# document

id: 7cb40e666c0ac93f9c5e2eb91bdb779e
created_at: 2023-03-16 14:02:11
updated_at: 2023-03-16 14:02:11
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"Large-scale changes in the position and strength of the North \r\nAtlantic jet stream and storm tracks associated with the NAO/NAM, \r\nas well as the related incidence in blocking episodes traditionally \r\nlinked to extreme events, affect surface climate conditions over the \r\nentire North Atlantic and surrounding land masses from daily to \r\nmulti-decadal time scales (Hurrell, 1995). Warmer and wetter weather \r\nover Northern Europe extending eastward in western Siberia with \r\nsimultaneous cooler and drier conditions over a broad Mediterranean \r\nbasin are distinct features of the positive boreal winter NAO/NAM \r\n(Figure AIV.1d,e). Over North America, regions around the Labrador \r\nSea are significantly colder, with excess sea ice formation, while \r\nmilder conditions dominate the Great Plains, forming altogether \r\na  typical quadripolar anomaly pattern for temperature linked to \r\nthe winter NAO/NAM"


id: 1076cf20502355ddd644ccc632604195
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" The latter structure is tightly related to the \r\nlatitudinal shift of the storm tracks that is particularly marked in \r\nprecipitation over the ocean (Figure AIV.1e). More remotely, changes \r\nin North Atlantic storm track position and transient eddy activity \r\nassociated with the positive (negative) phase of the NAO contribute \r\ndownstream to negative (positive) SLP anomalies in north-eastern \r\nEast Asia during boreal summer (Linderholm et al., 2011)."


id: f03859ce4e63429b97c3a2add6735
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"An alternative and simpler estimate of the NAM/NAO uses differences \r\nin SLP between fixed locations. The NAM index can be calculated \r\nfrom the difference in normalized zonally averaged SLP anomalies \r\nbetween 35degN and 65degN (Li and Wang, 2003), which reflects the \r\nlatitudinal fluctuation of the position and strength of the mid-latitude \r\nwesterlies at the hemispheric scale. Similarly, for the NAO, a weather \r\nstation-based index is traditionally calculated as the difference of \r\nnormalized SLP anomalies located in the Azores (Ponta Delgada) or \r\ncontinental Iberia (Lisbon/Gibraltar) minus those along the northern \r\ncentre of action around Iceland (Stykkisholmur/Reykjavik) (Jones \r\net al., 1997). Cornes et al. (2013) alternatively uses the Paris-London \r\ndifference because of data availability back to the 17th century"


id: 8a493deac658142374767bdc16311082
created_at: 2023-03-16 14:02:11
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" The \r\nstation-based NAO index is simple to compute, extends the longest \r\nover the instrumental period, and can be used year round, but caveats \r\nstand in its limitation to capture the seasonal latitudinal migration of \r\nthe NAO variability pattern as well as potential shifts in the North \r\nAtlantic pressure centres of action at interannual to decadal time \r\nscales (Ulbrich and Christoph, 1999). Because it uses fixed locations, \r\nthe index captures part of the variability that is not directly related to \r\nthe latitudinal seesaw pressure balance and associated large-scale \r\nfluctuations of the zonal flow that characterize the NAO dynamics, \r\nand it also fails representing the spatial asymmetries between the \r\npositive and negative phases of the mode (Cassou et al., 2004)."


id: 305225816e75efa155cc9da7e851016c
created_at: 2023-03-16 14:02:11
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"Related changes in both westerly winds at mid-latitudes and trade \r\nwinds in the tropics over the North Atlantic result in anomalous \r\nfluxes of sensible and latent heat at sea surface, which create ocean \r\ntemperature anomalies that extend down to the base of the deep mixed \r\nlayer during boreal winter (Deser and Timlin, 1997; Seager et al., 2000). \r\nThe SST imprint of the NAO has a tripolar shape with positive anomalies \r\nat mid-latitudes straddled by colder surface ocean in the subtropics \r\nand along the entire subpolar gyre (Figure AIV.1d). Weak feedbacks \r\nfrom SST on boreal winter NAO are found through the forced Rossby \r\nwave mechanism (Baker et al., 2019) and local phenomena such as \r\noceanic re-emergence in the North Atlantic Nordic seas (Cassou et al., \r\n2007). Negative NAO phases are characterized by a wavier jet stream \r\nand a southward shift of storm tracks, which favours cold outbreaks \r\nover Northern Europe (Cattiaux et al"


id: c74c315321b6a461f4c7540dc7c6302d
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", 2010) and reinforced storminess \r\nalong the Mediterranean basin leading to heavy precipitation events \r\n(Sanchez-Gomez et  al., 2008) and storm surges in southern Europe \r\n(Cid et  al., 2016). Those are also often related to a  collapse/split/\r\ndisplacement of the low-level stratospheric polar vortex and the \r\noccurrence of stratospheric sudden warming (Sections 2.3.1.4.5 and \r\n3.3.3.4). In boreal winter, NAM and NAO teleconnections are very \r\nsimilar over the Atlantic-surrounding continents."


id: c9aae2f08f967eb8e65714bfe7d5c0d3
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"The Southern Annular Mode (SAM), sometimes called the Antarctic \r\nOscillation (AAO), is the leading mode of large-scale atmospheric \r\ncirculation variability in the Southern Hemisphere extratropics in \r\nall seasons and at intra-seasonal to multi-decadal time scales, \r\nalbeit with season-dependent physical characteristics such as \r\nfraction of explained variance, latitudinal shift and intensity of the \r\nrelated anomalous atmospheric circulation and SST fingerprint. It is \r\ncharacterized by synchronous SLP or geopotential height anomalies \r\nof opposite signs between the mid- and high latitudes, modulating \r\nthe mean westerly winds near 50degS. The SAM affects the frequency, \r\nstrength and position of cold fronts, and extratropical synoptic to \r\nmesoscale weather systems (Thompson et al., 2000; Manatsa et al., \r\n2016; Mariani and Fletcher, 2016; Cerrone and Fusco, 2018)"


id: c6954896595ba3fbafcade76c779eb02
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" It controls \r\na  significant fraction of seasonal to multi-annual variance of the \r\nzonal mean precipitation in the Southern Hemisphere subtropics and \r\nextratropics (Kang et al., 2011). The SAM is also tightly linked to the \r\nfluctuations in the thermohaline properties of the Southern Ocean \r\nas well as its dynamics (Sen Gupta and England, 2006; Spence et al., \r\n2014), and it modulates the Antarctic sea ice extent from interannual \r\nto multi-decadal time scales (Ferreira et al., 2015)."


id: 912740fa2f5c1d68f0fe1f14ae85b8cb
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"Sea level pressure records from Stykkisholmur, Akureyri, Reykjavik \r\nand from Gibraltar, Lisbon and Ponta Delgada weather stations, all \r\nlocated close to the NAO centres of action (i.e., the Icelandic Low \r\nand the Azores High), allowed the reconstruction of the NAO index \r\nback to the mid-19th Century (Cropper et al., 2015; Hernandez et al., \r\n2020). The Paris-London sea level pressure difference used by Cornes \r\net al. (2013) extends back to 1692, but it captures only part of the \r\nlarge-scale NAO phenomena because of the spatial proximity of \r\nthe weather stations used. More reliable NAO reconstructions were \r\nrecently developed based on the reconstruction of the sea level \r\npressure field all over the North Atlantic and Europe (Delaygue et al., \r\n2019; Mellado-Cano et  al., 2019). Other long-term reconstructions \r\ninvolve proxy-based information from documents (Barriopedro et al., \r\n2014), tree rings (Cook et al., 2019), ice cores (Sjolte et al., 2018), \r\nstalagmites (Baker et al"


id: 4ac93a2b608fba98a7255aa84c616158
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", 2015), sediments (Faust et al., 2016) and \r\na combination of multi-proxy information (Ortega et al., 2015), with \r\nlarge uncertainties due to the diverse methodological approaches \r\nused for reconstruction (Michel et al., 2020) and disparate correlations \r\nbetween proxies and associated temporal resolution (Hernandez \r\net  al., 2020). An additional difficulty for reconstruction lies on the \r\nfact that the spatial structure of North Atlantic variability might be \r\ntime-scale dependent (Woollings et al., 2015). It is shown that on the "


id: b396bdfa8c7f341e4ab1b4677922c63e
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"The regional influence of the SAM is recorded in tree rings (Holz \r\net  al., 2017; Datwyler et  al., 2018), lake sediments (Fletcher et  al., \r\n2018; Moreno et  al., 2018), ice cores (Goodwin et  al., 2004) and \r\ncorals (Goodwin and Harvey, 2008) across the Southern Hemisphere \r\nextratropics. Multi-proxy-based reconstructions of the SAM have been \r\ndeveloped using temperature-sensitive proxy records that cover the \r\nlast millennium (Villalba et al., 2012; Abram et al., 2014; Datwyler et al., \r\n2018). Villalba et al. (2012) used tree ring records to reconstruct the \r\nsummer SAM since 1409, while Abram et al. (2014) combined ice-core \r\nrecords with tree ring data to derive a weighted composite-plus-scale \r\nreconstruction of the annual SAM index for the past millennium. \r\nDatwyler et al. (2018) further included proxies from corals, speleothems \r\nand lake sediments using different stationarity criteria for the proxy \r\nrecord selection to build an austral summer SAM reconstruction"


id: ed9495ca2e45a76f9cfc7642cc72ff8d
created_at: 2023-03-16 14:02:12
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" Hessl \r\net al. (2017) compared three paleoclimatic reconstructions of the SAM, \r\nhighlighting the need for additional proxy records over the Indian \r\nOcean sector, to fully account for the annular structural properties of \r\nthis mode of variability. Uncertainties also arise from the lack of proxy \r\ndata related to cold season conditions, given that reconstructions \r\nrelying on tree rings might be biased towards summer thus accounting \r\nonly for SAM austral summer variability (Hernandez et  al., 2020). \r\nSeveral studies attempted to reconstruct the SAM behaviour during \r\nthe Holocene, inferred through changes in the Southern Hemisphere \r\nwesterly wind belt, but uncertainties remain large without consensus \r\nin the SAM phases for most of the periods at multi-decadal time scales \r\n(Hernandez et al., 2020). These SAM reconstructions are assessed in \r\nSection 2.4.1.2 and compared with simulations in Section 3.7.2."


id: 5da823240eb01f9b2ba5ccff69dc1e97
created_at: 2023-03-16 14:02:12
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"The El Nino-Southern Oscillation (ENSO) refers to the large-scale \r\nalternation between anomalous warming and cooling of central/\r\neastern equatorial Pacific SSTs that coincide with changes in the \r\noverlaying winds and precipitation (Philander, 1990; Neelin et  al., \r\n1998; Wang, 2018). The main features of ENSO are displayed \r\nin Figure AIV.3. ENSO is the primary mode of tropical variability \r\non interannual time scales and is considered as an internally \r\noccurring coupled ocean-atmosphere phenomenon. Whilst the \r\nENSO phenomenon itself largely occurs in the tropical Pacific, it \r\ntriggers climate teleconnections over the entire basin and in many \r\nother parts of the world (Figure AIV.3c,d). ENSO is consistently the \r\nmain modulator of the global surface temperature at interannual \r\ntime scales (Pan and Oort, 1983; Trenberth et  al., 2002). ENSO is \r\nthe main source of climate predictability on seasonal to interannual \r\ntime scales (Philander, 1990; Smith et  al"


id: f9a13c472fa96dac75b0ead6df14de1e
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", 2012) and its status is \r\nconsequently closely monitored by meteorological institutes around \r\nthe globe. El Nino events are typically identified by warmer than \r\nnormal central/eastern equatorial Pacific SSTs, negative SLP anomaly \r\ndifference between the eastern and western portions of the tropical \r\nPacific Ocean, anomalous westerly surface winds, and increases in \r\ncloudiness and precipitation over the central and eastern equatorial \r\nPacific and adjacent land areas. By contrast, La Nina events are \r\ntypically identified by cooler than normal central/eastern equatorial \r\nPacific SSTs, positive surface pressure anomaly difference between \r\nthe eastern and western portions of the tropical Pacific Ocean \r\nand anomalous easterly surface winds reflecting altogether an \r\nintensification of the climatological east-west thermal gradient \r\nacross the equatorial Pacific Ocean."


id: 1364b0b651bd92cb78f44104b5c69a35
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"A particular aspect of ENSO diversity is whether the event is centred \r\nin the eastern Pacific (EP events) or central Pacific (CP or 'Modoki' \r\nevents). This distinction is important as the type of event leads to \r\ndifferent large-scale teleconnections and worldwide climatic impacts \r\n(e.g., Ashok et al., 2007; Ratnam et al., 2014; Capotondi et al., 2015; \r\nTimmermann et  al., 2018; Taschetto et  al., 2020). Differences can \r\nbe significant at regional scale because of the displaced source \r\nof diabatic heating associated with the position of the main SST \r\nanomalies. For instance, stronger precipitation teleconnections occur \r\nover Australia during CP than during EP events (Wang and Hendon, \r\n2007; Taschetto et al., 2009). It is noteworthy that central and eastern \r\nPacific SST indices are more highly correlated with each other during \r\nLa Nina than during El Nino events"


id: 4001bee3f92c04fb5e9d88ef88469883
created_at: 2023-03-16 14:02:12
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" This indicates that La Nina events \r\nhave less geographic diversity than El Nino events (Kug and Ham, \r\n2011) and that the CP/EP distinction is therefore more relevant \r\nfor the latter. In the historical instrumental records, Banholzer and \r\nDonner (2014) found that CP events had a  weaker influence on \r\nglobal surface temperature than EP events did."


id: 3c24f0e7e191b634fc999349ae2bf83c
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"However, the distinction between CP and EP events is not \r\nnecessarily clearly defined, as different methods yield an inconsistent \r\nclassification of the events, which implies that no robust consensus \r\non the classification of CP or EP events is possible (Wiedermann \r\net  al., 2016). Furthermore, Giese and Ray (2011) found that the \r\ncentral longitude of peak SST anomalies associated with individual \r\nEl Nino events had a distribution indistinguishable from a Gaussian \r\ndistribution peaked at 140degW, as opposed to a bimodal distribution \r\nimplied by the EP/CP paradigm. There are also occasional 'coastal \r\nNino' events, such as those of 1925 (Takahashi and Martinez, 2019) \r\nor 2017 (Rodriguez-Morata et  al., 2019), where abnormally warm \r\nwaters are confined to the South American coast. Even though these \r\nevents are not conventionally classified as El Ninos, they have some \r\nEl Nino-like teleconnections in South America"


id: 14815b336f18b3a57a18835f01e15256
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" Instrumental data \r\nhave historically been of limited value in assessing the classification \r\nof events before 1955, because of the almost total lack of SST \r\nmeasurements in the western half of the NINO3.4 region (L'Heureux \r\net al., 2013). It is not yet known whether newer SST dataset versions, \r\nwhich benefit from improved data archives (Section 2.3.1.1), have \r\nreduced this uncertainty."


id: 2663e86ea703db963d9fcaa82e4ad47e
created_at: 2023-03-16 14:02:12
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"ENSO has been monitored through a  range of indices, which are \r\nbased on SST or SLP anomalies during the instrumental period, \r\nand  various proxies on longer time scales. Differences between \r\nindices and thresholds used to define the occurrence of an El Nino or \r\nLa Nina event imply that there is no commonly accepted list of events \r\nand associated magnitude. The three most prominent (Pepler, 2016; \r\nL'Heureux et al., 2017) indices used to monitor ENSO are the averaged \r\nSST anomalies over the so-called NINO3 (5degS-5degN, 150degW-90degW) \r\nor NINO3.4 (5degS-5degN, 170degW-120degW) regions and the Southern \r\nOscillation Index (SOI) based on the normalized SLP difference \r\nbetween Darwin and Tahiti (Troup, 1965). Decomposition into EOFs \r\nof the tropical SST is also used in the literature. Those indices are \r\nhighly correlated, as shown in Figure AIV.3b. The correlation between \r\nNINO3.4 SST and the EOF-SST index is about 0.97, and between \r\nNINO3.4 and SOI it is about 0"


id: 4bef0aafaa448c915d69049a5014754f
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"Figure AIV.3 | The boreal winter El Nino-Southern Oscillation (ENSO) mode defined by December-January-February (DJF) sea surface temperature (SST) \r\nanomalies averaged over the NINO3.4 region (5degS-5degN, 170degW-120degW, cyan box) or extracted as the leading EOF mode over the tropical Pacific Ocean \r\n(the region denoted by the black box in a) for 1958-2019 using ERSSTv5. (a) SST anomalies regressed onto the NINO3.4 time series, which is shown in (b) as red \r\nand blue bars. Black curve in (b) represents the standardized leading principal component and the purple curve stands for the SOI index. The percentage of explained variance of \r\nthe leading EOF is given in the legend, as well as the correlation between all indices and the NINO3.4 time series. (c) Same as (a) but for land surface air temperature (shading; \r\nbased on Berkeley Earth) and 10 m level wind (arrows; m s-1 based on JRA-55) anomalies over 1958-2018"


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created_at: 2023-03-16 14:02:12
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" (d) Same as (a) but for precipitation anomalies (shading based \r\non GPCC for 1958-2016 and contours based on CMAP for 1979-2019 for every 0.5 mm day-1). Magenta dots in (c, d) stands for the Darwin and Tahiti weather station used \r\nfor the SOI computation. On maps, no overlay indicates regions where the regressions are significant based on t-test accounting for false detection rates at 10% and crosses \r\nindicates no significance. Significance for CMAP precipitation is materialized by red contours in (d). All fields have been linearly detrended prior to computation."


id: 2fd3707c21338bb88e6541894ccfcc84
created_at: 2023-03-16 14:02:12
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"ENSO has a  broad range of teleconnections, both in and outside \r\nthe Pacific region (Figure AIV.3c,d). Amongst the best-known \r\nteleconnections are the association of El Nino events with increased \r\nprecipitation in the south-western and south-eastern USA, eastern \r\nAfrica, coastal Ecuador and Peru, and northern Argentina, and \r\nwith decreased precipitation in eastern Australia and the Maritime \r\nContinent, southern Africa, the Amazonian basin and much of the \r\nIndian subcontinent. A general reverse association is found during \r\nLa Nina events although there are some asymmetries (Cai et al., 2010; \r\nTaschetto et al., 2020). ENSO also has a substantial impact on tropical \r\ncyclone occurrence (Wang and Chan, 2002; Chu, 2004; Kuleshov et al"


id: e3897474c71fde3f2adcd31a3a996ecd
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", \r\n2008), with El Nino typically being associated with reduced tropical \r\ncyclone activity in the North Atlantic, and an eastward displacement \r\nof western North Pacific and South Pacific tropical cyclone genesis \r\naway from the Philippine Sea and the Australian continent and into \r\nthe central Pacific. ENSO has been shown to control part of the \r\ninterannual variability of fire, including wildfire and human-triggered \r\nfires, in many regions of the world including all the continents \r\nsurrounding the Pacific (see for instance the devastating extreme \r\nfire emissions in Indonesia in 2015; Chisholm et al., 2016) but also \r\nremotely, for example, over the Arctic (Monks et al., 2012)."


id: 963c31e809610199e7eede2703362fc2
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"Model evidence suggests that ENSO characteristics vary on decadal \r\nto centennial time scales (e.g.,  Lewis and LeGrande, 2015; Deser \r\net  al., 2017), implying that a  minimum of several hundred years of \r\nobservations, which is much longer than the instrumental record, \r\nare required to determine the full natural range of ENSO complexity \r\n(Wittenberg, 2009). Thus, one way to understand if anthropogenic \r\nforcing has modulated ENSO is to understand ENSO behaviour prior \r\nto the observational record. Paleoclimate archives can be used to fill \r\nthis gap as they provide quantitative estimates of ENSO behaviour \r\nduring the pre-industrial era, while also providing information of ENSO \r\ncharacteristics under a variety of past natural climate background states \r\ncontrolled by external forcings"


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" Extensive proxy-based studies, using \r\na range of ENSO-sensitive records, have been carried out to assess the \r\nENSO changes on various paleoclimate time scales, specifically the last \r\nfew hundred years and back to the mid-Holocene (Section 2.4.2)."


id: 20105e4ad2dbc3fb59f9631c8a4636dd
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"Tropical climate impacts during ENSO events are largely driven by \r\na  reorganization of the zonal Walker circulation that results from \r\nanomalous diabatic heating associated with deep atmospheric \r\nconvection changes (e.g., Gill, 1980; Klein et al., 1999; Chiang and Sobel, \r\n2002; Choi et al., 2015). The latter are also responsible for atmospheric \r\nteleconnections to higher latitudes (Yeh et al., 2018), acting as a source \r\nof forced large-scale Rossby waves (Hoskins and Karoly, 1981). \r\nSpecific phases of the Pacific-North American (PNA) pattern (Horel \r\nand Wallace, 1981) and Pacific-South American (PSA) pattern (Karoly, \r\n1989) are then favoured in the Northern and Southern hemispheres, \r\nrespectively, and are associated with shift and modulation of the mean \r\nstorm tracks. These teleconnections influence temperature and rainfall \r\naround the globe, including extreme events (King et  al., 2014)"


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" For \r\ninstance, mild winter conditions dominate Alaska/Canada, connected \r\nto the south-eastward displacement and reinforcement of the Aleutian \r\nLow during El Nino, which generates anomalous southerlies and \r\nadvects warmer air northward. By contrast, the anomalous cyclonic \r\ncirculation favours cold outbreaks over the far-east Siberian region \r\nand Kamtchaka (Figure AIV.3c,d)."


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vector_id: 21836
content:
"Reconstructions of past ENSO behaviour traditionally merges \r\ninformation from multiple proxies that are considered sensitive \r\nto ENSO (Lu et al., 2018). This merging process may have multiple \r\npurposes, but it is generally used to extend the reconstruction \r\nlength and also to increase its signal-to-noise ratio. The resulting \r\nreconstructions can be broadly placed into two main categories: \r\n(i) those that utilize proxies in the central and eastern equatorial \r\nPacific itself; and (ii) those that utilize proxies from other regions \r\nimpacted through teleconnections, along with those that mix both \r\ncategories. ENSO reconstructions from category (i) are considered to \r\nbe a more accurate measure of ENSO behaviour. However, proxies \r\nfrom the central and eastern equatorial Pacific are generally rarer \r\nthan those outside the region, due to the small number of land/\r\nreefs, and they also tend to be of shorter duration"


id: f09710d4be7e393a9859cceaf0d2d10e
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
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index: climate_gpt_only_giec
vector_id: 35350
content:
" Thus, the ideal \r\ncase is a combination of both categories to get a robust quantitative \r\nestimate of ENSO behaviour in the past."


id: 55a394f5a19d580bca5c644dba6a2e2f
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 10955
content:
"Figure AIV.4 | The boreal spring Indian Ocean Basin (IOB) mode defined by March-April-May (MAM) SST anomalies averaged over the Indian Ocean (the \r\nregion denoted by the cyan box in a) or extracted as the leading EOF mode over the same domain (black lines) for 1958-2019 using ERSSTv5. (a) SST \r\nanomalies regressed onto the IOB index time series shown in (b) in red and blue bars, while the black curve in (b) represents the leading principal component time series. \r\nExplained variance and correlation between indices are given in the legend in (b). (c) Same as (a) but for land surface air temperature (shading; based on Berkeley Earth) and \r\n10 m level wind (arrows; m s-1 based on JRA-55) anomalies for 1958-2018. (d) Same as (a) but for precipitation anomalies (shading based on GPCC for 1958-2016 and \r\ncontours based on CMAP for 1979-2019 for every 0.3 mm day-1)"


id: 6547b53f666fc5a80a49eb54ca816263
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 13513
content:
" On maps, no overlay indicates regions where the regressions are significant based on t-test accounting \r\nfor false detection rates at 10% and crosses indicates no significance. Significance for CMAP precipitation is materialized by red contours in (d). All fields have been linearly \r\ndetrended prior to computation."


id: 814d2d34e5425dd744ba88f942b460f3
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
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index: climate_gpt_only_giec
vector_id: 17868
content:
"ENSO's equatorial Pacific SST anomalies typically dissipate by boreal \r\nsummer, when the lingering IOB then plays the leading role in exerting \r\nclimate anomalies in Asia and the western North Pacific (the Indian \r\nOcean capacitor effect; Xie et  al., 2009). The boreal summer IOB \r\ninduces meridionally dipolar anomalies of precipitation and surface \r\nair temperature in South East and East Asia, with wetter and cooler \r\nsummers in mid-latitude East Asia and drier and warmer summers in \r\nSouth East Asia during positive IOB events (Kosaka et al., 2013; Wang \r\net al., 2013). These conditions affect the occurrence of extremes such as \r\nheavy rainfall (often caused through atmospheric rivers; Kamae et al., \r\n2017) and heatwaves (Hu et al., 2012; Deng et al., 2019). The positive \r\nIOB suppresses summer tropical cyclone formation in the western \r\nNorth Pacific (Du et al., 2011; Ueda et al., 2018), leading to a delayed \r\nonset of the typhoon season (Zhao et  al., 2019)"


id: e454f44a5adb20f06ebe2ce0943c2878
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
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index: climate_gpt_only_giec
vector_id: 33399
content:
" These regional \r\ninfluences are already present in the spring season (Figure AIV.4c,d) \r\nbut tend to persist and even get reinforced due to the persistence of \r\nthe IOB, while following the seasonal climatological displacement of \r\ntropical dynamics (monsoon, etc.). In South Asia, tripolar precipitation \r\nanomalies form in boreal summer, with precipitation increase along \r\nthe Western Ghats and in Bangladesh, and decrease around the \r\nGanges Delta during the positive IOB (Chowdary et al., 2013, 2019)."


id: 32cdf08bb1a3c6658be570795e8af861
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 5413
content:
"The IOB and IOD are identified as the first and second EOF modes, \r\nrespectively, of monthly SST variability in the tropical Indian Ocean \r\n(Weare, 1979; Saji et al., 1999). The IOD can be extracted also as the \r\nfirst EOF of SST variability in boreal autumn, although the pattern \r\ndepends on the analysis period (Pillai et al., 2019). The IOD mode has \r\na sub-surface signature and can be also calculated as the leading \r\nEOF mode of the 20degC isotherm depth of the Indian Ocean seawater \r\ntemperature (Saji et al., 2006). Apart from the EOF definitions, the \r\ntropical Indian Ocean averaged SST (20degS-20degN, 40degE-100degE or \r\n40degE-120degE) is often used as the IOB mode index (Figure AIV.4a). \r\nFor March-April-May (MAM), its correlation with the leading PC \r\nof Indian Ocean SST (Figure AIV.4b) reaches 0.98"


id: b4ebfb2b8ef49dcf78e17ee8a08db431
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 26000
content:
" For the IOD, the \r\ndifference between normalized SST anomalies between the western \r\n(10degS-10degN, 50degE-70degE) and eastern (10degS-0deg, 90degE-110degE) \r\nequatorial Indian Ocean is a  widely used index (Saji et  al., 1999; \r\nFigure AIV.5). It is highly correlated (at 0.76) with the leading principal \r\ncomponent of the Indian Ocean September-October-November SST \r\nwhen assessed over 1958-2019 (Figure AIV.5b)."


id: 3adbf05f9f2ed67d164685130c05add4
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 6628
content:
"The positive phase of the IOD also decreases rainfall in western to \r\nsouth-eastern Australia (Ashok et al., 2003; Risbey et al., 2009), acting \r\nas preconditioning for fires (Cai et al., 2009). The rainfall anomalies \r\ncover the whole of Australia when combined with simultaneous ENSO \r\nwith the same polarity (Risbey et al., 2009). The IOD influences South \r\nAsian summer monsoon rainfall either directly through moisture \r\ntransport over the western Indian Ocean or modification of the local \r\nHadley cell (e.g., Ashok et al., 2001; Gadgil et al., 2004; Ashok and Saji, \r\n2007; Behera and Ratnam, 2018), or indirectly in the framework of \r\nthe tropospheric biennial oscillation (e.g., Meehl et al., 2003; Li et al., \r\n2006; Webster and Hoyos, 2010). IOD and ENSO often interfere in \r\ntheir influence on the South Asian summer monsoon (e.g.,  Ashok \r\net al., 2001, 2004b; Pepler et al., 2014; Cretat et al., 2017)"


id: 6ab2f650326abfedf7a1e8fd377918bf
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 14402
content:
" Opposing \r\ninfluences of IOD and ENSO have been found on surface temperature \r\nin summer in northern East Asia (Saji and Yamagata, 2003a). "


id: 99ba358f828f9355165cebbfea6abce7
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 21705
content:
"The AMM is thought to be primarily driven by air-sea heat fluxes \r\nand therefore thermodynamic in nature (Chang et al., 2000; Foltz and \r\nMcPhaden, 2010), while various mechanisms for generating AZM \r\nevents have been discussed. They mainly concern fluctuations in the \r\nwind field over the equatorial Atlantic. These wind-stress anomalies \r\ncan be excited by local SST changes, as part of Bjerknes feedback \r\n(i.e., weakening of trade winds and thermocline slope adjustments \r\nvia oceanic equatorial Kelvin wave propagation; Keenlyside and \r\nLatif, 2007; Deppenmeier et al., 2016). They can also be excited by \r\nmeridional advection of temperature anomalies (Richter et al., 2013), \r\ngenerated remotely as a  response to ENSO (Latif and Grotzner, \r\n2000; Tokinaga et  al., 2019) or to variations in the South Atlantic \r\nSubtropical High (Lubbecke et al., 2014), or occur as stochastic wind \r\nvariability (Richter et al., 2014)"


id: b2aee0b738b9405518546b4348baaaaa
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 25632
content:
" In contrast to previous mechanisms \r\ninvolving coupled atmosphere-ocean dynamics, Bellomo et  al. \r\n(2015) and Nnamchi et al. (2015) suggested that a large part of the \r\nSST variability in the eastern equatorial Atlantic can be explained \r\nby thermodynamic feedbacks excited by stochastic atmospheric \r\nperturbations. A recent study based on observational and reanalysis \r\nproducts by Nnamchi et al. (2021) indicates that the seasonality of \r\nAZM is indeed largely controlled by the variability of the atmospheric \r\ndiabatic heating. However, the balance between thermodynamic \r\nand dynamic processes determining SST variability in the equatorial \r\nAtlantic is subtle, and other studies (e.g., Jouanno et al., 2017; Dippe \r\net al., 2018) conclude that, even if the thermodynamic component is \r\nnot negligible, the dynamics is the dominant driver of the AZM."


id: 28a355c91f53ad00d78b812e288b6b2c
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 3820
content:
"Corals record interannual and longer variability in the Indian \r\nOcean (Zinke et  al., 2005, 2009). Several studies have developed \r\nIOD reconstructions based on oxygen isotope ratios from corals. \r\nThese reconstructions include those based on samples from Kenya \r\nsince 1887 (Kayanne et  al., 2006), from the Mentawai Islands in \r\nwestern Indonesia for the last 6500 years (Abram et  al., 2007), \r\nand in combination with samples from Bali in southern Indonesia \r\nand  the Seychelles since 1846 (Abram et  al., 2008). Abram et  al. \r\n(2020) developed a coral-based reconstruction of the IOD for the last \r\nmillennium, albeit with some discontinuity. Niedermeyer et al. (2014) \r\nuse isotope compositions in terrestrial plant wax to reconstruct \r\nrainfall changes over north-western Sumatra, which are highly \r\ncorrelated with the IOD. Compared to the IOD, proxy-based IOB \r\nreconstruction is limited. Yet Du et al"


id: 1c0a14476a120a436724ccdd6e02358a
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 1873
content:
" (2014) find that coral oxygen \r\nisotope records from the Seychelles are well correlated with the IOB, \r\nsuggesting that they can be used as an IOB proxy. Evidence from \r\nother proxies that are potentially associated with the Indian Ocean \r\nvariability is assessed in Section 2.4.3."


id: 56dad6f35e132d343315485a9f2c9606
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
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vector_id: 11141
content:
"Figure AIV.6 | The boreal summer Atlantic Zonal Mode (AZM) defined by June-July-August (JJA) box-averaged sea surface temperature (SST) anomalies \r\nover the ATL3 region (3degS-3degN, 0deg-20degW, cyan box in a) or estimated as the leading empirical orthogonal function (EOF) over the tropical Atlantic Ocean \r\n(the region denoted by the black box in a) for 1958-2019 using ERSSTv5. (a) SST anomalies regressed onto the ATL3 time series, which is shown in (b) as red and \r\nblue bars, while the black curve represents the leading principal component time series. Explained variance and correlation between indices are given in the legend in (b). \r\n(c) Same as (a) but for land surface air temperature (shading; based on Berkeley Earth) and 10 m level wind (arrows; m s-1 based on JRA-55 for 1958-2019) anomalies. (d)\r\nSame as (a) but for precipitation anomalies (shading based on GPCC for 1958-2016 and contours based on CMAP for 1979-2019 for every 0.2 mm day-1)"


id: c9436c0f9d5a6dbf597785ef42709293
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 29226
content:
" On maps, no overlay \r\nindicates regions where the regressions are significant based on t-test accounting for false detection rates at 10% and crosses indicates no significance. Significance for CMAP \r\nprecipitation is materialized by red contours in (d). All fields have been linearly detrended prior to computation."


id: de6d577428dda68a4387d9ba95245f15
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 32493
content:
"The AZM affects the Indian summer monsoon (ISM), altering the \r\nENSO-ISM connection (Kucharski et  al., 2007, 2008; Wang et  al., \r\n2009; Barimalala et  al., 2012, 2013; Kucharski and Joshi, 2017). \r\nSpecifically, warm SST in the equatorial Atlantic leads to decreased \r\nmonsoon rainfall over central India, and the opposite occurs for \r\nnegative Atlantic SST anomalies via strengthening of the Somali Jet \r\nand low-level convergence (Kucharski et al., 2007, 2008; Pottapinjara \r\net al., 2016). This latter relationship opens up the possibility of using \r\nAZM phases to predict ISM rainfall (Sabeerali et al., 2019). In this \r\nframework, the AZM modulates SST variability in the Indian Ocean \r\n(Kajtar et al., 2017). The number of monsoon depressions over the \r\nBay of Bengal increases during the cold phase of the AZM, which is \r\nremotely responsible for changes in low-level cyclonic vorticity and \r\nmid-tropospheric humidity (Pottapinjara et al., 2014)"


id: af54db6006b4206ae58835e864c2d47f
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
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index: climate_gpt_only_giec
vector_id: 25103
content:
" A relationship \r\nbetween AZM and the West African Monsoon is documented as \r\nwell, with a  reduction of rainfall over the Sahel and an increase \r\nover Guinea (Losada et  al., 2010). But the relationship with the \r\nSahel shows significant changes across the 20th century (Joly and \r\nVoldoire, 2010; Losada et al., 2012), which is strongly indicative for \r\nnon-stationarity of the teleconnection."


id: c160b59addfd5a6bfdadedff99ff091d
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 28012
content:
"A positive phase of the PDV is characterized by warm conditions in \r\nthe central-eastern tropical Pacific from the dateline to the coasts of \r\nthe Americas, encircled to the west in the mid-latitudes by negative \r\nSST anomalies forming a  so-called horseshoe pattern. The pattern \r\nhas a  certain similarity with ENSO and related anomalous SST \r\nfingerprints through atmospheric teleconnections. However, the PDV \r\nfeatures meridionally broader SST anomalies in the tropical Pacific \r\nextending to the subtropics compared to ENSO, and relatively stronger \r\nextratropical SST anomalies. Alternatively, the low-pass filtered \r\nSST regression on the PDO index, defined as the leading principal \r\ncomponents of SST (Mantua et  al., 1997) or sea surface height \r\n(Di Lorenzo et al., 2008) over the extratropical North Pacific north \r\nof 20degN, are used in literature for PDV. Their corresponding spatial \r\npatterns are, by definition, weighted to the respective hemisphere of \r\nthe Pacific. Figure AIV"


id: 62b01621901bf4095a33f73475500e30
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
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index: climate_gpt_only_giec
vector_id: 13097
content:
"8b provides evidence that they are all highly \r\ncorrelated to each other. The correlation value between the TPI and \r\nthe PDO index is equal to 0.76, and when low-pass filtered, it is \r\ngreater than 0.9 for all the combination of indices."


id: f6c2e5460d0b8ceb38d928e62721d1b
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 36334
content:
"Despite the PDO, SPDO and TPDV being considered to be physically \r\ndistinct modes (Newman et al., 2016), they are highly correlated in \r\nobservations (Chen and Wallace 2015) and they collectively constitute \r\nan equatorially symmetric pattern of basin-wide variability, often \r\ncalled the Inter-decadal Pacific Oscillation (IPO; Power et al., 1999; \r\nFolland et al., 2002; Henley et al., 2015). There is evidence that ENSO \r\nand the TPDV drive the PDO and SPDO in the North and South Pacific \r\nthrough tropical-extratropical teleconnections and the 'reddening' \r\n(i.e., selective retainment of decadal and longer variability) in the \r\nocean, ensuring the synchronization of decadal variability in both \r\nhemispheres (Newman et al., 2016). Besides, positive feedbacks are \r\nsuggested between the TPDV and the extratropical North Pacific \r\nthrough the Pacific Meridional Mode (Stuecker, 2018), which is the \r\nPacific analogue of the AMM (Section AIV.2"


id: 5d2a867f0f4b28e41982836581631b36
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
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index: climate_gpt_only_giec
vector_id: 12177
content:
"5; Chiang and Vimont, \r\n2004), and modulations of the shallow subtropical cell of the Pacific \r\nocean circulation (Farneti et al., 2014)."


id: 811e9a051624f603a01b24761a66b9d2
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 17835
content:
"Since AR5, there has been considerable progress in the understanding \r\nof PDV itself, but also in its critical role in modulating the evolution \r\nof observed global surface temperature (Cross-Chapter Box 3.1) as \r\nwell as regional climate through teleconnections. On interannual \r\ntime scales, ENSO is the leading internal driver of global surface \r\ntemperature variability (Section AIV.2.3). Since PDV encompasses \r\ndecadal modulations of El Nino versus La Nina occurrences, PDV \r\naffects global surface temperature on decadal time scales, with \r\nits positive and negative phases tied to higher and lower global \r\nsurface temperature, respectively. Indeed, studies identify PDV as the \r\nleading mode of variability associated with unforced decadal global \r\nsurface temperature fluctuations in observations and a majority of \r\nCMIP5 models (Brown et al., 2015; Dai et al., 2015), with additional \r\ninfluence from AMV (Section AIV.2.7; Tung and Zhou, 2013)"


id: 7ede9f97cdb4a6d499f45caf851f87ea
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
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index: climate_gpt_only_giec
vector_id: 17523
content:
" PDV \r\nthus plays an important role in decadal acceleration and slowdown \r\nof global surface temperature trends (Cross-Chapter Box 3.1). So far, \r\nlittle predictability has been found for decadal PDV, despite its global \r\nrelevance, and decadal prediction in the Pacific remains challenging \r\nwith the current decadal forecast system (Section  4.4.3.5). Finally, \r\nthere is an increasing body of evidence about the existence of \r\na network of teleconnections related to decadal climate-variability \r\nphenomena (Cassou et al., 2018; Cai et al., 2019). For instance, AMV \r\nhas been hypothesized to influence in part the phase of the PDV \r\n(McGregor et al., 2014; Ruprich-Robert et al., 2017), and consistently \r\nthere is a  significant negative correlation between PDV and AMV. \r\nOther studies suggest that the PDV may be the main driver of the "


id: f15c644161b578c74918ead820f1917f
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 35479
content:
"PDV reconstructions have been developed with tree rings in North \r\nAmerica and Asia (Biondi et al., 2001; D'Arrigo et al., 2001; Gedalof \r\nand Smith, 2001; MacDonald and Case, 2005; D'Arrigo and Wilson, \r\n2006; D'Arrigo and Ummenhofer, 2015; Buckley et  al., 2019), \r\ncorals in the Pacific (Linsley et al., 2015; Williams et al., 2017) and \r\nIndian Ocean (Crueger et al., 2009), historical documents in China \r\n(Shen et  al., 2006), ice cores (Vance et  al., 2015), and multi-proxy \r\napproaches (Verdon and Franks, 2006; Mann et al., 2009; McGregor \r\net al., 2010) covering the past several centuries up to a millennium. \r\nComparing 12 of these reconstructions, it has been found that \r\nrespective correlations are generally low (Henley, 2017), indicating \r\nlarge uncertainty in the PDV temporal history before instrumental \r\nrecords"


id: e80d3ac729e06909c5fad5fa664bff2
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
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index: climate_gpt_only_giec
vector_id: 34011
content:
" On longer time scales, a  variety of proxies consistently \r\ncapture the variations of the Aleutian Low intensity throughout the \r\nHolocene, which can be used to reconstruct the PDV (Section 2.4.5)."


id: d205efa45711fd3087035164c5bf380d
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 30572
content:
"A positive phase of the PDV brings anomalously warm conditions \r\nin the north-western corner of North America, with maximum \r\nloading over Alaska linked to reinforced and eastward-displaced \r\nAleutian Low and related anomalous southerlies (Figure AIV.8c). In \r\ncontrast, concurrent cold conditions dominate far-eastern Siberia \r\ndue to strengthened advection of Arctic air. The tail end of the storm \r\ntrack is strengthened leading to more precipitation along the Pacific \r\ncoast of North America, especially in boreal winter. A positive PDV \r\nalso induces a cooler and wetter climate in the southern part of the \r\nNorth American continent, with the wet anomalies being the most \r\npronounced in boreal summer (Figure AIV.8d). The negative phase \r\nof the PDV is an important cause of droughts in the western and \r\ncentral USA (Dai, 2013)"


id: b9b6678a7956c4c9e4543bf229a20c83
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 26759
content:
" Besides the direct influence of atmospheric \r\nteleconnections from the tropical branch of the mode, extratropical \r\nSST anomalies have been shown to modulate the amplitude of the \r\nprecipitation anomalies in North America (Burgman and Jang, 2015)."


id: 71991cd6eb50d1e761589823cd0badee
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 15504
content:
"The observed AMV is interpreted as the regional residual imprint in \r\nSST anomalies after removal of the externally forced signal. There \r\nis no unique and preferred way to remove the impact of external \r\nforcing in observations. Linear detrending is used in the traditional \r\ndefinition of the AMV index, which corresponds to the average over \r\nthe entire North Atlantic basin of the linearly detrended SST yearly \r\nanomalies on which a 10-year running mean is applied, as proposed \r\nby Enfield et  al. (2001). To remove the residual nonlinear global\u0002scale signal from the North Atlantic mean SST, different approaches \r\nhave been proposed either based on observations applying specific \r\nstatistics (e.g., Trenberth and Shea, 2006; Frajka-Williams et al., 2017; \r\nFrankignoul et al., 2017; Sutton et al., 2018; Yan et al"


id: ef820b39bb4815303affe5986c19f693
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 35172
content:
", 2019) or based \r\non model estimates of the externally forced signal including both \r\nnatural (solar and volcanoes) and anthropogenic (greenhouse gases \r\nand aerosols) influence assessed from historical simulations (Ting \r\net al., 2009; Terray, 2012; Steinman et al., 2015; Tandon and Kushner, \r\n2015). More specifically, in Trenberth and Shea (2006), the annual \r\nobserved global-mean SST anomaly time series is subtracted from \r\nthe observed annual North Atlantic spatially averaged time series to \r\nobtain the raw unfiltered AMV index. In Ting et al. (2009), a signal\u0002to-noise maximizing EOF analysis is applied to global annual mean \r\nSSTs derived from the CMIP5 multi-model ensemble to extract an \r\nestimation of the forced component, which is a priori removed before \r\ncomputation of the AMV index, then defined as a  low-frequency \r\nresidual. The latter technique has been retained in the so-called \r\nCMIP6 DCPP-C sensitivity experiments (Boer et al., 2016) aimed at "


id: d09f253e23d348979076a4e910cb89a9
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
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vector_id: 30349
content:
"Positive AMV is associated with much warmer conditions over \r\nthe north-western corner of the Atlantic, with marked positive \r\nanomalies on land around the Labrador Sea (Figure AIV.9c). There \r\nis a strong seasonality in the AMV teleconnection and this pattern \r\nis mostly explained by boreal winter atmospheric dynamics and \r\na  local reduction of sea ice formation. There is a  large-scale \r\ndecrease of the winter mid-latitude westerlies and, concurrently, \r\na weakening of the trade winds in the North Atlantic tropical basin, \r\nwhich is reminiscent of a  negative phase of the NAO (Peings and \r\nMagnusdottir, 2014; Gastineau and Frankignoul, 2015). Over Europe, \r\nAMV-related signals are weak on yearly average (Figure AIV.9c) but \r\nmask a  pronounced seasonality of the teleconnection as well as \r\nsome interplay between dynamical (i.e., atmospheric circulation) \r\nversus thermodynamical changes controlled by the AMV (O'Reilly \r\net al., 2017; Ruprich-Robert et al., 2017, Qasmi et al. 2020)"


id: 56ffb0c60028ca2a3b40524b7ab4526f
created_at: 2023-03-16 14:02:12
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content:
" Summer \r\ntends to be warmer during positive AMV in Europe (Sutton and \r\nDong, 2012; Nicoli et  al., 2020) with constructive contribution of \r\nboth dynamical and thermodynamical responses while destructive \r\ninterference prevails in boreal winter leading to colder conditions "


id: 512525f35de28d0fe5213c7267c497f4
created_at: 2023-03-16 14:02:12
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content:
"In terms of precipitation (Figure AIV.9d), positive AMV is associated \r\nwith a northward shift of the Atlantic ITCZ in response to changes in \r\nthe meridional SST gradient with respect to the equator (Martin and \r\nThorncroft, 2014; Friedman et al., 2017; Green et al., 2017). The AMV \r\ninfluence is the strongest in the boreal summer affecting the West \r\nAfrican Monsoon, leading to reinforced Sahel rainfall, and wetter \r\nconditions in the Caribbean basin through the modulation of the \r\noccurrence of hurricanes (Dunstone et al., 2011; Hermanson et al., \r\n2014). By contrast, dry conditions prevail in north-eastern Brazil \r\nand in the south-eastern corner of South America as well as in the \r\nGreat Plains in North America. The signal of the positive AMV lead \r\nto intensified precipitation and river runoff over northern Eurasia \r\n(Nicoli et al., 2020) and controls the multi-decadal variations in river \r\nflows over much of Europe (Boe and Habets, 2014; Hodgkins et al., \r\n2017)"


id: f5b6098d69ed3fc3abed09b71ea400a
created_at: 2023-03-16 14:02:12
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" Because of global teleconnections, especially in the tropics, \r\nthe AMV also affects the other monsoon systems with signals found \r\nover India, South East Asia and the Maritime Continent and, more \r\nbroadly, over the entire tropical Pacific (Chylek et al., 2014; Kucharski \r\net al., 2016; Ruprich-Robert et al., 2017)."


id: f9607ca5fadbc1c3344fca9a14343a19
created_at: 2023-03-16 14:02:12
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content:
"The Madden-Julian Oscillation (MJO) is the leading mode of tropical \r\nintra-seasonal variability with 20-90 days' time scale (Madden \r\nand Julian, 1994), and it is an important source of regional climate \r\nvariability and predictability across the globe at sub-seasonal time \r\nscales from a week to months (Madden and Julian, 1994; Zhang, 2005; \r\nVitart, 2017; Wheeler et al., 2017; Kim et al., 2018). It is characterized \r\nby planetary-scale disturbances (zonal wavenumber 1-3) of pressure, \r\nwind, clouds and rainfall moving predominantly eastward along the \r\nequator at average speed of 5 m s-1 (Roxy et al., 2019). More precisely, \r\nthe MJO is characterized by alternating regions of suppressed and \r\nenhanced convection coupled to an anomalous zonal overturning \r\ncirculation of the atmosphere (Zhang, 2005). The MJO is driven by \r\ninternal ocean-atmosphere processes that occur all year round but \r\nit exhibits prominent seasonality with more pronounced signals in \r\nboreal winter"


id: ef499e2c5e70cfe2cd5f8d3a6c55f23e
created_at: 2023-03-16 14:02:12
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content:
" During boreal summer, centres of convective activity \r\nassociated with the MJO are shifted away from the equator to \r\n10degN-20degN and propagate northward in the Asian monsoon region \r\n(Yasunari, 1980; Madden, 1986) in addition to eastward propagation, \r\noften called as the boreal summer intra-seasonal oscillation (BSISO; \r\nMadden, 1986; Kikuchi et al., 2012). Whereas the MJO is considered \r\npresent in all seasons, albeit with weaker amplitude in boreal summer, \r\nthe BSISO tends to prevail during boreal summer, with influences on \r\nmonsoons, tropical cyclones and other water cycle aspects (Kikuchi \r\net al., 2012; Lee et al., 2013)."


id: b7a8a3bcc2658fa2549e9d785c7c8777
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content:
"Paleoclimate reconstructions of AMV-related signals have been \r\ndeveloped using terrestrial, marine and ice-core proxies. The AMV \r\nreconstruction in the last 1200 years by J. Wang et al. (2017), using \r\nterrestrial proxy records, shows a  clear broad band of enhanced \r\nspectral power at multi-decadal time scales but no evidence of \r\na  distinct multi-decadal or centennial spectral peak (Singh et  al., \r\n2018). This is largely consistent with the record of the instrumental \r\nperiod and with previous proxy-based studies (Gray et al., 2004; Mann \r\net al., 2009) in the overlapping periods. Studies focusing on marine \r\nproxies also show persistent multi-decadal variability over the last \r\nthree centuries (Kilbourne et al., 2014; Svendsen et al., 2014; Moore \r\net al., 2017). Multi-decadal Arctic temperature variability related to \r\nthe AMV is also evident in ice-core records over the last millennium \r\n(Chylek et al., 2011; Zhou et al., 2016) and beyond (Knudsen et al., \r\n2011)"


id: ef78701d78e1771fdbb8457211c2a6bc
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content:
" High-resolution sedimentary records from the Arctic were \r\nrecently analysed to reconstruct the AMV over the last 2900 years \r\n(Lapointe et  al., 2020). However, this new reconstruction refers to "


id: ef4730279c61f1bd73e8e5884493cb88
created_at: 2023-03-16 14:02:12
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content:
"The real-time multivariate MJO (RMM) index has been widely used \r\nto detect the MJO, based on the first two combined EOF modes of \r\ndaily outgoing longwave radiation (OLR) and zonal winds at 850 and \r\n200 hPa averaged over 15degN-15degS (Wheeler and Hendon, 2004). The \r\nOLR represents convective activity and zonal winds at the lower and \r\nupper troposphere depict the overturning zonal circulation along the \r\nequator. The composite maps of convective activity and atmospheric \r\ncirculation (Figure AIV.10a) and the phase diagram (Figure AIV.10b) \r\nbased on eight phases constructed by the two PCs time series provide \r\neffective ways to understand and monitor spatial and temporal \r\nevolution of the MJO event. In general, the MJO disturbances are \r\ninitiated in the equatorial western Indian Ocean and travel eastward \r\nmainly over the Indo-Pacific warm pool region"


id: de8ad1100c52b2afe56e28e806946731
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content:
" However dynamical \r\nfields in composite maps indicate that the MJO also propagates over \r\nclimatological non-convective regions (e.g., the eastern Pacific, part of \r\nthe tropical Atlantic; Figure AIV.10a), then manifesting the planetary \r\nnature of the tropical intra-seasonal oscillation (Cassou,  2008). "


id: 89d8e36ff4d6d5110981300b53e7e512
created_at: 2023-03-16 14:02:12
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content:
"Figure AIV.10 | (a) The spatio-temporal properties of the Madden-Julian Oscillation (MJO). Daily composites of outgoing longwave radiation (OLR, W m-2, shaded) \r\nanomalies and stream-function anomalies at 300 hPa (STF, contours every 0.5 x 106 m2 s-1) for the eight MJO phases in boreal winter (1 December to 31 March) based on \r\nNOAA interpolated satellite products and NCEP-NCAR reanalysis over 1979-2019, respectively. Greenish (brownish) colours indicate enhanced (reduced) convective activity \r\nassociated with wet (dry) conditions. Solid (dashed) contours show for positive (negative) STF anomalies which stand for anomalous anticyclonic circulation in the Northern \r\n(Southern) Hemisphere. (b) Phase diagram of the MJO constructed from the two leading principal components drawn from empirical orthogonal decomposition of combined \r\nwind at 300 hPa + OLR fields following Wheeler and Hendon (2004) for winter 2007-2008 chosen as an example of active MJO years"


id: 915de967164a860c0dd82de0423506a4
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content:
" (c) Schematic representation of \r\nMJO-related tropical-Northern Hemisphere interactions and teleconnections inferred from observational and numerical studies (adapted from Stan et al., 2017). Because the \r\ninteractions and teleconnections of the Southern Hemisphere are not as well understood except for the Southern Annular Mode, they are not included in the current schematic."


id: 6cdf6b4ad7749f05ab352dd62c2eb5f0
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content:
"The schematic in Figure AIV.10c shows the main regions of \r\ninfluence of the MJO. During boreal winter, the MJO fingerprint \r\nis mostly confined to the deep tropics with a  zonal alternation of \r\nwet and dry zones depending on the MJO phase. For instance, in \r\nphase 4, enhanced rainfall is found over the Maritime Continent \r\nwhile suppression of convection is present over the central Pacific, \r\nnortheast Brazil and equatorial Africa (Figure AIV.10a). The opposite \r\nis found during phase 8. Anomalous upper-level divergence in \r\nthe deep tropics associated with altered convection and diabatic \r\nheating leads to anomalous vorticity in the subtropics (shown by \r\nstream-function dipoles straddling the equator in Figure AIV.10a) \r\nacting as an efficient and primary source of teleconnections outside \r\nthe tropics (L'Heureux and Higgins, 2008; Lin et  al., 2009; Lukens \r\net al., 2017)"


id: cc2934bca3ec80c868543794a19af26a
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content:
" The extratropical influence of the MJO may occur via \r\nthe excitation of forced tropospheric Rossby waves (Ferranti et al., \r\n1990; Matthews et al., 2004; Cassou, 2008; Stan et al., 2017) that \r\naffect the climate variability over the North Pacific (Ferranti et  al., \r\n1990; Higgins and Mo, 1997), the North Atlantic (Ferranti et al., 1990; \r\nCassou, 2008) and the Arctic (Zhou and Miller, 2005). For example, \r\ntropical convective heating associated with MJO phase 5 contributes \r\nto Arctic warming, while the opposite occurs for MJO phase 1 (Yoo \r\net  al., 2011, 2012). Phase 2-3 of the MJO is clearly associated \r\nwith the prevalence of the positive phase of the NAO with a 10- to \r\n15-day lag after the MJO forcing. Stratospheric pathways are also \r\nreported to support tropical-extratropical teleconnections though \r\nthe modified occurrence of sudden stratospheric warming (Liu et al., \r\n2014; Garfinkel and Schwartz, 2017) with 15- to 20-day delay leading \r\nto sub-seasonal predictability."


id: f783a9ab76d5526f1e9fe9935fc5b7fd
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content:
"The evaluation of the MJO in climate models is done through \r\nphenomenological (Waliser et  al., 2009) and process-oriented \r\n(Xavier, 2012) diagnostics. MJO simulation remains a  challenge in \r\nboth CMIP3 and CMIP5 models, even though overall improvements \r\nhave been reported in the most recent model versions (Waliser et al., \r\n2003; Lin et al., 2006; Sperber and Annamalai, 2008; Kim et al., 2009; \r\nXavier et al., 2010; Jiang et al., 2016). Poor MJO simulation in coupled \r\nclimate models has been attributed to many factors, including \r\nan insufficient buildup of atmospheric moisture for large-scale \r\norganized convection to occur (Kim et al., 2012; Mizuta et al., 2012), \r\nbiases in models' mean state (Inness et al., 2003; Hannah et al., 2015; \r\nKim, 2017), coupling frequency with the ocean and representation \r\nof air-sea interactions (Bernie et  al., 2008; DeMott et  al., 2015)"


id: 9930dca00ea493cb235f357532729de9
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content:
" \r\nIn CMIP5 simulations, the eastward propagation of boreal winter \r\nMJO convection from the Indian Ocean into the western Pacific \r\n(Hung et  al., 2013) and the northward propagation during boreal \r\nsummer (Sperber et al., 2013) are slightly better represented, even \r\nif major biases persist and still affect the monsoon systems. These \r\nimprovements have been attributed to advances in parametrizing \r\nmoist turbulence and convection (Hohenegger and Bretherton, 2011; \r\nMapes and Neale, 2011; Del Genio et  al., 2012; Kim et  al., 2012; \r\nHourdin et al., 2013), leading to greater MJO predictive skill of up \r\nto four weeks in operational sub-seasonal prediction centres (Vitart, \r\n2014). Super-parametrized models (Benedict and Randall, 2009), \r\nglobal cloud system-resolving models (Miyakawa et al., 2014) and \r\nhigh-resolution global climate models with an improved seasonal \r\ncycle (Mizuta et  al., 2012) have been shown to produce a  more \r\nrealistic simulation of the MJO"


id: a5f8ca5a603afaf3a2b5fd597eb96178
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content:
" Progresses in the representation of \r\nthe MJO across model generations are assessed in Sections 1.5.4.6 \r\nand 8.3.2.9.1."


id: adef3f8c8c603931b404d06eae314fd9
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content:
"During boreal summer, the MJO/BSISO convective activities and \r\nrelated impacts propagate northward/north-eastward over the Indian \r\nOcean-South Asia region (Yasunari, 1980; Annamalai and Sperber, \r\n2005), and northward/north-westward over the western North \r\nPacific-East Asia region (Kemball-Cook and Wang, 2001; Lee et al., \r\n2013). The MJO/BSISO significantly modulate occurrences of extreme \r\nrainfall and heatwaves over Asia (Hsu et al., 2016, 2017; Chen and \r\nZhai, 2017; Diao et al., 2018) and North America (Moon et al., 2013; Lee \r\nand Grotjahn, 2019). They also regulate tropical cyclone activities in \r\nthe Indian (Frank and Roundy, 2006), Pacific (Maloney and Hartmann, \r\n2000; Diamond and Renwick, 2015; Zhao et al., 2015) and Atlantic \r\n(Klotzbach and Oliver, 2015) tropical oceans. They contribute to the \r\nintra-seasonal fluctuations of monsoons, including timing of onset \r\nand termination during summer (Maloney and Shaman, 2008; Wheeler \r\net al., 2009; Lee et al"


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", 2013; Grimm, 2019), and active/break phases \r\n(Pohl and Camberlin, 2006; Maloney and Shaman, 2008; Joseph et al., \r\n2009; Lee et al., 2017)."


id: 5bc6d1d3ea7e809124c14c9bd3b946e2
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"The definition of the regional monsoons has been slightly modified \r\nin AR6 with respect to AR5, starting from the consideration that \r\nsome of the continental areas identified using the global metric have \r\na seasonality in precipitation that is not necessarily of monsoon origin. \r\nIn particular, the dotted regions in Figure AV.1 located over South \r\nAfrica, Central America and equatorial South America have a strong \r\nseasonality in precipitation but their qualification as monsoons is \r\na subject of discussion. In the assessment of the regional monsoons \r\nin Sections 8.3.2.4 and 8.4.2.4, these regions are not considered as \r\ndistinct regional monsoons, but they are discussed in Box 8.2, which \r\nis dedicated to changes in water cycle seasonality. The domains \r\nof the regional monsoons in AR6 are defined based on published \r\nliterature and expert judgement, and accounting for the fact that \r\nthe climatological summer monsoon rainy season varies across the \r\nindividual monsoon regions"


id: 6c95c5a9fcfe3349997cc2c92968aeb0
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vector_id: 14707
content:
" As shown in Figure AV.1, the AR6 regional \r\nmonsoons are: South and South East Asian (Section AV.4.1), East \r\nAsian (Section AV.4.2), West African (Section AV.4.3), North American \r\n(Section AV.4.4), South American (Section AV.4.5) and Australian-\r\nMaritime Continent (Section AV.4.6) monsoons. For each region, the \r\ndefinition, regional justification and key features are provided, along \r\nwith cross references to the main areas of assessment in AR6."


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"The SAsiaM rainy season from June to September contributes to more \r\nthan 75% of the annual rainfall over much of the region, including the \r\nsouthern slopes of the central and eastern Himalayas (Krishnan et al., \r\n2019b). Considering the spatial domain of the SAsiaM, monsoon \r\nprecipitation maxima are located over the west coast, north-east and \r\ncentral/north India as well as Myanmar and Bangladesh, whereas \r\nminima are located over north-west and south-eastern India, western \r\nPakistan, and south-eastern and northern Sri Lanka (Pant and Rupa \r\nKumar, 1997; Gadgil et al., 2010). Prior to the SAsiaM rainy season, \r\nareas in the north-western Himalaya receive precipitation during \r\nwinter and early spring from so-called 'western disturbances', which \r\nare extratropical synoptic systems originating over the Mediterranean \r\nregion and propagating eastward along the subtropical westerly jet \r\n(Madhura et al., 2014; Cannon et al., 2015; Dimri et al., 2015; Hunt \r\net al"


id: a7302e9435ebaa60787f9be27b837e2e
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content:
"Intra-seasonal variability of the EAsiaM has been mostly related to \r\nthe Madden-Julian Oscillation (MJO) (Yasunari, 1979; Zhang et al., \r\n2009; Chen et  al., 2015), to the phase of the Pacific-Japan mode \r\n(Nitta, 1987) or the Indian summer monsoon (Li et al., 2018) and to \r\nthe BSISO (Kikuchi et al., 2012; Chen et al., 2015), particularly for its \r\nonset (J.-Y. Lee et al., 2013). At interannual time scales, changes in \r\nEast Asian summer monsoon intensity lead to a position shift of the \r\nmonsoon rain belt under the influence of ENSO (Wang et al., 2000) \r\nand the western North Pacific subtropical high (Kosaka et al., 2013; \r\nWang et al., 2013), Arctic sea ice (Guo et al., 2014), and solar and \r\nvolcanic forcing (Peng et al., 2010; Man et al., 2012; Cui et al., 2014). \r\nVariability at inter-decadal time scales is more prominent in the \r\nsecond half of the 20th century (Jiang, 2005; Ding et al., 2008; Wang \r\net al"


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content:
"The basic features and interannual variations of the East Asian \r\nsummer monsoon are well reproduced in climate models. For \r\nexample, the climatological circulation structure is well reproduced \r\nin both atmospheric and coupled GCMs (Song and Zhou, 2013; Song \r\net al., 2013; Jiang et al., 2016, 2020), and the relationship between \r\nthe monsoon and ENSO is well represented (Sperber et  al., 2013; \r\nFu and Lu, 2017). In CMIP5 models, the main shortcomings relate \r\nto missing rainfall bands around 30degN and the northward shift of \r\nthe western North Pacific subtropical high (Huang et al., 2013). Most \r\ncoupled models show an inadequate strength of monsoon circulation \r\nover southern East Asia, and with little change in model performance \r\nfrom the Third Assessment Report (TAR) to AR6 (Jiang et al., 2016, \r\n2020). In coupled model simulations, air-sea coupling helps improve \r\nthe climatology and interannual variability of rainfall over the East \r\nAsia monsoon region (Wang et al"


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content:
"The West African monsoon (WAfriM) is a seasonal reversal in wind \r\nand precipitation extending over a vast and contrasted geographical \r\nregion, from the equator to the margins of the Sahara, and from the \r\nAtlantic coast inland. The WAfriM domain includes Togo, Guinea \r\nBissau, Gambia, Senegal, Mauritania, Guinea, Sierra Leone, Liberia, \r\nMali, Ivory Coast, Burkina Faso, Ghana, Benin, Chad, Cape Verde, \r\nnorthern Cameroon, Niger, Nigeria and the northern Central African \r\nRepublic. It is roughly bounded by 18degW-30degE, 5degN-18degN, and is \r\nshaded grey in Figure AV.1 (e.g., Adedokun, 1978). The West African \r\nmonsoon and Sahel are sometimes considered interchangeably. \r\nHowever, the Sahel lies in the northern part of the WAfriM region, \r\noften limited to the west and central north Africa (e.g.,  Nicholson \r\net al. (2018) considered the domain to be 20degW-30degE, 10degN-20degN) \r\nor sometimes extended to the east (e.g"


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content:
",  Giannini and Kaplan \r\n(2019) considered the domain to be 20degW-40degE, 10degN-20degN). The \r\nEast African region with the Greater Horn of Africa, which includes \r\nEthiopia, Sudan and South Sudan, lies on the fringes of both the West \r\nAfrican and Indian monsoons (Nicholson, 2017)."


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content:
"The WAfriM is characterized by the northward progression from \r\nMay to September of moist low-level south-westerlies from the Gulf \r\nof Guinea, meeting the dry north-easterlies (Harmattan) from the \r\nSaharan Heat Low at the inter-tropical discontinuity (e.g., Hamilton \r\net  al., 1945; Omotosho, 1988). In May and June, rainfall remains \r\nessentially along the Guinean coast with a  maximum occurring \r\nnear 5degN, then the rainfall maximum jumps suddenly over the \r\nSudan-Sahel zone near 12degN, followed by a  'little dry season' \r\nover the Guinea coast (Adejuwon and Odekunle, 2006). This \r\napparent shift is known as the West African monsoon jump and it \r\nis concomitant with the monsoon onset over the Sahel (Sultan and \r\nJanicot, 2003; Cook, 2015). Rainfall continues to progress towards \r\nthe north up to about 18degN-20degN"


id: cf7a804f834fbf56c04cc15032076fc4
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
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index: climate_gpt_only_giec
vector_id: 30175
content:
" The rainfall maximum occurs in \r\nthe Sahel in August/September, followed by a rapid retreat of rainfall \r\nto the Guinean coast and a second maximum occurs over this region \r\nin October/November."


id: 2e7222d8fb1a2b9e2bb64728aef22995
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 4757
content:
"At interannual time scales, tropical oceans (Atlantic, Pacific and Indian) \r\nappear to be the major drivers with their SST anomalies leading to \r\nvariations in the accumulated seasonal rainfall (e.g.,  Lamb, 1978; \r\nDiakhate et al. 2019). At intra-seasonal time scales, equatorial waves \r\n(e.g., Mekonnen et al., 2008; Janicot et al., 2010) and interactions \r\nwith mid-latitudes and the Mediterranean (e.g.,  Vizy and  Cook, \r\n2009; Roehrig et al., 2011) have an effect on WAfriM activity. African \r\nEasterly Waves (AEWs) and mesoscale convective systems (MCSs), \r\nincluding squall lines, are the prominent weather synoptic scale \r\naspects of the WAfriM, supplying almost all rainfall in the Sahel. The \r\nstrong coupling between AEWs and MCSs has been investigated \r\nin depth, as well as their interactions with the Saharan Heat Low, \r\nthe moisture supply from East Africa and the Mediterranean region \r\nor from near the equator (e.g., Diongue et al"


id: 1cfa84a9caae22e233c98abb3fc1761b
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
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vector_id: 2009
content:
", 2002; Brammer and \r\nThorncroft, 2017; Lafore et  al., 2017). Land surface processes are \r\nknown to influence WAfriM precipitation (Boone et al., 2016)."


id: 13290c0e979c688a439cb14c61e72cc1
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
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vector_id: 528
content:
"Simulation of West African climate, including the monsoon, has \r\nreceived specific attention under coordinated programmes in the \r\nrecent past: the African Multidisciplinary Monsoon Analysis Model \r\nIntercomparison Project (AMMA-MIP), the AMMA Land-surface \r\nModel Intercomparison Project (ALMIP), the West African Monsoon \r\nModelling and Evaluation (WAMME) project and the Coordinated \r\nRegional Downscaling Experiment (CORDEX; Raj et al., 2019). CMIP5 \r\nand CMIP6 struggle to reproduce the amplitude of observed decadal \r\nvariability in the 20th century and to represent the mean climatology \r\nincluding the northward propagation of the monsoon (Roehrig et al., \r\n2013; Monerie et  al., 2020; Sow et  al., 2020). A  higher horizontal \r\nresolution improves the representation of the intensity and spatial \r\ndistribution of WAfriM rainfall and related circulation, because of the \r\neffects of vegetation, orography and coastlines (Hourdin et al., 2010; \r\nSylla et al., 2010; Xue et al"


id: 7a29b61421a6f8c1b10e93b00f749c7a
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
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vector_id: 16792
content:
"The region is challenging for climate modelling for several \r\nreasons, including complex topography, the importance of \r\nMesoscale Convective Systems (MCSs), and sensitivity to SST bias \r\n(e.g., Pascale et al., 2019). Many CMIP3 and CMIP5 models with \r\nresolutions coarser than 100 km are unable to realistically resolve \r\nthe topography of the NAmerM region, thus inducing biases in \r\nsimulating the monsoon (Geil et al., 2013). Among other factors, \r\nthese biases are due to deficiencies in the simulation of the Gulf of \r\nCalifornia summer low-level flow (Kim et al., 2008; Pascale et al., \r\n2017) and to failures in representing properly the diurnal cycle \r\n(Risanto et al., 2019) and the decay (Bukovsky et al., 2015) of the \r\nNAmerM precipitation. Simulations at higher horizontal resolution \r\n(i.e., with at least 0"


id: e643b3bd18d387b0328ab3c240d699ae
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 33716
content:
"25deg grid) exhibit an improved representation \r\nof the regional topography, which provides a better representation \r\nof the regional circulation and therefore of the NAmerM (Varuolo\u0002Clarke et al., 2019). "


id: cf0214ae680b552974d3bc503b54b483
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 30102
content:
"The SAmerM displays considerable variability on time scales raging \r\nfrom intra-seasonal to decadal (Vera et  al., 2006; Marengo et  al., \r\n2012; Vuille et  al., 2012; Novello et  al., 2017). The Madden-Julian \r\nOscillation (MJO, Section AIV.2.8) influences the SACZ via changes \r\nin mid-latitude synoptic disturbances (Jones and Carvalho, 2002; \r\nLiebmann et  al., 2004). At interannual time scales, ENSO explains \r\nmost of the SAmerM variability (e.g., Paegle and Mo, 2002; Marengo \r\net al., 2012). Tropical Atlantic temperatures also affect the SAmerM, \r\nwith reduced atmospheric moisture transport to feed the monsoon \r\nunder warmer tropical North Atlantic conditions (e.g., Marengo et al., \r\n2008; Zeng et al., 2008). In addition to SSTs, interannual variability of \r\nthe SAmerM is linked to changes in land surface processes, cold-air \r\nincursions, the latitudinal location of the subtropical jet, and the \r\nSouthern Annular Mode (Section AIV.2.2; e.g"


id: b2b4187c5b9006d9fb980bce76295218
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 25639
content:
", Silvestri and Vera, 2003; \r\nLi and Fu, 2004; Collini et al., 2008; Yin et al., 2014). At inter-decadal \r\ntime scales, the SAmerM is influenced by important modes of climate \r\nvariability (e.g., Robertson and Mechoso, 2000; Paegle and Mo, 2002; \r\nChiessi et al., 2009; Silvestri and Vera, 2009)."


id: fe666f482ac9ec2a080f276f8fe9a537
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 37565
content:
"Indices are, in principle, computable from observations, reanalyses \r\nor model simulations, although it is important to consider scale in \r\ncomparing across datasets. For example, an extreme precipitation \r\nevent has a lower magnitude across a large grid cell than it would at \r\na single station within that grid cell. In many cases, CIDs are simply \r\ncharacterized by the exceedance of a  threshold for an Essential \r\nClimate Variable (ECV). For instance, the probability of crop failure \r\ndramatically increases as temperature rises above certain thresholds, \r\nwhich may differ from one species to another (Hatfield and Prueger, \r\n2015; Grotjahn, 2021). To assess the effect of climate change on \r\nthreshold-based indices (e.g., the change in the number of days with \r\nmaximum temperature above 35degC), a  bias adjustment of model \r\noutputs should be considered where sensible as model simulations \r\ncan have biases compared to observations and reanalyses \r\n(Section 10.3.1"


id: a11400c38e1573ef3e119e86f134af7c
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
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vector_id: 22890
content:
"In the climate science literature, a  number of indices are used to \r\ncharacterize and quantify one or several aspects of climate phenomena \r\noccurring due to natural variability or due to long-term changes in \r\nthe system. There is an extremely large number of examples. One can \r\ncite mean global climate indices, such as global mean sea level rise \r\nor global surface temperature, which characterize the state of the \r\nclimate system and act as a shifting baseline for regional changes. \r\nOne can also examine mean regional trends, for example in mean \r\nspring precipitation, which reflect large-scale patterns and alter the \r\nbackground conditions within which episodic hazards may occur"


id: 3feaa6df87eaa7316f85f3ce16b9ed39
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
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vector_id: 7442
content:
" \r\nOne can also calculate indices of extremes characterizing episodic \r\nevents within the tail of the distributions of specific variables within \r\ntheir variability range, for instance the annual maximal temperature \r\nat a given location or the 100-year return value of river discharge \r\ncharacterizing extreme floods. Such extreme indices have been the \r\nsubject of a number of studies and have been used to characterize \r\nhow climate change modifies extreme values of climate variables \r\nand subsequent impacts in the IPCC Special Report on 'Managing \r\nthe risks of Extreme Events and Disasters to Advance Climate Change \r\nAdaptation' (IPCC, 2012), as well other recent IPCC reports."


id: a707ac869062901e99c232cce112174d
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 23791
content:
"In Chapter  11, extreme weather and climate events (collectively \r\nreferred to as extremes) are assessed and the main focus is on extreme \r\nevents over land. Since the analysis of extremes often involves the \r\nexamination of the tails of the statistical distributions, a parametric \r\nor non-parametric approach can be used to define extremes. The \r\nnon-parametric approach is largely adopted in most of the literature \r\nto characterize moderate temperature and precipitation extremes \r\nwith shorter return periods. The Expert Team on Climate Change \r\nDetection and Indices (ETCCDI; https://www.wcrp-climate.org/etccdi) \r\ndefined 27 indices to characterize different aspects of moderate \r\ntemperature and precipitation extremes, which are described by Frich \r\net al. (2002), Alexander et al. (2006), Zhang et al. (2011), Donat et al. \r\n(2013), and Sillmann et al. (2013), and were also extensively used \r\nin previous IPCC reports"


id: 7a96bda576e89997f221e1a5383e956d
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 16861
content:
" In Chapter 11, a subset of these indices is \r\nassessed in detail (Sections 11.3 and 11.4). For events with longer \r\nreturn periods (e.g., events that occur once in 20 years or even more \r\nrarely), the parametric approach based on Extreme Value Theory (EVT; \r\nColes, 2001) is used and adopted in the literature (e.g., Kharin and \r\nZwiers, 2000; Brown et al., 2008; Kharin et al., 2013). These events are \r\nalso assessed throughout the chapter. Aside from temperature and \r\nprecipitation, the chapter also assesses indices used to characterize \r\ndroughts. Table AVI.1 lists the indices used."


id: 8e58c74ccca86458e1226b077783012e
created_at: 2023-03-16 14:02:12
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content_type: text
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vector_id: 19921
content:
"To quantify heat stress, yearly numbers of daily HI-threshold \r\nexceedances are calculated using a  threshold of 41degC, which \r\ncorresponds to conditions that the US National Weather Service \r\nclassifies into the category of 'Danger' (Blazejczyk et  al., 2012). \r\nBias-adjusted model simulations are used for calculating threshold \r\nexceedances of HI, employing the quantile delta mapping (QDM) \r\napproach as described by Cannon et al. (2015). The QDM approach \r\nadjusts the model data in the application period to fit the reference \r\ndata in the reference period (using quantile mapping). Subsequently, \r\nthe climate change signal is added for each quantile by considering \r\nthe change between the model's reference and application periods. \r\nQDM is directly applied to the HI data using WFDE5 as the reference \r\ndataset and 1981-2010 as the reference period. WFDE5 HI data are \r\nconservatively remapped to each model's grid before bias adjustment \r\nis performed"


id: d0b2034678c220bb4e401eb64b2caba4
created_at: 2023-03-16 14:02:12
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vector_id: 30362
content:
"Aerosol A suspension of airborne solid or liquid particles, with \r\ntypical particle size in the range of a few nanometres to several \r\ntens of micrometres and atmospheric lifetimes of up to several \r\ndays in the troposphere and up to years in the stratosphere. The \r\nterm aerosol, which includes both the particles and the suspending \r\ngas, is often used in this report in its plural form to mean 'aerosol \r\nparticles'. Aerosols may be of either natural or anthropogenic origin \r\nin the troposphere; stratospheric aerosols mostly stem from volcanic \r\neruptions. Aerosols can cause an effective radiative forcing directly \r\nthrough scattering and absorbing radiation (aerosol-radiation \r\ninteraction), and indirectly by acting as cloud condensation nuclei or \r\nice nucleating particles that affect the properties of clouds (aerosol-\r\ncloud interaction), and upon deposition on snow- or ice-covered \r\nsurfaces"


id: b62b874e9de125eccb29dd00008fa921
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
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vector_id: 26191
content:
" Atmospheric aerosols may be either emitted as primary \r\nparticulate matter or formed within the atmosphere from gaseous \r\nprecursors (secondary production). Aerosols may be composed of \r\nsea salt, organic carbon, black carbon (BC), mineral species (mainly \r\ndesert dust), sulphate, nitrate and ammonium or their mixtures. See \r\nalso Short-lived climate forcers (SLCFs). "


id: 68615056ea4181014f77c8125eafd445
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
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index: climate_gpt_only_giec
vector_id: 14006
content:
"A see-saw latitudinal fluctuation in Northern Hemisphere sea level \r\npressure or geopotential height between the Arctic and the mid\u0002latitudes. The NAM has some links with the stratospheric polar \r\nvortex and is related to the fluctuation in strength and latitude of the \r\nmean westerlies. Its variance is maximum in winter and its pattern \r\nhas a strong regional expression in the North Atlantic, being strongly \r\ncorrelated with the North Atlantic Oscillation index. The NAM is also \r\nknown as the Arctic Oscillation (AO). In its positive phase, the NAM is \r\ncharacterized by anomalously low pressure over the Arctic and high \r\npressure over the mid-latitudes/subtropics, with a strengthening of \r\nthe zonally averaged westerly winds on their polar flank that confines \r\ncolder air across the Arctic. The negative NAM phase is characterized \r\nby a more distorted wind pattern and jet meanders that increase \r\nstorminess in the mid-latitude regions. See Section AIV.2"


id: dea163be194448d063d56fd680cb6ce1
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 32523
content:
"Anthropocene A proposed new geological epoch resulting from \r\nsignificant human-driven changes to the structure and functioning of \r\nthe Earth System, including the climate system. Originally proposed \r\nin the Earth system science community in 2000, the proposed new \r\nepoch is undergoing a formalization process within the geological \r\ncommunity based on the stratigraphic evidence that human activities \r\nhave changed the Earth system to the extent of forming geological \r\ndeposits with a signature that is distinct from those of the Holocene, \r\nand which will remain in the geological record. Both the stratigraphic \r\nand Earth system approaches to defining the Anthropocene consider \r\nthe mid-20th century to be the most appropriate starting date \r\n(Steffen et al., 2016), although others have been proposed and \r\ncontinue to be discussed"


id: a2e17827bb206a1f6524e1f1a562116b
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 23193
content:
" The Anthropocene concept has already \r\nbeen informally adopted by diverse disciplines and the public to \r\ndenote the substantive influence of humans on the Earth system."


id: 2e9a164b0649a3c8025b1757814ae028
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 4775
content:
"Anthropogenic removals The withdrawal of greenhouse gases \r\n(GHGs) from the atmosphere as a result of deliberate human \r\nactivities. These include enhancing biological sinks of CO2 and using \r\nchemical engineering to achieve long-term removal and storage. \r\nCarbon dioxide capture and storage (CCS), which alone does not \r\nremove CO2 from the atmosphere, can help reduce atmospheric CO2\r\nfrom industrial and energy-related sources if it is combined with \r\nbioenergy production (BECCS), or if CO2 is captured from the air \r\ndirectly and stored (DACCS). [Note: In the 2006 IPCC Guidelines for \r\nnational GHG Inventories (IPCC, 2006), which are used in reporting of \r\nemissions to the UNFCCC, 'anthropogenic' land-related GHG fluxes \r\nare defined as all those occurring on 'managed land', i.e., 'where \r\nhuman interventions and practices have been applied to perform \r\nproduction, ecological or social functions'. However, some removals \r\n(e.g"


id: 37fc912764cdab74b3fde9ff435cfd12
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 6185
content:
"Atmosphere The gaseous envelope surrounding the Earth, \r\ndivided into five layers - the troposphere which contains half of \r\nthe Earth's atmosphere, the stratosphere, the mesosphere, the \r\nthermosphere and the exosphere, which is the outer limit of the \r\natmosphere. The dry atmosphere consists almost entirely of nitrogen \r\n(78.1% volume mixing ratio) and oxygen (20.9% volume mixing \r\nratio), together with a number of trace gases, such as argon (0.93% \r\nvolume mixing ratio), helium and radiatively active greenhouse gases \r\n(GHGs) such as carbon dioxide (CO2) (0.04% volume mixing ratio), \r\nmethane (CH4 ), nitrous oxide (N2O) and ozone (O3)"


id: 95516ef82a059f28ffbd09186ea6cd34
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 20988
content:
" In addition, \r\nthe atmosphere contains the GHG water vapour (H2O), whose \r\nconcentrations are highly variable (0-5% volume mixing ratio) as the \r\nsources (evapotranspiration) and sinks (precipitation) of water vapour \r\nshow large spatio-temporal variations, and atmospheric temperature \r\nexerts a strong constraint on the amount of water vapour an air \r\nparcel can hold. The atmosphere also contains clouds and aerosols. \r\nSee also Hydrological cycle, Stratosphere and Troposphere."


id: a12bca9861bbb150e8e36be0e1d99af1
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 22917
content:
"Climate change A change in the state of the climate that can be \r\nidentified (e.g., by using statistical tests) by changes in the mean and/\r\nor the variability of its properties and that persists for an extended \r\nperiod, typically decades or longer. Climate change may be due to \r\nnatural internal processes or external forcings such as modulations \r\nof the solar cycles, volcanic eruptions and persistent anthropogenic\r\nchanges in the composition of the atmosphere or in land use. Note \r\nthat the United Nations Framework Convention on Climate Change \r\n(UNFCCC), in its Article 1, defines climate change as: 'a change of \r\nclimate which is attributed directly or indirectly to human activity \r\nthat alters the composition of the global atmosphere and which is \r\nin addition to natural climate variability observed over comparable \r\ntime periods'"


id: b451aca38a63e15baf320ad06c85312e
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 25881
content:
" The UNFCCC thus makes a distinction between climate \r\nchange attributable to human activities altering the atmospheric \r\ncomposition and climate variability attributable to natural causes. See \r\nalso Climate variability, Detection and attribution, Global warming\r\nand Ocean acidification (OA)."


id: acb0aa211df38cb140c48d9f4d4ca315
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 24716
content:
"Climate model A qualitative or quantitative representation of \r\nthe climate system based on the physical, chemical and biological \r\nproperties of its components, their interactions and feedback \r\nprocesses and accounting for some of its known properties. The \r\nclimate system can be represented by models of varying complexity; \r\nthat is, for any one component or combination of components, \r\na spectrum or hierarchy of models can be identified, differing in such \r\naspects as the number of spatial dimensions, the extent to which \r\nphysical, chemical or biological processes are explicitly represented, \r\nor the level at which empirical parametrizations are involved. There is \r\nan evolution towards more complex models with interactive chemistry \r\nand biology. Climate models are applied as a research tool to study \r\nand simulate the climate and for operational purposes, including \r\nmonthly, seasonal and interannual climate predictions"


id: 3ed98391cfebc3455c1afba6d5230da7
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 7272
content:
" See also Earth \r\nsystem model (ESM), Earth system model of intermediate complexity \r\n(EMIC), Energy balance model (EBM), Simple climate model (SCM), \r\nRegional climate model (RCM), Dynamic global vegetation model \r\n(DGVM), General circulation model (GCM) and Emulators."


id: e8e4f0416d63579a529d82badf424e45
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 34157
content:
"CO2 equivalent (CO2-eq) emission The amount of carbon \r\ndioxide (CO2) emission that would have an equivalent effect on \r\na  specified key measure of climate change, over a specified time \r\nhorizon, as an emitted amount of another greenhouse gas (GHG) or \r\na mixture of other GHGs. For a mix of GHGs, it is obtained by summing \r\nthe CO2-equivalent emissions of each gas. There are various ways and \r\ntime horizons to compute such equivalent emissions (see greenhouse \r\ngas emission metric). CO2-equivalent emissions are commonly used \r\nto compare emissions of different GHGs, but should not be taken \r\nto imply that these emissions have an equivalent effect across all \r\nkey measures of climate change. [Note: Under the Paris Rulebook \r\n(Decision 18/CMA.1, annex, paragraph 37), parties have agreed to \r\nuse GWP-100 values from the IPCC AR5 or GWP-100 values from a \r\nsubsequent IPCC Assessment Report to report aggregate emissions \r\nand removals of GHGs"


id: d885966b4663a03c3c673ec822a029f5
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 31585
content:
" In addition, parties may use other metrics \r\nto report supplemental information on aggregate emissions and \r\nremovals of GHGs.]"


id: 897dfb1966d33d570ea9d6018af5dc3f
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 19171
content:
"El Nino-Southern Oscillation (ENSO) The term El Nino was \r\ninitially used to describe a warm-water current that periodically flows \r\nalong the coast of Ecuador and Peru, disrupting the local fishery. It has \r\nsince become identified with warming of the tropical Pacific Ocean \r\neast of the dateline. This oceanic event is associated with a fluctuation \r\nof a global-scale tropical and subtropical surface pressure pattern \r\ncalled the Southern Oscillation. This coupled atmosphere-ocean\r\nphenomenon, with preferred time scales of two to about seven years, \r\nis known as the El Nino-Southern Oscillation (ENSO). The warm and \r\ncold phases of ENSO are called El Nino and La Nina, respectively. ENSO \r\nis often measured by the surface pressure anomaly difference between \r\nTahiti and Darwin and/or the sea surface temperatures in the central \r\nand eastern equatorial Pacific. This phenomenon has a great impact \r\non the wind, sea surface temperature and precipitation patterns in the \r\ntropical Pacific"


id: 6cb8603926ca4de01f4118ffa3a763f
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 14719
content:
" It has climatic effects throughout the Pacific region \r\nand in many other parts of the world through global teleconnections. \r\nSee Section AIV.2.3 in Annex IV of the AR6 WGI report."


id: 1c690a598c7a5cc50a0988d56ffab1fe
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 1927
content:
"Energy budget (of the Earth) The Earth is a physical system \r\nwith an energy budget that includes all gains of incoming energy and \r\nall losses of outgoing energy. The Earth's energy budget is determined \r\nby measuring how much energy comes into the Earth system from \r\nthe Sun, how much energy is lost to space, and accounting for \r\nthe remainder on Earth and its atmosphere. Solar radiation is the \r\ndominant source of energy into the Earth system. Incoming solar \r\nenergy may be scattered and reflected by clouds and aerosols or \r\nabsorbed in the atmosphere. The transmitted radiation is then either \r\nabsorbed or reflected at the Earth's surface. The average albedo of \r\nthe Earth is about 0.3, which means that 30% of the incident solar \r\nenergy is reflected into space, while 70% is absorbed by the Earth"


id: 80553aca8a54ed850bf63eb309fec15f
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 17721
content:
" \r\nRadiant solar or shortwave energy is transformed into sensible heat, \r\nlatent energy (involving different water states), potential energy, and \r\nkinetic energy before being emitted as infrared radiation. With the \r\naverage surface temperature of the Earth of about 15degC (288 K), \r\nthe main outgoing energy flux is in the infrared part of the spectrum. \r\nSee also Sensible heat flux and Latent heat flux."


id: 9733becd54b491b1689befbe116f8a84
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 21303
content:
"Glacial or glaciation A period characterized by the establishment \r\nof expanded ice sheets and glaciers, and associated with global mean \r\nsea level (GMSL) substantially lower than present; generally coincides \r\nwith even-numbered marine isotope stages. Glacial intervals were \r\ninterrupted by interglacial intervals. The Last Glacial Maximum (LGM) \r\nis a specific interval within the most recent glaciation, when ice sheets \r\nwere near their global maximum volume (Clark et al., 2009; Gowan \r\net al., 2021) and GMSL was nearly at its lowest level (Lambeck et al., \r\n2014; Yokoyama et al., 2018). Local or regional glacial maxima may \r\nbe diachronous, for example ranging from about 29,000 years ago \r\nand 16,000 years ago. For purposes of global synthesis, IPCC AR6 \r\nadopts a practical chronostratigraphic definition of LGM of 23,000-\r\n19,000 years BP (before 1950; chronozone level 1 of Mix et al., 2001)"


id: 2ab7bcde2b707ddf6055d043012730dd
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
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vector_id: 4147
content:
" \r\nFor modelling purposes, LGM is defined by the model time step \r\nnearest to the centre of this interval, 21,000 years ago (Kageyama \r\net al., 2017). See also Deglacial or deglaciation or glacial termination, \r\nGlacial-interglacial cycles, Ice age and Interglacial or interglaciation."


id: aec73f750946f642f27bc9ba84931fcb
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 25028
content:
"The East Asian monsoon (EAsiaM) is the seasonal reversal in wind \r\nand precipitation occurring over East Asia, including eastern China, \r\nJapan and the Korean peninsula. In contrast to the other monsoons \r\nit extends quite far north, out of the tropical belt, and it is largely \r\ninfluenced by subtropical systems and by disturbances from the mid\u0002latitudes. The EAsiaM manifests during boreal summer with warm \r\nand wet southerly winds, but also during boreal winter with cold \r\nand dry northerly winds. In late April/early May, rainfall onsets in \r\nthe central Indochina Peninsula, and in mid-June the rainy season \r\narrives over East Asia with the formation of the Meiyu front along \r\nthe Yangtze River valley, Changma in Korea and Baiu in Japan. In \r\nJuly, the monsoon advances up to North China, the Korean peninsula \r\nand central Japan"


id: b8327781ccf807061bae141189d1ffee
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 26511
content:
" During boreal winter, strong north-westerlies \r\nmanifest over north and north-east China, Korea and Japan, while \r\nstrong north-easterlies arrive along the coast of East Asia. Further \r\ndetails on how EAsiaM is defined and used throughout the Report \r\nare provided in Annex V."


id: 6f05e4bdbf8b272750124b4c4dda3cf5
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
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vector_id: 15091
content:
"Greenhouse gas emission metric A simplified relationship \r\nused to quantify the effect of emitting a unit mass of a given \r\ngreenhouse gas on a specified key measure of climate change. \r\nA relative GHG emission metric expresses the effect from one gas \r\nrelative to the effect of emitting a unit mass of a reference GHG on \r\nthe same measure of climate change. There are multiple emission \r\nmetrics, and the most appropriate metric depends on the application"


id: 57df798d3ef553960a405698406072c2
created_at: 2023-03-16 14:02:12
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vector_id: 11327
content:
" \r\nGHG emission metrics may differ with respect to (i) the key measure \r\nof climate change they consider, (ii) whether they consider climate \r\noutcomes for a specified point in time or integrated over a specified \r\ntime horizon, (iii) the time horizon over which the metric is applied, \r\n(iv) whether they apply to a single emission pulse, emissions sustained \r\nover a period of time, or a combination of both, and (v)  whether \r\nthey consider the climate effect from an emission compared to the \r\nabsence of that emission or compared to a reference emissions level \r\nor climate state."


id: ae82e21d010c2f6b2dc333a48787145
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 24988
content:
"The West African monsoon (WAfriM) is a seasonal reversal in wind \r\nand precipitation whose domain includes Benin, Burkina-Faso, \r\nnorthern Cameroon, Cape Verde, northern Central African Republic, \r\nChad, Gambia, Ghana, Guinea, Guinea Bissau, Ivory Coast, Liberia, \r\nMali, Mauritania, Niger, Nigeria, Senegal, Sierra Leone and Togo. The \r\nWAfriM is characterized by the northward progression from May to \r\nSeptember of moist low-level south-westerlies from the Gulf of Guinea. \r\nIn May and June, rainfall essentially remains along the Guinean coast \r\nwith a maximum occurring near 5degN, followed by a sudden decrease \r\nof rainfall, marking the 'short dry season' in the Guinean coast and \r\nthe monsoon onset in the Sahel. Then rainfall continues to progress \r\nnorthward up to about 18-20degN, with a maximum near 12degN in late \r\nAugust/September, until it retreats starting from October towards \r\nthe Guinean coast for a second maximum"


id: 563ad5b88300a4719a0e96e5ea7294ac
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
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vector_id: 11051
content:
" Further details on how \r\nWAfriM is defined and used throughout the Report are provided in \r\nAnnex V."


id: 31c3ff9fc1cc48686fa6bf3c65065352
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 5239
content:
"Holocene The current interglacial geological epoch, the second \r\nof two epochs within the Quaternary Period, the preceding being \r\nthe Pleistocene. The International Commission on Stratigraphy (ICS) \r\ndefines the start of the Holocene Epoch at 11,700 years before 2000 \r\n(Walker et al., 2019) spanning the interval from 11,700 yr to the \r\npresent day. Together with the subadjacent Pleistocene, it comprises \r\nthe Quaternary System/Period. The Holocene record contains diverse \r\ngeomorphological, biological, climatological and archaeological \r\nevidence, within sequences that are often continuous and extremely \r\nwell-preserved at decadal, annual and even seasonal resolution. \r\nAs a consequence, the Holocene is perhaps the most intensively \r\nstudied series/epoch within the entire Geological Time Scale. Yet \r\nuntil recently little attention had been paid to a formal subdivision of \r\nthe Holocene. Here we describe an initiative by the Subcommission \r\non Quaternary Stratigraphy (SQS"


id: 6e1849754b09f9b976f0648b6098bd12
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
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vector_id: 14935
content:
" It encompasses the mid-Holocene \r\n(MH), the 1000-year-long interval centred at 6000 years before 1950; \r\na period of long-standing focus for climate modelling, with enhanced \r\nseasonality in the Northern Hemisphere and decreased seasonality in \r\nthe Southern Hemisphere. The early part of the Holocene is marked by \r\nthe late stages of deglaciation of Pleistocene land ice, sea level rise, \r\nand the occurrence of warm phases that affected different regions at \r\ndifferent times, often referred to as the 'Holocene Thermal Maximum'. \r\nIn addition, the epoch includes the post-glacial interval, which began \r\napproximately 7000 years ago when the fundamental features of the \r\nmodern climate system were essentially in place, as the influence of \r\nremnant Pleistocene ice sheets waned. See also Anthropocene."


id: 55f67d8d57facc8c1fec266b7b724f6f
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
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vector_id: 11011
content:
"Integrated assessment model (IAM) Models that integrate \r\nknowledge from two or more domains into a single framework. They \r\nare one of the main tools for undertaking integrated assessments. \r\nOne class of IAM used with respect to climate change mitigation may \r\ninclude representations of: multiple sectors of the economy, such as \r\nenergy, land use and land-use change; interactions between sectors; \r\nthe economy as a whole; associated greenhouse gas (GHG) emissions \r\nand sinks; and reduced representations of the climate system. This \r\nclass of model is used to assess linkages between economic, social \r\nand technological development and the evolution of the climate \r\nsystem. Another class of IAM additionally includes representations of \r\nthe costs associated with climate change impacts, but includes less \r\ndetailed representations of economic systems"


id: b0dfae2db7a2667a89105787d7440d77
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
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index: climate_gpt_only_giec
vector_id: 25343
content:
" These can be used to \r\nassess impacts and mitigation in a cost-benefit framework and have \r\nbeen used to estimate the social cost of carbon."


id: 96ef9c0a703dacc47a0568e8145d30d6
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 21263
content:
"Modes of climate variability Recurrent space-time structures \r\nof natural variability of the climate system with intrinsic spatial \r\npatterns, seasonality and time scales. Modes can arise through the \r\ndynamical characteristics of the atmospheric circulation but also \r\nthrough coupling between the ocean and the atmosphere, with some \r\ninteractions with land surfaces and sea ice. Many modes of variability \r\nare driven by internal climate processes and are a critical potential \r\nsource of climate predictability on sub-seasonal to decadal time \r\nscales. See Annex IV of the AR6 WGI report"


id: c95c8b7925e86f769671f12a3b1be0e2
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 29241
content:
" See also Annular modes, \r\nTropical Atlantic Variability (TAV), Indian Ocean Dipole (IOD), Indian \r\nOcean Basin (IOB) mode, Pacific Decadal Variability (PDV), Pacific \r\nDecadal Oscillation (PDO) (under Pacific Decadal Variability (PDV)), \r\nEl Nino-Southern Oscillation (ENSO), North Atlantic Oscillation \r\n(NAO), Northern Annular Mode (NAM) (under Annular modes), \r\nSouthern Annular Mode (SAM) (under Annular modes), Atlantic \r\nMeridional Mode (AMM) (under Tropical Atlantic Variability (TAV)), \r\nAtlantic Zonal Mode (AZM) (under Tropical Atlantic Variability (TAV)), \r\nMadden-Julian Oscillation (MJO), Atlantic Multi-decadal Variability \r\n(AMV) and Inter-decadal Pacific Oscillation (IPO) (under Pacific \r\nDecadal Variability (PDV))."


id: 5bd633d07e6432394695a90bbded0b36
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 11964
content:
"Pacific Decadal Variability (PDV) Coupled decadal-to-inter\u0002decadal variability of the atmospheric circulation and underlying \r\nocean that is typically observed over the entire Pacific Basin \r\nbeyond the El Nino-Southern Oscillation (ENSO) time scale. In the \r\nAR6 WGI report, PDV encapsulates the Pacific Decadal Oscillation \r\n(PDO), the South Pacific Decadal Oscillation (SPDO), tropical Pacific \r\ndecadal variability (also called decadal ENSO), and the Inter-decadal \r\nPacific Oscillation (IPO). Typically, the positive phase of the PDV is \r\ncharacterized by anomalously high sea surface temperatures in \r\nthe central-eastern tropical Pacific that extend to the extratropical \r\nNorth and South Pacific along the American coasts, encircled to the \r\nwest by cold sea surface anomalies in the mid-latitude North and \r\nSouth Pacific. The negative phase is accompanied by sea surface \r\ntemperature anomalies of the opposite sign"


id: b8edea43f9e14166219203a96b8eb854
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 26628
content:
" Those sea surface \r\ntemperature anomalies are linked to anomalies in atmospheric and \r\noceanic circulation throughout the whole Pacific Basin. The PDV is \r\nassociated with decadal modulations in the relative occurrence of \r\nEl Nino and La Nina. See Section AIV.2.6 in Annex IV of the AR6 \r\nWGI report."


id: c304ae1817db8f5fdf783709bfcc3f91
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
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index: climate_gpt_only_giec
vector_id: 28265
content:
"y()\r\nShared Socio-economic Pathways (SSPs) have been developed \r\nto complement the Representative Concentration Pathways \r\n(RCPs). By design, the RCP emission and concentration pathways \r\nwere stripped of their association with a certain socio-economic \r\ndevelopment. Different levels of emissions and climate change\r\nalong the dimension of the RCPs can hence be explored against \r\nthe backdrop of different socio-economic development pathways \r\n(SSPs) on the other dimension in a matrix. This integrative SSP-RCP \r\nframework is now widely used in the climate impact and policy \r\nanalysis literature, where climate projections obtained under the \r\nRCP scenarios are analysed against the backdrop of various SSPs. \r\nAs several emissions updates were due, a new set of emissions \r\nscenarios was developed in conjunction with the SSPs. Hence, the \r\nabbreviation SSP is now used for two things: On the one hand \r\nSSP1, SSP2, ..., SSP5 are used to denote the five socio-economic \r\nscenario families"


id: 69b05a6decd013ad6706530849992c28
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
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vector_id: 14224
content:
" On the other hand, the abbreviations SSP1-1.9, \r\nSSP1-2.6, ..., SSP5-8.5 are used to denote the newly developed \r\nemissions scenarios that are the result of an SSP implementation "


id: f85d6fcfd3981295dfafc5a3d4ef38c5
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 36588
content:
"Sea level change (sea level rise/sea level fall) Change to \r\nthe height of sea level, both globally and locally (relative sea level\r\nchange) at seasonal, annual, or longer time scales due to (i) a change \r\nin ocean volume as a result of a change in the mass of water in the \r\nocean (e.g., due to melt of glaciers and ice sheets), (ii) changes in \r\nocean volume as a result of changes in ocean water density (e.g., \r\nexpansion under warmer conditions), (iii) changes in the shape of the \r\nocean basins and changes in the Earth's gravitational and rotational \r\nfields, and (iv) local subsidence or uplift of the land. Global mean sea \r\nlevel (GMSL) change resulting from change in the mass of the ocean \r\nis called barystatic. The amount of barystatic sea level change due \r\nto the addition or removal of a mass of water is called its sea level \r\nequivalent (SLE). Sea level changes, both globally and locally, resulting \r\nfrom changes in water density are called steric"


id: 911f9362afa9dba253f8cc66d0af93a8
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 20369
content:
" Density changes \r\ninduced by temperature changes only are called thermosteric, while \r\ndensity changes induced by salinity changes are called halosteric. \r\nBarystatic and steric sea level changes do not include the effect of \r\nchanges in the shape of ocean basins induced by the change in the \r\nocean mass and its distribution. See also Vertical land motion (VLM), \r\nLand water storage, Glacial isostatic adjustment (GIA), Extreme sea \r\nlevel (ESL) and Storm surge."


id: 2025ad554d2f1d14726d4f3a39e27d2a
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 2501
content:
"Changes in Earth gravity, Earth rotation and viscoelastic solid Earth \r\ndeformation (GRD) result from the redistribution of mass between \r\nterrestrial ice and water reservoirs and the ocean. Contemporary \r\nterrestrial mass loss leads to elastic solid Earth uplift and a nearby \r\nrelative sea level fall (for a single source of terrestrial mass loss this \r\nis within ~2000 km, for multiple sources the distance depends on \r\nthe interaction of the different relative sea level patterns). Farther \r\naway (more than ~7000 km for a single source of terrestrial mass \r\nloss), relative sea level rises more than the global average, due (to \r\nfirst order) to gravitational effects. Earth deformation associated \r\nwith adding water to the oceans and a shift of the Earth's rotation \r\naxis towards the source of terrestrial mass loss leads to second-order \r\neffects that increase spatial variability of the pattern globally"


id: 78914085e4b35d682741d84ae1480438
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
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index: climate_gpt_only_giec
vector_id: 16532
content:
" GRD \r\neffects due to the redistribution of ocean water within the ocean \r\nitself are referred to as self-attraction and loading effects."


id: fb9d40f389b82776c3faab13c3fdb317
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 37089
content:
" See also Polar regions\r\ncarbonate chemistry measurements in, 717\r\ndeepening of the mixed-layer, 1226\r\ndynamic sea level change in, 1247\r\nin polar climate regions, 2016\r\nliquid freshwater column projections for, 1842\r\nmarine heatwave frequency projections, 1227\r\nocean acidification in, 720, 722\r\nocean heat content changes in, 1231\r\nsea ice change in, 1251\r\nsea ice projections (2081-2100), 557\r\nsea ice projections at 1.5degC to 2degC warming, \r\n1217\r\nsea surface temperature change, 1222\r\nstratification changes in, 1226\r\nsurface salinity projections, 1842\r\nArctic Oscillation* (AO), 489, 2156"


id: ac36a92a7ccfd5559fd8198a5c161ec8
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 24630
content:
"uncertainty from natural variability of, 1059\r\nuncertainty levels for projections, 198\r\nuser context for, 1431, 1432, 1863\r\nClimate change commitment*. See Committed \r\nclimate change\r\nClimate change signals\r\nemergence in seawater carbonate chemistry, 721\r\nemergence of, 157, 1853\r\nemergence of impact-relevant changes, 1770\r\nestimation of time-varying signals, 430\r\nfirst realization (r1) in simulations, 570\r\nmitigation effects on emergence of, 591\r\nprogression of changes in, 158\r\nprojection for ocean acidification emergence, 720\r\nreference period for emergence, 1853\r\nsignal-to-noise ratio in emergence, 1853, 1950\r\nsignatures of, 2002\r\nType I versus Type II errors in identification of, 1946\r\nvariability and emergence of, 193-194\r\nClimate drivers\r\nchanges in natural and anthropogenic, 296\r\nsolar and orbital forcing, 296-297\r\nvolcanic aerosol forcing, 298\r\nClimate-dust feedback, radiative forcing \r\ndue to, 858\r\nClimate emulators"


id: c056e82ec532612cdc597c1d88ea78d6
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 27821
content:
"scenario generation process, 236\r\nDimethyl sulphide (DMS), 832, 858\r\nDimming (solar radiation). See also Solar \r\nradiation modification* (SRM)\r\ndecadal variations in past, 1791\r\nobservational records, 925\r\nover Northern Hemisphere, 1077\r\nsolar radiation decline (1950s-1980s), 938\r\nDirect air carbon capture with carbon storage \r\n(DACCS), 757, 765\r\nDissolved inorganic carbon* (DIC), 723, 742\r\nDissolved oxygen, 1787, 1842"


id: c72b70f7a0bcee5dd6f178bc0fceda0e
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 28923
content:
"net effect from climate drivers, 312\r\nnet energy flux of the Earth system, 931\r\nof assessed climate drivers, 310\r\nof black carbon, 847\r\nof climate drivers, 290\r\nquantification and representation in models, \r\n941-942\r\nquantification following perturbation, 941-953\r\nquantification of perturbations, 925\r\nshort-lived gases association with, 290\r\nsolar radiation modification by aerosols, 860\r\nspecific forcing agents, 942-944\r\nstratospheric aerosol injection effects, 860\r\nsummary by time period and driver, 960\r\ntemporal evolution of, 311\r\ntime evolution of 20-year mean averages, 853\r\ntime series for SSP1-1.9, 2145\r\ntime series for SSP1-2.6, 2146\r\ntime series for SSP2-4.5, 2146\r\ntime series for SSP3-7.0, 2147\r\ntime series for SSP5-8"


id: f5f092f10556e7a7b1c510763e36842f
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 36205
content:
"climate features of, 1975\r\nwarming trends in, 1976, 1978\r\nwildfire risk in, 1976\r\nEast Southern Africa (ESAF)\r\nclimatic features, 1967\r\ntropical cyclone projections, 135, 1795\r\nEcological drought. See Agricultural and \r\necological droughts*\r\nEconomic sectors/stakeholders\r\nclimate services for, 1433\r\nphysical and cultural diversity of, 1461\r\nprioritizing metrics for use by, 172\r\nspatial and temporal scales of, 1431\r\ntransdisciplinary approaches to interactions, \r\n1432\r\nuseful regional information, 1461\r\nEcosystems*, large-scale indicator of change, \r\n314\r\nEddy\r\nexchanges between gyres, 1234, 2156\r\nlarge eddy simulation, 972\r\ntransient activity of, 2158\r\nEffective feedback parameter, 933\r\nEffective radiative forcing* (ERF)"


id: f5f52611c941f1ba79a8719618a6b1e7
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content:
"Hadley circulation* (HC)\r\nAR5 report on trends in strength, 335\r\neffects on tropical cyclones, 1528\r\nextent and intensity trends, 336\r\nhuman influence on, 459, 461\r\nNorthern and Southern hemisphere changes, 459\r\npoleward expansion of, 426, 606, 1528\r\npoleward shift of dryland areas, 1849\r\nstratospheric ozone depletion effects \r\n(1981-2000), 459, 1124, 1373\r\nstrength and widening, 1093\r\nHail \r\nAsian changes, 1803\r\nEuropean changes, 1825\r\nfrequency and distribution changes, 1771, 1785\r\nNorth American change, 1834\r\nHalogenated greenhouse gases\r\neffective radiative efficiencies, 946\r\neffective radiative forcing increase, 947\r\nnatural sources, 304\r\nozone-depleting substances in, 304\r\nsummary of changes in, 305\r\ntypes and current status of, 304\r\nHalons, historical abundances forcing, 843, 2143\r\nHalosteric sea level change* defined, 1220\r\nHazards*. See also Climate hazards and \r\nconsequences\r\ncharacteristics of, 1875\r\ndefined for AR6, 201\r\nHeat extremes"


id: 17a45b2eb419bb21c1002122dea4f9f2
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updated_at: 2023-03-16 14:02:12
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content:
" See Snow and ice\r\nIdealized energy balance models/emulators, \r\n1011\r\nImage transformation techniques in attribution \r\napproaches, 1416\r\nIndian Ocean. See also Equatorial Indian Ocean\r\nfreshening projections, 1844\r\nsurface salinity projections, 1842\r\nwarming rate of, 143, 1222\r\nwarming trends, 2010\r\nIndian Ocean Basin*/Indian Ocean Dipole* (IOB/\r\nIOD)\r\nanthropogenic forcing effects on, 1104\r\nAR5 assessment, 373\r\nassociated with climate anomalies, 2167\r\nAustralasian teleconnections with, 1986\r\nboreal autumn sea surface temperature, 2166\r\nclimate variability and, 2156\r\nclimatic effects in Africa, 1968\r\nCMIP simulations of, 499-500, 589\r\ncoral reconstructions of, 2168\r\ndecadal mode of, 2167\r\neffects on East Asian winter monsoon, 1973\r\nEl Nino-Southern Oscillation association, 2167\r\nextreme event association in Small Islands, 2013\r\nglobal climate change projections, 611"


id: 16f729ab6944c1b9ee8826111adc0aa1
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
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content:
" See Paleoclimate \r\n(proxy) reconstructions\r\nlmer Drought Severity Index (PDSI), 1572, \r\n2009\r\nris Agreement goals\r\n1.5degC or 2degC warming limitation goal, 189\r\nbalance between emissions and removals, 242\r\nemissions metrics for, 1018, 1542\r\nfor NOx emissions, 881\r\nframing of report for climate policy, 172\r\nglobal stocktake for, 162\r\nlimiting global temperature increases, 192\r\nlimiting of global average temperature increase"


id: 842d9572ba2bd9f5ac0a651df54229b2
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
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vector_id: 18334
content:
"Reanalysis*\r\napplications of, 215\r\nas evidence in assessments, 151\r\natmospheric, 213\r\nintercomparison of models, 213\r\nmodel output constrained by observations, 212\r\nocean, 214\r\nof pre-instrumental era, 214\r\nprecipitation minus evaporation, 334\r\nsparse input of instrumental era, 214\r\nReasons for Concern* (RFCs)\r\nAR6 WGII assessment of hazards, 1771\r\necosystem impact in Siberia, 1976\r\nrisk with climate change consequences, 1857\r\nrisk with low-likelihood outcomes, 199\r\nReduced-complexity climate model, 962. See\r\nClimate model emulators\r\nReduced Complexity Model Intercomparison \r\nProject (RCMIP), 963\r\nReference Land and Ocean Regions, 208\r\nReforestation*"


id: fd0016d2c60da87dec7269bef6098d4b
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updated_at: 2023-03-16 14:02:12
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content:
"simultaneous changes in CH4, aerosol, and ozone \r\nprecursors, 30\r\nSouth Asia projections, 1981\r\ntemperature effects of regional emissions of \r\nshort-lived climate forcers, 887\r\ntime evolution of short-lived climate forcers and \r\nhydrofluorocarbons, 886\r\ntime series for all climate forcers for SSP1-1.9, \r\n2145\r\nuncertainty of emissions representation in, 566\r\nused in WGI, 232\r\nuse with models, 224\r\nwarming levels and patterns of change with, 56\r\nwarming with stringent mitigation policy, 821\r\nzonal wind and westerly jet projections, 604\r\nhoreline progradation, 135, 139, 142\r\nhoreline retreat, 139, 141, 1853"


id: 6f3016a8685f33fbc8ff02c17b4d89bb
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content:
"5degC \r\n(SR1.5)\r\nchanges in global monsoons with warming, 615\r\nemission pathways to limit warming, 189\r\nframing for climate policy relevance, 172\r\nglobal and regional climate links, 1378\r\nglobal warming levels above 1850-1900, 1936\r\nmethane and sulphur dioxide emissions and \r\nlimiting warming, 825"


id: ea61c990422f68bffd51e7bc1d629fb7
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content:
" See also Cryosphere*\r\nindicators of changes in, 291\r\nerrestrial snow cover. See Snow cover extent* \r\n(SCE)\r\nerrestrial vegetation. See Vegetation\r\nhermal expansion*, 1220\r\nhermal radiation, changes since 1970s, 939\r\nhermodynamic response to warming\r\nenhanced moisture flux with warming, 601\r\nglobal monsoon precipitation enhancement, 603\r\nhermosteric* change (in ocean). See also Steric \r\nsea level change*\r\nanthropogenic forcing of, 481\r\ncontribution to steric sea level change, 1220\r\nglobal mean sea level changes with, 1298\r\nin upper 700 m (1970-2005), 1290\r\nranges of sea level change from, 1245\r\nhermosteric* sea level rise"


id: b4ef10b206f90dbea6cef3064950b760
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content:
"'Climate Change 2022: Impacts, Adaptation and Vulnerability', the \r\nWorking Group II contribution to the Intergovernmental Panel on Climate \r\nChange's (IPCC) Sixth Assessment Report presents a comprehensive \r\nassessment of the current state of knowledge of the observed impacts \r\nand projected risks of climate change as well as the adaptation options. \r\nThe report confirms the strong interactions of the natural, social and \r\nclimate systems and that human-induced climate change has caused \r\nwidespread adverse impacts to nature and people. It is clear that \r\nacross sectors and regions, the most vulnerable people and systems are \r\ndisproportionately affected and climate extremes have led to irreversible \r\nimpacts. The assessment underscores the importance of limiting global \r\nwarming to 1.5degC if we are to achieve a fair, equitable and sustainable \r\nworld"


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" While the assessment concluded that there are feasible and \r\neffective adaptation options which can reduce risks to nature and \r\npeople, it also found that there are limits to adaptation and that there \r\nis a need for increased ambition in both adaptation and mitigation. \r\nThese and other findings confirm and enhance our understanding of the \r\nimportance of climate resilient development across sectors and regions \r\nand, as such, demands the urgent attention of both policymakers and \r\nthe general public."


id: 14a057231c65d99a31079589fd1714bb
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"We are also grateful to the governments that supported their scientists' \r\nparticipation in developing this report and that contributed to the IPCC \r\nTrust Fund to provide for the essential participation of experts from \r\ndeveloping countries and countries with economies in transition. We \r\nwould like to express our appreciation to the government of Ethiopia \r\nfor hosting the scoping meeting for the IPCC's Sixth Assessment Report, \r\nto the governments of South Africa, Nepal, Portugal and Guatemala for \r\nhosting drafting meetings of the Working Group II contribution and to \r\nthe government of Germany for hosting the Twelfth Session of Working \r\nGroup II held virtually for approval of the Working Group II Report. The \r\ngenerous financial support by the government of Germany and the \r\nlogistical support by the Alfred Wegener Institute Helmholtz Centre for \r\nPolar and Marine Research (Germany), enabled the smooth operation \r\nof the Working Group II Technical Support Unit in Bremen, Germany"


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" \r\nAdditional funding from the Governments of Germany, Norway and \r\nNew Zealand provided key support to the Technical Support Unit office \r\nin Durban, South Africa."


id: 55438bb45959192bc18b0d5eafdf365b
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"The Working Group II contribution to the Sixth Assessment Report \r\nof the Intergovernmental Panel on Climate Change (IPCC) provides \r\na comprehensive assessment of the scientific, technical and socio\u0002economic literature relevant to impacts, adaptation and vulnerability. \r\nIt builds upon the Working Group II contribution to the IPCC's Fifth \r\nAssessment Report, the three Special Reports of the Sixth Assessment \r\ncycle: 'Global Warming of 1.5degC. An IPCC Special Report on the \r\nimpacts of global warming of 1.5degC above pre-industrial levels and \r\nrelated global greenhouse gas emission pathways, in the context of \r\nstrengthening the global response to the threat of climate change, \r\nsustainable development, and efforts to eradicate poverty (SR1"


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"5)'; \r\n'Climate Change and Land: An IPCC Special Report on climate change, \r\ndesertification, land degradation, sustainable land management, food \r\nsecurity, and greenhouse gas fluxes in terrestrial ecosystems (SRCCL)'; \r\n'IPCC Special Report on the Ocean and Cryosphere in a Changing \r\nClimate (SROCC)', and the Working Group I contribution to the IPCC \r\nSixth Assessment Report."


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"Drafting Authors: Hans-O. Portner (Germany), Debra C. Roberts (South Africa), Helen Adams \r\n(UK), Carolina Adler (Switzerland/Chile/Australia), Paulina Aldunce (Chile), Elham Ali (Egypt), \r\nRawshan Ara Begum (Malaysia/Australia/Bangladesh), Richard Betts (UK), Rachel Bezner Kerr \r\n(Canada/USA), Robbert Biesbroek (The Netherlands), Joern Birkmann (Germany), Kathryn Bowen \r\n(Australia), Edwin Castellanos (Guatemala), Gueladio Cisse (Mauritania/Switzerland/France), \r\nAndrew Constable (Australia), Wolfgang Cramer (France), David Dodman (Jamaica/UK), Siri \r\nH"


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content:
" Lisa F. Schipper (Sweden/\r\nUK), Daniela Schmidt (UK/Germany), David Schoeman (Australia), Rajib Shaw (Japan), Chandni \r\nSingh (India), William Solecki (USA), Lindsay Stringer (UK), Adelle Thomas (Bahamas), Edmond \r\nTotin (Benin), Christopher Trisos (South Africa), Maarten van Aalst (The Netherlands), David Viner \r\n(UK), Morgan Wairiu (Solomon Islands), Rachel Warren (UK), Pius Yanda (Tanzania), Zelina Zaiton \r\nIbrahim (Malaysia)"


id: 6b8ab6d4899249fca3e1e9d120c51c46
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"socioeconomic details and assumptions, WGII chapters contextualize impacts with respect to exposure, vulnerability and adaptation as \r\nappropriate for their literature, this includes assessments regarding sustainable development and climate resilient development. There are \r\nmany emissions and socioeconomic pathways that are consistent with a given global warming outcome. These represent a broad range \r\nof possibilities as available in the literature assessed that affect future climate change exposure and vulnerability. Where available, WGII \r\nalso assesses literature that is based on an integrative SSP-RCP framework where climate projections obtained under the RCP scenarios \r\nare analysed against the backdrop of various illustrative SSPs22. The WGII assessment combines multiple lines of evidence including \r\nimpacts modelling driven by climate projections, observations, and process understanding. {1.2, 16.5, 18.2, CCB CLIMATE, WGI AR6 \r\nSPM.C, WGI AR6 Box SPM.1, WGI AR6 1"


id: 6b3b038fe1038abb401b5b821bc7f09f
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"(b) Climate change has already had diverse adverse impacts on human systems, including on water security and food production, health and well-being, and cities, settlements and \r\ninfrastructure. The + and - symbols indicate the direction of observed impacts, with a - denoting an increasing adverse impact and a +- denoting that, within a region or globally, both \r\nadverse and positive impacts have been observed (e.g., adverse impacts in one area or food item may occur with positive impacts in another area or food item). Globally, '-' denotes an \r\noverall adverse impact; 'Water scarcity' considers, e.g., water availability in general, groundwater, water quality, demand for water, drought in cities. Impacts on food production were \r\nassessed by excluding non-climatic drivers of production increases; Global assessment for agricultural production is based on the impacts on global aggregated production; 'Reduced \r\nanimal and livestock health and productivity' considers, e.g"


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", heat stress, diseases, productivity, mortality; 'Reduced fisheries yields and aquaculture production' includes marine and \r\nfreshwater fisheries/production; 'Infectious diseases' include, e.g., water-borne and vector-borne diseases; 'Heat, malnutrition and other' considers, e.g., human heat-related morbidity \r\nand mortality, labour productivity, harm from wildfire, nutritional deficiencies; 'Mental health' includes impacts from extreme weather events, cumulative events, and vicarious or \r\nanticipatory events; 'Displacement' assessments refer to evidence of displacement attributable to climate and weather extremes; 'Inland flooding and associated damages' considers, \r\ne.g., river overflows, heavy rain, glacier outbursts, urban flooding; 'Flood/storm induced damages in coastal areas' include damages due to, e.g., cyclones, sea level rise, storm surges. \r\nDamages by key economic sectors are observed impacts related to an attributable mean or extreme climate hazard or directly attributed"


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" Key economic sectors include standard \r\nclassifications and sectors of importance to regions (for methodology and detailed references to chapters and cross-chapter papers see SMTS.1 and SMTS.1.2)."


id: 4ef4c2f83fd437cf755d6afb0b18ce7
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"confidence). In rural areas vulnerability will be heightened by compounding processes including high emigration, reduced habitability and \r\nhigh reliance on climate-sensitive livelihoods (high confidence). Key infrastructure systems including sanitation, water, health, transport, \r\ncommunications and energy will be increasingly vulnerable if design standards do not account for changing climate conditions (high \r\nconfidence). Vulnerability will also rapidly rise in low-lying Small Island Developing States and atolls in the context of sea level rise and \r\nin some mountain regions, already characterised by high vulnerability due to high dependence on climate-sensitive livelihoods, rising \r\npopulation displacement, the accelerating loss of ecosystem services and limited adaptive capacities (high confidence). Future exposure \r\nto climatic hazards is also increasing globally due to socioeconomic development trends including migration, growing inequality and \r\nurbanization (high confidence)"


id: df1aa62d7568dfa5e045539301d65f6a
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" {4.5, 5.5, 6.2, 7.2, 8.3, 9.9, 9.11, 10.3, 10.4, 12.3, 12.5, 13.6, 14.5, 15.3, 15.4, 16.5, CCP2.3, CCP4.3, \r\nCCP5.2, CCP5.3, CCP5.4, CCP6.2, CCB MIGRATE}"


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"(b) The Reasons for Concern (RFC) framework communicates scientific understanding about accrual of risk for five broad categories. Diagrams are shown for each RFC, assuming \r\nlow to no adaptation (i.e., adaptation is fragmented, localized and comprises incremental adjustments to existing practices). However, the transition to a very high risk level has an \r\nemphasis on irreversibility and adaptation limits"


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" Undetectable risk level (white) indicates no associated impacts are detectable and attributable to climate change; moderate risk \r\n(yellow) indicates associated impacts are both detectable and attributable to climate change with at least medium confidence, also accounting for the other specific criteria for key \r\nrisks; high risk (red) indicates severe and widespread impacts that are judged to be high on one or more criteria for assessing key risks; and very high risk level (purple) indicates \r\nvery high risk of severe impacts and the presence of significant irreversibility or the persistence of climate-related hazards, combined with limited ability to adapt due to the nature \r\nof the hazard or impacts/risks. The horizontal line denotes the present global warming of 1.09degC which is used to separate the observed, past impacts below the line from the future \r\nprojected risks above it"


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" RFC1: Unique and threatened systems: ecological and human systems that have restricted geographic ranges constrained by climate-related conditions and \r\nhave high endemism or other distinctive properties. Examples include coral reefs, the Arctic and its Indigenous Peoples, mountain glaciers and biodiversity hotspots. RFC2: Extreme \r\nweather events: risks/impacts to human health, livelihoods, assets and ecosystems from extreme weather events such as heatwaves, heavy rain, drought and associated wildfires, \r\nand coastal flooding. RFC3: Distribution of impacts: risks/impacts that disproportionately affect particular groups due to uneven distribution of physical climate change hazards, \r\nexposure or vulnerability. RFC4: Global aggregate impacts: impacts to socio-ecological systems that can be aggregated globally into a single metric, such as monetary damages, lives \r\naffected, species lost or ecosystem degradation at a global scale"


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" RFC5: Large-scale singular events: relatively large, abrupt and sometimes irreversible changes in systems caused \r\nby global warming, such as ice sheet disintegration or thermohaline circulation slowing. Assessment methods are described in SM16.6 and are identical to AR5, but are enhanced \r\nby a structured approach to improve robustness and facilitate comparison between AR5 and AR6. "


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"(f) Examples of regional key risks. Risks identified are of at least medium confidence level. Key risks are identified based on the magnitude of adverse consequences (pervasiveness \r\nof the consequences, degree of change, irreversibility of consequences, potential for impact thresholds or tipping points, potential for cascading effects beyond system boundaries); \r\nlikelihood of adverse consequences; temporal characteristics of the risk; and ability to respond to the risk, e.g., by adaptation. The full set of 127 assessed global and regional key \r\nrisks is given in SM16.7. Diagrams are provided for some risks"


id: 246f32f8e02cd39a2ec22a3456e393c6
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" The development of synthetic diagrams for Small Islands, Asia and Central and South America were limited by the \r\navailability of adequately downscaled climate projections, with uncertainty in the direction of change, the diversity of climatologies and socioeconomic contexts across countries \r\nwithin a region, and the resulting low number of impact and risk projections for different warming levels. Absence of risks diagrams does not imply absence of risks within a region. \r\n(Box SPM.1) {Figure TS.4, Figure 2.11, Figure SM3.1, Figure 7.9, Figure 9.6, Figure 11.6, Figure 13.28, 16.5, 16.6, Figure 16.15, SM16.3, SM16.4, SM16.5, SM16.6 (methodologies), \r\nSM16.7, Figure CCP4.8, Figure CCP4.10, Figure CCP6.5, WGI AR6 2, WGI AR6 SPM A.1.2, WGI AR6 Figure SPM.8}"


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"settlements, climate impacts to key infrastructure are leading to losses and damages across water and food systems, and affect \r\neconomic activity, with impacts extending beyond the area directly impacted by the climate hazard (high confidence). In Amazonia, \r\nand in some mountain regions, cascading impacts from climatic (e.g., heat) and non-climatic stressors (e.g., land use change) will result \r\nin irreversible and severe losses of ecosystem services and biodiversity at 2degC global warming level and beyond (medium confidence). \r\nUnavoidable sea level rise will bring cascading and compounding impacts resulting in losses of coastal ecosystems and ecosystem \r\nservices, groundwater salinisation, flooding and damages to coastal infrastructure that cascade into risks to livelihoods, settlements, \r\nhealth, well-being, food and water security, and cultural values in the near to long-term (high confidence). (Figure SPM.3) {Figure TS.10, \r\n2.5, 3.4, 3.5, Box 7.3, Box 8.7, Box 9.4, 11"


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"5, Box 11.1, 12.3, 13.9, 14.6, 15.3, 16.5, 16.6, CCP1.2, CCP2.2, CCP5.2, CCP5.3, CCP6.2, \r\nCCP6.3, Box CCP6.1, Box CCP6.2, CCB EXTREMES, WGI AR6 Figure SPM.8d}"


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content:
"Figure SPM.4 |  (b) Climate responses and adaptation options, organized by System Transitions and Representative Key Risks, are assessed at global scale \r\nfor their likely ability to reduce risks for ecosystems and social groups at risk, as well as their relation with the 17 Sustainable Development Goals (SDGs).\r\nClimate responses and adaptation options are assessed for observed benefits (+) to ecosystems and their services, ethnic groups, gender equity, and low-income groups, or observed \r\ndis-benefits (-) for these systems and groups. Where there is highly diverging evidence of benefits/ dis-benefits across the scientific literature, e.g., based on differences between \r\nregions, it is shown as not clear or mixed (*). Insufficient evidence is shown by a dash. The relation with the SDGs is assessed as having benefits (+), dis-benefits (-) or not clear or \r\nmixed (*) based on the impacts of the climate response and adaptation option on each SDG"


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" Areas not coloured indicate there is no evidence of a relation or no interaction with the \r\nrespective SDG. The climate responses and adaptation options are drawn from two assessments. For comparability of climate responses and adaptation options see Table SM17.5. \r\n{17.2, 17.5, CCB FEASIB}"


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"Authors: Hans Portner (Germany), Debra C. Roberts (South Africa), Helen Adams (UK), \r\nIbidun Adelekan (Nigeria), Carolina Adler (Switzerland/Chile/Australia), Rita Adrian (Germany), \r\nPaulina Aldunce (Chile), Elham Ali (Egypt), Rawshan Ara Begum (Malaysia/Australia/Bangladesh), \r\nBirgit Bednar-Friedl (Austria), Rachel Bezner Kerr (Canada/USA), Robbert  Biesbroek (The \r\nNetherlands), Joern  Birkmann (Germany), Kathryn Bowen (Australia), Martina Angela Caretta \r\n(Sweden), Jofre  Carnicer (Spain), Edwin Castellanos (Guatemala), Tae Sung Cheong (Republic \r\nof Korea), Winston Chow (Singapore), Gueladio  Cisse (Mauritania/Switzerland/France), Susan \r\nClayton (USA), Andrew Constable (Australia), Sarah R"


id: 612ee85702bb061a42bfafd59acf9595
created_at: 2023-03-16 14:02:12
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content:
" Poloczanska (UK/Australia/\r\nGermany), Anjal Prakash (India), Benjamin Preston (USA), Marie-Fanny Racault (UK/France), Diana \r\nReckien (The Netherlands/Germany), Aromar Revi (India), Steven K. Rose (USA), E. Lisa F. Schipper \r\n(Sweden/UK), Daniela Schmidt (UK/Germany), David Schoeman (Australia), Rajib Shaw (Japan), \r\nNicholas P. Simpson (Zimbabwe/South Africa), Chandni Singh (India), William Solecki (USA), \r\nLindsay Stringer (UK), Edmond Totin (Benin), Christopher Trisos (South Africa), Yongyut Trisurat \r\n(Thailand), Maarten van Aalst (The Netherlands), David Viner (UK), Morgan Wairiu (Solomon \r\nIslands), Rachel Warren (UK), Philippus Wester (Nepal/The Netherlands), David Wrathall (USA), \r\nZelina Zaiton Ibrahim (Malaysia)"


id: 886f5442482804f0f7adc6053ea683cc
created_at: 2023-03-16 14:02:12
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content:
"Portner, H.-O., D.C. Roberts, H. Adams, I. Adelekan, C. Adler, R. Adrian, P. Aldunce, E. Ali, R. Ara Begum, B. Bednar\u0002Friedl, R. Bezner Kerr, R. Biesbroek, J. Birkmann, K. Bowen, M.A. Caretta, J. Carnicer, E. Castellanos, T.S. Cheong, \r\nW. Chow, G. Cisse, S. Clayton, A. Constable, S.R. Cooley, M.J. Costello, M. Craig, W. Cramer, R. Dawson, D. Dodman, \r\nJ. Efitre, M. Garschagen, E.A. Gilmore, B.C. Glavovic, D. Gutzler, M. Haasnoot, S. Harper, T. Hasegawa, B. Hayward, \r\nJ.A. Hicke, Y. Hirabayashi, C. Huang, K. Kalaba, W. Kiessling, A. Kitoh, R. Lasco, J. Lawrence, M.F. Lemos, R. Lempert, \r\nC. Lennard, D. Ley, T. Lissner, Q. Liu, E. Liwenga, S. Lluch-Cota, S. Loschke, S. Lucatello, Y. Luo, B. Mackey, K. Mintenbeck, \r\nA. Mirzabaev, V. Moller, M. Moncassim Vale, M.D. Morecroft, L. Mortsch, A. Mukherji, T. Mustonen, M. Mycoo, J. Nalau, \r\nM. New, A. Okem, J.P. Ometto, B. O'Neill, R. Pandey, C. Parmesan, M. Pelling, P.F. Pinho, J. Pinnegar, E.S. Poloczanska, \r\nA. Prakash, B. Preston, M.-F"


id: db2ade6dd3c58dc28cf5c6521b7a25ce
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 31995
content:
" Racault, D. Reckien, A. Revi, S.K. Rose, E.L.F. Schipper, D.N. Schmidt, D. Schoeman, R. Shaw, \r\nN.P. Simpson, C. Singh, W. Solecki, L. Stringer, E. Totin, C.H. Trisos, Y. Trisurat, M. van Aalst, D. Viner, M.Wairiu, R.Warren, \r\nP.Wester, D.Wrathall, and Z. Zaiton Ibrahim, 2022: Technical Summary. [H.-O. Portner, D.C. Roberts, E.S. Poloczanska, \r\nK. Mintenbeck, M. Tignor, A. Alegria, M. Craig, S. Langsdorf, S. Loschke, V. Moller, A. Okem (eds.)]. In: Climate Change \r\n2022: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the \r\nIntergovernmental Panel on Climate Change [H.-O. Portner, D.C. Roberts, M. Tignor, E.S. Poloczanska, K. Mintenbeck, \r\nA. Alegria, M. Craig, S. Langsdorf, S. Loschke, V. Moller, A. Okem, B. Rama (eds.)]. Cambridge University Press, Cambridge, \r\nUK and New York, NY, USA, pp. 37-118, doi:10.1017/9781009325844.002."


id: 63ce545cad5fb4092b69d8df71d05a9f
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content:
"This technical summary complements and expands the key findings of \r\nthe Working Group (WG) II contribution to the Sixth Assessment Report \r\n(AR6) presented in the Summary for Policymakers and covers literature \r\naccepted for publication by 1 September 2021. It provides technical \r\nunderstanding and is developed from the key findings of chapters and \r\ncross-chapter papers (CCPs) as presented in their executive summaries \r\nand integrates across them. The report builds on the WGII contribution \r\nto the Fifth Assessment Report (AR5) of the IPCC and three special \r\nreports of the AR6 cycle providing new knowledge and updates. The \r\nthree special reports are the Special Report on Global Warming of \r\n1.5degC (2018), an IPCC special report on the impacts of global warming \r\nof 1"


id: 118c58f1514e2a3a54b2598aad9f0ec5
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content:
"5degC above pre-industrial levels and related global greenhouse \r\ngas emission pathways in the context of strengthening the global \r\nresponse to the threat of climate change, sustainable development \r\nand efforts to eradicate poverty; the Special Report on Climate Change \r\nand Land, which is concerned with climate change, desertification, \r\nland degradation, sustainable land management, food security, and \r\ngreenhouse gas fluxes in terrestrial ecosystems (2019); and the Special \r\nReport on the Ocean and Cryosphere in a Changing Climate (2019). \r\nThe WGII assessment integrates with the WGI (the physical science \r\nbasis) and WGIII (mitigation of climate change) contributions and \r\ncontributes to the Synthesis Report."


id: 12abc1ce5597f2920fa414d0f1e62412
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content:
"Confidence in the key findings of this assessment is communicated \r\nusing the IPCC calibrated uncertainty language. This calibrated \r\nlanguage is designed to consistently evaluate and communicate \r\nuncertainties that arise from incomplete knowledge due to a lack \r\nof information or from disagreement about what is known or even \r\nknowable. The IPCC calibrated language uses qualitative expressions \r\nof confidence based on the robustness of evidence for a finding \r\nand (where possible) uses quantitative expressions to describe the \r\nlikelihood of a finding. Each finding is grounded in an evaluation of \r\nunderlying evidence and agreement. A level of confidence is expressed \r\nusing five qualifiers, very low, low, medium, high and very high, and \r\ntypeset in italics, for example, medium confidence"


id: 5accc23026334088562e7962006bc1e5
created_at: 2023-03-16 14:02:12
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" The following terms \r\nhave been used to indicate the assessed likelihood of an outcome or \r\na result: virtually certain 99-100% probability, very likely 90-100%, \r\nlikely 66-100%, as likely as not 33-66%, unlikely 0-33%, very unlikely\r\n0-10%, exceptionally unlikely 0-1%. Assessed likelihood is typeset in \r\nitalics, for example, very likely. This is consistent with AR5 and the \r\nother AR6 reports. (Figure TS.1) {1.3.4}"


id: dc0fbc1efb1414ec104c676685f955a4
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content:
"The contribution of Working Group II (WGII) to the Sixth Assessment \r\nReport (AR6) of the IPCC summarizes the current understanding of \r\nobserved climate change impacts on ecosystems, human societies \r\nand their cities, settlements, infrastructures and industrial systems, as \r\nwell as vulnerabilities and future risks tied to different socioeconomic \r\ndevelopment pathways. The report is set against a current backdrop \r\nof rapid urbanisation, biodiversity loss, a growing and dynamic global \r\nhuman population, significant inequality and demands for social justice, \r\nrapid technological change, continuing poverty, land degradation \r\nand food insecurity, and risks from shocks such as pandemics and \r\nincreasingly intense extreme events from ongoing climate change. \r\nThe report also assesses existing adaptations and their feasibility and \r\nlimits"


id: e0e47f519e64e9492974ec58f0edc39
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content:
" Any success of adaptation is dependent on the achieved level of \r\nmitigation and the transformation of global and regional sustainability \r\noutlined in the Sustainable Development Goals (SDGs). Accordingly, \r\nadaptation is essential for climate resilient development. Compared to \r\nearlier IPCC assessments, this report integrates more strongly across \r\nthe natural, social and economic sciences, highlighting the role of social \r\njustice and diverse forms of knowledge, such as Indigenous knowledge \r\nand local knowledge, and reflects the increasing importance of urgent \r\nand immediate action to address climate risk. {1.1.1}"


id: 2ca6ec3b1182f9b89cabf7dfaeb9daf6
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content:
"Vulnerability is a component of risk, but also, independently, an important focus. Vulnerability in this report is defined as the propensity \r\nor predisposition to be adversely affected and encompasses a variety of concepts and elements, including sensitivity or susceptibility \r\nto harm and lack of capacity to cope and adapt (Annex II: Glossary). Over the past several decades, approaches to analysing and \r\nassessing vulnerability have evolved. An early emphasis on top-down, biophysical evaluation of vulnerability included--and often started \r\nwith--exposure to climate hazards in assessing vulnerability. From this starting point, attention to bottom-up, social and contextual \r\ndeterminants of vulnerability, which often differ, has emerged, although this approach is incompletely applied or integrated across \r\ncontexts. Vulnerability is now widely understood to differ within communities and across societies, also changing through time"


id: a9fae4b9c4bd7e323e371a896b60b618
created_at: 2023-03-16 14:02:12
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content:
" In WGII \r\nAR6, assessment of the vulnerability of people and ecosystems encompasses the differing approaches that exist within the literature, \r\nboth critiquing and harmonising them based on available evidence. In this context, exposure is defined as the presence of people; \r\nlivelihoods; species or ecosystems; environmental functions, services and resources; infrastructure; or economic, social or cultural assets \r\nin places and settings that could be adversely affected. Potentially affected places and settings can be defined geographically, as well as \r\nmore dynamically, for example through transmission or interconnections through markets or flows of people. {1.2.1, Annex II: Glossary}"


id: 1539486d86478188914315d3200f12a5
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content:
"Adaptation in this report is defined, in human systems, as the process of adjustment to actual or expected climate and its effects in \r\norder to moderate harm or exploit beneficial opportunities. In natural systems, adaptation is the process of adjustment to actual climate \r\nand its effects; human intervention may facilitate this (see Annex II: Glossary). Adaptation planning in human systems generally entails \r\na process of iterative risk management. Different types of adaptation have been distinguished, including anticipatory versus reactive, \r\nautonomous versus planned and incremental versus transformational adaptation. Adaptation is often seen as having five general stages: \r\n(a) awareness, (b) assessment, (c) planning, (d) implementation and (e) monitoring and evaluation. Government, non-government, and \r\nprivate-sector actors have adopted a wide variety of specific approaches to adaptation that, to varying degrees, conform to these five \r\ngeneral stages"


id: 555438c8ef3975c1868632b6a470a7b8
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" Adaptation in natural systems includes autonomous adjustments through ecological and evolutionary processes. It also \r\ninvolves the use of nature through ecosystem-based adaptation. The role of species, biodiversity and ecosystems in such adaptation \r\noptions can range from the rehabilitation or restoration of ecosystems (e.g., wetlands or mangroves) to hybrid combinations of so\u0002called green and grey infrastructure (e.g., horizontal levees). The WGII AR6 emphasises the assessment of observed adaptation-related \r\nresponses to climate change, governance and decision-making in adaptation and the role of adaptation in reducing key risks and global\u0002scale reasons for concern, as well as limits to such adaptation. {1.2.1, 17.4}"


id: 72b031fa5c6e91fba144691be690fc4
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vector_id: 15661
content:
"Assessments of climate risks consider possible future climate change, societal development and responses. This report assesses literature \r\nincluding that based on climate model simulations that are part of the fifth and sixth Coupled Model Intercomparison Project phase \r\n(CMIP5, CMIP6) of the World Climate Research Programme. Future projections are driven by emissions and/or concentrations from \r\nillustrative Representative Concentration Pathways (RCPs)1\r\n and Shared Socio-economic Pathways (SSPs)2 scenarios, respectively3. Climate \r\nimpacts literature is based primarily on climate projections assessed in AR5 or earlier, or assumed global warming levels, though some \r\nrecent impacts literature uses newer projections based on the CMIP6 exercise"


id: 3253d3ab114bf0ac0298facfbb90773b
created_at: 2023-03-16 14:02:12
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content:
" Given differences in the impacts literature regarding \r\nsocioeconomic details and assumptions, WGII chapters contextualize impacts with respect to exposure, vulnerability and adaptation as \r\nappropriate for their literature, this includes assessments regarding sustainable development and climate resilient development. There are \r\nmany emissions and socioeconomic pathways that are consistent with a given global warming outcome. These represent a broad range \r\nof possibilities as available in the literature assessed that affect future climate change exposure and vulnerability. Where available, WGII \r\nalso assesses literature that is based on an integrative SSP-RCP framework where climate projections obtained under the RCP scenarios \r\nare analysed against the backdrop of various illustrative SSPs2\r\n. The WGII assessment combines multiple lines of evidence including \r\nimpacts modelling driven by climate projections, observations, and process understanding. {1.2, 16.5, 18"


id: 7cd9f4ae16a672ef8b3adabb6682e38d
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vector_id: 17241
content:
" RFC5: Large-scale singular events: relatively large, abrupt and sometimes irreversible changes in systems caused \r\nby global warming, such as ice sheet disintegration or thermohaline circulation slowing. Assessment methods are described in SM16.6 and are identical to AR5, but are enhanced \r\nby a structured approach to improve robustness and facilitate comparison between AR5 and AR6. Risks for (c) terrestrial and freshwater ecosystems and (d) ocean ecosystems. "


id: d46f6ed9f09fac64ba742db3bb340471
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content:
"(f) Examples of regional key risks. Risks identified are of at least medium confidence level. Key risks are identified based on the magnitude of adverse consequences (pervasiveness \r\nof the consequences, degree of change, irreversibility of consequences, potential for impact thresholds or tipping points, potential for cascading effects beyond system boundaries); \r\nlikelihood of adverse consequences; temporal characteristics of the risk; and ability to respond to the risk, e.g., by adaptation. The full set of 127 assessed global and regional key \r\nrisks is given in SMTS.4 and SM16.7. Diagrams are provided for some risks"


id: 55d1ab82f5ebd62e4da24efd728409c
created_at: 2023-03-16 14:02:12
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content:
" The development of synthetic diagrams for Small Islands, Asia and Central and South America were \r\nlimited by the availability of adequately downscaled climate projections, with uncertainty in the direction of change, the diversity of climatologies and socio-economic contexts \r\nacross countries within a region, and the resulting low number of impact and risk projections for different warming levels. Absence of risks diagrams does not imply absence \r\nof risks within a region. (Box TS.2) {Figure 2.11, Figure SM3.1, Figure 7.9, Figure 9.6, Figure 11.6, Figure 13.28, 16.5, 16.6, Figure 16.15, SM16.3, SM16.4, SM16.5, SM16.6 \r\n(methodologies), SM16.7, Figure CCP4.8, Figure CCP4.10, Figure CCP6.5, WGI AR6 2, WGI AR6 SPM A.1.2, WGI AR6 Figure SPM.8}"


id: 212f87e06ef67f02af1368adb7c3d1a
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updated_at: 2023-03-16 14:02:12
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index: climate_gpt_only_giec
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content:
"are estimated to increase nutrition-related diseases and the number of \r\nundernourished people by 2050, affecting tens (under low vulnerability \r\nand low warming) to hundreds of millions of people (under high \r\nvulnerability and high warming, i.e., SSP-3-RCP6.0), particularly among \r\nlow-income households in low- and middle-income countries in sub\u0002Saharan Africa, South Asia and Central America (high confidence), for \r\nexample, between 8 million under SSP1-6.0 to up to 80 million people \r\nunder SSP3-6.0. At 3degC or higher global warming levels, adverse \r\nimpacts on all food sectors will become prevalent, further stressing \r\nfood availability (high confidence), agricultural labour productivity and \r\nfood access (medium confidence). Regional disparity in risks to food \r\nsecurity will grow at these higher warming levels, increasing poverty \r\ntraps, particularly in regions characterised by a high level of human \r\nvulnerability (high confidence). {4.5.1, 4.6.1, 5.2.2, 5.4.3, 5.4.4, 5.5"


id: 1ae9287bd1b77dbfca952375b050b075
created_at: 2023-03-16 14:02:12
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content:
"3, \r\n5.8.3, 5.9.3, 5.12.4, 7.3.1, 9.8.2, 9.8.5, 13.5.1, 14.5.4, 16.5.2, 16.6.3, \r\nCCB MOVING PLATE}"


id: feda202e518ebd7133ce81ef15c137c1
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updated_at: 2023-03-16 14:02:12
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content:
"decrease by 5.7% +- 4.1% and 15.5% +- 8.5% under SSP1-2.6 and \r\nSSP5-8.5 respectively by 2080-2099 relative to 1995-2014 (medium \r\nconfidence), affecting food provisioning, revenue value and distribution. \r\nCatch composition will change regionally, and the vulnerability of \r\nfishers will partially depend on their ability to move, diversify and \r\nleverage technology (medium confidence). Global marine aquaculture \r\nwill decline under increasing temperature and acidification conditions \r\nby 2100, with potential short-term gains for finfish aquaculture in \r\nsome temperate regions and overall negative impacts on bivalve \r\naquaculture due to habitat reduction (medium confidence). Changes \r\nin precipitation, sea level rise, temperature and extreme events will \r\nnegatively affect food provisioning from inland aquatic systems \r\n(medium confidence), which provide a significant source of livelihoods \r\nand food for direct human consumption, particularly in Asia and Africa. \r\n{3.4.2, 3.4.3, 3.5"


id: d6929e7c4ea47115e306b602e240f0ac
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content:
"3, 3.6.2, 3.6.3, 5.8.3, 5.9.3, 5.13, 9.8.5, 13.5.1, 14.5.2, \r\nCCP6.2.3, CCP6.2.4, CCP6.2.5, CCP6.2.6, CCP6.2.8, CCB MOVING \r\nPLATE, CCB SLR}"


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created_at: 2023-03-16 14:02:12
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index: climate_gpt_only_giec
vector_id: 6660
content:
"TS.C.6.4 Climate impacts on food systems are projected to \r\nincrease undernutrition and diet-related mortality and risks \r\nglobally (high confidence). Reduced marine and freshwater fisheries \r\ncatch potential is projected to increase malnutrition in East, West \r\nand Central Africa (medium to high confidence) and in subsistence\u0002dependent communities across North America (high confidence). \r\nBy 2050, disability-adjusted life years due to undernutrition and \r\nmicronutrient deficiencies are projected to increase by 10% under \r\nRCP8.5 (medium evidence, high agreement). These projected changes \r\nwill increase diet-related risk factors and related non-communicable \r\ndiseases globally and increase undernutrition, stunting and related \r\nchildhood mortality, particularly in Africa and Asia (high confidence)"


id: 7c546e1e3a7ab182afd68593812920f9
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 17159
content:
" \r\nNear-term projections (2030) of undernutrition are the highest for \r\nchildren (confidence), which can have lifelong adverse consequences \r\nfor physiological and neurological development as well as for earnings \r\ncapacity. Climate change is projected to put 8  million (SSP1-6.0) \r\nto 80  million people (SSP3-6.0) at risk of hunger in mid-century, \r\nconcentrated in sub-Saharan Africa, South Asia and Central America \r\n(high confidence). These climate change impacts on nutrition could \r\nundermine progress towards the eradication of child undernutrition \r\n(high confidence). {4.5.3, 5.2.2, 5.12.4, Box 5.10, 7.3.1, 9.8.5, 9.10.2, \r\n10.4.7, Figure  10.11, 13.7.1, 14.5.6, 15.3.4, CCP6.2, CCB MOVING \r\nPLATE}"


id: 3c1930a4f04ae6d7787c197dee7a7cc9
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 6814
content:
"TS.C.6.5 Vector-borne disease transmission is projected to \r\nexpand to higher latitudes and altitudes, and the duration \r\nof seasonal transmission risk is projected to increase (high \r\nconfidence), with the greatest risk under high emissions \r\nscenarios. Dengue vector ranges will increase in North America, Asia, \r\nEurope and sub-Saharan Africa under RCP6 and RCP8.5, potentially \r\nputting another 2.25 billion people at risk (high confidence). Higher \r\nincidence rates of Lyme disease are projected for the Northern \r\nHemisphere (high confidence). Climate change is projected to increase \r\nmalaria's geographic distribution in endemic areas of sub-Saharan \r\nand southern Africa, Asia and South America (high confidence), \r\nexposing tens of millions more people to malaria, predominately \r\nin east and southern Africa, and up to hundreds of millions more \r\nexposed under RCP8.5 (high confidence). {7.3.1, 9.10.2, Figure 9.32, \r\n10.4.7, Figure 10.11, 11.3.6, 12.3.2, 12.3.5, 12.3.6, Figure 12.5, 13.7"


id: 3badfcb08b7ba3d70492497ccfeb86d
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index: climate_gpt_only_giec
vector_id: 6746
content:
"TS.C.7.1 Future climate-related migration is expected to vary \r\nby region and over time, according to future climatic drivers, \r\npatterns of population growth, adaptive capacity of exposed \r\npopulations and international development and migration \r\npolicies (high confidence). Future migration and displacement \r\npatterns in a changing climate will depend not only on the physical \r\nimpacts of climate change, but also on future policies and planning at \r\nall scales of governance (high confidence). Projecting the number of \r\npeople migrating due to slow onset events is difficult due to the multi\u0002causal nature of migration and the dominant role that socioeconomic \r\nfactors have in determining migration responses (high confidence)"


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vector_id: 4321
content:
" \r\nIncreased frequency of extreme heat events and long-term increases \r\nin average temperatures pose future risks to the habitability of \r\nsettlements in low latitudes; this, combined with the urban heat island \r\neffect, may in the long term affect migration patterns in exposed areas, \r\nespecially under high emissions scenarios, but more evidence is needed.\r\nHigh emissions/low development scenarios raise the potential for both \r\nincreased rates of migration and displacement and larger involuntary \r\nimmobile populations that are highly exposed to climatic risks but lack \r\nthe means of moving to other locations (medium confidence). {4.5.7, "


id: c6c1a61dd80fd774879eae52657d9c2c
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 28843
content:
"TS.C.9 Climate change increases risks for a larger number of \r\ngrowing cities and settlements across wider areas, especially in \r\ncoastal and mountain regions, affecting an additional 2.5 billion \r\npeople residing in cities mainly in Africa and Asia by 2050 (high \r\nconfidence). In all cities and urban areas, projected risks faced \r\nby people from climate-driven impacts has increased (high \r\nconfidence). Many risks will not be felt evenly across cities and \r\nsettlements or within cities. Communities in informal settlements \r\nwill have higher exposure and lower capacity to adapt (high \r\nconfidence). Most at risk are women and children who make up \r\nthe majority populations of these settlements (high confidence). \r\nRisks to critical physical infrastructure in cities can be severe \r\nand pervasive under higher warming levels, potentially resulting \r\nin compound and cascading risks, and can disrupt livelihoods \r\nboth within and across cities (high confidence)"


id: 135895d24b154b6a52b098d95a5b02f0
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
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index: climate_gpt_only_giec
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content:
" In coastal cities \r\nand settlements, risks to people and infrastructure will get \r\nprogressively worse in a changing climate, sea level rise and with \r\nongoing coastal development (very high confidence). {2.6.5, 6.1, \r\n6.1.4, 6.2, 9.9.4, 16.5, 14.5.5, Box 14.4, CCP2.2}"


id: 5ba59d07a5ea00ba4f7edc3f38e16f4b
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 11928
content:
"TS.C.9.2 Asian and African urban areas are considered high-risk \r\nlocations from projected climate, extreme events, unplanned \r\nurbanisation and rapid land use change (high confidence). These \r\ncould amplify pre-existing stresses related to poverty, informality, \r\nexclusion and governance, such as in African cities (high confidence). \r\nClimate change increases heat stress risks in cities (high confidence) \r\nand amplifies the urban heat island across Asian cities at 1.5degC and \r\n2degC warming levels, both substantially larger than under present \r\nclimates (medium confidence). Urban population exposure to extreme \r\nheat in Africa is projected to increase from 2 billion person-days per \r\nyear in 1985-2005 to 45  billion person-days by the 2060s (1.7degC \r\nglobal warming with low population growth) and to 95 billion person\u0002days (2.8degC global warming with medium-high population growth) \r\n(medium confidence)"


id: 9a21731987d996b7e4d5259c8ddc598e
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 21774
content:
" Risks driven by flooding and droughts will also \r\nincrease in cities (high confidence). Urban populations exposed to \r\nsevere droughts in West Africa will increase (65.3+-34.1  million) at \r\n1.5degC warming and increase further at 2degC (medium confidence). \r\nUrban land in flood zones and drylands exposed to high-frequency \r\nfloods is expected to increase by as much as 2600% and 627% \r\nrespectively across East, West and Central Africa by 2030. Higher \r\nrisks from temperature and precipitation extremes are projected for \r\nalmost all Asian cities under RCP8.5 (medium confidence), impacting \r\non freshwater availability, regional food security, human health and \r\nindustrial outputs. {4.3.4, 4.3.5, 4.5.4, 6.1, 6.2, Table 6.3, Table 6.4, \r\n9.9.4, 10.3.7, 10.4.6, 15.3.3, 15.3.4, 15.4.3, CCP2.2, CCP6.2.7, CWGB \r\nURBAN}"


id: 1a5d410be7cc7153976060a122d83035
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
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index: climate_gpt_only_giec
vector_id: 1618
content:
"TS.C.9.3 Globally, urban key infrastructure systems are \r\nincreasingly sites of risk creation that potentially drive \r\ncompounding and cascading risks (high confidence). Unplanned \r\nrapid urbanisation is a major driver of risk, particularly where \r\nincreasing climate-driven risks affect key infrastructure and potentially \r\nresult in compounding and cascading risks as cities expand into \r\ncoastal and mountain regions prone to flooding or landslides that \r\ndisrupt transportation networks, or where water and energy resources \r\nare inadequate to meet the needs of growing settlements (high \r\nconfidence). These infrastructure risks expand beyond city boundaries; \r\nclimate-related transport and energy infrastructure damage is \r\nprojected to be a significant financial burden for African countries, \r\nreaching tens to hundreds of billions of US dollars under moderate \r\nand high emissions scenarios (high confidence)"


id: cbd1bfa828c00d27775e3f4695ae8d90
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 29606
content:
" Projected changes \r\nin both the hydrological cycle and the cryosphere will threaten urban \r\nwater infrastructure and resource management in most regions \r\n(very high confidence). South and Southeast Asian coastal cities can \r\nexperience significant increases in average annual economic losses \r\nbetween 2005 and 2050 due to flooding, with very high losses in \r\neast Asian cities under RCP8.5 (high confidence). By 2050, permafrost \r\nthaw in the pan-Arctic is projected to impact 69% of infrastructure, \r\nmore than 1200 settlements, 36,000 buildings, and 4 million people \r\nin Europe under RCP4.5. In small islands, degraded terrestrial \r\necosystems decrease resource provision (e.g., potable water) and \r\namplify the vulnerability of island inhabitants (high confidence). \r\nProjections suggest that 350 million (+- 158.8 million) more people in \r\nurban areas will be exposed to water scarcity from severe droughts \r\nat 1.5degC warming and 410.7 million (+- 213.5) at 2degC warming (low \r\nconfidence)"


id: 7f4d2aa571c8efd83e1b23c8dbdb14d4
created_at: 2023-03-16 14:02:12
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vector_id: 17581
content:
"TS.C.10.1 Without limiting warming to 1.5degC global warming \r\nlevel, many key risks are projected to intensify rapidly in \r\nalmost all regions of the world, causing damage to assets and \r\ninfrastructure and losses to economic sectors and entailing high \r\nrecovery and adaptation costs (high confidence). Severe risks are \r\nmore likely in developing regions that are already hotter and in regions \r\nand communities with a large portion of the workforce employed \r\nin highly exposed industries (e.g., agriculture, fisheries, forestry, \r\ntourism, outdoor labour). In addition to market damage and disaster \r\nmanagement costs, substantial costs of climate inaction are projected \r\nfor human health (high confidence). At higher levels of warming, climate \r\nimpacts will pose risks to financial and insurance markets, especially if \r\nclimate risks are incompletely internalised (medium confidence), with \r\nadverse implications for the stability of markets (low confidence)"


id: 2b6a966cf337c892609582b03bc82745
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 4269
content:
" While \r\nthe overall economic consequences are clearly negative, opportunities \r\nmay arise for a few economic sectors and regions, such as from longer \r\ngrowing seasons or reduced sea ice, primarily in northern latitudes \r\n(medium to high confidence). {4.4.4, 4.7.5, 9.11.2, 10.4.6, 11.6, 13.9.2, \r\n13.10.3, 14.5.4, 14.5.5, 14.5.7, 14.5.8, 14.5.9, Box  14.5, Box  14.6, \r\n16.5.2, 16.5.3, CCP4.2, CCP6.2, CCB INTEREG}"


id: 4a4f55f2ff1a1d66d29ff032b06a9d10
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 9098
content:
"increased relative to the range reported in AR5, though there is low \r\nagreement and significant spread within and across methodology types \r\n(e.g., statistical, structural, meta-analysis), resulting in an inability to \r\nidentify a best estimate or robust range (high confidence). Under high \r\nwarming (>4degC) and limited adaptation, the magnitude of decline in \r\nannual global GDP in 2100 relative to a non-global-warming scenario \r\ncould exceed economic losses during the Great Recession in 2008-\r\n2009 and the COVID-19 pandemic in 2020. Much smaller effects are \r\nestimated for less warming, lower vulnerability and more adaptation \r\n(medium confidence). Regional estimates of GDP damage vary (high \r\nconfidence). Severe risks are more likely in (typically hotter) developing \r\ncountries (medium confidence). For Africa, GDP damage is projected to \r\nbe negative across models and approaches (high confidence). {4.4.4, \r\n4.7.5, 9.11.2, 10.4.6, 13.10.2, 13.10.3, 14.5.8, Box 14.6, 16.5.2, 16.6"


id: 38e251cd347c5a11aff8bb486d99e334
created_at: 2023-03-16 14:02:12
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index: climate_gpt_only_giec
vector_id: 6321
content:
"TS.C.11.2 Climate hazards cause multiple impacts, interacting \r\nto compound risks to food security, nutrition and human health \r\n(high confidence). Compound risks to health and food systems \r\n(especially in tropical regions) are projected from simultaneous \r\nreductions in food production across crops, livestock and fisheries \r\n(high confidence), heat-related loss of labour productivity in \r\nagriculture (high confidence), increased heat-related mortality (high \r\nconfidence), contamination of seafood (high confidence), malnutrition \r\n(high confidence) and flooding from sea level rise (high confidence). \r\nMalnourished populations will increase through direct impacts on \r\nfood production with cascading impacts on food prices and household \r\nincomes, reducing access to safe and nutritious food (high confidence)"


id: 7789b01db0d6938eebeaadbe4b8428d2
created_at: 2023-03-16 14:02:12
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vector_id: 16362
content:
" \r\nFood safety will be undermined from increased food contamination \r\nfor seafood with marine toxins from harmful algal blooms and \r\nchemical contaminants, worsening health risks (high confidence). \r\n(Figure TS.10 COMPLEX RISK) {4.5.1, 5.2.2, 5.4.3, 5.8.1, 5.8.3, 5.11.1, \r\n5.12, Figure 5.2, 5.12.4, Box 5.10, 7.3.1, 9.10.2, 9.8.2, 9.8.3, 14.5.6, \r\nCCP5.2.3, CCP6.2.3, CCB ILLNESS}"


id: 2d680504629d4e0e2f0a363ee888108e
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 4585
content:
"TS.C.13.4 Solar radiation modification (SRM) approaches have \r\nthe potential to offset warming and ameliorate other climate \r\nhazards, but their potential to reduce risk or introduce novel \r\nrisks to people and ecosystems is not well understood (high \r\nconfidence). SRM effects on climate hazards are highly dependent \r\non deployment scenarios, and substantial residual climate change or \r\novercompensating change would occur at regional scales and seasonal \r\ntime scales (high confidence). Due in part to limited research, there is \r\nlow confidence in projected benefits or risks to crop yields, economies, \r\nhuman health or ecosystems. Large negative impacts are projected \r\nfrom rapid warming for a sudden and sustained termination of SRM \r\nin a high-CO2 scenario. SRM would not stop CO2 from increasing in the \r\natmosphere or reduce resulting ocean acidification under continued \r\nanthropogenic emissions (high confidence)"


id: dff8cf6582b10f75870eae000cba08
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 32728
content:
" There is high agreement \r\nin the literature that for addressing climate change risks SRM is, at \r\nbest, a supplement to achieving sustained net zero or net negative CO2\r\nemission levels globally. Co-evolution of SRM governance and research \r\nprovides a chance for responsibly developing SRM technologies with \r\nbroader public participation and political legitimacy, guarding against \r\npotential risks and harms relevant across a full range of scenarios. \r\n{CWGB SRM}"


id: 7a8eb43d02fe136cca80524da2d26ef8
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 16855
content:
"Figure TS.5 ECOSYSTEMS  | (a) Left: Observed global and regional impacts on ecosystems and human systems attributed to climate change. Confidence levels reflect uncertainty \r\nin attribution of the observed impact to climate change. For more details and line of sight to chapters and cross-chapter papers see Figure TS.3a, SMTS.1 and Table SMTS.1. Right: \r\nObserved species richness across latitude for three historical periods. {3.4.3, Figure 3.18}. (b) Left: Global warming levels (GMST) modelled across the ranges of more than 30,000 \r\nmarine and terrestrial species. Middle: Global warming levels (GSAT); change indicated by the proportion of species (modelled n=119,813 species globally) for which the climate \r\nis projected to become unsuitable across their current distributions. Right: Modelled 12,796 marine species globally. {2.5.1, Figure 2.6, 3.4.3, Figure 3.18, Figure 3.20a, CCP1.2.4, \r\nFigures AI.6, AI.15, AI.16}. (c) {2.6.2, Table 2.6, 3.6.2, Figure 3.24}"


id: b6ffcd0db2fb3b1c7c928bf13b744dc0
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 26322
content:
" (d) Some actions facilitate sustainable use but also increase space for nature. {2.4 2, 2.6.2, 2.6.3, 2.6.5, 2.6.7, \r\n2.6.8, 3.6.2, 3.6.5, Table 3.30, 5.6.3, Box 5.11, 9.3.1, 9.3.2, 9.6.3, 9.6.4, 9.12 .3, 10.4.2, 10.4.3, 11.3.1, 11 .3.2, 11 .7.3, 12.5. 1, 12. 5.2, 12.5.9, 12.6.1, 13.3.2, 13.4.2, 13.5 \r\n.2, 13.10.2, 14.5.1, 14.5.2, Box 14.2, Box 14.7, 15.5.4, 15.3.3, Table 15 .6, 16.5.2, 16.6.3, CCP1.3, CCP3. 2.2, CCP4.4.1, CCP5 .2.5, CCP5.4.1, CCP6.3.2, CCP7.5, CCP7 .5. 1, \r\nCCPBox7.1, Table CCP7 .3, CCB EXTREMES, CCB NATURAL}"


id: 4d635674b629b197ab8e1983c4dc54e9
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 9608
content:
"Figure TS.6 FOOD-WATER | (a) {5.4.1.1, Box 5.1, FAQ 5.1, SM5.1, Figure Al.20}. (b) Projected increase in the global share of area and population impacted from droughts. \r\nChanges are calculated based on the RCP6.0 concentration pathway for Terrestrial Water Storage (TWS) droughts, which can be considered to be a combination of agricultural, \r\necological and hydrological droughts. TWS is the sum of continental water stored in canopies, snow and ice, rivers, lakes and reservoirs, wetlands, soil and groundwater. {Figure 4.19; \r\n4.4.5}. (c) Projected impacts are for RCP4.5 mid 21st century, taking into account adaptation and CO2 fertilisation for the crop yield productivity {4.3.1, 4.2.7, 4.5.1, Figure 4.2, \r\n5.5.3, 5.4.1, Figure 5.3, Figure 9.22, 15.3.3, 15.3.4}. (d) Projections used five CMIP5 climate models, three global hydrological models from ISIMIP, and three Shared Socioeconomic \r\nPathways (SSPs).{Box 4.1, Figure Box 4.1.1, Figure AI.48}. (e) {4.6.2, Figure 4.29, Figure 4.28, SM4"


id: f48cc7249bdbc37dee0dae1d02f87a6f
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 35981
content:
"Figure TS.7 VULNERABILITY | (a) The global map of vulnerability is based on two comprehensive global indicator systems, namely INFORM Risk Index and WorldRiskIndex \r\n(2019). Climate change hazards and exposure levels are not included in this figure. The relative level of average national vulnerability is shown by the colours. Vulnerability values \r\nare based on the average of the two indices, classified into 5 classes using the quantile method. A hexagon binning method was used to simplify the global map and enlarge small \r\nstates. The map combines information about the level of vulnerability (independent of the population size) with two classes of population density (high density >= 20 people/km2 \r\nand low density < 20 people/km2). The selected examples of local vulnerable populations underscore that there are also highly vulnerable populations in countries with overall low \r\nrelative vulnerability {8.3.2, Figure 8"


id: cc8df48882171f9ba3bfd47423fe438a
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
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index: climate_gpt_only_giec
vector_id: 29721
content:
"6} (b) This figure shows regional averages for selected aspects of human vulnerability. The indicators are a selection of the indicator systems \r\nused within the global vulnerability map (panel a). The colours represent the average value of the respective indicator for the regional level; classified into three classes using natural \r\nbreaks. This regional information reveals that within all regions challenges exist in terms of different aspects of vulnerability, however, in some regions these challenges are more \r\nsevere and accumulate in multiple-dimensions. For example, the indicator \"dependency ratio\" measures the ratio of the number of children (0-14 years old) and older persons \r\n(65 years or over) to the working-age population (15-64 years old). {8.3.2, Figure 8"


id: de856e0cf26eda5a5cc0a93b6722a778
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 32514
content:
"7} (c) The pie charts show the number of deaths (mortality) per hazard (storm, flood, drought, \r\nheatwaves and wildfires) event per continental region based on Emergency Events Database (EM-DAT) (Centre for Research on the Epidemiology of Disasters, 2020). The size of the \r\npie chart represents the average mortality per hazard event while slices of each pie chart show the absolute number of deaths from each hazard. This reveals that significantly more \r\nfatalities per hazard (storms, floods, droughts, heatwaves and wildfires) did occur in the past decade in more vulnerable regions, e.g. Africa and Asia. {Figure 8.6} (d) The figure \r\nshows constraints that make it harder to plan and implement human adaptation. Across regions and sectors, the most significant challenges to human adaptation are financial, \r\ngovernance, institutional and policy constraints"


id: f0992e571736aa45c59c108f858e7e59
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 35352
content:
" The ability of actors to overcome these socio-economic constraints largely influences whether additional adaptation is able to be \r\nimplemented and prevent limits to adaptation from being reached. Low: <20% of assessed literature identifies this constraint; Medium: 20-40% of assessed literature identifies \r\nthis constraint; High: >40% of assessed literature identifies this constraint. {9.3, 16.4.3, Figure 16.8}"


id: 6588e9c8e02cc527b8d16493cfc4e6ec
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 13550
content:
"TS.D.1.5 Systemic barriers constrain the implementation of \r\nadaptation options in vulnerable sectors, regions and social \r\ngroups (high confidence). Key barriers are limited resources, lack \r\nof private-sector and citizen engagement, insufficient mobilisation of \r\nfinance (including for research), lack of political leadership, limited \r\nresearch and/or slow and low uptake of adaptation science and \r\na low sense of urgency. Most of the adaptation options to the key \r\nrisks depend on limited water and land resources (high confidence). \r\nGovernance capacity, financial support and the legacy of past urban \r\ninfrastructure investment constrain how cities and settlements \r\nare able to adapt (high confidence)"


id: b4605bc8243bea63c9f932f8131a3692
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 25896
content:
" Critical urban capacity gaps \r\ninclude limited ability to identify social vulnerability and community \r\nstrengths, the absence of integrated planning to protect communities, \r\nthe lack of access to innovative funding arrangements and a limited \r\ncapability to manage finance and commercial insurance (medium \r\nconfidence). Prioritisation of options and transitions from incremental \r\nto transformational adaptation are limited due to vested interests, \r\neconomic lock-ins, institutional path dependencies and prevalent \r\npractices, cultures, norms and belief systems. For example, Africa faces \r\nsevere climate data constraints and inequities in research funding and \r\nleadership that reduce adaptive capacity (very high confidence)--from \r\n1990 to 2019 research on Africa received just 3.8% of climate-related \r\nresearch funding globally, and 78% of this funding for Africa went \r\nto European Union- and North America-based institutions and only \r\n14.5% to African institutions. {3.6.3, 9.1.5, 9.5"


id: b8103fb5489df77796af2f9a486cb41d
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 26491
content:
"TS.D.2 There is increasing evidence on limits to adaptation \r\nwhich result from the interaction of adaptation constraints \r\nand the speed of change (high confidence). In some natural \r\nsystems, hard limits have been reached (high confidence) and \r\nmore will be reached beyond 1.5degC (medium confidence). \r\nSurpassing such hard, evolutionary limits causes local species \r\nextinctions and displacements if suitable habitats exist (high \r\nconfidence). Otherwise, species' existence is at very high risk \r\n(high confidence). In human, managed and natural systems, soft \r\nlimits are already being experienced (high confidence). Financial \r\nconstraints are key determinants of adaptation limits in human \r\nand managed systems, particularly in low-income settings \r\n(high confidence), while in natural systems key determinants \r\nfor limits are inherent traits of the species or ecosystem (very \r\nhigh confidence). (Figure TS.7 VULNERABILITY) {2.4.2, 2.6.1, 3.3, \r\n3.4.2, 3.4.3, 15.5.4, CCP5.3.2, CCP7.5"


id: 66c441e9347299898d4d00e29a2c1299
created_at: 2023-03-16 14:02:12
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content_type: text
index: climate_gpt_only_giec
vector_id: 13763
content:
"TS.D.1.6 Insufficient financing is a key driver of adaptation gaps \r\n(high confidence). Annual finance flows targeting adaptation \r\nfor Africa, for example, are billions of US dollars less than the \r\nlowest adaptation cost estimates for near-term climate change \r\n(high confidence). Finance has not targeted more vulnerable countries \r\nand communities. From 2014 to 2018 a greater amount of financial \r\ncommitments to developing countries was in the form of debt rather \r\nthan grants, and--excluding multilateral development banks--only \r\n51% of commitments targeting adaptation were dispersed (compared \r\nto 85% for other development projects). Tracked private-sector finance \r\nfor climate change action has grown substantially since 2015, but \r\nthe proportion directed towards adaptation has remained small (high \r\nconfidence); in 2018 contributions were 0.05% of total climate finance \r\nand 1% of adaptation finance"


id: 4ebfcb5997dcda9924c48e1edfdaf6e9
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
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index: climate_gpt_only_giec
vector_id: 9821
content:
" Globally, private-sector financing of \r\nadaptation has been limited, especially in developing countries (high \r\nconfidence). {3.6.3, 4.7,4, 4.7.5, 4.8.2, 6.4.5, Table 6.10, 9.4.1, 12.5.4, \r\n12.5.8, 15.6.3, 17.4.3, CCB FINANCE}"


id: 121a65d3f6ef1fa392db59451ca6ed2b
created_at: 2023-03-16 14:02:12
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content_type: text
index: climate_gpt_only_giec
vector_id: 345
content:
"TS.D.2.2 Limits to adaptation have been observed for terrestrial \r\nand aquatic species and ecosystems and for some human and \r\nmanaged systems in specific geographies such as small island \r\nstates and mountain regions (high confidence). Beginning at \r\nbelow 1.5degC, autonomous and evolutionary adaptation responses \r\nby more terrestrial and aquatic species and ecosystems will face hard \r\nlimits, resulting in species extinctions, loss of ecosystem integrity and \r\na resulting loss of livelihoods (high confidence). Examples of hard \r\nlimits being exceeded include observed population losses and species \r\nextinctions and loss of whole ecosystems from certain locations (e.g., \r\nirrecoverable loss of tropical coral reefs locally). Large local population \r\ndeclines of wild species have already impacted human food sources \r\nand livelihoods (e.g., for Indigenous Arctic communities)"


id: d8b8c876857c9e8c68f5004a1aba86e7
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
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content:
" Soft limits are \r\ncurrently being experienced in particular by individuals, households, cities \r\nand settlements along the coast and by small-scale farmers (medium \r\nconfidence). As sea levels rise and extreme events intensify, coastal \r\ncommunities face limits due to financial, institutional and socioeconomic \r\nconstraints and a short timeline for adaptation implementation, reducing "


id: 9cd153e62082bddaa250ef8375371fba
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
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content:
"them of food and livelihoods and reinforces and entrenches \r\nexisting inequalities (high confidence). Rights-based approaches \r\nto adaptation, participatory methodologies and inclusion of local \r\nand Indigenous knowledge, combined with informed consent, deliver \r\nmechanisms to avoid these pitfalls (medium confidence). Adaptation \r\nsolutions benefit from engagement with Indigenous and marginalised \r\ngroups, solve past equity and justice issues and offer novel approaches \r\n(medium confidence). Indigenous knowledge is a powerful tool to \r\nassess interlinked ecosystem functions across terrestrial, marine and \r\nfreshwater systems, bypassing siloed approaches and sectoral problems \r\n(high confidence). Lastly, engagement with Indigenous knowledge \r\nand marginalised groups often offers an intergenerational context for \r\nadaptation solutions needed to avoid maladaptation (high confidence). \r\n{2.6.5, 4.6.9, 8.4, 8.4.5, 5.12.8, 5.13.4, 11.4.1, 11.4.2, 12.5.8, 13.8.1, \r\nBox 13.2, 14.4, 14.5.9, 5"


id: c277a6858e7f4d8d9f202c9e9d66ce6f
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
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vector_id: 28184
content:
"TS.D.4 Diverse, self-sustaining ecosystems with healthy bio\u0002diversity provide multiple contributions to people that are \r\nessential for climate change adaptation and mitigation, thereby \r\nreducing risk and increasing societal resilience to future climate \r\nchange (high confidence). Better ecosystem protection and \r\nmanagement is key to reduce the risks that climate change poses \r\nto biodiversity and ecosystem services and build resilience; it \r\nis also essential that climate change adaptation be integrated \r\ninto the planning and implementation of conservation and \r\nenvironmental management if it is to be fully effective in future \r\n(high confidence). Risks to ecosystems from climate change can \r\nbe reduced by protection and restoration and also by a range \r\nof targeted actions to adapt conservation practice to climate \r\nchange (high confidence)"


id: c71b893e0bf55434fe959cf824f8b0c
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
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content:
" Protected areas are key elements of \r\nadaptation but need to be planned and managed in ways that \r\ntake account of climate change, including shifting species distri\u0002butions and changes in biological communities and ecosystem \r\nstructure. Adaptation to protect ecosystem health and integrity \r\nis essential to maintain ecosystem services, including for climate \r\nchange mitigation and the prevention of greenhouse gas \r\nemissions. (Figure TS.12, Figure TS.5 ECOSYSTEMS) {2.5.4, 2.6.2, \r\n2.6.3, 2.6.6, 2.6.7, 3.6.2, 3.6.3, 3.6.5, 4.6.6, Box  4.6, 5.14.1, \r\n12.5.1, 13.3.2, 13.4.2, Box 14.7, 15.5.4, 15.5.6, CCP1, CCP5.4.1, \r\nCCP5.4.2, CCB NATURAL}"


id: bc19cb2bdd9bc569e394a6274154db78
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
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vector_id: 27162
content:
"TS.D.3.3 Reliance on hard protection against sea level rise can \r\nlead to development intensification, which compounds risk and \r\nlocks in exposure of people and assets as socioeconomic and \r\ngovernance barriers and technical limits are reached. Avoiding \r\nmaladaptive responses to sea level rise depends on immediate mitigation \r\nand application of adaptive planning that sets out near-term, low-regret \r\nactions while keeping open options to account for ongoing committed \r\nsea level rise (very high confidence). Such forward-looking adaptive \r\npathway planning and iterative risk management can address the \r\ncurrent path dependencies that lead to maladaptation and can enable \r\ntimely adaptation alignment with long implementation lead times, as \r\nwell as addressing uncertainty about rate and magnitude of local sea \r\nlevel rise, and ensuring that adaptation will be more effective (medium \r\nconfidence)"


id: e92068632ed2fa9863488abf081fab71
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 34192
content:
" As sea level rise advances, only avoidance and relocation \r\nwill eliminate coastal risks (high confidence). Other measures only \r\ndelay impacts for a time, increasing residual risk, perpetuating risk and \r\ncreating ongoing legacy effects and inevitable property and ecosystem \r\nlosses (high confidence). While relocation may in the near term appear \r\nsocially unacceptable, economically inefficient or technically infeasible, \r\nit may become the only feasible option as protection costs become \r\nunaffordable and technical limits are reached (medium confidence). \r\n{3.4.2, 3.5.5, 3.6.3, 11.7.3, Box  11.6, 12.5.7, 12.5.8, 13.10, 15.3.4, \r\n15.5.1, 15.5.2, 15.5.3, CCP2.2.3, CCP4, CCB DEEP, CCB SLR}"


id: 2bc487fe05227db5c78b17281876cbf4
created_at: 2023-03-16 14:02:12
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content:
"TS.D.4.1 Ecosystem protection and restoration can build resil\u0002ience of ecosystems and generate opportunities to restore eco\u0002system services with substantial co-benefits (high confidence) \r\nand provision of ecosystem-based adaptation.7 Ecosystem-based \r\nadaptation includes protection and restoration of forests, grasslands, \r\npeatlands and other wetlands, blue carbon systems (mangroves, salt \r\nmarshes and seagrass meadows), and agroecological farming practices. \r\nIn coastal systems, nature-based solutions, including ecosystem-based \r\nadaptation, can reduce impacts for human settlements until sea level \r\nrise results in habitat loss. High rates of warming and drought may \r\nseverely threaten the success of nature-based solutions such as forest \r\nexpansion or peatland restoration. Ecosystem-based adaptation is \r\nbeing increasingly advocated in coastal defence against storm surges, \r\nterrestrial flood regulation, reducing urban heat and restoring natural \r\nfire regimes"


id: 3ed012a226a1b8f8dca928f3d71ccf0b
created_at: 2023-03-16 14:02:12
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content:
" Nature-based solutions, including ecosystem-based \r\nadaptation, can therefore reduce risks for ecosystems and benefit \r\npeople, provided they are planned and implemented in the right way \r\nand in the right place. For example, coastal wetlands and ecosystems \r\ncan also be seriously damaged by coastal defences designed to protect "


id: 7e41deef9d42de4702f8855dae68ca0f
created_at: 2023-03-16 14:02:12
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vector_id: 17427
content:
"TS.D.3.4 Maladaptation can be reduced using the principles of \r\nrecognitional, procedural and distributional justice in decision\u0002making, responsibly evaluating who is regarded as vulnerable \r\nand at risk, who is part of decision-making, who is the beneficiary \r\nof adaptation measures and integrated and flexible governance \r\nmechanisms that account for long-term goals (high confidence). \r\nExamples include selecting native and appropriate species in habitat \r\nrestoration, monitoring key social and environmental indicators for \r\nadaptation progress, embedding strong monitoring and evaluation \r\nprocesses, considering measures of efficiency and social welfare, \r\nand social and political drivers and power relationships"


id: 4d03567a743232be666b45c2d4129379
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
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vector_id: 9540
content:
"Figure TS.12 |  This figure shows the interconnectedness between different ecosystems and system transitions, with human activities in urban, rural and \r\ncoastal locations embedded in ecosystems. Maintaining biosphere integrity is essential for biodiversity, human and societal health and a precondition for climate resilient \r\ndevelopment. Panel a) illustrates how adaptation, mitigation and development actions characterised by exploitation and degradation lead to unsustainable development and \r\nadverse outcomes for human well-being and ecosystem integrity. Panel b) illustrates how adaptation options, implemented in an integrated way with mitigation and development \r\nand based on ecosystem stewardship, can support climate resilient development (Figure TS.13). The protection or restoration of one or more of these ecosystems also provides \r\nbenefits to the other ecosystems and enhances the services provided that improve livelihoods"


id: ec7b9c75b69a2316f2670e18a338e228
created_at: 2023-03-16 14:02:12
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content_type: text
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vector_id: 34688
content:
" Protecting and restoring ecosystem health as a part of societal development and \r\nthrough societal choices is a key transformative solution space for climate resilient development {2.5, 2.6, 3.5, 3.6, 4.3, 5.13, 6.3, 7.4, CCP1, CCP3, CCP5, Box 18.5}"


id: 4533f97d78962a32256d42fde3da6065
created_at: 2023-03-16 14:02:12
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content_type: text
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vector_id: 8314
content:
"TS.D.6.5 Key innovations in adaptation in social policy and \r\nnature-based solutions have not been matched by innovation \r\nin adaptation finance, which tends to favour established \r\nmechanisms, often led by grey/physical infrastructure at the \r\nnational scale. Social policy innovations include social safety nets, \r\ninclusive approaches to disaster risk reduction and the integration \r\nof climate adaptation into education. Nature-based solutions \r\ninclude green and blue infrastructure in and around cities, including \r\nhinterlands, that increase water access and reduce hazards for cities \r\nand settlements, for example reforestation of hill-slope and coastal \r\nareas. In Europe, many urban innovations are pilot tested, but their up\u0002scaling remains challenging. Where inclusive approaches to adaptation \r\npolicy and action are supported, this can enable wider gains of more \r\nequitable urbanisation (medium confidence). (Figure  TS.9 URBAN) \r\n{2.6.3, 4.6.5, 4.7.1, 6.3.3, 6.3.5, 6.4.3, 12.5.5, 13"


id: 2ccd1e6b006059f0813225ad06d38ed4
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
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vector_id: 4482
content:
"TS.D.6.3 Globally, urban adaptation gaps exist for all climate \r\nchange-driven risks, although the limits to adaptation are \r\nunevenly distributed (medium confidence). Governance capacity, \r\nfinancial support and the legacy of past urban infrastructure \r\ninvestment constrain how cities and settlements can adapt to key \r\nclimate risks (medium confidence). The gap between what can be \r\nadapted to and what has been adapted to is uneven; it is larger for \r\nthe poorest 20% of populations than for the wealthiest 20%. The \r\nadaptation gap is also geographically uneven; it is highest in Africa \r\n(medium confidence). Limits to adaptation are often most pronounced \r\nin rapidly growing urban areas and smaller settlements, including \r\nthose without dedicated local government"


id: 35ba01b5dc0aae7cfaf6ec179acf110c
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
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content:
" At the same time, legacy \r\ninfrastructure in large and mega cities, designed without taking \r\nclimate change risk into account, and past adaptation decisions \r\nconstrain innovation, leading to stranded assets and with increasing \r\nnumbers of people unable to avoid harm, including heat stress and \r\nflooding, without transformative adaptation (medium confidence). \r\n{6.3, 6.4, 12.5.5, 13.2, 13.2.3, 13.6.2, 13.6.2, 13.11.3, Box  14.4, \r\nCCP2.3.6, CCP2.4, CCP2.5, CWGB URBAN}"


id: 412c4ed87272609efbdf921b6d05a5c9
created_at: 2023-03-16 14:02:12
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content:
"TS.D.6.6 Many urban adaptation plans focus narrowly on climate \r\nrisk reduction and specific climate-associated risks, missing \r\nopportunities to advance co-benefits with climate mitigation \r\nand sustainable development (high confidence). This narrow \r\napproach limits opportunity for urban and infrastructure adaptation \r\nto tackle the root causes of inequality and exclusion, especially \r\namong marginalised groups, including women. Urban adaptation \r\nmeasures have many opportunities to contribute to climate resilient \r\ndevelopment pathways (medium confidence). They can enhance \r\nsocial capital, livelihoods, human and ecological health and contribute \r\nto low-carbon futures. Urban planning, social policy and nature\u0002based solutions bring great flexibility with co-benefits for climate \r\nmitigation and sustainable development"


id: d317fbee9a97458d3b4d573de1425843
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
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content:
" Participatory planning for \r\ninfrastructure provision and risk management in informal, precarious \r\nand underserved neighbourhoods, the inclusion of Indigenous \r\nknowledge and local knowledge, and communication and efforts \r\nto build local leadership especially among women and youth are \r\nexamples of inclusive approaches with co-benefits for equity. Targeted \r\ndevelopment planning across the range of innovation and investment \r\nin social policy, nature-based solutions and grey/physical infrastructure \r\ncan significantly increase the adaptive capacity of urban settlements \r\nand cities and their contribution to climate resilient development (high \r\nconfidence). (Figure TS.9 URBAN) {4.6.5, 6.1, 6.3, 6.4, Box 6.6, 7.4.1, \r\n7.4.2, 7.4.3, 10.5, 10.6, 12.5.5, 12.5.7, 13.11.3, 14.5.5, 15.6.1, 15.7, \r\nCCP5.4.3, CCB COVID, CCB FEASIB}"


id: f413d21818c8e0e00d997ebf38c45620
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
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index: climate_gpt_only_giec
vector_id: 35910
content:
"TS.D.6.4 The greatest gaps between policy and action are in \r\nprojects to integrate justice concerns into adaptation action, \r\naddress complex interconnected risks where solutions lie \r\noutside as well as within a city, for example in the food-energy-\r\nwater-health nexus, and resolve compound risks such as the \r\nrelationships between air quality and climate risk (medium \r\nconfidence). The most critical capacity gaps at the city and community \r\nlevels that hinder adaptation include an ability to identify social \r\nvulnerability and community strengths and to plan in integrated ways to \r\nprotect communities, alongside the ability to access innovative funding \r\narrangements and manage finance and commercial insurance, as well \r\nas locally accountable decision-making with sufficient access to science, \r\ntechnology and local knowledge to support application of adaptation \r\nsolutions at scale"


id: 6f6204a1a9d74ccf3f4480326ea4a389
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
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content:
" As ecosystems provide important additional benefits \r\nto human well-being and coastal livelihoods, urban adaptation \r\nstrategies can be developed for settlements and nearby ecosystems; \r\ncombining these with engineering solutions can extend their lifetime \r\nunder high rates of sea level rise (medium confidence). In Central and \r\nSouth America, the adoption of nature-based solutions and hybrid \r\n(green-grey) infrastructure are still emerging. Monitoring and evaluation \r\nframeworks that incorporate questions of justice, ecological health and \r\nmulti-sector considerations can help to move away from more narrow, \r\nstatic, indicator-based approaches to adaptation. (high confidence) \r\n{4.6.5, Box 4.8, 5.12.5, 6.1, 6.3, 6.4, 10.3.4, 12.5.5, 13.6.1, 13.6.2}"


id: 2cc5c71eb53e690d1a9fbda117172399
created_at: 2023-03-16 14:02:12
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vector_id: 4478
content:
"TS.D.7.5 Adaptation is costly, but the benefit-to-cost ratio is high \r\nfor urbanised coastal areas with high concentrations of assets \r\n(high confidence). Protection has a high benefit-cost ratio during the \r\n21st century but can become unaffordable and insufficient to reduce \r\ncoastal risk (e.g., due to salinisation, drainage of rivers and excess \r\nwater), reaching technical limits (high confidence). Hard protection \r\nsets up lock-in of assets and people to risks and reaches limits by the \r\nend of the century or sooner, depending on the scenario, local sea level \r\nrise effects and community tolerance thresholds (high confidence). \r\nConsidering coastal retreat as part of the solution space could lower \r\nglobal adaptation costs but would result in large land losses and high \r\nlevels of migration for South and Southeast Asia in particular and in \r\nrelative terms, small island nations would suffer most (high confidence)"


id: ee1d1eb3ba762ac5ed220e8c7d7deef3
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
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content:
" \r\nSolutions include disincentivising developments in high-risk areas and \r\naddressing existing social vulnerabilities now (high confidence). {3.4.2, \r\n3.5.5, 3.6.3, 5.13.4, 9.4.1, Box 11.6, 13.2, 14.5.3, 15.5.1, 15.5.2, 15.5.3, \r\n16.5.2, CCP2.3, CCB MIGRATE, CCB NATURAL, CCB SLR}"


id: 6e5d6ad7f31b7eb7d0e5c16584496699
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 14987
content:
"TS.D.7.1 As the scale and pace of sea level rise accelerates \r\nbeyond 2050, long-term adjustments may in some locations be \r\nbeyond the limits of current adaptation options and for some \r\nspecies and some locations could be an existential risk in the \r\n21st century (medium confidence). Nature-based interventions, \r\nfor example wetlands and salt marshes, can reduce impacts and \r\ncosts while supporting biodiversity and livelihoods but have limits \r\nunder high warming levels and rapid sea level rise (high confidence). \r\nEcological limits and socioeconomic, financial and governance barriers \r\nwill be reached first and are determined by the type of coastline and \r\ncity or settlement (medium confidence). Accommodation can reduce \r\nimpacts on people and assets but can address only limited sea level \r\nrise. Considering the long term now will help to avoid maladaptive \r\nlock-in, to build capacity to act in a timely and pre-emptive manner \r\nand to reduce risks to ecosystems and people. {3.4.2, 3"


id: 5bce74dc746e5a16358a69aeacdd4b3b
created_at: 2023-03-16 14:02:12
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content:
"TS.D.8.2 Continued investment in general health systems and in \r\nsystems enhancing health protection is an effective adaptation \r\nstrategy in the short to medium term (high confidence). Although \r\nsome mortality and morbidity from climate change are already \r\nunavoidable, targeted adaptation and mitigation actions can reduce \r\nrisks and vulnerabilities (high confidence). The burden of diseases \r\ncould be reduced and resilience increased through health systems, \r\ngenerating awareness of climate change impacts on health (medium \r\nconfidence), strengthening access to water and sanitation (high \r\nconfidence), integrating vector control management approaches (very \r\nhigh confidence), expanding existing early-warning monitoring systems \r\n(high confidence), increasing vaccine development and coverage \r\n(medium confidence), improving the heat resistance of the built \r\nenvironment (medium confidence) and building financial safety nets \r\n(medium confidence). {2.6.4, 4.6.4, 5.12.5, 5.14, 7.4.1, 7"


id: e1a6de215bb0715bf323b43db675559d
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
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index: climate_gpt_only_giec
vector_id: 32996
content:
"4.2, Table 7.2, \r\n9.10.3, 10.4.7, 11.3.6, 12.5.6, Table 12.9, 13.7.2, Figure 13.25, 14.5.6, \r\nTable 14.5, CCP6.2.6, CCB FEASIB, CCB ILLNESS}"


id: c6e1c4ee2c1af4b6da21c310de63966f
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
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vector_id: 28874
content:
"TS.D.7.7 Experience in coastal cities and settlements highlights \r\ncritical enablers for addressing coastal hazard risk compounded \r\nby sea level rise (high confidence). These enablers include building \r\nand strengthening governance capacity and capabilities to tackle \r\ncomplex problems; taking a long-term perspective in making short\u0002term decisions; enabling more effective coordination across scales, \r\nsectors and policy domains; reducing injustice, inequity and social \r\nvulnerability; and unlocking the productive potential of coastal \r\nconflict while strengthening local democracy (medium evidence, \r\nhigh agreement). Flexible options enable responses to be adjusted as \r\nclimate risk escalates and circumstances change, which may increase \r\nexposure  (medium confidence)"


id: dcfffdc7290ba33b78f36e53e6a30505
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
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index: climate_gpt_only_giec
vector_id: 32266
content:
" Legal and financial provisions can \r\nenable managed retreat from the most at-risk locations (medium \r\nconfidence) but require coordination, trust and legitimate decisions by \r\nand across policy domains and sectors (high confidence) that prioritise \r\nvulnerability, justice and equity (medium confidence). Inclusive, \r\ninformed and meaningful deliberation and collaborative problem\u0002solving depend on safe arenas for engagement by all stakeholders \r\n(high confidence). {CCP2.4, Table CCP2.1, Table CCP2.2, CCB SLR}"


id: da50651b16b910b47d0bbf5c9b4f3562
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 31870
content:
"TS.D.8.3 Many adaptation measures that benefit health and \r\nwell-being are found in other sectors (e.g., food, livelihoods, \r\nsocial protection, water and sanitation, infrastructure) (high \r\nconfidence). Such cross-sectoral solutions include improved air quality \r\nthrough renewable energy sources (very high confidence), active \r\ntransport (e.g., walking and cycling) (high confidence) and sustainable \r\nfood systems that lead to healthier diets (high confidence). Heat \r\nAction Plans have strong potential to prevent mortality from extreme \r\nheat events and elevated temperature (high confidence). Nature\u0002based solutions reduce a variety of risks to both physical and mental \r\nhealth and well-being (high confidence). For example, integrated \r\nagroecological food systems offer opportunities to improve dietary \r\ndiversity while building climate-related local resilience to food insecurity \r\n(high confidence), especially when combined with gender equity and \r\nsocial justice"


id: 610a83ea2e95e2b86e3418d1e7175fad
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 12838
content:
" Social policy-based adaptation, including education and \r\nthe adaptation of health systems, offers considerable future scope. The \r\ngreatest gaps between policy and action are in failures to manage \r\nadaptation of social infrastructure (e.g., community facilities, services \r\nand networks) and failure to address complex interconnected risks \r\nfor example in the food-energy-water-health nexus or the inter\u0002relationships of air quality and climate risk (medium confidence). \r\n{2.6.7, 4.6.4, 4.7.1, 5.12.5, 5.14.1, 6.3.1, 6.4.3, 6.4.5, 6.4.5, 6.4.5, 7.4.2, \r\n9.10.3, 10.4.7, 11.3.6, 12.5.6, Table 12.9, 13.7.2, Figure 13.25, 14.5.6, \r\nTable 14.5, CCB GENDER, CCB HEALTH, CCB NATURAL}"


id: 2a4cf5bc6ab0bab15615617feae7912a
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 4084
content:
"TS.D.8.6 Reducing future risks of involuntary migration and \r\ndisplacement due to climate change is possible by improving \r\noutcomes of existing migration patterns, addressing vulner\u0002abilities that pose barriers to in situ adaptation and livelihood \r\nstrategies and meeting existing migration agreements and \r\ndevelopment objectives (medium confidence). Properly support\u0002ed and where levels of agency and assets are high, migration as an \r\nadaptation to climate change can reduce exposure and socioeconomic \r\nvulnerability (medium confidence). However, migration becomes a risk \r\nwhen climate hazards cause an individual, household or community \r\nto move involuntarily or with low agency (high confidence). Inability \r\nto migrate (i.e., involuntary immobility) in the face of climate hazards \r\nis also a potential risk to exposed populations (medium confidence)"


id: 2881d2338b58f014356256f8fd6335ac
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vector_id: 3787
content:
" \r\nBroad-based institutional and cross-sectoral efforts to build adaptive \r\ncapacity, including meeting the SDGs, reduce future risks of climate\u0002related involuntary displacement and immobility (medium confidence), \r\nwhile policies such as the Global Compact on Safe, Orderly and Reg\u0002ular Migration (medium confidence) that are aimed at ensuring safe \r\nand orderly movements of people within and between states are \r\npotential components of climate resilient development pathways that \r\ncan improve migration as an adaptation. {4.6.8, 7.4.4, 9.3.1, 12.5.8, \r\nCCP5.4.2, CCB FEASIB, CCB MIGRATE}"


id: c80ed6018a1333d4c736ee72f893c7da
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 29055
content:
"TS.D.11.3 Adaptation options that are feasible and effective to the \r\n3.4 billion people living in rural areas around the world and who \r\nare especially vulnerable to climate change, include the provision \r\nof basic services, livelihood diversification and strengthening of \r\nfood systems (high confidence). The vulnerability of rural areas to \r\nclimate risks increases due to the long distances to urban centres and \r\nthe lack of or deficient critical infrastructure such as roads, electricity \r\nand water. Providing critical infrastructure, including through distributed \r\ngeneration power systems through renewable energy, has provided many \r\nco-benefits (high confidence). Biodiversity management strategies have \r\nsocial co-benefits, including improved community health, recreational \r\nactivities and ecotourism, which are co-produced by harnessing ecological \r\nand social capital to promote resilient ecosystems with high connectivity \r\nand functional diversity"


id: 4dad6da60405af96620d84fb197bc7a3
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
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vector_id: 9651
content:
" Strengthening local and regional food systems \r\nthrough strategies such as collective trademarks, participatory guarantee \r\nsystems and city-rural links build rural livelihoods, resilience and self\u0002reliance (medium confidence). Livelihood diversification is a key coping \r\nand adaptive strategy to climatic and non-climatic risks. There is high \r\nevidence (medium agreement) that diversifying livelihoods improves \r\nincomes and reduces socioeconomic vulnerability, but feasibility changes \r\ndepending on livelihood type, opportunities and local context. Key \r\nbarriers to livelihood diversification include sociocultural and institutional \r\nbarriers as well as inadequate resources and livelihood opportunities that \r\nhinder the full adaptive possibilities of existing livelihood diversification \r\npractices (high confidence). (Figure TS.11b) {4.6.2, 4.7.1, 5, 8, 14.5.9, CCB \r\nFEASIB}"


id: 2e81a5927cc8e05106f311d569d94539
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 4764
content:
"TS.D.11.1 A sub-set of adaptation options has been implemented \r\nthat cuts across sectors to enable sector-specific adaptation \r\nresponses. These options, such as disaster risk management, climate \r\nservices and risk sharing, increase the feasibility and effectiveness \r\nof other options by expanding the solution space available (high \r\nconfidence). For example, carefully designed and implemented disaster \r\nrisk management and climate services can increase the feasibility \r\nand effectiveness of adaptation responses to improve agricultural \r\npractices, income diversification, urban and critical services and \r\ninfrastructure planning (very high confidence). Risk insurance can be a \r\nfeasible tool to adapt to transfer climate risks and support sustainable \r\ndevelopment (high confidence). They can reduce both vulnerability and \r\nexposure, support post-disaster recovery and reduce financial burden \r\non governments, households and business. {3.6.3, 3.6.5, 4.6, 4.7.1, \r\n5.4.4, 5.6.3, 5.5.4, 5"


id: 62582ba228c84a2c7a6b29f4cdebe365
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 13029
content:
"8.4, 5.9.4, 5.12.4, 5.14.1, 5.14.2, 13.11.2, 14.7.2, \r\n15.5.7, CCB FEASIB, CCB GENDER, CCB MOVING PLATE}"


id: 2f3bce00e33a589b6d94f4066aa9deef
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 4848
content:
"TS.D.11.2 Transformations for energy include the options of \r\nefficient water use and water management, infrastructure \r\nresilience and reliable power systems, including the use of \r\nintermittent renewable energy sources, such as solar and wind \r\nenergy, with the use of storage (very high confidence). These \r\noptions are not sufficient for the far-reaching transformations required \r\nin the energy sector, which tend to focus on technological transitions \r\nfrom a fossil-based to a renewable energy regime. A resilient power \r\ninfrastructure is considered for energy generation, transmission \r\nand distribution systems. Distributed generation utilities, such as \r\nmicrogrids, are increasingly being considered, with growing evidence \r\nof their role in reducing vulnerability, especially within underserved \r\npopulations (high confidence)"


id: 5a29b21374d26286f3f537918282247c
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
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vector_id: 11694
content:
" Infrastructure resilience and reliable \r\npower are particularly important in reducing risk in peri-urban and \r\nrural areas when they are supported by distributed generation of \r\nrenewable energy by isolated systems (high confidence). The option \r\nfor a resilient power infrastructure is considered for all types of \r\npower generation sources and transmission and distribution systems. \r\nEfficient water use and water management especially in hydropower \r\nand combined cycle power plants in drought-prone areas have a \r\nhigh feasibility (high confidence) with multiple co-benefits (medium "


id: fdd552049579f82d8e3de71d0bf40b1d
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 37455
content:
"TS.E.1.3 System transitions are highly feasible. For energy system \r\ntransitions, there is medium confidence in the high feasibility \r\nof resilient infrastructure and efficient water use for power \r\nplants and high confidence in the synergies of this option with \r\nmitigation. For coastal ecosystem transitions, there is medium to high \r\nconfidence that ecosystem conservation and biodiversity management \r\nare increasing adaptive and ecological capacity with socioeconomic \r\nco-benefits and positive synergies with carbon sequestration. However, \r\nopportunity costs can be a barrier. For land ecosystem transitions, there \r\nis high confidence in the role of agroforestry to increase ecological and \r\nadaptive capacity, once economic and cultural barriers and potential \r\nland use change trade-offs are overcome. There is high confidence in \r\nimproved cropland management and its economic feasibility due to \r\nimproved productivity"


id: 12d3504b84571774958217ff6271c68b
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 465
content:
" For efficient livestock systems, there is medium \r\nconfidence  in the high technological and ecological feasibility. \r\n(Figure TS.11a) {CCB FEASIB}"


id: 2e572e42f8878c929856271fee242f1
created_at: 2023-03-16 14:02:12
updated_at: 2023-03-16 14:02:12
content_type: text
index: climate_gpt_only_giec
vector_id: 4732
content:
"TS.E.1.1  Prevailing development pathways do not advance \r\nclimate resilient development (very high confidence). Societal \r\nchoices in the near term will determine future pathways. There \r\nis no single pathway or climate that represents climate resilient \r\ndevelopment for all nations, actors or scales, as well as globally, \r\nand many solutions will emerge locally and regionally. Global trends \r\nincluding rising income inequality, urbanisation, migration, continued \r\ngrowth in greenhouse gas emissions, land use change, human \r\ndisplacement and reversals of long-term trends toward increased \r\nlife expectancy run counter to the SDGs as well as efforts to reduce \r\ngreenhouse gas emissions and adapt to a changing climate.  With \r\nprogressive climate change,  enabling conditions  will diminish, and \r\nopportunities for successfully transitioning systems for both mitigation \r\nand adaptation will become more limited (high confidence)"


id: c9b640f1e790ed02c0c235ce84264493
created_at: 2023-03-16 14:02:13
updated_at: 2023-03-16 14:02:13
content_type: text
index: climate_gpt_only_giec
vector_id: 29291
content:
" Investments \r\nin economic recovery from COVID-19 offer opportunities to promote \r\nclimate resilient development (high confidence). (Figure TS.13) {16.6.1, \r\n17.2.1, 18.2, 18.4, CCP5.4.4, CCB COVID}"


id: 6084bb6975fefdfba9cf3d3484dca29c
created_at: 2023-03-16 14:02:13
updated_at: 2023-03-16 14:02:13
content_type: text
index: climate_gpt_only_giec
vector_id: 12758
content:
"TS.E.1.4 For urban and infrastructure system transitions, there \r\nis  medium confidence  for sustainable land use and urban \r\nplanning. There is  high confidence  in the economic and ecological \r\nfeasibility of green infrastructure and ecosystem services, as well as \r\nsustainable urban water management, once institutional  barriers in \r\nthe form of limited social and political acceptability are overcome. \r\nSocial safety nets, disaster risk management and climate services and \r\npopulation health and health  systems are considered overarching \r\nadaptation options due to their applicability across all system \r\ntransitions. There is medium to high confidence in the high feasibility of \r\ndisaster risk management and the use of demand-driven and context\u0002specific climate services as well as in the socioeconomic feasibility of \r\nsocial safety nets"


id: 8f22038eee0e45b8f1c379f1557415c
created_at: 2023-03-16 14:02:13
updated_at: 2023-03-16 14:02:13
content_type: text
index: climate_gpt_only_giec
vector_id: 20061
content:
" Improving health systems through enhancing access \r\nto medical services and developing or strengthening surveillance \r\nsystems can have high feasibility when there is a robust institutional \r\nand regulatory framework (high confidence). (Figure  TS.8 HEALTH, \r\nFigure TS.9 URBAN, Figure TS.11a, Figure TS.13) {6.3, CCB FEASIB}"


id: 9e4e2877236e196ec07b8140d00f397b
created_at: 2023-03-16 14:02:13
updated_at: 2023-03-16 14:02:13
content_type: text
index: climate_gpt_only_giec
vector_id: 22431
content:
"TS.E.1.2  System transitions can enable climate resilient devel\u0002opment when accompanied by appropriate enabling conditions \r\nand inclusive arenas of engagement (very high confidence). Five \r\nsystem transitions are considered: energy, industry, urban and infra\u0002structure, land and ecosystems, and society. Advancing climate resilient \r\ndevelopment in specific contexts may necessitate simultaneous progress \r\non all five transitions. Collectively, these system transitions can widen \r\nthe solution space and accelerate and deepen the implementation \r\nof sustainable development, adaptation and mitigation actions by \r\nequipping actors and decision makers with more effective options (high \r\nconfidence). For example, urban ecological infrastructure linked to an \r\nappropriate land use mix, street connectivity, open and green spaces \r\nand job-housing proximity provides adaptation and mitigation benefits \r\nthat can aid urban transformation (medium confidence)"


id: 79855a6e2e9fc6bb4024abbe827eb13a
created_at: 2023-03-16 14:02:13
updated_at: 2023-03-16 14:02:13
content_type: text
index: climate_gpt_only_giec
vector_id: 16693
content:
" These system \r\ntransitions are necessary precursors for more fundamental climate \r\nand sustainable-development transformations but can simultaneously \r\nbe outcomes of transformative actions. Enhancing equity and agency \r\nare cross-cutting considerations for all five transitions. Such transitions \r\ncan generate benefits across different sectors and regions, provided "


id: 70e6eef4575473781a239d5b0c232606
created_at: 2023-03-16 14:02:13
updated_at: 2023-03-16 14:02:13
content_type: text
index: climate_gpt_only_giec
vector_id: 15396
content:
"TS.E.1.5  There are multiple possible pathways by which \r\ncommunities, nations and the world can  pursue climate \r\nresilient development. Moving towards different pathways \r\ninvolves confronting complex synergies and trade-offs between \r\ndevelopment pathways and the options, contested values and \r\ninterests that underpin climate mitigation and adaptation \r\nchoices (very high confidence).  Climate resilient development \r\npathways are trajectories for the pursuit of climate resilient development \r\nand navigating its complexities. Different actors, the private sector and \r\ncivil society, influenced by science, local and Indigenous knowledges, \r\nand the media, are both active and passive in designing and navigating \r\nclimate resilient development pathways. Increasing levels of warming \r\nmay narrow the options and choices available for local survival and \r\nsustainable development for human societies and ecosystems"


id: c2cf460dd42f0ab85da79f56b7676b96
created_at: 2023-03-16 14:02:13
updated_at: 2023-03-16 14:02:13
content_type: text
index: climate_gpt_only_giec
vector_id: 28230
content:
" Limiting \r\nwarming to Paris Agreement goals will reduce the magnitude of climate \r\nrisks to which people, places, the economy and ecosystems will have \r\nto adapt. Reconciling the costs, benefits and trade-offs associated with "


id: c8e4408cf3a34b183f67f5ed5a9bf0d8
created_at: 2023-03-16 14:02:13
updated_at: 2023-03-16 14:02:13
content_type: text
index: climate_gpt_only_giec
vector_id: 29168
content:
"TS.E.1.6. Economic sectors and global regions are exposed to \r\ndifferent opportunities and challenges in facilitating climate \r\nresilient development, suggesting adaptation and mitigation \r\noptions should be aligned to local and regional context and \r\ndevelopment pathways (very high confidence). Given their current \r\nstate of development, some regions may prioritise poverty and inequality \r\nreduction and economic development over the near term as a means \r\nof building capacity for climate action and low-carbon development \r\nover the long term. In contrast, developed economies with mature \r\neconomies and high levels of resilience may prioritise climate action to \r\ntransition their energy systems and reduce greenhouse gas emissions. \r\nSome interventions may be robust in that they are relevant to a broad \r\nrange of potential development trajectories and could be deployed \r\nin a flexible manner"


id: 211fe50eefe1add67307e7c6ff78717c
created_at: 2023-03-16 14:02:13
updated_at: 2023-03-16 14:02:13
content_type: text
index: climate_gpt_only_giec
vector_id: 2645
content:
" However, other types of interventions, such as \r\nthose that  are dependent upon emerging technologies, may require \r\na specific set of enhanced enabling conditions or factors, including \r\ninfrastructure, supply chains, international cooperation and education \r\nand training that currently limit their implementation to certain settings. \r\nNotwithstanding national and regional differences, development \r\npractices that are aligned to people, prosperity, partnerships, peace \r\nand the planet as defined in Agenda 2030 could enable more climate \r\nresilient development. (high confidence) {18.5, Figure 18.1}"


id: ac9ed6b1d11341be2bf4d34698b0efce
created_at: 2023-03-16 14:02:13
updated_at: 2023-03-16 14:02:13
content_type: text
index: climate_gpt_only_giec
vector_id: 24705
content:
"TS.E.1.7 Pursuing climate resilient development involves \r\nconsidering a broader range of sustainable development \r\npriorities, policies and practices, as well as enabling societal \r\nchoices to accelerate and deepen their implementation  (very \r\nhigh confidence).  Scientific assessments of climate change have \r\ntraditionally framed solutions around the implementation of specific \r\nadaptation and mitigation options as mechanisms for reducing \r\nclimate-related risks. They have given less attention to a fuller set of \r\nsocietal priorities and  the role of non-climate policies, social norms, \r\nlifestyles, power relationships and worldviews in enabling climate action \r\nand sustainable development"


id: 88ed35946a8c8bb3b0245d349ca285f4
created_at: 2023-03-16 14:02:13
updated_at: 2023-03-16 14:02:13
content_type: text
index: climate_gpt_only_giec
vector_id: 19082
content:
" Because climate resilient development \r\ninvolves different actors pursuing plural development trajectories  in \r\ndiverse contexts, the pursuit of solutions that are equitable for all \r\nrequires opening the space for engagement and action to a diversity \r\nof people, institutions, forms of knowledge and worldviews. Through \r\ninclusive modes of engagement that enhance knowledge sharing and \r\nrealise the productive potential of diverse perspectives and worldviews, \r\nsocieties could alter institutional structures and arrangements, \r\ndevelopment processes, choices and actions that have precipitated \r\ndangerous climate change, constrained the achievement of SDGs and \r\nthus limited pathways to achieving climate resilient development"


id: 510082047464816f1a8367e66713fb6b
created_at: 2023-03-16 14:02:13
updated_at: 2023-03-16 14:02:13
content_type: text
index: climate_gpt_only_giec
vector_id: 10193
content:
" The \r\ncurrent decade is critical to charting climate resilient development \r\npathways that catalyse the transformation of prevailing development \r\npractices and offer the greatest promise and  potential for human \r\nwell-being and planetary health (very high confidence). {18.4, Box 18.1}"


id: a888c276083e4555a2e2695f45c95e50
created_at: 2023-03-16 14:02:13
updated_at: 2023-03-16 14:02:13
content_type: text
index: climate_gpt_only_giec
vector_id: 24044
content:
"TS.E.2.3 Climate adaptation actions are grounded in local \r\nrealities so understanding links with SDG 5 on gender \r\nequality ensures that adaptive actions do not worsen existing \r\ngender and other inequities within society (e.g., leading to \r\nmaladaptation practices)  (high confidence). Adaptation actions \r\ndo not automatically have positive outcomes for gender equality. \r\nUnderstanding the positive and negative links of adaptation actions \r\nwith gender equality goals (i.e., SDG 5) is important to ensure that \r\nadaptive actions do not exacerbate existing gender-based and \r\nother social inequalities. Efforts are needed to change unequal \r\npower dynamics and to foster inclusive decision-making for climate \r\nadaptation to have a positive impact for gender equality  (high \r\nconfidence)"


id: 618bd772598401c5e302e62a923a3c1f
created_at: 2023-03-16 14:02:13
updated_at: 2023-03-16 14:02:13
content_type: text
index: climate_gpt_only_giec
vector_id: 12914
content:
"  There are very few examples of successful integration \r\nof gender and other social inequities in climate policies to address \r\nclimate change vulnerabilities and questions of social justice (very \r\nhigh confidence). Yet inequities in climate change literacy compounds \r\nwomen's vulnerability to climate change through its negative effect \r\non climate risk perception {4.8.3, 9.4.5, 16.1.4, 17.5.1, CCB GENDER}"


id: 709afb9a579bbe5cee50595d753625f5
created_at: 2023-03-16 14:02:13
updated_at: 2023-03-16 14:02:13
content_type: text
index: climate_gpt_only_giec
vector_id: 15333
content:
" Confidence levels on each cell are indicated as *= low confidence, ** = medium confidence, *** = high confidence.\r\n(see also SMTS.4, Table SMTS.5) {16.5.2, Table SM16.4}"


id: 308318bb720022c102ff525f2e585d75
created_at: 2023-03-16 14:02:13
updated_at: 2023-03-16 14:02:13
content_type: text
index: climate_gpt_only_giec
vector_id: 5061
content:
"TS.E.2.6 Procedural and distributional justice and flexible \r\ninstitutions facilitate successful adaptation and minimise \r\nmaladaptive outcomes. Reorienting existing institutions to become \r\nmore flexible (e.g., through capacity building and institutional reform) \r\nand inclusive is key to building adaptive governance systems that are \r\nequipped to take long-term decisions (medium confidence). Enhancing \r\nclimate governance, institutional capacity and differentiated policies \r\nand regulation from the local to global scale enables and accelerates \r\nclimate resilient development. Transforming financial systems to \r\ndeliver the SDGs, while accelerating system transitions and addressing \r\nphysical and transition risks, is a precondition. Changes in lifestyles, \r\nhuman behaviour and preferences can have a significant impact \r\non adaptation implementation, demand and hence emissions and \r\ndecision-making around climate action (high confidence)"


id: 3cccd81ae324db2b339cfec545636b98
created_at: 2023-03-16 14:02:13
updated_at: 2023-03-16 14:02:13
content_type: text
index: climate_gpt_only_giec
vector_id: 6933
content:
" Additionally, \r\nthe use of customary and traditional justice systems, such as those \r\nof Indigenous peoples, can enhance the equity of adaptation policy \r\nprocesses (high confidence). {4.8,  4.6,8, 5.2.3,  13.8,  15.6.1, 15.6.3, \r\n15.6.4, 15.6.5, 17.1, 18.4}"


id: 14a84649af83c8763eec2b03e1d9b370
created_at: 2023-03-16 14:02:13
updated_at: 2023-03-16 14:02:13
content_type: text
index: climate_gpt_only_giec
vector_id: 756
content:
"TS.E.3.2 Some communities/regions are resilient with strong \r\nsocial safety nets and social capital that support responses \r\nand actions already occurring, but there is limited information \r\non the effectiveness of adaptation practices and the scale of \r\naction needed (high confidence). Among island communities, \r\ngreater insights into which drivers weaken local communities and \r\nIndigenous Peoples' resilience, together with recognition of the \r\nsociopolitical contexts within which communities operate, can assist \r\nin identifying opportunities at all scales to enhance climate adaptation \r\nand enable action towards climate resilient development pathways \r\n(medium evidence, high agreement). Adaptation responses to climate\u0002driven impacts in mountain regions vary significantly in terms of goals \r\nand priorities, scope, depth and speed of implementation, governance \r\nand modes of decision-making and the extent of financial and other \r\nresources to implement them (high confidence)"


id: 8832024006a73376ac92b88ccc719852
created_at: 2023-03-16 14:02:13
updated_at: 2023-03-16 14:02:13
content_type: text
index: climate_gpt_only_giec
vector_id: 18951
content:
" Adaptation in Africa \r\nhas multiple benefits, and most assessed adaptation options have \r\nmedium effectiveness at reducing risks for present-day global warming, \r\nbut their efficacy at future warming levels is largely unknown (high \r\nconfidence). In Australia and New Zealand, a range of incremental \r\nand transformative adaptation options and pathways is available as \r\nlong as enablers are in place to implement them (high confidence). \r\nSeveral enablers can be used to improve adaptation outcomes and \r\nto build resilience (high confidence), including better governance and \r\nlegal reforms; improving justice, equity and gender considerations; \r\nbuilding human resource capacity; increased finance and risk transfer \r\nmechanisms; education and awareness programmes; increased access \r\nto climate information; adequately downscaled climate data; inclusion \r\nof Indigenous knowledge; and integrating cultural resources into \r\ndecision-making (high confidence). {9.3, 9.6.4, 9.8.3, 9.11.4, 11.7"


id: 609d6cacc23f5b5a06a6f80479a66d12
created_at: 2023-03-16 14:02:13
updated_at: 2023-03-16 14:02:13
content_type: text
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vector_id: 12769
content:
"3, \r\n14.4, Box 14.1, 15.6.1, 15.6.5, 15.7, 15.6.3, 15.6.4, 15.6.5, CCP5.2.4, \r\nCCP5.2.7, CCP6.3, CCP6.4, Box CCP6.2, CCB GENDER}"


id: 70cfe49aa795f08578ab7bd7adfc4ff
created_at: 2023-03-16 14:02:13
updated_at: 2023-03-16 14:02:13
content_type: text
index: climate_gpt_only_giec
vector_id: 15380
content:
"TS.E.3 A focus on climate risk alone does not enable effective \r\nclimate resilience (high confidence).  The integration of \r\nconsideration of non-climatic drivers into adaptation pathways \r\ncan reduce climate impacts across food systems, human \r\nsettlements, health, water, economies and livelihoods (high \r\nconfidence). Strengthened health, education and basic social \r\nservices are vital for improving population well-being and \r\nsupporting climate resilient development (high confidence). The \r\nuse of climate-smart agriculture technologies that strengthen \r\nsynergies among productivity and mitigation is growing as \r\nan important adaptation strategy (high confidence). Pertinent \r\ninformation for farmers provided by climate information services \r\nis helping them to understand the role of climate  compared \r\nwith other drivers in perceived productivity changes (medium \r\nconfidence)"


id: bde7d958a2290df4f41915ca7f1ea3c4
created_at: 2023-03-16 14:02:13
updated_at: 2023-03-16 14:02:13
content_type: text
index: climate_gpt_only_giec
vector_id: 27436
content:
" Index insurance builds resilience and contributes \r\nto adaptation both by protecting farmers' assets in the face \r\nof major climate shocks, by promoting access to credit and \r\nby adopting improved  farm technologies and practices (high \r\nconfidence). {3.6.4, 4.6, 4.7.1, 7.4.6, Box 9.1, Box 9.7, Box 9.8, \r\nBox 9.9, Box 9.10, Box 9.11, 12.5.4}"


id: 4b9d883cc4bfa90e48cccd2de7db9a27
created_at: 2023-03-16 14:02:13
updated_at: 2023-03-16 14:02:13
content_type: text
index: climate_gpt_only_giec
vector_id: 9316
content:
"TS.E.5.3 Climate governance will be most effective when it has \r\nmeaningful and ongoing involvement of all societal actors from \r\nlocal to global levels (very high confidence). Actors, including \r\nindividuals and households, communities, governments at all levels, \r\nprivate-sector businesses, non-governmental organisations, Indigenous \r\nPeoples, religious groups and social movements, at many scales and in \r\nmany sectors, are adapting already and can take stronger adaptation \r\nand mitigation actions. Many forms of adaptation are more effective, \r\nmore cost-efficient and more equitable when organised inclusively \r\n(high confidence). Greater coordination and engagement across levels \r\nof government, business and community serves to move from planning \r\nto action and from reactive to proactive adaptation (high confidence)"


id: 2d586a48b4159c20e1884f330404b1bf
created_at: 2023-03-16 14:02:13
updated_at: 2023-03-16 14:02:13
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content:
" \r\nInclusion of all societal actors helps to secure credibility, relevance and \r\nlegitimacy, while fostering commitment and social learning (medium to\r\nhigh confidence), as well as equity and well-being, and reduces long\u0002term vulnerability across scales (high evidence, medium agreement). \r\nSocial movements in many cities, including those led by youth, have \r\nheightened public awareness about the need for urgent, inclusive \r\nadaptation that can enhance well-being, foster formal and informal \r\ncooperation and coherence between different institutions and build \r\nnew adaptive capacities. City and local governments remain key actors \r\nfacilitating climate change adaptation in cities and settlements (medium \r\nconfidence). Private and business investment in key infrastructure, \r\nhousing construction and insurance can drive adaptive action at \r\nscale but can exclude the priorities of the poor (medium confidence)"


id: ae96f3a26973e6f42b969235474e5899
created_at: 2023-03-16 14:02:13
updated_at: 2023-03-16 14:02:13
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content:
" \r\nNetworked community actions can address neighbourhood-scale \r\nimprovements and vulnerability at scale (very high confidence). {1.4.2, \r\n3656164945Box94114111421463Box148172}"


id: e3655b02823ad1e7233a4862bd6f9a3c
created_at: 2023-03-16 14:02:13
updated_at: 2023-03-16 14:02:13
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index: climate_gpt_only_giec
vector_id: 33259
content:
"TS.E.5 Governance arrangements and practices are presently \r\nineffective to reduce risks, reverse path dependencies and \r\nmaladaptation and facilitate climate resilient development (very \r\nhigh confidence). Governance for climate resilient development \r\ninvolves diverse societal actors, including the most vulnerable, \r\nwho can work collectively, drawing upon local and Indigenous \r\nknowledges and science, and are supported by strong political will \r\nand climate change leadership (medium confidence)"


id: e4f72e8477510b148f6487b726121202
created_at: 2023-03-16 14:02:13
updated_at: 2023-03-16 14:02:13
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content:
" Governance \r\npractices will work best when they are coordinated within and \r\nbetween multiple scales and levels (institutional, geographical \r\nand temporal) and sectors, with supporting financial resources, \r\nare tailored for local conditions, are gender-responsive and \r\ngender-inclusive and are founded upon enduring institutional and \r\nsocial learning capabilities to address the complexity, dynamism, \r\nuncertainty and contestation that characterise escalating climate \r\nrisk (medium confidence). {1.4.2, 3.6.2, 3.6.3, 4.8, 4.8.1, 4.8.2, \r\n4.8.3, 4.8.4, 4.8.5, 4.8.6, 4.8.7, 6.4.3, 6.4.4, 9.4.5, 17.4, 17.6}"


id: 2592c322a6d27f6e76596184b309c423
created_at: 2023-03-16 14:02:13
updated_at: 2023-03-16 14:02:13
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index: climate_gpt_only_giec
vector_id: 3339
content:
"TS.E.5.5 Multi-lateral governance efforts can help reconcile \r\ncontested interests, worldviews and values about how to \r\naddress climate change (medium confidence). Policy responses \r\nand strategies that localise development and expand the adaptation \r\nand mobility options of populations exposed to climatic risks can also \r\nreduce risks of climate-related conflict and political instability (high \r\nagreement, medium evidence). Formal institutional arrangements for \r\nnatural resource management can contribute to wider cooperation and \r\npeacebuilding (high confidence). Reducing vulnerability depends on \r\nthe inclusive engagement of the most vulnerable, is gender-responsive \r\nand includes key societal actors from civil society, the private sector \r\nand government, with an especially important role played by local \r\ngovernment in partnership with local communities"


id: ff93129d58a8f84ddcae110474daac2
created_at: 2023-03-16 14:02:13
updated_at: 2023-03-16 14:02:13
content_type: text
index: climate_gpt_only_giec
vector_id: 37757
content:
" Strong governance \r\nand gender-sensitive approaches to natural resource management \r\nreduce the risk of intergroup conflict in climate-disrupted areas \r\n(medium confidence). {3.6.3, 3.6.4, 3.6.5, 4.8.5, 4.8.6, 4.8.7, 6.1, 7.4.4, \r\n7.4.5, CCB COVID, CCB GENDER, CCB HEALTH, CCB INDIG}"


id: d9fd095842c76a4e60a1d6d1f7af027
created_at: 2023-03-16 14:02:13
updated_at: 2023-03-16 14:02:13
content_type: text
index: climate_gpt_only_giec
vector_id: 31823
content:
"TS.E.6.1 Large-scale, transformational adaptation necessitates \r\nenabling improved approaches to governance and coordination \r\nacross sectors and jurisdictions to avoid overwhelming current \r\nadaptive capacities and to avoid future maladaptive actions \r\n(high confidence). Response options in one sector can become \r\nresponse risks that exacerbate impacts in other sectors. A deliberate \r\nshift from primarily technological adaptation strategies to those \r\nthat additionally incorporate behavioural and institutional changes, \r\nadaptation finance, equity and environmental justice and that align \r\npolicy with global sustainability goals will facilitate transformational \r\nadaptation (high confidence). Application and efficacy testing \r\nof climate resilient development, or adaptation pathways, show \r\npromise for implementing transformational approaches (medium \r\nconfidence), including expansion of ecosystem-based adaptation \r\napproaches"


id: 5ef4988fef3079141bcaba993b73337f
created_at: 2023-03-16 14:02:13
updated_at: 2023-03-16 14:02:13
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index: climate_gpt_only_giec
vector_id: 12483
content:
" Climate information services that are demand driven \r\nand context specific, combined with climate change literacy, have \r\nthe potential to improve adaptation responses (high confidence). \r\n{5.14.3, 9.4.5, 14.7.2, 14.6, 17.6}"


id: ebc7ffb051c07b3da2d43823653242f9
created_at: 2023-03-16 14:02:13
updated_at: 2023-03-16 14:02:13
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index: climate_gpt_only_giec
vector_id: 34600
content:
"TS.E.5.6 A range of governance processes, practices and tools \r\nthat are applicable across a range of temporal and spatial \r\nscales are available to support inclusive decision-making for \r\nadaptation and risk management in diverse settings (high \r\nconfidence). National guidance and laws, policies and regulations, \r\ndecision tools that can be tailored to local circumstances, innovative \r\nengagement processes and collaborative governance can motivate \r\nbetter understanding of climate risk and build climate resilient \r\ndevelopment (high confidence). Collaborative networks and \r\ninstitutions, including among local communities and their governing \r\nauthorities, can help resolve conflicts (high confidence). A combination \r\nof robust climate information, adaptive decision-making under \r\nuncertainty, land use planning, public engagement and conflict \r\nresolution approaches can help to address governance constraints to \r\nprepare for climate risks and build adaptive capacity (high confidence)"


id: fc79ffd5a633433535188651c79f1b61
created_at: 2023-03-16 14:02:13
updated_at: 2023-03-16 14:02:13
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content:
" \r\nNew modelling, monitoring and evaluation approaches, alongside \r\ndisruptive technologies, can help understand the societal implications \r\nof trade-offs and build integrated pathways of low-regret anticipatory \r\noptions, established jointly across sectors in a timely manner, to avoid \r\nlocked-in development pathways (high confidence). {3.6.2, 3.6.3, 3.6.4, \r\n3.6.5, 5.14.1, 5.14.4, 11.4.1, 11.4.2, 11.7.1, 11.7.3, Box 11.5, 15.5.3, \r\n15.5.4, 15.6.3, 15.6.4, 15.6.5, 17.3.1, 17.3.2, 17.4.2, 17.4.4, 17.6, \r\nCCP2.4.3, CCB DEEP, CCB NATURAL, CCB SLR, CWGB BIOECONOMY}"


id: fa88030a31c72a3778104d20b73d0352
created_at: 2023-03-16 14:02:13
updated_at: 2023-03-16 14:02:13
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content:
"appropriate enabling conditions (high confidence). These enabling \r\nconditions include effective governance and information flow, policy \r\nframeworks that incentivise sustainability solutions, adequate financing \r\nfor adaptation, mitigation and sustainable development, institutional \r\ncapacity, science, technology and innovation, monitoring and evaluation \r\nof climate resilient development policies, programmes and practices \r\nand international cooperation. Investment in social and technological \r\ninnovation could generate the knowledge and entrepreneurship needed \r\nto catalyse system transitions and their transfer. The implementation \r\nof policies that incentivise the deployment of low-carbon technologies \r\nand  practices within specific sectors, such as energy, buildings \r\nand agriculture, could accelerate greenhouse gas mitigation and \r\ndeployment of climate resilient infrastructure in both urban and rural \r\nareas"


id: af8fc403671a962170de069b312c84aa
created_at: 2023-03-16 14:02:13
updated_at: 2023-03-16 14:02:13
content_type: text
index: climate_gpt_only_giec
vector_id: 25138
content:
" Civic engagement is an important element of building societal \r\nconsensus and reducing barriers to action on adaptation, mitigation \r\nand sustainable development (very high confidence). {18.4}"


id: 2694ec19cb7569f1eb65c739a98046ac
created_at: 2023-03-16 14:02:13
updated_at: 2023-03-16 14:02:13
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vector_id: 3478
content:
"Figure TS.AII.1 |  Synthesis of the severity conditions for Representative Key Risks (RKRs) by the end of this century. The figure does not aim to describe severity \r\nconditions exhaustively for each RKR, but rather to illustrate the risks highlighted in this report (Sections 16.5.2.3.1 to 16.5.2.3.8). Coloured circles represent the levels of warming \r\n(climate), exposure/vulnerability and adaptation that would lead to severe risks for particular key risks and RKRs. Each set of three circles represents a combination of conditions that \r\nwould lead to severe risk with a particular level of confidence, indicated by the number of black dots to the right of the set, and for a particular scope, indicated by the number of \r\nstars to the left of the set. The two scopes are 'broadly applicable', meaning applicable pervasively and even globally, and 'specific', meaning applicable to particular areas, sectors \r\nor groups of people"


id: ac6217f42a633fccb2221b60fc07c797
created_at: 2023-03-16 14:02:13
updated_at: 2023-03-16 14:02:13
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index: climate_gpt_only_giec
vector_id: 24655
content:
" Details of confidence levels and scopes can be found in Section 16.5.2.3. In terms of severity condition levels (Section 16.5.2.3), for warming levels (coloured \r\ncircles labelled 'C' in the figure), high refers to climate outcomes consistent with RCP8.5 or higher, low refers to climate outcomes consistent with RCP2.6 or lower, and medium \r\nrefers to intermediary climate scenarios. Exposure-vulnerability levels are determined relative to the range of future conditions considered in the literature. For adaptation, high \r\nrefers to near maximum potential and low refers to the continuation of today's trends. Despite being intertwined in reality, exposure-vulnerability and adaptation conditions are \r\ndistinguished to help understand their respective contributions to risk severity. {Figure 16.10}"


id: f5662adbbe172581c9dec6894e02fd5
created_at: 2023-03-16 14:02:13
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content:
"Contributing Authors: Maarten van Aalst (Netherlands), Gusti Anshari (Indonesia), Richard Betts \r\n(UK), Edward R. Carr (USA), Vanesa Castan Broto (UK), Connor J. Cavanagh (Norway), Eva Davidsdottir \r\n(Norway), Greg Flato (Canada), Katja Frieler (Germany), Nathan Gillett (Canada/UK), Lorena B. \r\nGonzalez (Mexico), Marjolin Haasnoot (the Netherlands), Jo S Hermansen (Norway), Pandora Hope \r\n(Australia), Christian Huggel (Switzerland), Darrell S. Kaufman (USA), Wolfgang Kiessling (Germany), \r\nTimothy A. Kohler (USA), Aris Koutroulis (Greece), Sivapuram Venkata Rama Krishna Prabhakar \r\n(Japan/India), Judy Lawrence (New Zealand), Timo Leiter (Germany/UK), Martina K. Linnenluecke \r\n(Australia), Tabea Lissner (Germany), Leonardo Martinez-Diaz (USA), Md Shahin Mia (Malaysia/\r\nBangladesh), Guy Midgley (South Africa), Jan Minx (Germany), Tero Mustonen (Finland), Johanna \r\nNalau (Australia/Finland), Ben Orlove (USA), Friederike Otto (UK/Germany), Camille Parmesan \r\n(France/UK/USA), Karishma V"


id: 82d011317650509ac9b56157ad0928d5
created_at: 2023-03-16 14:02:13
updated_at: 2023-03-16 14:02:13
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index: climate_gpt_only_giec
vector_id: 18106
content:
" Patel (USA), Asif Raihan (Bangladesh), Nussaibah Raja (Mauritius), \r\nLauren Rickards (Australia), Joeri Rogelj (UK/Belgium), Maisa Rojas (Chile), Steven Rose (USA), \r\nLisa Schipper (Sweden/UK), Daniela N. Schmidt (UK/Germany), Sonia I. Seneviratne (Switzerland), \r\nNicholas Philip Simpson (South Africa), Chandni Singh (India), Jo Skeie Hermansen (Norway), Aimee \r\nSlangen (the Netherlands), William Solecki (USA), Daithi Stone (New Zealand), Laurent Terray \r\n(France), Adelle Thomas (Bahamas), Joe Thwaites (UK), Edmond Totin (Benin), Christopher Trisos \r\n(South Africa), Robert Vautard (France), Michael Weisberg (USA), Xuebin Zhang (Canada)"


id: 318a746c9bb596a9f3a7af695927587c
created_at: 2023-03-16 14:02:13
updated_at: 2023-03-16 14:02:13
content_type: text
index: climate_gpt_only_giec
vector_id: 5206
content:
"Since IPCC AR5, human influence on the Earth's climate has \r\nbecome unequivocal, increasingly apparent and widespread, \r\nreflected in both the growing scientific literature and in \r\nthe perception and experiences of people worldwide (high \r\nconfidence). Current changes in the climate system and those \r\nexpected in the future will increasingly have significant and \r\ndeleterious impacts on human and natural systems. The impacts of \r\nclimate change and extreme weather events have adversely affected, \r\nor caused the loss of ecosystems including terrestrial, freshwater, \r\nocean and coastal ecosystems, including tropical coral reefs; reduced \r\nfood security; contributed to migration and displacement; damaged \r\nlivelihoods, health and security of people; and increased inequality"


id: c72bf68dfe115a6aa9eb4ebdef5353e5
created_at: 2023-03-16 14:02:13
updated_at: 2023-03-16 14:02:13
content_type: text
index: climate_gpt_only_giec
vector_id: 28924
content:
" \r\nClimate change impacts are concurrent and interact with other \r\nsignificant societal changes that have become more salient since AR5, \r\nincluding a growing and urbanising global population; significant \r\ninequality and demands for social justice; rapid technological change; \r\ncontinuing poverty, land and water degradation, biodiversity loss; food \r\ninsecurity; and a global pandemic. {1.1.1; 1.3; Cross-Working Group \r\nBox ATTRIBUTION in Chapter 1}"


id: ecd15a38b91b0494c977f426bee4bb7a
created_at: 2023-03-16 14:02:13
updated_at: 2023-03-16 14:02:13
content_type: text
index: climate_gpt_only_giec
vector_id: 34738
content:
"Risks from climate change differ through space and time and \r\ncascade across and within regions and systems. The total risk \r\nin any location may thus differ from the sum of individual \r\nrisks if these interactions, as well as risks from responses \r\nthemselves, are not considered (high confidence). The risks \r\nof climate change responses include the possibility of mitigation or \r\nadaptation responses not achieving their intended objectives or having \r\ntrade-offs or adverse side effects for other societal objectives. Another \r\ncore area of complexity in climate risk is the behaviour of systems, \r\nwhich includes multiple stressors unfolding together, cascading or \r\ncompounding interactions within and across sectors and regions, and \r\nnonlinear responses and the potential for surprises. All of this is crucial \r\nfor effective decision making and decision-support methods"


id: 2fac6245c20b252bb38522adcf05e863
created_at: 2023-03-16 14:02:13
updated_at: 2023-03-16 14:02:13
content_type: text
index: climate_gpt_only_giec
vector_id: 4922
content:
" The key \r\nrisks assessed in this report become important in interaction with the \r\ncultures, values, ethics, identities, experiences and knowledge systems \r\nof affected communities and societies. {1.3.1}"


id: 69233ac626b3547101e43b79b0349b90
created_at: 2023-03-16 14:02:13
updated_at: 2023-03-16 14:02:13
content_type: text
index: climate_gpt_only_giec
vector_id: 14136
content:
"solution space, overcome soft limits to adaptation, reduce residual risk \r\nto tolerable levels and achieve societal goals. If such a transformation \r\nis not pursued or is not successful and risk remains above intolerable \r\nlevels, a forced transformation may occur that is less consistent with \r\nsocietal goals. The literature describes incremental and transformational \r\nchange as linked processes. The transformational adaptation literature \r\nsuggests shifts from incremental to transformational processes are \r\nmade possible by knowledge and skills, as well adjustments to vision, \r\nagendas and coalitions achieved through monitoring and learning. \r\nThe socio-ecological and sustainability transitions literature suggests \r\nthat actors seeking deliberate transformation may take incremental \r\nsteps that aim to induce societal tipping point behaviour in the \r\nnear or longer term"


id: 4d3808f37647e3ec5240111cb14d8a3e
created_at: 2023-03-16 14:02:13
updated_at: 2023-03-16 14:02:13
content_type: text
index: climate_gpt_only_giec
vector_id: 9581
content:
"The concept of limits to adaptation is dynamic in terms of \r\nthe temporal, spatial and contextual dimensions of climate \r\nchange risks, impacts and response. Socioeconomic, technological, \r\ngovernance and institutional systems or policies can be changed or \r\ntransformed in response to the different dimensions of adaptation \r\nlimits to climate change and extreme events. Adaptation limits can be \r\nsoft or hard. Soft adaptation limits occur when options may exist but \r\nare currently not available to avoid intolerable risks through adaptive \r\nactions. Hard adaptation limits occur when no adaptive actions \r\nare possible to avoid intolerable risks. The levels of GHG emissions \r\nreduction, adaptation and risk management measures are the key \r\nfactors determining if and when adaptation limits are reached. When \r\na limit (soft) is reached, then intolerable risks and impacts may occur \r\nand additional adaptations (incremental or transformational) would \r\nbe required"


id: c11f6b5e68b32095027da668942c1f7e
created_at: 2023-03-16 14:02:13
updated_at: 2023-03-16 14:02:13
content_type: text
index: climate_gpt_only_giec
vector_id: 27963
content:
" Transformational adaptation can allow a system to extend \r\nbeyond its soft limits and prevent soft limits from becoming hard \r\nlimits. The loss and damage associated with the future climate change \r\nimpacts, beyond the limits to adaptation, is an area of increasing \r\nfocus. However, it is yet to be fully developed in terms of assessment \r\nmethods, including non-economic values and identifying means to \r\navoid and reduce both economic (loss of asset, infrastructure, land \r\netc.) and non-economic (loss of societal beliefs and values, cultural \r\nheritage, biodiversity and ecosystem services) losses and damages. \r\n{1.4.4.1; 1.4.4.2}"


id: 8051102836d44e5c02ca2aadc89975b4
created_at: 2023-03-16 14:02:13
updated_at: 2023-03-16 14:02:13
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vector_id: 17715
content:
"Transformation is understood as a collective action challenge \r\namong actors with both common and differing values interacting \r\nwith a mix of competition and cooperation. Significant innovations \r\noften begin in niches or protected spaces, sometimes introduced by \r\nnew entrants or outsiders. The drivers of transformation are multi\u0002dimensional, involving social, cultural, economic, environmental, \r\ntechnical and political processes. The combination of these creates the \r\npotential for abrupt and systemic change, the stability of entrenched \r\nand interlocked power structures, and the importance of individual \r\nbeliefs and behaviours. Decision frameworks that consider multiple \r\nobjectives and multiple scenarios can avoid privileging some views \r\nover others and help multiple actors to identify resilient and equitable \r\nsolutions to complex, deeply uncertain challenges"


id: eb9a9d6a21457fcf00fba4574c8f389
created_at: 2023-03-16 14:02:13
updated_at: 2023-03-16 14:02:13
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vector_id: 34570
content:
" Nonetheless, \r\ncommon goals and narratives are both enablers of transformation and \r\nhelp align the activities of multiple, loosely co-ordinated actors. {1.5.2}"


id: d5f566f7adbf5bf0ccfe30c49d0f1476
created_at: 2023-03-16 14:02:13
updated_at: 2023-03-16 14:02:13
content_type: text
index: climate_gpt_only_giec
vector_id: 31177
content:
"Numerous additional significant climate-related changes have unfolded \r\nworldwide since the publication of the IPCC Fifth Assessment Report \r\n(AR5) in 2014 (IPCC, 2014a). Consistent with projections, multiple \r\nconcurrent changes in the physical climate system have grown more \r\nsalient, including increasing global temperatures, loss of ice volume, \r\nrising sea levels and changes in global precipitation patterns (AR6 WGI \r\nChapter 1, Chen et al., 2021). The changes in the physical climate system, \r\nmost notably more intensive extreme events, have adversely affected \r\nnatural and human systems around the world. This has contributed to \r\na loss and degradation of ecosystems, including tropical coral reefs; \r\nreduced water and food security; increased damage to infrastructure; \r\nadditional mortality and morbidity; human migration and displacement; \r\ndamaged livelihoods; increased mental health issues; and increased \r\ninequality"


id: 419c3d076dfd3327bad3dbb43c7c63af
created_at: 2023-03-16 14:02:13
updated_at: 2023-03-16 14:02:13
content_type: text
index: climate_gpt_only_giec
vector_id: 7720
content:
" Since AR5, a growing literature attributes change in specific \r\nclimate variables to observed damages to specific localised human and \r\nnatural systems in many regions of the world (Figure 1.1, also see Cross\u0002Working Group BOX ATTRIBUTION in Chapter 1)."


id: fe8bd9ff074dc18d9312207b35f78110
created_at: 2023-03-16 14:02:13
updated_at: 2023-03-16 14:02:13
content_type: text
index: climate_gpt_only_giec
vector_id: 37588
content:
"Concurrently, since AR5, a growing share of people around the world \r\nperceive a changing climate, regard these changes as significant and \r\nconsider climate action to be a matter of high urgency (Wilson and \r\nOrlove, 2019; Section  17.4.5). A survey, representing over half the \r\nworld's population, found that almost two-thirds of people across \r\n50 countries view climate change as an emergency (Flynn et al., 2021), \r\ncompared to just over half across 23 countries in 2013 (Fagan, 2019). \r\nThe highest level of support for climate action is among small island \r\ndeveloping states (SIDS) (74%), followed by high-income countries \r\n(72%), middle-income countries (62%) and, then, least developed \r\ncountries (58%) (Flynn et al., 2021). Notably, after mid-2018, global \r\nmedia showed a large increase number of mentions of 'global \r\nwarming', 'climate change' and similar terms (Thackeray et al., 2020)"


id: 185b74ddccb2982b177372decd47697b
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updated_at: 2023-03-16 14:02:13
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index: climate_gpt_only_giec
vector_id: 1326
content:
" \r\nThe business community now consistently includes climate change, \r\nincluding 'climate action failure' as a major risk (World Economic Forum, \r\n2021). In late 2019, protests calling for strengthened climate action \r\nreached an unprecedented level of over 6000 events in 185 countries, \r\nwith a reported estimate of 7.6 million participants, largely led by the \r\n'Fridays for Future' youth movement (Chase-Dunn and Almeida, 2020)."


id: 8b635e30b2b7b535a3dbdf86fd8ea6e1
created_at: 2023-03-16 14:02:13
updated_at: 2023-03-16 14:02:13
content_type: text
index: climate_gpt_only_giec
vector_id: 19450
content:
"Since AR5, climate action has grown at all levels of governance as \r\nwell as among non-governmental organisations, small and large \r\nenterprises, and citizens. Two international agreements--the United \r\nNations Framework Convention on Climate Change (UNFCCC) Paris \r\nAgreement and the 2030 Agenda for Sustainable Development--\r\njointly provide overarching goals for climate action. The 2015 Paris \r\nAgreement frames direct local, national and private sector actions \r\naligned with long-term goals addressing mitigation, adaptation and \r\nfinance. For mitigation, the agreement calls for 'holding the increase \r\nin global average temperature to well below 2degC above pre-industrial \r\nlevels', 'pursuing efforts to limit the temperature increase to 1.5degC' and \r\n'reaching net zero greenhouse gas (GHG) emissions in the second half \r\nof this century' (UNFCCC, 2016)"


id: 5f8ac2a68c33552e130b8c6ea27aa91c
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index: climate_gpt_only_giec
vector_id: 12606
content:
" For adaptation, the agreement calls \r\nfor 'increasing the ability to adapt to the adverse impacts of climate \r\nchange and foster climate resilience' (UNFCCC, 2016, Article 2), as \r\nwell as having a dedicated 'global goal on adaptation' (Lesnikowski \r\net al., 2017; Persson, 2019). For finance, the agreement seeks to make \r\n'financial flows consistent with a pathway towards low GHG emissions \r\nand climate resilient development'."


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"Risk Reduction (SFDRR) (Tozier de la Poterie and Baudoin, 2015; UNISDR, \r\n2015), the finance-oriented Addis Ababa Action Agenda (UN, 2015) and \r\nthe New Urban Agenda (UN, 2017). For example, the SFDRR recognises \r\nsome disasters as 'exacerbated by climate change and increasing in \r\nfrequency and intensity, significantly [impeding] progress towards \r\nsustainable development' (UNISDR, 2015). The Convention on Biological \r\nDiversity (CBD) is one of the key international legal instruments for \r\nsustainable development for 'the conservation of biological diversity, the \r\nsustainable use of its components and the fair and equitable sharing \r\nof the benefits arising out of the utilisation of genetic resources' (CBD, \r\n2011). The CBD and its Aichi targets recognises that biodiversity is \r\naffected by climate change, with negative consequences for human well\u0002being, but biodiversity, through ecosystem services, contributes to both \r\nclimate change mitigation and adaptation (CBD, 2010)"


id: 5076333a2aa4f418f20b2ea3814f169f
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" There is concern \r\nthat many of the proposed post-2020 biodiversity targets of the CBD \r\nmay not be met due to climate change impacts (post-2020 biodiversity \r\ntargets from Chapter 2; Arneth et al., 2020)."


id: 3876998a2f85b09a0053b510809850ea
created_at: 2023-03-16 14:02:13
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"Knowledge about adaptation has significantly expanded since AR5 \r\n(Cross-Chapter Box  ADAPT in Chapter 1). While understanding \r\nregarding the extent of adaptation gaps remains limited, the available \r\nevidence suggests significant adaptation gaps exist (high confidence). \r\nMany current adaptation efforts constitute adaptation planning, rather \r\nthan implementation (Section  16.3). Most current implementation \r\nefforts represent incremental as opposed to transformational \r\nadaptation despite the proximity to adaptation limits (Sections 17.2; \r\n17.5.2). Some current adaptation efforts are considered maladaptive \r\nbecause they increase some climate-related risks even if they reduce \r\nothers (Sections 1.4.2.4; 17.5; Chapter 16). Gaps exist in key enablers \r\nof adaptation, such as finance (Cross-Chapter Box FINANCE in Chapter \r\n17)"


id: eb50c7c42cb8904ea905835b15a5ce84
created_at: 2023-03-16 14:02:13
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" Given the long time scales involved with many adaptation actions \r\nand the potential to significantly reduce longer-term costs with near\u0002term actions, closing many adaptation gaps requires actions over the \r\nnext few years by governments, business, civil society and individuals \r\nat a scale and speed significantly faster than that represented by \r\ncurrent trends."


id: 8e0b45e4a7486174d8f2bb5f77484c35
created_at: 2023-03-16 14:02:13
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"First, this AR6 assessment has an increased focus on risk- and solutions\u0002frameworks. The risk framing can move beyond the limits of single \r\nbest estimates or most-likely outcomes and include high-consequence \r\noutcomes for which probabilities are low or in some cases unknown \r\n(Jones et  al., 2014; Mach and Field, 2017). In this report, the risk \r\nframing for the first time spans all three Working Groups, includes \r\nrisks from the responses to climate change, considers dynamic and \r\ncascading consequences (Section  1.3.1.1), describes with more \r\ngeographic detail risks to people and ecosystems, and assesses such \r\nrisks over a range of scenarios (Chapter 16). The focus on solutions \r\nencompasses the interconnections among climate responses, \r\nsustainable development, and transformation, and the implications \r\nfor governance across scales within the public and private sectors \r\n(Section  17.5.2; Chapter 18)"


id: df7a4ebe689a2537570c98328b7ee7c5
created_at: 2023-03-16 14:02:13
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" The assessment therefore includes \r\nclimate-related decision making and risk management, climate \r\nresilient development pathways, implementation and evaluation \r\nof adaptation, and also limits to adaptation, and loss and damage \r\n(Cross-Chapter Box LOSS in Chapter 17; Section 1.4.4). Specific focal \r\nareas reflect contexts increasingly important for the implementation \r\nof responses, such as cities (Chapter 6)."


id: 60ecf6726993fc697d43ea44ec9bddcf
created_at: 2023-03-16 14:02:13
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"Risk framing is increasingly used to assess climate change impacts on \r\nhuman and natural systems (Sections 1.2.4.1; 16.1; 17.3; Cross-Chapter \r\nBox CLIMATE in Chapter 1; IPCC, 2012; Mach and Field, 2017; O'Neill \r\net al., 2017; Connelly et al., 2018; see also Oppenheimer et al., 2014). \r\nA risk framing reflects key dimensions of the climate challenge. These \r\nfeatures include the changing likelihoods of many different outcomes \r\n(including adverse consequences and beneficial opportunities), \r\nuncertainties that will persist, and different and contested values, \r\npriorities and goals (Jones and Preston, 2011; Mach et al., 2016). The \r\nIPCC AR6 and associated special reports apply a broad definition of \r\nrisk. WGI Cross-Chapter Box 1.3"


id: ffb8d8cc7dffabe178f54e3e9d6b7c53
created_at: 2023-03-16 14:02:13
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" WGI uses the Climatic Impact Driver \r\nterminology, rather than hazard, to neutrally assess changing climatic \r\nconditions that are relevant to human and natural systems, leaving \r\nthe determination of positive/negative consequences and resulting \r\nimpacts and risks for WGII assessment (WGI Section  12.3). In most \r\ncases, throughout this WGII report, the term 'risk' refers to the risks of \r\nclimate change impacts. The full assessment, however, incorporates all \r\nrelevant risks from climate change impacts and responses."


id: 2c59d710fab216ddec4e3180f5ef82e6
created_at: 2023-03-16 14:02:13
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"Vulnerability is a component of risk, but also an important focus \r\nindependently. Vulnerability in this report is defined as the propensity \r\nor predisposition to be adversely affected. It encompasses a variety \r\nof concepts and elements, including sensitivity or susceptibility \r\nto harm and lack of capacity to cope and adapt (see Annex II: \r\nGlossary). Over the past several decades, approaches to analysing \r\nand assessing vulnerability have evolved. An early emphasis on top\u0002down, biophysical evaluation of vulnerability included--and often \r\nstarted with--exposure to climate hazards in assessing vulnerability. \r\nFrom this starting point, attention to bottom-up, social and contextual \r\ndeterminants of vulnerability, which often differ, has emerged, \r\nalthough this approach is incompletely applied or integrated across \r\ncontexts (Bergstrand et al., 2015; Rufat et al., 2015; Spielman et al., \r\n2020; Taberna et al., 2020)"


id: 63eaf8c363e497bf34fe4c32b40d153d
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" Vulnerability is now widely understood \r\nto differ within communities and across societies, also changing \r\nthrough time (Kienberger et  al., 2013; Jurgilevich et  al., 2017; see \r\nalso Chapter 16). In the WGII AR6, assessment of the vulnerability \r\nof people and ecosystems encompasses the differing approaches \r\nthat exist within the literature, both critiquing and harmonising them \r\nbased on available evidence. In this context, exposure is defined \r\nas the presence of people; livelihoods; species or ecosystems; \r\nenvironmental functions, services, and resources; infrastructure; \r\nor economic, social, or cultural assets in places and settings that \r\ncould be adversely affected (Annex II: Glossary). Potentially affected \r\nplaces and settings can be defined geographically, as well as more \r\ndynamically, for example through transmission or interconnections \r\nthrough markets or flows of people."


id: f4414b49e538eae07e8da35eb2480bb8
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"The broad definition of risk involves quantitative and integrative \r\nunderstandings of risk ( Oppenheimer et al., 2014; Mach and Field, 2017; \r\nsee also Section 17.3). Risk is sometimes defined as the probability \r\nof a consequence, multiplied by the magnitude of that consequence, \r\nacknowledging both the diversity of possible consequences and \r\nthe relevance of values. Yet it also applies in circumstances where \r\nprobabilities cannot be fully quantified (e.g., Adger et al., 2013). For \r\nexample, in some cases the probability and magnitude of consequences \r\nmay be more uncertain, dependent on complex dimensions of the \r\nclimate (e.g., a cyclone, high tide, and heatwave co-occurring) or the \r\nvulnerability of different communities (e.g., the ways in which social \r\nnetworks and community cohesion support the most vulnerable \r\nindividuals during disasters) (Ford et al., 2018). The determinants of \r\nrisk vary dynamically through space and time (Jurgilevich et al., 2017; \r\nViner et  al"


id: 39e36fc252f4fe3094ff6cdb90469f71
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", 2020). They interact, compound, and cascade (Dawson, \r\n2015; Adger et al., 2018; see also Section 16.1.2)."


id: 220c3d53a79824093742e9d8a9f23f5d
created_at: 2023-03-16 14:02:13
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"climate change adaptation and disaster risk reduction communities (e.g., \r\nIPCC, 2012; UNDRR, 2019, especially Chapter 13 in that report). Building \r\non the scientific literature and adaptation and risk reduction practice, the \r\nIPCC Special Report on Extremes resulted in several major IPCC advances \r\nthat continue through the present report, including emphasis on risk and \r\nclimate-related extremes (e.g., Burton et al., 2012; Lavell et al., 2012) \r\nand re-conceptualisation of vulnerability to encompass both social \r\nand biophysical orientations (i.e., bridging contextual/bottom-up and \r\nclimate-driven/top-down approaches) (Polsky et al., 2007; Cardona et al., \r\n2012). Linking disaster risk reduction and climate change adaptation can \r\nalso be an important basis for discussion in climate negotiations on the \r\nallocation of funds needed for tackling climate change, especially in \r\ndeveloping countries and SIDS (Begum et al., 2014)"


id: ce7c9ea65889beec11a71ad918cc15f6
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" The integration of \r\ndisaster risk management and climate change adaptation in the IPCC \r\nAR6 is seen, for example, in the assessment of key risks within and \r\nacross sectors and regions, along with global-scale reasons for concern, \r\nwhich is attuned to extreme events and disasters (Oppenheimer et al., \r\n2014; see also Chapter 16). Additionally, the assessment of adaptation \r\nhas prioritised these interconnections (e.g., Mimura et  al., 2014), as \r\nhave literature and practice especially in the context of sustainable \r\ndevelopment (e.g., Schipper et al., 2016)."


id: d0b20a74b071313b9cfddea1974027d7
created_at: 2023-03-16 14:02:13
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"Adaptation in this report is defined, in human systems, as the process \r\nof adjustment to actual or expected climate and its effects, in order \r\nto moderate harm or exploit beneficial opportunities. In natural \r\nsystems, adaptation is the process of adjustment to actual climate \r\nand its effects; human intervention may facilitate adjustment to \r\nexpected climate and its effects (see Annex II: Glossary). Adaptation \r\nplanning in human systems generally entails a process of iterative risk \r\nmanagement. Different types of adaptation have been distinguished, \r\nincluding anticipatory versus reactive, autonomous versus planned \r\nand incremental versus transformational adaptation (Chapters 16-\r\n18; IPCC WGII glossaries for the TAR, AR4, AR5, and AR6 (Annex II)). \r\nAdaptation is often seen as having five general stages: (a) awareness, \r\n(b) assessment, (c) planning, (d) implementation and (e) M&E (Moser \r\nand Boykoff, 2013; Jones et al., 2014; Mimura et al., 2014; Noble et al"


id: 57f0203e40e01743dec7fb92ef9d07af
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", \r\n2014; see also Section 17.4). Government, non-government and private \r\nsector actors have adopted a wide variety of specific approaches to \r\nadaptation that, to varying degrees, address these five general stages. \r\nAdaptation in natural systems includes 'autonomous' adjustments \r\nthrough ecological and evolutionary processes. It also involves the use \r\nof nature through ecosystem-based adaptation. The role of species, \r\nbiodiversity and ecosystems in such adaptation options can range \r\nfrom the rehabilitation or restoration of ecosystems (e.g., wetlands or \r\nmangroves) to hybrid combinations of 'green and grey' infrastructure \r\n(e.g., horizontal levees) (Chapters 2 and 3; IPBES, 2018)."


id: de539778615426dee587f9dcdbd0aa3d
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"Resilience in this report is defined as the capacity of social, economic \r\nand environmental systems to cope with a hazardous event or trend \r\nor disturbance, responding or reorganising in ways that maintain their \r\nessential function, identity and structure, while also maintaining the \r\ncapacity for adaptation, learning and transformation (see Annex II: \r\nGlossary). Resilience is an entry point commonly used, although under \r\na wide spectrum of meanings (Reghezza-Zitt et al., 2012; Flood and \r\nSchechtman, 2014; Aldunce et al., 2015; Tanner et al., 2015; Fisichelli \r\net  al., 2016; Meerow et  al., 2016; Moser et  al., 2019a). Resilience \r\nas a system trait overlaps with concepts of vulnerability, adaptive \r\ncapacity, and thereby risk. Resilience as a strategy overlaps with risk \r\nmanagement, adaptation and also transformation (Woodruff et  al., \r\n2018; Moser et al., 2019a)"


id: e2581c67d49e1a802ae75c1c6abe3f11
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" Implemented adaptation is often organised \r\naround resilience as bouncing back and returning to a previous state \r\nafter a disturbance (Fisichelli et al., 2016)."


id: 8d0f47be5600f9e452c06bb66b71e4b3
created_at: 2023-03-16 14:02:13
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"A narrative contains a storyline in addition to a set of actors \r\n(Elliott, 2005). A storyline is a series of events including their \r\ncausal connections within a narrative. The IPCC and climate change \r\nliterature more broadly often use the terms storylines and narratives \r\ninterchangeably (O'Neill et al., 2017; see also WGI Cross-Chapter Box 6 \r\nin Chapter 1; Sections 1.4.4; 10.5.3). A scenario storyline refers to a \r\nnarrative description of a scenario including its main characteristics, \r\nrelationships between driving forces and how these factors evolve \r\n(AR6 WGI Section 1.4.4.2, Chen et al., 2021). Storylines are used to \r\nassess risks related to low-likelihood, but high-impact events (Sutton, \r\n2018). In this use of the terms, narratives and storylines do not \r\ninclude specific actors"


id: 9061768de08ea6b665bda655c32bb90f
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" There is also a critical literature on the use of \r\nnarratives and storylines based on projected scenarios, which points \r\nout the conservative character of these concepts whose performative \r\neffect tends to preserve the status quo and the current socioeconomic \r\nrelationships. (Malm and Hornborg, 2014; Chollet and Felli, 2015; \r\nLovbrand et al., 2015; Demortain, 2019; Theys and Cornu, 2019)."


id: a33c4719b949ffa416fc3ab712a093a3
created_at: 2023-03-16 14:02:13
updated_at: 2023-03-16 14:02:13
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content:
"Often, development and adaptation communities of practice default to \r\npersistence and stability in their use of resilience (Cote and Nightingale, \r\n2012; MacKinnon and Derickson, 2013). Such a framing aligns resilience \r\nwith a long-standing but increasingly questioned belief that sustainable \r\ndevelopment can be achieved through incremental adjustments in \r\nbehaviour and advances in technology that allow for the persistence of \r\nexisting socioeconomic and socio-ecological arrangements (Klauer, 1999; \r\nBanerjee, 2003; Redclift, 2005; UN Inter-agency Task Force on Financing \r\nfor Development 2019; Chapter 18, Section 1.5). However, the literature \r\nincreasingly suggests that the achievement of sustainable development \r\nwill require transformative change in socio-ecological systems at scales \r\nranging from the community to the globe"


id: bea90620cc5c6912afbf468df0e7c155
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" The concept of climate \r\nresilient development, initially introduced in AR5 and now a key focus in \r\nthis report (see Chapter 18), engages with such transformations and the \r\nassociated questions of justice, power and politics as shaped by internal, \r\nendogenous social factors and their interactions with other drivers of \r\nchange (Eriksen et al., 2015; Nightingale, 2015b; Carr, 2019; Nightingale \r\net al., 2019; see also Chapter 18)."


id: 6df9ca203819deebf39cc368e4942bd0
created_at: 2023-03-16 14:02:13
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content:
"Standard research communication may fail to engage policymakers, \r\nmedia and the public at large (WGI AR6 Section 1.2.4, Chen et al., 2021). \r\nRather, policies and decision making tend to be based on narratives \r\nand storylines (Roe, 1994; Roe, 2017). Although mathematical models \r\nand narratives are often presumed to be antithetical, in practice they \r\nmay be complementary and work together (Morgan and Wise, 2017). \r\nCommunicating research insights through storylines and narratives \r\nmay have a better chance of transmitting key messages. AR6 employs \r\nthese communication tools in many places, for instance storylines \r\nfor constructing and communicating regional climate information \r\nor climate services (WGI AR6 Chapter 10, Doblas-Reyes et al., 2021; \r\nWGI AR6 Chapter 12, Ranasinghe et  al., 2021) or 'low likelihood \r\nhigh warming storylines' (Chapter 4)"


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" To better communicate deep \r\nuncertainty in sea level rise projections, WGI uses storylines to describe \r\nthe physical events that would have to unfold to generate its high-end \r\nestimates (Cross-Chapter Box DEEP in Chapter 17)."


id: ee820199221910a69e9b61637ed59050
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updated_at: 2023-03-16 14:02:13
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content:
"AR6 WGI adopts a common set of reference years and time periods to assess observed and projected climate change, namely the pre\u0002industrial period, the current 'modern' period and future reference time periods. The IPCC Glossary (2021b) defines the pre-industrial \r\nperiod as 'the multi-century period prior to the onset of large-scale industrial activity around 1750. The reference period 1850-1900 is \r\nused to approximate pre-industrial global mean surface temperature (GMST).' The 'modern' period is defined as 1995 to 2014 in AR6, \r\nwhile three future reference periods are used for presenting climate change projections, namely near term (2021-2040), mid-term (2041-\r\n2060) and long term (2081-2100), in both the AR6 WGI and WGII reports. Importantly, the historical rate of warming assessed by WGI \r\nin AR6 is different to that assessed in AR5 and Special Report on Global Warming of 1.5degC (SR1.5, IPCC, 2018b), due to methodological \r\nupdates (see WGI Cross-Chapter Box 2"


id: a65b29605c2b1db46cbddce2b68f7acf
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"3 in Chapter 2 for details (Gulev, 2021)). This means that the 'modern' period is assessed as slightly \r\nwarmer compared to 1850-1900 than it would have been with AR5-era methods. This also has implications for the projected timing of \r\nreaching policy-relevant levels of global warming, which need to be understood."


id: d52d117d9e0969790eb35013d64e60d3
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"The plausibility of emissions levels as high as the emissions scenario conventionally associated with the RCP8.5 and SSP5-8.5 concentration \r\npathways has been called into question since AR5, as has the emissions pathway feasibility of the low scenarios (Hausfather and Peters, \r\n2020; Rose and and Scott, 2020). However, these views are contested (Schwalm et al., 2020, for RCP8.5) It is important to realise that \r\nemissions scenarios and concentration pathways are not the same thing and higher concentration pathways, such as RCP8.5 could arise \r\nfrom lower emissions scenarios if carbon cycle feedbacks are stronger than assumed in the integrated assessment models (IAMs) used to \r\ncreate the standard scenarios (Booth et al., 2017). In the majority of full-complexity Earth System Models, these feedbacks are stronger \r\nthan in the IAMs (Jones et al., 2013), so the RCP8.5 concentration pathway cannot be ruled out purely through consideration of the \r\neconomic aspects of emissions scenarios"


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" Nonetheless, the likelihood of a climate outcome, and the overall distribution of climate \r\noutcomes, are a function of the emissions scenario's likelihood. Note that the original RCPs were created explicitly to facilitate a broad \r\nrange of climate modelling experiments, with the expectation that other issues, such as socioeconomic uncertainty, could be subsequently \r\nexplored (Moss et al., 2010)."


id: f985cc5f371b0328f4c40fc58ef0f62e
created_at: 2023-03-16 14:02:13
updated_at: 2023-03-16 14:02:13
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index: climate_gpt_only_giec
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content:
"An important feature of the AR6 cycle is a stronger emphasis on the use of future GWLs to support consistency and comparability across \r\nthe three IPCC Working Groups' contributions to the AR6 and improve communication. The common range of GWLs relative to the 1850 \r\nto 1900 period, termed the 'Tier 1' range by WGI, are 1.5, 2.0, 3.0 and 4.0degC. The use of GWLs assists in the comparison of climate states \r\nacross climate change scenarios (projections) and in assessing the broader literature, as well as for cross-chapter and cross-working \r\ngroup comparisons. They facilitate the integration of climate projections, impacts, adaptation challenges and mitigation challenges within \r\nand across the three Working Groups as there is a close connection between the level of global warming and climate change impacts. Of \r\nparticular interest is the timing of when the 'Tier 1' GWLs are reached, relative to the period 1850-1900, under the five SSP x-y scenarios, \r\nas well as RCP scenarios"


id: 77df5cce69f76efb626d4c11ee40d1fa
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updated_at: 2023-03-16 14:02:13
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index: climate_gpt_only_giec
vector_id: 16419
content:
" For climate change impacts and adaptation responses, linking GWLs to RCP and SSP climate projections using \r\na climate information translation resource is of great relevance for the WGII contribution to AR6."


id: 5160982e1c7950f8545370b01bd00545
created_at: 2023-03-16 14:02:13
updated_at: 2023-03-16 14:02:13
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index: climate_gpt_only_giec
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content:
"WGII's common climate dimensions include (a) a common range of GWLs from WGI, (b) common ranges for other climate variables, \r\n(c) information for translating climate variable levels to climate projections and vice versa. See Table Cross-Chapter Box CLIMATE.1 for \r\nglobal warming level ranges by time periods for RCP and SSP climate projections, and Table Cross-Chapter Box CLIMATE.2 for information \r\nregarding the timing for when GWLs are reached in climate projections. The common GWL range is based on WGI's 'Tier 1' dimensions of \r\nintegration range: 1.5degC, 2degC, 3degC and 4degC. The first table illustrates the greater levels of projected global warming with higher emissions \r\npathways, as well as the increasing uncertainty in the climate response over time for a given pathway. The second table illustrates \r\nsignificant uncertainty in the timing for passing GWL thresholds which can narrow for a given GWL, the higher the emissions pathway"


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" \r\nFinally, given the importance of geographic heterogeneity in projected changes in future climate, Table Cross-Chapter Box CLIMATE.3a \r\nand 3b are provided with ranges for select climate variables (temperature, precipitation, ocean) by GWL and continent (or ocean biome). \r\nThe ranges illustrate spatial heterogeneity in potential physical changes in levels and uncertainty that are relevant to assessing climate \r\nimpacts risk. There is significantly more spatial heterogeneity than represented in the table that is relevant to local decision makers (see, \r\nfor instance, WGI Interactive Atlas)."


id: 9b3e9d641d075dff80f2e5ee4bc8fbba
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"Figure Cross-Chapter Box CLIMATE.1 |  Illustration of the use of global warming levels (GWLs) as a dimension of integration for impact studies: \r\nprojected changes in river flows in major basins at 4degC global warming from four different multi-model ensembles. Results are shown for projected flow \r\nchanges direct from Earth System Models (ESMs) in CMIP5 and CMIP6, for the Joint UK Land Environment Simulator (JULES) land surface model driven by meteorological \r\noutputs of the HadGEM3 and EC-Earth model in the High-End cLimate Impacts and eXtremes (HELIX) ensemble (Betts et al., 2018; Koutroulis et al., 2019), and nine \r\nhydrological models driven by a subset of five CMIP5 ESMs in the Inter-Sectoral Impacts Model Intercomparison Project (ISIMIP; Warszawski et al., 2014). Dots show results \r\nfrom individual models, blue for increased flows and red for decreased flows, black circles show the median for each ensemble, and black bars show the 95% confidence \r\nrange in the median"


id: e101ab9cce549283751ef2eef439b0a
created_at: 2023-03-16 14:02:13
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"Common ranges for other 'climate' variables, such as minimum and maximum temperatures and regional climates, are available based \r\non WGI projections. They are based on feasible combinations with GWLs taken into consideration using the WGI Interactive Atlas. Climate \r\ninformation translation may have been necessary within chapters for mapping the WGII literature and assessments of the common \r\nclimate dimensions. WGII's climate impacts literature is based primarily on climate projections around AR5 and earlier or assumed \r\ntemperature levels, though some recent impacts literature uses newer climate projections based on the CMIP6 exercise. Thus, it was \r\nimportant to be able to map climate variable levels to climate projections of different vintages and vice versa and adjust variables, \r\nwhen possible, to a common reference year"


id: 2499f19b80fea6ddc216ed8d433ca974
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" WGII chapters and CCPs only provide climate impact information for the common climate \r\ndimensions that their literature supports and where there is sufficient evidence."


id: bfc1b9b9dc516aca23f7db6335ca24a7
created_at: 2023-03-16 14:02:13
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"In an assessment using multiple lines of evidence, including models, observational constraints and improved understanding of climate \r\nsensitivity, WGI project a central estimate of the 20-year average warming crossing the 1.5degC GWL in the early 2030s in all scenarios \r\nassessed, except SSP5-8.5 (Lee et al., 2021). This is about 10 years earlier than the midpoint of the likely range (2030-2052) assessed in \r\nSR1.5, which assumed continuation of the observed warming rate reported at that time. However, this does not imply that the projected \r\nimpacts of 1.5degC will be reached 10 years earlier, because roughly half of the 10-year difference is a result of updating the diagnosed \r\nhistorical rate of warming due to methodological advances, new datasets and other improvements (Gulev et al., 2021)"


id: cc29fa7561bb203aeaba5522228b4978
created_at: 2023-03-16 14:02:13
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content:
" The other half of \r\nthe 10-year difference arises because, for central estimates of climate sensitivity, most scenarios show stronger warming over the near \r\nterm than was assessed as 'current' in SR1.5 (medium confidence)."


id: 8625f496b3d0e780030f2c8d460358d9
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"Pathways are one element of a larger scenario (O'Neill et  al., 2017), \r\nfocusing on just one element of a larger system of drivers, emissions \r\nor concentrations. Scenarios provide one means to represent deep \r\nuncertainty when there is disagreement or uncertainty about conceptual \r\nmodels (Cross-Chapter Box DEEP in Chapter 17; IPCC, 2019b). In addition, \r\nscenarios provide several important functions in decision support. A lack \r\nof strong association with probabilities enables scenarios to promote \r\nbuy-in from parties to a decision who hold different expectations about \r\nthe future, helping them to expand the range of futures and options \r\nthey consider"


id: 3c0c4c1fde410ec623656fd5e77ddf3c
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" The process of generating scenarios can serve as the focus \r\nof participatory stakeholder exercises and processes, and scenarios can \r\nalso be used to support risk management by stress-testing alternative \r\npolicies and identifying robust and adaptive policies under conditions of \r\ndeep uncertainty (Cross-Chapter Box DEEP in Chapter 17)."


id: ccfcfae8d458026e42b6b7e6c49dcff
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"resources. The key risk assessment in the IPCC AR5 informed the long\u0002term temperature goal in the 2015 Paris Agreement--limiting the \r\nincrease in global mean temperature to well below 2degC and pursuing \r\nefforts towards limiting warming to 1.5degC (Oppenheimer et al., 2014; \r\nPachauri et al., 2014). The IPCC Special Report on Global Warming of \r\n1.5degC, responding to an invitation by UNFCCC, used new scientific \r\ninformation to provide a specific risk assessment associated with the \r\nambitious warming levels targeted by the Paris Agreement (Hoegh\u0002Guldberg et al., 2019). The Special Reports on Oceans and Land further \r\nadvanced the methods of transparent risk assessment (Zommers et al., \r\n2020)"


id: d40c8cfd096e3e5d513fae909a4aece5
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" The current assessment expands significantly from the previous \r\nreports, aiming to inform and advance understanding of the following \r\ncore themes: (a) the ways changes in vulnerability and exposure \r\nmodulate risks of climate change impacts and risk complexity in \r\naddition to warming; (b) the knowledge basis relevant to continued \r\nrefinement of temperature goals; (c) the effectiveness of adaptation \r\nsolutions; (d) the management of risks at higher levels of warming, \r\nshould ambitious climate change mitigation be unsuccessful, including \r\nlimits to adaptation; and (e) the benefits of climate change mitigation \r\nand emissions reductions (Section 16.1)."


id: 133880ef6822da456714360527a047ba
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"To encompass the nature of climate risk, IPCC assessment since \r\nthe Third Assessment Report has used five overarching domains, \r\nnamed 'reasons for concern', to assess increasing risk for societies \r\nand ecosystems under climate change (IPCC, 2014b; O'Neill et  al., \r\n2017; see also Section  16.5; WGI Section  1.2.4.1). The reasons for \r\nconcern approach has enabled evidence to be combined with expert \r\njudgement, in order to provide a holistic assessment across multiple \r\nlines of evidence (O'Neill et al., 2017). The approach also respects the \r\nuncertainties inherent to climate risk and highlights the ways in which \r\nvalues are relevant in connecting scientific knowledge to societal \r\ndecision making and risk management"


id: d429ce74544536c0a00ba02c32c728ff
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" The different reasons for \r\nconcern underscore that there is no single metric that can reflect all \r\ndimensions of climate-related risk and the diversity of consequences \r\nfor lives and livelihoods, health and well-being, economic and \r\nsociocultural assets, infrastructure and ecosystems (Mach and Field, \r\n2017; see also Section 1.4.1.2)."


id: 2c6fe8ab6665a542b642a253d782ac16
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"magnitude of global warming, socioeconomic development pathways \r\nand levels of adaptation (Section  16.6). Risk levels are determined \r\nthrough a formal elicitation approach for both representative key \r\nrisks and reasons for concern, following the authors' assessment of \r\nthe literature. The reasons for concern consider unique and threatened \r\nsystems (RFC1), such as coral reefs or Arctic Sea ice systems that \r\nhave especially high vulnerability and low capacity to adapt. They \r\nalso include the role of extreme weather events (RFC2), such as heat \r\nwaves, heavy rain, drought, coastal flooding or wildfires. The reasons \r\nfor concern address both the distributional and the aggregate impacts\r\nof climate change (RFC3, RFC4), including the unfairness factor for \r\npopulations that have contributed little in terms of historic emissions \r\nbut that are disproportionately vulnerable to the impacts of a changing \r\nclimate"


id: 5a986d669717ebbe4bd67dded2ef9bc5
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" The final reason for concern relates to large-scale singular \r\nevents, nonlinearities and tipping points (RFC5), including ice sheet \r\ncollapse and ecosystem regime shifts."


id: a7185d4d763092c5cdd26945a5ecf898
created_at: 2023-03-16 14:02:13
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content:
"human and ecological systems, and also responses (see Section 1.2.1; AR6 \r\nGlossary, IPCC, 2021b; WGI AR6 Cross-Chapter Box 2 in Chapter 1, Chen \r\net al., 2021; Oppenheimer et al., 2014). The determinants of risk all can \r\nvary and change through space and time in response to socioeconomic \r\ndevelopment and decision making (Figures  1.4; 1.5; Section  16.1). \r\nHazards are affected by current and future changes in climate, including \r\naltered climate variability and shifts in frequency and intensity of \r\nextreme events (WGI AR6 Chapter 12, Ranasinghe et al., 2021). Such \r\nhazards can be sudden, for example, a heat wave or heavy rain event, or \r\nslower onset, for example, land loss, degradation and erosion linked to \r\nmultiple climate hazards compounding. The severity of climate change \r\nimpacts will depend strongly on vulnerability, which is also dynamic and \r\nincludes the sensitivity and adaptive capacity of affected human and \r\necological systems (McDowell et al"


id: 957f9e9abad513fbf8ee8faf44e44db1
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", 2016; Jurgilevich et al., 2017; Ford \r\net al., 2018; Viner et al., 2020). As a result, risks vary at fine scale across \r\ncommunities and societies and also among people within societies, \r\ndepending, for example, on intersecting inequalities and context-specific \r\nfactors such as culture, gender, religion, ability and disability, or ethnicity \r\n(Kuruppu, 2009; Jones and Boyd, 2011; Carr and Thompson, 2014; \r\nalso Section 16.1.4). The dynamic social distribution of impacts is the \r\nsubject of increasing attention within climate assessment and responses, \r\nincluding the role of adaptation, iterative risk management and climate \r\nresilient sustainable development (Section 16.1)."


id: d6297c46ecf8e70ae99cda856701cba4
created_at: 2023-03-16 14:02:13
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content:
"The nature of climate risk also involves risks from responses \r\nthemselves (Figure 1.5c). The risks of climate change responses include \r\nthe possibility of responses not achieving their intended objectives or \r\nhaving trade-offs or adverse side effects for other societal objectives \r\n(Annex II: Glossary; Section  16.1). In particular, human responses \r\nmay create novel hazards and unexpected side effects and entail \r\nopportunity costs and path dependencies (Boonstra, 2016). Such \r\nfeedback loops can unfold at local and global scales, including large\u0002scale interactions among climate, ecological and human systems with \r\nhuman behaviour and decision making affecting such interactions. \r\nResponse risks can originate from uncertainty in implementation, \r\nmaladaptation, action effectiveness, technology development or \r\nadoption, or transitions in systems (see Sections 1.4 and 1.5). Typical \r\nrisks may be related to regulation, litigation, competition, sociopolitics \r\nor reputation"


id: 7accf0568978381517c578d0c5c6a4c7
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content:
" Interactions across responses can importantly involve \r\nco-benefits for other objectives, such as for human health and \r\nwell-being which may be improved from both reduced air pollution \r\n(e.g., AR6 WGI Chapter 6, Szope et  al., 2021; WGIII, IPCC, 2022) \r\nand enhanced adaptation to climate change. The nature of risk also \r\nentails residual impacts that will occur even with ambitious societal "


id: c431d343067bfea4de36d243506632d0
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content:
"In contrast to previous assessments, AR6 is increasingly focused on the \r\nneeds for and challenges of assessing the societal response to climate \r\nchange. The accurate tabulation of adaptation, a key question for \r\nexamining the solution space, is difficult (Chapter 16; Cross-Chapter \r\nBox ADAPT in Chapter 1), since many forms of adaptation activity are \r\nunder-represented in the peer-reviewed and grey literature. Moreover, \r\nthe related question of assessing the effectiveness of adaptation, that \r\nis, the extent to which it reduces risk, is also difficult. Estimating risk \r\nreduction often involves counterfactuals, for instance, quantifying the \r\ndamage a flood would have caused had a community not adapted prior \r\nto a storm or projecting the damage averted by today's adaptation in \r\nsome future storm (see Cross-Chapter Box PROGRESS in Chapter 17)"


id: c34613d0baaee8695094406073fcefe2
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" \r\nMany socioeconomic drivers affect risk, so attribution for any observed \r\nor projected changes must be allocated among those that are due to \r\nadaptation and those due to economic development, cultural changes \r\nand other types of policies and trends. For instance, many measures of \r\nsustainable development overlap with those for adaptive capacity and \r\nboth can reduce climate risk while also yielding benefits irrespective \r\nof future climate regimes (UNEP, 2018). There are also many different \r\ngoals for adaptation both among and within different jurisdictions, so \r\nthat adaptation efforts deemed effective by some individuals may not \r\nbe deemed effective by others (Dilling et al., 2019)."


id: cab690f4bf32eb6c8bf408d940aa6756
created_at: 2023-03-16 14:02:13
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content:
"Since AR5, social science literature on how individuals and societies \r\nperceive and respond to climate risk has advanced dramatically (Renn, \r\n2008; Jones et al., 2014; Taylor et al., 2014; Neaves and Royer, 2017; Van \r\nValkengoed and Steg, 2019). The literature is increasingly integrating \r\nand advancing long-standing scholarship on environmental and \r\nsocial governance, human dimensions of environmental change, risk \r\nperception and communication, and enabling conditions for effective \r\npolicymaking. These emergent literatures on climate risk, human \r\naction and solution are reflected in three broad areas of analysis: \r\n(a) root drivers (i.e., role of cultural norms and social practice, social \r\nstructures and economic development status that shape physical \r\nand social vulnerability; (b) context-specific barriers and enablers \r\n(i.e"


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", governance structures, institutional structure and function, risk \r\nperceptions, access to financing and knowledge availability and needs) \r\nand (c) the solution-proximate decision space (i.e., climate urgency and \r\ncatalysing conditions, risk communication strategies, M&E strategies) \r\n(see Solecki et al., 2017; Jorgenson et al., 2019)."


id: e1340c3e309fd8b31928104ab8f17cfe
created_at: 2023-03-16 14:02:13
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content:
"While scientific knowledge is vital, IK and LK are also necessary for \r\nunderstanding and acting effectively on climate risk (IPCC, 2014a; IPCC, \r\n2019b, SROCC Chapter 1; see also Section 2.4). Indigenous knowledge\r\nrefers to the understandings, skills and philosophies developed by \r\nsocieties with long histories of interaction with their natural surroundings \r\n(IPCC, 2019a). Local knowledge is defined as the understandings and \r\nskills developed by individuals and populations, specific to the places \r\nwhere they live (IPCC, 2019a). These definitions relate to the debates on \r\nthe world's cultural diversity (UNESCO, 2018a), which are increasingly \r\nconnected to climate change debates (UNESCO, 2018b). However, there \r\nis agreement that, in the same way that there is not a unique definition \r\nof Indigenous Peoples because it depends on self-determination (see \r\nbelow), there is not a single definition of neither IK and LK. Therefore, \r\ncontextualisation is greatly needed"


id: b1b85cec7aa949ee657c4a799368e1d8
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" IK and LK will shape perceptions \r\nwhich are vital to managing climate risk in day-to-day activities and \r\nlonger-term actions."


id: 4eb2c815b8ecc1496962454f3bcdf574
created_at: 2023-03-16 14:02:13
updated_at: 2023-03-16 14:02:13
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content:
"The unambiguous framing of what is being attributed to what is a crucial first step for an assessment (Easterling et al., 2016; Hansen et al., \r\n2016; Stone et al., 2021), followed by the identification of the possible and plausible drivers of change and the development of a hypothesis \r\nor theory for the linkage (see Figure ATTRIBUTION.1 in Chapter 1). The next step is to clearly define the indicators of the observed change \r\nor event and note the quality of the observations. There has been significant progress in the compilation of fragmented and distributed \r\nobservational data, broadening and deepening the data basis for attribution research (Poloczanska et al., 2013; Ray et al., 2015; Cohen \r\net al., 2018; WGI Section 1.5). The quality of the observational record of drivers should also be considered (e.g., volcanic eruptions: WGI \r\nSection 2.2.2). Impacted systems also change in the absence of climate change"


id: 8388097d8edd1e38ce042f2be6431bbd
created_at: 2023-03-16 14:02:13
updated_at: 2023-03-16 14:02:13
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content:
" This baseline and its associated modifiers such as agricultural \r\ndevelopments or population growth need to be considered, alongside the exposure and vulnerability of people depending on these systems."


id: 2fa55fdddc555e3187730cc72309ac89
created_at: 2023-03-16 14:02:13
updated_at: 2023-03-16 14:02:13
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index: climate_gpt_only_giec
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content:
"Attribution of changes in atmospheric GHG concentrations to anthropogenic activity\r\nAR6 WGI Chapter 5 (Canadell et al., 2021) presents multiple lines of evidence that unequivocally establish the dominant role of \r\nhuman activities in the growth of atmospheric CO2, including through analysing changes in atmospheric carbon isotope ratios and the \r\natmospheric O2:N2 ratio (WGI Section 5.2.1.1, Canadell et al., 2021). Decomposition approaches can be used to attribute emissions \r\nunderlying those changes to various drivers such as population, energy efficiency, consumption or carbon intensity (Hoekstra and van \r\nden Bergh, 2003; Raupach et al., 2007; Rosa and Dietz, 2012). Combined with attribution of their climate outcomes, the attribution of \r\nthe sources of GHG emissions can inform the attribution of anthropogenic climate change to specific countries or actors (Matthews, \r\n2016; Otto et al., 2017; Skeie et al., 2017; Nauels et al"


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content:
"Changes in large-scale climate variables (e.g., global mean temperature) have been reliably attributed to anthropogenic and natural \r\nforcings (e.g., Hegerl et al., 2010; Bindoff and et al., 2014; WGI Section 1.3.4). The most established method is to identify the 'fingerprint' \r\nof the expected space-time response to a particular climate forcing agent such as the concentration of anthropogenically induced \r\nGHGs or aerosols, or natural variation of solar radiation. This technique disentangles the contribution of individual forcing agents to an \r\nobserved change (e.g., Gillett et al., 2021). New statistical approaches have been applied to better account for internal climate variability \r\nand the uncertainties in models and observations (e.g., Naveau et al., 2018; Santer et al., 2019; WGI Section 3.2)"


id: 1309859efad0097621fefbecea04cedb
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updated_at: 2023-03-16 14:02:13
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" There are many other \r\napproaches, for example, global mean sea level change has been attributed to anthropogenic climate forcing by attributing the individual \r\ncontributions from, for example, glacier melt or thermal expansion, while also examining which aspects of the observed change are \r\ninconsistent with internal variability (WGI Sections 3.5.2; 9.6.1.4)."


id: 1e29472af745e0829fa83ff78bb061a1
created_at: 2023-03-16 14:02:13
updated_at: 2023-03-16 14:02:13
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index: climate_gpt_only_giec
vector_id: 2184
content:
"New methods have emerged since AR5 to attribute the change in likelihood or characteristics of weather or climate events or classes of \r\nevents to underlying drivers (National Academies of Sciences, 2016; Stott et al., 2016; Jezequel et al., 2018; Wehner et al., 2019; Wang et \r\nal., 2020; WGI Sections 10.4.1; 11.2.2). Typically, historical changes, simulated under observed forcings, are compared to a counterfactual \r\nclimate simulated in the absence of anthropogenic forcing. Another approach examines facets of the weather and thermodynamic status \r\nof an event through process-based attribution (Hauser et al., 2016; Shepherd et al., 2018; Grose et al., 2020; WGI Section 10.4.1; WGI \r\nChapter 11). Events where attributable human influences have been found include hot and cold temperature extremes (including some \r\nwith widespread impacts), heavy precipitation, and certain types of droughts and tropical cyclones (e.g., Vogel et al., 2019; Herring et al., \r\n2021; WGI Section 11.9)"


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" Event attribution techniques have sometimes been extended to 'end-to-end' assessments from climate forcing \r\nto the impacts of events on natural or human systems (Otto et al., 2017; examples in Table 16.1; Section 16.2)."


id: 27caf6beaa28bc65e4ad980f3aba9e30
created_at: 2023-03-16 14:02:13
updated_at: 2023-03-16 14:02:13
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index: climate_gpt_only_giec
vector_id: 3669
content:
"Impact attribution covers a diverse set of qualitative and quantitative approaches, building on experimental approaches, observations from \r\nremote sensing, long-term in situ observations and monitoring efforts, teamed with LK, process understanding and empirical or dynamical \r\nmodelling (Section 16.2; Stone et al., 2013; Cramer et al., 2014). The attribution of a change in a natural or human system (e.g., wild species, \r\nnatural ecosystems, crop yields, economic development, infrastructure or human health) to changes in climate-related systems (i.e., climate \r\nand ocean acidification, permafrost thawing or sea level rise) requires accounting for other potential drivers of change, such as technological \r\nand economic changes in agriculture affecting crop production (Hochman et al., 2017; Butler et al., 2018), changes in human population \r\npatterns and vulnerability affecting flood or wildfire induced damages (Huggel et al., 2015; Sauer et al"


id: 63464a8ab54d4debd704dd9a64ea5481
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updated_at: 2023-03-16 14:02:13
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vector_id: 13187
content:
", 2021), or habitat loss driving declines \r\nin wild species (IPBES, 2019b). These drivers are accounted for by estimating a baseline condition that would exist in the absence of climate \r\nchange. The baseline might be stationary and be approximated by observations from the past, or it may change over time and be simulated \r\nby statistical or process-based impact models (Section 16.2; Cramer et al., 2014). Assessment of multiple independent lines of evidence, \r\ntaken together, can provide rigorous attribution when more quantitative approaches are not available (Parmesan et al., 2013). These include \r\npalaeodata, physiological and ecological experiments, natural 'experiments' from very long-term datasets indicating consistent responses \r\nto the same climate trend/event and 'fingerprints' in species' responses that are uniquely expected from climate change (e.g"


id: 78679d5bd5e87738190fb9ed82dae980
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", poleward \r\nrange boundaries expanding and equatorial range boundaries contracting in a coherent pattern worldwide, Parmesan and Yohe, 2003). \r\nMeta-analyses of species/ecosystem responses, when conducted with wide geographic coverage, also provide a globally coherent signal of \r\nclimate change at an appropriate scale for attribution to anthropogenic climate change (Parmesan and Yohe, 2003; Parmesan et al., 2013)."


id: 758fae81f13d328947bc737e89257bb1
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"Observations provided by the historical, archaeological, and palaeontological records, together with paleoclimatic data, demonstrate \r\nthat climatic variability has high potential to affect biodiversity and human society (high confidence). The evolution of the Earth's biota \r\nhas been punctuated by global biodiversity crises often triggered by rapid warming (high confidence) (Figure PALEO.1; Bond and Grasby, \r\n2017; Benton, 2018; Foster et al., 2018;). These so-called hyperthermal events were marked by rapid warming of >1degC, which coincided \r\nwith global disturbances of the carbon and water cycles, and by reduced oxygen and pH in seawater ( Foster et al., 2018; Clapham and \r\nRenne, 2019). Magnitudes of global temperature shifts in hyperthermal events were sometimes greater than those predicted for the \r\ncurrent century but extended over longer periods of time"


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" Rates inferred from paleo records that are coarsely resolved are inevitably lower \r\nthan those from direct observations during recent decades, and caution must be exercised when describing the rate of recent temperature \r\nchanges as unprecedented (Kemp et al., 2015). Mass extinctions, each with greater than 70% marine species extinctions, occurred when \r\nthe magnitude of temperature change exceeded 5.2degC (Song et al., 2021), albeit species extinctions occurred at lower magnitudes of \r\nwarming (medium confidence)."


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content:
"Responses of biota to rapid climate change have included range shifts (very high confidence), phenotypic plasticity (high confidence), \r\nevolutionary adaptation (medium confidence), and species extinctions, including mass extinctions (very high confidence). While \r\nknowledge about the relative roles of these processes in promoting survival during times of climate change is still limited (Nogues-Bravo \r\net al., 2018), they have influenced the evolutionary trajectories of species and entire ecosystems (high confidence), and also the course \r\nof human history (medium confidence)"


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" The combined ecological and evolutionary responses to ancient rapid warming events ranged \r\nfrom extinction of 81% of marine animal species and 70% of terrestrial tetrapod species on land at the end of the Permian period (~ \r\n252 million years ago, Ma) (Smith and Botha, 2005; Stanley, 2016) to low rates of species extinctions but biome- and range shifts on land \r\nand in the ocean at the Palaeocene-Eocene Thermal Maximum (PETM, ~ 56 Ma) (Figure PALEO.1; Ivany et al., 2018; Fraser and Lyons, \r\n2020; Huurdeman et al., 2021). Temperature and deoxygenation were key drivers of past biotic responses in the oceans (Gibbs et al., \r\n2016; Penn et al., 2018; Section 3.3) (high confidence), whereas on land the interplay between temperature and precipitation is less well \r\nestablished in ancient hyperthermals (Frank et al., 2021) (medium confidence)"


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" Climate-driven extinction risk increased by up to 40% \r\nwhen a short-term climate change added to a long-term trend in the same direction, for example when a long-term warming trend was \r\nfollowed by rapid warming (Mathes et al., 2021)."


id: cbdf8e2636dec434baa5f1af6701ab74
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"Population range shifts including migrations are common adaptations to climate changes across multiple time scales and ecological \r\nsystems in the past and in response to current warming (high confidence). Poleward expansions and retractions ( Reddin et al., 2018; \r\nWilliams et al., 2018; Fordham et al., 2020) as well as migration upslope and downslope in response to warming and cooling were \r\ncommon adaptations (Ortega-Rosas et al., 2008; Iglesias et al., 2018;). During warming periods, diversity loss was common near the \r\nequator (medium confidence)(Kiessling et al., 2012; Kroger, 2017; Yasuhara et al., 2020), while diversity gains and forest expansion \r\noccurred in high latitudes (Brovkin et al., 2021). Comparison of contemporary shells and skeletons with historical collections in museums \r\n(Barnes et al., 2011) and the analysis of skeletons of long-lived organisms (Cantin et al"


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", 2010) indicate significant climate-induced \r\nchange in organismic growth rates today (high agreement, medium confidence)."


id: 5ae6409cd0176609ec438f54e5d9163f
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"The emergence of agriculture (~10.2 ka) in southwest Asia was associated with stable (within +-1degC global mean annual on multi-century \r\ntime scale; WGI Chapter 2) warm and moist conditions (Richerson et al., 2001; Rohling et al., 2019; Palmisano et al., 2021). Variability in \r\nresource availability and agricultural production, entrained by climatic variability, is implicated in the disruption and decline of numerous \r\npast human societies (medium confidence) (d'Alpoim Guedes and Bocinsky, 2018; Cookson et al., 2019; Jones, 2019; Park et al., 2019). \r\nThese crises are partially caused by regional climate anomalies including Holocene 'Rapid Climate Change Events' (Rohling et al., 2019) \r\nnot visible in the globally averaged conditions shown in Figure PALAEO.2. Such anomalies affected human population size (Clark et al., \r\n2019; Kuil et al., 2019; Riris and Arroyo-Kalin, 2019), health (Campbell and Ludlow, 2020) and social stability/conflict (Buntgen et al., \r\n2011; Kohler et al"


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", 2014), and triggered migrations (D'Andrea et al., 2011; Schwindt et al., 2016; Chiotis, 2018; Pei et al., 2018) or retarded \r\nthem (Betti et al., 2020; FAQ 14.2). Populations have also been impacted by sea level change in coastal areas (Turney and Brown, 2007; \r\nCross-Chapter Box SLR in Chapter 3)."


id: 74428b08beab920833221d3b402d1dae
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"Evidence for widespread droughts ~4.2 ka, lasting for several centuries in some regions, has been tentatively linked to declines of the \r\nAkkadian Empire (Weiss, 2017; Carolin et al., 2019), the Indus Valley (Giosan et al., 2018; Sengupta et al., 2020), and the Egyptian \r\nOld Kingdom and Yangtze River Valley (Ran and Chen, 2019). Deteriorating climates often exacerbate accumulating weaknesses in \r\nsocial systems to which population growth and urban expansion contribute (Knapp and Manning, 2016; Lawrence et al., 2021; Scheffer \r\net al., 2021). The rather narrow climatic niche favoured by human societies over the last 6000 years is poised to move on the Earth's \r\nsurface at speeds unprecedented in this time span (IPCC, 2021a), with consequences for human well-being and migration that could be \r\nprofound under high-emission scenarios (Xu et al., 2020)"


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content:
" This will overturn the long-lasting stability of interactions between humans and \r\ndomesticated plants and animals as well as challenge the habitability for humans in several world regions (Horton et al., 2021) (medium \r\nconfidence)."


id: 878da389ded3f82edadfd30b380c83ce
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"Indigenous Peoples have been faced with adaptation challenges \r\nfor centuries and have developed coping strategies in changing \r\nenvironments (Coates, 2004). Along with other local groups, they hold \r\nrelevant knowledge about the environment and environmental change, \r\nthe impact of those changes on ecosystems and livelihoods, and \r\npossible effective adaptive responses (see Cross-Chapter Box  INDIG \r\nin Chapter 18). Therefore, the participation of Indigenous Peoples in \r\nclimate change decisions and the inclusion of Indigenous knowledge \r\nin the IPCC assessment process should be of high priority (following \r\nrecommendations in UNESCO, 2018b, and UN, 2020). Furthermore, the \r\nparticipation of scientifically trained climate specialists with indigenous \r\nbackgrounds is valuable to the work of IPCC because the assessment \r\nmust reflect a diverse range of views and expertise (for examples of \r\nIK, see Cross-Chapter Box INDIG in Chapter 18)"


id: 7b2c67769b7c2949c4a152fcfdb5706e
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" Article 31 of the UN \r\nDeclaration on the Rights of Indigenous Peoples (2007) supports the \r\ninclusion of IK and LK in the IPCC assessment process, calling for the \r\nuse of IK and LK to be protected and validated by Indigenous Peoples \r\nthemselves and their inclusion as active participants in the assessment \r\n(Klenk et  al., 2017). Paying special attention to the mechanism \r\nwhereby some forms of knowledge have been excluded in previous \r\nreports--such as the use of technical knowledge or acronyms, or the \r\ndeployment of discipline-specific validation mechanism--is a first step "


id: 7fd7474b29e2a6ff9ec544a7d769dbb3
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"Moreover, IK and LK may be particularly relevant to ensuring that \r\nclimate action does not cause further harm, and also addresses \r\nhistorical injustices committed against Indigenous Peoples and other \r\nmarginalised social groups, recognising them as active agents of their \r\nown change (Nursey-Bray et  al., 2019). There are between 370 and \r\n500 million people in at least 90 countries belonging to about 5,000 \r\ndifferent ethnic groups that are classified as 'Indigenous' (Sangha et al., \r\n2019). Although there is no single, universal definition of Indigenous \r\nPeoples, core criterion within both the ILO Convention on Indigenous \r\nand Tribal Peoples (1989) and the UN Declaration on the Rights of \r\nIndigenous Peoples (UN, 2007) include: (a) self-determination and (b) \r\nthe recognition that Indigenous Peoples as distinct social and cultural \r\ngroups that retain collective ancestral ties to the lands they inhabited \r\nor to the lands from which they have been displaced"


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" Indigenous \r\nPeoples attribute cultural and spiritual values to land, environmental \r\nfeatures and landscapes (ILO, 2013; ILO, 2019). Indigenous Peoples \r\nsuffer disproportionally. For example, they are three times more likely \r\nto live in extreme poverty than non-Indigenous Peoples; they are also \r\nmore likely to suffer discrimination and violence (UN, 2020). At the \r\nsame time, Indigenous Peoples have long led climate change and \r\nenvironmental protection agendas."


id: 5496fb8c4a7fab52db317dd7eb7fd1c0
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"The AR4 was the first IPCC report to explicitly discuss the value of IK \r\nand LK in adaptation and mitigation processes. AR5 recognised the \r\nimportance of creating synergies across disciplines in the production \r\nof knowledge, acknowledging the importance of 'non-scientific' \r\nsources such as IK, which may not follow discipline conventions but \r\nnevertheless reflects the outcomes of learning across generations \r\n(Burkett et al., 2014). This also explains the importance of including IK \r\nand LK and diverse stakeholder interests and values in local decision \r\nmaking processes (Jones et  al., 2014). Such processes should be \r\ndone in partnership with IK and LK knowledge holders and, when \r\npossible, be led by them (Inuit Tapiriit Kanatami, 2018). Recent IPCC \r\nreports have included distinct sections dedicated to IK and LK (e.g., \r\nIPCC, 2019b)"


id: 10182c613f5a787d27bd25a27589ca9d
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" The IPCC Special Report on Climate Change and Land \r\n(SRCCL) includes a section on 'Local and Indigenous knowledge for \r\naddressing land degradation' (2019a) and the IPCC Special Report \r\non Ocean and Cryosphere (SROCC) describes LK as 'what non\u0002Indigenous communities, both rural and urban, use on a daily and \r\nlifelong basis,' a type of knowledge which is recognised as 'multi\u0002generational, embedded in community practices and cultures, and \r\nadaptive to changing conditions' (2019b). The IPCC Special Report \r\non Global Warming of 1.5degC emphasised the high vulnerability of \r\nIndigenous Peoples to climate change. It stated that disadvantaged \r\nand vulnerable populations, including Indigenous Peoples and certain \r\nlocal communities, are at disproportionately higher risk of suffering \r\nadverse consequences with global warming of 1.5degC and beyond \r\n(IPCC, 2018b)"


id: 3a9c9f4a968b6ea9f93f6934043564d7
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" The report also assessed evidence in relation to the \r\nimportance of including IK and LK in adaptation options, explaining \r\ntheir role in early warning systems and arguing that they are part of a \r\nrange of approaches to catalyse wide-scale values and are consistent \r\nwith adapting to and limiting global warming to 1.5degC (IPCC, 2018b)."


id: b4f93dbb56b2f412218395529dbf3afd
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content:
"Since AR5, several academic publications have directly addressed the \r\nchallenges of including IK and LK in climate research ( Ford et al., 2016; \r\nYeh, 2016; David-Chavez and Gavin, 2018) and demonstrated its value \r\nin building resilience to extreme events related to climate change \r\n(Janif et al., 2016; Olazabal et al., 2021). For instance, IK and LK has \r\nproved useful in land management methods that reduce wildfire risk \r\n(Nepstad et al., 2006; Cook et al., 2012; Welch et al., 2013; Mistry et al., \r\n2016). Since IK is traditionally communicated through storytelling \r\nand oral history, there are practical challenges to integrating it into \r\nan assessment that prioritises scientific knowledge. There is a need \r\nfor increased critical engagement towards the co-production of \r\nknowledge (Ford et al., 2016). Scholars now recognise the ontological \r\nand epistemological differences in approaches, understandings and \r\neffects of climate change (Yeh, 2016)"


id: f104ce9939a6fd9f0294eec5a5c52bcd
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" One common strategy has \r\nbeen assessing Indigenous observations of climate change alongside \r\nscientific data (Klein et  al., 2014a) as a means to bridge the gap \r\nbetween scientific inquiry and Indigenous knowledge systems \r\n(Fernandez-Llamazares et  al., 2017). The Intergovernmental Science\u0002Policy Platform on Biodiversity and Ecosystem Services (IPBES) and \r\nthe CBD have helped illustrate how to bridge multiple knowledge \r\nsystems, particularly those conceived from different ontologies. Rather \r\nthan viewing IK as a single source of knowledge to be compared with \r\nscientific data, recent scholarship suggests assessments, such as the "


id: 9e16f28ab5e933acf78eaf8a411cfbcf
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"The guidance to authors additionally identifies other practices and \r\napproaches relevant in applying expert judgement and developing \r\nassessment findings (Mastrandrea et al., 2010; Mastrandrea and Mach, \r\n2011; Mach and Field, 2017). First, authors are encouraged to carefully \r\nconsider appropriate generalisation within assessment findings, \r\nemphasising insights that are integrative, nuanced and rigorous (IAC, \r\n2010; Mastrandrea et  al., 2010; NEAA, 2010). Second, authors are \r\ninstructed to attend to potential biases, including in group dynamics, \r\nsuch as tendencies towards overconfidence and anchoring or Type I \r\n(false positive) error aversion (Mastrandrea et al., 2010; Brysse et al., \r\n2013; Anderegg et al., 2014; Morgan, 2014). Third, particular attention \r\nis drawn to the importance of evaluating and communicating ranges \r\nof potential outcomes to inform decision making and risk management \r\n(Mastrandrea et al., 2010)"


id: 37938c5b5c7ba0d819b38cbf43c68061
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" In some cases, deep uncertainties related to \r\nparameters or processes that are unknown or disagreed upon strongly \r\nbenefit from dedicated methods of assessment and decision support \r\n(see Cross-Chapter Box  DEEP in Chapter 17). Fourth, the guidance \r\nexplores the different ways that framings of conclusions can shape \r\ntheir interpretation by readers. Finally, the guidance underscores \r\nthe importance of reflecting upon all sources of uncertainty, which \r\ncan include deep, difficult-to-quantify and easy-to-underestimate \r\nuncertainties arising from incomplete understanding of relevant \r\nprocesses or competing conceptualisations across the literature \r\n(Mastrandrea et  al., 2010). A detailed review of literature assessing \r\nIPCC uncertainty characterisation methods is provided in WGI 1.2.3.1."


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"Procedural justice addresses the fairness of the processes by which \r\ndecisions are made and the legitimacy of those making the decisions \r\n(Gutmann and Thompson, 2009; Kitcher, 2011). Criteria include \r\ntransparency, the application of neutral principles among parties, \r\nrespect for participants' rights and inclusive participation in decision \r\nmaking, which often takes the form of participatory processes. Article \r\n6 of the Framework Convention creates a binding commitment on \r\nparties to promote public participation in addressing climate change. \r\nIncreased participation by civil society in climate policy discussion, \r\nincluding new forums such as the Local Communities and Indigenous \r\nPeople's Platform of the UNFCCC work toward this goal (UNFCCC, \r\n2021). Genuine, not merely formal, participation requires communities \r\nbe well-acquainted with the climate change risks they face and are \r\ngiven a full voice in the process of adaptation planning"


id: 32dd43728a874a9783d52a908b60c7f1
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" Many local \r\ncommunities, especially those most vulnerable to climate change, \r\nremain excluded, which is inconsistent with principles of procedural \r\njustice. In addition to a normative principle, models of decision making \r\nalso suggest that diverse, representative decision makers can be \r\nexpected to make better decisions than more limited groups (Hong \r\nand Page, 2004; Landemore, 2013; Singer, 2019)."


id: b06ac8cd853503f074e7b8589812dc7
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content:
"The multi-criteria concept of well-being has been increasingly \r\nemployed as a structured framework for measuring social progress \r\nin many areas of public policy (Lamb and Steinberger, 2017) including \r\nclimate and health (Chapter 7) and, to a lesser extent, in other areas \r\nof the climate change adaptation literature (Singh et al., 2021). Well\u0002being reflects the ability of a person to pursue and realise the goals \r\nthat they value (Sen, 1985). The disaster risk management community \r\nemploys well-being to evaluate mental health impacts in terms of \r\npeoples' abilities to cope with trauma and loss because of natural \r\ndisasters (Berry et  al., 2010; MacDonald et  al., 2015; Willox et  al., \r\n2015). The term appears in the literature with concepts such as \r\nhuman security (Koren and Butler, 2006; Adger, 2010; Pasgaard et al., \r\n2017), subjective well-being or happiness (Sekulova and van den \r\nBergh, 2013; Rehdanz et al"


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", 2015; Fanning and O'Neill, 2019), welfare \r\n(Gough, 2015) and living standards or quality of life (Degorska and \r\nDegorski, 2018; Rao and Min, 2018)."


id: 73c574d0b7b682f3d38498ca92cebab9
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content:
"Recent work has used quantified measures of well-being and multi\u0002objective decision-support tools to balance among equity and efficiency \r\nobjectives in disaster risk management (Section  1.5.2; Chapter 17; \r\nMarkhvida et al., 2020). Rather than focus on the economic value of \r\nlost assets, the well-being measure evaluates disaster impacts and \r\nrecovery policies by considering the fraction of consumption lost at \r\nthe household level for different income cohorts. Not surprisingly, poor \r\nhouseholds account for twice as much of the disaster losses when \r\nevaluated by effects on well-being rather than by asset losses. The \r\nmost effective policy responses also differ when using well-being and \r\nasset loss-based measures. Ciullo et. al"


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content:
" (2020) compare flood control \r\nstrategies using multi-objective decision criteria that include both \r\nbenefit-cost and distributional components, show how the favoured \r\nstrategy can depend on whether one seeks equitable risk or equitable \r\nrisk reduction, and propose tools that can help embed both ethical \r\nand efficiency considerations in adaptation decisions. Widespread use \r\nof such approaches could strengthen consideration of climate justice \r\nalong with efficiency in the evaluation of climate risks and adaptation \r\n(Section 1.5.2; Dryzek et al., 2013)."


id: 7647e702aa4674044952a2d7ba83cf84
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content:
"Aggregate, monetised economic measures are, however, insufficient \r\nto address issues of climate justice fully or to reflect that wide range \r\nof worldviews and values that different people bring to questions of \r\nclimate action and development (Chambwera et al., 2014). While recent \r\nwork has enriched the consideration of distributive justice in aggregate \r\nsocial welfare functions (Adler, 2012), multi-objective approaches that \r\nseparately report several biophysical and socioeconomic attributes \r\ncan prove valuable (Section  17.3.3). Many adaptation measures, in \r\nparticular those that encompass transformational social changes \r\n(Section 1.5), involve complicated trade-offs among multi-dimensional \r\nbenefits and costs (Adger, 2016). Different people commonly value \r\nsuch trade-offs differently, particularly in heterogeneous societies"


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" \r\nMulti-objective measures can thus enhance transparency, fairness, \r\nlegitimacy and participation by highlighting the different outcomes \r\nthat different people and communities might find important, making \r\nthe specific trade-offs more transparent and explicit, and avoiding \r\nprivileging any particular view on the appropriate trade-offs (Lempert \r\net al., 2018; Siders, 2019b; Siders and Keenan, 2020)."


id: d7be0cd1f84e01133e8522994575bde2
created_at: 2023-03-16 14:02:13
updated_at: 2023-03-16 14:02:13
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"Adaptation actions taken by individuals, social groups and organisations \r\nin response to climate and environmental stimuli depend, in part, on \r\nthe options they have (see Chapters 16 and 17). Actions previously \r\ntaken can reduce the scale of responses needed subsequently, increase \r\nthe options available, reduce barriers to additional action and increase \r\ncapacity to respond. Successful adaptation sufficient to meet the goals \r\nof the Paris Agreement and SDGs needs to involve actors at many \r\nscales and in many sectors, including individuals and households, \r\ncommunities, governments at all levels, private sector businesses, non\u0002governmental organisations, religious groups and social movements. \r\nThis report highlights the increased range of societal actors engaged \r\nin adaptation and the need for multi-level and polycentric governance"


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" \r\nThe section describes key concepts related to the process of adaptation \r\nand assessment of how human choices and exogenous changes can \r\nexpand and contract the set of available solutions."


id: 8b1fd7da40cb7db54056bb159b7f4f9e
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"Previous IPCC reports have described in detail adaptation for individual \r\nactors as an iterative risk management process of scoping (identifying \r\nrisks, vulnerabilities, objectives and decision-making criteria), \r\nanalysis (identifying options, assessing risks, evaluating trade-offs) \r\nand implementation (implementing chosen options, monitoring, and \r\nreviewing and learning) (Jones et. al. 2014). This AR6 report expands \r\nthe focus to consider adaptation processes with multiple actors and \r\na richer temporal dimension in which actions taken at one time can \r\nexpand or contract the set of feasible, effective and just options \r\navailable at another time, thereby increasing or decreasing the ability of \r\nadaptation to reduce risks (Section 17.1)"


id: e21649d3230cbe137be36eda9c3a1e32
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" This AR6 report also expands \r\nthe focus to include decision processes the implement both adaptation \r\nand mitigation (Chapter 18), as well as heightened attention to M&E, \r\nwhich is a key prerequisite for successful iterative risk management \r\nand achieving effective and just adaptation outcomes at local to \r\nglobal levels (Sections  1.4.3; 17.5.2). The challenges of implications \r\nfor adaptation, mitigation and sustainable development outcomes \r\nresult from decision-making process at different levels (Von Stechow \r\net al., 2015; Bertram et al., 2016). Overcoming these challenges often \r\nrequires significant learning and innovative ways of linking science, \r\npractice and policy at all scales (Shaw and Kristjanson, 2014)."


id: c178ea0bf3af4df6bbe424a5ebab7ee4
created_at: 2023-03-16 14:02:13
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"Adaptation actions span a vast range of activities. Successful adaptation \r\ngenerally requires a portfolio of actions, often implemented by multiple \r\nactors in different sectors, often in different places and over time \r\n(Section  17.2.2). Useful taxonomies include categorising such actions \r\naround representative key risks (Figure  17.3), and by human systems \r\nand scenarios of adaptation extent for four components of adaptation \r\n(depth, scope, speed and limits) (Table 16.2). As shown in Chapter 17, for \r\ninstance, ecosystem-based adaptation, hardening buildings and physical \r\nbarriers, and changes to zoning and planned retreat can reduce risks to \r\ncoastal socio-ecological systems. Restoration and protection of forests, \r\nenhancing ecosystem connectivity through corridors and ecosystem\u0002based adaptation can reduce risks to terrestrial and ocean ecosystems"


id: dcd9ea61eb82a47e0d397c769540840f
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" \r\nIncreased use of grey, green and blue infrastructure and upgrading \r\ndesign standards, city plans and more redundancy in power systems and \r\nother networks can reduce risks associated with critical infrastructure. \r\nInsurance and diversified or changed livelihoods can reduce risks to "


id: 12072c76ff4a3eeb250d1a407e36145
created_at: 2023-03-16 14:02:13
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"According to the UNFCCC, adaptation finance includes public, private and alternative sources of finance for supporting adaptation \r\nactions, whereby adaptation and resilience are often used interchangeably in this context. Adaptation finance constitutes a crucial \r\nenabling condition and shaper of the solution space, depending on other enabling conditions, such as proper planning, implementation \r\nand governance, which are also the triggers for investments and finance to flow, ensuring positive adaptation outcomes. Details of \r\nadaptation finance can be found in Chapter 17 (Cross-Chapter Box FINANCE in Chapter 17; Section 17.4). The adaptation and resilience \r\noptions offer multiple benefits, including avoiding risks and losses, economic growth and well-being, as well as social and environmental \r\nbenefits (Agrawal and Lemos, 2015; Bayleyegn et al., 2018; Global Commission on Adaptation, 2019)"


id: 9d6d26034d3e3fc7129b28c79d2f4817
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" Hence, the rate of return on \r\nadaptation is large; for example, there is a huge potential of net benefits, that is USD 7.1 trillion while investing USD 1.8 trillion globally \r\nin climate resilience and adaptation options, such as early warning systems, climate-resilient infrastructure, improved dryland agriculture \r\ncrop production, global mangrove protection and building resilience of water resources (Global Commission on Adaptation, 2019). These \r\nnet benefits resulted primarily from reducing future losses and risk, increasing productivity and innovation, and social and environmental \r\nbenefits. Despite significant uncertainty and concerns over undue focus on efficiency (monetary), benefit-cost estimates often ignore \r\nimportant issues of non-economic values, effectiveness of risk reduction and climate justice (procedural/distributional)."


id: 5429ce5850ca54c3d995b2259b2ded71
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"The current public and private financial flows to adaptation are much smaller than needed (Cross-Chapter Box FINANCE in Chapter 17). Only \r\na small portion of overall adaptation finance needs is likely to be covered by public sector finance. Private sector investment thus needs to \r\nplay a crucial role. Hence, tracking adaptation finance flows is important for enabling effective planning and prioritisation of investments, \r\nassessing whether needs are being met, and ensuring accountability towards funding commitments, such as the USD 100 billion promised \r\nto developing countries per year by 2020 under the Paris Agreement (Donner et al., 2016)"


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" Since AR5, significant progress has been made \r\nin tracking adaptation finance flows through UNFCCC channels, multi-lateral development banks and bilateral finance (Cross-Chapter \r\nBox FINANCE in Chapter 17), but large information gaps on adaptation finance via national public finance, commercial lenders, investors, \r\nasset managers and insurers, company finance, and individuals and households remain. That these financial flows do not occur suggests \r\nmisaligned incentives and other governance challenges that could be addressed as part of a response to climate change (Chapter 17). \r\nAcross regions and sectors, financial constraints have been identified as the most common and important determinants leading to limits \r\nto adaptation (Chapter 16)."


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"Nature-based solutions (NbS) (Section 2.6; Cross-Chapter Box NATURAL in Chapter 2) provides an example of how innovative ideas \r\ncan expand the climate solution space (IPCC, 2018b; Seddon et al., 2019). A commonly used definition of NbS is that of The World \r\nConservation Union (IUCN), which defines it as 'actions to protect, sustainably manage, and restore natural or modified ecosystems, that \r\naddress societal challenges effectively and adaptively, simultaneously providing human well-being and biodiversity benefits' (Cohen\u0002Shacham et al., 2016). In the context of IPCC, it focuses on NbS which deliver climate change adaptation or mitigation benefits. NbS \r\ngenerally benefits biodiversity and supports its role in both climate mitigation and adaptation"


id: 8f385ee5d5f46fcc357e7f5cf10519b5
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" While the carbon-sequestering mitigation \r\nrole of increasing forest and tree cover has dominated much of the earlier discussions, the role of NbS in promoting adaptation of natural \r\necosystems and human societies to climate change is being increasingly emphasised. The details of different categories of ecosystem \r\nservices in the ocean or on land, including biodiversity, food provision, other provisioning services (e.g., medicinal and commercial \r\nproducts), and regulating and cultural services are described in Chapters 2 and 3."


id: 19969c7b1348a9641e0a86121ff720a8
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"Forest restoration would certainly contribute substantially towards climate-proofing and achievement of several SDGs as well as the Paris \r\nAgreement. There is increasing evidence that diverse, native tree species plantations are more likely to be resilient to climate change in \r\ncontrast to fast-growing monocultures, (Hulvey et al., 2013) often of exotic species. At the same time, other natural ecosystems such as \r\nsavannas, grasslands, peatlands, wetlands and mangroves have considerable value in acting as carbon sinks as well as providing other \r\necosystem services such as hydrological regulation, coastal protection, maintaining biodiversity and contributing to human livelihoods \r\nespecially pastoralists and fishermen (Veldman et al., 2015; Conant et al., 2017; Leifeld and Menichetti, 2018; Seddon et al., 2019)"


id: cdaa4ec9eebd80883fa4feb1b8e05faa
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" \r\nCoastal and marine ecosystems including wetlands and mangroves have featured prominently in studies of NbS in climate adaptation \r\nand mitigation potential for 'blue carbon' sequestration (Inoue, 2019; Sections 3.6.2.1; 6.3.3; Cross-Chapter Paper 2.3.2.3). Agroecological \r\npractices such as agroforestry, intercropping, rotational grazing, organic manuring, and integrating livestock production with cropping \r\netc can also consider as NbS which contribute to both climate mitigation and adaptation (Altieri and Nicholls, 2017; Webb et al., 2017; \r\nBezner Kerr et al., 2019; Leakey, 2020; Box 5.10)."


id: f725d72d182df8a1a0e4740003705709
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"Much of the conceptual framework for NbS has come from initiatives to bring environmental, social and economic dimensions to the \r\nsame level of importance, particularly in the context of a highly urbanised society (Section 6.3.3; Faivre et al., 2017; Nesshover et al., \r\n2017). Emphasis has been placed on urban storm water management (Section 2.6.5.2) and heat mitigation using measures, such as \r\nsustainable drainage systems, urban forests, parks and green roof-tops, apart from coastal defences using NbS (Section 13.6.2.3) This has \r\ntriggered much debate on how distinct the concept of NbS is in relation to other similar concepts, such as ecosystem-based adaptation \r\n(EbA) approaches (Section 9.11.4.2). There are calls for an assessment framework for proving the 'effectiveness and efficiency' of NbS \r\nin providing superior ecosystem and societal benefits (Calliari et al., 2019). Instead, EbA can be treated as a subset of NbS (Chapter 2)"


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" \r\nHowever, the time frame of EbA is also an important consideration; thus, grassland and forest restoration would operate at different \r\ntime scales, while mangrove restoration can promote adaptation only at local to national scales, depending on the extent and nature of \r\ncoastlines (Taillardat et al., 2018). Given the complex nature of plant and animal species adapting to climate change through dispersal \r\nand migration to more suitable habitat, this also means that landscape-scale approaches, as opposed to purely protected areas, are \r\nneeded to promote adaptation, conserve and sustainably use biodiversity, and sustain livelihoods (Vos et al., 2008; Sukumar et al., 2016)."


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"The concept of the solution space provides a framework for assessing \r\nhow the options available for adaptation for any particular community \r\nare not constant over time and can depend on the past, current and \r\nfuture choices of many actors. The solution space is defined as the \r\nspace within which opportunities and constraints determine why, \r\nhow, when and who adapts to climate risks (Haasnoot et al., 2020). \r\nThe concept aims to capture the dynamic inter-temporal, spatial and \r\njurisdictional interconnections among adaptation actions. A larger \r\nsolution space indicates people and organisations with more options \r\nfor adapting to and reducing their risk from climate change. Both \r\nhuman choices and exogenous changes in human and natural systems \r\naffect the future solution space. For instance, changes such as the \r\nmagnitude and rate of climate change may shrink the space"


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" Economic \r\ngrowth can generate more resources that expand the solution space as \r\ncan implemented adaptation actions such as pilot projects, awareness \r\nraising and changes in laws and regulations."


id: a8822d7adbdfe3558d3ed96366f8f470
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"The empirical literature on adaptation governance has advanced \r\nstrongly since AR5. It shows that stronger general governance \r\ncapabilities are usually associated with more ambitious adaptation \r\nplans and more effective implementation of such plans (UNEP, 2014; \r\nChen et al., 2016; Keskitalo and Preston, 2019b: 24; ND-GAIN, 2019; \r\nOberlack, 2017; Oberlack and Eisenack, 2018; Woodruff and Regan, \r\n2019; UNEP, 2018; UNEP et al., 2021). Governance capabilities are, to a \r\nsignificant degree, but not exclusively, a function of available financial \r\nresources and technology. They are also a function of social capital \r\nand societal institutions, including well-functioning local, regional and \r\nnational governments, and collaboration among these governmental \r\nactors and non-governmental stakeholders, including civil society and \r\nthe private sector. The literature also points to governance conditions \r\nthat are likely to enable transformational adaptation (Maor et  al"


id: 7da9f73f38c7325754089b3315e06828
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"AR5 used the concept of solution space in its SPM Figure  8 (IPCC, \r\n2014c). Several AR6 chapters, in particular Chapters 13, 14 and 18, use \r\nthe concept to address challenges salient in AR6. In any assessment \r\nof solutions, what is feasible, effective and just depends not only \r\non the potential solution itself but the particular biophysical and \r\nsocietal context in which it might occur (Section  17.5; Wise et  al., \r\n2014; Gorddard et  al., 2016). Solutions can also be space and time \r\ndependent because the biophysical and societal context can change \r\nover space and time (Section 18.1.4). In addition, the large gap that \r\nexists between current climate action and that needed to meet policy \r\ngoals suggests that decision makers may not only seek to implement \r\navailable solutions but seek to actively expand the set of solutions \r\n(Chapters 17; 18)"


id: 7a91cee7effdd303207a6827f76faa5d
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" Finally, as used in this report, the concept of solution \r\ndoes not fully engage with questions of 'by whom?' and 'for whom?' In \r\nmany cases solutions would necessarily be implemented by multiple, \r\nindependent actors interacting with varying degrees of cooperation \r\nand competition (Sections 1.5.2)."


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"Existing comparative data for adaptive capacity worldwide is at a rather \r\ncoarse level of temporal and spatial resolution. It can, nonetheless, \r\nprovide a very general picture of rates of change in adaptive capacity at \r\nthe national scale, and differences between countries. Further empirical \r\nresearch is needed to identify the most important predictors of variation \r\nacross countries and time, though the available data suggests that \r\ndiffering national income and education levels play a major role in \r\naccounting for differences in adaptive capacity (Andrijevic et al., 2020). \r\nSpatially more resolved (sub-national) data is needed because a large \r\nbody of case study research suggests that there is strong variation within \r\ncountries, particularly the large ones (e.g., India, China, Brazil, United \r\nStates) (Chapter 16; see also Nalau and Verrall, 2021 and Cross-Chapter \r\nBox  ADAPT)"


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" Moreover, higher degrees of adaptive capacity do not \r\nmean that adaptation action will follow automatically, nor that it will \r\nsucceed in terms of equity and effectiveness in reducing vulnerability \r\nto climate change and enhancing well-being. How differences across \r\nand within countries in climate risk exposure translate into adaptation \r\naction, contingent on differences in adaptive capacity, can to some \r\nextent be inferred from case studies, but this remains to be studied at a \r\nlarger, comparable and generalisable scale."


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"Governance and governing refer to the structures, processes and actions \r\nthrough which private and public actors interact to address societal \r\ngoals. This includes formal and informal institutions and the associated \r\nnorms, rules, laws and procedures for deciding, managing, implementing \r\nand monitoring policies and measures at any geographic or political \r\nscale, from global to local. Governance systems and the specific societal \r\ninstitutions through which they are organised are crucial to the feasibility \r\nand success of climate change adaptation, both in terms of its effectiveness \r\nin reducing climate risk and vulnerability, as well as equity (including \r\nclimate justice), with respect to incremental as well as transformational \r\nadaptation"


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" This is why AR6 WGII pays even more attention than previous \r\nassessments to governance as an important enabling condition, and to \r\nthe wide range of new actors beyond governments involved in planning, \r\nimplementing, monitoring and evaluating adaptation action. The \r\nassessments in subsequent chapters of AR6 WGII show that successful \r\nand equitable collective adaptation efforts at different levels and scales, \r\nbased on key principles of iterative risk management, require strong, \r\nusually multi-level. governance systems. Multi-level governance refers \r\nto the dispersion of governance across multiple levels of jurisdiction and \r\ndecision making, including global, regional, national and local, as well as "


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"to evolve, as does understanding of appropriate response options and \r\nthe actors involved. In addition, norms, values and practices may change \r\nin response to changing conditions (Jones et al., 2014). Much learning \r\nby individuals, communities and organisations is unplanned (National \r\nResearch Council, 2009), as is much current adaptation (Berrang-Ford, \r\n2020), which can reduce near-term costs and administrative complexity, \r\nbut may prove maladaptive (Section 1.4.2.4). Iterative risk management \r\n(Section 1.2.1.1) and related concepts such as risk governance (Renn, \r\n2008) describe a planned learning process of ongoing assessment, \r\naction and reassessment. Iterative risk management can be as simple \r\nas scheduling future updates of assessments and plans, as with the \r\n5-year updates of the global stocktake after 2023 called for in the \r\nParis Agreement, or encompass more elaborate learning processes, \r\nsuch as dynamic adaptive pathways (Haasnoot et al"


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", 2019b; Cross\u0002Chapter Box  DEEP in Chapter 17) which include specific near-term \r\nactions, specific trends to monitor and specific contingency actions to \r\ntake depending on the future conditions observed. While often more \r\neffective at meeting goals, such planned learning processes may pose \r\nimplementation and governance challenges (Metzger et al., 2021)"


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"Maladaptation can result from many potential barriers, including \r\nadministrative, human, financial and technical resource constraints \r\n(Hassanali, 2017; Pardoe et  al., 2018; Singh et  al., 2018); lack of \r\ntransparency and/or capacity in governance (Friend et  al., 2014); \r\nunreliable information on climate impacts and the lack of key \r\npolicy guidelines (Pilato et  al., 2018); entrenched institutional, legal \r\nand technical obstacles (Gao, 2018) and low literacy, including \r\nenvironmental and scientific literacy (Wright et  al., 2014); exclusion \r\nof vulnerable groups (Forsyth, 2018); governance fragmentation (that \r\nis, a fragmentation of laws, regulations, and policy requirements); \r\nand limited cross-sectoral collaboration, meaning that there is \r\nlimited coordination and that top-down planning approaches are not \r\nconnected to local dynamics (Archer et al., 2014; Pardoe et al., 2018)"


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" \r\nThis report draws attention to maladaptation challenges recognising \r\nthat not all adaptation-related responses reduce risks (Chapter 16). \r\nBesides, maladaptation is the opposite of successful adaptation, which \r\nis associated with reduction of climate risks and vulnerabilities for \r\nhumans and ecosystems, increased well-being and co-benefits with \r\nother sustainable development objectives. (Chapter 17)"


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"Academic work on adaptation now spans an increasing number of disciplines and countries and is published across diverse academic \r\noutlets and disciplines, with 28.5% annual average increase in adaptation specific publications (Nalau and Verrall, 2021). This expands \r\nthe range of considerations and perspectives within adaptation research and increases the challenge of identifying and synthesising all \r\nrelevant research on adaptation in reviews or assessments (Berrang-Ford et al., 2015; Webber, 2016; Singh et al., 2020; Sietsma et al., \r\n2021). Also, large bodies of research and knowledge exist that support climate adaptation ideas, theoretical development and practical \r\nimplementation, but are not explicitly framed as climate change adaptation (Dupuis and Biesbroek, 2013; Biesbroek et al., 2018; Keskitalo \r\nand Preston, 2019a)"


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" Therefore, debates still emerge about what actually counts as 'adaptation' (Dupuis and Biesbroek, 2013), and what \r\nknowledge is being assessed and measured for this purpose."


id: 10ba6fffcfa6e5d20f980bd9bc619f51
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"One of the contributions of research 'on adaptation' is to track and debate the broader trends, core characteristics and overall assumptions \r\nembedded in adaptation knowledge. This reflexive turn about the foundational assumptions is itself one emerging trend in adaptation \r\nresearch (e.g., Preston et al., 2013; Nalau et al., 2015; Juhola, 2016; Atteridge and Remling, 2017). This signals the influence of more \r\nsocial science in adaptation research and increased awareness of the practical value of being transparent and critically reflective about \r\nthe content, topics, frames and approaches that researchers use (Lacey et al., 2015; Nalau et al., 2021; Singh et al., 2021). For example, \r\ndifferent conceptions of adaptation contribute to different definitions of 'adaptation success', different ideas about what 'effective' \r\nadaptation practice looks like and, thus, different conclusions about what is and is not working well (Berrang-Ford et al., 2019; Dilling \r\net al"


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", 2019; Magnan et al., 2020; Owen, 2020; Eriksen et al., 2021; Singh et al., 2021; Section 17.5.1.1). This diversity adds richness and \r\noptions, but also poses challenges in constructing a conventional evidence base for decision and policymaking. Adaptation researchers \r\nare increasingly expected to offer clear and confident advice on adaptation success, yet are also increasingly aware of how context\u0002specific and contested success is (see also Lacey et al., 2015 on ethics)."


id: d74961bab54b5cd6fa18d959f80c0bc5
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"Helping to differentiate adaptation from mitigation during this period was a focus on theoretical principles and a framing of adaptation \r\nas local and context specific, in contrast to mitigation's global character (Nalau et al., 2015; Westoby et al., 2020), leading to locally \r\noriented adaptation research and practice, including the rise of community-based adaptation (Kirkby et al., 2017). Since AR5, however, \r\nadaptation has extended beyond the local, recognising the 'borderless' character of many climate change risks and vulnerabilities (Benzie \r\nand Persson, 2019) and framing adaptation and global adaptation governance as a global public good (Persson, 2019). Encompassing \r\nthis expanded scale is challenging for adaptation research compared to treating adaptation as a local issue, which fits more easily with \r\nsocial research methods. Adaptation now works across scales (Biesbroek et al"


id: fd1d93af744ff81373113b65081e98b6
created_at: 2023-03-16 14:02:13
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", 2013; Dzebo and Stripple, 2015; Keskitalo and Preston, \r\n2019a) and attends simultaneously to both the opportunities and risks arising from climate change (Juhola, 2016; Keskitalo and Preston, \r\n2019a). This suggests that empirical adaptation research should incorporate multi-scalar research designs and methods."


id: 67a69c05e2710fff2fe76b4941db9ca2
created_at: 2023-03-16 14:02:13
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content:
"A strong focus has been and remains on case studies of adaptation practice, but adaptation science literature reviews have become \r\ncommon. Recent systematic reviews cover topics such as adaptation effectiveness (Owen, 2020), public participation and engagement \r\n(Hugel and Davies, 2020), the role of LK (Klenk et al., 2017), adaptive capacity (Mortreux and Barnett, 2017; Siders, 2019a; Mortreux et al., \r\n2020), evolution of adaptation science (Nalau and Verrall, 2021), empirical adaptation research in the Global South (Vincent and Cundill, \r\n2021), how cities are adapting (Reckien et al., 2018), how decisions can be made (Siders and Pierce, 2021), IK (Petzold et al., 2020) \r\nand SIDS (Robinson, 2020). Review papers have developed common methodologies for how to undertake robust reviews in adaptation \r\nresearch (Berrang-Ford et al., 2015; Biesbroek et al., 2018; Lesnikowski et al., 2019a; Singh et al"


id: a3234217e790e239ee28d2e6ba732969
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", 2020), and noted an existing imbalance \r\nas the majority of the literature still originates from the Global North compared to Global South (Robinson, 2020; Nalau and Verrall, 2021; \r\nSietsma et al., 2021)."


id: 1605cd298f7a865385c171046ee4290b
created_at: 2023-03-16 14:02:13
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content:
"At the same time, adaptation research is also challenged by increasing attention to transformational adaptation, which refers to fundamental \r\nchanges going beyond existing practices, including new approaches to adaptation decision making (Section 1.5). Whereas AR5 noted \r\ntransformational adaptation as an area of future research (Klein et al., 2014b), it has continued to grow in profile since then. Rather than \r\na future or fringe consideration--for example, an extreme action necessitated by the limits of incremental adaptation-- transformational \r\nadaptation is increasingly an option that decision makers are considering today. This increasing attention to transformational adaptation \r\nis driven by a growing recognition of climate risks and impacts, as well as the need for urgent, systemic action as laid out in the IPCC's \r\nrecent special reports (IPCC, 2018c)"


id: ae5155136ef49ce5f9eb4dff74d4511a
created_at: 2023-03-16 14:02:13
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content:
" Yet what incremental and transformational adaptation look like, how they relate in practice and how \r\nto appropriately choose incremental or transformational options is uncertain and increasingly debated ( Section 17.2.2.3; Termeer et al., \r\n2016; Few et al., 2017; Vermeulen et al., 2018; Magnan et al., 2020; Wilson et al., 2020). One of the main challenges is now to generate \r\nempirical evidence and policy relevant insights on transformational adaptation (e.g., Jakku et al., 2016). Transformative approaches are \r\nespecially being discussed in the context of COVID-19 (Schipper et al., 2020; Cross-Chapter Box COVID in Chapter 7)."


id: f5442ef918ea6a1102e9d1a16653e456
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content:
"Another characteristic of recent adaptation research is a stronger focus on ethics, justice and power (Byskov et al., 2021; Coggins et al., 2021; \r\nEriksen et al., 2021; Singh et al., 2021). Researchers and practitioners are increasingly impatient to address the root causes of vulnerability \r\nand use inclusive climate adaptation processes to generate effective adaptation responses for marginalised and misrecognised groups \r\n(Tschakert et al., 2013; Eriksen et al., 2015; Scoones et al., 2015; Gillard et al., 2016; Wisner, 2016). Increasingly ambitious, normative \r\nadaptation research often challenges technological solutions that simply reinforce the existing status quo (Nightingale et al., 2019, p. \r\n2) and calls for 'socially just pathways for change'"


id: a6d5a1e60dd335d98fd4190948e3bb65
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content:
" Here work on adaptation overlaps with mitigation, transitions and other large-scale \r\nsocial change, encouraging the move towards more systemic, integrated approaches that discern between options according to multiple \r\ncriteria (Goldman et al., 2018)."


id: c3c81e2c2a8aa9ae358f17ece8368ad5
created_at: 2023-03-16 14:02:13
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vector_id: 28372
content:
"how climate justice intersects with other justice agendas. Attention is also turning to relations and tensions between different adaptation \r\napproaches, scales, constraints, limits, losses, enablers and outcomes (Barnett et al., 2015; Pelling et al., 2015; Mechler and Schinko, 2016; \r\nCrichton and Esteban, 2017; Gharbaoui and Blocher, 2017; Deshpande et al., 2018; McNamara and Jackson, 2019). Evident here is an \r\nongoing, serious knowledge gap around the long-term repercussions of adaptation interventions. There is growing awareness of the need \r\nto address the potential for maladaptation (Sections 1.4.2.4; 5.13.3; 15.5.1; 17.5.2; Chapter 4). Concerns about maladaptation have led \r\nto renewed calls to open the 'black box' of decision making to examine the influence of power relationships, politics and institutional \r\nculture (Biesbroek et al., 2013; Eriksen et al., 2015; Goldman et al., 2018), including the power-adaptation linkage itself (Woroniecki \r\net al"


id: ae665bf44d12cfcf3c4f96bd6e28c48b
created_at: 2023-03-16 14:02:13
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vector_id: 24963
content:
", 2019), external factors outside the decision-making process (Eisenack et al., 2014) and the influence of leadership on adaptation \r\nprocesses and outcomes (Meijerink et al., 2014; Vignola et al., 2017)."


id: 6a758de5d6ca309269ceacdd38ab91f5
created_at: 2023-03-16 14:02:13
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vector_id: 14357
content:
"The effectiveness of adaptation efforts also depends on the constraints \r\nand limits that human and natural systems face when confronted with \r\nincreasingly higher levels of climate risks. The concept of adaptation \r\nlimits strongly affects any appropriate balance among adaptation and \r\nmitigation actions in the sense that less mitigation makes adaptation \r\nharder or even infeasible. Adaptation limits refer to the point at which \r\nan actor's objectives (or system needs) cannot be secured from intolerable \r\nrisks through adaptive actions (Annex II: Glossary). Adaptation limits \r\ncan be soft or hard. Soft adaptation limits occur when options may \r\nexist but are currently not available to avoid intolerable risks through \r\nadaptive actions and hard adaptation limits occur when no adaptive \r\nactions are possible to avoid intolerable risks"


