared to the traditional AMV index."


id: 5fb8e6bb04046b6729f3a3fc782a516c
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"subtropical North Atlantic basin to a  lower extent (Figure AIV.9a). \r\nConcurrent, albeit weaker, anomalous cooling is found over the \r\nSouth Atlantic but also along the Pacific cold tongue sandwiched by \r\nwarmer SST, a pattern reminiscent of the PDV (Section AIV.2.6). What \r\nstands out is the apparent inter-basin connectivity in AMV-related \r\nSST anomalies, which is strongly indicative of the existence of \r\na  network of teleconnections linking neighbouring ocean basins, \r\nthe tropics and the extratropics, including Northern Hemisphere \r\npolar regions, and the oceans and land regions (Cassou et al., 2018). \r\nThe AMV, together with the PDV, has been shown to modulate the \r\nobserved time-evolving global surface temperature on multi-decadal \r\ntime scales since pre-industrial time (Cross-Chapter Box 3.1), whilst \r\nthe level of their respective influence remains difficult to quantify \r\nbecause of their inter-connectivity (Tung and Zhou, 2013; Wu et al., \r\n2019; Li et al., 2020)."


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content:
"All AMV time series consistently show a few distinct phases over the \r\napproximately 120-year instrumental record, namely warm periods \r\nover 1930-1965 and since 1995, and cool periods between 1900 \r\nand 1925 and over 1965-1995 (Figure AIV.9b). The uncertainty \r\nintroduced by the different definitions of the index is mainly related \r\nto the strength of the phase of the AMV and in particular the most \r\nrecent ones, which are the most sensitive to the estimation of the \r\nexternally forced signal."


id: 50cd9ce933f16de3dc50a050187e25ed
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content:
"The temporal evolution of AMV in the instrumental records is \r\nassessed in Section 2.4.6. The transition between phases has been \r\nshown to involve various atmospheric and oceanic processes, leading \r\nto some predictability, but also influences from external forcing (both \r\nanthropogenic and natural), whose respective weight and interplay \r\nremain outstanding issues, as assessed in detail in Sections 3.7.7 \r\nand 9.2.3."


id: 818f5a848e53954274b2678481a0d198
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"The term 'Atlantic Multi-decadal Oscillation' (AMO) has been \r\nintroduced in the literature (Kerr, 2000) and used in previous IPCC \r\nreports to describe this phenomenon and in particular to make the \r\ndistinction with the so-called North Atlantic tripole pattern connected \r\nto the NAO (Section AIV.2.1) at interannual time scales (Enfield et al., \r\n2001). However, since the observed slow variations of the North \r\nAtlantic SSTs consist of a broad band of low-frequency signals (Zhang, \r\n2017; Sutton et al., 2018) rather than a single preferred time scale, \r\nas also confirmed from last-millennium reconstructions (e.g., Singh \r\net  al., 2018), the denomination 'Atlantic Multi-decadal Variability' \r\nwherein a distinct oscillatory time scale is less clearly implied, appears \r\nmore appropriate to describe decadal climate variability phenomena \r\nas also suggested for the Pacific (Section AIV.2.6). The term AMV will \r\nbe accordingly used subsequently and throughout the entire report."


