# document

id: 89a070f1305d55eec7db0efdf7a88075
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
content_type: text
index: climate_gpt_only_giec
vector_id: 19188
content:
" \r\nAlthough the natural cycling rate of this amount is theoretically \r\nenough to meet global human and ecosystem needs, there are large \r\ngeographical and seasonal differences that influence the availability \r\nof freshwater to meet regional demands. "


id: 1263b29a5c57443e40a0c6e89a254504
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
content_type: text
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vector_id: 395
content:
"The consequences of climate change on terrestrial ecosystems and \r\nhuman societies are primarily experienced through changes to \r\nthe global water cycle (Jimenez Cisneros et al., 2014). Changes in \r\nthe quantity and seasonality of water due to climate change have \r\nlong been recognized by IPCC and global development agencies as \r\nheavily influencing the food security and economic prosperity of many \r\ncountries, particularly in the arid and semi-arid areas of the world \r\nincluding Asia, Africa, Australia, Latin America, the Mediterranean, \r\nand small island developing states (Bates et al., 2008; Schewe \r\net al., 2014; Mekonnen and Hoekstra, 2016). Having too much or \r\ntoo little water increases the likelihood of flooding and drought, \r\nas precipitation variability increases in a  warming climate (Stocker \r\net al., 2013; Hoegh-Guldberg et al., 2018). Climate change poses \r\na threat to both regional water availability and global water security"


id: 2a518a7bfaa04aa537f540ee93579696
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
content_type: text
index: climate_gpt_only_giec
vector_id: 4087
content:
" \r\nChanges in precipitation and glacier runoff and snowmelt influence \r\nother hydroclimate variables like surface and subsurface runoff, and \r\ngroundwater recharge, which are critical to the water, food and energy \r\nsecurity of many regions (Oki and Kanae, 2006; Jimenez Cisneros \r\net al., 2014; Schewe et al., 2014; Mekonnen and Hoekstra, 2016). "


id: 82e528b917d66ca4ad66c4b12d0db028
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
content_type: text
index: climate_gpt_only_giec
vector_id: 18118
content:
"Freshwater is the most essential natural resource on the planet \r\n(Mekonnen and Hoekstra, 2016; Djehdian et al., 2019) and underpins \r\nalmost all Sustainable Development Goals (SDGs), which require \r\naccess to adequate and safe resources for drinking and sanitation \r\n(SDG 6) and many other purposes. Freshwater supports a range of \r\nhuman activities from irrigation to industrial processes including \r\nthe generation of hydro-electricity and the cooling of thermoelectric \r\npower plants (Bates et al., 2008; Schewe et al., 2014). These activities \r\nrequire sufficient quantities of freshwater that can be drawn from \r\nrivers, lakes, groundwater stores, and in some cases, desalinated sea \r\nwater (Schewe et al., 2014). Recent estimates of global water pools \r\nand fluxes suggest that half of global river discharge is redistributed \r\neach year by human water use (Abbott et al., 2019)"


id: ac2d879ba0cb4bd7c5911196df9d3ce2
created_at: 2023-03-16 14:02:03
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vector_id: 24623
content:
" This emphasizes \r\nthe need to consider both anthropogenic climate change and \r\ndirect human influences, such as population increase or migration, \r\neconomic development, urbanization, and land use change, when \r\nplanning water-related mitigation or adaptation strategies (Jimenez \r\nCisneros et al., 2014)."


id: ceb3d9e35ac52062537b4eae8bc762a7
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
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vector_id: 30062
content:
"Based on long-term observational evidence (Hartmann et al., 2013), \r\nAR5 concluded it was likely that anthropogenic influence has affected \r\nthe water cycle since the 1960s (IPCC, 2018). Detectable human \r\ninfluence on changes to the water cycle were found in atmospheric \r\nmoisture content (medium confidence), global-scale changes of \r\nprecipitation over land (medium confidence), intensification of heavy \r\nprecipitation events over land regions where sufficient data networks \r\nexist (medium confidence), and very likely changes to ocean salinity \r\nthrough its connection with evaporation minus precipitation change \r\npatterns (Sections  2.5, 2.6, 3.3, 7.6, 10.3 and 10.4; Stocker et al., \r\n2013). The AR5 also reported that it is very likely that global surface air \r\nspecific humidity increased since the 1970s"


id: a9a033f0eb5644f5f0c6674e466f0952
created_at: 2023-03-16 14:02:03
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" There was low confidence\r\nin the observations of global-scale cloud variability and trends, medium \r\nconfidence in reductions of pan-evaporation, and medium confidence\r\nin the non-monotonic changes of global evapotranspiration since the \r\n1980s. In terms of streamflow and runoff, AR5 identified that there \r\nis low confidence in the observed increasing trends of global river \r\ndischarge during the 20th century. Similarly, AR5 concluded that there \r\nis low confidence in any global-scale observed trend in drought or \r\ndryness (lack of rainfall) since the mid-20th century. Yet, the frequency \r\nand intensity of drought likely increased in the Mediterranean and \r\nWest Africa, while they likely decreased in central North America and \r\nnorth-western Australia since 1950. "


id: 3e4a934bb7672b7e3392a5bae91e05c3
created_at: 2023-03-16 14:02:03
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"Understanding the interactions between the water and energy \r\ncycles is one of the four core projects of the World Climate Research \r\nProgramme (WCRP). Latent heat fluxes, released by condensation of \r\natmospheric water vapour and absorbed by evaporative processes, are \r\ncritical to driving the circulation of the atmosphere on scales ranging \r\nfrom individual thunderstorm cells to the global circulation of the \r\natmosphere (Stocker et al., 2013; Miralles et al., 2019). Water vapour \r\nis the most important gaseous absorber in the Earth's atmosphere, \r\nplaying a key role in the Earth's radiative budget (Schneider et al., 2010). \r\nAs atmospheric water vapour content increases with temperature, it \r\nhas a  considerable influence on climate change (Section  7.4.2.2). \r\nAdditionally, a  small fraction of the atmospheric water content is \r\nliquid or solid and has a major effect on both solar and longwave \r\nradiative fluxes, from the Earth's surface to the top of the atmosphere"


id: 40e353133c02c7f349fdb5c7891f1fd7
created_at: 2023-03-16 14:02:03
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" \r\nThe cloud response to anthropogenic radiative forcings, both in the \r\ntropics and in the extratropics (Zelinka et al., 2020), is therefore also \r\ncrucial for understanding climate change (Section 7.4.2.4)."


id: 983a43c34f12015355295f97b9924e21
created_at: 2023-03-16 14:02:03
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vector_id: 21459
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"The terrestrial water and carbon cycles are also strongly coupled (Cross\u0002Chapter Box 5.1). As atmospheric carbon dioxide (CO2) concentration \r\nincreases, the physical environment in which plants grow is altered, \r\nincluding the availability of soil moisture necessary for plants' CO2\r\nuptake and, potentially, the effectiveness of CO2 removal techniques \r\nto mitigate climate change (Section  5.6.2.1.2). Rising surface CO2\r\nconcentrations also modify stomatal (small pores at the leaf surface) \r\nregulation as well as the plants' biomass, thus affecting ecosystem \r\nphotosynthesis and transpiration rates and leading generally to \r\na net increase in water use efficiency (Lemordant et al., 2018). These \r\ncoupled changes have profound implications for the simulation of the \r\ncarbon and water cycles (Gentine et al., 2019; see also Section 5.4"


id: 4d22bb6b71d75dbcb370ed3eb78f8600
created_at: 2023-03-16 14:02:03
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"1), \r\nwhich can be better assessed with the new generation Earth system \r\nmodels, although both the carbon concentration and carbon-climate \r\nfeedbacks remain highly uncertain over land (Section  5.4.5; Arora \r\net al., 2020). The water constraints on the terrestrial carbon sinks \r\nare a matter of debate regarding the feasibility or efficiency of some \r\nland-based CO2 removal and sequestration techniques requested to \r\ncomply with the Paris Agreement (Section 5.6.2.2.1; Fuss et al., 2018; \r\nBelyazid and Giuliana, 2019)."


id: dcb4999f1a098f7a48b49a389c69f8d0
created_at: 2023-03-16 14:02:03
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vector_id: 32222
content:
"Water cycle projections in AR5 (Collins et al., 2013) were \r\nconsidered primarily in terms of water vapour, precipitation, surface \r\nevaporation, runoff, and snowpack. Globally-averaged precipitation \r\nwas projected to increase with global warming with virtual certainty \r\n(Chapter 12 Executive Summary and Section 12.4.1.1). Regionally, \r\nprecipitation in some areas of the tropics and polar regions could \r\nincrease by more than 50% by the end of the 21st century under \r\nthe RCP8.5 emissions scenario, while precipitation in large areas \r\nof the subtropics could decrease by 30% or more (AR5 FAQ 12.2, \r\nFigure  12.22). Overall, the contrast of annual mean precipitation \r\nbetween dry and wet regions and between dry and wet seasons \r\n('wet get wetter, dry get drier') was projected to increase over \r\nmost of the globe with high confidence (Chapter  12 Executive \r\nSummary and Section 12.4.5.2)"


id: 4edea679668dba129cdf1d17327b48a8
created_at: 2023-03-16 14:02:03
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vector_id: 9844
content:
" Globally, the frequency of intense \r\nprecipitation events was projected to increase while the frequency \r\nof all precipitation events was projected to decrease, leading to \r\nthe contradictory-seeming projection of a  simultaneous increase \r\nin both droughts and floods (12.2 and Section  12.4.5.5 in AR5 \r\nWGI). Surface evaporation change was projected to be positive \r\nover most of the ocean and to generally follow the pattern of \r\nprecipitation change over land (Chapter  12 Executive Summary, \r\nand Section  12.4.5.4). Near-surface relative humidity reductions \r\nover many land areas were projected to be likely, with medium \r\nconfidence (Section 12.4.5.1). General decreases in soil moisture in \r\npresent-day dry regions were considered likely, and projected with "


id: 9d02bb251ba95579005c277e8ac7c328
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
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vector_id: 22245
content:
"The Special Report on climate change, desertification, land \r\ndegradation, sustainable management, food security, and \r\ngreenhouse gas (GHG) fluxes in terrestrial ecosystems (SRCCL) has \r\nclear connections with the water cycle. This Report indicates that since \r\n1850-1900, land surface temperature has risen nearly twice as much \r\nas global surface temperature (high confidence), with an increase \r\nin dry climates (high confidence). Land surface processes modulate \r\nthe likelihood, intensity and duration of many extreme events \r\nincluding droughts (medium confidence) and heavy precipitation \r\n(medium confidence). The direction and magnitude of hydrological \r\nchanges induced by land use change and land surface feedbacks \r\nvary with location and season (high confidence). Desertification \r\nexacerbates climate change through feedbacks involving vegetation \r\ncover, greenhouse gases and mineral dust aerosol (high confidence)"


id: b296d8e3204ff4033a7a1dc94382e4c9
created_at: 2023-03-16 14:02:03
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" \r\nUrbanization increases extreme rainfall events over or downwind of \r\ncities (medium confidence). Intensification of rainy events increase \r\ntheir consequences on land degradation. "


id: fa7e946c79d1dc47548bc870e6044dec
created_at: 2023-03-16 14:02:03
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vector_id: 36929
content:
"The SR1.5 assessed the impacts of global warming of 1.5degC above \r\npre-industrial levels. The dominant human influence on observed \r\nglobal warming and related water cycle changes was confirmed. \r\nFurther evidence that anthropogenic global warming has caused \r\nan increase in the frequency, intensity and/or amount of heavy \r\nprecipitation events at the global scale (medium confidence), as \r\nwell as in drought occurrence in the Mediterranean region (medium \r\nconfidence) was also reported. Chapter 3 of SR1.5 (Hoegh-Guldberg \r\net al., 2018) highlights that each half degree of additional global \r\nwarming influences the climate response. Heavy precipitation shows \r\na  global tendency to increase more at 2degC compared to 1.5degC, \r\nthough there is low confidence in projected regional differences \r\nin heavy precipitation at 1.5degC compared to 2degC global warming, \r\nexcept at high latitudes or at high altitude where there is medium \r\nconfidence"


id: 1719cc64c0be2b63afd6804b13d4a34d
created_at: 2023-03-16 14:02:03
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content:
" A key finding is that 'limiting global warming to 1.5degC \r\ncompared to 2degC would approximately halve the proportion of the \r\nworld population expected to suffer water scarcity, although there \r\nis considerable variability between regions (medium confidence)' \r\n(SR1.5). This is consistent with greater adverse impacts found at \r\n2degC compared to 1.5degC for a number of dryness or drought indices \r\n(Schleussner et al., 2016; Lehner et al., 2017; Greve et al., 2018). There \r\nis also medium confidence that land areas with increased runoff and \r\nexposure to flood hazards will increase more at 2degC compared to \r\n1.5degC of global warming."


id: 1e0f23b4dc3c00a4b5be57be0e0f8e98
created_at: 2023-03-16 14:02:03
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"The AR5 report was a major step forward in the assessment of the \r\nhuman influence on the Earth's water cycle, yet regional projections \r\nof precipitation and water resources often remained very uncertain \r\nfor a range of reasons including modelling uncertainty and the large \r\ninfluence of internal variability (Sections  1.4.3 and 8.5.2; Hawkins \r\nand Sutton, 2011; Deser et al., 2012). Since AR5, longer and more \r\nhomogeneous observational and reanalysis datasets have been \r\nproduced along with new ensembles of historical simulations driven \r\nby all or individual anthropogenic forcings. These factors, together \r\nwith improved detection-attribution tools, has enabled a  more \r\ncomprehensive assessment and a  better understanding of recent \r\nobserved water cycle changes, including the competing effects of \r\nGHGs and aerosol emissions. New paleoclimate reconstructions \r\nhave been also developed, particularly from the SH, that were not \r\navailable at the time of AR5"


id: ea6d19414da905e4a80f8f3818c2e640
created_at: 2023-03-16 14:02:03
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vector_id: 34390
content:
" There have also been advances in \r\nmodelling clouds, precipitation, surface fluxes, vegetation, snow, \r\nfloodplains, groundwater and other processes relevant to the water \r\ncycle. Convection permitting and cloud-resolving models have been \r\nimplemented over increasingly large domains and can be used as \r\nbenchmarks for the evaluation of the current-generation climate \r\nmodels. The added value of increased resolution in global or regional \r\nclimate models can be also assessed more thoroughly based on \r\ndedicated model intercomparison projects (Sections 10.3.3 and 8.5.1). \r\nOngoing research activities on decadal predictions and observational \r\nconstraints are aimed at narrowing the plausible range of near-term \r\n(2021-2040) to long-term (2081-2100) water cycle changes."


id: 18fc48a414aeb065b64e0ecb60b247a9
created_at: 2023-03-16 14:02:03
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content:
"Chapter  8  has multiple links across all AR6 WGI chapters, so \r\nnecessarily includes references to other chapter subsections and \r\nfigures. Model evaluation of large-scale circulation and precipitation \r\nis mostly covered by Chapter  3, while hydrological extremes are \r\ncovered by Chapter 11. Chapter 8 focuses on key processes relevant \r\nto the water cycle and their resolution-dependent representation in \r\nmodels. Observed and projected changes in large-scale circulation and \r\nprecipitation are primarily assessed in Chapters 2, 3 and 4. Beyond \r\nglobal and regional mean precipitation amounts, Chapter  8  also \r\nfocuses on other precipitation properties (e.g.,  frequency, intensity \r\nand seasonality) and other water cycle variables (evapotranspiration, \r\nrunoff, soil moisture and aridity, solid and liquid freshwater reservoirs). \r\nKey regional phenomena (e.g"


id: 2404a29b3a07b2056b4f87469f25700b
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
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vector_id: 3109
content:
",  tropical overturning circulations, \r\nmonsoons, extratropical stationary waves and storm tracks, modes \r\nof variability and related teleconnections) are also assessed given \r\ntheir major dynamical contribution to regional water cycle changes. \r\nAlthough the biosphere and the cryosphere are key components of \r\nthe water cycle, a more comprehensive assessment of their responses \r\ncan be found in Chapters 5 and 9, respectively. Further assessment "


id: 7126a1a3cd5fb6f45b1462c46f1b232f
created_at: 2023-03-16 14:02:03
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content:
"The chapter starts with theoretical evidence that link small-scale \r\nprocesses and drivers, as well as global energy budget and large\u0002scale circulation constraints to physically-understood changes in the \r\nglobal water cycle (Section 8.2). Observed and projected water cycle \r\nchanges (Sections 8.3 and 8.4, respectively) are assessed in separate \r\nsections, but with a  parallel structure to facilitate comparison of \r\na specific topic across sections. Projections are primarily assessed on \r\nthe basis of contrasted emissions scenarios to emphasize the water \r\ncycle response to mitigation. Unless otherwise specified, projected \r\nanomalies are estimated relative to the 1995-2014 baseline \r\nclimatology and are assessed over 20-year time slices, 2021-2040, \r\n2041-2060 and 2081-2100 for near-, mid- and long-term changes \r\nrespectively"


id: 5f3d7c830226186ad4a6a20215ac93bd
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
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vector_id: 12550
content:
" Beyond multi-model ensemble means, model response \r\nuncertainty, the influence of natural climate variability, and the \r\npotential non-linearities in the regional water cycle response are \r\nalso considered (Section 8.5). Low likelihood but physically plausible "


id: 70a773d0afedd905510abbee6123c6b6
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
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"Figure 8.3 | Schematic representation of fast and slow responses of the atmospheric energy balance and global precipitation to radiative forcing.\r\n('Baseline' panel) The atmospheric energy budget: (1) responds instantaneously to radiative forcings; (2) leading to rapid atmospheric adjustments; and (3) slower semi-rapid \r\nadjustments involving the land surface and vegetation that further modify atmospheric circulation patterns. (4) This slow precipitation response to global mean surface air \r\ntemperature is quantified as (a) the hydrological sensitivity, e, and the total precipitation response, including initial rapid adjustments, is termed the apparent hydrological \r\nsensitivity, ea. (b-d) The slow precipitation response over land and ocean develops over time"


id: b2638bb216cf0d2c912bbc9074fd5c4d
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
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vector_id: 25582
content:
" Large, filled arrows (in panels from 'baseline' to 4) depict fluxes or circulation \r\nchange while small arrows (1-4) denote increases (|) or decreases (|) in variables (P is precipitation; L is atmospheric longwave radiative cooling; S is solar radiation absorption \r\nby the atmosphere; H is sensible heat flux; E is surface evaporative heat flux; and T is temperature). Adapted from Allan et al. (2020) with statistics taken from Figures 7.2 and \r\nFigure 8.1."


id: 4b618b8cb8faca775fb65567cc455d54
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
content_type: text
index: climate_gpt_only_giec
vector_id: 9273
content:
"Observed estimates of hydrological sensitivity (e = 3.2 +- 0.8% degC-1) \r\nbased on interannual variability (Allan et al., 2020) or responses to \r\nEl Nino-Southern Oscillation (ENSO) of 9% degC-1 (Adler et al., 2017) are \r\nnot suitable to assess the magnitude of e (Figure 8.4). This is because \r\nthese relationships depend on amplifying feedbacks associated with \r\nENSO-related cloud changes (G.L. Stephens et al., 2018) that may not \r\nbe relevant for longer term climate change. However, there is robust \r\nevidence and high agreement across observations, modelling and \r\nsupporting physics that precipitation increases at a lower % degC-1 rate \r\nthan water vapour content in the global mean (Held and Soden, 2006; \r\nCollins et al., 2013; Allan et al., 2020), implying an increased residence \r\ntime of atmospheric water vapour (Hodnebrog et al., 2019b; Dijk et al., \r\n2020)"


id: d42977fed2caef0a676cddaed22f73f5
created_at: 2023-03-16 14:02:03
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vector_id: 30901
content:
" Increasing global precipitation, evaporation and moisture fluxes \r\nwith warming thereby drive an intensification but not acceleration of \r\nthe global water cycle (Sections 8.3.1.1 and 8.4.1.1). "


id: fae998832196bd5fd106aee00ffdfcdd
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
content_type: text
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vector_id: 36997
content:
"While thermodynamics exert a  strong control on water vapour \r\nchanges, global mean precipitation and evaporation are constrained \r\nby the balance of energy fluxes in the atmosphere and at the surface \r\n(Figure 8.3). Global mean precipitation increases of 1-3% per 1 degC of \r\nwarming, as estimated in AR5 (Collins et al., 2013), are explained as \r\na combination of rapid (or fast) atmospheric adjustments and slow \r\ntemperature-driven responses (Figure 8.3, panels 1-4) to radiative \r\nforcings (Andrews et al., 2010; Bala et al., 2010; Cao et al., 2012). Fast \r\natmospheric adjustments are caused by near-instantaneous (hours \r\nto days) changes in the atmospheric energy budget (Figure  8.3, \r\npanels 1-3) and atmospheric properties (e.g.,  temperature, clouds \r\nand water vapour) in direct response to the radiative effects of \r\na forcing agent (Sherwood et al., 2015)"


id: 814eaeb3c67e7d145a616f5d518cb84
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
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vector_id: 17870
content:
" A further relatively fast (days \r\nto months) adjustment of the climate system involves interactions \r\nwith vegetation and land surface temperature (Figure 8.3, panel 3), \r\nwhich respond more rapidly than ocean temperature to a radiative \r\nforcing (Cao et al., 2012; Dong et al., 2014). The slower temperature\u0002dependent precipitation response is driven by the increased \r\natmospheric radiative cooling rate of a  warming atmosphere. \r\nWarming drives increases in precipitation intensity while frequency is \r\ndominated by rapid atmospheric adjustments to the radiative forcing \r\nbased on abrupt 4xCO2 CMIP6 simulations (Douville and John, 2021). \r\nSince AR5, many new studies applying the dual rapid adjustment and \r\nslow response framework show that global precipitation responses \r\nto different forcing agents are physically well understood (Flaschner \r\net al., 2016; MacIntosh et al., 2016; Samset et al., 2016; Myhre et al., \r\n2018a)"


id: c5f57691b502c62f9f6c54e1e6761c7
created_at: 2023-03-16 14:02:03
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vector_id: 28723
content:
" Further confidence in the coupled processes involved are \r\nprovided by simple models representing the energy budget and \r\nthermodynamic constraints that limit global mean evaporation to \r\naround 1.5% degC-1 (Siler et al., 2019). This strengthens the physical \r\nlink between energy budget and thermodynamic drivers of the global \r\nwater cycle (Section 8.2.2.1)."


id: 83f18fd01e920a091568b5f8ff509bb2
created_at: 2023-03-16 14:02:03
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vector_id: 18291
content:
"The overall global mean rate of precipitation change per 1 degC of \r\nGSAT increase, apparent hydrological sensitivity (ea), is reduced \r\ncompared to hydrological sensitivity by the direct influence of \r\nradiative forcing agents on the atmospheric energy balance. Rapid \r\natmospheric adjustments that alter precipitation are primarily \r\ncaused by GHGs and absorbing aerosols, with high agreement and \r\nmedium evidence across idealized simulations (Flaschner et al., 2016; \r\nSamset et al., 2016). A range of rapid precipitation adjustments to \r\nCO2 between models are also attributed to vegetation responses \r\nleading to a re-partitioning of surface latent and sensible heat fluxes \r\n(DeAngelis et al., 2016). Values obtained from six CMIP5 models \r\nsimulating the Last Glacial Maximum (LGM; 21,000-19,000 years \r\nago) and pre-industrial period (ea = 1.6-3.0%degC-1) are larger than for \r\neach corresponding abrupt 4xCO2 experiment (ea = 1.3-2"


id: 15430973738ba058c9ff092576f8e1a5
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
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vector_id: 858
content:
"6% degC-1) \r\ndue to differences in the mix of forcings, vegetation and land surface \r\nchanges and a  higher thermodynamic %  degC-1 evaporation scaling \r\nin the colder state (Figure  8.4, Section  8.4.1.1; G.  Li et al., 2013). \r\nUpdated estimates across comparable experiments from 22 CMIP5/\r\nCMIP6 models (Rehfeld et al., 2020) display a  consistent range \r\n(ea = 1.7 +- 0.6% degC-1). Confirming ea in observations (Figure 8.4) is \r\ndifficult due to measurement uncertainty, varying rapid adjustments \r\nto radiative forcing and unforced variability (Dai and Bloecker, 2019; \r\nAllan et al., 2020)."


id: a755bb766a5cac5ab616963a2b914bc9
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
content_type: text
index: climate_gpt_only_giec
vector_id: 23840
content:
"aerosol) produce only a small rapid adjustment of the global water \r\ncycle and therefore larger ea than drivers that immediately modulate \r\nthe atmospheric energy budget such as GHGs and absorbing aerosol \r\n(Salzmann, 2016; Samset et al., 2016; Lin et al., 2018; F. Liu et al., \r\n2018). Thus, global precipitation appears more sensitive to radiative \r\nforcing from sulphate aerosols (2.8 +- 0.7% degC-1; eae) than GHGs \r\n(1.4 +- 0.5% degC-1; ea<e) while the response to black carbon aerosol \r\ncan be negative (-3.5 +- 5.0% degC-1; ea<<e) due to strong atmospheric \r\nsolar absorption (Samset et al., 2016). Therefore, artificially reducing \r\nsurface-absorbed sunlight through solar radiation modification \r\nstrategies to mitigate GHG warming will not mitigate precipitation \r\nchanges (see Sections  4.6.3.3, 6.4.7 and 8.6.3). Aerosol-induced \r\nprecipitation changes depend upon the type of aerosol species \r\nand their spatial distribution"


id: c711151518c1b70389dd234b022aba0
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
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index: climate_gpt_only_giec
vector_id: 28904
content:
" Global mean precipitation increases \r\nafter complete removal of present-day anthropogenic aerosol \r\nemissions (see also Section 4.4.4) in four different climate models \r\n(ea = 1.6-5.5% degC-1) are mainly attributed to sulphate aerosol as \r\nopposed to other aerosol species (Samset et al., 2018b). Idealized \r\nmodelling studies show that sulphate aerosol increases over Europe \r\nproduce a  larger global precipitation response than an equivalent \r\nincrease in aerosol burden or radiative forcing over Asia, explained \r\nby differences in cloud climatology and cloud-aerosol interaction \r\n(Kasoar et al., 2018; L. Liu et al., 2018). The vertical profiles of black \r\ncarbon and ozone further influence the magnitude of the rapid global \r\nprecipitation response, yet are difficult to observe and simulate (Allen \r\nand Landuyt, 2014; MacIntosh et al., 2016; Stjern et al., 2017; Sand \r\net al., 2020). "


id: 38d56abc24ca88da8fb273bf017a501c
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
content_type: text
index: climate_gpt_only_giec
vector_id: 6313
content:
"In summary, there is high confidence that global mean evaporation \r\nand precipitation increase with global warming, but the estimated \r\nrate is model-dependent (very likely range of 1-3 % degC-1). The global \r\nincrease in precipitation is determined by a robust response to global \r\nsurface temperature only (very likely 2-3% degC-1) that is partly offset \r\nby fast atmospheric adjustments to the vertical profile of atmospheric \r\nheating by GHGs and aerosols. Global precipitation increases due to \r\nGHGs are offset by the well-understood overall surface radiative \r\ncooling effect by aerosols (high confidence). Over land, the average \r\nwarming-related increase in precipitation is expected to be smaller \r\nthan over the ocean due to increasing land-ocean thermal contrast \r\nand surface feedbacks, but the overall precipitation increase over \r\nland is generally reinforced by fast atmospheric responses to GHGs \r\nthat strengthens convergence of winds (medium confidence)"


id: 8a12bc53edabafb98cbd8bd0cc8eb666
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
content_type: text
index: climate_gpt_only_giec
vector_id: 19252
content:
" Global \r\nmean precipitation and evaporation increase at a  lower rate than \r\natmospheric moisture per 1degC of global warming (high confidence), \r\nleading to longer water vapour lifetime in the atmosphere and driving \r\nchanges in precipitation intensity, duration and frequency and an \r\noverall intensification but not acceleration of the global water cycle."


id: 1b4b8e91eabaf7fb9a7c6af8ee5025fd
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
content_type: text
index: climate_gpt_only_giec
vector_id: 1756
content:
"Hydrological sensitivity is generally lower over land but with a large \r\nuncertainty range (e = -0.1 to 3.0%  degC-1) relative to the oceans \r\n(e = 2.3 to 3.3% degC-1) based on multi-model 4 x CO2 CMIP6 simulations \r\n(Pendergrass, 2020b), broadly consistent with comparable CMIP5 \r\nexperiments (T.B.  Richardson et al., 2018a; Samset et al., 2018a). \r\nSuppressed hydrological sensitivity over land (Figures 8.3d and 8.4) \r\nis associated with greater warming compared with the oceans, which \r\nalters atmospheric circulation and precipitation patterns (Saint-Lu \r\net al., 2020). Also, since oceans supply much of the moisture to fuel \r\nprecipitation over land, the slower ocean warming rate means there \r\nis insufficient moisture supplied to maintain continental relative \r\nhumidity levels (Byrne and O'Gorman, 2018), which can inhibit \r\nconvection (J. Chen et al., 2020a). Land surface feedbacks involving \r\nsoil-vegetation-atmosphere coupling further drive continental drying \r\n(Berg et al"


id: e4f4458311c2708a0b04fda04ba66e81
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
content_type: text
index: climate_gpt_only_giec
vector_id: 33507
content:
", 2016; Kumar et al., 2016; Chandan and Peltier, 2020). \r\nThe suppressed hydrological sensitivity is counteracted by rapid \r\nprecipitation responses in most GHG-forced simulations, explained \r\nby increases in surface downward longwave radiation due to CO2\r\nincreases that rapidly warm the land, destabilize the troposphere and \r\nstrengthen vertical motion in the short term (Chadwick et al., 2014; \r\nT.B. Richardson et al., 2016, 2018a). There is medium understanding \r\nof how land-sea warming contrast governs rapid precipitation \r\nresponses based on idealized modelling that shows similar spatial \r\npatterns of precipitation response to radiative forcing from GHGs, \r\nsolar forcing and absorbing aerosols (Xie et al., 2013; Samset et al., \r\n2016; Kasoar et al., 2018). Rapid precipitation adjustments to CO2\r\nhave been counteracted by cooling from anthropogenic aerosol \r\nincreases over land (Box 8.1) but this compensation is expected to \r\ndiminish as aerosol forcing declines (T.B"


id: 6b3595aa140f4c4abe87d4b55abb4034
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
content_type: text
index: climate_gpt_only_giec
vector_id: 14493
content:
"Since AR5, numerous studies have confirmed that changes in P-E \r\nwith warming over land cannot be interpreted simply as a  'wet \r\nregions get wetter, dry regions get drier' response (Chadwick et al., \r\n2013; Greve et al., 2014; Roderick et al., 2014; Byrne and O'Gorman, \r\n2015; Scheff and Frierson, 2015). Firstly, P-E is a simplistic diagnostic \r\nof the water cycle that inadequately describes 'dryness' or aridity (Fu \r\nand Feng, 2014; Roderick et al., 2014; Greve and Seneviratne, 2015; \r\nScheff and Frierson, 2015; Greve et al., 2019; Vicente-Serrano et al., \r\n2020). Secondly, terrestrial P-E is generally positive and balanced \r\nby surface runoff and percolation into subsurface soils and \r\naquifers (Figure 8.1). As a result, the simple thermodynamic scaling \r\n(Figure 8.5b) predicts that P-E over land will become more positive \r\n(wetter) with warming (Greve et al., 2014; Roderick et al., 2014; \r\nByrne and O'Gorman, 2015)"


id: 6f8afd0870e359c8b230ef65aa7a16e3
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
content_type: text
index: climate_gpt_only_giec
vector_id: 15180
content:
" This is not necessarily true, however, \r\nin the dry seasons and regions where terrestrial water is lost to \r\nthe atmosphere and exported (Sheffield et al., 2013; Kumar et al., \r\n2015; Keune and Miralles, 2019). Thirdly, regional P-E patterns over \r\nland are affected by changes in atmospheric circulation, oceanic \r\nmoisture supply and land surface feedbacks. As the land warms \r\nmore than oceans, spatial gradients in temperature and relative \r\nhumidity influence moisture supply and reduce P-E over some land \r\nregions, such as southern Chile and Argentina around 30degS-50degS \r\nas captured by an extended thermodynamic scaling (Figure 8.5b). \r\nDrying of soils can be amplified by vegetation responses (Berg et al., \r\n2016; Byrne and O'Gorman, 2016; Lambert et al., 2017) but limited \r\nby atmospheric circulation feedbacks (Zhou et al., 2021). Changes \r\nin soil moisture and rainfall intensity (Sections 8.2.3.2 and 8.2.3"


id: 1e56a93af1b19be7b519be4717bbffd9
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
content_type: text
index: climate_gpt_only_giec
vector_id: 2213
content:
"3) \r\ncan alter the partitioning of precipitation between evaporation and \r\nrunoff, further complicating terrestrial P-E responses (Short Gianotti \r\net al., 2020)."


id: 71414e206a07d469a87ceda8b7c59719
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
content_type: text
index: climate_gpt_only_giec
vector_id: 15458
content:
"2013). These can dominate regional water cycle changes, affecting \r\nthe availability of freshwater and the occurrence of climate extremes. \r\nAtmospheric circulation changes generally dominate the spatial \r\npattern of rapid precipitation adjustments (Section 8.2.1) to different \r\nforcing agents in the tropics (Bony et al., 2013; He and Soden, 2015; \r\nT.B. Richardson et al., 2016, 2018a; Tian et al., 2017; X. Li et al., 2018). \r\nRadiative forcing with heterogeneous spatial patterns such as ozone \r\nand aerosols (including cloud interactions; Section 6.4.1 and Box 8.1) \r\ndrive substantial responses in regional atmospheric circulation \r\nthrough uneven heating and cooling effects (L. Liu et al., 2018; Dagan \r\net al., 2019b; Wilcox et al., 2019). Changes in atmospheric circulation \r\nare also driven by slower, evolving patterns of warming and associated \r\nchanges in temperature and moisture gradients (Bony et al., 2013; \r\nSamset et al., 2016, 2018a; Ceppi et al., 2018; Ma et al., 2018)"


id: f3c06b6b54b7ea4e17e230d427a39cd8
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
content_type: text
index: climate_gpt_only_giec
vector_id: 35865
content:
" There is \r\nstrong evidence that large regional water cycle changes arise from the \r\natmospheric circulation response to radiative forcings and associated \r\nSST pattern evolution but low agreement in the sign and magnitude \r\n(Chadwick et al., 2016b). The role of prolonged weather regimes in \r\ndetermining wet and dry extremes is also better understood since AR5 \r\n(Kingston and McMecking, 2015; Schubert et al., 2016; D. Richardson \r\net al., 2018; Barlow et al., 2019). Advances in knowledge of expected \r\nlarge-scale dynamical responses of the water cycle are further \r\nassessed in this section (see also Figure 8.21)."


id: 81e22c52132508c8f11014828ee27314
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
content_type: text
index: climate_gpt_only_giec
vector_id: 17960
content:
"Long-term weakening of the tropical atmospheric overturning \r\ncirculation is expected as climate warms in response to elevated \r\nCO2 (Collins et al., 2013). A  weaker circulation is required \r\nto reconcile  global mean low-level water vapour increases \r\n(around  7%  degC-1) with the smaller global precipitation responses \r\nof about 1-3% degC-1 (Section 8.2.1). The slowdown can occur in both \r\nthe Hadley and Walker circulations, but occurs preferentially in the \r\nWalker circulation in most climate models (Vecchi and Soden, 2007) \r\nbut this response has been questioned on the basis of model bias \r\nin east Pacific SST (Seager et al., 2019a). Weakening is expected \r\nto drive P-E decreases over the western Pacific and increases over \r\nthe eastern Pacific"


id: 2607c108246d30e4c8209c3fecb825
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
content_type: text
index: climate_gpt_only_giec
vector_id: 3403
content:
" However, the driving mechanisms for Walker \r\ncirculation weakening differ to those involved in determining ENSO \r\nvariability, so it is too simplistic to interpret changes as an El Nino \r\npattern of regional hydrological cycle extremes (Sohn et al., 2019). \r\nInternal variability is also capable of temporarily strengthening the \r\nWalker circulation (Section 2.3.1.4.1; L'Heureux et al., 2013; Chung \r\net al., 2019) while regional responses depend on the pattern of \r\nwarming (Sandeep et al., 2014). "


id: fb6770150fa0b79956f3441403abc0e
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
content_type: text
index: climate_gpt_only_giec
vector_id: 37058
content:
"Monsoons are key components of the tropical overturning circulation \r\nthat can be understood as a  balance between net energy input \r\n(e.g., radiative and turbulent fluxes) and the export of moist static energy. \r\nThis is determined by contrasting surface heat capacity between ocean \r\nand land and modified through changes in atmospheric dynamics, \r\ntropical tropospheric stability and land surface properties (Jalihal et al., \r\n2019). Thermodynamic increases in moisture transport are expected to \r\nincrease monsoon strength and area (Christensen et al., 2013). Since \r\nAR5, evidence continues to demonstrate that monsoon circulation is \r\nsensitive to spatially varying radiative forcing by anthropogenic aerosols \r\n(Hwang et al., 2013; R.J. Allen et al., 2015; Z. Li et al., 2016b) and GHGs \r\n(Dong and Sutton, 2015). Changes in SST patterns also play a role (Guo \r\net al., 2016; W. Zhou et al., 2019; Cao et al"


id: 6ccf53c42b392ed6bc42516b644086a3
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
content_type: text
index: climate_gpt_only_giec
vector_id: 14736
content:
", 2020) by altering cross\u0002equatorial energy transports and land-ocean temperature contrasts. \r\nThis evidence continues to support a thermodynamic strengthening of \r\nmonsoon precipitation that is partly offset by slowing of the tropical \r\ncirculation but with weak evidence and low agreement for regional \r\naspects of circulation changes. Disagreement between paleoclimate \r\nand modern observations, physical theory and numerical simulations \r\nof global monsoons have been partly reconciled (Section  3.3.3.2) \r\nthrough improved understanding of regional processes (Harrison et al., \r\n2015; R. Bhattacharya et al., 2017; Bhattacharya et al., 2018; Biasutti \r\net al., 2018; D'Agostino et al., 2019; Jalihal et al., 2019; Seth et al., \r\n2019), although interpreting past changes in the context of future \r\nprojections requires careful account of differing forcings and feedbacks \r\n(D'Agostino et al., 2019)"


id: 6eac62c618451c7c88c73ded05959439
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
content_type: text
index: climate_gpt_only_giec
vector_id: 15037
content:
" Assessment of past changes and future \r\nprojections in regional monsoons are provided in Sections 2.3.1.4.2, \r\n8.3.2.4 and 8.4.2.4. "


id: 1ffd8f91982545af44a45e4869eace5f
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
content_type: text
index: climate_gpt_only_giec
vector_id: 2475
content:
"The regional Inter-tropical Convergence Zone (ITCZ) position, width \r\nand strength determine the location and seasonality of the tropical \r\nrain belt. Since AR5, multiple studies have linked cross-equatorial \r\nenergy transport to the mean ITCZ position (Donohoe et al., 2013; \r\nFrierson et al., 2013; Bischoff and Schneider, 2014; Boos and Korty, \r\n2016; Loeb et al., 2016; Adam et al., 2018; Biasutti and Voigt, 2019). \r\nMulti-model studies agree that aerosol cooling in the NH led to \r\na southward shift in the ITCZ and tropical precipitation after the 1950s \r\nup to the 1980s that is linked with the 1980s Sahel drought (Box 8.1; \r\nSection 8.3.2.4 and 10.4.2.1). In particular, aerosol-cloud interaction \r\nwas identified as a potentially important driver of this shift (Chung \r\nand Soden, 2017) but this is uncertain since observations suggest that \r\nmodels may overestimate (Malavelle et al., 2017; Toll et al., 2017) or \r\nunderestimate (Rosenfeld et al"


id: 5818dd30173b534ddd8656e3287f0a50
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
content_type: text
index: climate_gpt_only_giec
vector_id: 11367
content:
", 2019) the aerosol cloud-mediated \r\ncooling effects. In addition, greenhouse gas forcing has been invoked \r\nin explaining much of the increase in Sahel precipitation since the \r\n1980s through enhanced meridional temperature gradient, with only \r\na secondary role for aerosol (Dong and Sutton, 2015)."


id: cc568ab3279b9d0af7b07731994ac5f4
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
content_type: text
index: climate_gpt_only_giec
vector_id: 29688
content:
"Since AR5, understanding of poleward expansion of the Hadley Cells \r\nhas improved (Section 2.3.1.4.1) but its role in subtropical drying is \r\nlimited to the zonal mean and dominated by ocean regions (Byrne \r\nand O'Gorman, 2015; Grise and Polvani, 2016; He and Soden, 2017; \r\nSchmidt and Grise, 2017; Siler et al., 2018; Chemke and Polvani, 2019; \r\nGrise and Davis, 2020). Over subtropical land, evolving SST patterns \r\nand land-ocean warming contrasts, that are partly explained by rapid \r\nresponses to CO2 increases, can dominate aspects of the atmospheric \r\ncirculation response (Byrne and O'Gorman, 2015; He and Soden, 2015; \r\nChadwick et al., 2017; H. Yang et al., 2020) and resultant regional \r\nwater cycle changes, particularly for projected drying in semi-arid, \r\nwinter-rainfall dominated subtropical climates (Deitch et al., 2017; \r\nBrogli et al., 2019; Seager et al., 2019b; Zappa et al., 2020)"


id: 57c5fa7c8175e641d5f89288d8e5bcd7
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
content_type: text
index: climate_gpt_only_giec
vector_id: 11306
content:
" Poleward \r\nexpansion of the tropical belt is expected to drive a corresponding \r\nshift in mid-latitude storm tracks, but the controlling mechanisms \r\ndiffer between hemispheres. Southern Hemisphere expansion is driven \r\nby GHG forcing and amplified by stratospheric ozone depletion, while "


id: 25816bfaf6544e205a7235a16b015dd3
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
content_type: text
index: climate_gpt_only_giec
vector_id: 3325
content:
"Understanding of how ITCZ width and strength respond to a warming \r\nclimate has improved since AR5 (Byrne and Schneider, 2016; Harrop and \r\nHartmann, 2016; Popp and Silvers, 2017; Dixit et al., 2018; Zhou et al., \r\n2020). Studies suggest that convection gets stronger and more focused \r\nwithin the core of the ITCZ (Lau and Kim, 2015; Byrne et al., 2018). This \r\nleads to drying on the equatorward edges of the ITCZ and a moistening \r\ntendency in the ITCZ core (Byrne and Schneider, 2016). Feedbacks \r\ninvolving clouds have been identified as an important mechanism \r\nleading to tightening and strengthening of the ITCZ (Popp and Silvers, \r\n2017; Su et al., 2017, 2019, 2020; Talib et al., 2018). Stronger ascent \r\nin the core amplifies the 'wet get wetter' response while reduced \r\nmoisture inflow near the ITCZ edges reduces this response below the \r\n7%  degC-1 thermodynamic increase in moisture transport"


id: d13a3090c812f7ea6419edf6dace5bac
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
content_type: text
index: climate_gpt_only_giec
vector_id: 30459
content:
" Thus, there \r\nis a  range of evidence and medium agreement for strengthening \r\nand contraction of the ITCZ with warming that sharpens contrasts \r\nbetween wet and dry regimes. However, understanding of how the \r\nregional ITCZ location responds in a  warming climate is not robust "


id: 2b814db2465e9386e2c441b3916dc4bd
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
content_type: text
index: climate_gpt_only_giec
vector_id: 4285
content:
"Declining ice-sheet mass, glacier extent and Northern Hemisphere \r\n(NH) sea ice, snow cover and permafrost (Collins et al., 2013; \r\nVaughan et al., 2013) is an expected consequence of a  warming \r\nclimate (Sections  2.3.2, 3.4, 4.3.2.1 and 9.3-9.5). A  decline in \r\nmountain snow cover and increased snow and glacier melt will \r\nalter the amount and timing of seasonal runoff in mountain regions \r\n(Sections  3.4.2, 3.4.3 and 9.5). Earlier and more extensive winter \r\nand spring snowmelt (X. Zeng et al., 2018) can reduce summer and \r\nautumn runoff in snow-dominated river basins of mid-high latitudes \r\nof the NH (Rhoades et al., 2018; Bloschl et al., 2019). Since AR5, \r\nan earlier but less rapid snowmelt has been explained by reduced \r\nwinter snowfall and less intense solar radiation earlier in the season \r\n(Musselman et al., 2017; Wu et al., 2018; Grogan et al., 2020)"


id: c2275908e72588e43eee6d53714075e1
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
content_type: text
index: climate_gpt_only_giec
vector_id: 28128
content:
" \r\nReduced snow cover also increases energy available for evaporation, \r\nwhich can dominate declining river discharge based on modelling \r\nof the Colorado River (Milly and Dunne, 2020). An increase in the \r\nfraction of precipitation falling as rain compared with snow can lead \r\nto declines in both streamflow and groundwater storage in regions \r\nwhere snowmelt is the primary source of recharge (Earman and \r\nDettinger, 2011; Berghuijs et al., 2014). Such regions include western \r\nSouth America and western North America, semi-arid regions which \r\nrely on snowmelt from high mountain chains (Ragettli et al., 2016; \r\nMilly and Dunne, 2020). Rain-on-snow melt events reduce at lower \r\naltitudes due to declining snow cover but increase at higher altitudes \r\nwhere snow tends to be replaced by rain based on observations and \r\nmodelling (Musselman et al., 2018; Pall et al., 2019), thereby altering \r\nseasonal and regional characteristics of flooding (Section 11.5)."


id: f7cda225410a5d4578e1f60f42d5bb29
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
content_type: text
index: climate_gpt_only_giec
vector_id: 36508
content:
"The influence of amplified Arctic warming on mid-latitude regional \r\nwater cycles is not well understood based on simple physical \r\ngrounds due to the large number of competing physical processes \r\n(Cross-Chapter Box 10.1). The thermal gradient between polar and \r\nlower latitude regions decreases at low levels due to Arctic warming \r\namplification. However, at higher altitudes, the corresponding \r\nthermal gradient increases with warming due to cooling of the Arctic \r\nstratosphere and this is consistent with a strengthening of the winter \r\njet stream in both hemispheres, yet there is low agreement on the \r\nprecise mechanisms (Vallis et al., 2015; Vihma et al., 2016). Changes \r\nin the strength of the polar stratospheric vortex can also alter the \r\nmid-latitude circulation in winter, but responses are not consistent \r\nacross models (Oudar et al., 2020a). Nevertheless, thermodynamic \r\nstrengthening of moisture convergence into weather systems and \r\npolar regions is robust (Section 8.2.2"


id: e549f6b9f3f3d7dfc53bd4fab60908b9
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vector_id: 33570
content:
"In summary, there is high confidence that altered atmospheric \r\nwind patterns in response to radiative forcing and evolving \r\nsurface temperature patterns will affect the regional water cycle \r\nin most regions. Mean tropical circulation is expected to slow with \r\nglobal warming (high confidence) but temporary multi-decadal \r\nstrengthening is possible due to internal variability (medium \r\nconfidence). Slowing of the tropical circulation reduces the meridional \r\nP-E gradient over the Pacific and can partly offset thermodynamic \r\namplification of P-E patterns and strengthening of monsoons (high \r\nconfidence) but regional characteristics of tropical rain belt changes \r\nare not well understood. There is medium confidence in processes \r\ndriving strengthening and tightening of the ITCZ that increase \r\nthe contrasts between wet and dry tropical weather regimes and \r\nseasons"


id: 272aa22aa247797d67a1dcc1cfa0fee3
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
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vector_id: 3557
content:
" There is high confidence in understanding of how radiative \r\nforcing and global warming drive a  poleward expansion of the \r\nsubtropics and mid-latitude storm tracks but only low confidence in \r\nhow poleward expansion influences drying of subtropical and mid\u0002latitude climates. There is low confidence in understanding how Arctic \r\nwarming amplification affects mid-latitude regional water cycles but \r\nhigh confidence that thermodynamic strengthening of precipitation \r\nwithin weather systems and in monsoons and polar regions is robust \r\nto large-scale circulation changes."


id: 8cd793a7f7c6bb17b371361d7a75ade4
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
content_type: text
index: climate_gpt_only_giec
vector_id: 19676
content:
"Seasonal melt water from high mountain glaciers in Asia (see Cross\u0002Chapter Box 10.4) supply the basic needs of 221 +- 97 million people \r\n(Pritchard, 2019; Immerzeel et al., 2020). Glacier-melt in response to \r\nwarming can initially lead to increased runoff volumes, especially in \r\npeak summer flows, but they will eventually decline as most glaciers \r\ncontinue to shrink. SROCC concluded there is high confidence that \r\nthe peak runoff has already been passed for some smaller glaciers \r\n(Hock et al., 2019a). Increased precipitation and glacier-melt can also \r\ncontribute to rising lake levels and flood hazards in regions such as \r\nthe inner Tibetan Plateau, Patagonia, Peru, Alaska and Greenland \r\n(Lei et al., 2017; Shugar et al., 2020; Stuart-Smith et al., 2020)"


id: 1eaf8aae6d68d6db4dcab0e38b6d483f
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
content_type: text
index: climate_gpt_only_giec
vector_id: 2270
content:
" Since \r\nAR5, evidence from multiple locations (New Zealand, Greenland, \r\nAntarctica) shows that intrusions of warm, moist air are important in \r\ncontrolling glacier mass balance, the likelihood of extreme ablation \r\nor snowfall events depending on air temperature (Gorodetskaya \r\net al., 2014; Mackintosh et al., 2017; Mattingly et al., 2018; \r\nLittle et al., 2019; Oltmanns et al., 2019; Wille et al., 2019; Adusumilli \r\net al., 2021). Sensible heating from warm air and increased longwave \r\nradiation from atmospheric moisture and low clouds drive melt \r\nevents (Stuecker et al., 2018). "


id: 287106a818a630c9d8b30f350102b8a1
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
content_type: text
index: climate_gpt_only_giec
vector_id: 3777
content:
"evidence from simplified calculations, convection resolving models and \r\nobservations that thermodynamics drives an increase in convective \r\navailable potential energy (CAPE) with warming and therefore the \r\nintensity of convective storms (Singh and O'Gorman, 2013; Romps, \r\n2016; Barbero et al., 2019). Also, declining relative humidity over land \r\n(Sections 2.3.1.3.2 and 8.2.2.1) increases lifting condensation level, \r\nthereby delaying but intensifying convective systems (Louf et al., \r\n2019; J. Chen et al., 2020a). Larger systems are linked with increasing \r\ntropopause height (Lenderink et al., 2017) that can also amplify storm \r\nprecipitation (Prein et al., 2017). However, the heaviest rainfall is not \r\nnecessarily associated with the most intense (deepest) storms based \r\non satellite data (Hamada et al., 2015; Hamada and Takayabu, 2018)"


id: e0f05fb65d80d291b6d24cb995d923ed
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
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index: climate_gpt_only_giec
vector_id: 32889
content:
" \r\nPrecipitation intensification can exceed thermodynamic expectations \r\nwhere and when additional latent heating invigorates individual \r\nstorms (Section  11.4.1) as implied by medium agreement across \r\nmodelling and observational studies (Berg et al., 2013; Molnar et al., \r\n2015; Scoccimarro et al., 2015; Prein et al., 2017; Zhou and Wang, \r\n2017; Nie et al., 2018; Kendon et al., 2019; Z.  Zhang et al., 2019). \r\nThis intensification depends on time of day, based on convection\u0002permitting simulations (E.P. Meredith et al., 2019)."


id: ad68388a0484a9eb7bbf15acf874139
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
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vector_id: 24819
content:
"Intensification of sub-daily rainfall is inhibited in regions and seasons \r\nwhere available moisture is limited (Prein et al., 2017). However, \r\na fixed threshold temperature above which precipitation is limited \r\nby moisture availability is not supported by modelling evidence \r\n(Neelin et al., 2017; Prein et al., 2017). Enhanced latent heating \r\nwithin storms can also suppress convection at larger scales due to \r\natmospheric stabilization as demonstrated with high resolution, \r\nidealized and large ensemble modelling studies (Loriaux et al., 2017; \r\nChan et al., 2018; Nie et al., 2018; Tandon et al., 2018; Kendon et al., \r\n2019). Stability is also increased by the direct radiative heating effect \r\nof higher CO2 concentrations (Baker et al., 2018) and  influenced \r\nby aerosol effects on the atmospheric energy budget and cloud \r\ndevelopment (Box  8.1)"


id: cf351e4cc1983a1d66deb26e4b6b52f5
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
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index: climate_gpt_only_giec
vector_id: 30137
content:
" Since AR5, modelling evidence shows \r\nincreases in convective precipitation extremes are limited by droplet/\r\nice fall speeds (Singh and O'Gorman, 2014; Sandvik et al., 2018) \r\nbut these processes are only crudely represented (Tapiador et al., \r\n2019a). Idealized regional and coupled global models combined \r\nwith limited observational evidence shows that instantaneous \r\nprecipitation extremes are sensitive to microphysical processes, while \r\ndaily extremes are determined more by the degree of convective \r\naggregation (Bao and Sherwood, 2019; Pendergrass, 2020a). "


id: d0fcb2ff14272277a24d393ef2a13ec7
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
content_type: text
index: climate_gpt_only_giec
vector_id: 30412
content:
"Evidence that heavy precipitation events (from sub-daily up to \r\nseasonal time scales) intensify as the planet warms has strengthened \r\nsince AR5 (Section 11.4, Box 11.1 and Cross-Chapter Box 3.2) based \r\non improved physical understanding, extensive modelling and \r\nincreasing observational corroboration (O'Gorman, 2015; Fischer \r\nand Knutti, 2016; Neelin et al., 2017). There is robust evidence, with \r\nmedium agreement across a range of modelling and observational \r\nstudies, of thermodynamic intensification of wet seasons (Chou \r\net al., 2013; Liu and Allan, 2013; Dunning et al., 2018; Lan et al., \r\n2019; Zhang and Fueglistaler, 2019). Extreme daily precipitation is \r\nexpected to increase at close to the 7% degC-1 increase in the near\u0002surface atmospheric moisture-holding capacity determined by the \r\nClausius-Clapeyron equation (Section 11.4, Figure 8"


id: 780059916ae28c99b70c6a3b448c262c
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
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index: climate_gpt_only_giec
vector_id: 16441
content:
"4), with limited \r\nevidence that higher rates apply for shorter duration precipitation \r\nevents (Formayer and Fritz, 2017; Lenderink et al., 2017; Ali et al., \r\n2018; Guerreiro et al., 2018; Burdanowitz et al., 2019; W.  Zhang \r\net al., 2019a). However, observed estimates sample multiple synoptic \r\nweather states, mixing thermodynamic and dynamic factors, so are \r\nnot directly relatable to climate change responses (Bao et al., 2017; \r\nDrobinski et al., 2018). The contrasting spatial scales sampled by the \r\nobservations and models (from global to cloud resolving) explain \r\nthe large range of daily and sub-daily precipitation scaling with \r\ntemperature assessed in Figure 8.4."


id: 4ab9f2a6bfbf47235075f8bbd0f4ed33
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
content_type: text
index: climate_gpt_only_giec
vector_id: 9167
content:
"Dynamical changes modify and can dominate thermodynamic drivers \r\nof local rainfall and flood hazard change (Box  11.1). For example, \r\nincreased land-ocean temperature gradients (Section 8.2.2.2) explain \r\nmore intense rain from convective systems over the Sahel based on \r\nsatellite data since the 1980s (Taylor et al., 2017) and dynamical \r\nfeedbacks can invigorate active to break phase transition over India \r\n(Karmakar et al., 2017; Roxy et al., 2017). Satellite data shows long-lived, \r\norganized mesoscale convective systems contribute disproportionally \r\nto extreme tropical precipitation (Roca and Fiolleau, 2020). Since AR5, \r\nthe spatial variability in soil moisture has been linked with the timing \r\nand location of convective rainfall by altering the partitioning between \r\nlatent and sensible heating. This was demonstrated for the Sahel, \r\nEurope and India in observations (C.M. Taylor et al., 2013; Taylor, 2015; \r\nPetrova et al., 2018; Barton et al"


id: 32a5c1808fd35196cf7f6218b4587427
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
content_type: text
index: climate_gpt_only_giec
vector_id: 5385
content:
"The amount and intensity of rainfall within extratropical storms is \r\nexpected to increase with atmospheric moisture. This is particularly \r\nevident for atmospheric rivers (see Glossary) and research since AR5 \r\nhas confirmed their link with flooding and terrestrial water storage \r\n(Froidevaux and Martius, 2016; Paltan et al., 2017; Waliser and \r\nGuan, 2017; Adusumilli et al., 2019; Ionita et al., 2020; Payne et al., \r\n2020). There is robust evidence based on simple physics and detailed \r\nmodelling that extratropical cyclone rainfall, including atmospheric \r\nriver events, will intensify through increased atmospheric moisture \r\nflux (Lavers et al., 2013; Ramos et al., 2016; Yettella and Kay, 2017; \r\nV. Espinoza et al., 2018; Algarra et al., 2020; Xu et al., 2020; Zavadoff \r\nand Kirtman, 2020; Zhao, 2020), although changes in dynamical \r\naspects will modify responses regionally (Section  8.4.2.8)"


id: e89d160f178193c336b1901749076bcf
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
content_type: text
index: climate_gpt_only_giec
vector_id: 34109
content:
" For \r\nexample, stronger latitudinal temperature gradients in the high\u0002latitude upper troposphere drive increased extratropical storm \r\nspeed around 30degN-70degN based on CMIP5 simulations (Dwyer and \r\nO'Gorman, 2017), causing reduced precipitation accumulation."


id: a2a85f9eb68b4a08b30c214ccd0e144a
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
content_type: text
index: climate_gpt_only_giec
vector_id: 23157
content:
"In summary, there is very high confidence that heavy precipitation \r\nevents will become more intense in a  warming climate. There is \r\nhigh confidence that increased moisture and its convergence within \r\nextratropical and tropical cyclones and storms will increase rainfall \r\ntotals during wet events at close to the 7%  degC-1 thermodynamic \r\nresponse, with low confidence of higher rates for sub-daily intensities. \r\nThere is medium confidence that more intense but less frequent \r\nrainfall increases the proportion of rainfall leading to surface runoff \r\nand focused groundwater recharge from temporary water bodies. \r\nThere is low confidence in how the frequency of flooding will change \r\nregionally as it is strongly dependent on catchment characteristics, \r\nantecedent conditions and how atmospheric circulation systems \r\nrespond to climate change, which is less certain than thermodynamic \r\ndrivers (Section 11.5)"


id: 9ea91dd9222d24168082f835082e1acf
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
content_type: text
index: climate_gpt_only_giec
vector_id: 22501
content:
" However, there is high confidence that increases \r\nin precipitation intensity and amount during very wet events (from \r\nsub-daily up to seasonal time scales) will intensify severe flooding \r\nwhen these extremes occur."


id: 9b88284eee0d12ad513ac75a3875fbf4
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
content_type: text
index: climate_gpt_only_giec
vector_id: 22024
content:
"The response of flood hazard to changing rainfall characteristics \r\ndepends on time and space scale and the nature of the land surface \r\n(Section 11.5.1 and FAQ 8.2). Sustained and heavy rainfall can lead \r\nto widespread flooding and landslides while intensification of short\u0002duration intense rainfall can increase the severity and frequency \r\nof flash flooding (Marengo et al., 2013; Chan et al., 2016; Gariano \r\nand Guzzetti, 2016; Sandvik et al., 2018). Flooding events in many \r\ntropical regions (e.g., north-western South America, southern Africa \r\nand Australasia) are associated with ENSO variability (Emerton et al., \r\n2017; Takahashi and Martinez, 2019; Pabon-Caicedo et al., 2020) and \r\namplified by thermodynamic increases in water vapour. Flood hazard \r\nfrom heavy rainfall is modulated by snowmelt (Section  8.2.3.1), \r\nvegetation characteristics (Page et al., 2020; Murphy et al., 2021) and \r\ndirect human intervention (Sections  8.2.3.4 and FAQ 8"


id: 734dc90ea8309c408110c9e3665f59db
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
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index: climate_gpt_only_giec
vector_id: 15760
content:
"2) but also \r\ncan be compounded by sea level rise (Sections 4.3.2.2 and 9.6.4) in \r\ncoastal and delta regions (Bevacqua et al., 2019; Ganguli and Merz, \r\n2019; Eilander et al., 2020). Antecedent soil moisture conditions are \r\nan important modulator of flooding (Section 11.5.1) but become less \r\nimportant for smaller catchments and for more severe floods (Wasko \r\nand Nathan, 2019). Depleted soil moisture after more intense dry \r\nseasons (Section  8.2.2.1) can allow greater uptake of wet season \r\nrainfall before soils saturate. Since AR5, evidence confirms that more \r\nintense rainfall increases the proportion of runoff and reservoir \r\nrecharge relative to infiltration into the soil (Eekhout et al., 2018; Yin "


id: 3c8ce28c25820d467165066cad91759a
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
content_type: text
index: climate_gpt_only_giec
vector_id: 6895
content:
"Regional changes in aridity  - broadly defined as a  deficit of \r\nmoisture  - are expected to occur in response to anthropogenic \r\nforcings as a consequence of shifting precipitation patterns, warmer \r\ntemperatures, changes in cloudiness (affecting solar radiation), \r\ndeclining snowpack, changes in winds and humidity, and vegetation \r\ncover (Figure  8.6). Evapotranspiration (see Annex VII: Glossary) \r\nis a  key component of aridity, and is composed of two main \r\nprocesses: evaporation from soil, water and vegetation surfaces; \r\nand transpiration, the exchange of moisture between plants and \r\natmosphere through plant stomata. On a  global level, warmer \r\ntemperatures increase evaporative demand in the atmosphere, and \r\nthus (assuming sufficient soil moisture is available) increase moisture \r\nloss from evapotranspiration (high confidence) (Dai et al., 2018; \r\nVicente-Serrano et al., 2020)"


id: 96aaa888bb99ed9346860dd141e6b150
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
content_type: text
index: climate_gpt_only_giec
vector_id: 21225
content:
" On a regional level, aridity is further \r\nmodulated by seasonal rainfall patterns, runoff, water storage, and \r\ninteractions with vegetation. "


id: 5535428287362305e02763f95a906b9f
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
content_type: text
index: climate_gpt_only_giec
vector_id: 10866
content:
"et al., 2020). Societies have developed a  variety of strategies to \r\nmanipulate the water cycle to increase resiliency in the face of water \r\nscarcity, including irrigation, creation of artificial reservoirs, and \r\ngroundwater pumping. While potentially buffering water resource \r\ncapacity, in some cases these interventions may unexpectedly \r\nincrease vulnerability (medium confidence). For example, while \r\nincreased irrigation efficiency may ensure more water is available \r\nto crops, the corresponding reduction in runoff and subsurface \r\nrecharge may exacerbate hydrologic drought (Grafton et al., 2018). \r\nFurthermore, while building dams and increasing surface reservoir \r\ncapacity can boost water resources, they may actually increase \r\ndrought vulnerability if demands rise to take advantage of the \r\nincreased supply or if over-reliance on these surface reservoirs is \r\nencouraged (Di Baldassarre et al., 2018)"


id: 5f8661c91fba078e545e98a16737df8d
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
content_type: text
index: climate_gpt_only_giec
vector_id: 12600
content:
" Interactions between \r\nadaptation, vulnerability, and drought impacts are discussed further \r\nin WGII (Chapters 2 and 4)."


id: fe2519adbe14c40659982685c0d2ffbc
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
content_type: text
index: climate_gpt_only_giec
vector_id: 37512
content:
"A drought is a period of abnormally dry weather that persists for long \r\nenough to cause a serious hydrological imbalance (Glossary; Wilhite \r\nand Glantz, 1985; Wilhite, 2000; Cook et al., 2018). Most droughts \r\nbegin as persistent precipitation deficits ('meteorological drought') \r\nthat propagate over time into deficits in soil moisture, streamflow, \r\nand water storage (Figure 8.6), leading to a reduction in water supply \r\n('hydrological drought'). Increased atmospheric evaporative demand \r\nincreases plant water stress, leading to 'agricultural and ecological \r\ndrought' (Williams et al., 2013; C.D. Allen et al., 2015; Anderegg et al., \r\n2016; McDowell et al., 2016; Grossiord et al., 2020). Evaporative \r\ndemand affects plants in two ways. It increases evapotranspiration, \r\ndepleting soil moisture and stressing plants through lack of water \r\n(Teuling et al., 2013; Sperry et al"


id: 2303a857ae3e2ea2952763ade9399bb9
created_at: 2023-03-16 14:02:03
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vector_id: 2954
content:
", 2016), and also directly affects plant \r\nphysiology, causing a decline in hydraulic conductance and carbon \r\nmetabolism, leading to mortality (Figure 8.6; Breshears et al., 2013; \r\nHartmann, 2015; McDowell and Allen, 2015; Fontes et al., 2018). \r\nWhile droughts are traditionally viewed as 'slow moving' disasters \r\nthat typically take months or years to develop, rapidly evolving and \r\noften unpredictable flash droughts can also occur (Otkin et al., 2016, \r\n2018). Flash droughts can develop within a  few weeks, causing \r\nsubstantial disruption to agriculture and water resources (Pendergrass \r\net al., 2020). Conversely, droughts that persist for a long time (usually \r\na decade or more) are called megadroughts. Droughts span a large \r\nrange of spatial and temporal scales, arise through a  variety of \r\nclimate system dynamics (e.g., internal atmospheric variability, ocean \r\nteleconnections), and can be amplified or alleviated by a variety of \r\nphysical and biological processes"


id: 5d8bdce94ca628538765d76fb1c3f5fd
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
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index: climate_gpt_only_giec
vector_id: 12241
content:
" As such, droughts occupy a unique \r\nspace within the framework of extreme climate and weather events, \r\npossessing no singular definition."


id: 576d40275a5e11a502f9cc5bc11c442f
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
content_type: text
index: climate_gpt_only_giec
vector_id: 11247
content:
"Human activities influence the regional water cycle directly through \r\nmodifying and exploiting stores and flows from rivers, lakes and \r\ngroundwater and by altering land cover characteristics. These \r\nactions alter surface energy and water balances through changes in \r\npermeability, surface albedo, evapotranspiration, surface roughness \r\nand leaf area. Direct redistribution of water by human activities for \r\ndomestic, agricultural and industrial use of about 24,000  km3 yr-1\r\n(Figure 8.1) is equivalent to half the global river discharge or double \r\nthe global groundwater recharge each year (Abbott et al., 2019). Since \r\nAR5, both modelling studies and observations have demonstrated \r\nthat land use change can drive local and remote responses in \r\nprecipitation and river flow by altering the surface energy balance, \r\nmoisture advection and recycling, land-sea thermal contrast and \r\nassociated wind patterns (Alter et al., 2015; Wey et al., 2015; De Vrese \r\net al., 2016; Pei et al"


id: e771898afb6f19584e7271959929d3d
created_at: 2023-03-16 14:02:03
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vector_id: 33906
content:
", 2016; Wang-Erlandsson et al., 2018; Vicente\u0002Serrano et al., 2019). There is robust evidence that a warming climate \r\ncombined with direct human demand for groundwater will deplete \r\ngroundwater resources in already dry regions (Wada and Bierkens, \r\n2014; D'Odorico et al., 2018; Jia et al., 2019)."


id: 4ad507d4706ebd6c5e34f3b0752d0c92
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
content_type: text
index: climate_gpt_only_giec
vector_id: 9190
content:
"While the role of precipitation in droughts is obvious, other climatic \r\ndrivers are also important, such as temperature, radiation, wind, \r\nand humidity (Figure  8.6). These factors have a  strong influence on \r\natmospheric evaporative demand, which affects evapotranspiration \r\nand soil moisture (Figure  8.6). In snow-dominated regions, high \r\ntemperatures increase the fraction of precipitation falling as rain \r\ninstead of snow and advance the timing of spring snowmelt (high \r\nconfidence) (Vincent et al., 2015; Mote et al., 2016, 2018; Berg and \r\nHall, 2017; Solander et al., 2018). This can result in lower than normal \r\nsnowpack levels (a 'snow drought'), and thus reduced streamflow, even \r\nif total precipitation is at or above normal for the cold season (Harpold \r\net al., 2017). Plants also affect the severity of droughts by modulating \r\nevapotranspiration (Figure  8.6)"


id: 8801274b06c6ca644b4af4686233c6cc
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
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index: climate_gpt_only_giec
vector_id: 18921
content:
" As discussed above, the  effect of \r\nelevated CO2 on plants has the potential to both increase and reduce \r\nwater loss through evapotranspiration via enhanced WUE and plant \r\ngrowth, respectively (Figure 8.6), but there is low confidence in whether \r\none process dominates over another at the global scale. "


id: 240ef64cc456315cb410eacf542fbd0d
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
content_type: text
index: climate_gpt_only_giec
vector_id: 3116
content:
"Changes in land use from forest to agriculture can exert profound \r\nregional effects on the water cycle (FAQ 8.1) by modifying the \r\nsurface energy balance and moisture recycling (Krishnan et al., 2016; \r\nPaul et al., 2016; Llopart et al., 2018; Singh et al., 2019). There is \r\nmedium evidence from modelling and observations over the Amazon \r\nand East Africa that deforestation drives increased streamflow \r\n(Dos Santos et al., 2018; Guzha et al., 2018; Levy et al., 2018) but \r\nlimited evidence that increases in global runoff due to deforestation \r\nare counterbalanced by decreases resulting from irrigation (Hoegh\u0002Guldberg et al., 2018). Total Amazon deforestation drives reductions \r\nin precipitation but with a large 90% confidence range (-38 to +5 %) \r\nbased on 44 primarily pre-AR5 climate model simulations (Spracklen \r\nand Garcia-Carreras, 2015) with smaller reductions (-2.3 to -1.3 %) \r\nattributed to observed Amazon deforestation up to 2010"


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vector_id: 5579
content:
" Climate \r\nmodel development has reduced this uncertainty range but has not \r\naltered the median change (Lejeune et al., 2015). Large-scale global \r\ndeforestation (20 million km2) simulated by 9 CMIP6 models confirms \r\na large range in precipitation amount reduction of -37 +- 54 mm yr-1\r\nover the deforested regions (Boysen et al., 2020). However, small\u0002scale deforestation can increase precipitation locally (Lawrence and \r\nVandecar, 2015). A 50-60% deforestation rate corresponded to a wet \r\nseason delay of about one week and greater chance of dry spells \r\nof eight days or longer based on correlation analysis of rain gauge \r\nand land-use data for South America (Leite-Filho et al., 2019). Forest \r\nand grassland fires can also modify hydrological response at the \r\nwatershed scale (Havel et al., 2018). Afforestation or reforestation \r\naimed at removing CO2 from the atmosphere can also alter the water \r\ncycle at the regional scale (Section 8.4.3 and Cross-Chapter Box 5.1)."


id: 2a35ae0c85a28722f56bd7175b63684e
created_at: 2023-03-16 14:02:03
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vector_id: 4065
content:
"and atmospheric water vapour locally (Jia et al., 2019; Mishra \r\net al., 2020). Irrigation can explain declining groundwater storage \r\nin some regions, including north-western India and North America \r\n(Asoka et al., 2017; G. Ferguson et al., 2018). Simulations spanning \r\n1960-2010 indicate that approximately 30% of the present human \r\nwater consumption is supplied from non-sustainable water resources \r\n(Wada and Bierkens, 2014). However, there is only limited evidence \r\nthat groundwater extraction is lowering streamflow (Mukherjee \r\net al., 2018; de Graaf et al., 2019). Model experiments show that \r\nirrigation can either aggravate or alleviate climate-induced changes \r\nof surface or subsurface water (Leng et al., 2015). Widespread \r\nextraction of water from rivers can reduce flows and decrease the \r\nlevel and area of inland seas and lakes (Wurtsbaugh et al., 2017; \r\nTorres-Batllo et al., 2020; X. Wang et al., 2020)"


id: 5404a091f5b3a4803a7bcdc5f85fcbb8
created_at: 2023-03-16 14:02:03
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content:
" Between 1985 and \r\n2015, about 139,000 km2 of inland water areas have become land, \r\nwhile creation of dams has converted about 95,000 km2 of land to \r\nwater, particularly in the Amazon and Tibetan Plateau (Donchyts et al., \r\n2016). Direct management of river flow is comparable in magnitude \r\nto climate change effects for snow-fed rivers at a continental scale \r\nbased on a global analysis and a study of 96 Canadian catchments \r\n(Tan and Gan, 2015; Arheimer et al., 2017). "


id: 4c36f69058b0f7adad80db54723d554f
created_at: 2023-03-16 14:02:03
updated_at: 2023-03-16 14:02:03
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vector_id: 9413
content:
"The SRCCL assessed with medium confidence that mean and extreme \r\nprecipitation is increased over and downwind of urban areas (Jia \r\net al., 2019). There is medium confidence that altered thermodynamic \r\nand aerodynamic properties of the land surface from urbanization \r\naffects evaporation and increases precipitation over or downwind of \r\ncities (Box 10.3) due to altered stability and turbulence (Han et al., \r\n2014; Pathirana et al., 2014; Jiang et al., 2016; D'Odorico et al., 2018; \r\nSarangi et al., 2018; Boyaj et al., 2020). However, reduced biogenic \r\naerosol, but increased anthropogenic aerosol emissions modify \r\ncloud microphysics and precipitation processes (Box  8.1; Schmid \r\nand Niyogi, 2017; D'Odorico et al., 2018; Fan et al., 2020; Zheng \r\net al., 2020). Urbanization also decreases permeability of the surface, \r\nleading to increased surface runoff (Chen et al., 2017; Jia et al., 2019)"


id: 120cb3cc80672cb9f9a83d0cdf5ccab5
created_at: 2023-03-16 14:02:03
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content:
" \r\nLarge-scale infrastructure, such as the construction and operation of \r\ndikes, weirs, and hydropower plants, also alters surface energy and \r\nmoisture fluxes, potentially influencing the regional water cycle. \r\nLimited modelling evidence suggests that large-scale solar and wind \r\nfarms can increase precipitation locally (over the Sahel and North \r\nAmerica) when dynamic vegetation responses are represented \r\n(Y. Li et al., 2018; Pryor et al., 2020), with remote effects also possible \r\n(Lu et al., 2021). "


id: 7182bb4c5f09bce8f871e5e319d440be
created_at: 2023-03-16 14:02:03
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content:
"Dimming over the NH causes a relative cooling, compared to the Southern Hemisphere (SH), which induces a southward shift of the \r\nnorthern edge of the tropical rain belt (Section 3.3.2.3; Allen et al., 2014; Bronnimann et al., 2015). CMIP5 simulations show that most of the \r\ncooling is caused by the aerosol cloud-mediated effect (Chung and Soden, 2017). Dimming also weakens monsoon flow and precipitation, \r\noffsetting or even overcoming the expected precipitation increase due to increased GHGs (Ayantika et al., 2021). The oceanic response to \r\na weakened monsoon cross-equatorial flow can further weaken the South Asian monsoon through an amplifying feedback loop (Swapna \r\net al., 2012; Krishnan et al., 2016; Patil et al., 2019). These processes partially explain (medium confidence) the southward shift of the NH \r\ntropical edge of the tropical rain belt from the 1950s to the 1980s (Allen et al., 2014; Bronnimann et al"


id: a0aa71f8b9f1a7e22e8f6f698923f521
created_at: 2023-03-16 14:02:03
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content:
", 2015) and the severe drought in \r\nthe Sahel that peaked in the mid-1980s (Rotstayn et al., 2002; Undorf et al., 2018b). These processes also explain (high confidence) the \r\nobserved decrease of South East Asian monsoon precipitation during the second half of the 20th century (Figure 8.7; Bollasina et al., 2011; \r\nSanap et al., 2015; Krishnan et al., 2016; Lau and Kim, 2017; Lin et al., 2018; Undorf et al., 2018b). "


id: 78368aa5a8594f05c66bc0ed5fbb454f
created_at: 2023-03-16 14:02:03
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vector_id: 16480
content:
"Cloud droplets nucleate on pre-existing aerosol particles which act as cloud condensation nuclei (CCN). Anthropogenic aerosols \r\nadd CCN, compared to a pristine background, and produce clouds with more numerous and smaller droplets, slower to coalesce \r\ninto raindrops and to freeze into ice hydrometeors at temperatures below 0degC. Adding CCN suppresses light rainfall from shallow \r\nand short-lived clouds, but it is compensated by heavier rainfall from deep clouds. Adding aerosols to clouds in extremely clean air \r\ninvigorates them by more efficient vapour condensation on the added drop surfaces (Koren et al., 2014; Fan et al., 2018). Clouds \r\nforming in more polluted air masses (hence with more numerous and smaller drops) need to grow deeper to initiate rain (Freud \r\nand Rosenfeld, 2012; Konwar et al., 2012; Campos Braga et al., 2017)"


id: e347830b1a17fb1d0860758cac1125b0
created_at: 2023-03-16 14:02:03
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vector_id: 33251
content:
" This leads to larger amount of cloud water evaporating aloft \r\nwhile cooling and moistening the air there at the expense of the lower levels, which leads to convective invigoration (Dagan et al., \r\n2017; Chua and Ming, 2020), followed by convergence, air mass destabilization and added rainfall in an amplifying feedback loop "


id: ec0225375cce0636e31efbfd33aa2bcb
created_at: 2023-03-16 14:02:04
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vector_id: 34629
content:
"Ice nucleating particle (INP) initiate ice precipitation from persistent supercooled water clouds that have cloud droplets too small for \r\nefficient warm rain, or expedite mixed-phase precipitation in short-lived supercooled rain clouds (Creamean et al., 2013). Most INPs \r\nare desert and soil dust particles, rather than air pollution aerosols (DeMott et al., 2010). Biogenic particles from terrestrial and marine \r\norigin are more rare, but important at temperatures above about -15degC (Murray et al., 2012; DeMott et al., 2016). Dust particles \r\nfrom long-range transport across the Pacific were found to enhance snow-forming processes over the Sierra Nevada in California \r\n(Creamean et al., 2013; Fan et al., 2014). The impact of INPs was demonstrated by glaciogenic cloud seeding experiments, which \r\nenhanced orographic supercooled clouds with medium confidence of success (French et al., 2018; Rauber et al., 2019; Friedrich et al., \r\n2020)"


id: 2c027401a8de4dea0777a5f55a536aab
created_at: 2023-03-16 14:02:04
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vector_id: 4355
content:
" There are still major gaps in understanding the effects of INPs mainly on deep convective clouds (Kanji et al., 2017; Stanford \r\net al., 2017; Korolev et al., 2020)."


id: 21f4885076e0df1f1dcfa8aae2b90111
created_at: 2023-03-16 14:02:04
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vector_id: 2788
content:
"This section focuses on the evaluation and attribution of past and \r\nrecent water cycle changes using observational datasets, theoretical \r\nunderstanding and model simulations. Paleoclimate records and \r\nhistorical observations provide evidence for past water cycle changes \r\ncaused both by natural variability and human activities (Haug et al., \r\n2003; Buckley et al., 2010; Pederson et al., 2014). Key elements of \r\nthe observed water cycle changes are assessed in this section, \r\nincluding flux and storage variations across the atmosphere, the \r\ncontinents and to a lesser extent the ocean and cryosphere, as well \r\nas related changes in large-scale atmospheric circulation and modes \r\nof variability. Particular emphasis is placed on assessing changes \r\nacross regions and seasons (Box 8.2). Detailed regional assessments \r\nare presented in Chapters 10, 11, 12 and Atlas"


id: 5361863e546197aa4ea567975cc09e8e
created_at: 2023-03-16 14:02:04
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content:
" Further information \r\nconcerning large-scale observed water cycle changes and their \r\nattribution is available in Sections 2.3.1.3 and 3.3.2. "


id: 90c226a603c269b304c06465afa75bf
created_at: 2023-03-16 14:02:04
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vector_id: 20317
content:
"Declining near-surface relative humidity over land areas (e.g.,  the \r\nUSA, Mediterranean, South Asia, South America and southern Africa) \r\nis evident in surface observations (Willett et al., 2014, 2020; Dunn \r\net al., 2017). This is consistent with a faster rate of warming over \r\nland than ocean (Sections 2.3.1.3 and 8.2.2.1; Byrne and O'Gorman, \r\n2018). CMIP5 simulations underestimate the observed decreases \r\nin relative humidity over much of global land during 1979-2015 \r\n(Douville and Plazzotta, 2017; Dunn et al., 2017) even when observed \r\nSSTs are prescribed (-0.05 to -0.25% per decade compared with an \r\nobserved rate of -0.4 to -0.8% per decade). It is not yet clear if this \r\ndiscrepancy is related to internal variability or can be explained by \r\ndeficiencies in models (Vanniere et al., 2019; Douville et al., 2020)\r\nor observations (Willett et al., 2014)"


id: c6669fd39848f03f22eea907145a3bbb
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vector_id: 28789
content:
" Over the NH mid-latitude \r\ncontinents, there is medium confidence that human influence has \r\ncontributed to a decrease in near-surface relative humidity in summer\r\n(Sections 2.3.1.3 and 3.3.2.3). "


id: 20062477f91b8de846ff471da99b8f56
created_at: 2023-03-16 14:02:04
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vector_id: 2478
content:
"Water vapour transport (or convergence) estimates from observations \r\nhave substantial uncertainties even in regions of high quality \r\nradiosonde data. Consequently many studies use reanalyses for \r\nwater transport estimates instead of instrumental observations. For \r\nexample, increases in low-level (800-1000 hPa) moisture convergence \r\ninto the tropical wet regime with a smaller outflow increase in the \r\nmid-troposphere (400-800 hPa) with warming was detected in one \r\nreanalysis (ERA-Interim; Allan et al., 2014). Modelling evidence \r\ncombined with statistical analysis demonstrate consistency between \r\nreanalysis moisture convergence and P-E over land (Robertson et al., \r\n2016)"


id: c3597c67adf1cc0d43f65bb0a5bb961b
created_at: 2023-03-16 14:02:04
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vector_id: 28300
content:
" Advances in reanalysis representation of atmospheric moisture \r\nand winds in addition to new observational isotope analysis have \r\nimproved the ability to identify the main sources of water vapour for \r\nkey continental regions and quantify the relative contributions from \r\nmoisture advection and recycling (Gimeno et al., 2012; van Der Ent \r\net al., 2014; Joseph et al., 2016). "


id: 50540fade329b97899c19268b5e377c0
created_at: 2023-03-16 14:02:04
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vector_id: 10085
content:
"Observed changes in moisture transport can also arise from changes \r\nin atmospheric circulation as well as thermodynamics. For instance, \r\nmoisture transport into the Arctic region estimated from reanalyses \r\ndatasets is consistent with radiosonde data (Dufour et al., 2016),\r\nwith increases since 1979 linked to atmospheric circulation (Nygard \r\net al., 2020). Moisture transport into the Eurasian Arctic was \r\nidentified to increase by 2.6% per decade during 1948-2008 based \r\non a reanalysis estimate (X. Zhang et al., 2013). More intense moist \r\nintrusions associated with atmospheric rivers affecting the Arctic and \r\nEurope have been documented since 1979, but with a  substantial \r\ninfluence from decadal internal variability (Ummenhofer et al., 2017; \r\nMattingly et al., 2018). A recent strengthening of tropical circulation \r\nand associated moisture convergence has been identified since \r\naround 2000 for the Amazon region (Arias et al., 2015; Barichivich \r\net al., 2018; J.C"


id: 4c04540f120c6b04de5edda039cc85e7
created_at: 2023-03-16 14:02:04
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vector_id: 9384
content:
" Espinoza et al., 2018; X.Y. Wang et al., 2018). This was \r\nalso strenghtened by increased moisture transport from the North \r\nAtlantic, driving more abundant latent heat release (Segura et al., \r\n2020) and leading to an increased frequency of extreme floods in the \r\nnorthern Amazon (Barichivich et al., 2018; Heerspink et al., 2020). \r\nOverall, increased moisture transport has been linked to increased "


id: 9c30e8dbd506fd3986b5e20d9da17b46
created_at: 2023-03-16 14:02:04
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vector_id: 22131
content:
"Since AR5, it is very likely that increases in global atmospheric water \r\nvapour were observed based on in situ, satellite and reanalysis data \r\n(with medium confidence in the magnitude; Section 2.3.1.3). Satellite \r\nrecords show increases in upper tropospheric water vapour (constant \r\nrelative humidity while temperatures have increased) since 1979 \r\n(E.-S. Chung et al., 2014; Blunden and Arndt, 2020), to which human \r\ninfluence has likely contributed (Section 3.3.2.2). Combined satellite \r\nand reanalysis estimates and CMIP6 atmosphere-only simulations \r\n(1988-2014) show global mean precipitable water vapour increases \r\nof 6.7 +-  0.3 %  degC-1, very close to the Clausius-Clapeyron rate \r\n(Allan et al., 2020). Satellite-based products show increases close \r\nto the Clausius-Clapeyron rate over the ice-free oceans (about 7 to \r\n9 % degC-1; 1998-2008), but reanalysis estimates outside this range \r\n(Schroder et al"


id: 6b581703052b60ee3f9c581126d62ca9
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vector_id: 14507
content:
", 2019) are an expected consequence of their changing \r\nobserving systems (Allan et al., 2014; Parracho et al., 2018). Increases \r\nin precipitable water vapour are found over the central and sub\u0002Arctic based on multiple reanalyses with some corroboration from "


id: d4aae7e0544a73cd388ddfe16f501c7b
created_at: 2023-03-16 14:02:04
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vector_id: 30986
content:
"The AR5 concluded that it is likely there has been an overall increase \r\nin annual mean precipitation amount over mid-latitude land areas in \r\nthe NH, with low confidence since 1901, but medium confidence after \r\n1951. There is further evidence of a faster increase since the 1980s \r\n(medium confidence) (Sections 2.3.1.3.4 and 3.3.2.2). Precipitation \r\nhas increased from 1950 to 2018 over mid-high latitude Eurasia, \r\nmost of North America, south-eastern South America, and north\u0002western Australia, while it has decreased over most of Africa, eastern \r\nAustralia, the Mediterranean region, the Middle East, and parts of \r\nEast Asia, central South America, and the Pacific coasts of Canada, as \r\nsimulated by the CMIP5 multi-ensemble mean (Dai, 2021). Since AR5, \r\nthere have been updates of several precipitation datasets, including \r\nsatellite estimates, reanalysis and merged products (Adler et al., \r\n2017; Roca, 2019)"


id: d1e7baeb0bd4121657876ed27d367be
created_at: 2023-03-16 14:02:04
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content:
" However, observational uncertainties remain \r\nan issue for assessing regional trends in seasonal or annual mean \r\nprecipitation amount (Hegerl et al., 2015; Maidment et al., 2015; \r\nSarojini et al., 2016; Beck et al., 2017), as well as the convective and \r\nstratiform types of precipitation (e.g., Ye et al., 2017). Precipitation \r\ntrends at regional scales are dominated by internal variability across \r\nmuch of the world (Knutson and Zeng, 2018). Regional changes in \r\nprecipitation amounts can also be obscured by contrasting responses \r\nto GHG compared with aerosol forcings (Wu et al., 2013; Hegerl \r\net al., 2015; Xie et al., 2016; Zhao and Suzuki, 2019; Zhao et al., \r\n2020) and changes in precipitation intensity versus frequency (Shang \r\net al., 2019). "


id: 7d6b89bc9933aa1b5721d28a48018760
created_at: 2023-03-16 14:02:04
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content:
"In the tropics, increases in precipitation amount are observed in \r\nconvergence zones and decreases in the descending branches \r\nof the atmospheric circulation since 1979 (Chou et al., 2013; Liu \r\nand Allan, 2013; Gu et al., 2016; Polson et al., 2016; Polson and \r\nHegerl, 2017), consistent with increased moisture transports with \r\nwarming (Gimeno et al., 2020). Over tropical land areas, there is \r\nsubstantial variability in the 'wet convergent regimes get wetter' \r\nand 'dry divergent regimes get drier' pattern of trends observed \r\nsince 1950 that are modulated by decadal changes in ENSO (Liu \r\nand Allan, 2013; Gu and Adler, 2018). CMIP6 models indicate an \r\nincreased contrast between wet and dry regions in the tropics and \r\nsubtropics (Figure 8.7; Schurer et al., 2020). This provides further \r\nevidence that rainfall has increased in wet regimes, and slightly \r\ndecreased in dry regimes over the period 1988-2019 (Figure 3.14)"


id: ca5837e9520d9e4a6007eb1bd457725e
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content:
" \r\nThis greater contrast is primarily attributable to greenhouse gas \r\nforcings, although the observed trends are statistically larger than \r\nthe model responses (Section 3.3.2.3)."


id: d2026d3e1afa1a8e65a8ccda9099ecd1
created_at: 2023-03-16 14:02:04
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vector_id: 30570
content:
"of 0.12  mm  day-1 per decade, while the number of days with \r\nprecipitation exceeding 0.1  mm  day-1 significantly decreased at \r\na rate of 0.9 days per decade (Shang et al., 2019). There is consistency \r\nin trend estimates during 1998-2015 over mainland China among \r\nsatellite-based products and station data, which show increased \r\nprecipitation amounts in autumn and winter and decreases in summer \r\n(Chen and Gao, 2018), consistent with a decreased intensity of East \r\nAsian monsoon precipitation (Lin et al., 2014; Deng et al., 2018). \r\nFurther assessment of precipitation changes over the South and \r\nSouth East Asian and the East Asian monsoon regions is presented in \r\nSection 8.3.2.4. An increasing trend in the frequency of heavy rainfall \r\noccurrences at the expense of low and moderate rainfall occurrences \r\nis found over central India (Krishnan et al., 2016; Roxy et al., 2017) \r\nand over eastern China with the latter due to increasing high aerosol \r\nlevels (Qian et al., 2009; J"


id: 9d43fc80b0eac6e9f04b692670e55e1a
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vector_id: 22268
content:
" Guo et al., 2017; Xu et al., 2017; Day et al., \r\n2018), consistent with the effects of absorbing aerosol on stability \r\nand convective inhibition (Box 8.1)."


id: 88eb6e5de50e96f9ddbed253bd2a4918
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
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index: climate_gpt_only_giec
vector_id: 19080
content:
"Observed precipitation records since the early 1900s show \r\nincreases in precipitation totals over central and north-eastern \r\nNorth America that are attributable to anthropogenic warming but \r\nlarger in magnitude than found in CMIP5 simulations (Knutson and \r\nZeng, 2018; Guo et al., 2019). Decreases in precipitation amount \r\nover the central and south-western USA and increases over the \r\nnorth-central USA during 1983-2015 (Cui et al., 2017; P. Nguyen \r\net al., 2018), are not clearly associated with forced responses in \r\nCMIP6 simulations (Figure  8.7; see also Section  10.4.2.3). Over \r\nEurope, precipitation trends since 1979 do not show coherence \r\nacross datasets (Zolina et al., 2014; P. Nguyen et al., 2018). Longer \r\nrecords since 1910 show increases for much of Scandinavia, north\u0002western Russia, and parts of north-western Europe/United Kingdom \r\nand Iceland (Knutson and Zeng, 2018)"


id: 1942b9b31ef47667c6634d08f55c4c1b
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
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index: climate_gpt_only_giec
vector_id: 1462
content:
" Records since 1930 show \r\nincreases of annual preciptation amount over western Russia (see \r\nalso Atlas.8.2). Widespread increases in daily precipitation intensity \r\nappear clearly over regions with a  high density of rain gauges, \r\nsuch as Europe and North America over the 1951-2014 period \r\n(Alexander, 2016). Observations during 1966-2016 over northern \r\nEurasia show increases in the contribution of heavy convective \r\nshowers to total precipitation by 1-2% on average (with local \r\ntrends of up to 5%) for all seasons except for winter (Chernokulsky \r\net al., 2019). Increases in convective precipitation intensity have \r\nbeen identified, particularly on sub-daily time scales, using a range \r\nof modelling and observational data (Berg et al., 2013; Kanemaru \r\net al., 2017; Pfahl et al., 2017)."


id: 3aeeda57cb0543442815603560bbf087
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
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vector_id: 6636
content:
"Over South America, there is observational and paleoclimate \r\nevidence of declining precipitation amount during the past 50 years \r\nover the Altiplano and central Chile, primarily explained by the PDO \r\nbut with at least 25% of the decline attributed to anthropogenic \r\ninfluence (Morales et al., 2012; Neukom et al., 2015; Boisier et al., \r\n2016; Seager et al., 2019b; Garreaud et al., 2020). In contrast, \r\na significant rainfall increase has been detected over the Peruvian-\r\nBolivian Altiplano (from observational data and satellite-based \r\nestimations) since the 1980s (Figure 8.7; Imfeld et al., 2020; Segura \r\net al., 2020). Long-term (1902-2005) precipitation data indicate \r\npositive trends over south-eastern South America and negative \r\ntrends over the southern Andes, with at least a partial contribution \r\nfrom anthropogenic forcing (Gonzalez et al., 2014; Vera and Diaz, \r\n2015; Diaz and Vera, 2017; Boisier et al., 2018; Knutson and Zeng, \r\n2018; see further assessment in Section 10.4"


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content:
"2.2 and Atlas.7.2.2). \r\nThe Peruvian Amazon has exhibited significant rainfall decreases \r\nduring the dry season since 1980 (Lavado et al., 2013; Ronchail \r\net al., 2018). Increases in wet season rainfall in the northern and \r\ncentral Amazon since the 1980s and decreases during the dry \r\nseason in the southern Amazon (Barreiro et al., 2014; Gloor et al., \r\n2015; Martin-Gomez and Barreiro, 2016; J.C. Espinoza et al., 2018; \r\nX.Y. Wang et al., 2018; Haghtalab et al., 2020) are not explained \r\nby radiative forcing based on CMIP6 experiments (Figure 8.7) and \r\ntrends are insignificant over longer periods since 1930 (Kumar \r\net al., 2013) or more recently, since 1973 (Almeida et al., 2017). \r\nSee Section 8.3.2.4.5 for monsoon-related changes"


id: 7da1badc5227a42ca1daff760397c3ec
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
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content:
" For the tropical \r\nAndes region, trends in annual precipitation show heterogenous \r\npatterns, ranging between -4% per decade and +4% per decade \r\nin the northern and southern tropical Andes for a 30-year period at \r\nthe end of the 20th century, although increases during 1965-1984 \r\nand decreases since 1984 have been registered in Bolivia (Carmona \r\nand Poveda, 2014; Pabon-Caicedo et al., 2020). \r\nOver China, annual precipitation totals changed little from 1973 \r\nto 2016, but precipitation intensity significantly increased at a rate "


id: 58dbe1b00e014744a4e01564340c79d2
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
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index: climate_gpt_only_giec
vector_id: 11474
content:
"In summary, regional changes in precipitation amounts can be \r\nobscured by the contrasting responses to GHG and aerosol forcings \r\nacross much of the 20th century and can thus be dominated \r\nby internal variability at decadal to multi-decadal time scales \r\n(high confidence). There is, however, a  detectable increase in \r\nnorthern high-latitude annual precipitation over land which has \r\nbeen primarily driven by human-induced global warming (high \r\nconfidence) (Section  3.3.2). Human influence has strengthened \r\nthe zonal mean precipitation contrast between the wet tropics \r\nand dry subtropics since the 1980s (medium confidence), although \r\nregional studies suggest a more complex precipitation response to \r\nevolving anthropogenic forcings"


id: 6b94cebc0ee8c94f2cab9a9d53a2c9d2
created_at: 2023-03-16 14:02:04
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vector_id: 14553
content:
" There is high confidence that daily \r\nmean precipitation intensities have increased since the mid-20th \r\ncentury in a majority of land regions with available observations \r\nand it is likely that such an increase is mainly due to GHG forcing \r\n(see Section 11.4). Section 8.3.2.4 assesses monsoon precipitation \r\nchanges in detail."


id: 9504b3c2d2aa76a453d22cc6e88ebb81
created_at: 2023-03-16 14:02:04
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vector_id: 20953
content:
"Plant water use efficiency (WUE) is expected to rise with CO2 levels \r\n(high confidence) (Section 8.2.3.3 and Box 5.2), and can in theory \r\ncounteract rising evapotranspiration in a  warmer atmosphere \r\n(Section  8.2.3.3). However, observational studies suggest that this \r\nmay not be the case in some ecosystems. For example, Frank et al. \r\n(2015) found that while the WUE increased in European forests across \r\nthe 20th century, transpiration also increased due to more plant \r\ngrowth, a  lengthened growing season, and increased evaporative \r\ndemand. Likewise Guerrieri et al. (2019) observed that while WUE \r\nand photosynthesis increased in North American forests, stomatal \r\nconductance experienced only modest declines that were restricted \r\nto moisture-limited forests. Other studies further suggest that in \r\nmany ecosystems increased WUE will not compensate for increased \r\nplant growth, amplifying declines in surface water availability \r\n(De Kauwe et al., 2013; Ukkola et al., 2016b; A"


id: 456149d91ba99b0323fd937286ed898f
created_at: 2023-03-16 14:02:04
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vector_id: 8334
content:
" Singh et al., 2020), \r\nwhile drought conditions can also offset the CO2 fertilization effect \r\nand lead to a  decline in WUE (N.  Liu et al., 2020). There is low \r\nconfidence regarding the impact of plant physiological effects on \r\nobserved trends in evapotranspiration."


id: 8fe379f9da3f02ed98861f7c9368d84f
created_at: 2023-03-16 14:02:04
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content:
"The AR5 assessed that there was medium confidence that pan \r\nevaporation declined in most regions over the last 50 years, yet \r\nmedium confidence that evapotranspiration increased from the early \r\n1980s to the late 1990s. Since AR5, these conflicting observations \r\nhave been attributed to internal variability and by the fact that \r\nevapotranspiration is less sensitive to trends in wind speed and is partly \r\ncontrolled by vegetation greening (K.  Zhang et al., 2015; Y.  Zhang \r\net al., 2016; Z.  Zeng et al., 2018b). Observation-based estimates \r\nshow a robust positive trend in global terrestrial evapotranspiration \r\nbetween the early 1980s and the early 2010s (Miralles et al., 2014b; \r\nZ.  Zeng et al., 2014, 2018b; K.  Zhang et al., 2015; Y.  Zhang et al., \r\n2016). The rate of increase varies among datasets, with an ensemble \r\nmean terrestrial average rate of 7.6 +- 1.3 mm yr-1 per decade for \r\n1882-2011 (Z. Zeng et al., 2018a)"


id: 6cdb733b3a1ea4f271061ba83bc009d9
created_at: 2023-03-16 14:02:04
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vector_id: 14746
content:
" In addition, a decreasing trend \r\nin pan evaporation plateaued or reversed after the mid-1990s \r\n(C.M. Stephens et al., 2018) has been reported as due to a shift from \r\na dominant influence of wind speed to a dominant effect of water \r\nvapour pressure deficit, which has increased sharply since the 1990s \r\n(Yuan et al., 2019). The absence of a  trend in evapotranspiration \r\nin the decade following 1998 was shown to be at least partly an \r\nepisodic phenomenon associated with ENSO variability (Miralles \r\net al., 2014b; K. Zhang et al., 2015; Martens et al., 2018). Thus, there \r\nis medium confidence that the apparent pause in the increase in \r\nglobal evapotranspiration from 1998 to 2008 is mostly due to internal \r\nvariability. In contrast to AR5, there are now consistent trends in \r\npan evaporation and evapotranspiration at the global scale, given \r\nthe recent increase in both variables since the mid-1990s (medium \r\nconfidence)"


id: f4ef70b13c47aaae5ae9cec718d33aed
created_at: 2023-03-16 14:02:04
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vector_id: 36043
content:
" Given the growing number of quantitative studies, there \r\nis high confidence that global terrestrial annual evapotranspiration \r\nhas increased since the early 1980s."


id: 11115f5e2d26ce8849b4726edaa3d718
created_at: 2023-03-16 14:02:04
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vector_id: 167
content:
"The AR5 reported low confidence in the assessment of trends in \r\nglobal river discharge during the 20th century. This is because many \r\nstreamflow observations have been impacted by land use and dam \r\nconstruction, and the largest river basins worldwide differ in many \r\ncharacteristics, including geography and morphology. In regions \r\nwith seasonal snow storage, AR5 WGII assessed that there is robust \r\nevidence and high agreement that warming has led to earlier spring \r\ndischarge maxima and robust evidence of earlier breakup of Arctic \r\nriver ice, as well as indications that warming has led to increased \r\nwinter flows and decreased summer flows where streamflows are \r\nlower and that the observed increases in extreme precipitation led \r\nto greater probability of flooding at regional scales with medium \r\nconfidence"


id: e36f49771b5a791a5a1008cea680b9c7
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
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vector_id: 33264
content:
" The SROCC found robust evidence and high agreement\r\nthat discharge due to melting glaciers has already reached its \r\nmaximum point and has begun declining with smaller glaciers, but \r\nonly low confidence that anthropogenic climate change has already \r\naffected the frequency and magnitude of floods at the global scale. "


id: 69ddc53183c5b143219a095627abb1dd
created_at: 2023-03-16 14:02:04
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vector_id: 14255
content:
"Significant trends in streamflow and continental runoff were observed \r\nin 55 out of 200 large river basins during 1948-2012, with an even \r\ndistribution of increasing and decreasing trends (Section 2.3.1.3.6; \r\nDai, 2016). A global detection and attribution study shows that the \r\nsimulation of spatially heterogeneous historical trends in streamflow \r\nis consistent with observed trends only if anthropogenic forcings are \r\nconsidered (Gudmundsson et al., 2019). Section  3.3.2.4 assesses \r\nwith medium confidence that anthropogenic climate change has \r\naltered regional and local streamflows, although a significant trend \r\nhas not been observed in the global average (Sections 2.3.1.3.6 and \r\n3.3.2.3). Multiple human-induced and natural drivers have been \r\nshown to play an important but variable role in observed regional \r\ntrends of streamflow for several different areas (Fenta et al., 2017; \r\nFicklin et al., 2018; Glas et al., 2019; Vicente-Serrano et al., 2019)"


id: b474a887ae8663fc0d79d525e311f798
created_at: 2023-03-16 14:02:04
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vector_id: 25918
content:
" \r\nFor instance, decreasing runoff during the dry season has been \r\nobserved over the Peruvian Amazon since the 1980s (Lavado et al., \r\n2013; Ronchail et al., 2018). Up to 30-50% of the recent multi\u0002decadal decline in streamflow across the Colorado River Basin can \r\nbe attributed to anthropogenic warming and its impacts on snow and \r\nevapotranspiration (Woodhouse et al., 2016; McCabe et al., 2017; \r\nUdall and Overpeck, 2017; Xiao et al., 2018; Milly and Dunne, 2020). \r\nIn the Upper Missouri River basin, Martin et al. (2020) found that \r\nwarming temperatures have contributed to streamflow reductions \r\nsince at least the late 20th century. Cold regions in the NH have \r\nexperienced an earlier occurrence of snowmelt floods, an overall \r\nincrease in water availability and streamflow during winter, and \r\na  decrease in water availability and streamflow during the warm \r\nseason (Aygun et al., 2019). "


id: ba7d566918ad1406f4421cecad0b8e8c
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content:
"forcing (Marvel et al., 2019), but then emerges again from 1981 to \r\npresent, although it is not detectable in the GLEAM nor MERRA-2 \r\nsoil moisture reanalysis products. Likewise, Bonfils et al. (2020) \r\ninvestigated changes in precipitation, temperature and continental \r\naridity in CMIP5 historical simulations and found that the dominant \r\nmultivariate fingerprint, an amplification of wet-dry latitudinal \r\npatterns and progressive continental aridification, was associated \r\nwith greenhouse gas emissions (Figure  8.9a,d), and the second \r\nleading fingerprint was associated with anthropogenic aerosols \r\n(Figure 8.9e,h). This study found that the anthropogenic greenhouse \r\ngas signal is statistically detectable in reanalyses over the 1950-2014 \r\nperiod (signal-to-noise ratio above 1.96). Gu et al. (2019) found that \r\na global trend in declining soil moisture is detectable in the GLDAS-2 \r\nreanalysis product and is attributable to greenhouse gas forcing. \r\nPadron et al"


id: 9229669c9e2b6ea7c4ba9729ef04e59e
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content:
" (2020) reconstructed the global patterns of dry season \r\nwater availability from 1902-2014, and found it extremely likely\r\n(99% range) that trends in the last three decades of the analysis \r\nperiod could be attributed to anthropogenic forcing, mainly due to \r\nincreases in evapotranspiration. It is very likely (>90% range) that \r\nanthropogenic forcing has affected global patterns of soil moisture \r\nover the 20th century."


id: b7d374387c3d7e63c19c3d9244476701
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
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vector_id: 26457
content:
"The Mediterranean region has experienced notable changes \r\nin drought and aridity. A number of studies have identified a decline \r\nin precipitation since 1960 and attributed this to anthropogenic \r\nforcing (Hoerling et al., 2012; Gudmundsson and Seneviratne, 2016; \r\nKnutson and Zeng, 2018; Seager et al., 2019b). Kelley et al. (2015) \r\nshowed that climate change caused a  three-fold increase in the \r\nlikelihood of the 2007-2010 meteorological drought in the eastern \r\nMediterranean. However, historical trends in precipitation across \r\nthe Mediterranean are spatially variable and contain substantial \r\ndecadal variability, such that an anthropogenic influence may not \r\nbe detectable in all areas (Zittis, 2018; Vicente-Serrano et al., 2021). \r\nRecords of soil moisture provide a  clearer signal, indicating that \r\nhigher temperatures and increased atmospheric demand have played \r\na strong role in driving Mediterranean aridity (Vicente-Serrano et al., \r\n2014)"


id: 6b377296549075fde04214ba3be8c880
created_at: 2023-03-16 14:02:04
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vector_id: 14494
content:
" Hydrological modeling suggests that the recent decline in \r\nsoil moisture in the Mediterranean is unprecedented in the last \r\n250 years (Hanel et al., 2018). Paleoclimate evidence extends this \r\nview, additionally indicating that dryness in the Mediterranean is \r\napproaching an extreme condition compared to the last millennium \r\n(Markonis et al., 2018) and that the 15-year drought in the Levant \r\n(1998-2012) has an 89% likelihood of being the driest of the last \r\n900 years (Cook et al., 2016a). Marvel et al. (2019) found that the \r\nMediterranean region contributes strongly to the anthropogenic \r\nwarming component of the global trend in aridity. There is high \r\nconfidence that anthropogenic forcings are causing increased aridity \r\nand drought severity in the Mediterranean region."


id: 29301b97f1fd59e500d099d6ebd30898
created_at: 2023-03-16 14:02:04
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content:
"Several studies have analyzed CMIP5 and land surface models and \r\ndetected a significant summer drying trend in the NH across the late \r\n20th century that is attributable to anthropogenic forcings (Mueller \r\nand Zhang, 2016; Douville and Plazzotta, 2017). This trend is mainly \r\ndriven by dryland areas such as the western USA and west-central \r\nAsia, where both reanalysis products and satellite data confirm \r\nthere has been a persistent decline in soil moisture since 1990 (Y. Liu \r\net al., 2019a). In the western USA, snow deficits have very likely\r\ncontributed to recent drying (Mote et al., 2018). Spring snow water \r\nequivalent across the Sierra Nevada Mountains reached a  record \r\nlow in 2015 (Margulis et al., 2016; Mote et al., 2016), possibly the \r\nlowest of the last five hundred years (Belmecheri et al., 2016). Over \r\nthe longer California drought (2011-2015) anthropogenic warming \r\nalone reduced snowpack levels in the Sierras by 25% (Berg and Hall, \r\n2017)"


id: b2b2845079083a8a85bcdd87ca75a709
created_at: 2023-03-16 14:02:04
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vector_id: 25637
content:
" The north-western USA also experienced snow drought in \r\n2015, despite near-normal levels of total cold season precipitation \r\n(Mote et al., 2016; Marlier et al., 2017). There is high confidence that \r\nanthropogenic warming contributed to these recent snow droughts \r\n(Belmecheri et al., 2016; Mote et al., 2016). "


id: 4bb37252dfbb52e74eee9d1527c069e8
created_at: 2023-03-16 14:02:04
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content:
"Millennium drought in south-eastern Australia was the worst \r\nexperienced since 1783 (Gergis et al., 2012), and that the spatial \r\nextent and duration of cool season (April to September) rainfall \r\nanomalies were either very much below average or unprecedented \r\nover at least the last 400 years (Freund et al., 2017). Other \r\npaleoclimate studies suggest that the Millennium drought in \r\neastern Australia was not unusual in the context of natural \r\nvariability reconstructed over the past millennium (Palmer et al., \r\n2015; Cook et al., 2016c; Kiem et al., 2020). While there is currently \r\nlow confidence that recent droughts in eastern Australia can be \r\nclearly attributed to human influence (Cai et al"


id: fb83cfd520c026eebc76be72c434c53f
created_at: 2023-03-16 14:02:04
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vector_id: 37070
content:
", 2014; Delworth \r\nand Zeng, 2014; Rauniyar and Power, 2020), there is emerging \r\nevidence that declines in April to October rainfall in south-eastern \r\nAustralia since the 1990s would not have been as large without the \r\ninfluence of increasing levels of atmospheric GHGs (Rauniyar and \r\nPower, 2020). "


id: 70e723ad3108532bcbd24cd6ddb9f8a7
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
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vector_id: 15397
content:
"intense in recent decades, continuing across rainy seasons (Hoell \r\net al., 2017b; Nicholson, 2017), and this trend appears to be unusual \r\nin the context of the last 1500 years (Tierney et al., 2015). Knutson \r\nand Zeng (2018) attribute decreased annual precipitation over the \r\nSudan to anthropogenic forcing, but other studies argue that the \r\nrecent trend cannot yet be distinguished from natural variability, at \r\nleast over parts of this region (Hoell et al., 2017b; Philip et al., 2018). \r\nThere remains low confidence due to limited evidence that drying \r\nthe north-eastern Africa is attributable to human influence. In the \r\nWestern Cape region of South Africa, human influence increased \r\nthe likelihood of the severe 2015-2017 drought by a  factor of \r\n3-6, depending on the analysis (Otto et al., 2018; Pascale et al., \r\n2020). Anthropogenic forcing also contributed to the 2018 drought, \r\nmainly by increasing evapotranspiration (Nangombe et al., 2020)"


id: c1409a81209bf6f79623fbbbc96c9658
created_at: 2023-03-16 14:02:04
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vector_id: 27994
content:
" \r\nWhile some analysis of instrumental precipitation data in this region \r\ndetect a slight long-term drying trend consistent with the simulated \r\nanthropogenic response (Seager et al., 2019b), there is strong \r\nmulti-decadal variability in the data (Wolski et al., 2021). However, \r\na  study of streamflow in southern Africa detected a  significant \r\ndecline (Gudmundsson et al., 2019; see also Section 10.6.2). There \r\nis medium confidence in the long-term drying trend in this region \r\nand its attribution to anthropogenic forcing, and medium confidence\r\nthat anthropogenic warming has contributed to recent severe \r\ndrought events."


id: 579eaa3d0b179c655fc8be7499e3b0b8
created_at: 2023-03-16 14:02:04
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index: climate_gpt_only_giec
vector_id: 11281
content:
"The AR5 and SROCC found, with very high confidence, a  general \r\ndecline in glaciers due to climate change in recent decades. There \r\nis very high confidence that during the decade 2010-2019 glaciers \r\nlost more mass than in any other decade since the beginning of the \r\nobservational record (Sections 2.3.2.3 and 9.5.1). Human influence \r\nis very likely the main driver of the global, near-universal retreat of \r\nglaciers since the 1990s (Section 3.4.3.1). In Table 9.5, the contribution \r\nof glaciers to sea level rise for different periods is presented; in \r\n1971-2018 glacier mass loss contributed 20.9 [10.0  to 31.7] mm \r\nor 22.2% of the sea level rise during that period. The highest mass \r\nloss rates are observed in the southern Andes, New Zealand, Alaska, \r\nCentral Europe and Iceland while the largest mass loss are observed in \r\nAlaska, the periphery of Greenland and Arctic Canada (Section 9.5.1 \r\nand Figure 9.20)"


id: 7e80c420016521a52feea4d0af42e32e
created_at: 2023-03-16 14:02:04
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vector_id: 17464
content:
" Predominantly, runoff from small glaciers such as in \r\nCanada has decreased because of glacier mass loss, while runoff from \r\nlarger glaciers such as in Alaska has typically increased (Bolch et al., \r\n2010; Thomson et al., 2011; Tennant et al., 2012; WGMS, 2017; Huss \r\nand Hock, 2018). Asia contains the largest concentration of glaciers \r\noutside the polar regions where the total glacier mass change is \r\n-16.3 +- 3.5 Gt yr-1 over 2000-2016 with considerable intra-regional \r\nvariability (Brun et al., 2017). Mass losses of glaciers in Asia between "


id: a05e1f4f054bf82fd0f2b45ec470da7d
created_at: 2023-03-16 14:02:04
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vector_id: 22767
content:
"autumn and winter seasons (Barry and Gan, 2020). Such recent \r\npositive trends in snow cover extent are however at odds with \r\nother surface and satellite datasets and with the negative trends \r\nsimulated by most CMIP5 and CMIP6 models (Mudryk et al., 2017, \r\n2020). Hernandez-Henriquez et al. (2015) also detected positive \r\ntrends in October to November SCE in in the NOAA SCE Climate \r\nData Record (NOAA-CDR), which are not replicated in other datasets \r\n(Section 9.5.3). Wu et al. (2018) found slower snowmelt rates over \r\nthe NH in 1980-2017, with higher ablation rates in locations with \r\ndeep snow water equivalent (SWE), but due to the reduction of \r\nSWE in deep snowpacks, moderate/high ablation rates showed \r\ndecreasing trends. Santolaria-Otin and Zolina (2020) reported \r\nweak but significant decline in SCE in autumn over northern \r\nEurasia and North America during 1979-2005, and similarly for \r\nspring, except for northern Siberia which showed higher spring \r\nSCE"


id: 3c33ea9605180b675fe33e7b45fa550a
created_at: 2023-03-16 14:02:04
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vector_id: 6833
content:
"  Kapnick and Hall (2012) detected significant loss of spring \r\nmountain snowpack in western USA in 1950-2008. For Canada, \r\nextensive decreasing snow depths, SCE and duration were detected \r\nsince mid-1970s, especially in western Canada during winter and \r\nspring (DeBeer et al., 2016). Berghuijs et al. (2014) show that across \r\nthe continental USA, catchments with more snowfall than rainfall \r\ngenerally have higher mean streamflow, which will probably \r\ndecrease with smaller fractions of precipitation falling as snow \r\nbecause of climate warming."


id: 87fa10cf32fd39f2848c69d641e7bbea
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
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vector_id: 18918
content:
"The AR5 assessed that Northern Hemisphere (NH) snow cover \r\nextent (SCE) has decreased since the late 1960s, especially in \r\nspring (very high confidence). This is confirmed by recent studies \r\n(Section 2.3.2.2; Kunkel et al., 2016). AR6 assesses that NH spring \r\nsnow cover has been decreasing since 1978 (very high confidence) \r\nand  that this trend extends back to 1950 (high confidence) \r\n(Section  9.5.3). Human-caused global warming is the dominant \r\ndriver of this observed decline (Section 3.4.2; Estilow et al., 2015). \r\nModel simulations suggest that surface temperature responses at \r\nhemispheric/regional scales explain between 40% and 85% of the \r\nSCE trend variability (Mudryk et al., 2017). A decreasing trend in \r\nsnowfall has also been detected in the NH (Figure 8.1; Rupp et al., \r\n2013). Snowfall as a  proportion of precipitation has decreased \r\nsignificantly in recent years (Berghuijs et al., 2014)"


id: 2599de0ee48852ae0d905a699eed2746
created_at: 2023-03-16 14:02:04
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vector_id: 3344
content:
" However, a late\u000220th-century increase in snowfall in West Antarctica observed in ice \r\ncores has been linked to a  combination of factors including the \r\nanthropogenically forced deepening of the Amundsen Sea Low \r\n(Thomas et al., 2015, 2017)."


id: 6c5edf4d50a47317414d82ce66e44d15
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vector_id: 14674
content:
"Increasing global freshwater withdrawals, primarily associated \r\nwith the expansion of irrigated agriculture in drylands, have led \r\nto global groundwater depletion that has an estimated range of \r\nabout 100 and about 300 km3 yr-1 from hydrological models and \r\nvolumetric-based calculations (Bierkens and Wada, 2019). The \r\nmagnitude of this change is such that its estimated contribution \r\nto global sea level rise is in the order of 0.3 to 0.9 mm yr-1 (Wada \r\net al., 2010; Konikow, 2011; Doll et al., 2014; Pokhrel et al., 2015; \r\nde Graaf et al., 2017; Hanasaki et al., 2018). Groundwater depletion \r\nhas been observed regionally in The USA High Plains, California's \r\nCentral Valley (Scanlon et al., 2012), north-west India (Rodell et al., \r\n2009; Asoka et al., 2017), Upper Ganges in India (MacDonald \r\net al., 2016), North China Plain (Feng et al., 2013), north-central \r\nMiddle East region of Tigris-Euphrates-Western Iran (Voss et al., \r\n2013), Central Asia (Hu et al"


id: 35c80aafde4c9f5f42e41bc5ef98a003
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
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vector_id: 5876
content:
", 2019), and North Africa (Bouchaou \r\net al., 2013). The regional contribution of agricultural irrigation to \r\ngroundwater depletion was previously highlighted by SRCCL but \r\nno formal assessment of observed changes in global or regional \r\ngroundwater featured in AR5. "


id: f42246aaef6bc19728150a49d7c6abce
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 35921
content:
"Following a  global review of groundwater and climate change \r\n(R.G. Taylor et al., 2013a) and AR5 WGII, evidence of an association \r\nbetween heavy or extreme precipitation and groundwater recharge \r\nhas continued to grow, especially in tropical (Asoka et al., 2018; \r\nCuthbert et al., 2019a; Kotchoni et al., 2019) and subtropical regions \r\n(Meixner et al., 2016). Stable-isotope ratios of oxygen and hydrogen \r\nat 14 of 15 sites across the tropics trace groundwater recharge to \r\nintensive monthly rainfall, commonly exceeding the 70th intensity \r\npercentile, approximately (Jasechko and Taylor, 2015). Further, heavy \r\nrainfall recharging groundwater resources is often influenced by \r\nclimate variability such as ENSO and PDO (R.G. Taylor et al., 2013b; \r\nKuss and Gurdak, 2014; Asoka et al., 2017; Cuthbert et al., 2019b; \r\nKolusu et al., 2019; Shamsudduha and Taylor, 2020)"


id: c1556e2748849ef1bfa5c2e319a5b0e1
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
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index: climate_gpt_only_giec
vector_id: 28004
content:
" Additionally, \r\nincreases in groundwater storage estimated from GRACE for 37 \r\nof the world's large-scale aquifer systems from 2002 to 2016 are \r\ngenerally found to result from episodic recharge associated with \r\nextreme (>90th percentile) annual precipitation. "


id: a40d6d1d0d7c425cb323fead9d3b2302
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 23368
content:
"The AR5 concluded it is likely that the tropical belt, as delimited by \r\nthe Hadley circulation, has widened since the 1970s. Observations \r\nin the satellite era indicate precipitation increases in the core of \r\nthe Pacific Inter-tropical Convergence Zone (ITCZ) and decreases on \r\nthe ITCZ margins (Gu et al., 2016; Su et al., 2017). As the satellite \r\nperiod has lengthened, observations have increasingly been used to \r\nassess trends in the ITCZ and tropical rain belt. Since AR5, significant \r\nnarrowing and strengthening of the Pacific ITCZ after 1979 have been \r\nidentified in atmospheric reanalyses (Wodzicki and Rapp, 2016), but \r\nno change in the ITCZ location (Byrne et al., 2018). Atmospheric \r\nmodel simulations suggest that with a narrower ITCZ, the subtropical \r\njet becomes baroclinically unstable at a  lower latitude and allows \r\nmid-latitude eddies to propagate farther equatorward (Watt-Meyer \r\nand Frierson, 2019)"


id: b98291fd6c771d6977bbafeb71528422
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
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vector_id: 26727
content:
" Observational analyses also show that the \r\nITCZ narrowing (Zhou et al., 2020) is associated with increased \r\nprecipitation in the ITCZ core region that is strongly coupled to \r\nincreasing Outgoing Longwave Radiation (OLR) in the expanding \r\ndry zones, particularly over land regions in the subtropics and mid\u0002latitudes (Lau and Tao, 2020). In addition, an eastward movement of \r\nthe South Pacific Convergence Zone (SPCZ) between 1977 and 1999 \r\nhas been reported, with associated significant precipitation trends in \r\nthe South Pacific regions (Salinger et al., 2014). "


id: de51d91347b5b1ee00fd2bbf38a8a61b
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
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vector_id: 32469
content:
"The poleward shift of the HC is closely related to migration of the \r\nlocation of tropical cyclone trajectories in both hemispheres (Sharmila \r\nand Walsh, 2018; Studholme and Gulev, 2018), with a  very likely\r\npoleward shift over the western North Pacific Oceans since the 1940s \r\n(Section  11.7.1.2). Moreover, the Western North Pacific Subtropical \r\nHigh has extended westward since the 1970s, resulting in a monsoon \r\nrain band shift over China, with excessive rainfall along the middle \r\nand lower reaches of the Yangtze River valley along about 30degN over \r\neastern China. At the same time, the effect of anthropogenic aerosols \r\ndominated the response to GHG increases over East Asia, resulting in \r\na weakening of the East Asian summer monsoon and causing a drying \r\ntrend in north-eastern China (Hu, 2003; Yu and Zhou, 2007; T. Wang \r\net al., 2013; Z. Li et al., 2016b; Lau and Kim, 2017) and northern parts of \r\nSouth Asia (Section 8.3.2.4.2; Preethi et al., 2017)"


id: a1cf95bb7a5d5ef3e0a0fe3ae94b1232
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
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index: climate_gpt_only_giec
vector_id: 23024
content:
" During 1977-2007, \r\nthe precipitation variability over the eastern USA increased due to \r\nchanges in the intensity and position of the western ridge of the North \r\nAtlantic Subtropical High (Li et al., 2011; Diem, 2013). "


id: a051a8e90d64a2c142bf398282bcaf6b
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 22758
content:
"Reconstructions in the Sahel (Carre et al., 2019) and Belize (Ridley \r\net al., 2015) support the southward displacement of the tropical \r\nrain belt since 1850 and the narrowing trend of the tropical rainbelt \r\ndetected in observations (Rotstayn et al., 2002; Hwang et al., 2013). \r\nDecreasing precipitation trends in the NH during the 1950s to 1980s \r\nhave been attributed to anthropogenic aerosol emissions from North \r\nAmerica and Europe, which peaked during the late1970s and declined \r\nthereafter following improved air quality regulations, causing dimming \r\n(brightening) through reduced (increased) surface solar radiation \r\n(Box 8.1 Figure 1), in agreement with model simulations (Chiang et al., \r\n2013; Hwang et al., 2013). This is consistent with energetic constraints \r\nwhere tropical precipitation shifts are anti-correlated with cross\u0002equatorial energy transport (Section 6.3.3, Box 8.1)"


id: 7282769fe74f9433ef55ceead2ff74c8
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
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vector_id: 15637
content:
" It also provides \r\na physical mechanism for the severe drought in the Sahel that peaked \r\nin the mid-1980s (Sections 8.3.2.4.3 and 10.4.2.1) and the southward \r\nshift of the NH tropical edge from the 1950s to the 1980s (Allen et al., "


id: b8d0f343207e594eacfcb538467becab
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 26602
content:
"Since AR5, an improved understanding of the key drivers of the \r\nrecent HC expansion has been achieved, identifying the role of \r\nboth internal variability and anthropogenic climate change. Part of \r\nthe recent expansion (1979-2005) of the HC has been driven by \r\na swing from warm to cold phase of the Pacific Decadal Variability \r\n(PDV; Meehl et al., 2016; Grise et al., 2019). The presence of large \r\nmulti-decadal variability in 20th-century reanalyses means there \r\nis limited evidence on the human influence on the recent HC \r\nstrengthening, yet the southward shift of the southern edge and \r\nwidening of the SH HC appeared as robust features in all reanalysis \r\ndatasets, and their trends have accelerated during 1979-2010 \r\n(D'Agostino and Lionello, 2017). As assessed in Section  3.3.3"


id: 941f8d34362913d3a855ba5271833b61
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
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vector_id: 20816
content:
"1, \r\nGHG increases and stratospheric ozone depletion have contributed \r\nto the expansion of the zonal mean HC in the SH since around \r\n1980, and the expansion of the NH HC has not exceeded the range \r\nof internal variability (medium confidence). Moreover, Antarctic \r\nozone depletion can cause a poleward shift in the SH mid-latitude \r\njet and HC (Sections 3.3.3 and 6.3.3.2). Further assessment of the \r\nattribution of recently observed changes in the HC extent and \r\nintensity is found in Section 3.3.3.1."


id: f7555ff105f213c7d45f375d0a8d7cfc
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
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index: climate_gpt_only_giec
vector_id: 36430
content:
"Large human populations in the monsoon regions of the world \r\nheavily depend on freshwater supply for agriculture, water resources, \r\nindustry, transport and various socio-economic activities. The effects \r\nof GHG forcing combined with water vapour feedback (R.J. Allen et al., \r\n2015; Dong and Sutton, 2015; Evan et al., 2015; Dunning et al., 2018) \r\nand cloud feedbacks (Stephens et al., 2015; Potter et al., 2017) are \r\nfundamental to monsoon precipitation changes in a warming world. \r\nSince AR5 there has been improved understanding of precipitation \r\nchanges associated with regional monsoons. Sections  2.3.1.4.2 \r\nand 3.3.3.2 provide an assessment of observed changes and \r\nattribution for the global monsoon. Here we provide an assessment \r\nof the observed changes in regional monsoons (see Annex V  and \r\nFigure 8.11) and underlying causes. In AR6, the definition of regional \r\nmonsoons slightly differs from AR5 and the rationale for it is provided \r\nin Annex V (see Glossary)"


id: 56d9b3728298383e55db64bb10f3694c
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
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vector_id: 11139
content:
"Figure 8.11 | Regional monsoon precipitation changes from observations and model attribution. Precipitation changes during 1951-2014 are shown as least\u0002square linear trends in box-whisker plots (first and fourth rows) over the six regional monsoons, for example, North American monsoon (NAmerM, July-August-September, JAS), \r\nWest African monsoon (WAfriM, June-July-August-September, JJAS), South and South East Asian monsoon (SAsiaM, June-July-August-September, JJAS), East Asian monsoon \r\n(EAsiaM, June-July-August), South American monsoon (SAmerM, December-January-February, DJF), Australian and Maritime Continent monsoon (AusMCM, December-\r\nJanuary-February, DJF), and over the two land domains, for example, equatorial America (EqAmer, June-July-August, JJA) and South Africa (SAfri, December-January-February, \r\nDJF), as identified in the map shown in the middle and as described in Annex V"


id: a90a28f4aeb90b9690c0808e41c20828
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 24115
content:
" Precipitation changes are computed from observations and from Detection and Attribution Model \r\nIntercomparison Project (DAMIP) CMIP6 experiments over the historical period with all-forcing (ALL), GHG-only forcing (labelled GHG), Aerosol-only (AER) and Natural (NAT) \r\nforcings prescribed. Observations are based on the CRU (light green) and GPCC (light blue) datasets and the APHRODITE (light orange) dataset for SAsiaM and EAsiaM. CMIP6 \r\nsimulations are taken from nine CMIP6 models contributing to DAMIP, with at least three members. Ensembles are weight-averaged for the respective model ensemble size. \r\nObserved trends are shown as coloured circles and the simulated trends from the CMIP6 multi-model experiments are shown as box-whisker plots. Precipitation anomaly time\u0002series are shown in the second and third row"


id: 10498ad766a2f5962975141bf95589f6
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
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vector_id: 48
content:
" The thick black line is the multi-model ensemble-mean precipitation anomaly time-series from the ALL experiment and the grey \r\nshading shows the spread across the multi-model ensembles. An 11-year running mean has been applied on the precipitation anomaly time-series prior to calculating the \r\nmulti-model ensemble mean. Further details on data sources and processing are available in the chapter data table (Table 8.SM.1)."


id: 22a6a431f3b5ff13f1107452e4a4f517
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
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index: climate_gpt_only_giec
vector_id: 2908
content:
"Evidence from several climate modelling studies indicates that the \r\nobserved decrease in the regional monsoon precipitation during the \r\nsecond half of the 20th century is dominated by the radiative effects \r\nof NH anthropogenic aerosols, with smaller contributions due to \r\nvolcanic aerosols from the Mount Pinatubo (1991) and El Chichon \r\n(1982) eruptions (Bollasina et al., 2011; Polson et al., 2014; Sanap \r\net al., 2015; Krishnan et al., 2016; Liu et al., 2016; Lau and Kim, 2017; \r\nLin et al., 2018; Takahashi et al., 2018; Undorf et al., 2018a, b; Patil \r\net al., 2019; M. Singh et al., 2020; see Box 8.1, Figure 1 and Figure 8.11). \r\nLand-use changes over South and South East Asia and  the rapid \r\nwarming trend of the equatorial Indian Ocean during the recent few \r\ndecades also appear to have contributed to the observed decrease \r\nin monsoon precipitation (Roxy et al., 2015; Krishnan et al., 2016; \r\nSingh, 2016)"


id: 907e9594eb44c98f23e774f261c1cd04
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 20267
content:
" Overall, the magnitude of the precipitation response \r\nto anthropogenic forcing exhibits large spread across CMIP5 models \r\npointing to the strong internal variability of the regional monsoon \r\n(Saha et al., 2014; Salzmann et al., 2014; Sinha et al., 2015), \r\nincluding variations linked to phase changes of the Pacific Decadal \r\nVariability (Section AVI.2.6; X. Huang et al., 2020a), uncertainties in \r\nrepresenting aerosol-cloud interactions (Takahashi et al., 2018), and \r\nthe effects of local compared with remote aerosol forcing (Bollasina \r\net al., 2014; Polson et al., 2014; Undorf et al., 2018b). CMIP3 and \r\nCMIP5 models do not accurately reproduce the observed seasonal \r\ncycle of precipitation over the major river basins of South and South \r\nEast Asia, limiting the attribution of observed regional hydroclimatic \r\nchanges (Hasson et al., 2014, 2016; Biasutti, 2019)"


id: 1fa101ec6de4f760c1b935f21094679f
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 2408
content:
" While warm \r\nrain processes and organized convection are known to dominate \r\nthe heavy orographic monsoon rainfall over the Western Ghats \r\nmountains (Shige et al., 2017; Choudhury et al., 2018), in various \r\nparts of India (Konwar et al., 2012) and East Asia (Section 11.7.3.1), \r\nthere are uncertainties in representing the regional physical processes \r\nof the monsoon environment, including cloud-aerosol interactions \r\n(Sarangi et al., 2017), land-atmosphere (e.g., Barton et al., 2020) and \r\nocean-atmosphere coupling (Annamalai et al., 2017), in state-of\u0002the-art climate models (see also Section 8.5.1)."


id: 5c95660ae0addcb2dfab5b318bb4b64b
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
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index: climate_gpt_only_giec
vector_id: 12084
content:
"The AR5 reported a  decreasing trend of global land monsoon \r\nprecipitation over the last half-century, with primary contributions \r\nfrom the weakened summer monsoon systems in the Northern \r\nHemisphere (NH). Since AR5, several studies have documented \r\nlong-term variations and changes in the South and South East \r\nAsian summer monsoon (SAsiaM) rainfall. The SAsiaM strengthened \r\nduring past periods of enhanced summer insolation in the NH, \r\nsuch as the early-to-mid Holocene warm period around 9000 to \r\n6000 years before the present (BP) (Masson-Delmotte et al., 2013; \r\nMohtadi et al., 2016; Braconnot et al., 2019) and weakened during \r\ncold periods (high confidence), such as the Last Glacial Maximum \r\n(LGM) and Younger Dryas (Shakun et al., 2007; Cheng et al., 2012; \r\nDutt et al., 2015; Chandana et al., 2018; Hong et al., 2018; E. Zhang \r\net al., 2018)"


id: be49578cc6b1436c5b0ea0b3b70e5556
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 27489
content:
" These long-time scale changes in monsoon intensity are \r\ntightly linked to orbital forcing and changes in high-latitude climate \r\n(Braconnot et al., 2008; Battisti et al., 2014; Araya-Melo et al., 2015; \r\nRachmayani et al., 2016; Bosmans et al., 2018; E. Zhang et al., 2018). \r\nA weakening trend of the SAsiaM during the last 200 years has been \r\ndocumented based on tree ring oxygen isotope chronology from the \r\nnorthern Indian subcontinent (Xu et al., 2018) and South East Asia \r\n(Xu et al., 2013), oxygen isotopes in speleothems from northern India \r\n(Sinha et al., 2015), and tree ring width chronologies from the Indian \r\ncore monsoon region (Shi et al., 2017). Nevertheless, the detection \r\nof century-long decreases in regional monsoon rainfall is obscured \r\nby the presence of multi-decadal time scale precipitation variations \r\n(Turner and Annamalai, 2012; Knutson and Zeng, 2018) which are \r\nevident in long-term rain guage records extending back to the early \r\n1800s (Sontakke et al"


id: ee41bce3af6cb07f60b0dcd78677a925
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 34954
content:
"A significant decline in summer monsoon precipitation is observed \r\nover India since the mid-20th century, which is accompanied by \r\na weakening of the large-scale monsoon circulation (Mishra et al., \r\n2012; Abish et al., 2013; Krishnan et al., 2013, 2016; Saha et al., 2014; \r\nRoxy et al., 2015; Guhathakurta et al., 2017; Samanta et al., 2020). \r\nThis precipitation decline is corroborated by a  decreasing trend in \r\nthe frequency of monsoon depressions that form over Bay of Bengal \r\n(Prajeesh et al., 2013; Vishnu et al., 2016), an increasing trend in \r\nthe frequency and duration of monsoon breaks or 'dry spells' (Singh \r\net al., 2014), significant decreases in soil moisture and increases in \r\ndrought severity across different parts of India post-1950 (Niranjan \r\nKumar et al., 2013; Ramarao et al., 2015, 2019; Krishnan et al., 2016; \r\nGaneshi et al., 2020; Mujumdar et al., 2020)"


id: c6c08fc499bb51ff695dba4c743c230c
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 28842
content:
" While recent studies \r\nhave reported an apparent recovery of the Indian summer monsoon \r\nover a relatively short period since 2003 (Jin and Wang, 2017; Hari \r\net al., 2020), long-term trends for the period 1951-2015 indicate \r\nan overall decrease in the regional monsoon precipitation (Kulkarni \r\net al., 2020; Ayantika et al., 2021). A case study on the Indian summer \r\nmonsoon is provided in Section 10.6.3."


id: b45ad07d8c165610a7f20f8ceb1a3638
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 25892
content:
"During the recent past, long-term rain gauge observations display \r\nsubstantial variability in the WAfriM precipitation over the \r\n20th century (Section 10.4.2.1). The WAfriM experienced the wettest \r\ndecade of the 20th century during the 1950s and early 1960s (high \r\nconfidence), over much of the western and central Sahel region, \r\nfollowed abruptly by the driest years during 1970-1989 (Ali and \r\nLebel, 2009; Nicholson, 2013; Descroix et al., 2015). The percentage \r\ndeficit in the annual rainfall during 1970-1989, relative to the long\u0002term mean, ranged from 60% in the north of Sahel to 25-30% in \r\nthe south (Le Barbe et al., 2002; Lebel et al., 2003). The long decline \r\nin annual rainfall is related to a decrease of rain occurrence over the \r\nSahel (Le Barbe and Lebel, 1997; Frappart et al., 2009; Bodian et al., \r\n2016) and the Soudano-Guinean sub-region of West Africa (Le Barbe \r\net al., 2002), even though the interannual variability pattern is \r\nmore complex (Balme et al., 2006)"


id: b08103de7bd36a8cec2c21b5fbcc5c32
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 25291
content:
" Decrease of rainfall occurrences \r\nresulted from decreases in large convective events in the core of the \r\nrainy season (Bell et al., 2006), that modulate interannual variability \r\nof the WAfriM (Panthou et al., 2018)."


id: 18e1f40a9ded50ac536911d2eba381a8
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 1418
content:
"Internal variability and volcanic eruptions also contributed to the \r\nweakened EAsiaM (Hsu et al., 2014; Qian and Zhou, 2014; Zhou et al., \r\n2017a; Knutson and Zeng, 2018). Since the late 1970s, the EAsiaM \r\nweakening has been also linked to SST changes in the Pacific Ocean \r\nwith warm conditions in the central-eastern tropical part and cold \r\nones in the north, similar to a positive phase of the Pacific Decadal \r\nVariability (PDV; Section AVI.2.6; Z. Li et al., 2016b; Zhou et al., 2017a). \r\nIn the late 1990s the transition from a positive to a negative PDV has \r\nbeen associated with the recent recovery observed in the EAsiaM \r\nstrength (Zhou et al., 2017a). Atlantic Multi-decadal Variability (AMV) \r\nalso has an influence on the EAsiaM via the global teleconnection \r\npattern propagating from the North Atlantic through the westerly jet \r\n(Zuo et al., 2013; Wu et al., 2016a, b)"


id: fad08bb9cf068df35379b3fd7771b5c1
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 36984
content:
" This North Atlantic influence \r\nhas contributed to the increase of precipitation over the Huaihe\u0002Huanghe valley since the late 1990s (Y. Li et al., 2017). When PDV \r\nand AMV are in opposite phase, the former has a larger influence in \r\ndriving the southern flooding and northern drought pattern over the \r\nregion (Q. Yang et al., 2017)."


id: de1803dc99997e59b93d79b44eb23c2a
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 32439
content:
"SST variations in the tropical Atlantic, Pacific and Mediterranean Sea \r\n(Rodriguez-Fonseca et al., 2015; Diakhate et al., 2019). Increased \r\nfrequency of extreme rainfall events impacts high flow occurrences \r\nof the large Sahelian rivers as well as small to meso-scale catchments \r\n(Wilcox et al., 2018). Overall, extreme intense precipitation events are \r\nmore frequent in the Sahel since the beginning of the 21st century \r\n(Giannini et al., 2013; Panthou et al., 2014, 2018; Sanogo et al., \r\n2015; Taylor et al., 2017). Intensification of mesoscale convective \r\nsystems associated with extreme rainfall in the WAfriM is favoured \r\nby enhancement of meridional temperature gradient by the warming \r\nof the Sahara desert (Taylor et al., 2017) at a pace that is two to four \r\ntimes greater than that of the tropical-mean temperature (K.H. Cook \r\net al., 2015; Vizy et al., 2017)"


id: b61a911aa705f55ec3773830a7a8ed2f
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
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index: climate_gpt_only_giec
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content:
" Periods of monsoon-breaks and the \r\npersistence of low rainfall events are still prominent, particularly \r\nafter the onset, thus exposing West Africa simultaneously to the \r\npotential impacts of dry spells (W.  Zhang et al., 2017b) and also \r\nextreme localized rains and floods (Engel et al., 2017; Lafore et al., \r\n2017). Occurrence of extreme events is compounded by land use and \r\nland cover changes leading to increased runoff (Bamba et al., 2015; \r\nDescroix et al., 2018). "


id: 343c4afe3c10bac8ef338ce7d92d2ae4
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
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vector_id: 5628
content:
"The Sahel drought from the 1970s until the early 1990s was related to \r\nanthropogenic emissions of sulphate aerosols in the Atlantic, which \r\nled to an inter-hemispheric pattern of SST anomalies and associated \r\nregional precipitation changes (Section 6.3.3.2 and Box 8.1). Also the \r\ncombined effects of anthropogenic aerosols and GHG forcing appear \r\nto have contributed to the late twentieth century drying of the Sahel \r\nthrough their effect on SST, by cooling the North Atlantic and warming \r\nthe tropical oceans (Giannini and Kaplan, 2019; Hirasawa et al., \r\n2020). Subsequent aerosol removal led to SST warming of the North \r\nAtlantic, shifting the ITCZ further northward and strengthening the \r\nWAfriM (Giannini and Kaplan, 2019). The recent recovery has been \r\nascribed to prevailing positive SST anomalies in the tropical North \r\nAtlantic potentially associated with a positive phase of the Atlantic \r\nMulti-decadal Oscillation (Diatta and Fink, 2014; Rodriguez-Fonseca \r\net al., 2015)"


id: 8c8e91532acfe3096a87e72b4810c61d
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 19634
content:
" The Sahel rainfall recovery has also been attributed to \r\nhigher levels of GHG in the atmosphere and increases in atmospheric \r\ntemperature (Dong and Sutton, 2015). "


id: daf65e5860655d5a43d7ad9fd37f71c0
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 31971
content:
"An increase in intense rainfall and severe weather events has been \r\nobserved in several locations, especially in south-western Arizona \r\nsince 1991, resulting from increases in atmospheric moisture content \r\nand instability; a  change that has been confirmed by convective\u0002permitting model simulations (Luong et al., 2017; Pascale et al., \r\n2019). A dense network of 59 rain gauges located in south-eastern \r\nArizona suggests an intensification of monsoon sub-daily rainfall \r\nsince the mid-1970s (Demaria et al., 2019), as expected by a stronger \r\nglobal warming signature for sub-daily rather than daily or monthly \r\nprecipitation accumulation (Section 11.4). Section 10.4.2.3 provides \r\nfurther details on changes in precipitation in south-western North \r\nAmerica. Evidence from multiple reanalyses suggests that increases \r\nin NAmerM rainfall have contributed to the increasing trend of global \r\nmonsoon precipitation (Section 2.3.1.4.2; Lin et al., 2014)"


id: 6ba02ff585061d4e6ff47cafca07697f
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
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index: climate_gpt_only_giec
vector_id: 14555
content:
" In addition, \r\nmore frequent occurrence of earlier retreats of the NAmerM since \r\n1979 is documented (Arias et al., 2012, 2015), in association with the \r\npositive phase of the Atlantic Multi-decadal Variability (AMV) and \r\na westward expansion of the North Atlantic Subtropical High (W. Li \r\net al., 2011, 2012). "


id: b9767df44c4cc9ce6fd1cb086209969a
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 26722
content:
"Since AR5, there has been improved understanding of changes in the \r\nSouth American monsoon (SAmerM) as evidenced from paleoclimate \r\nrecords, instrumental observations and climate model simulations. \r\nHowever, general circulation models (GCMs) still exhibit difficulties \r\nin reproducing SAmerM precipitation amount (Rojas et al., 2016; \r\nD'Agostino et al., 2020b). Paleoclimate evidence suggests a relatively \r\nstronger SAmerM during the 1400-1600 period (Bird et al., 2011b; \r\nVuille et al., 2012; Ledru et al., 2013; Apaestegui et al., 2014; Novello \r\net al., 2016; Wortham et al., 2017). Last millennium GCM simulations \r\nare able to reproduce stronger SAmerM during the 1400-1600 \r\nperiod in comparison with warmer epochs such as the 900-1100 \r\nperiod (Rojas et al., 2016) or the current warming period (Diaz and \r\nVera, 2018). PMIP3/CMIP5 simulations indicate a consistent weaker \r\nSAmerM during the mid-Holocene (6000 years ago; see Cross\u0002Chapter Box  2"


id: c655afe9931754a8aabac2b693851fcb
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 28781
content:
"1) in comparison to current conditions (Bird et al., \r\n2011a; Mollier-Vogel et al., 2013; Prado et al., 2013a; D'Agostino \r\net al., 2020b), thus favouring savannah/grassland-like vegetation \r\n(Smith and Mayle, 2018), in agreement with climate reconstructions \r\nfrom different proxies (Prado et al., 2013b). Signals of weak and \r\nstrong SAmerM during mid-Holocene and LGM, respectively, are \r\nevident also in high-resolution long-term (i.e., more than about \r\n22,000 years) rainfall reconstructions based on oxygen isotopes in \r\nspeleothems from Brazil (Novello et al., 2017; Strikis et al., 2018; \r\nCampos et al., 2019)."


id: dcf0948c9c9ae62f0057c2ab366b9d3c
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 32255
content:
"Significant increases in precipitation have been observed over \r\nsouth-eastern Brazil during 1902-2005 while non-significant \r\ndecreases have been found over central Brazil (Vera and Diaz, \r\n2015). In Bolivia, increases were observed during 1965-1984, while \r\nreductions have occurred since then (Seiler et al., 2013). However, \r\nthe Peruvian Amazon does not reveal significant changes in mean \r\nrainfall during 1965-2007 (Lavado et al., 2013; Ronchail et al., 2018). \r\nHistorical simulations from CMIP5 ensembles adequately capture \r\nthe observed summer precipitation amount over central and south\u0002eastern Brazil, thereby providing high confidence in interpreting the \r\nobserved variability of SAmerM for the period 1960-1999 (Gulizia \r\nand Camilloni, 2015; Pascale et al., 2019)"


id: 3ba3829eeabfc1912da08ac32a7c8fdf
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 6737
content:
" Also, CMIP5 simulations \r\nindicate that the anthropogenic forcing associated with increased \r\nGHG emissions is necessary to explain the positive trends in upper\u0002troposphere zonal winds observed over the South American Altiplano \r\n(Vera et al., 2019). However, the detection of anthropogenically\u0002induced signals for precipitation is still ambiguous in monsoon \r\nregions, like the SAmerM (Hoegh-Guldberg et al., 2018)."


id: 35d9b7d2dd8ad6e4672c5ae03eb6ecb7
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 5884
content:
"In contrast, it has been shown that observed trends in blocking are \r\nsensitive the choice of the blocking index, and that there is a large \r\ninternal variability that complicates the detection of forced trends \r\n(Barnes et al., 2014; Cattiaux et al., 2016; Woollings et al., 2018), \r\ncompromising the attribution of any observed changes in blocking. \r\nMany climate models still underestimate the occurrence of blocking, \r\nat least in winter over north-eastern Atlantic and Europe (Dunn\u0002Sigouin and Son, 2013), which leads to caution in the interpretation \r\nof their results for these regions. However, over the Pacific Ocean \r\nthere have been large improvements in the simulation of blocking \r\nfor the last 20 years (Davini and D'Andrea, 2016; Patterson et al., \r\n2019). In the SH, increases in blocking frequency have occurred in the \r\nSouth Atlantic in austral summer (Dennison et al., 2016) and in the \r\nsouthern Indian Ocean in austral spring (Schemm, 2018)"


id: b8ab1b5bd22057459b06d1e8e9cf1783
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
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index: climate_gpt_only_giec
vector_id: 26580
content:
" A reduced \r\nblocking frequency has been found over the south-western Pacific \r\nin austral spring (Sections 2.3.1.4.3 and 3.4.1.3.3; Schemm, 2018)."


id: 8a2f635a6d1bc8ee61e462027b8a4bb5
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 19271
content:
"number of cyclones from 1979 show a large spread of trends across \r\ndifferent estimates (Section 2.3.1.4.3; Neu et al., 2013; Z. Li et al., \r\n2016a; Grieger et al., 2018) resulting in low confidence in any clear \r\nincrease of in the total number of cyclones. However, starting from \r\nthe early 1990s, most reanalyses show increases in the total cyclone \r\nnumber by about 2-5% per decade (Figure 8.12). Increasing trends \r\nin the total number of cyclones are dominated by the increase in \r\nthe number of shallow and moderate cyclones (which are more \r\ndependent on the datasets and identification methods used) than \r\nwith decreasing number of deep cyclones since the early 1990s \r\n(Tilinina et al., 2013; Chang, 2018). In the SH the variability of the \r\ntotal number of cyclones is characterized by strong inter-decadal \r\nvariability preventing a  clear assessment of trends"


id: c66d90eb35962904a64fddd058337800
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 28792
content:
" However, in \r\ncontrast to the NH,there is a  significant increasing trend in the \r\nnumber of deep cyclones (about 10% over 1979-2018) in ERA5, \r\nERA-Interim, JRA55 and MERRA, and in the CFSR dataset after 2000 \r\n(Figure 8.12; Reboita et al., 2015; X.L. Wang et al., 2016)."


id: ef3ceb69157878521f970dedfab74d46
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 35110
content:
"Changes in the number of deep storms, which are often associated \r\nwith heavier precipitiation over the North Atlantic and North \r\nPacific, exhibit strong seasonal differences and decadal variability \r\n(Colle et al., 2015; Chang et al., 2016; Matthews et al., 2016; Priestley \r\net al., 2020a). An increase in the number of summer cyclones over the \r\nAtlantic-European sector (Tilinina et al., 2013) is consistent with \r\nthe  increase in the strength of the strongest fronts over Europe \r\n(Schemm et al., 2018). Chang et al. (2016) reported a decrease in the \r\nnumber of strong summer storms in the latitudinal band 40degN-75degN \r\nover the last decades, however, the assessment of seasonal trends in \r\nthe Atlantic-European sector is complicated by the choice of region, \r\nattribution of tracks to the region selected, and thresholds used to \r\nidentify trajectories, leading to low confidence on regional seasonal \r\ntrends. For the SH, Grieger et al"


id: 8d10b869d35ca3c0d3ca04dd029a827b
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
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index: climate_gpt_only_giec
vector_id: 19721
content:
" (2018) reported a growing number \r\nof cyclones over sub-Antarctic region in the austral-summer during \r\n1979-2010, while statistically significant trends were absent \r\nduring the austral winter. "


id: 37a2fbaa6e13435ff486ae74f7ee1997
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 6132
content:
"Atmospheric rivers (ARs) are long, narrow (up to a  few hundred \r\nkilometres wide), shallow (up to few kilometres deep) and transient \r\ncorridors of strong horizontal water vapour transport that are typically \r\nassociated with a low-level jet stream ahead of the cold front of an \r\nextratropical cyclone (Ralph et al., 2018). Atmospheric rivers were \r\nnot assessed in AR5. ARs are associated with atmospheric moisture \r\ntransport from the tropics to the mid- and high latitudes  (Zhu \r\nand Newell, 1998), although the drivers of moisture transport \r\nrelative to the different airstreams within extratropical cyclones \r\nremains a subject of current study (Dacre et al., 2019). While much \r\nprevious research has focused on the west coast of North America, \r\nARs occur throughout extratropical and polar regions (e.g.,  Guan \r\nand Waliser, 2015) and are often associated with locally-heavy \r\nprecipitation, including a  substantial fraction of all mid-latitude \r\nextreme precipitation events (e.g"


id: 83853d031b52c96068f7e00d5a5f992a
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
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index: climate_gpt_only_giec
vector_id: 18223
content:
", Waliser and Guan, 2017). ARs \r\nalso affect East Asia strongly during the period from late spring to \r\nsummer (Kamae et al., 2017). ARs can be related to warming/melt \r\nevents trough the intrusions of warm and moist air in Antarctica, "


id: 3d82b331e118ea88834ad0c4feab566e
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 7034
content:
"Tropical SSTs and associated global circulation may increase rainfall \r\nin West Africa, as observed in some years during 1950-2015, despite \r\nthe presence of El  Nino (Pomposi et al., 2020). During an El  Nino \r\nsummer, equatorial convective systems and the associated Walker \r\ncirculation tend to shift eastward, leading to decreases in Indian \r\nsummer monsoon rainfall (Li and Ting, 2015; Roy et al., 2019). This \r\nteleconnection is modulated by Indian Ocean Variability (Terray et al., \r\n2021), as observed during the extreme positive IOD event in 2019 \r\n(Ratna et al., 2021). Since the end of the 19th century, synchronous \r\nhydroclimate changes (medium confidence) have been identified \r\nover south-eastern Australia and South Africa (Gergis and Henley, \r\n2017) modulated by ENSO, as well as other regional fluctuations like \r\nthe Botswana High over southern Africa (Driver and Reason, 2017). \r\nOver southern South America, the ENSO influence on precipitation \r\n(Cai et al., 2020; Poveda et al"


id: 891157084963d2c2a507103775ad354a
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 19106
content:
", 2020) interacts with the influence of \r\nSAM (Pedron et al., 2017), exhibiting large multi-decadal variations \r\nbecause of changes in the correlation between the two large-scale \r\nmodes (Vera and Osman, 2018). Other processes underlying ENSO \r\nteleconnections of relevance for water cycle changes include water \r\nvapour and moisture transports, like over the Middle East (Sandeep \r\nand Ajayamohan, 2018), south-eastern China (S.  Yang et al., \r\n2018b), or central Asia (X. Chen et al., 2018), south-eastern South \r\nAmerica (Martin-Gomez et al., 2016; Martin-Gomez and Barreiro, \r\n2016), Australia (Rathore et al., 2020) and southern USA (Okumura \r\net al., 2017). "


id: 113c169cc82bd3ae15f389319a395e9c
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 188
content:
"There is no evidence of a trend in the Indian Ocean Dipole (IOD; Section \r\nAIV.2.4) mode and associated anthropogenic forcing (Sections 2.4.3 \r\nand 3.7.4). The AR5 concluded that the IOD is likely to remain active, \r\naffecting climate extremes in Australia, Indonesia and East Africa. \r\nSince the AR5, IOD teleconnections have been identified extending \r\nfurther to the Middle East (Chandran et al., 2016), to the Yangtze \r\nriver (Xiao et al., 2015), where in boreal summer and autumn positive \r\nIOD events tend to increase the precipitation in the south-eastern \r\nand central part of the basin, and to the southern Africa extreme \r\nwet seasons (Hoell and Cheng, 2018). During the last millenium, \r\nthe combined effect of a positive IOD and El Nino conditions have \r\ncaused severe droughts over Australia (Abram et al., 2020)"


id: a1a52a9c081c29c98186ab1668e4d8e0
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 22988
content:
" In the \r\nsatellited period, it is found more effective in inducing significant \r\ndecrease of rainfall over Indonesia, with the opposite occurring for \r\nnegative IOD events (As-syakur et al., 2014; Nur'utami and Hidayat, \r\n2016; Pan et al., 2018). Similarly, over the Ganges and Brahmaputra \r\nriver basins major droughts have been recorded during co-occurring \r\nEl Nino and positive IOD, while floods occurred during La Nina and \r\nnegative IOD conditions (Pervez and Henebry, 2015). Over equatorial \r\nEast Africa the IOD affects the short rain season (medium confidence) \r\nexacerbating flooding and inundations independently of ENSO \r\n(Behera et al., 2005; Conway et al., 2005; Ummenhofer et al., 2009; \r\nHirons and Turner, 2018). Extreme conditions, like the 2019 Australian \r\nbushfires and African flooding, have been associated with strong \r\npositive IOD conditions (Cai et al., 2021)."


id: bb185e7f88158494b47fec99914ee2c6
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 26979
content:
"A positive trend has been observed in the Northern Annular Mode \r\n(NAM; Section AIV.2.1) in the second half of the 20th century, which \r\npartially reversed since the 1990s (Section 2.4.5.1), but the detection \r\nand attribution of these changes remain difficult (Section  3.7.1). \r\nThe linkages of the NAM with weather and climate extremes in the \r\nnorthern extratropics are still unclear in models and observations \r\n(Vihma, 2014; Overland et al., 2016; Screen et al., 2018). However, \r\nrobust links are identified between precipitation trends and \r\nvariability in Europe and the phases of the Atlantic component of \r\nthe NAM, that is, the NAO (Moore et al., 2013; Comas-Bru and \r\nMcDermott, 2014). Reduced winter precipitation is well correlated \r\nwith the NAO over Southern Europe and Mediterranean countries \r\n(Kalimeris et al., 2017; Corona et al., 2018; Vazifehkhah and Kahya, \r\n2018; Neves et al., 2019)"


id: ef9e76b0239f3bc2785b42972851c1a5
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
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index: climate_gpt_only_giec
vector_id: 35200
content:
" NAO teleconnections in those regions \r\ninclude influences on groundwater and streamflow (Zamrane et al., \r\n2016; Massei et al., 2017; Jemai et al., 2018). Remote teleconnections \r\nof the NAO have been identified over Northern China, the Yangtze \r\nRiver valley and India (Jin and Guan, 2017; Di Capua et al., 2020). The \r\nsummer phase of the NAO is significantly correlated with variations \r\nin summer rainfall in East China, with the thermal forcing of the \r\nTibetan Plateau providing a  link to this Eurasian teleconnection \r\n(Z. Wang et al., 2018). "


id: 54954fa8efde4df49f93205b2f12845
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 10764
content:
"Simulated changes in MJO precipitation amplitude are extremely \r\nsensitive to the pattern of SST warming (Takahashi et al., 2011; \r\nMaloney and Xie, 2013; Arnold et al., 2015) and ocean-atmosphere \r\ncoupling (DeMott et al., 2019; Klingaman and Demott, 2020). In \r\nagreement with results from previous model generations, most \r\nCMIP5 models still underestimate MJO amplitude, and struggle \r\nto generate a  coherent eastward propagation of precipitation \r\nand wind (Hung et al., 2013; Jiang et al., 2015; Ahn et al., 2017), \r\naffecting regional surface climate in the tropics and extratropics. \r\nIn addition, most CMIP5 models simulate an MJO that propagates \r\nfaster compared with observations, with a poorly represented intra\u0002seasonal precipitation variability (Ahn et al., 2017). Over the Indian \r\nOcean, the propagation speed of convection in some CMIP5 models \r\ntends to be slower than observed due to a strong persistence of \r\nequatorial precipitation (Hung et al., 2013; Jiang et al., 2015)"


id: 43b3976928d7d6282cfeb238a351ea55
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 8059
content:
" Among \r\nother processes, improving the moisture-convection coupling, \r\nthe  representation of moist convection, the interaction between \r\nlower tropospheric heating and boundary layer convergence, and the \r\ntopography of the Maritime Continent improve simulations of \r\nthe MJO (Ahn et al., 2017, 2020a; Kim and Maloney, 2017; Yang \r\nand Wang, 2019; H. Tan et al., 2020; Y.-M. Yang et al., 2020). In \r\nfact, CMIP6 models reproduce the amplitude and propagation of \r\nthe MJO better than CMIP5 models due to increased horizontal \r\nmoisture advection over the Maritime Continent (Ahn et al., 2020b). \r\nDespite the diverse theories of MJO evolution and processes that "


id: c428e86f6a64100885772f5c798ec269
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 28427
content:
"In the Southern Hemisphere (SH), an observed positive trend is \r\nidentified in the strength of the Southern Annular Mode (SAM, \r\nSection AIV.2.2) since 1950, especially in austral summer (high \r\nconfidence, Section  2.4.1.2). While stratospheric ozone depletion \r\nand GHG increases largely contributed to this change, climate \r\nmodels still have trouble simulating the SAM and its response to \r\nozone and GHGs (Section 3.7.2). Shifts in the south-westerly winds \r\n(Fletcher et al., 2018) and the expansion of the SH Hadley cell (Kang \r\nand Polvani, 2011; H.  Nguyen et al., 2018) influence SAM-related \r\nrainfall anomalies in in southern South America and southern \r\nAustralia during the austral spring-summer. Over New Zealand, \r\nlarge-scale SLP and zonal wind patterns associated with SAM \r\nphases modulate regional river flow (Li and McGregor, 2017). The \r\nSAM also influences precipitation and water vapour changes over \r\nAntarctica via moisture fluxes (Marshall et al"


id: f6aaf4e653f0d94cf000608e6c731a67
created_at: 2023-03-16 14:02:04
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index: climate_gpt_only_giec
vector_id: 36324
content:
", 2017; Oshima and \r\nYamazaki, 2017; Grieger et al., 2018) but CMIP5 models are limited \r\nin their ability to simulate these regional teleconnections (Marshall \r\nand Bracegirdle, 2015; Palerme et al., 2017). SAM and its interaction "


id: 8675d7958c0df94868104cb77fe65cc2
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 18696
content:
"In this Report, Chapter 4 provides an updated assessment of global \r\nannual precipitation (Section 4.3.1), finding that it is very likely that \r\nannual precipitation averaged over all land regions continuously \r\nincreases as global surface temperatures increase in the 21st \r\ncentury (high confidence). CMIP6 projections for long-term changes \r\nin P-E (Figure 8.13) show that, for all scenarios, P-E increases over \r\nthe tropics and high latitudes and decreases over the subtropics, \r\nresulting from a  thermodynamically driven amplification of P-E \r\npatterns (Section  8.2.2.1). Both the intensity of changes and the \r\nspread among the models is larger for the higher emissions scenarios. \r\nA  less coherent latitudinal pattern and smaller magnitude of P-E \r\nchanges over land reflect the complex influence of land-ocean \r\nwarming contrast, atmospheric circulation change and vegetation \r\nfeedbacks (Section  8.2.2.1)"


id: 1308d3acf90abddd3ae18170e921c1ef
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
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index: climate_gpt_only_giec
vector_id: 506
content:
" However, stronger atmospheric \r\nmoisture transport, increases in precipitation and evaporation over \r\nglobal land and ocean and larger continental runoff that is in part \r\nfed by melting of glaciers characterizes a more intense water cycle \r\nwith global warming."


id: 3e4b1a7ddb9d906cf00befb27b036fbd
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vector_id: 7171
content:
"Most projected changes in the water cycle are not expected to be \r\nuniform in space or time. They are driven by both dynamical and \r\nthermodynamical processes (Section  8.2) and have not necessarily \r\nemerged yet in the recent observational record (Section 8.3) as they \r\nare superimposed on substantial natural fluctuations in weather and \r\nclimate. Therefore, projecting regional water cycle changes remains \r\nchallenging. However, a number of physically understood responses \r\ncan be evaluated using both CMIP5 and CMIP6 models, which are \r\nimportant for guiding decision making that anticipates, prepares for, \r\nand responds to water cycle changes. In this section, global maps of \r\nprojected changes in water cycle variables are assessed using the \r\nWGI AR6 'simple method' (see Cross-Chapter Box Atlas1), which uses \r\nhatching to highlight where less than 80% of the models agree on \r\nthe sign of projected changes. This choice differs from Section 4.2.6 \r\nfor a  number of reasons"


id: d1e691e7dca126834c101be9675b5b48
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
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vector_id: 30547
content:
" These include the weak signal-to-noise \r\nratio of projected hydrological changes in low to medium emissions \r\nscenarios, the sensitivity of their statistical significance to the \r\nbaseline reference period, and the non-Gaussian distribution of many \r\nwater cycle variables (see Cross-Chapter Box Atlas.1 for more details \r\non strengths and limitations of the hatching methods implemented \r\nwithin AR6)."


id: 30b1161ea6d89862b2e15c8cc970f66f
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 5080
content:
"Global and global land mean water cycle changes from CMIP6 \r\nprojections are shown in Table 8.1. Increases in global and continental \r\nprecipitation, P-E and runoff in both the mid-term and long-term \r\nillustrate the future intensification of the water cycle, with the \r\nmagnitude of change increasing with emissions scenarios. Consistent \r\nwith AR5, CMIP6 simulations of global mean precipitation show \r\na systematic multi-model mean increase of 1.6 to 2.9 % degC-1 warming \r\n(apparent hydrological sensitivity; Section  8.2.1) by 2081-2100 \r\nrelative to present day across the new SSP scenarios (using global \r\nsurface air temperature change from Table 4.1). It is well understood \r\nthat rising concentrations of CO2 drive a long-term increase in global \r\nprecipitation with warming, but with the increase partly offset by \r\nrapid atmospheric adjustments to the direct atmospheric heating \r\nfrom radiative forcing agents (Section  8.2.1)"


id: 2d66cdd4b63eb736b106befc04ea53cf
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
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index: climate_gpt_only_giec
vector_id: 4584
content:
" The largest apparent \r\nhydrological sensitivity is found for SSP1-1.9, where the suppressing \r\neffects on precipitation from atmospheric heating by greenhouse \r\ngases (GHGs) rapidly reduce as their concentration falls. Additional \r\nwarming due to reduced aerosol loadings under the SSP scenarios \r\n(Lund et al., 2019) further increases global precipitation (Rotstayn \r\net al., 2013; Wu et al., 2013; Salzmann, 2016; T.B. Richardson et al., "


id: 3560539d8cb0bb082e1d736839f4aaca
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 5808
content:
"While projections of water vapour are well understood due to the \r\nconstraints of the Clausius-Clapeyron relationship, projections \r\nof water vapour transport are complicated regionally by the role of \r\nchanges in the wind field, which is influenced by a  wide variety \r\nof  factors. Additionally, there has been relatively little general \r\nevaluation of moisture transport in models. In CMIP5 models, \r\nboth the mean and variability of the vertically-integrated moisture \r\ntransport is projected to increase, largely due to increases in water \r\nvapour (Lavers et al., 2015), with substantial regional differences \r\n(Levang and Schmitt, 2015). Single-model studies have illustrated \r\nprojected increases in low-altitude moisture transport into \r\nconvergence regions (Allan et al., 2014) and from ocean to land \r\n(Zahn and Allan, 2013) that are consistent with present day trends"


id: 6c27d55f63aef95d594f2d906edcb3d3
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 14645
content:
" \r\nIncreases in moisture transport have been linked to increases in large \r\nprecipitation accumulations over land (Norris et al., 2019). Based \r\non robust physics and supported by modelling studies, it is well \r\nunderstood that moisture transport increases into convergent parts \r\nof the atmospheric circulation such as storm systems, the tropical rain \r\nbelt and high latitudes (Section 8.2.2.1), but changes in atmospheric \r\ncirculation that are less well understood alter moisture transport \r\nregionally (Section 8.2.2.2). Therefore, given the limited examination \r\nof moisture transport in models, regional projections should be \r\nconsidered with caution. Changes in moisture transport specifically \r\nassociated with monsoons, atmospheric rivers, and other specific \r\ncirculation features are discussed further in the following sections."


id: 3e11fa2d51633c6c63a813e78ca8800
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 7125
content:
"Geographical patterns of projected precipitation changes show \r\nsubstantial seasonal contrasts and regional differences, including \r\nover land (Figure 8.14 and Figure 4.27). Projections for 2081-2100 \r\nunder the SSP2-4.5 scenario suggest increased precipitation over \r\nthe tropical oceans, north-eastern Africa, the Arabian Peninsula, \r\nIndia, south-eastern Asia and the Polar regions while decreased \r\nprecipitation is projected mainly over the subtropical regions \r\n(Section  4.5.1.4). Precipitation changes contrast regionally in the \r\ntropics with wetter wet seasons over South Asia, central Sahel and \r\neastern Africa, but less precipitation over Amazonia and coastal \r\nWest Africa (Section  8.4.2.4). These large-scale responses are \r\nassociated with stronger moisture transports in a warmer climate \r\nthat are modulated by the greater warming over land than ocean, \r\natmospheric circulation responses and land surface feedbacks \r\n(Section  8.2.2)"


id: a357d7f4006cfef9f4ce502818d71f63
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 23260
content:
" There is agreement across CMIP5 and CMIP6 \r\nmodelling studies that precipitation increases in wet parts of the \r\natmospheric circulation and decreases in dry parts (Liu and Allan, \r\n2013; Kumar et al., 2015; Deng et al., 2020; Schurer et al., 2020) \r\nalthough these regions shift with atmospheric circulation changes. \r\nThe overall pattern is robust across different model scenarios and \r\ntime horizons (Tebaldi and Knutti, 2018), but some deviations \r\nfrom the mean pattern cannot be excluded due to the multiple \r\ntime scales and non-linear atmospheric or land surface processes \r\ninvolved (Section 8.5.3). Near-term regional changes in precipitation \r\nare more uncertain because of a  stronger sensitivity to natural \r\nvariability (Section  8.5.2) and non-GHG anthropogenic forcings \r\n(Section 4.4.1.3 and 8.4.3.1)."


id: 7acad5be35f06637b67b2ed0e3608592
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 16902
content:
"Projected changes in regional precipitation also arise as a response \r\nto changes in large-scale atmospheric circulation (Section  8.2.2.2 \r\nand 8.4.2), both in the tropics (Chadwick et al., 2016b; Byrne et al., \r\n2018) and extratropics (Shaw, 2019; Oudar et al., 2020b). Despite \r\nvariability in simulated changes, CMIP5 climate models consistently \r\nproject large rainfall changes (of varying sign) over considerable \r\nproportions of tropical land during the 21st century (Chadwick \r\net al., 2016b). Since AR5, some robust responses in large-scale \r\ncirculation patterns have been identified. For example, and as further \r\nassessed in Section 8.4.2, CMIP6 models project a northward shift \r\nin the tropical rain belt over eastern Africa and the Indian Ocean \r\nand a  southward shift in the eastern Pacific and Atlantic oceans \r\n(Mamalakis et al., 2021)"


id: 65e9a08e053bed5fcfe00dc0857ba6af
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 13622
content:
" A projected strengthening and tightening \r\nof the tropical rain belt increases the contrasts between wet and \r\ndry tropical weather regimes and seasons. It is less clear how the \r\nwell understood poleward expansion of the subtropics and mid\u0002latitude storm tracks influences precipitation over subtropical and \r\nmid-latitude continents (Section 8.2.2.2). "


id: a736e7520569e23391eeb6666a7b8981
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
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vector_id: 23819
content:
"An ensemble of 31 CMIP6 models under the SSP5-8.5 scenario \r\nprojects increases precipitation by 10-30% over much of the USA \r\nand decreases by 10-40% over Central America and the Caribbean \r\nby 2080-2099 (Almazroui et al., 2021). This CMIP6 ensemble also \r\nprojects an increase in annual precipitation over the southern \r\nArabian Peninsula and a  decrease over the northern Arabian \r\nPeninsula, as also projected by CMIP3 and CMIP5 models (Almazroui \r\net al., 2020a). Annual mean precipitation is projected to increase over \r\nSouth Asia during the 21st century under all scenarios, although the \r\nrate of change varies within the region based on 27 CMIP6 models \r\n(Almazroui et al., 2020c). CMIP6 projections also display a reduction \r\nin annual mean precipitation over northern and southern Africa \r\nwhile increases are projected over Central Africa, under the SSP1-2.6, \r\nSSP2-4.5 and SSP5-8.5 scenarios (Almazroui et al., 2020b)"


id: 5604100f97267099a9cd76a8106a8ebb
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
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index: climate_gpt_only_giec
vector_id: 11023
content:
" The AR6 \r\nAtlas assesses that regions where annual mean rainfall is likely to \r\nincrease include the Ethiopian Highlands, East, South and North Asia, \r\nsouth-eastern South America, northern Europe, northern and eastern \r\nNorth America, and the Polar Regions. In contrast, regions where "


id: aae7980aeb2f4719e120cd585ecbbbf7
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 24407
content:
"In summary, it is virtually certain that global precipitation will \r\nincrease with warming due to increases in GHG concentrations \r\nand decreases in air pollution. There is high confidence that total \r\nprecipitation will increase in the high latitudes, with a  shift from \r\nsnowfall to rainfall except in the coldest regions and seasons. \r\nThere is also high confidence that precipitation will decrease over \r\nthe Mediterranean, southern Africa, Amazonia, Central America, \r\nsouth-western South America, south-western Australia and coastal \r\nWest Africa and that monsoon precipitation will increase over South \r\nAsia, East Asia and central-eastern Sahel. See Section  8.4.2.4 for \r\na more detailed assessment of changes in regional monsoons. Daily \r\nmean precipitation intensities, including extremes, are projected to \r\nincrease over most regions (high confidence). The number of dry days \r\nis projected to increase over the subtropics, Amazonia, and Central \r\nAmerica (medium confidence)"


id: 18c88c86486e8f7906e7e69265f77116
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
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index: climate_gpt_only_giec
vector_id: 1398
content:
" There is high confidence in an overall \r\nincrease in precipitation variability over most land areas."


id: 3a281504ee10bbd990fea5ac25f7da32
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 6516
content:
"The AR5 did not highlight observed changes in water cycle seasonality and SRCCL mostly emphasized changes in vegetation \r\nseasonality. Since AR5, a  number of relevant studies have been published, but often with conflicting results. Based on three in \r\nsitu datasets, reduced precipitation seasonality was identified over 62% of the terrestrial ecosystems analysed from 1950-2009 \r\n(Murray-Tortarolo et al. 2017). In contrast, both in situ and satellite data show a general increase in the annual range of precipitation \r\nfrom 1979 to 2010, which is dominated by wetter wet seasons (Chou et al., 2013). This paradox may be partly explained by a larger \r\naerosol radiative forcing in the middle of the 20th century as well as by internal variability (Kumar et al., 2015; see also Box 8.1). For \r\ninstance, the 'long rains' over East Africa experienced declining trends in the 1980s and 1990s (Nicholson, 2017), which was linked \r\nto anthropogenic aerosols and SST patterns (Rowell et al"


id: e92be6191f954656267faa4a0aea38f7
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
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vector_id: 34203
content:
", 2015), followed by a recent recovery that was linked to internal variability \r\n(Wainwright et al., 2019). Two satellite datasets revealed decreased rainfall seasonality in the tropics but an increased seasonality in \r\nthe subtropics and mid-latitudes since 1979, without clear attribution (Marvel et al., 2017)."


id: 7e420f1ddc4dd0f58927436d82d55252
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 17428
content:
"Additional changes in seasonality may manifest in the timing and duration of wet seasons. A later monsoon onset trend was reported \r\nthroughout India from 1901 to 2013 (Sahany et al., 2018). Conversely, an earlier rainfall onset was implicated in increased springtime \r\nrainfall over the Tibetan Plateau in recent decades (W. Zhang et al., 2017a). Winter and early spring precipitation over the north-western \r\nHimalaya for the period 1951-2007 shows an increasing trend of daily precipitation extremes in association with enhanced amplitude \r\nvariations of extratropical synoptic-scale systems known as 'Western Disturbances'(Madhura et al., 2014; Cannon et al., 2015; Krishnan \r\net al., 2019). In China, an earlier onset was observed during 1961-2012 (Deng et al., 2019). In the African Sahel, rainfall has been most \r\nconcentrated in the peak of the rainy season since the end of the 20th century (Biasutti, 2019)"


id: d0d155327a5d4574c790f63a8c10bc4a
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
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index: climate_gpt_only_giec
vector_id: 30388
content:
" A shift in the seasonality of Sahelian \r\nrainfall, including delayed cessation has also been reported (Section 10.4.2.1; Nicholson, 2013; Dunning et al., 2018). Over southern Africa, \r\nan observed earlier onset (1985-2007) is in contrast to a simulated historical and projected future delay in the wet season (Maidment \r\net al., 2015; Dunning et al., 2018). An increasingly early onset of the North American monsoon has been observed from 1978 to 2009 \r\n(Arias et al., 2015). Seasonality changes in the South American monsoon indicate delayed onsets since 1978 (Fu et al., 2013; Yin et al., \r\n2014; Arias et al., 2015; Debortoli et al., 2015; Arvor et al., 2017; Giraldez et al., 2020; Haghtalab et al., 2020; Correa et al., 2021). "


id: f875ffdcfc214cade508fce283eaf4a1
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
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index: climate_gpt_only_giec
vector_id: 36600
content:
"Since AR5, several studies have further documented a projected increase in rainfall seasonality and the understanding of the underlying \r\nmechanisms has been improved (Sections 8.2.1 and 8.3.2). CMIP5 models show that the seasonal concentration of annual precipitation \r\nwill increase over many regions by the end of the 21st century, with robust model agreement in most subtropical regions where an \r\nincrease in the mean number of dry days was also reported in the RCP8.5 scenario (Pascale et al., 2016). The semi-arid, winter rainfall \r\ndominated subtropical climate is projected to shift poleward and eastward, with the equatorward margins replaced by a more arid \r\nclimate type. However, evolving SST patterns and land-ocean warming contrasts cause more complex responses (Alessandri et al., \r\n2015; Polade et al., 2017; Brogli et al., 2019; Zappa et al., 2020). Projections over California show a stronger and shorter wet season \r\n(Polade et al., 2017; Dong et al., 2019)"


id: 485f0b7e2fa622f90e70282da643fbc0
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
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vector_id: 8759
content:
" Decreases in future winter and spring rainfall are projected over south-western Australia (Hope \r\net al., 2015). Central Asia is projected to experience wetter winters, associated with an increase in snow depth in the north-eastern \r\nregions (Y. Li et al., 2019). Even in a +2degC climate, both extreme precipitation and dryness will increase significantly in the extratropics, \r\namplifying the seasonal precipitation range (Fujita et al., 2019). A single-model study shows that the annual range of precipitation \r\nincreases globally by 2.6%  per 1degC of global warming in stabilized low-warming scenarios (Z. Chen et al., 2020a)."


id: cbf7af6497ae2ca08b95d96dc2965c9d
created_at: 2023-03-16 14:02:04
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vector_id: 29634
content:
"In the tropics, an amplified annual cycle (by about 3-5% degC-1) of global land monsoon hydroclimates (precipitation, precipitation minus \r\nevaporation (P-E), and runoff) is projected by CMIP5 models under the RCP8.5 scenario, mostly due to a more intense wet season \r\n(W. Zhang et al., 2019b). A longer rainy season is projected by CMIP6 models over most regional monsoon areas except in the Americas \r\n(Moon and Ha, 2020). A delayed onset and cessation of the wet season over West Africa and the Sahel (Dunning et al., 2018) and \r\na slightly delayed onset of South Asian monsoon rainfall (Hasson et al., 2016) are projected by CMIP5 models. CMIP5 projections suggest \r\na strengthening of the annual cycle and a lengthening of the dry season in Southern Amazonia (Fu et al., 2013; Reboita et al., 2014; Boisier \r\net al., 2015; Pascale et al., 2016; Sena and Magnusdottir, 2020). This is further verified by the projections from six CMIP6 models (Moon \r\nand Ha, 2020)"


id: dd1198b64da33ad0b910a6209a1a92f6
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
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vector_id: 32280
content:
" A wet season shorter by 5-10 days by the end to the 21st century is projected for southern Africa (Dunning et al., 2018). "


id: 794519d5278cc96401d64207c8069f2b
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 16645
content:
"An increase in streamflow seasonality is projected over several large rivers in the low-mitigation RCP8.5 scenario, but with only small \r\nchanges in the seasonality timing, except in northern high latitudes due to the earlier but potentially slower snowmelt in a warmer world \r\n(Eisner et al., 2017; Musselman et al., 2017). At the end of the century in a high-emissions scenario, peak snowmelt timing is projected to \r\noccur one month earlier and peak water volume is 79% lower in the eastern USA (Rhoades et al., 2018). Earlier snowmelt is projected, for \r\nexample, by 30 days at the end of the 21st century in RCP4.5 for the Sierra Nevada in the western USA (F. Sun et al., 2018). Sub-seasonal \r\nchanges in water availability were found in many regions in the RCP8.5 scenario. However, these should be considered with caution given \r\nthe magnitude of model errors (C.R. Ferguson et al., 2018)"


id: 414238ceec15529e8bbb0f8900b76321
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
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index: climate_gpt_only_giec
vector_id: 7662
content:
" Increases in the seasonality of water availability has been found to be more \r\npronounced in areas with high atmospheric evaporative demand, giving rise to a pattern of seasonally variable regimes becoming even \r\nmore variable (Konapala et al., 2020). RCP4.5 and RCP8.5 projections show a pronounced soil drying in summer and autumn over western \r\nEurope, and a springtime drying over northern Europe due to an earlier snowmelt (Ruosteenoja et al., 2018)."


id: 16f97a83d76a2c8d270c82604f5a6089
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 1140
content:
"A simple relative seasonality metric (Walsh and Lawler, 1981) applied to global projections based on CMIP6 models and SSP scenarios \r\nsupports previous CMIP5 findings, especially the amplified seasonality of precipitation around the Mediterranean, and across southern \r\nAfrica, California, southern Australia and the Amazon (Box 8.2, Figure 1). While such changes are not significant in the low-emissions \r\nSSP1-2.6 scenario, they are consistent with the increased frequency of dry days projected over the same regions (Figure  8.16). \r\nIn monsoon regions outside the Americas, rainfall seasonality does not show a significant increase even in high-emissions scenarios. \r\nThis challenges previous CMIP5 findings based on the difference between maximum and minimum monthly precipitation in a year \r\n(W. Zhang et al., 2019b) and higher sensitivity to the projected increase in precipitation extremes (Section 11.4.5)"


id: 881a2148c1fcded54f3067d572acf028
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 18935
content:
" In the northern high \r\nlatitudes, milder winters are associated with wetter conditions and a decrease in precipitation seasonality."


id: 218660d57a359b8415a31353a8d8c262
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
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vector_id: 2697
content:
"Box 8.2, Figure 1 | Projected long-term changes in precipitation seasonality. Global maps of projected changes in precipitation seasonality (simply defined \r\nas the sum of the absolute deviations of mean monthly rainfalls from the overall monthly mean, divided by the mean annual rainfall as in Walsh and Lawler, 1981) \r\naveraged across available CMIP6 models (number provided at the top right of each panel) in the SSP1-2.6 (b), SSP2-4.5 (c) and SSP5-8.5 (d) scenario respectively. \r\nThe simulated 1995-2014 climatology is shown in panel (a). All changes are estimated in 2081-2100 relative to 1995-2014. Uncertainty is represented using the \r\nsimple approach. No overlay indicates regions with high model agreement, where >=80% of models agree on sign of change. Diagonal lines indicate regions with \r\nlow model agreement, where <80% of models agree on sign of change. For more information on the simple approach, please refer to the Cross-Chapter Box Atlas.1"


id: ce7ab9dd4c3876fbed5c496848f10c82
created_at: 2023-03-16 14:02:04
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vector_id: 30023
content:
" \r\nFurther details on data sources and processing are available in the chapter data table (Table 8.SM.1)."


id: a214b50a531111e0d238d762828dcf46
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
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index: climate_gpt_only_giec
vector_id: 23065
content:
"in evapotranspiration can also be influenced by changes in soil \r\nmoisture and vegetation, which modulate the moisture flux from the \r\nland to the atmosphere. Several studies of CMIP5 projections suggest \r\nthat increases in plant water use efficiency will limit or counteract \r\nrising evapotranspiration (Milly and Dunne, 2016; Swann et al., \r\n2016; Lemordant et al., 2018; Y. Yang et al., 2018). However, other \r\nstudies have found that transpiration increases due to the impact of \r\nclimate change on growing season length, leaf area, and evaporative \r\ndemand (Section  8.2.3.3; Frank et al., 2015; Mankin et al., 2017, \r\n2018, 2019; Guerrieri et al., 2019; S.  Zhou et al., 2019; Vicente\u0002Serrano et al., 2020). The parametrizations accounting for these \r\ncomplex physiological processes in global climate models may also \r\nbe insufficient (Franks et al., 2017; Peters et al., 2018; Peano et al., \r\n2019)"


id: a9db2ca84319f44c483302bd2690511d
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 24257
content:
" Thus, there is currently low confidence in the role of vegetation \r\nphysiology in modulating future projections of evapotranspiration. "


id: 34dfcb8e76a4e211def108ead92a8a22
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 5726
content:
"Since AR5, studies confirm that global mean annual runoff increases \r\nwith global surface temperature increase (X.  Zhang et al., 2014, \r\n2018; Lehner et al., 2019), but varies regionally (Chen et al., 2017; \r\nH. Yang et al., 2017; Cook et al., 2020). CMIP5 models display a large \r\nspread in the ratio of runoff to precipitation for the present-day \r\nclimate, which applies also to future runoff changes under global \r\nwarming (Lehner et al., 2019). In studies of CMIP6 projections, runoff \r\nincreases in most parts of the northern high latitudes and Asia and \r\nnorth and eastern Africa, and decreases in the Mediterranean region, \r\nsouthern Africa, southern Australia and in parts of western Africa, \r\nas well as in Central and South America (Greve et al., 2018; Cook \r\net al., 2020). Projected changes in runoff also vary seasonally"


id: db60b6044797ed48825c301c823e6746
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 32027
content:
" In the \r\nNorthern Hemisphere (NH), runoff increases during winter since more \r\nprecipitation falls as rain than snow and decreases in the summer as \r\nless snow is available to contribute to runoff during the warm season \r\n(Cook et al., 2020). Global maps of projected changes for December-\r\nJanuary-February and June-July-August are shown in Figure 8.18, \r\nshowing projected changes becoming larger and more consistent \r\nin the higher emissions scenarios. Runoff projections for CMIP6 are \r\nalso shown in Figure 8.16 for tropical and extratropical averages at \r\na range of global mean warming levels and in Table 8.1 for global \r\nland in different future scenarios. In the tropics, both the mean and \r\ninterannual variability of runoff increase with warming. The increase \r\nin variability is roughly twice as large as the increase in the mean, \r\nand has a large spread across models"


id: 6af11963a98f031d441cb975b79a7c5d
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
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index: climate_gpt_only_giec
vector_id: 14445
content:
" In the extratropics, changes \r\nare small in the summer but there are large increases in the winter, \r\nwith the mean increasing much more than the variability, in contrast \r\nto the tropics. "


id: b9a4d9c9e346e5623a3f13064b55220b
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 26749
content:
"The AR5 assessed that projected changes in runoff had low confidence\r\nover the period 2016-2035; however, under the RCP8.5 scenario, \r\nrunoff increases by 2100 are likely in high northern latitudes. This is \r\nconsistent with projected regional precipitation increases, based on \r\nconsistency of changes across different generations of models and \r\ndifferent forcing scenarios, and with runoff decreases being likely\r\nin southern Europe, the Middle East and southern Africa. There was \r\nconsiderable uncertainty in the magnitude and direction of change \r\nfor some regions, largely driven by the uncertainty in projected \r\nprecipitation changes, particularly across south Asia. For flooding, \r\nAR5 assessed with medium confidence that flooding would increase \r\nover parts of South and South East Asia, tropical Africa, north-east \r\nEurasia, and South America, and decrease for parts of Northern \r\nand Eastern Europe, Anatolia, Central Asia, Central North America, \r\nand southern South America. The SR1"


id: a5afe0443b4986eec7dd78fc472d2751
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
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vector_id: 23634
content:
"complicating factors for projecting both pluvial (overland) and fluvial \r\n(river) flooding that limit confidence in their assessment. In addition to \r\nprecipitation, flooding also depends on basin and river characteristics \r\nsuch as permeability, antecedent soil moisture, and antecedent flow \r\nlevels for river flooding, so projections of extreme precipitation and \r\nflooding are not always closely linked (Section  8.2.3.2). Possible \r\nchanges in water resources management and land use add another \r\nlayer of complexity to future changes. There is medium confidence\r\nin a general increase in pluvial and fluvial flooding, although there \r\nare large regional variations, discussed further in Sections  11.5.5, \r\nand 12.4. There is medium confidence in a  substantial increase \r\nin the frequency of extreme sea level events for coastal regions \r\n(Section  9.6.4.2) and the associated coastal flooding is regionally \r\nassessed in Section  12.4"


id: daf4f5c081649979a25abd179fc45910
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
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vector_id: 31970
content:
" The risk of glacier lake outburst floods \r\n(GLOFs) is expected to increase with glacier melting in some high \r\nmountain regions (Section 12.4)."


id: 8dd539f3b0f268ba2efdd3c2cebf4624
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 19844
content:
"Soil moisture in the top soil layer (10 cm) shows more widespread \r\ndrying than total soil moisture, reflecting a  greater sensitivity of \r\nthe upper soil layer to increasing evaporative demand (Figure 8.19; \r\nBerg et al., 2017). Conversely, total column soil moisture represents \r\nthe carry-over of moisture from previous seasons deeper in the soil \r\ncolumn, and potentially higher sensitivity to vegetation processes \r\n(Berg et al., 2017; Kumar et al., 2019). Central America, the \r\nAmazonian basin, the Mediterranean region, southern Africa, and \r\nsouth-western Australia are projected to experience significant \r\ndeclines in total soil moisture, whereas declines in Europe (north \r\nof the Mediterranean), western Siberia, and north-eastern North \r\nAmerica are limited to the surface (Figure 8.19)"


id: 386cc7a79ee915c18f3d13eb3bc01052
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
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vector_id: 6251
content:
" It should be noted \r\nthat because models differ in their number of hydrologically active \r\nlayers, there is less confidence in total soil moisture projections than \r\nsurface soil moisture projections. Based on surface soil moisture \r\nprojections, more than 40% of global land areas (excluding \r\nAntarctica and Greenland) are expected to experience robust year\u0002round drying, even under lower emissions scenarios (Cook et al., \r\n2020). The percentage of land area experiencing drying is slightly \r\nlower when runoff is used as an aridity metric instead (20-30%); \r\ntaking this into consideration, it is estimated that about a  third \r\nof global land areas will experience at least moderate drying in \r\nresponse to anthropogenic emissions, even under SSP1-2.6 (medium \r\nconfidence) (Cook et al., 2020)."


id: d8de60e6f4cc20216a08ca5fff17d2c6
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
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vector_id: 31630
content:
"In summary, there is medium confidence that global runoff will \r\nincrease with global warming, but with large regional and seasonal \r\nvariations. There is high confidence that runoff will increase in the \r\nnorthern high latitudes and decrease in the Mediterranean region and \r\nsouthern Africa. There is medium confidence that runoff will increase \r\nin regions of central and eastern Africa, and decrease in Central \r\nAmerica and parts of southern South America, with the magnitude \r\nof the change increasing with emissions. There is medium confidence\r\nthat the seasonality of runoff and streamflow will increase with \r\nglobal warming in the subtropics. In snow-dominated regions, there \r\nis high confidence that peak flows associated with spring snowmelt \r\nwill occur earlier in the year and medium confidence that snowmelt\u0002induced runoff will decrease with reduced snow, except in glacier-fed \r\nbasins where runoff may increase in the near term"


id: 7f58e503c847364bc4ef89f899ddafec
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 17590
content:
" There is medium \r\nconfidence that flooding in general will increase, although with \r\nconsiderable variation based on geographic region and flood type. \r\nThese projected climate-related changes will occur in the context of \r\nhuman-caused land-use and land-cover changes, which may also \r\nhave a large influence."


id: 283e1ca901cf374ae2be409d3b636541
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 3738
content:
"southern Africa, and south-western Australia, on the order of \r\n0.5  to  1  month for a  moderate emissions scenario (SSP2-4.5) and \r\ntwo months for a high-emissions scenario (SSP5-8.5; Ukkola et al., \r\n2020). Drought intensity is projected to increase in the tropics, mainly \r\nin the Amazonian basin, Central Africa, and southern Asia, as well as \r\nin Central America and south-western South America (Ukkola et al., \r\n2020). The CORDEX South Asia multi-model ensemble projections \r\nindicate an increase in the frequency and severity of droughts over \r\ncentral and northern India during the 21st century, under the RCP4.5 \r\nand RCP8.5 scenarios (medium confidence) (Mujumdar et al., 2020)"


id: abdeadf380838cb25d37b9fb8e2b2456
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 24560
content:
" \r\nUnder intermediate or high-emissions scenarios, the likelihood of \r\nextreme droughts (events that have magnitudes equal to or less than \r\nthe 10th percentile of the 1851-1880 baseline period) increases by \r\n200-300% in the Amazonian basin, south-western North America, \r\nCentral America, the Mediterranean, southern Africa, and south\u0002western South America (Cook et al., 2020). Even under a  low\u0002emissions scenario (SSP1-2.6), the likelihood of extreme droughts \r\nincreases by 100% in south-western North America, south-western \r\nSouth America, the Amazon, the Mediterranean, and southern Africa \r\n(Cook et al., 2020). Thus, there is high confidence that drought \r\nseverity and intensity will increase in the Mediterranean, southern "


id: e4e6b6db814dc40fd132f0ff3d7cf62b
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 33491
content:
"Paleoclimate records provide context for these future expected changes \r\nin drought and aridity. In the Mediterranean, western North America, \r\nand Central Chile, there is high confidence that climate change will shift \r\nsoil moisture (as represented by the Palmer Drought Severity Index) \r\noutside the range of observed and reconstructed values spanning the \r\nlast millennium (Figure  8.20; Cook et al., 2014; Otto-Bliesner et al., \r\n2016). Warmer temperatures, leading to increased evaporative losses, \r\nare clearly implicated in the projected future drying in these semi-arid \r\nregions (Dai et al., 2018), emphasizing the central role that warming \r\nplays in driving increased evaporative demand (Vicente-Serrano et al., \r\n2020). In contrast, future trajectories are more uncertain in regions \r\nlike Central Asia and eastern Australia-New Zealand where projected \r\nchanges in precipitation and soil moisture are less coherent (Figure 8.19 \r\nand 8.20; Hessl et al., 2018)"


id: cb9f1e28dd8f135bf8b69f9f8c869019
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 29580
content:
" More information on projected changes \r\nin drought, including specific categories or drought, can be found in \r\nSection 11.6.5 and Section 12.4."


id: 71326b994f528bd240923dfe4a8220a2
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 15445
content:
"Groundwater projections were not assessed in AR5. Groundwater \r\nprocesses are not explicitly included in most current CMIP6 models \r\nand so must be calculated separately with hydrologic models \r\n(e.g.,  R.G. Taylor et al., 2013; Cuthbert et al., 2019a). A  range of \r\nfactors are important in assessing groundwater projections, including \r\nthe mean difference between precipitation and evaporation, the \r\nintensity of precipitation (R.G. Taylor et al., 2013a), and in changes \r\nin snow (Tague and Grant, 2009), glaciers (Gremaud et al., 2009), \r\nand permafrost (Okkonen and Klove, 2011). Climate impacts on \r\ngroundwater are occurring in the context of severe and growing \r\nhuman-caused groundwater depletion (WGII; Konikow and Kendy, \r\n2005; Rodell et al., 2018; Bierkens and Wada, 2019), and water \r\nscarcity issues (Mekonnen and Hoekstra, 2016)"


id: d166f1d2d23010b234c0929442317ce
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 30480
content:
" Climate-related \r\nchanges to the water cycle can influence water demand (for example, \r\nprecipitation decreases in an irrigated area), and anthropogenic \r\ngroundwater depletion can influence the water cycle through \r\ninteractions with surface energy fluxes, surface water, and vegetation \r\n(Cuthbert et al., 2019a), although uncertainties in estimates of future \r\ngroundwater depletion are large (Smerdon, 2017; Bierkens and Wada, \r\n2019). Some aspects of groundwater change will be irreversible, \r\nincluding the increase of saltwater intrusion into coastal aquifers \r\nwith sea level rise (Werner and Simmons, 2009), and depletion of \r\nfossil aquifers and aquifers with very long recharge times (Bierkens \r\nand Wada, 2019). "


id: 7419bd19bcba1384bcc3d630d641fdc3
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
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vector_id: 15866
content:
"CMIP5 projections show no consistent shift in the zonal mean \r\nposition of the ITCZ (Donohoe et al., 2013; Donohoe and Voigt, 2017; \r\nByrne et al., 2018). The ITCZ position is strongly connected to cross\u0002equatorial energy transport (Kang et al., 2008; Bischoff and Schneider, \r\n2014), which also shows no consistent change in future projections \r\n(Donohoe et al., 2013). Since AR5 it has been reported that most \r\nCMIP5 models project a narrowing of the ITCZ in response to surface \r\nwarming together with intensified ascent in the core region and \r\nweakened ascent on the ITCZ edges (Lau and Kim, 2015; Byrne et al., \r\n2018), implying a narrowing of precipitation regions influenced by \r\nthe ITCZ. Modelled changes in the width and intensity of the zonal \r\nmean ITCZ are strongly anti-correlated, for example, narrowing is \r\nassociated with increased intensity while broadening with decreased \r\nintensity"


id: f81630108a8d6d80edd127388610ac9f
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 36546
content:
" Such changes are associated with changes in tropical high \r\ncloud fraction and outgoing longwave radiation (Su  et al., 2017; \r\nByrne et al., 2018)."


id: 47886b88f0bfe0be2184cd1804699dbe
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 8640
content:
"Since AR5, the paradox between the projected weakening and the \r\nobserved strengthening of the Walker circulation since the 1990s \r\n(Section  8.3.2.2) has triggered debate about the drivers of these \r\nchanges (England et al., 2014; McGregor et al., 2014; Kociuba and \r\nPower, 2015; Vilasa et al., 2017; Chung et al., 2019). Projected changes \r\nin equatorial SST gradients are not entirely consistent with observed \r\ntrends (Coats and Karnauskas, 2017; Seager et al., 2019a), and one \r\nCMIP5 model that projects a  future strengthening of the Walker \r\ncirculation is more consistent with observations than other models \r\n(Kohyama et al., 2017). Other studies suggest that these differences \r\narise from the dominant influence of internal climate variability to \r\nthe observed trends (Chung et al., 2019), or as a  consequence of \r\na  systematic cold bias of most CMIP5 models in their Equatorial \r\nPacific cold tongues (Seager et al., 2019a)"


id: b49251f12dde6aee0d76dc2dc174c025
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
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index: climate_gpt_only_giec
vector_id: 25943
content:
" However, the latter \r\nhypothesis is based on a simplified model of tropical Pacific dynamics \r\nand is not consistent with the current physical understanding of the \r\ntropical circulation response to increasing CO2 levels (Section 8.2.2.2) \r\nor with independent paleoclimate evidence suggesting a  weaker \r\nWalker circulation under warmer climates (Tierney et al., 2019; \r\nMcClymont et al., 2020). Different time scales of the tropical Pacific "


id: 28a472fe296760eda82663d402291f5d
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 3821
content:
"and Ajayamohan, 2015). Greater warming over the Asian land region \r\ncompared to the ocean contributes to intensification of the monsoon \r\nlow-level south-westerly winds and precipitation (Endo et al., 2018), \r\neven though the combined effect of upper and lower tropospheric \r\nwarming makes the Asian monsoon circulation response rather \r\ncomplicated. A  high resolution model projection, based on the \r\nRCP8.5 scenario, indicates that a  northward shift of the low-level \r\njet and associated weakening of the large-scale monsoon circulation \r\ncan induce a large reduction in the genesis of monsoon low pressure \r\nsystems by the late 21st century (Sandeep et al., 2018). Experiments \r\nwith constant forcing indicate that at 1.5degC and 2degC global warming \r\nlevels, mean precipitation and monsoon extremes are projected to \r\nintensify in summer over India and South Asia (Chevuturi et al., 2018; \r\nD. Lee et al., 2018) and that a 0"


id: e6d06e7441e77a4979f32c0e87f7c535
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
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index: climate_gpt_only_giec
vector_id: 33796
content:
"5degC difference would imply a 3% \r\nincrease of precipitation (Chevuturi et al., 2018). CMIP5 models \r\nproject an increase in short intense active days and decrease in long \r\nactive days, with no significant change in the number of break spells \r\nfor India (Sudeepkumar et al., 2018)."


id: 6b165ee6057b6c89d14a0981432d6903
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 14466
content:
"Inter-hemispheric mass exchange can act as a  bridge connecting \r\nSH circulation with EAsiaM rainfall, however this inter-hemispheric \r\nlink is projected to weaken in a future warmer climate as seen from \r\na  CCSM4 projection using the RCP8.5 scenario (Yu et al., 2018). \r\nA  comparison of 1.5degC and 2degC global warming levels reveals \r\nhow a  0.5degC difference could result in precipitation enhancement \r\nover large areas of East Asia (D. Lee et al., 2018; J. Liu et al., 2018; \r\nChen et al., 2019), with substantial increases in the frequency and \r\nintensity of extremes (Chevuturi et al., 2018; D. Li et al., 2019). Future \r\nmonsoon projections from the CMIP6 models show increase of \r\nEAsiaM precipitation across all the scenarios (Z. Chen et al., 2020b), \r\nthough with a large model spread mostly on the long-term and in \r\nthe higher emissions scenarios (Figure 8.22)"


id: 3779b0037161e3bf82023045cfaca59f
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 6108
content:
" Considering all the five \r\nscenarios used across the report, changes in precipitation, runoff \r\nand P-E over the EAsiaM are positive and become larger for highest \r\nemissions scenarios and for the long-term mean, except for the mid\u0002term SSP1-1.9 scenario where the changes are close to zero or even \r\nnegative (Table 8.2). Additionally, CMIP6 models confirm a projected \r\nincreased length of the EAsiaM season due to early onset and late \r\nretreat (Ha et al., 2020)."


id: 46f1e8091f7e1d8784feda40e6e022f3
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 8546
content:
"CMIP5 projections indicated a possible intensification of the EAsiaM \r\ncirculation during the 21st century, in addition to precipitation \r\nincrease, although there is a  lack of consensus on changes in the \r\nwestern North Pacific subtropical high, this is an important feature \r\nof the EAsiaM circulation (Kitoh, 2017). Furthermore, the EAsiaM \r\nprecipitation enhancements in the CMIP5 projections are prominent \r\nover the southern part of the Baiu rainband by the late 21st century, \r\nwith no significant changes in the Meiyu precipitation over central\u0002eastern China (Horinouchi et al., 2019). It was also shown that the \r\nBaiu precipitation response in CMIP5 projections is accompanied by \r\na southward retreat of the western North Pacific subtropical high and \r\na southward shift of the East Asian subtropical jet (Horinouchi et al., \r\n2019)"


id: 5667bb0db0dc42d8ac3e4fb425afa7d1
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 11073
content:
" According to the high-resolution MRI-AGCM global warming \r\nexperiments, future summer precipitation could potentially increase \r\non the southern side and decrease on the northern side of the present\u0002day Baiu location in response to downward-motion tendencies which \r\ncan offset the 'wet-gets-wetter' effect, but is subject to large model \r\nuncertainties (Ose, 2019). Future projections of land warming over \r\nthe Eurasian continent (Endo et al., 2018) and intensified land-sea \r\nthermal contrast (Z. Wang et al., 2016; Tian et al., 2019) can potentially "


id: bd167d991e5710f8dd670b93e49198e8
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 27314
content:
"and demise (Cook et al., 2013). It is recognized that CMIP5 models \r\nare generally too coarsely-resolved to simulate the Gulf of California \r\nand the moisture surges associated with the NAmerM (Pascale et al., \r\n2017). Under different RCPs, CMIP5 models tend to project a reduction \r\nin NAmerM precipitation but an increase in extreme precipitation \r\nevents (Torres-Alavez et al., 2014; Bukovsky et al., 2015; Pascale \r\net al., 2019). The almost unchanged or slight decrease in NAmerM \r\ntotal precipitation amount under global warming projections is at \r\nodds with paleoclimate records that suggest increased monsoon \r\nprecipitation under past warm conditions (D'Agostino et al., 2019; \r\nSeth et al., 2019). However, there is low agreement on how those \r\nchanges and the mechanisms that drive them are affected under \r\ndifferent RCPs since most simulations are model-dependant (Cook \r\nand Seager, 2013; Geil et al., 2013; Pascale et al., 2019)"


id: 5c605939ac2eeb3cdab1bf9d95c01468
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 12047
content:
" Projections \r\nfrom six CMIP6 models show a  shortening of the NAmerM under \r\nthe SSP5-8.5 scenario due to earlier demises (Moon and Ha, \r\n2020). In addition, CMIP6 projections show a decrease in NAmerM \r\nprecipitation under SSP2-4.5 and SSP5-8.5 scenarios by the end of \r\nthe 21st century with large inter-model spread (Figure  8.22). This \r\nresult is also supported by the analysis of 31 CMIP6 models under \r\nthe SSTP5-8.5 scenario for the 2080-2099 period (Almazroui et al., \r\n2021). Non-linearities and uncertainties in the NAmerM projected \r\nchanges are valid for many water cycle variables, like precipitation, \r\nrunoff and P-E (Table 8.2). "


id: ed8acbccbf5ec1efa26d41963cb480d5
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 34835
content:
"increases in rainfall in eastern-central Sahel and a decrease in the \r\nwest for the end of the 21st century (Roehrig et al., 2013; Biasutti, \r\n2019; Monerie et al., 2020). However, the magnitude of WAfriM \r\nprojected precipitation depends on the convective parametrization \r\nused (Hill et al., 2017), and large uncertainties remain in WAfriM \r\nprojections because of large inter-model spread, particularly over \r\nthe western Sahel (Roehrig et al., 2013; Biasutti, 2019; Monerie \r\net al., 2020). CMIP6 models show a  general increase of WAfriM \r\nprecipitation across all future scenarios but with a substantial model \r\nspread for the SSP5-8.5 scenario (Figure 8.22). This sensitivity arises \r\nfrom the combined and contrasting influences of anthropogenic \r\ngreenhouse gas and aerosol forcing that affect WAfriM precipitation \r\n(particularly over the Sahel) directly and also indirectly through \r\nsubtropical North Atlantic SST changes (Giannini and Kaplan, 2019)"


id: 5febbb21babb6e54a1582e214e573462
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vector_id: 12674
content:
" \r\nThe large model spread and associated uncertainties in projected \r\nprecipitation changes is reflected also in runoff and P-E changes \r\n(Table 8.2). Regional climate models (RCMs) ensembles (e.g., Klutse \r\net al., 2018) agree with CMIP5 projected rainfall trends but some \r\nindividual models show rainfall declines (e.g.,  Sylla et al., 2015; \r\nAkinsanola et al., 2018), highlighting the existing large uncertainties \r\nin RCMs WAfriM rainfall projections."


id: a944f4801da21f4da4e269b48ef3255c
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 24159
content:
"Since AR5, some studies indicate that the SAmerM would experience \r\nchanges in its seasonal cycle, with delayed monsoon onsets under \r\nincreasing GHG emissions associated to different RCPs (Fu et al., \r\n2013; Reboita et al., 2014; Boisier et al., 2015; Pascale et al., 2016; \r\nSeth et al., 2019; Sena and Magnusdottir, 2020). In contrast, other \r\nstudies indicate projected earlier onsets and delayed retreats of the \r\nSAmerM under the RCP8.5 scenario based on six CMIP5 models \r\n(Jones and Carvalho, 2013). These differences have been linked to \r\nthe methodology used to determine monsoon timing, and sensitivity \r\nto the monsoon domain considered (Section 8.3.2.4.5; Correa et al., \r\n2021). Recent studies provide further evidence for the projection \r\nof delayed SAmerM onsets by the late 21st century (Sena and \r\nMagnusdottir, 2020). An analysis of six CMIP6 models under the \r\nSSP5-8.5 scenario confirm the projections of delayed SAmerM onsets \r\nby the end of the 21st century (Moon and Ha, 2020)"


id: 8684665ad54b2ffb8c68b76ab4181d3c
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 18705
content:
" In addition, \r\nprojected changes in the intensity and length of the SAmerM season \r\nhave been found to be model-dependent (Pascale et al., 2019). The \r\nanalysis of CMIP5 projections of total monsoon rainfall indicate mixed \r\nsignals in the Amazon and SAmerM regions (Jones and Carvalho, \r\n2013; Marengo et al., 2014), with some studies suggesting increased "


id: 5188f558b03d3812309972db5373d16c
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 10278
content:
"summer precipitation in the core SAmerM region (Kitoh et al., 2013; \r\nSeth et al., 2013). Dynamical downscaling of CMIP5 projections \r\nunder the RCP4.5 and RCP8.5 scenarios with the Eta RCM suggests \r\nreductions of austral summer precipitation over the SAmerM region \r\nthroughout the 21st century (Chou et al., 2014). Further analysis \r\nusing 15 different CMIP6 models for the SSP2-4.5 scenario suggest \r\nreductions in total SAmerM rainfall (B. Wang et al., 2020). However, \r\nother analyses of CMIP6 projections under different SSP scenarios \r\ndo not report clear changes in the SAmerM precipitation throughout \r\nthe 21st century (Figure 8.22; Z. Chen et al., 2020b; Jin et al., 2020). \r\nSimilar uncertainties for all the SSP scenarios used across the report \r\nare found for other water cycle variables, including runoff and P-E \r\n(Table 8.2). Furthermore, there is disagreement in projected extreme \r\nprecipitation in the region, with some CMIP5-based studies suggest \r\nreductions (Marengo et al"


id: 89acede46bd9a759957912df9b53c1a5
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 19198
content:
", 2014), while others indicate increases \r\nbased on CMIP5 and CMIP6 models (Kitoh et al., 2013; Sena and \r\nMagnusdottir, 2020). "


id: 5c88c59cf3c827dce7bdc3181849694b
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 12077
content:
"Blocking projections are assessed in this Report in \r\nChapter  4  (Section  4.5.1.6), and model performance in simulating \r\nblocking is also discussed in Chapter  3  (Section  3.3.3.3). CMIP5 \r\nprojections suggest a complex response in blocking frequencies with \r\nan eastward shift in NH winter blocking, mid-latitude decreases in \r\nboreal summer except in eastern Europe-western Russia, and SH \r\ndecreases in the Pacific sector during austral spring and summer. \r\nCMIP6 projections (Figure. 4.28) show a notable decrease in blocking \r\nactivity over Greenland and the North Pacific for the SSP3-7.0 and \r\nSSP5-8.5 scenarios. However, the continued large differences among \r\ncurrent models as well as the sensitivity to blocking detection methods \r\nlimits confidence in projected regional changes in blocking (see also \r\nSection 10.3.3.3.1)"


id: a8f5b4eaca97193e82025dbfea2ee0ff
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 24108
content:
" The influence of blocking on multiple elements of \r\nthe water cycle means that the uncertainty in blocking projections adds \r\na corresponding layer of uncertainty to water cycle projections. "


id: bdaa901d1dbb372c26d3e6f18b41ab9a
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 27407
content:
"While the potential role of increasing hydrologic extremes with \r\nquasi-resonant stationary waves during NH summer has received \r\nconsiderable attention (see Section 8.3.2.6), as yet there is no clear \r\nevidence in model projections that this variability will increase (Teng \r\nand Branstator, 2019). The influence of the Arctic on mid-latitude \r\ncirculation is assessed in Cross-Chapter Box 10.1, which reports that \r\nthere is low confidence in the dominant contribution of Arctic warming \r\ncompared to other drivers in future projections. Potential changes to \r\nthe stratospheric polar vortex in CMIP5 models have a substantial \r\ninfluence on tropospheric stationary waves and associated hydrologic \r\nimpacts in both the NH (Zappa and Shepherd, 2017) and SH (Mindlin \r\net al., 2020). CMIP5 models have some important limitations in their \r\nrepresentation of stationary waves (Lee and Black, 2013; Simpson \r\net al., 2016; Garfinkel et al"


id: 90c0e70456e52d71398a5923aa90b9bd
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 20315
content:
"Biases remain in cyclone locations, intensities, cloud features, and \r\nprecipitation (Catto, 2016, Chang et al., 2016). Uncertainties in \r\nprojected precipitation changes in many mid-latitude regions can \r\nbe explained to a large degree by uncertainties in projected storm \r\ntrack or ETC changes. Multiple studies (Chang et al., 2013; Zappa \r\net al., 2015; Chang, 2018) have shown strong relationships between \r\nmodel-projected precipitation change in many regions and model\u0002projected change in storm track activity near that regions. While front \r\nfrequency is well represented, frontal precipitation frequency is too \r\nhigh and the intensity is too low (Catto et al., 2015). Some of the \r\nbias in storm tracks appears to be related to limitations in model \r\nrealization of blocking (Zappa et al., 2014). The CMIP6 generation \r\nof models has improved representation of storm tracks in both \r\nhemispheres (Bracegirdle et al., 2020a; Harvey et al., 2020)"


id: 325d257931b952351964217c6ef73e09
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 5333
content:
" Simulation \r\nof storm tracks and their associated precipitation generally improve \r\nwith increasing resolution beyond that used in most current climate \r\nmodels (Jung et al., 2006; Michaelis et al., 2017; Barcikowska et al., \r\n2018). In terms of projections, the decreases in cyclone occurrence \r\nover the Mediterranean were replicated in a higher resolution model \r\n(Raible et al., 2018)."


id: 743ae8af543a31836aded1e695634923
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 15882
content:
"In AR5, several factors were identified as relevant to the uncertainties \r\nin projections of cyclone intensity, frequency, location of storm tracks \r\nand precipitation associated with ETCs. These include horizontal \r\nresolution, resolution of the stratosphere, and how changes in the \r\nAtlantic meridional overturning circulation (AMOC) were simulated. \r\nSince AR5, projections of extratropical cyclones and storm tracks have \r\nbeen examined further, largely confirming previous assessments. In \r\nparticular, extratropical cyclone precipitation scales with the product \r\nof cyclone intensity (as measured by near-surface wind speed) \r\nand atmospheric moisture content (Pfahl and Sprenger, 2016). \r\nBooth et al. (2018) showed that the fraction of rainfall generated \r\nby the convection scheme in simulated extratropical cyclones is \r\nhighly model- and resolution-dependent, which may be a source of \r\nuncertainty regarding their precipitation response to anthropogenic \r\nforcings"


id: 54690980455cbfeb9348c42806077d8d
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 10742
content:
" Also, increased moisture availability may increase the \r\nmaximum intensity of individual storms while reducing the overall \r\nfrequency as poleward energy transport becomes more efficient."


id: 3aa6758ff3ea2cd1c87234241b85e159
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 6598
content:
"CMIP6 projections indicate that the amplitude of ENSO (Annex IV.2.3) \r\nvariability will not substantially change during the 21st century (high \r\nconfidence) (Section  4.4.3.2). However, rainfall variability related \r\nto ENSO is projected to increase significantly by the second half of \r\nthe 21st century, regardless of ENSO amplitude (Section  4.5.3.2). \r\nRegional precipitation variability associated with ENSO increases \r\ndue to increases in atmospheric moisture, regardless of changes in \r\nENSO variability itself (Pendergrass et al., 2017). In many regions, \r\nthe magnitude of the projected changes related to ENSO is small \r\ncompared with historical interannual variability (Bonfils et al., \r\n2015; Power and Delage, 2018; Perry et al., 2020). Uncertainties \r\nin precipitation projections related to ENSO depend on internal \r\nvariability associated with the mode (Section 8.5"


id: 4388e21cd12b848ab184e91775185df7
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 8036
content:
"2), hence the need \r\nto have relatively large ensembles (about 15 members) to adequately \r\nestimate uncertainty (Deser et al., 2018; N. Maher et al., 2018; C. Sun \r\net al., 2018; Zheng et al., 2018)."


id: 31a3efadf2caf454e61dfcc210b2e54e
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 5219
content:
"Under continued global warming, more intense moisture transport \r\nwithin atmospheric river events is projected to increase the magnitude \r\nof heavy precipitation events on the west coast of the USA (Ralph \r\nand Dettinger, 2011; Lavers et al., 2015; Warner and Mass, 2017), \r\nin Western Europe (Lavers et al., 2015; Ralph et al., 2016; Ramos \r\net al., 2016), and in East Asia (very likely) (Kamae et al., 2019). All \r\nCMIP5 models analysed agreed under a range of scenarios, except \r\nover the Iberian Peninsula (Ramos et al., 2016) where there is only \r\nlow confidence in projected changes. Kamae et al. (2019) reported \r\na 1% increase per degC warming in the frequency of atmospheric rivers \r\naffecting East Asia, but this is strongly affected by SST changes. \r\nEmerging evidence of possible regional changes due to dynamical \r\nfactors are uncertain (Lavers et al., 2013; Gao et al., 2015; Payne \r\nand Magnusdottir, 2015)"


id: f296cd1e1a2b193e50dd395e72ce86bd
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 35697
content:
" The frequency, magnitude and duration of \r\natmospheric rivers making landfall along the North American west \r\ncoast are projected to increase (Gershunov et al., 2019). In contrast, \r\nV. Espinoza et al. (2018) suggest that the number of atmospheric river \r\nevents is projected to slightly decrease globally. "


id: 7dd99b292d8bb7fdfdf0bbcdf4b52cb
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 17370
content:
"Sensitivity studies generally project increases in Madden Julian \r\nOscillation (MJO, Annex IV.2.8) precipitation amplitude in a warmer \r\nclimate, with increases of up to 14% degC-1 of warming (Arnold et al., \r\n2013, 2015; Caballero and Huber, 2013; Liu and Allan, 2013; Maloney \r\nand Xie, 2013; Schubert et al., 2013; Subramanian et al., 2014; \r\nCarlson and Caballero, 2016; Pritchard and Yang, 2016; Adames \r\net al., 2017a; Wolding et al., 2017; Haertel, 2018). However, in CMIP5 \r\nmodels with realistic historical MJO behaviour, the precipitation \r\namplitude over the Indo-Pacific warm pool region changes from \r\n-4% to +8% degC-1 in the RCP8.5 scenario relative to the end of the \r\n20th century (Bui and Maloney, 2018; Maloney et al., 2019)"


id: 981510d47c74dddab5a3b28c5ca82af2
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 21435
content:
" When \r\nsimulated MJO precipitation amplitude increases with warming, the \r\nleading factor for such change is the intensification of the lower \r\ntropospheric vertical moisture gradient, that supports stronger \r\nvertical moisture advection per unit diabatic heating (Arnold et al., \r\n2015; Adames et al., 2017a, b; Wolding et al., 2017). In idealized \r\nsimulations with constant CO2 forcing with El  Nino-like patterns, \r\nthe MJO activity penetrates farther east into the central and east \r\nPacific with increased warming (Subramanian et al., 2014; Adames \r\net al., 2017a). Increased MJO convective variability in a  warmer \r\nclimate does not reflect into increased ability of the MJO to force the \r\nextratropics (Wolding et al., 2017)."


id: 2b08f7a492f27c4063373c50818d0a35
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 4203
content:
"Understanding the limits to projecting water cycle changes are \r\nfundamental for refining climate and hydrological models needed to \r\ndevelop successful climate change adaptation strategies. Regional \r\nwater cycle projections depend on a  range of model-dependent \r\nresponses (Section 8.5.1) and are also strongly influenced by internal \r\nvariability, especially in the near term (Section 8.5.2; Hawkins and \r\nSutton, 2012; Rowell, 2012; Orlowsky and Seneviratne, 2013; Kent \r\net al., 2015; Fatichi et al., 2016; Greve et al., 2018; Chegwidden \r\net al., 2019). CMIP6 models show that different model responses \r\nto the same forcing scenario remain the main source of uncertainty \r\nfor projected changes in regional precipitation (Figure 8.23; Lehner \r\net al., 2020). Section  8.5.3 assesses the potential for non-linear \r\nresponses when shifting from low- to high-global warming levels \r\n(Section  8.4.2.4; James et al., 2017)"


id: 21e7b1a6e711a9cdcf088f97eda74724
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 2779
content:
" While regional uncertainties \r\nrelated to downscaling methods (Section 10.3.3) and impact models \r\n(WGII Chapter 4) are not covered here, the added value of regional \r\nclimate models is briefly discussed (Section 8.5.1.2.2) with a focus on \r\nwater cycle changes."


id: 5b2cac4490b8d27ae6fd6234009cea39
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 11852
content:
"The AR5 Chapter  7  recognized that the simulation of clouds and \r\nprecipitation remains challenging for state-of-the-art GCMs. \r\nModel development and evaluation have continued since \r\nAR5, with a  particular emphasis on the representation of new \r\nmodel components, like interactive vegetation, aerosols and \r\nbiogeochemical cycles. For example, the comparison of simulated \r\ntropical precipitation across three successive generations of CMIP \r\nmodels (including CMIP6) indicates overall little improvement for the \r\nsummer monsoons, the double-ITCZ bias, the diurnal cycle and the \r\nfrequency of precipitation (Fiedler et al., 2020). Some of these issues \r\nare related to inherent model limitations in three specific areas: \r\natmospheric convection, cloud-aerosol interactions and land surface \r\nprocesses (ocean and cryosphere-related processes are addressed \r\nin Chapter 9). These limitations do not weaken the overall progress \r\nmade in the large-scale simulation of present-day climate (FAQ 3"


id: d2a77e6484fd1cfe7909327f39bc4632
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 30665
content:
"3 \r\nand Section  3.3.2.3), even though the improvement of CMIP6 \r\ncompared with CMIP5 models is limited (Figure 3.12) and is generally \r\nless systematic or obvious at the regional scale (e.g., Gusain et al., \r\n2020; Monerie et al., 2020; Oudar et al., 2020a). Instead, they call for \r\na careful interpretation of hydrological projections with the full range \r\nof plausible outcomes, rather than only considering the most likely \r\nscenarios (Sutton, 2018, 2019). "


id: 977d868bb3179b9b3bbe529f0e4b1fa6
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 21358
content:
"Since AR5, there have been improvements in the representation of \r\nconvective clouds and related precipitation in GCMs. For instance, \r\nthe drizzle issue (too light and too frequent rainfall events) has led \r\nto modifications in the deep convection triggering scheme (Rochetin \r\net al., 2014b; Han et al., 2017; Xie et al., 2018; Wu et al., 2019). \r\nAlthough high-resolution studies have highlighted these limitations, \r\nmost GCMs still rely on a convective available potential energy (CAPE) \r\nclosure which has been adapted to various cloud regimes (Bechtold \r\net al., 2014; Han et al., 2017; Walters et al., 2019) or evaluated \r\nagainst convection-permitting models (CPMs; J. Chen et al., 2020a). \r\nTo increase the sensitivity of convection to tropospheric humidity, \r\nseveral models now include a  representation of deep convective \r\nentrainment dependent on relative humidity (Bechtold et al., 2008; \r\nHan et al., 2017; M. Zhao et al., 2018; Walters et al., 2019)"


id: f156d272d85b730bdaf877a59da90077
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 35474
content:
" Other \r\nefforts have focused on the improvement of shallow convection and \r\nlow-level cloudiness due to their major contribution to uncertainty \r\nin climate sensitivity (Section 7.4.2.4). A cloud-regime-based study \r\nhowever highlights an apparent disconnection between cloud \r\nand precipitation processes in GCMs (Tan et al., 2018), suggesting \r\nthat a  good representation of clouds does not lead to systematic \r\nimprovement in simulated precipitation. A global simulation in which \r\nthe parametrized convection is switched off shows a strong influence \r\nof parametrized convection on daily precipitation extremes (P. Maher \r\net al., 2018). Regional simulations at a 25km resolution suggest that \r\nan explicit deep convection can be beneficial even at such a relatively \r\ncoarse resolution (Vergara-Temprado et al., 2020). Perturbed physics \r\nensembles (PPE, Section 1.4"


id: e9058f498ba7db544dc63154b55f78da
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 34180
content:
"4) make it possible to identify parameters \r\nin the convection scheme that are most important in determining \r\nfuture precipitation changes (Bernstein and Neelin, 2016)."


id: 6a7f4f89bfb62b625d0bf2341fe5408b
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 14371
content:
"Since AR5, spatial aggregation of tropical convection has also received \r\ngrowing attention in both observational (Holloway et al., 2017) and \r\nmodelling studies (Muller and Bony, 2015; Wing et al., 2017; Tan \r\net al., 2018). The changing degree of convective organization was \r\nhighlighted as a  key mechanism for dynamic changes in extreme \r\nprecipitation (Pendergrass, 2020a). Yet, convective parametrizations \r\ndo not represent all aspects of mesoscale convective systems (Hourdin \r\net al., 2013; Park et al., 2019). This is related to the complexity of \r\nmechanisms involved from synoptic to mesoscale dynamics, which \r\nare only partially resolved by models. Cloud-resolving models (CRMs, \r\nSection  8.5.1.2.2) represent a  useful benchmark for improving the \r\nparametrization of mesoscale convective systems. Machine learning \r\ncan also be used to parametrize moist convection after training \r\nthe model with a conventional or a super parametrization scheme \r\n(Gentine et al"


id: 52cc43c09ff4ac3274b012f1ae75e164
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 10481
content:
"improvements in their simulation of the tropical precipitation \r\nclimatology compared to CMIP5 (Figure  3.10; Fiedler et al., 2020). \r\nFor instance, the double-ITCZ syndrome is still prominent (Tian and \r\nDong, 2020) despite being reduced in some models (e.g., Qin and \r\nLin, 2018). This systematic bias was shown to arise from atmospheric \r\nprocesses including cloud feedbacks (Tian, 2015; Dixit et al., 2018; \r\nTalib et al., 2018) and the SST threshold at which deep convection \r\noccurs in the tropics (Oueslati and Bellon, 2015; Xiang et al., 2017; \r\nAdam et al., 2018). Such biases can also arise from a  too weak \r\nsensitivity of seasonal tropical precipitation to local SSTs compared \r\nwith observations (Good et al., 2021). These biases are large enough \r\nto alter forced precipitation changes, and consequently limit our \r\nconfidence in projected precipitation changes (Samanta et al., 2019; \r\nAadhar and Mishra, 2020)"


id: 79294f81c162ce1ee0422cf9b3221b07
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 16624
content:
" Observational constraints can be used to \r\nnarrow model response uncertainties (DeAngelis et al., 2015; G. Li \r\net al., 2017; Ham et al., 2018; Watanabe et al., 2018), although there \r\nis still no consensus that model selection or weighting is a reliable \r\nalternative to the 'one-model-one-vote' approach used in Section 8.4 \r\n(Box  4.1). The detrimental influence of model errors can also be \r\nmitigated by focusing on phenomena or events (Polson and Hegerl, \r\n2017; Weller et al., 2017), implementing bias adjustment techniques \r\n(Section 10.2.3.2), or adopting a non-probabilistic storyline approach \r\n(Zappa and Shepherd, 2017). "


id: d91815a18cbec449b5426e394c389e00
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 31662
content:
"A major challenge in representing convective clouds and related \r\nprecipitation events in GCMs is a  lack of sophisticated cloud \r\nmicrophysics in convective parametrization schemes (e.g., Fan et al., \r\n2016). Most of these schemes only include simple microphysical \r\ntreatments, such as direct partition between cloud condensation and \r\nprecipitation, and do not include advanced treatment of conversion \r\namong different types of hydrometeors. As such these schemes are \r\nunable to simulate microphysical cloud and precipitation responses \r\nto aerosol-related perturbations in cloud droplet concentration \r\nand ice crystals (see Box 8.1), or perturbations in thermodynamical \r\nstates from global warming. Efforts have been made to include more \r\nadvanced cloud microphysical treatment in cumulus parametrizations \r\n(Song and Zhang, 2011; Grell and Freitas, 2014; Berg et al., 2015) or \r\nto use explicit cloud microphysics schemes in climate models with \r\na  'super parametrization' (Wang et al"


id: b3146ccbaebf4763f09295c9de6111c8
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 25690
content:
", 2015), which have been \r\nshown to improve the performance in simulating cloud properties \r\nand precipitation. However, few of these improvements have been \r\nincorporated into CMIP6 climate models so the projected precipitation \r\nresponse to anthropogenic perturbation may still be hindered by the \r\ninadequate microphysical treatment in cumulus parametrization \r\n(Smith et al., 2020). "


id: 9617c4c67e7f1fdd122b46330a69dec6
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 21121
content:
"Land surface processes determine the partitioning of net surface \r\nradiation into sensible, latent and ground heat fluxes, the partitioning \r\nof precipitation into evapotranspiration and runoff, and the net \r\nterrestrial carbon flux at the Earth's surface. They are relevant for \r\nsimulating the terrestrial water cycle responses to climate change, as \r\nwell as the response to land use change (FAQ 8.1). Even basic land \r\nsurface properties such as albedo (Terray et al., 2018) or the ratio of \r\ntranspiration to total evaporation (Chang et al., 2018) still need to \r\nbe improved in state-of-the-art coupled GCMs. Runoff sensitivities \r\nare also not well constrained in these models, which display a large \r\nspread for the present-day climate, influencing simulated changes \r\nunder global warming (Lehner et al., 2019)"


id: 24d625f2ac21581a1726306810c107e1
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 3238
content:
" Earth System Models \r\n(ESMs) incorporate some combined biophysical and biogeochemical \r\nprocesses to a  limited extent, and many relevant processes about \r\nhow plants and soils interactively respond to climate changes are yet \r\nto be considered (e.g., Y. Liu et al., 2020). Consequently, land surface \r\nprocesses and their atmospheric coupling contribute to the range in \r\nwater cycle projections (Jia et al., 2019). "


id: 9dfc57ff1473664625a255cb08ea4b7d
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 22378
content:
"In AR5 Chapter 7, there was low confidence in the representation \r\nof cloud-aerosol interactions in climate models. Despite progresses \r\nin this field since AR5, cloud-aerosol interactions remain a  major \r\nobstacle to understanding climate and severe weather (Varble, 2018). \r\nHigh aerosol concentrations have been observed to suppress rain in \r\nwater clouds (Campos Braga et al., 2017; Fan et al., 2020). However, \r\nsuch aerosol effects are muted in GCMs, which tend to produce \r\nprecipitation from shallow clouds too frequently at the expense \r\nof rain intensity (Suzuki et al., 2015; Jing et al., 2017). This arises \r\nfrom incomplete knowledge of how clouds adjust to aerosol primary \r\neffects such as cloud condensation nuclei (CCN). The adjustment \r\noccurs mainly as a dynamic response to the impacts of CCN on cloud \r\ndroplet size and number concentrations on precipitation-forming \r\nprocesses (Rosenfeld et al., 2008; Goren and Rosenfeld, 2014; Koren \r\net al., 2014; Camponogara et al., 2018)"


id: 2d0d59bf2882a96c6f2fa307722c46d0
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 4526
content:
"Progress in the representation of land surface heterogeneity has \r\nbeen made, in the form of improved mapping of root zone storage \r\ncapacity (Wang-Erlandsson et al., 2016), improved vegetation \r\nstand, disturbance and fire dynamics (F.  Li et al., 2013; Fisher \r\net al., 2018; Haverd et al., 2018; Yue et al., 2018; Zou et al., 2019), \r\nbetter representation of urban surfaces (Box 10.3), and the explicit \r\nrepresentation of inland water bodies (Gu et al., 2015; Verseghy and \r\nMacKay, 2017). The representation of realistic snow and vegetation \r\ncover significantly affects the simulation of the land surface energy \r\nand water budgets at multiple time scales (Loranty et al., 2014; \r\nBartlett and Verseghy, 2015; Thackeray et al., 2015; Qiu et al., \r\n2016; Thackeray and Fletcher, 2016; L. Wang et al., 2016; Alessandri \r\net al., 2017)"


id: 69fdf67daaf38fe82f135a65b91aaab3
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 14276
content:
" Groundwater remains inadequately represented in \r\nmany models, which limits our current understanding of the two\u0002way interactions between groundwater and the rest of the hydrologic \r\ncycle (R.G. Taylor et al., 2013a; Leng et al., 2014; Vergnes et al., 2014; \r\nPokhrel et al., 2015; Maxwell and Condon, 2016; Collins, 2017; \r\nScanlon et al., 2018; Condon et al., 2020). Land management exerts \r\nan increasing influence on the water cycle (Abbott et al., 2019) whose \r\nrepresentation in the current-generation climate models is generally \r\nincomplete (Section 10.3.3.7.2)."


id: e89a0ff4b2d29351bc4063d5e1693e63
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 34103
content:
"The physiological response of plants to increasing atmospheric CO2 is \r\ngenerally accounted for, but only using empirical models of stomatal \r\nconductance that are characterized by a  single critical parameter \r\nof intrinsic water-use efficiency (Franks et al., 2017, 2018). This \r\nreflects a lack of structural diversity and caution about the consensus \r\nof the photosynthesis response to increasing CO2 (Knauer et al., \r\n2015; Huang et al., 2016), which has implications for the ability of \r\nthe current-generation models to account for uncertainty in future \r\nevapotranspiration changes. Most CMIP5 models underestimate the \r\nratio of plant transpiration to total terrestrial evapotranspiration, \r\nwhich may suggest that they also underestimate the impact of plant \r\nphysiology on the water cycle (Lian et al., 2018)"


id: 19bc1e056232e7ae6016df397f1d0d69
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 1520
content:
" Plant hydraulics \r\nare not explicitly considered in many land surface models, which \r\nmay lead to an underestimation of the influence of the increasing \r\natmospheric moisture stress on plant transpiration under climate \r\nchange (Massmann et al., 2019; Grossiord et al., 2020; Y.  Liu \r\net al., 2020). Most ESMs underestimate the water use efficiency \r\nmeasured at many sites and, consequently overestimate the ratio of \r\nevapotranspiration to precipitation (J. Li et al., 2018). "


id: 31dd96d528aafacbb538fec86fb99c0a
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 5259
content:
"Aside from land surface models (LSMs), global hydrological models \r\n(GHMs) have been further developed for off-line simulations of the \r\nhydrological impacts of both climate change and water management \r\n(Jimenez Cisneros et al., 2014; Schewe et al., 2014; Doll et al., 2016, \r\n2018; Pokhrel et al., 2016, 2017; Veldkamp et al., 2018). GHMs can \r\nequal or outweigh the contribution of GCMs to uncertainties in \r\nhydrological projections at the regional scale (Giuntoli et al., 2015). \r\nHistorical GHM simulations are currently not sufficient to improve \r\nregional water cycle projections, due to modelling uncertainties in \r\nboth the driving GCMs and land surface hydrology (Pechlivanidis \r\net al., 2017; Samaniego et al., 2017; Hattermann et al., 2018; \r\nKrysanova et al., 2018). Biophysical vegetation processes are still \r\nnot accounted for in many GHMs, which may lead to inadequate \r\nprojections of terrestrial runoff and water resources"


id: 57f897b1c4953c3b34646d287533c923
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 11345
content:
" However, \r\nhydrological models that do simulate these effects often disagree \r\n(Prudhomme et al., 2014), so do not necessarily provide the added \r\nvalue of a more sophisticated representation of vegetation processes \r\nand land surface conditions (Doll et al., 2016). "


id: 67b9b97ae2107366708d2e7b0b385d53
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 13911
content:
"Since AR5, horizontal resolution has increased in most global climate \r\nmodels, which has led to several improvements in the simulation of \r\nthe water cycle (see also Section 10.3.1.1), not only in areas with \r\nsteep or complex orography, but also over the tropical oceans and \r\nwithin the North Pacific and North Atlantic storm tracks (Piazza \r\net al., 2016; Roberts et al., 2018; Bui et al., 2019; Chen and Dai, 2019; \r\nVanniere et al., 2019). Yet, the added value of higher resolution global \r\nclimate models is not systematic (Johnson et al., 2016; Ogata et al., \r\n2017; D. Huang et al., 2018; Mahajan et al., 2018; Vanniere et al., \r\n2019) and needs careful assessment (Haarsma et al., 2016; Caldwell \r\net al., 2019). Several AGCM studies suggest that increased spatial \r\nresolution leads to better simulation of the atmospheric moisture \r\ntransport from ocean to land, the geographical distribution of \r\nannual mean precipitation (Demory et al"


id: 9fa04a8aabc2d2de74f96bc6d52314c3
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 22656
content:
", 2014), and the frequency \r\ndistribution of daily precipitation intensities (L. Zhang et al., 2016; \r\nChen and Dai, 2019) including extremes in many (Jacob et al., 2014; \r\nWestra et al., 2014), but not all cases (Bador et al., 2020). "


id: 2830569ff6663a1b4b3b91cedfa26ce4
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 3734
content:
"Part of the improvement in simulated precipitation accuracy is related \r\nto improved simulation of the frequency and/or mean intensity of \r\ntropical (Roberts et al., 2015; Walsh et al., 2015) and extratropical \r\n(Hawcroft et al., 2016) cyclones. Idealized regional experiments also \r\nshow that the North Atlantic storm track response to global warming \r\ncan be amplified in higher resolution models (Willison et al., 2015). \r\nIncreased atmospheric horizontal resolution can be also important \r\nfor simulating Northern Hemisphere (NH) blockings (Davini et al., \r\n2017; Schiemann et al., 2017) and synoptic features of the East \r\nAsian summer monsoon (Yao et al., 2017; Kusunoki, 2018). Variable \r\nresolution based on grid stretching may be a valuable alternative for \r\nsimulating regional phenomena like monsoons (Sabin et al., 2013; \r\nKrishnan et al., 2016) or tropical cyclones (Harris et al., 2016; Chauvin \r\net al"


id: e3f3c04399d41adae7d1521f935ab1dc
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
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index: climate_gpt_only_giec
vector_id: 33341
content:
", 2017), while avoiding inconsistencies in the forcings or physics \r\nthat can be found in RCMs driven by GCMs (Boe et al., 2020; Tapiador \r\net al., 2020)."


id: dc341730fb734e63909c2b95836f4c60
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 32157
content:
"Regional Climate Models (RCMs) are used to dynamically downscale \r\nglobal model simulations for a particular region (usually at a spatial \r\nresolution of the order of 10 to 50 km; see Section 10.3.3). The AR5 \r\nreported that RCMs are useful for regions with variable topography \r\nand for small-scale phenomena. However, they inherit biases from \r\ntheir driving GCMs and thus may lack physical consistency with \r\nthem. Since AR5, the application of RCMs has largely increased due \r\nto international model intercomparison projects such as CLARIS\u0002LPB (Sanchez et al., 2015). Many studies have focused on present\u0002day climatological precipitation, showing with high confidence\r\nimprovements in its monthly to seasonal accumulation and spatial \r\ndistribution (Dosio et al., 2015; Giorgi et al., 2016; Bozkurt et al., 2019; \r\nFalco et al., 2019; Di Virgilio et al"


id: 145c0d2a86a01349627357f130fd16c9
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 713
content:
", 2020), although the modelling of \r\nprecipitation remains the 'Achilles heel' of both GCMs and RCMs and \r\nshould be considered cautiously when informing regional climate \r\nchange adaptation strategies (Tapiador et al., 2019b)."


id: fdb77d9a0448ad4bbdad687929d004ae
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 37436
content:
"Estimating internal variability is an important challenge in the \r\nassessment of human-induced changes in the water cycle since its \r\nmagnitude and range of variability can exceed the anthropogenic \r\nsignal, at least at the regional scale and for near-term projections \r\nor low-emissions scenarios (Sections  4.4.1.4 and 8.4.2.9; Deser \r\net al., 2012; Shepherd, 2014; Xie et al., 2015; Sarojini et al., 2016; \r\nDai and Bloecker, 2019; Lehner et al., 2020). Underestimating \r\ninternal variability in models may result in the overestimation of \r\nanthropogenic climate change because the 'noise' in the signal-to\u0002noise ratio is underestimated (Knutson and Zeng, 2018). There is \r\nmedium confidence that this underestimation affects global water \r\ncycle projections, for instance, in terms of drought persistence and \r\nseverity in the south-western USA, eastern Australia, southern Africa, \r\nthe Mediterranean, the southern Amazonian basin and China (Ault \r\net al., 2014; Cook et al., 2018; Gu et al"


id: b28623f1246479e6f3e6724fc8895b66
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 25605
content:
", 2018). In CMIP6 models, \r\nthe uncertainty in future projections of 20-year mean precipitation \r\nchanges attributable to internal variability ranges from 41% in \r\nthe near term (2021-2040) to 5% in the long term (2081-2100) \r\n(Figure 8.23). For decadal-mean precipitation changes, the relative \r\ncontribution of internal variability is even larger when using large \r\nensembles (Lehner et al., 2020)."


id: 6d739b27a55f18aec3b965dfee6cc7f9
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 14829
content:
"Over the 20th century, CMIP5 models show a realistic magnitude of \r\ndecadal precipitation variability, if not a slight overestimation in some \r\nregions (Knutson and Zeng, 2018). However, the relatively short and \r\nhuman-influenced instrumental record limits our ability to quantify the \r\nmagnitude of internal variability in the water cycle, particularly over \r\nlong time scales (decadal and beyond). Global extended reanalyses \r\n(Section 1.5.2) have been used to derive long-term variability in the \r\nregional water cycle components (Caillouet et al., 2017), merged with \r\nhistorical meteorological and hydrological local observations (Bonnet \r\net al., 2017; Devers et al., 2020). Specific assessment of these types \r\nof methodology and related uncertainties is provided in Chapter 10 \r\n(Sections  10.2 and 10.3)"


id: 5be5cb256487910680c91b2ff50e8592
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 11972
content:
" Paleoclimate archives (tree rings, corals, \r\nice core, speleothems, lake and ocean sediments) provide extended \r\nreconstructions of key water cycle metrics and large-scale circulation \r\nfeatures. Some studies have suggested that CMIP5 models \r\nunderestimate internal variability at decadal and longer time scales, \r\nand therefore may be missing important processes in the climate \r\nsystem (Ault et al., 2012, 2013; Bunde et al., 2013; Franke et al., 2013; \r\nCheung et al., 2017; Hope et al., 2017; Kravtsov, 2017; Cassou et al., \r\n2018). However, recent assessments using paleoclimate records have \r\nfound that CMIP5 models are able to reproduce decadal-to-centennial \r\nvariability, including the severity, persistence and spatial extent of \r\nmegadroughts (Coats et al., 2015; Stevenson et al., 2015; PAGES \r\nHydro2K Consortium, 2017), once signal reddening (autocorrelation) \r\nin proxy archives is accounted for (Dee et al., 2017; PAGES Hydro2K \r\nConsortium, 2017)"


id: bd57403d529113b1e5d8879c7c4056d0
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 27354
content:
"Beyond modelling uncertainties, internal variability and unpredictable \r\nnatural forcings may also lower the degree of confidence in projected \r\nwater cycle changes, especially in the near term (2021-2040) and \r\nregional-scale projections (Hawkins and Sutton, 2011; Kent et al., \r\n2015; Thompson et al., 2015; Fatichi et al., 2016; McKinnon and \r\nDeser, 2018; Chen and Brissette, 2019; Lehner et al., 2020). Although \r\nthere is low confidence that the main modes of climate variability \r\n(Annex IV) are altered in a  warmer climate (Sections  4.4.3 and \r\n4.5.3), increasing contrast between wet and dry weather regimes \r\n(Section 8.2.2.1) will amplify their influence on water cycle variability \r\n(Section 8.4.2.9) and therefore contribute to uncertainties in near\u0002term precipitation changes (Figure  8.23). The role of internal \r\nvariability as source of uncertainties in regional climate projections \r\nis assessed in Section 10.3.4.3"


id: bca38acac95e51e45b37f49a43c9a79
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 27254
content:
" Here we assess the role of internal \r\nvariability in influencing water cycle projections using paleoclimate \r\nreconstructions, pre-industrial model simulations, and large single \r\nmodel ensembles (Section 8.5.2.1). Implications for the predictability \r\nof near-term water cycle changes are specifically assessed, as \r\nthey show significant but model-dependent regional hydrological "


id: 33e4df125e817a15bf6d9af171c20e3b
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 5578
content:
"In AR5, decadal prediction of precipitation over some land areas \r\nshowed improved skill due to specified radiative forcing, with almost \r\nno added value from ocean initialization. Since AR5, more studies \r\nhave been devoted to understanding the potential or effective \r\nwater cycle predictability related to ocean multi-decadal variability. \r\nDecadal hindcast experiments based on large ensembles highlight \r\nincreasing skill scores in annual mean precipitation three to seven \r\nyears ahead, at least over the Sahel and Europe (Yeager et al., 2018). \r\nThere is relatively high predictability of the AMV impacts over the \r\nMediterranean basin, Central Asia and the Americas (from the USA to \r\nnorthern South America) during boreal summer, but in boreal winter \r\nthe signal-to-noise ratio shows only weak predictability over land \r\n(Yamamoto and Palter, 2016; Ruprich-Robert et al., 2017)"


id: 4465e544661c4dee90592b3b85488941
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 8183
content:
" The link \r\nbetween South Asian summer monsoon changes and the AMOC \r\nand the decadal variability in the Pacific Ocean open the possibility \r\nof increased predictability for the near future (Kushnir et al., 2017; \r\nX. Huang et al., 2020b; Sandeep et al., 2020)."


id: 622cfe5a6ce71691186f1978794b721e
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 13000
content:
"Since AR5, SMILEs have helped quantify the time of emergence of \r\nclimate change signals (see Sections  1.4.2.2 and 10.4.3). Results \r\nfrom SMILEs indicate that by 2000-2009 (compared to 1950-1999), \r\nsimulated anthropogenic shifts in mean annual precipitation \r\nalready emerged over 36-41% of the globe including high latitudes \r\n(Frankcombe et al., 2018; Kumar and Ganguly, 2018), the eastern \r\nsubtropical oceans, and the tropics (Zhang and Delworth, 2018). \r\nBy 2050 (2100), more than 60% (85%) of the globe is projected to \r\nshow detectable anthropogenic shifts in mean annual precipitation \r\n(Zhang and Delworth, 2018). Other SMILE results for the 1950-2100 \r\nperiod (Kay et al., 2015; Sigmond and Fyfe, 2016) indicate that \r\ninternal variability can obscure the detection of the anthropogenic \r\nhydroclimatic signal until the middle to late 21st century in many \r\nparts of the world for both mean and extreme precipitation \r\n(Martel et al., 2018; Dai and Bloecker, 2019)"


id: db5486bfe5615546fd89aac33ca2c1a8
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 32014
content:
" A  common finding \r\nis that changes in the characteristics of wet extreme events will \r\nemerge earlier than changes in average conditions (Gaetani et al., \r\n2020; Hawkins et al., 2020; Kusunoki et al., 2020). An assessment "


id: 1a649f412feecf871e7bf84d628ad18e
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 1626
content:
"Volcanic eruptions can affect climate projections in the near term \r\n(2021-2040; Section  4.4.4 and Cross-Chapter Box  4.1). In this \r\nchapter, they are of interest because they can trigger a  transient \r\ndeparture from the water cycle response to anthropogenic radiative \r\nforcing. Major volcanic eruptions temporarily reduce total global and \r\nwet tropical region precipitation (high confidence) (Iles and Hegerl, \r\n2014), can weaken or shift the ITCZ (Iles and Hegerl, 2014; Colose \r\net al., 2016; Liu et al., 2016), and reduce summer monsoon rainfall \r\n(medium confidence) (Pausata et al., 2015b; Zambri and Robock, \r\n2016; Zambri et al., 2017; Zuo et al., 2019; M. Singh et al., 2020). \r\nMonsoon precipitation in one hemisphere can be enhanced by the \r\nremote volcanic forcing occurring in the other hemisphere (medium \r\nconfidence) (Pausata et al., 2015a; Liu et al., 2016; Zuo et al., 2019)"


id: ce35840045fa860bab915d0e90ce2ce7
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 29963
content:
" \r\nOver the Sahel, the sign of hydrological changes depend on the \r\nhemisphere where the volcanic eruptions occur (J.M.  Haywood \r\net al., 2013). Out of phase changes in the Sahel and the Amazonian \r\nbasin are expected from the effect of volcanic aerosols on tropical \r\nAtlantic SST and the ITCZ (Hua et al., 2019). Over the last millennium, \r\nuncertainties remain in the symmetry/asymmetry of the monsoon \r\nresponse because it is difficult to estimate the exact latitude and \r\nseason of past volcanic eruptions further back in time (Colose et al., \r\n2016; Fasullo et al., 2019)."


id: 6cf032adc788b17594c4c7dc8718bf2c
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 14759
content:
"Since AR5, there is further evidence that the pattern-scaling technique \r\nhas limitations (Lopez et al., 2014; Wartenburger et al., 2017; Tachiiri \r\net al., 2019), and that alternative approaches, such as multiple \r\nregressions using the land-sea warming contrast as an additional \r\npredictor, offer added value (Joshi et al., 2013). The  simplest \r\ntraditional pattern-scaling approach approximates future changes by \r\nthe product of a time-evolving global surface temperature change \r\nand a  pattern that varies spatially but is constant across time, \r\nscenarios, and models. This technique was shown to be more robust \r\nacross scenarios rather than across models, with better results for \r\ntemperature compared with precipitation (Tebaldi and Arblaster, \r\n2014; see also Section 4.2.4). One approach which avoids scaling is to \r\nconsider a period in a different scenario with the same global surface \r\ntemperature change (Herger et al., 2015)"


id: 9417ea98e9b6db594a4ce075f71eb381
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 20807
content:
" It is attractive as it provides \r\npatterns of any temporal resolution that are consistent across \r\nvariables. Nonetheless, this technique is still only based on global \r\nsurface temperature and is not necessarily suitable for precipitation \r\nchanges projected in stabilized versus transient scenarios (at the \r\nsame global warming level) given the fast-atmospheric adjustment \r\nto GHG radiative forcing (Sections 8.2.1 and 8.4.1.1)."


id: 56aa6579f85cd9da5a77f85a526d540a
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
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vector_id: 11115
content:
"abrupt quadrupling of atmospheric CO2 (Webb et al., 2017). Preliminary \r\nanalyses based on CMIP5 models showed that annual precipitation \r\nchanges following a doubling step change in CO2 from pre-industrial \r\nlevels are not necessarily consistent with the response to the step \r\nfrom doubling to quadrupling despite a  similar change in radiative \r\nforcings (Good et al., 2016a; Ceppi and Shepherd, 2017). Beyond the \r\nvisual comparison of the climate response at various global warming \r\nlevels (e.g., Figure 4.35), the linearity across global warming levels can \r\nbe assessed by using the highest emissions scenario and comparing \r\nseasonal mean relative precipitation changes at +2degC versus +4degC \r\nabove pre-industrial (1850-1900) temperatures (Figure  8.25). The \r\nresults support the previous finding (Good et al"


id: d25ec6aa36d28add33b7228940627f98
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
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vector_id: 30625
content:
", 2016b) that a second \r\n2degC warming does not necessarily lead to the same precipitation \r\nanomaly pattern as the first 2degC, especially in the tropics where regional \r\ndifferences can be large but not necessarily consistent among different \r\nmodels. They are also consistent with a  recent analysis of CMIP5 \r\nmodels showing that the projected drying in the Mediterranean and \r\nin Chile is substantially faster than the increase in GSAT, and therefore \r\ndoes not scale linearly with global warming (Zappa et al., 2020). "


id: 303204b438b96edabc3ed092d23cdc28
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 5011
content:
"Non-linearities in the climate response are thought to arise from \r\nmultiple factors. These include state-dependent ice-albedo feedback \r\nand its potential influence on Northern Hemisphere (NH) storm \r\ntracks (Peings and Magnusdottir, 2014; Semenov and Latif, 2015; see \r\nalso Cross-Chapter Box 10.1 and Section 8.6.1.2); a state-dependent \r\nsensitivity of tropical precipitation to increased SST (Schewe and \r\nLevermann, 2017; He et al., 2018); a complex response of the Atlantic \r\nmeridional overturning circulation (AMOC; Sections  9.2.4.1 and \r\n8.6.1.1) and its model- and magnitude dependent teleconnections \r\nwith regional temperature and precipitation (Kageyama et al., \r\n2013; Jackson et al., 2015; Qasmi et al., 2017, 2020); and other \r\natmospheric and terrestrial (Section 8.5.3.2) processes such as cloud \r\nand land surface feedbacks (Ceppi and Gregory, 2017; King, 2019)"


id: a121bff9ada8d41e4eb5b651c6e19aca
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 22908
content:
" \r\nThe response of convective precipitation may exhibit non-linearities \r\nbecause it is itself modulated by both dynamics and atmospheric \r\nwater content, each responding independently to warming (Chadwick \r\nand Good, 2013; Neupane and Cook, 2013). "


id: 3244e785c34edbdcb8c14777a0b90781
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
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index: climate_gpt_only_giec
vector_id: 5320
content:
"Land surface responses and feedbacks represent a potential source of \r\nnon-linearity for the water cycle response, at least at regional and local \r\nscales. The forced response of soil moisture and freshwater resources \r\nnot only depends on precipitation, but also on evaporation (Laine \r\net al., 2014), snowmelt (Thackeray et al., 2016), and runoff (X. Zhang \r\net al., 2018) which are intrinsically non-linear processes depending \r\non soil moisture or temperature thresholds. Bare ground evaporation \r\nis, for instance, usually estimated as a non-linear function of surface \r\nsoil moisture (Jefferson and Maxwell, 2015). Plant transpiration \r\nrequires more complex formulations with non-linear dependencies \r\non multiple environmental factors including root-zone soil moisture \r\nand atmospheric CO2 concentration (Franks et al., 2017). Globally, \r\nland surface evaporation is both energy and soil-moisture limited, \r\nbut one of these limitations can become dominant depending on \r\nregions and seasons"


id: 31d4a833a518caaf8e88b033a33560db
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 5253
content:
" Non-linearities may be particularly strong in \r\ntransitional regimes where and when soil moisture limitation plays \r\na major role (Berg and Sheffield, 2018b)."


id: 84796ccc643fc3e433ab8e8f270a4864
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 18373
content:
"Compared to atmospheric circulation and seasonal mean precipitation, \r\nextreme precipitation has been found to scale more accurately with \r\nlocal and global mean temperature (Chou et al., 2012; Pendergrass \r\net al., 2015). The projected increase in the magnitude of extreme \r\nprecipitation is generally proportional to the global warming level, \r\nwith an increase of around 7%  per  1degC warming (Section  11.4.5) \r\nalthough this rate shows seasonal and geographical variations and \r\nis slightly less for five-day than for one-day precipitation maxima. \r\nProjected changes in extreme precipitation are the result of both \r\nthermodynamical and more model-dependent and potentially less \r\nlinear dynamical contributions (Pfahl et al., 2017). Projected changes \r\nin precipitation extremes are also potentially sensitive to a  non\u0002linear response of spatial convective organization (Pendergrass et al"


id: 8ec921b5518d2e42d7191610227d8fb6
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 19990
content:
", \r\n2016), and can exhibit a  quadratic rather than linear response to \r\nglobal warming (Pendergrass et al., 2019)."


id: bfe5f87e5d3f62445771e583f1dcc1d
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
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index: climate_gpt_only_giec
vector_id: 27749
content:
"Land surface runoff and groundwater recharge are highly non\u0002linear process, depending for instance on rainfall intensity, soil \r\ninfiltration capacity, vertical profile of soil moisture and water table \r\ndepth. A non-linear relationship between rainfall and groundwater \r\nrecharge was observed in the tropics where intense seasonal \r\nrainfalls associated with internal climate variability contribute \r\ndisproportionately to recharge (R.G. Taylor et al., 2013a; Cuthbert \r\net al., 2019a). Groundwater fluxes in arid regions are generally less \r\nresponsive to climate variability than in humid regions, which can \r\ntemporarily buffer climate change impacts on water resources or lead \r\nto a long, initially hidden, hydrological responses to global warming \r\n(Cuthbert et al., 2019a)"


id: 71ff0c0f5f74c6feff79913a44cccafe
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
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index: climate_gpt_only_giec
vector_id: 15561
content:
" Hydrological model simulations driven by \r\nindividual and combined forcing show that decreased precipitation \r\ncan cause larger deficits in soil moisture, streamflow and water table \r\ndepth than other forcings, but also that these factors are not linearly \r\ncumulative when applied in combination (Hein et al., 2019). Surface \r\nrunoff was found to scale only approximately with global warming \r\n(Tanaka et al., 2017). Significant non-linearities were found in the \r\nprojected annual mean runoff response to global warming in CMIP5 \r\nprojections, which could not be entirely explained by precipitation \r\nchanges (X.  Zhang et al., 2018). Similar non-linear behaviours are \r\nfound in CMIP6 models over the Amazon, Yangtze, Niger, Euphrates \r\nand Mississippi river basins (Figure 8.26), highlighting the need to \r\nreassess the assumption of linearity when estimating regional water \r\ncycle changes. "


id: b738ec0bdb6fc3b2894553b3d60d246e
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 26358
content:
"Multiple lines of evidence, including both paleoclimate reconstructions \r\nand simulations, suggest that a  severe weakening or collapse of \r\nAtlantic Meridional Overturning Circulation (AMOC, see Glossary) \r\ncauses abrupt and profound changes in the global hydrological cycle \r\n(Chiang and Bitz, 2005; Broccoli et al., 2006; Chiang and Friedman, \r\n2012; Jackson et al., 2015; Renssen et al., 2018). Deep water \r\nformation in the North Atlantic is dependent on a delicate balance of \r\nheat and salt fluxes (Buckley and Marshall, 2016); disruption in either \r\nof these due to melting ice sheets, a  change in precipitation and \r\nevaporation, or ocean circulation can force AMOC to cross a tipping \r\npoint (SROCC; Drijfhout et al., 2015). During the last deglacial \r\ntransition, one such slowdown in AMOC - during the Younger Dryas \r\nevent (12,800-11,700 years ago) - caused worldwide changes in \r\nprecipitation patterns. These included a southward migration of the \r\ntropical ITCZ (Peterson et al"


id: 95c72fa0b8e9732358dc578f7cdba2c2
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
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vector_id: 21063
content:
", 2000; McGee et al., 2014; Schneider \r\net al., 2014; Mohtadi et al., 2016; Reimi and Marcantonio, 2016; \r\nP.X.  Wang et al., 2017) and systematic weakening of the African \r\nand Asian monsoons (Tierney and DeMenocal, 2013; Otto-Bliesner \r\net al., 2014; Cheng et al., 2016; Grandey et al., 2016; Wurtzel et al., \r\n2018). Conversely, the Southern Hemisphere (SH) monsoon systems \r\nintensified (Cruz et al., 2005; Ayliffe et al., 2013; Strikis et al., 2015, \r\n2018; Campos et al., 2019). Drying occurred in Meso-America \r\n(Lachniet et al., 2013) while the North American monsoon system \r\nwas largely unaffected (Bhattacharya et al., 2018). The mid-latitude \r\nregion in North America was wetter (Polyak et al., 2004; Grimm et al., \r\n2006; Wagner et al., 2010; Voelker et al., 2015), while Europe was \r\ndrier (Genty et al., 2006; Rach et al., 2017; Naughton et al., 2019)"


id: e00f266433bdc8dc74ea78f278920d7c
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 32744
content:
" \r\nA transient coupled climate model simulation was able to reproduce \r\nthe large-scale precipitation response to such an event (Figure 8.27a; \r\nLiu et al., 2009). "


id: 4c086d4e5aee0e54e6a74eca3b6392ac
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 9386
content:
"These patterns of past hydroclimatic change are relevant for future \r\nprojections because it is very likely that AMOC will weaken by 2100 \r\nin response to increased greenhouse gas emissions (Section 9.2.3.1; \r\nWeaver et al., 2012; Drijfhout et al., 2015; Bakker et al., 2016; \r\nReintges et al., 2017). Furthermore, there is medium confidence\r\nthat the decline in AMOC will not involve an abrupt collapse before \r\n2100 (Section 9.2.3.1). The response of precipitation to hypothetical \r\nAMOC collapse under elevated greenhouse gases bears resemblance \r\nto the paleoclimate response during the Younger Dryas event, with \r\nsome important differences due to effects of increased CO2 on global \r\nprecipitation patterns (Figure  8.27b). As with the paleoclimate \r\nevents, AMOC collapse results in a southward shift in the ITCZ that \r\nis most pronounced in the tropical Atlantic. This could cause drying \r\nin the Sahel region (Defrance et al"


id: 372c5b19836541c0f7197398414e9c5d
created_at: 2023-03-16 14:02:04
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content:
", 2017) as well as Meso-America \r\nand northern Amazonia (Parsons et al., 2014; Y. Chen et al., 2018). "


id: 74b7eca958541c3ee233e148f7925802
created_at: 2023-03-16 14:02:04
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vector_id: 15959
content:
"This close association between the land surface and the water cycle \r\nmakes the Amazon a  potential hotspot for abrupt change (Torres \r\nand Marengo, 2014). Both deforestation and drying are projected to \r\nincrease by 2100, resulting in a worst-case scenario of up to a 50% \r\nloss in forest cover by 2050 (Soares-Filho et al., 2006; Boisier et al., \r\n2015; ter Steege et al., 2015; Gomes et al., 2019). Deforestation in \r\nthe Amazon also raises the probability of catastrophic fires (Brando \r\net al., 2014). The combination of deforestation, drier conditions, \r\nand increased fire can push the rainforest ecosystem past a tipping \r\npoint, beyond which there is rapid land surface degradation, a sharp \r\nreduction in atmospheric moisture recycling, an increase in the \r\nfraction of precipitation that runs off, and a  further shift towards \r\na drier climate (Staal et al., 2015; Boers et al., 2017; Zemp et al., 2017; \r\nRuiz-Vasquez et al., 2020)"


id: 3eb63588c7d127000e6b60f177ceebac
created_at: 2023-03-16 14:02:04
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vector_id: 7246
content:
" A rapid drop in precipitation has a direct \r\nimpact on river flows, driving basin-scale shifts from a regulated to \r\nunregulated state (Salazar et al., 2018). Regional climate modeling \r\nexperiments confirm that increased deforestation leads to a  drier \r\nclimate, although not all models show a true tipping point, at least \r\nunder present-day climatic conditions (Lejeune et al., 2015; Spracklen \r\nand Garcia-Carreras, 2015)."


id: 16e9283c5290ee9b554f3a198b41a83a
created_at: 2023-03-16 14:02:04
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index: climate_gpt_only_giec
vector_id: 1130
content:
"CMIP5 and CMIP6 models, some of which include dynamic \r\nvegetation schemes, cannot simulate the magnitude, nor the spatial \r\nextent, of greening and precipitation change associated with the \r\nlast Green Sahara under standard mid-Holocene (6,000 years ago) \r\nboundary conditions (high confidence) (Figure 3.11; Harrison et al., \r\n2014; Tierney et al., 2017; Brierley et al., 2020). This result remains \r\nunchanged since AR4 (Jansen et al., 2007). This may be due to \r\nclimatological biases in the models (Harrison et al., 2015) or could \r\nimply that the strength of the feedbacks between vegetation and \r\nthe water cycle in the models is too weak (Hopcroft et al., 2017). To \r\ndate, climate models still only produce the amount and spatial extent \r\nof rainfall that is needed to sustain a Green Sahara if they are given \r\nprescribed changes in the land surface, such as albedo, soil moisture, \r\nvegetation cover and/or dust emissions (Pausata et al., 2016; Skinner \r\nand Poulsen, 2016; Tierney et al"


id: 35d4960a0348a7ca0e51409b14fb37a8
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
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index: climate_gpt_only_giec
vector_id: 5883
content:
"Greening of the Sahara and Sahel regions in North Africa, in \r\nresponse to an increase in precipitation, has long been considered \r\nan amplifying mechanism that can lead to abrupt change. Although \r\nthe high surface albedo of the desert stabilizes the energy balance \r\nof the system (Charney, 1975), greening can induce strong, positive \r\nfeedbacks between the land surface and precipitation that can shift \r\nthe region into a 'Green Sahara' state. The fact that the transition \r\nphase between a Desert Sahara and Green Sahara is not theoretically \r\nstable (Brovkin et al., 1998) creates a tipping point and allows for the \r\npossibility of an abrupt shift between dry and wet climate regimes. \r\nPaleoclimate reconstructions provide evidence of past Green Sahara \r\nstates (DeMenocal and Tierney, 2012), under which rainfall rates \r\nincreased by an order of magnitude (Tierney et al., 2017), leading \r\nto a vegetated landscape (Jolly et al"


id: f2843a0018547a639b3dc1c2e3f8c9ed
created_at: 2023-03-16 14:02:04
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vector_id: 35681
content:
", 1998) with large lake basins \r\n(Gasse, 2000; Drake and Bristow, 2006). The underlying driver of the \r\nGreen Sahara is the periodic increase in summer insolation associated \r\nwith the orbital precession cycle (Kutzbach, 1981). In this sense, Green \r\nSaharas are not direct analogues for a  response to anthropogenic \r\ngreenhouse gas emissions (GHGs), as these past states were forced \r\nby natural, seasonal changes in solar radiation. However, the climate \r\ndynamics of Green Sahara periods (which have global impacts, \r\nPausata et al., 2020), and the speed of the transitions between Desert \r\nSaharas and Green Saharas, are relevant for future projections."


id: dd33c868a86e1609347164bb8341fb55
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 32302
content:
"Since AR5, paleoclimatic studies have improved our view of the \r\ntiming, spatial extent, and speed of transitions associated with the \r\nearly Holocene (11,000-5,000 years ago) Green Sahara. Observed \r\ntransitions into and out of Green Sahara states are always \r\nfaster than the underlying forcing, in agreement with theoretical \r\nconsiderations (high confidence) (Tierney and DeMenocal, \r\n2013; Shanahan et al., 2015; Tierney et al., 2017). However, \r\nthere is low confidence in the duration of the transition because \r\nsedimentary records cannot typically resolve changes on decadal \r\nto multi-decadal time scales (Tierney and DeMenocal, 2013). Both \r\npaleoclimate data and modelling experiments suggest that the \r\ntiming and speed of the transition was spatially heterogeneous \r\n(high confidence), with northern Saharan locations becoming drier \r\nthousands of years before more equatorial locations (Shanahan \r\net al., 2015; Tierney et al., 2017; Dallmeyer et al., 2020)"


id: 2e45cf4cafe91db0f467bbc3841a0acc
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
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index: climate_gpt_only_giec
vector_id: 4715
content:
"increasing stability in the atmosphere (Cook et al., 2013; Huang et al., \r\n2014) can drive positive feedbacks (see also Section  6.3.6). Thus \r\nthere is strong potential for dust to contribute to abrupt changes in \r\nthe water cycle, especially in semi-arid regions where wind erosion \r\nis highly sensitive to vegetation cover and drought variability (Yu \r\net al., 2015). One such event occurred over the Central USA during \r\nthe 1930s: the Dust Bowl drought, an iconic event characterized by \r\nwidespread land degradation and historically unprecedented levels \r\nof dust storm activity (Hansen and Libecap, 2004; Lee and Gill, \r\n2015). While initialized by warm sea surface temperatures in the \r\nNorth Atlantic, modeling work indicates that land cover changes and \r\nresulting dust emissions contributed to the severity and spatial extent \r\nof the drought by further suppressing precipitation (Cook et al., 2009; \r\nHu et al., 2018; Cowan et al., 2020)"


id: c688cc1c67538b027ea08105f7ee0e4d
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
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index: climate_gpt_only_giec
vector_id: 28802
content:
" There is also increasing evidence \r\nthat dust aerosol feedbacks are necessary to explain the magnitude \r\nof rainfall increase during the mid-Holocene Green Sahara (Pausata \r\net al., 2016; Tierney et al., 2017)."


id: 94614bf948e7f42864e4162692030341
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 20855
content:
"Solar radiation modification (SRM) techniques seek to reduce the \r\nimpacts of climate change by modifying the Earth's radiation budget, \r\neither by reflecting incoming solar radiation or increasing the amount \r\nof heat lost to space. Note that, following SR1.5, the definition of \r\nSRM in this Report refers to changes in both solar and longwave \r\nradiation (Section 4.6.3.3 and Glossary). A variety of methods have \r\nbeen proposed, including injection of aerosols or their precursors \r\ninto the stratosphere, cloud brightening, and cirrus cloud thinning \r\n(Table 4.8). Since SRM alters the planetary energy balance, changes in \r\nthe hydrological cycle are theoretically expected (Section 8.2). These \r\nchanges can be abrupt if the initial magnitude of SRM is large, rather \r\nthan increased gradually. Since AR5, a diversity of SRM techniques \r\nhave been tested using climate model simulations, with an increasing \r\nfocus on consequences for regional water availability"


id: 81e3aa50b9daf0863ff061dcc5ba21c9
created_at: 2023-03-16 14:02:04
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vector_id: 17961
content:
"However, heavier rainfall does not always lead to greater flooding. This is because flooding also depends \r\nupon the type of river basin, the surface landscape, the extent and duration of the rainfall, and how wet the \r\nground is before the rainfall event (FAQ 8.2, Figure 1). Some regions will experience a drying in the soil as the \r\nclimate warms, particularly in subtropical climates, which could make floods from a rainfall event less probable \r\nbecause the ground can potentially soak up more of the rain. On the other hand, less frequent but more intense \r\ndownpours can lead to dry, hard ground that is less able to soak up heavy rainfall when it does occur, resulting \r\nin more runoff into lakes, rivers and hollows. Earlier spring snowmelt combined with more precipitation falling \r\nas rain rather than snow can trigger flood events in cold regions. Reduced winter snow cover can, in contrast, \r\ndecrease the chance of flooding arising from the combination of rainfall and rapid snowmelt"


id: 6350653e24e64257a6277f02d810798f
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
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index: climate_gpt_only_giec
vector_id: 13198
content:
" Rapid melting of \r\nglaciers and snow in a warming climate is already increasing river flow in some regions, but as the volumes of ice \r\ndiminish, flows will peak and then decline in the future. Flooding is also affected by changes in the management \r\nof the land and river systems. For example, clearing forests for agriculture or building cities can make rainwater \r\nflow more rapidly into rivers or low-lying areas. On the other hand, increased extraction of water from rivers can \r\nreduce water levels and the likelihood of flooding. "


id: 44baabd301c0ff05816bb92c2a88bcbf
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
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index: climate_gpt_only_giec
vector_id: 8239
content:
"A drought is broadly defined as drier than normal conditions; that is, a moisture deficit relative to the average \r\nwater availability at a given location and season. Since they are locally defined, a drought in a wet place will \r\nnot have the same amount of water deficit as a drought in a dry region. Droughts are divided into different \r\ncategories based on where in the water cycle the moisture deficit occurs: meteorological drought (precipitation), \r\nhydrological drought (runoff, streamflow, and reservoir storage), and agricultural or ecological drought (plant \r\nstress from a combination of evaporation and low soil moisture). Special categories of drought also exist. For \r\nexample, a snow drought occurs when winter snowpack levels are below average, which can cause abnormally \r\nlow streamflow in subsequent seasons. And while many drought events develop slowly over months or years, \r\nsome events, called flash droughts, can intensify over the course of days or weeks"


id: 6c88682c71a52fb0c0fba0034a11d4c8
created_at: 2023-03-16 14:02:04
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index: climate_gpt_only_giec
vector_id: 14700
content:
" One such event occurred \r\nin 2012 in the Midwestern region of North America and had a severe impact on agricultural production, with \r\nlosses exceeding $30 billion US dollars. Droughts typically only become a concern when they adversely affect \r\npeople (reducing water available for municipal, industrial, agricultural, or navigational needs) and/or ecosystems \r\n(adverse effects on natural flora and fauna). When a drought lasts for a very long time (more than two decades) \r\nit is sometimes called a megadrought."


id: 94caac6aeb9120f330dfa74a386e49ef
created_at: 2023-03-16 14:02:04
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vector_id: 20921
content:
"Most droughts begin when precipitation is below normal for an extended period of time (meteorological \r\ndrought). This typically occurs when high pressure in the atmosphere sets up over a  region, reducing cloud \r\nformation and precipitation over that area and deflecting away storms. The lack of rainfall then propagates \r\nacross the water cycle to create agricultural drought in soils and hydrological drought in waterways. Other \r\nprocesses act to amplify or alleviate droughts. For example, if temperatures are abnormally high, evaporation \r\nincreases, drying out soils and streams and stressing plants beyond what would have occurred from the lack of \r\nprecipitation alone. Vegetation can play a critical role because it modulates many important hydrologic processes \r\n(soil water, evapotranspiration, runoff). Human activities can also determine how severe a  drought is"


id: f0d565810c44d7b66a8a2b95d347fadc
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
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vector_id: 35383
content:
" For \r\nexample, irrigating croplands can reduce the socio-economic impact of a drought; at the same time, depletion of \r\ngroundwater in aquifers can make a drought worse."


id: e579df53dd359ea0eb9b873cbf0ede97
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vector_id: 33611
content:
"The effect of climate change on drought varies across regions. In the subtropical regions like the Mediterranean, \r\nsouthern Africa, south-western Australia and south-western South America, as well as tropical Central America, \r\nwestern Africa and the Amazon basin, precipitation is expected to decline as the world warms, increasing the \r\npossibility that drought will occur throughout the year (FAQ 8.3, Figure 1). Warming will decrease snowpack, \r\namplifying drought in regions where snowmelt is an important water resource (such as in south-western South \r\nAmerica). Higher temperatures lead to increased evaporation, resulting in soil drying, increased plant stress, and \r\nimpacts on agriculture, even in regions where large changes in precipitation are not expected (such as central \r\nand northern Europe). If emissions of greenhouse gases are not curtailed, about a third of global land areas \r\nare projected to suffer from at least moderate drought by 2100"


id: 440b47712cbcbbf6faae812ed681987d
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
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vector_id: 8118
content:
" On the other hand, some areas and seasons \r\n(such as high-latitude regions in North America and Asia, and the South Asian monsoon region) may experience \r\nincreases in precipitation as a result of climate change, which will decrease the likelihood of droughts. FAQ 8.3, \r\nFigure 1 highlights the regions where climate change is expected to increase the severity of droughts."


id: ff2e207ae414209c91bfa4872bf4dcb1
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 37702
content:
"Cecile Agosta (France), Kyle Armour (United States of America), Mathias Aschwanden (Switzerland), \r\nJonathan L. Bamber (United Kingdom), Sophie Berger (France/Belgium), Fabio Boeira Dias (Finland/\r\nBrazil), Jason E. Box (Denmark/United States of America), Eleanor J. Burke (United Kingdom), \r\nKevin D. Burke (United States of America), Xavier Capet (France), John A.  Church (Australia), \r\nLee de Mora (United Kingdom), Chris Derksen (Canada), Catia M. Domingues (Australia, United \r\nKingdom/Brazil), Jakob  Dorr (Norway/Germany), Paul J. Durack (United States of America/\r\nAustralia), Thomas L. Frolicher (Switzerland), Thian Y. Gan (Canada/Malaysia), Gregory G. Garner \r\n(United States of America), Sebastian  Gerland (Norway/Germany), Heiko  Goelzer (Norway/\r\nGermany), Natalya  Gomez (Canada), Irina  V.  Gorodetskaya (Portugal/Belgium, The Russian \r\nFederation), Jonathan M. Gregory (United Kingdom), Robert Hallberg (United States of America), \r\nF"


id: fc225a7d46cc1371ca8c208a6d8fd61
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 37171
content:
" Alexander Haumann (United States of America/Germany), Tim H. J. Hermans (The Netherlands), \r\nEmma M. Hill (Singapore/United States of America, United Kingdom), Regine Hock (United States of \r\nAmerica, Norway/Germany), Stefan Hofer (Norway/Austria), Romain Hugonnet (France, Switzerland/\r\nFrance), Philippe Huybrechts (Belgium), A.K.M. Saiful Islam (Bangladesh), Laura C. Jackson (United \r\nKingdom), Nicolas C. Jourdain (France), Andreas Kaab (Norway/Germany), Nicole S. Khan (China/\r\nUnited States of America), Shfaqat Abbas  Khan (Denmark), Matthew Kirwan (United States of \r\nAmerica), Roxy Mathew Koll (India), James Kossin (United States of America), Anders Levermann \r\n(Germany), Sophie Lewis (Australia), Shiyin Liu (China), Daniel Lowry (New Zealand/United States \r\nof America), Marta  Marcos (Spain), Ben  Marzeion (Germany), Matthew  Menary (France/United \r\nKingdom), Sebastian H. Mernild (Norway, Denmark/Norway), Philip Orton (United States of America), \r\nMatthew D"


id: cff1168961631291d7fa771127749ef
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 30239
content:
" Palmer (United Kingdom), Frank Pattyn (Belgium), Brodie Pearson (United States of \r\nAmerica/United Kingdom), Cecile Pellet (Switzerland), Chris Perry (United Kingdom), Mark D. Pickering "


id: 775cc74681816178e5dd3803b87d9a0d
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
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vector_id: 16339
content:
"This chapter assesses past and projected changes in the ocean, \r\ncryosphere and sea level using paleoreconstructions, instrumental \r\nobservations and model simulations. In the following summary, we \r\nupdate and expand the related assessments from the IPCC Fifth \r\nAssessment Report (AR5), the Special Report on Global Warming of \r\n1.5degC (SR1.5) and the Special Report on Ocean and Cryosphere in \r\na Changing Climate (SROCC). This chapter covers major advances since \r\nSROCC, including the synthesis of extended and new observations. \r\nThese advances allow for improved assessment of past change, \r\nprocesses and budgets for the last century, and the use of a hierarchy \r\nof models and emulators, which provide improved projections and \r\nuncertainty estimates of future change"


id: 349bda21fbcab8b5c6f9827cf50c1301
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
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vector_id: 5683
content:
" In addition, the systematic use \r\nof model emulators makes our projections of ocean heat content, land \r\nice loss and sea level rise fully consistent with each other and with \r\nthe assessed equilibrium climate sensitivity and projections of global \r\nsurface air temperature across the entire report. In this executive \r\nsummary, uncertainty ranges are reported as very likely ranges and \r\nexpressed by square brackets, unless otherwise noted."


id: 973926d2806b1a0884f81a65d4dade9
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
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vector_id: 21306
content:
"The heat content of the global ocean has increased since at \r\nleast 1970, and will continue to increase over the 21st century \r\n(virtually certain). The associated warming will likely continue \r\nuntil at least 2300, even for low-emissions scenarios, because \r\nof the slow circulation of the deep ocean. Ocean heat content \r\nhas increased from 1971 to 2018 by 0.396 [0.329 to 0.463, likely\r\nrange] yottajoules and will likely increase until 2100 by two to four \r\ntimes that amount under SSP1-2.6 and four to eight times that amount \r\nunder SSP5-8.5. The long time scale also implies that the amount of \r\ndeep-ocean warming will only become scenario-dependent after about \r\n2040 (medium confidence), and that the warming is irreversible over \r\ncenturies to millennia (very high confidence). On annual to decadal \r\ntime scales, the redistribution of heat by the ocean circulation dominates \r\nspatial patterns of temperature change (high confidence)"


id: a85eaf859351a9c70873cb344eeec0f0
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
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vector_id: 24016
content:
" At longer time \r\nscales, the spatial patterns are dominated by additional heat, primarily \r\nstored in water masses formed in the Southern Ocean, and by weaker \r\nwarming in the North Atlantic where heat redistribution caused by \r\nchanging circulation counteracts the additional heat input through the \r\nsurface (high confidence). {9.2.2, 9.2.4, 9.6.1, Cross-Chapter Box 9.1}"


id: 99c42da5924f4594effd648b570099db
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
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vector_id: 21715
content:
"The Arctic Ocean will likely become practically sea ice free1\r\nduring the seasonal sea ice minimum for the first time before \r\n2050 in all considered SSP scenarios. There is no tipping \r\npoint for this loss of Arctic summer sea ice (high confidence). \r\nThe practically ice-free state is projected to occur more often with higher \r\ngreenhouse gas concentrations, and it will become the new normal for \r\nhigh-emissions scenarios by the end of this century (high confidence). \r\nBased on observational evidence, Coupled Model Intercomparison \r\nProject Phase 6 (CMIP6) models and conceptual understanding, the \r\nsubstantial satellite-observed decrease of Arctic sea ice area over \r\nthe period 1979-2019 is well described as a linear function of global \r\nmean surface temperature, and thus of cumulative anthropogenic \r\ncarbon dioxide (CO2) emissions, with superimposed internal variability \r\n(high confidence)"


id: 56a2f8bdaa8a35e66daf472e9bea402c
created_at: 2023-03-16 14:02:04
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vector_id: 11109
content:
" According to both process understanding and \r\nCMIP6 simulations, a  practically sea ice-free state will likely be \r\nobserved some years before additional (post-2020) cumulative \r\nanthropogenic CO2 emissions reach 1000 GtCO2. {4.3.2, 9.3.1}"


id: 9bc3aa6bcd6a7f28fe9e927013d5931a
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
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index: climate_gpt_only_giec
vector_id: 22067
content:
"Both the Greenland Ice Sheet (virtually certain) and the \r\nAntarctic Ice Sheet (likely) will continue to lose mass \r\nthroughout this century under all considered SSP scenarios. \r\nThe related contribution to global mean sea level rise until \r\n2100 from the Greenland Ice Sheet will likely be 0.01 to \r\n0.10 m under SSP1-2.6, 0.04 to 0.13 m under SSP2-4.5 and \r\n0.09-0.18 m under SSP5-8.5, while the Antarctic Ice Sheet will \r\nlikely contribute 0.03 to 0.27 m under SSP1-2.6, 0.03 to 0.29 m \r\nunder SSP2-4.5, and 0.03 to 0.34 m under SSP5-8.5. The loss of \r\nice from Greenland will become increasingly dominated by surface \r\nmelt, as marine margins retreat and the ocean-forced dynamic \r\nresponse of ice-sheet margins diminishes (high confidence). In the \r\nAntarctic, dynamic losses driven by ocean warming and ice-shelf \r\ndisintegration will likely continue to outpace increasing snowfall this \r\ncentury  (medium confidence)"


id: 830b64041679546549f31b0dc998547b
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 18145
content:
" Beyond 2100, total mass loss from \r\nboth ice sheets will be greater under high-emissions scenarios than \r\nunder low-emissions scenarios (high confidence). The assessed likely\r\nranges consider those ice-sheet processes in whose representation \r\nin current models we have at least medium confidence, including \r\nsurface mass balance and grounding-line retreat in the absence of \r\ninstabilities. Under high-emissions scenarios, poorly understood \r\nprocesses related to marine ice sheet instability and marine ice cliff \r\ninstability, characterized by deep uncertainty, have the potential to \r\nstrongly increase Antarctic mass loss on century to multi-century time \r\nscales. {9.4.1, 9.4.2, 9.6.3, Box 9.3, Box 9.4}"


id: 34ac1faf9e427025a46e3a90dd8556de
created_at: 2023-03-16 14:02:04
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vector_id: 5689
content:
"Regional sea level change has been the main driver of changes \r\nin extreme still water levels across the quasi-global tide gauge \r\nnetwork over the 20th century (high confidence) and will be \r\nthe main driver of a  substantial increase in the frequency \r\nof extreme still water levels over the next century (medium \r\nconfidence). Observations show that high-tide flooding events that \r\noccurred five times per year during the period 1960-1980 occurred, \r\non average, more than eight times per year during the period \r\n1995-2014 (high confidence). Under the assumption that other \r\ncontributors to extreme sea levels remain constant (e.g., stationary \r\ntides, storm-surge, and wave climate), extreme sea levels that \r\noccurred once per century in the recent past will occur annually or \r\nmore frequently at about 19-31% of tide gauges by 2050 and at \r\nabout 60% (SSP1-2.6) to 82% (SSP5-8.5) of tide gauges by 2100 \r\n(medium confidence)"


id: b87f88a5fb7f9f2cc0fd04ac8cc654b8
created_at: 2023-03-16 14:02:04
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vector_id: 26556
content:
" In total, such extreme sea levels will occur \r\nabout 20 to 30 times more frequently by 2050 and 160 to 530 times \r\nmore frequently by 2100 compared to the recent past, as inferred \r\nfrom the median amplification factors for SSP1-2.6, SSP2-4.5, and \r\nSSP5-8.5 (medium confidence). Over the 21st century, the majority \r\nof coastal locations will experience a  median projected regional \r\nsea level rise within +-20% of the median projected GMSL change \r\n(medium confidence). {9.6.3, 9.6.4}"


id: 9df6441adaaf5fee5bb91b20c0c00eb
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
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index: climate_gpt_only_giec
vector_id: 22372
content:
" Committed \r\nGMSL rise over 2000 years will be about 12 to 16 m with 4degC of \r\npeak warming and 19 to 22 m with 5degC of peak warming (medium \r\nagreement, limited evidence). {9.3.1, 9.4.1, 9.4.2, 9.5.1, 9.5.2, \r\n9.5.3, 9.6.3}"


id: efc1563727dd0552510ec5c844fe3ed2
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
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vector_id: 35227
content:
"At sustained warming levels between 1.5degC and 2degC, the Arctic \r\nOcean will become practically sea ice-free in September in some \r\nyears (medium confidence); the ice sheets will continue to lose mass \r\n(high confidence), but will not fully disintegrate on time scales of \r\nmultiple centuries (medium confidence); there is limited evidence\r\nthat the Greenland and West Antarctic ice sheets will be lost almost \r\ncompletely and irreversibly over multiple millennia; about 50 to 60% \r\nof current glacier mass excluding the two ice sheets and the glaciers \r\nperipheral to the Antarctic Ice Sheet will remain, predominantly in \r\nthe polar regions (low confidence); Northern Hemisphere spring \r\nsnow cover extent will decrease by up to 20% relative to 1995-2014 \r\n(medium confidence); the permafrost volume in the top 3 m will \r\ndecrease by up to 50% relative to 1995-2014 (medium confidence)"


id: 819b60141f5240a114c734290c3b60aa
created_at: 2023-03-16 14:02:04
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vector_id: 17923
content:
" \r\nCommitted GMSL rise over 2000 years will be about 2 to 6 m \r\nwith 2degC of peak warming (medium agreement, limited evidence). \r\n{9.3.1, 9.4.1, 9.4.2, 9.5.1, 9.5.2, 9.5.3, 9.6.3}"


id: d578fdd09de9f7ecb06ae67dd7ac782d
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
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index: climate_gpt_only_giec
vector_id: 31108
content:
"Changes in Earth gravity, Earth rotation and viscoelastic solid Earth deformation (GRD) - result from the redistribution of \r\nmass between terrestrial ice and water reservoirs and the ocean. Contemporary terrestrial mass loss leads to elastic solid Earth uplift \r\nand a nearby RSL fall. (For a single source of terrestrial mass loss, this is within about 2000 km; for multiple sources, the distance \r\ndepends on the interaction of the different RSL patterns.) Farther away (around more than 7000 km for a single source of terrestrial \r\nmass loss), RSL rises more than the global average, due to first-order gravitational effects. Earth deformation associated with adding \r\nwater to the ocean and a shift of the Earth's rotation axis towards the source of terrestrial mass loss leads to second-order effects \r\nthat increase spatial variability of the pattern globally"


id: 60a7521efb9c73750c162c2e0ee76bad
created_at: 2023-03-16 14:02:04
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vector_id: 12779
content:
" GRD effects due to the redistribution of ocean water within the ocean itself are \r\nreferred to as self-attraction and loading effects. There is high confidence in the understanding of GRD processes."


id: 24f904c610e7bd03b048556b8f077f3e
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
content_type: text
index: climate_gpt_only_giec
vector_id: 3259
content:
"Extreme sea level is an exceptionally low or high local sea surface height arising from combined short-term phenomena (e.g., storm \r\nsurges, tides and waves). RSL changes affect extreme sea levels directly by shifting the mean water levels, and indirectly by modulating \r\nthe depth for propagation of tides, waves and/or surges. Extreme sea levels can be influenced by changes in the frequency, tracks, \r\nor strength of weather systems, or anthropogenic changes such as dredging. Extreme still water level refers to the combined \r\ncontribution of RSL change, tides and storm surges. Wind-generated waves also contribute to coastal sea level. Extreme total water \r\nlevel is the extreme still water level plus wave setup (time-mean sea level elevation due to wave energy dissipation). When considering \r\ncoastal impacts, swash (vertical displacement up the shore-face induced by individual waves) is also important and included in Extreme \r\ncoastal water level"


id: 27799f1f9849e4b97e895849217fdbbe
created_at: 2023-03-16 14:02:04
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content:
" There is low to medium confidence in the understanding of extreme sea level processes (Sections 9.6.4 and 12.4)."


id: 2d0c3ae10cd740b1995acdede119af48
created_at: 2023-03-16 14:02:04
updated_at: 2023-03-16 14:02:04
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content:
"Figure 9.3 | Sea surface temperature (SST) and its changes with time. (a) Time series of global mean SST anomaly relative to 1950-1980 climatology. Shown \r\nare paleoclimate reconstructions and PMIP models, observational reanalyses (HadISST) and multi-model means from the Coupled Model Intercomparison Project (CMIP) \r\nhistorical simulations, CMIP projections, and HighResMIP experiment. (b) Map of observed SST (1995-2014 climatology HadISST). (c) Historical SST changes from observations. \r\n(d) CMIP 2005-2100 SST change rate. (e) Bias of CMIP. (f) CMIP change rate. (g) 2005-2050 change rate for SSP5-8.5 for the CMIP ensemble. (h) Bias of HighResMIP (bottom \r\nleft) over 1995-2014. (i) HighResMIP change rate for 1950-2014. (j) 2005-2050 change rate for SSP5-8.5 for the HighResMIP ensemble. No overlay indicates regions with \r\nhigh model agreement, where >=80% of models agree on sign of change"


id: 9ae7067157a6366d1add5c4027d103d3
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content:
" Diagonal lines indicate regions with low model agreement, where <80% of models agree on sign of \r\nchange (see Cross-Chapter Box Atlas.1 for more information). Further details on data sources and processing are available in the chapter data table (Table 9.SM.9)."


id: dce6b162696b138ca5e5caafb7b8471e
created_at: 2023-03-16 14:02:04
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vector_id: 32251
content:
"It is  virtually certain  that SST will continue to increase in the \r\n21st  century, at a  rate depending on future emissions scenarios. \r\nThe  future global mean SST increase projected by CMIP6 models \r\nfor the period 1995-2014 to 2081-2100 is 0.86 [5-95% range: \r\n0.43-1.47] degC under SSP1-2.6, 1.51 [1.02 to 2.19] degC under SSP2-4.5, \r\n2.19 [1.56 to 3.30] degC under SSP3-7.0, and 2.89 [2.01 to 4.07] degC under \r\nSSP5-8.5 (Figure  9.3). While under SSP1-2.6, the CMIP6 ensemble \r\nconsistently projects that it is very likely at least 83% of the world \r\nocean surface will have warmed by 2100, and under SSP5-8.5, at least \r\n98% of the world ocean surface will have warmed. The spatial pattern \r\nof future change is consistent with observed SST change over the \r\n20th century, though with notable regional differences (Figure 9.3)"


id: 645d4562bd54fb48d4df097dda4d17da
created_at: 2023-03-16 14:02:04
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content:
" \r\nLong-term change in SST patterns is important for regional impacts \r\nbut also affects radiative feedbacks, and therefore long-term change \r\nin climate sensitivity (Section 7.4.4.3). In the Southern Ocean, CMIP6 \r\nmodels project that SSTs will eventually consistently increase in the \r\n21st  century, at a  rate dependent on future scenarios (Figure  9.3 \r\nand Section 9.2.3.2; Bracegirdle et al., 2020). Yet, there is only low \r\nconfidence that this Southern Ocean warming will emerge by the \r\nend of the century (Section 7.4.4.1), due to the inconsistent historical \r\nand near-term simulations and observations over the 20th  century \r\n(Figure  9.3). Furthermore, the equilibrium SST pattern from proxy \r\nrecords or simulated by climate models under CO2 forcing stand in \r\ncontrast with the cooling trends in the Southern Ocean observed over \r\nthe past decades (Section 7.4.4.1.2)"


id: 3e07ff490ad09e33e1f7118aa68b6501
created_at: 2023-03-16 14:02:05
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vector_id: 7118
content:
" Similarly, the SST change pattern \r\nobserved in the tropical Pacific Ocean will transition on centennial \r\ntime scales to a mean pattern resembling the El Nino pattern (medium \r\nconfidence) (Annex IV). However, it is difficult to delineate a climate \r\nchange trend ressembling an El Nino pattern and El Nino variability \r\n(Wittenberg, 2009; Collins et  al., 2010) without large ensembles \r\n(Kay et  al., 2015). Several Pliocene SST reconstructions indicate \r\nenhanced warming in the centre of the eastern Pacific equatorial \r\ncold tongue upwelling region, consistent with reconstruction of \r\nenhanced subsurface warming and enhanced warming in coastal \r\nupwelling regions (Section  7.4.4.2.2). The North Atlantic subpolar \r\ngyre is projected to continue to warm more slowly than surrounding \r\nregions (Suo et  al., 2017), as the Gulf Stream concurrently warms \r\nrapidly (Figure 9.3; Cheng et al"


id: bd3ae9c43853ee69f26ecdeaaade0d45
created_at: 2023-03-16 14:02:05
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vector_id: 27336
content:
", 2013) and the Atlantic Meridional \r\nOverturning Circulation further declines under greenhouse gas forcing, \r\nalthough models disagree about the rate of change (Figure 9.3 and \r\nSection 9.2.3.1). In summary, CMIP6 models show a future pattern of \r\nSST change comparable to historical trends with intensity depending "


id: 3f22bdecff8de6d451dd93a225b756a1
created_at: 2023-03-16 14:02:05
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vector_id: 7322
content:
"In AR5 (Flato et  al., 2013), a  marginal improvement was noted in \r\nCoupled Model Intercomparison Project Phase 5 (CMIP5) climate \r\nmodel SST biases compared to Phase 3 (CMIP3) models in AR4, with \r\na reduction in the magnitude of biases. The AR5 noted that, in several \r\nregions, large SST biases are symptomatic of errors in the representation \r\nof important processes, such as dynamics in the equatorial Pacific and \r\nNorth Atlantic, and Southern Ocean. Common regional biases in SST \r\nor historical SST trends are not exclusively linked to the representation \r\nof the ocean (high confidence), but can have multiple causes, \r\nincluding: errors in the representation of long-term historical trends \r\nin equatorial winds (Section 9.2.1.2); misrepresentation of the forced \r\nequatorial ocean response (Karnauskas et al., 2012; Kohyama et al., \r\n2017; Coats and Karnauskas, 2018); thermocline depth errors (Linz \r\net  al"


id: a72bf7f79dcca6036bc6ffcfedb1a97b
created_at: 2023-03-16 14:02:05
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vector_id: 23810
content:
", 2014); errors in atmospheric model cloud-related shortwave \r\nradiation (Hyder et al., 2018); biases in ocean circulation variability (C. \r\nWang et al., 2014); and deficiencies in upper ocean (Q. Li et al., 2019) \r\nand atmospheric (Bates et al., 2012) boundary layer parametrizations. \r\nIn CMIP6, the mid-latitude biases in the Northern Hemisphere are \r\nimproved in the  multi-model mean, and the inter-model standard \r\ndeviation of the zonal mean SST error is significantly decreased \r\nin the northern Hemisphere south of 50degN compared to CMIP5, \r\nthough biases in equatorial regions remain essentially unchanged \r\n(Section 3.5.1.1 and Figures 3.23, 3.24 and 9.3). Some long-standing \r\nocean model biases have been reduced through increases in model \r\nresolution in CMIP6 (Bock et al., 2020) and improved parametrizations \r\n(Fox-Kemper et al., 2011; Q. Li et al., 2016; Qiao et al., 2016; Reichl \r\nand Hallberg, 2018)"


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vector_id: 5826
content:
" The High Resolution Model Intercomparison \r\nProject (HighResMIP) ensemble (Figure 9.3) has smaller cold biases \r\nin the North Atlantic and the tropical Pacific, and smaller warm biases \r\nin the upwelling regions off the western coasts of Africa, North and \r\nSouth America  (Roberts et  al., 2018, 2019; Caldwell et  al., 2019; \r\nDocquier et al., 2019). In summary, CMIP6 models show persistent \r\nregional biases in representing the climatological SST state (very high \r\nconfidence), but higher resolution reduces some biases, particularly \r\nin the North Atlantic and eastern boundary upwelling systems \r\n(Figure 9.3; high confidence).\r\nThe CMIP6 models represent the observed trends in SST patterns with \r\ngreater fidelity than CMIP5, with the ocean area that is inconsistent \r\nwith the observed trends decreasing by about three  quarters from \r\nCMIP5 to CMIP6 (Olonscheck et  al., 2020). In some regions, the "


id: 39186178254e562c773b38b634fcaf67
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 6356
content:
"The SROCC (Abram et al., 2019) and AR5 (Rhein et al., 2013) assessed \r\nthat observations of air-sea fluxes had not yet reached the density \r\nor accuracy to directly detect trends beyond the noise. New evidence \r\nsince SROCC confirms that direct heat and freshwater flux trends \r\nhave not emerged yet as spatial (Figure 9.4), annual (Yu, 2019), and \r\ndecadal (Zanna et al., 2019) variability overwhelm detection. Since \r\nAR5, comprehensive comparisons (Bentamy et al., 2017; Valdivieso \r\net al., 2017; Yu et al., 2017) have used updated and new surface \r\nflux products to improve surface flux uncertainty estimates, and \r\nthese comparisons note that implied global energy imbalances \r\noften exceed the observed ocean warming. Flux estimates using \r\ntop of atmosphere observations and atmospheric fluxes from \r\nreanalysis have improved over past products (Trenberth and Fasullo, \r\n2018) but require consistency adjustments (Trenberth et al., 2019) \r\nas the energy budget is not closed"


id: 35295cfcc8d74aa9f6915e0e88d79a93
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
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index: climate_gpt_only_giec
vector_id: 5774
content:
" Adjustments are needed for \r\nall flux products, and they remain less accurate than direct ocean \r\nheat content change measurements (Cheng et  al., 2017). Some \r\nregional changes are likely robust in both satellite observations and \r\nprojections (Figure 9.4). Recent satellite-based surface flux products \r\nwith improved retrieval algorithms and new satellites, for example, \r\nJ-OFURO3 (Tomita et al., 2019) and OAFlux-HR (Yu, 2019), provide "


id: dbb46ae2c55b913b070cea063ff312c0
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 32084
content:
"Air-sea flux biases result from common causes in most models, and \r\nmany are the same as during AR5 (Rhein et al., 2013). Important \r\ncurrents (e.g., Gulf Stream, Kuroshio, Antarctic Circum-polar Current \r\npatterns) are often found in erroneous locations in models, affecting \r\nSST and flux signatures (Bates et al., 2012; Beadling et al., 2020; J.-\r\nL.F. Li et al., 2020), but their locations are improved in high-resolution \r\nocean models (Chassignet et  al., 2017, 2020; Hewitt et  al., 2020), \r\nand high-resolution coupled models reduce the mean air-sea flux \r\nbiases (Delworth et  al., 2012; Sakamoto et  al., 2012; Small et  al., \r\n2014; Haarsma et  al., 2016; Caldwell et  al., 2019; L.C Jackson \r\net al., 2020). Oceanic variability stems either from internal chaotic \r\nvariability or atmospheric forcing (Hasselmann, 1976; Serazin \r\net  al., 2016, 2017)"


id: 512a585e8637763cb87eeab7e448f801
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 10227
content:
" Large-scale variability in the ocean tends to \r\nfollow atmospheric forcing in low-resolution models, while in high\u0002resolution coupled models ocean variability drives atmospheric \r\nvariability on small scales (Bishop et al., 2017; Small et al., 2019), \r\nallowing these high-resolution models to mimic the coupling with \r\nclouds, precipitation, and atmospheric and oceanic boundary layers \r\napparent in observations (Chelton and Xie, 2010; Frenger et  al., \r\n2013). Even coarse-resolution models, such as the ocean and sea ice \r\ncomponents used in CMIP6, show significant sensitivity in the mean \r\nand variability of SST and sea ice to modest changes in flux forcing \r\n(Tsujino et al., 2020). Finally, there is still considerable disagreement \r\nbetween different parametrizations of air-sea fluxes used in models \r\nand strong scatter in direct observations (Renault et  al., 2016; \r\nBrodeau et al., 2017)"


id: 1e59a50339862498e991d78dd62ff659
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 2215
content:
" In summary, there is very high confidence that \r\nair-sea heat flux and stress biases are reduced in coupled models \r\nwith high ocean resolution over coarse-resolution models, although \r\nthe effect on trends remain unclear."


id: ee50ab96a9236e9514ad5d67ae84498d
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 34965
content:
"a complete suite of turbulent fluxes including heat, moisture, and \r\nmomentum. When combined with satellite-based surface radiation \r\nfrom Clouds and the Earth's Radiant Energy System (CERES) Energy \r\nBalanced and Filled (EBAF; Kato et al., 2018) and precipitation from \r\nGlobal Precipitation Climatology Project (GPCP; Adler et al., 2003), \r\nfull ocean-surface forcing is available since 1987 (Figure 9.4). These \r\nproducts agree with sparse buoy and ship observations within \r\n30 W m-2 (Bentamy et al., 2017; Cronin et al., 2019). While patterns \r\nagree between models and satellites in net fluxes (Figure  9.4), \r\nthe trend magnitudes are substantially weaker in models. The \r\nfluxes tending to warm the North Atlantic and Southern Ocean \r\nare consistent with the largest changes observed in the surface \r\nproperties and water masses (Sections 9.2.1.1, 9.2.2.1 and 9.2.2.3)"


id: b09d89b3bdf5b61fe976f561361c642e
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 25308
content:
" \r\nThe observed trend toward a  saltier Atlantic Ocean and a  fresher \r\nIndian Ocean, as well as trends in evaporation minus precipitation \r\n(E-P) patterns in the equatorial Pacific (see also Section  8.3.1) \r\nenhance the present mean pattern of wetting and drying. Elsewhere \r\npatterns are less clear, with only partial, large-scale agreement \r\nwith the 'wet gets wetter' simplification (Sections 3.3.2.3, 4.4.1 \r\nand 4.5.1). In summary, globally integrated and large-scale fluxes \r\nare more reliably inferred from heat content and salinity change, \r\nwhile regional trends are rarely robust in observations; where they \r\nare robust, they tend to be underestimated or in disagreement \r\nin models (very high confidence)."


id: ebcf7f30c9237a019a1bc0e4d4e07dc3
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 34606
content:
"There is low confidence in long-term wind stress trends in most \r\nregions, but a few locations have likely trends over the scatterometer \r\nera and in projections, as shown in Figure  9.4 (Desbiolles et  al., \r\n2017; Young and Ribal, 2019; Yu, 2019). The AR5 (Rhein et al., 2013) \r\nassessed with medium confidence that zonal wind stress over the \r\nSouthern Ocean increased from the early 1980s to the 1990s (medium \r\nconfidence) (Figure  9.4). Over 1995-2014, the zonal wind  stress \r\nover the Southern Ocean continued to increase, westerly winds in \r\nthe North Pacific and North Atlantic weakened, while the easterly \r\nequatorial Pacific winds of the Walker circulation strengthened \r\n(Figure  9.4). In historical simulations, CMIP5 models projected \r\nannular modes (Annex IV) to move poleward and strengthen in both \r\nhemispheres (Yang et al., 2016), while in CMIP6 models westerlies \r\nonly strengthen over the Southern Ocean, with a weaker trend than \r\nrecently observed (Figure 9.4 and Sections 4"


id: 9e311576d78fc5cdf6cf3adb79190ef3
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 22411
content:
"5.1 and 4.5.3). In the \r\ntropical Pacific Ocean, a  weakening trend in easterly winds and \r\nWalker circulation in the 20th century has been inferred based on \r\nobserved sea level pressure data (Vecchi et  al., 2006; Vecchi and \r\nSoden, 2007) and coral proxies (Carilli et al., 2014) and is projected \r\nto continue by CMIP6 models (Figure  9.4). Yet, over 1995-2014 \r\nobserved winds have strengthened (Figure  9.4). The observed \r\nstrengthening may have been influenced by a combination of factors \r\n(Section 7.4.4.2.1), but there is low confidence in the attribution of \r\nthis signal to anthropogenic warming (Section 3.3.3.1) and medium \r\nconfidence  that it reflects internal variability (Section  8.3.2.3). \r\nNear-term projected changes over the Southern Ocean result from \r\nozone recovery and greenhouse gases (Sections 4.3.3 and 4.4.3)"


id: 9a88137c41d7d52b1cd88f797903db4e
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 21843
content:
" \r\nOverall, there is only low confidence in observed and projected wind \r\nstress trends in most regions because trends in oceanic wind stresses \r\nduring the satellite era have not emerged or are inconsistent with \r\nhistorical simulated changes."


id: d338a4d44793465713414ef1f3f61e9a
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 30758
content:
"The density difference from surface to deep ocean is the upper-ocean \r\nstratification. The AR5 (Rhein et  al., 2013) assessed that it is very \r\nlikely that the thermal contribution to stratification over the fixed \r\n0-200 m layer increased by about 1% per decade between 1971 and \r\n2010 (based on linear trend consistently across reports). The SROCC \r\n(Bindoff et  al., 2019) found it very likely that density stratification \r\nincreased by 0.46-0.51% per decade between 60degS and 60degN from \r\n1970 to 2017). New published estimates based on a  variety of \r\ndifferent interpolated observations show that SROCC assessed rate \r\nis too low, even using the same data and methods (Li et al., 2020). \r\nThe 1960-2018 stratification increase is estimated at 1.2  +-  0.1% \r\nper decade from the IAP dataset, 1.2 +- 0.4% per decade from the \r\nIshii product, 0.7 +- 0.5% per decade from the EN4 dataset, 0.9 \r\n+- 0.5% per decade from ORAS4, and 1.2 +-0"


id: ed5b4cc320e0c6c7eb37a10c8b580601
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 34817
content:
"3% per decade from the \r\nNational Centers for Environmental Information (NCEI) dataset (G. Li \r\net al., 2020). The improved methodology for computing stratification \r\nchange on individual profiles before gridding yields a global annual \r\nmean increase of 0-200 m stratification change of 0.8 +- 0.2% per \r\ndecade between 1960 and 2018 (Yamaguchi and Suga, 2019) and \r\na  global summer mean increase of 0-200 m stratification change \r\nof 1.3 +- 0.3% per decade between 1970 and 2018 (Sallee et  al., \r\n2021) is of a similar magnitude to the long-term trend (Yamaguchi \r\nand Suga, 2019; G. Li et  al., 2020). In summary, there is limited \r\nevidence that focusing on changes over a fixed depth range might "


id: 7f19961ac425e21cb47504b7c15c0922
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 17546
content:
"The SROCC assessed that upper-ocean stratification will continue \r\nto increase in the 21st century under increased radiative forcing (high \r\nconfidence), due to increased surface temperature and high-latitude \r\nsurface freshening (Bindoff et  al., 2019). New climate model \r\nsimulations concur with SROCC assessment of a future increase of \r\nthe 0-200 m stratification under increased radiative forcing in all \r\nregions of the world ocean (Kwiatkowski et al., 2020). In addition, \r\nCMIP6 climate models project a  shallowing of the mixed-layer \r\nin summer and winter by the end of the century under increased \r\nradiative forcing (Figure  9.5; Kwiatkowski et  al., 2020), with the \r\nexception of the Arctic showing deepening of the mixed layer as \r\na result of sea ice retreat (Figure 9.5; Lique et al., 2018)"


id: fef960e0dd37cee18436e70cbc735697
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 37663
content:
" The regions \r\nof largest shallowing are associated with the deepest climatological \r\nmixed layer, in both winter and summer, particularly affecting the \r\nNorth Atlantic and the Southern Ocean basins (Figure  9.5). While \r\nCMIP6 models tend to project shallowing mixed layers under \r\na warming climate, except at high latitudes (Figure 9.5; Lique et al., \r\n2018; Kwiatkowski et al., 2020), a deepening in the summer mixed\u0002layer depth by intensification of the surface winds and storms may \r\nexplain inconsistency among models in many regions (Figure  9.5; \r\nYoung and Ribal, 2019), although model mixed-layer biases are \r\nlarge in the summer in the Southern Ocean (Belcher et  al., 2012; \r\nSallee et al., 2013a; Q. Li et al., 2016; Tsujino et al., 2020). Lack of \r\nobserved ocean turbulence and climate model limitations do not allow \r\nfor direct assessment of ocean surface turbulence change and limit \r\nconfidence in past and future mixed-layer change"


id: 51ee686f7603f93f6cbbcdf8a306a856
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 10349
content:
" Understanding \r\nof turbulent processes, their representation in ocean and climate \r\nmodels, and their effect on mixed-layer biases have been an active \r\nand rapidly evolving topic of research since AR5 (Buckingham et al., \r\n2019; Q. Li  et  al., 2019). Small-scale mixed-layer processes are "


id: 31ae8fa989d4286d26e42c155a2837cb
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 5225
content:
"While AR5 and SROCC did not assess change in mixed-layer depth, the \r\nreported changes in stratification can modulate the surface mixed\u0002layer depth, which is set by a balance between fluxes and dynamical \r\nmixing (winds, tides, waves, convection) acting against the background \r\nstratification and restratification processes (solar and dynamical). \r\nDespite the large stratification increase observed at a  global scale, \r\nnew evidence shows that summer mixed-layer depth deepened \r\nconsistently over the globe at a rate of 2.9 +- 0.5% per decade from \r\n1970 to 2018, with the largest deepening observed in the Southern \r\nOcean, corresponding to overall deepening from 3-15 m per decade \r\ndepending on region (Somavilla et al., 2017; Sallee et al., 2021). While \r\nthe shorter observational record in winter (compared to summer) \r\ndoes not allow global winter mixed-layer trends to be reliably \r\nassessed (Sallee et al"


id: 972280ac9edfa38904c1c07532056cf8
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 21295
content:
", 2021), winter mixed-layer depths deepening \r\nat rates of 10  m per decade have been reported at individual \r\nlong-term mid-latitude monitoring sites (Somavilla et  al., 2017). \r\nProjections agree that shoaling of mixed-layer depth is expected in \r\nthe 21st  century, but only for strong emissions scenarios, and only \r\nin some regions (Figure 9.5). In summary, there is limited observational\r\nevidence that the mixed layer is globally deepening, while models \r\nshow no emergence of a trend until later in the 21st century under \r\nstrong emissions."


id: 437a4fb9d69c487646f2275eac956bf8
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 8026
content:
"The SROCC established that MHWs have occurred in all basins over the last decades. Additional evidence documenting widespread \r\noccurrence of marine heat waves in all basins and marginal seas continues to accumulate (Y. Li et al., 2019; Yao et al., 2020). The SROCC \r\nhighlighted the role of large-scale climate modes of variability in amplifying or suppressing MHW occurrences, which has since \r\nbeen further corroborated, increasing confidence in climate modes as important drivers of MHWs (Holbrook et al., 2019; Sen Gupta \r\net al., 2020). More generally, understanding of processes leading to MHWs has increased since SROCC, including air-sea heat flux \r\n(Section 9.2.1.2), increased horizontal heat advection, shoaling of the mixed-layer and suppressed mixing processes (Section 9.2.1.3), \r\nreduced coastal upwelling and Ekman pumping (Section 9.2.3.5), changes in eddy activities and planetary waves, and the re-emergence \r\nof warm subsurface anomalies (Holbrook et al., 2020; Sen Gupta et al"


id: b064f8e6be32a2ede107200b5a398a3a
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
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index: climate_gpt_only_giec
vector_id: 25275
content:
"The SROCC reported with high confidence that MHWs - defined as days exceeding the 99th percentile in sea surface temperature \r\n(SST) from 1982 to 2016 - have very likely doubled in frequency between 1982 and 2016. Additional observation-based evidence \r\nand acquisition of longer observation time series since SROCC have confirmed and expanded on this assessment: since the 1980s \r\nMHWs have also become more intense and longer (Frolicher and Laufkotter, 2018; Smale et al., 2019; Laufkotter et al., 2020). Satellite \r\nobservations and reanalyses of SST show an increase in intensity of 0.04degC per decade from 1982 to 2016, an increase in spatial \r\nextent of 19% per decade from 1982 to 2016, and an increase in annual MHW days of 54% between the 1987-2016 period compared \r\nto 1925-1954 (Frolicher et al., 2018; Oliver, 2019). The SROCC assessed that 84-90% of all MHWs that occurred between 2006 and \r\n2015 are very likely caused by anthropogenic warming"


id: cb6411f65a2ebc16289d6bfb4becaa68
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 29552
content:
" There is new evidence since SROCC that the frequency of the most impactful \r\nmarine heatwaves over the last few decades has increased more than 20-fold because of anthropogenic global warming (Laufkotter \r\net al., 2020). In summary, there is high confidence that MHWs have increased in frequency over the 20th century, with an approximate \r\ndoubling from 1982 to 2016, and medium confidence that they have become more intense and longer since the 1980s."


id: 2b9a6d0d294e6cd5e1c541bfac7f35ee
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 4296
content:
"Consistent with SROCC, future MHWs are defined with reference to the historical climate conditions. The SROCC assessed that MHWs will \r\nvery likely further increase in frequency, duration, spatial extent and intensity under future global warming in the 21st century. The CMIP6 \r\nprojections allow us to confirm this assessment and quantify future change based on global mean probability ratio change (Box 9.2, \r\nFigure 1): they project MHWs will become four times (5-95% range: 2-9 times] more frequent in 2081-2100 compared to 1995-2014 \r\nunder SSP1-2.6, or eight times (3-15 times) more frequent under SSP5-8.5. The SROCC highlighted that future change of MHWs will \r\nnot be globally uniform, with the largest changes in the frequency of marine heatwaves being projected to occur in the western tropical \r\nPacific and the Arctic Ocean (medium confidence). New evidence from the latest generation of climate models confirms and complements \r\nSROCC assessment (Box 9.2, Figure 1)"


id: 6aaee276ac4ee901b3acf10b26d188b1
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
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index: climate_gpt_only_giec
vector_id: 14397
content:
" Moderate increases are projected for mid-latitudes, and only small increases are projected for \r\nthe Southern Ocean (medium confidence) (Hayashida et al., 2020). While under the SSP5-8.5 scenario, permanent MHWs (more than \r\n360 days per year) are projected to occur in the 21st century in parts of the tropical ocean, the Arctic Ocean and around 45degS, the \r\noccurrence of such permanent MHWs can largely be avoided under the SSP1-2.6 scenario (Frolicher et al., 2018; Oliver et al., 2019; Plecha \r\nand Soares, 2020). The resolution of current climate models (CMIP5 and CMIP6) capture the broad features of MHWs, but they may have \r\na bias towards weaker and longer MHWs in the historical period (medium confidence) (Frolicher et al., 2018; Pilo et al., 2019; Plecha and \r\nSoares, 2020) and greater intensification in western boundary current regions (Hayashida et al., 2020)."


id: 184b224c139bd52318b1e17ecb4ca9aa
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 1315
content:
"Ocean warming is not uniform with depth. The AR5 (Rhein  et  al., \r\n2013) assessed that, since 1971, ocean warming was virtually \r\ncertain for the upper 700 m and likely for the 700-2000  m layer. \r\nBoth AR5 and SROCC (Bindoff et al., 2019) assessed that the deep \r\nocean below 2000  m had likely warmed since 1992, especially \r\nin the Southern Ocean. Section  2.3.3.1 provides an updated \r\nassessment of ocean temperature change for different depth layers, \r\ntime periods and observation-based reconstructions (Table  2.7). \r\nSection 2.3.3.1 confirms the previous assessment that it is virtually \r\ncertain that the upper ocean (0-700  m) has warmed since 1971, \r\nthat ocean warming at intermediate depths (700-2000 m) is very \r\nlikely since 2006, and that it is likely that ocean warming has \r\noccurred below 2000 m since 1992. Section 3.5.1"


id: 81aa86ebf3d0bc70d5cf6ea912d69b2b
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
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index: climate_gpt_only_giec
vector_id: 17931
content:
"3 assessed that it \r\nis extremely likely that human influence was the main driver of the \r\nocean heat content increase observed since the 1970s, which extends \r\ninto the deeper ocean (very high confidence), and shows that biases \r\nin potential temperature have a complex pattern (Figure 3.25). In the \r\npresent section, we assess the regional patterns of this warming and \r\nassociated processes driving regional ocean warming."


id: 7c1cfaf3634a46ceb329ef443ea4ba69
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 17127
content:
"Ocean warming - that is, increasing ocean heat content (OHC) - is \r\nan important aspect of energy on Earth: SROCC (Bindoff et al., 2019) \r\nreported that there is high confidence that ocean warming during \r\n1971-2010 dominated the increase in the Earth's energy inventory, \r\nwhich is confirmed by the Box 7.2 assessment that the ocean has \r\nstored 91% of the total energy gained from 1971 to 2018. As reported \r\nin Sections 2.3.3.1, 3.5.1.3 and 7.2.2.2, Box 7.2 and Cross-Chapter \r\nBox 9.1, confidence in the assessment of global OHC change since \r\n1971 is strengthened compared to previous reports, and extended \r\nbackward to include likely warming since 1871. Table  7.1 updates \r\nthe estimates of total ocean heat gains from 1971 to 2018, 1993 to \r\n2018 and 2006 to 2018. Section 3.5.1.3 assesses that it is extremely \r\nlikely that anthropogenic forcing was the main driver of the OHC \r\nincrease over the historical period. Section  2.3.3"


id: 8bca15fb469e57e019231b5f07934784
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
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index: climate_gpt_only_giec
vector_id: 19513
content:
"1 reports that \r\ncurrent multi-decadal to centennial rates of OHC gain are greater \r\nthan at any point since the last deglaciation (medium confidence)."


id: 335a4b591a2053b2db15734bb81b1d94
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
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vector_id: 5499
content:
"Below 2000 m, direct observations of full-depth ocean temperature \r\nchange are limited to ship-based, high-quality deep-ocean \r\ntemperature measurements. Such high-quality full-depth ship-based \r\nsampling has improved from 1990 to the present due to the World \r\nOcean Circulation Experiment (WOCE) and the Global Ocean \r\nShip-based Hydrographic Investigations Program (GO-SHIP; Sloyan \r\net al., 2019). The SROCC (Bindoff et al., 2019) assessed that the likely\r\nwarming of the ocean since the 1990s below 2000 m is associated \r\nwith a marked regional pattern, with larger warming in the Southern \r\nOcean. In the deep North Atlantic, warming has reversed to cooling \r\nover the past decade, possibly due to internal variability fed by North \r\nAtlantic Deep Water (Section  9.2.2.3). Over the past decade, the \r\nwarming rate of Antarctic Bottom Water (AABW; Section 9.2.2.3) has \r\nbeen dependent on origin: slower from the Weddell Sea and faster \r\nfrom the Ross Sea and Adelie Land"


id: 869b97ab518a4c6f60622dab99aa5e67
created_at: 2023-03-16 14:02:05
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content:
" One new study (Purkey et al., \r\n2019) strengthens confidence in AABW warming: below 4000  m \r\na  monotonic, basin-wide, and multi-decadal temperature change \r\nis found in the southern Pacific basin, with larger warming rates \r\nnear the bottom water formation sites than further downstream. \r\nNew analysis of one model provides limited evidence that the sparse "


id: 1664b14fc9d4c0bb2bd1fb23c499a77a
created_at: 2023-03-16 14:02:05
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content:
"Different processes drive OHC patterns over a range of time scales. \r\nRecent literature has highlighted the role of ocean circulation \r\nvariability in driving OHC patterns by decomposing the global pattern \r\nof OHC change into a  combination of added heat due to climate \r\nchange taken up under fixed ocean circulation ('added heat'), and \r\nredistribution of heat associated with changing ocean currents \r\n('redistributed heat'; Gregory et al., 2016; Bronselaer and Zanna, 2020; \r\nCouldrey et al., 2021). Redistributed heat alters regional patterns of \r\nheat storage and carbon storage (Cross-Chapter Box 5.3; Bronselaer \r\nand Zanna, 2020; Todd et al., 2020; Couldrey et al., 2021) but does \r\nnot affect the global OHC. There is medium confidence that decadal \r\nvariability of the ocean circulation strengthened the rate of ocean \r\nwarming in the Southern Hemisphere compared to the Northern \r\nHemisphere in the decade from 2005 (Rathore et al., 2020; L. Wang \r\net al., 2021; Zika et al., 2021)"


id: e08d82428604bfba6042673ba7c3b902
created_at: 2023-03-16 14:02:05
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vector_id: 32828
content:
"pattern observed is predominantly due to heat redistribution with \r\nregions of both warming and cooling (Figure 9.6; Zika et al., 2021); \r\nhowever, extending analysis back to 1972 shows the importance of \r\nadded heat setting a large-scale warming pattern with mid-latitude \r\nmaxima consistent with subduction of water masses, particularly in \r\nSouthern Hemisphere Mode Waters (Section 9.2.2.3, and Figures 9.6 \r\nand 9.8; Bronselaer and Zanna, 2020). The longer the analysis \r\nwindow, the more added heat dominates over redistributed heat. \r\nThis  translates into more ocean area with statistically significant \r\nwarming trends and less area with statistically significant cooling \r\ntrends (Johnson and Lyman, 2020). The region where added heat \r\nis most compensated for by redistributed cooling is in the northern \r\nNorth Atlantic basin, where changes in the subpolar gyre circulation \r\nand Atlantic Meridional Overturning Circulation (AMOC) result in \r\ncooling (Section 9.2.3.1; Williams et al"


id: 7dc75f2b5ec0f230234fd70fea9dcc63
created_at: 2023-03-16 14:02:05
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content:
", 2015; Piecuch et al., 2017; \r\nZanna et al., 2019; Bronselaer and Zanna, 2020). In summary, and \r\nstrengthening SROCC assessment, ocean warming is not globally \r\nuniform due to patterns of uptake predominantly along known water \r\nmass pathways, and due to changing ocean circulation redistributing \r\nheat within the ocean (high confidence)."


id: 3f8da4ce87116398c79607759e1b48da
created_at: 2023-03-16 14:02:05
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content:
"(Muilwijk et al., 2018; Q. Wang et al., 2019; Tsubouchi et al., 2021), \r\nsimilar to SROCC assessment, and consistent with observed increase \r\nin OHC in the ice-free Arctic ocean (Mayer et al., 2019). It is estimated \r\nthat an increase of 0.021 PW of OHT occurred after 2001 into the \r\nArctic, which is sufficient to account for the recent OHC change in the \r\nnorthern seas (Tsubouchi et al., 2021). However, these trends cannot \r\nyet be attributed to anthropogenic forcing due to potential internal \r\nvariability (Muilwijk et al., 2018; Wang et al., 2019). New evidence \r\nstrengthens the case that El Nino-Southern Oscillation (ENSO) and the \r\nNorthern Annular Mode affect interannual OHT variability (Trenberth \r\net  al., 2019) and shows that a  slowing AMOC reduces northward \r\nOHT in the Atlantic at 26.5degN (Section 9.2.3.1 and Figure 9.8; Bryden \r\net al., 2020). Despite a decrease of AMOC northward heat (0.17 PW) \r\nand mass (2"


id: c2ed338dc2e29361a9e5f0985ce2ac5e
created_at: 2023-03-16 14:02:05
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vector_id: 28246
content:
"5 Sverdrup (Sv); 1 Sv = 109 kg s-1) transport, OHT has \r\nincreased toward the Arctic through increased upper northern North \r\nAtlantic temperatures and stronger wind-driven gyres (medium \r\nconfidence) (Section  9.2.3.4 and Figure  9.11; Singh et  al., 2017; \r\nOldenburg et al., 2018). In summary, OHT has increased toward the \r\nArctic in recent decades, which at least partially explains the recent \r\nOHC change in the Arctic (medium confidence)."


id: d30fc4b5d5d92af620392e725dfdb9e3
created_at: 2023-03-16 14:02:05
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vector_id: 30728
content:
"0-2000 m layer will increase from 2017 to 2100 by 0.900 +- 0.345 YJ \r\n(1 YJ = 1024 Joules) under RCP2.6 and 2.150 +- 0.540  YJ under \r\nRCP8.5. Updating SROCC estimates with CMIP6 projections gives \r\nheat content increases and 17-83% ranges in the 0-2000 m layer \r\nbetween 1995-2014 and 2081-2100 of 1.06 (0.80-1.31) YJ, \r\n1.35 (1.08-1.67) YJ, 1.62 (1.37-1.91) YJ, 1.89 (1.60-2.29) YJ under \r\nscenarios SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5, respectively \r\n(Figure 9.6 and Table 9.1). The two-layer model used here to calculate \r\nthermosteric sea level rise (9.SM.4) and tuned for AR6-assessed \r\nequilibrium climate sensitivity (ECS; Section  7.SM.2), provides \r\nconsistent 17-83% ranges of 1.18 (0.99-1.42) YJ, 1.56 (1.33-1.86) YJ, \r\n1.90 (1.63-2.21) YJ, 2.23  (1.92-2.64) YJ under scenarios SSP1-2.6, \r\nSSP2-4.5, SSP3-7.0, and SSP5-8.5, respectively (Table  9.1)"


id: ce2794a78e95fc544518c8f95241ff9c
created_at: 2023-03-16 14:02:05
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vector_id: 29950
content:
" Based \r\non CMIP6 models and the two-layer model, it is likely that, between \r\n1995-2014 and 2081-2100, OHC will increase two to four times the \r\namount of the 1971-2018 OHC increase under SSP1-2.6, and four \r\nto eight times that amount under SSP5-8.5. The CMIP6 models show \r\nthat OHC dependence on scenarios begins only after about 2040 \r\n(Figure 9.6)."


id: 12aeee1cd507dff5c53cd835e9bdc808
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
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vector_id: 439
content:
"CMIP5 and CMIP6 models simulate OHC changes that are consistent \r\nwith the updated observational and improved estimates of OHC \r\nover the period 1960 to 2018 (Figures 9.6, 9.7 and 9.8), and they \r\nreplicate the vertical partitioning of OHC change for the industrial \r\nera, although with a  tendency to underestimate OHC gain \r\nshallower than 2000 m and overestimate it deeper than 2000 m \r\n(Section 3.5.1.3). The AR5 (Flato et al., 2013) assessed that climate \r\nmodels transport heat downward more than the real ocean. Since \r\nAR5, studies have shown that increasing the horizontal resolution of \r\nocean models tends to increase agreement of vertical heat transport \r\nwith observations as the dependency on ad-hoc choices of eddy \r\nparametrizations is relaxed (Griffies et al., 2015; Chassignet et al., \r\n2020). The magnitude of the AMOC and Indonesian Throughflow \r\naffect future OHC change  - for example, through overestimated \r\nmodelled downward heat pumping (Kostov et al"


id: 2fbc0109218cabe0e1c384a6d4eca846
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
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vector_id: 4928
content:
", 2014) - and there \r\nare indications of greater model consistency in these transports at \r\nhigher resolution (Figure 9.10; Chassignet et al., 2020; L.C. Jackson \r\net al., 2020). Climate models tend to reproduce the observed added \r\nheat, but redistributed heat is less well represented (Figure  9.8; \r\nBronselaer and Zanna, 2020; Dias et al., 2020; Couldrey et al., 2021). \r\nSince redistributed heat dominates historical OHC change, historical \r\nsimulations poorly reproduce regional patterns, but as future OHC \r\nchange will become dominated by added heat, more skill in future \r\nmodelled OHC patterns is expected (Bronselaer and Zanna, 2020). \r\nIn summary, climate models have more skill in representing OHC \r\nchange from added heat than from ocean circulation change (high \r\nconfidence)"


id: 5a91303f2b8be85cf145ef043c90703f
created_at: 2023-03-16 14:02:05
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vector_id: 11764
content:
" Since added heat dominates over redistributed heat \r\non a  centennial scale (especially under high-emissions scenarios) \r\nconfidence in future modelled OHC patterns at the end of the \r\n21st century is greater than at decadal scale."


id: 6a6bd98d174e33e63711517047961f75
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
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vector_id: 14349
content:
"The OHC patterns projected by CMIP6 models (Figures 9.6 and 9.7) \r\nare similar to the CMIP5 projections assessed in SROCC (Bindoff et al., \r\n2019): faster warming in all water mass subduction regions \r\n(e.g., subtropical cells and mode waters); deeper penetration in the \r\ncentre of subtropical gyres; slower northern North Atlantic warming \r\ndue to slowing AMOC; and slower subpolar Southern Ocean \r\nwarming  due upwelled pre-industrial water masses. Decreased \r\naerosol forcing will allow Northern Hemisphere ocean warming \r\nto be faster and less dominated by Southern Hemisphere change \r\n(Shi  et  al., 2018; Irving et  al., 2019). Since SROCC, distinguishing \r\nbetween added and redistributed heat has aided in understanding \r\nprojections (Bronselaer and Zanna, 2020; Dias et al., 2020; Couldrey \r\net  al., 2021). The near-term decades will feature patterns strongly \r\ninfluenced by heat redistribution and internal variability (Rathore \r\net  al., 2020)"


id: a863ad630b581ffdba35f6f0e4312d19
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
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index: climate_gpt_only_giec
vector_id: 24018
content:
" Strengthening Southern Hemisphere westerlies are \r\nprojected, except for stringent mitigation scenarios (Bracegirdle et al., \r\n2020), and will cause a northward and downward OHT. There is low \r\nagreement in future Southern Ocean warming across model results \r\ndue to uncertainties in the magnitude of westerly wind changes \r\n(Figure 9.4; Liu et al., 2018; He et al., 2019; Dias et al., 2020; Lyu et al., \r\n2020b) and the degree of eddy compensation of overturning across \r\ndifferent parametrizations and resolutions (Section 9.2.3.2; Beal and \r\nElipot, 2016; Mak et al., 2017; Roberts et al., 2020). By 2100, however, \r\nthe OHC change will be dominated by the added heat response, \r\nparticularly for strong warming scenarios (Garuba and Klinger, 2018; \r\nBronselaer and Zanna, 2020) with added heat following unperturbed \r\nwater mass pathways in the North Atlantic and Southern Ocean \r\n(Figure  9.8; Dias et  al., 2020; Couldrey et  al., 2021)"


id: a94d922bffae4ac048ac14be41f77355
created_at: 2023-03-16 14:02:05
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vector_id: 24165
content:
" There is high \r\nconfidence that projected weakening of the AMOC (Section 9.2.3.1) \r\nwill cause a decrease in northward OHT in the Northern Hemisphere \r\nmid-latitudes (Figure 9.8 and Sections 9.2.3.1 and 4.3.2.3; Weijer et al., \r\n2020) associated with a dipole pattern of Atlantic OHC redistributed \r\nfrom northern to low latitudes that may override added heating in \r\nthe northern North Atlantic (Figures 9.6, 9.7 and 9.8). Variations \r\nin  the degree of AMOC redistributed heat (Menary  and Wood, \r\n2018) causes large intermodel spread in SST (Figure  9.3) and  OHC \r\nchange (Figure 9.6; Kostov et al., 2014; Bronselaer and Zanna, 2020; "


id: 5552512edad858e0684e00227d9d03e9
created_at: 2023-03-16 14:02:05
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vector_id: 10893
content:
"from ice core rare gas elemental and isotopic ratios document a rise \r\nin global OHC relative to the Last Glacial Maximum of >17,000 ZJ \r\n(change in mean ocean temperature >3.1degC; 1 ZJ = 1021 Joules) \r\n(Figure 9.9; Bereiter et al., 2018; Baggenstos et al., 2019; Shackleton \r\net al., 2019, 2020). This temperature increase is significantly larger \r\nthan the modelled OHC changes associated with collapse of AMOC \r\nalone, and tracks rising Southern Ocean SST (Uemura et al., 2018), \r\nstrengthening of the deep abyssal overturning cell (Du et al., 2020) \r\nand increased North Atlantic water in the Southern Ocean (Wilson \r\net  al., 2020). This underscores the importance of Antarctic abyssal \r\nventilation on long-term oceanic heat budgets (Section  9.2.3.2). \r\nAn  ensemble of four intermediate-complexity models project \r\n10,000-year future responses to CO2 emissions (Clark et al"


id: 253b490e9d2efbb4ee7a8efb624c51e2
created_at: 2023-03-16 14:02:05
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content:
", 2016) with \r\nSST change peaking around 2300 and a varying scenario-dependent \r\nmagnitude approaching the scale of glacial-to-interglacial changes \r\nin paleodata (Figure 9.9). Long-term OHC commitments relative to \r\n1850-1900 conditions are 2.6, 9.7, 15.2, 21.6, and 28.0 YJ (with \r\nmean ocean temperature change as much as 5.1degC) for emissions \r\nof 0, 1280, 2560, and 3840 and 5120 Gt after 2000 CE respectively, \r\nwith OHC peaking near 4000 CE, reflecting whole-ocean warming \r\nlagging SST by thousands of years. The exact timing is uncertain, \r\nsubject to rates of high-latitude meltwater input (Van Breedam et al., \r\n2020) and circulation time (Gebbie and Huybers, 2019). In summary, \r\nthere is very high confidence that there is a long-term commitment \r\nto increased OHC in response to anthropogenic CO2 emissions, which \r\nis essentially irreversible on human time scales."


id: 49c10ca2f21128f2225f7f34fd842240
created_at: 2023-03-16 14:02:05
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vector_id: 9000
content:
"Figure 9.9 | Long-term trends of ocean heat content (OHC) and surface temperature. (a, b) Ice-core rare gas estimates of past mean OHC (ZJ), scaled to global \r\nmean ocean temperature (degC), and to steric global mean sea level (GMSL) (m) per CCB-2 (red dashed line), compared to surface temperatures (black solid line, gold solid \r\nline; degC rightmost axis). Southern Ocean sea surface temperature (SST) from multiple proxies in 11 sediment cores and from ice core deuterium excess (Uemura et  al., \r\n2018). (a) Penultimate glacial interval to last interglacial, 150,000-100,000 yr B2K (before 2000) (Shackleton et al., 2020). (b) Last glacial interval to modern interglacial, \r\n40,000-0 yr B2K (Baggenstos et al., 2019; Shackleton et al., 2019). Changes in OHC (dashed lines) track changes in Southern Ocean SST (solid lines). (c) Long-term projected \r\n(2000 to 12000 CE) changes of OHC (dashed lines) in response to four greenhouse gas emissions scenarios (Clark et al"


id: f30b1ae8bfcde54a73c273dbd3489f64
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vector_id: 35754
content:
", 2016) scale similarly to large-scale paleo changes but \r\nlag projected global mean SST (solid lines). (d) model simulated 1500-1999 OHC (Gregory et al., 2006) and 1955-2019 observations (Levitus et al., 2012) updated by NOAA \r\nNODC. All data expressed as anomalies relative to pre-industrial time. Further details on data sources and processing are available in the chapter data table (Table 9.SM.9)."


id: 1b69678c3fd544374e82b02e00a95734
created_at: 2023-03-16 14:02:05
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vector_id: 1774
content:
"Ocean circulation changes also affect salinity, largely on annual \r\nto decadal time scales (Du et  al., 2019; Liu et  al., 2019; Holliday \r\net al., 2020). For instance, in the subpolar North Atlantic, increasing \r\nnorthward transport of Atlantic waters entering the subpolar gyre \r\nfrom the South have compensated the salinity decrease expected \r\nfrom increased Greenland meltwater flux since the early 1990s \r\n(Dukhovskoy et  al., 2016, 2019; Stendardo et  al., 2020). After \r\nthe mid-2010s the trend reversed towards a  broad freshening, \r\nthe  largest in 120 years, in the North Atlantic (Holliday et  al., \r\n2020). The long-term freshening in the Pacific Ocean has also been \r\nsubject to decadal variability, such as a marked salinification since \r\n2005 associated with increased surface fluxes (G. Li et al., 2019)"


id: 3adcbf34f54df55ce6ca79448d2d77f9
created_at: 2023-03-16 14:02:05
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" \r\nLocal salinity anomalies forced by water cycle intensification can \r\nbe weakened by rapid exchange between basins with opposing \r\ntrends, such as by water mass exchange in shallow wind-driven cells \r\nbetween the tropics and the subtropics (Levang and Schmitt, 2020). \r\nSimilarly, eddy exchanges between neighbouring gyres can partly \r\ncounterbalance decadal time scale long-term subpolar freshening \r\nand affect deep convection (Levang and Schmitt, 2020). There is high \r\nconfidence that, at annual to decadal time scales, regional salinity \r\nchanges are driven by ocean circulation change superimposed on \r\nlonger-term trends."


id: 689ddd8b9eeed8e0587261fa0353026b
created_at: 2023-03-16 14:02:05
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vector_id: 14052
content:
"Globally the mean salinity contrast at near-surface between high- and \r\nlow-salinity regions increased 0.14 [0.07 to 0.20] from 1950 to \r\n2019 (Section  2.3.3.2). At regional scale, SROCC (Meredith et  al., \r\n2019) assessed an Arctic liquid freshwater trend of 600 +- 300 km3 yr-1\r\n(600+-200 Gt yr-1) between 1992 and 2012, reflecting changes \r\nassociated with continental freshwater imports that affect ocean \r\nmass (land ice, rivers) as well as changes in sea ice volume. Since AR5, \r\nregional observation-based analyses not assessed in SROCC further \r\nconfirm the long-term, large-scale and regional patterns of salinity \r\nchange, both at the ocean surface and in the subsurface ocean, \r\nincluding almost 120 years of changes in the North Atlantic (Friedman \r\net al., 2017) and 60 years of monitoring in the subpolar North Pacific \r\n(Cummins and Ross, 2020)"


id: b58718f8070a95bd8eca22ae1de29de3
created_at: 2023-03-16 14:02:05
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vector_id: 26091
content:
" These longer time series also provide \r\ncontext to detect large multi-annual change from 2012 to 2016 in \r\nthe subpolar North Atlantic, unprecedented over the centennial \r\nrecord (Holliday et al., 2020). In summary, there is high confidence\r\nthat salinity trends have extended for more than 60 to 100 years in \r\nthe regions with long historical observation records, such as the North \r\nPacific and the North Atlantic basin."


id: 277f971896f2179abe3ef61ae4c00dbe
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vector_id: 3615
content:
"While there is low confidence in direct estimates of trends in \r\nsurface freshwater fluxes (Sections 2.3.1.3.5, 8.3.1.1 and 9.2.1.2), \r\nas discussed in SROCC (Meredith et al., 2019), observational studies \r\ncoupled with modelling studies suggest that surface flux changes \r\ndrive many observed near-surface salinity changes, on top of \r\nchanges specific to polar regions. Advances in salinity observations - \r\nfor example, the Argo program (Riser et al., 2016); Soil Moisture and \r\nOcean Salinity (SMOS), Aquarius and Soil Moisture Active Passive \r\n(SMAP; Supply et  al., 2018; Vinogradova et  al., 2019)  - combined \r\nwith process studies (SPURS-1/2; Lindstrom et  al"


id: 538e16bb6577494e750a78377dd37871
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vector_id: 10613
content:
", 2015; SPURS-2 \r\nPlanning Group 2015) and methodological and numerical advances, \r\nhave increased understanding of how subsurface salinity anomalies \r\nlink to surface fluxes, and thus increase confidence that near-surface \r\nand subsurface salinity pattern changes since the 1950s are linked \r\nto changing surface freshwater fluxes (Zika et al., 2018; Cheng et al., \r\n2020) with an additional contribution from changes in sea ice and \r\nland ice discharge at high latitudes (Haumann et  al., 2016; Purich \r\net  al., 2018; Dukhovskoy et  al., 2019; Rye et  al., 2020). There is \r\ntherefore medium confidence in the processes linking surface fluxes \r\nto surface and subsurface salinity change."


id: 6f4418cea6c4446ecc17cff2c0e1b4f3
created_at: 2023-03-16 14:02:05
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vector_id: 15134
content:
"The SROCC (Bindoff et al., 2019) assessed that projected salinity \r\nchanges in the subsurface ocean reflect changes in the rates of \r\nformation of water masses or their newly formed properties. \r\nAdditional consistent newer evidence based on CMIP5 and regional \r\nclimate models confirms that 21st  century projections adhere \r\nto the 'fresh gets fresher, salty gets saltier' paradigm, through \r\nsubduction of freshening high-latitude waters into the ventilated \r\nwater masses in both hemispheres in the Pacific, Indian and \r\nSouthern Ocean - especially the Arctic and upper Southern Ocean, \r\nand saltier subtropical and Mediterranean surface waters - lead to \r\nsaltier pycnoclines and North Atlantic mode water (Metzner et al., \r\n2020; Parras-Berrocal et al., 2020; Silvy et al., 2020; Soto-Navarro \r\net  al., 2020)"


id: f5e36a74157b42fa81ee5fb0dbd2122
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vector_id: 36200
content:
" Overall, projections confirm SROCC assessment \r\nthat fresh ocean regions will continue to get fresher and salty \r\nocean regions will continue to get saltier in the 21st  century \r\n(medium confidence)."


id: 6231cd19b459ebf2c82a565e4abbda3f
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vector_id: 13004
content:
"Climate models from CMIP3 to CMIP5 generally simulated shallower \r\nand lighter SAMW and AAIW than is observed (Flato et al., 2013). \r\nNew analysis of CMIP5 models suggests that the freshening of \r\nthese water masses is one of the most prominent projected salinity \r\nchanges in the world ocean, and that this freshening emerged from \r\ninternal variability as early as the 1980s to 1990s (Silvy et al., 2020).\r\nTrends in North Atlantic Deep Water (NADW) are obscured by decadal \r\nvariability (Rhein et al., 2013; Bindoff et al., 2019). The AR5 (Rhein \r\net al"


id: 88c675f1dec5b7c85e04dc27f2678cc2
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 19055
content:
", 2013) assessed that it is very likely that the temperature, salinity, \r\nand formation rate of the Upper NADW (formed by deep convection \r\nin the Labrador and Irminger Seas) is dominated by strong decadal \r\nvariability related to the North Atlantic Oscillation (NAO) and it is \r\nlikely that Lower NADW (formed in the Nordic Seas and supplied to \r\nthe North Atlantic by deep overflows over the sills between Scotland \r\nand Greenland) cooled from 1955 to 2005. New insights from \r\nobservations have emphasized the stability of the deep overflows \r\nassociated with Lower NADW (Hansen et al., 2016; Jochumsen et al., \r\n2017; Osterhus et al., 2019) and even slight warming in the Faroe \r\nBank Channel (Hansen et al., 2016). As a result, the AR5 assessment \r\nthat Lower NADW likely cooled between 1955 and 2005 is revised \r\nto: it is likely that any observed changes in temperature, salinity, \r\nand formation rate of the Lower NADW are dominated by decadal \r\nvariability"


id: a3cd756d18770df860cf978e409d4092
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 23335
content:
" For CMIP5 models, it was shown that AMOC variability is \r\nlinked to variability in NADW formation (Heuze, 2017) and projected \r\nAMOC decline to decreased NADW formation (both Lower NADW \r\nand Upper NADW; Heuze et al., 2015). For CMIP6 models, projected \r\nAMOC decline is also associated with a decline in NADW formation \r\n(Reintges et al., 2017; Weijer et al., 2020). The link between AMOC \r\nand NADW formation appears insensitive to the large range in \r\nmodel bias in NADW water mass characteristics (Heuze, 2017). Many \r\nmodels may overestimate deep water formation in the Labrador Sea, \r\nbut at least one new model is consistent with recent Overturning in \r\nthe Subpolar North Atlantic Program (OSNAP) observations showing \r\nvery weak overturning in the western subpolar gyre, where Labrador \r\nSea water is formed (Menary et al., 2020a)"


id: 730a7a494aebacb253e83d7b693634f
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 15714
content:
" The CMIP6 models show \r\na reduced bias in NADW properties compared to CMIP5 models, but \r\nstill feature varying locations of deep convection in the subpolar \r\ngyre: some convect only in the Labrador Sea (6/35 models), most in \r\nboth the Labrador and Irminger Seas (26/35 models; as is observed), \r\nand some only in the Irminger Sea (3/35 models), but in general, the \r\narea where deep convection takes place has expanded relative to \r\nCMIP5, which appears unrealistic (Heuze, 2021). Models with most \r\ndeep convection in the subpolar gyre feature the smallest bias in \r\nNADW characteristics, partly associated with NADW formed in the \r\nNordic Seas (as observed) being largely unable to leave the area \r\n(Heuze, 2021) due to inaccurate overflows (Danabasoglu et al., 2010; \r\nDeshayes et al., 2014; Wang et al., 2015)"


id: f38fbce94c2af4073155841e499c55fd
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 35832
content:
" Despite the wide range in \r\nmodel bias, it remains very likely that any long-term (multi-decadal \r\nor longer) decrease in AMOC is accompanied by a decline in NADW \r\nformation, associated with lighter densities in the northern North \r\nAtlantic and Arctic basins.\r\nThe SROCC (Meredith et al., 2019) assessed that the global volume \r\nof Antarctic Bottom Water (AABW) had decreased and warmed \r\nsince the 1980s, most noticeably near Antarctica. The SROCC also \r\nnoted freshening in the Indian and Pacific sectors of the Southern "


id: 854795ee1dde939507cf042f20682bbc
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 18496
content:
"Subtropical mode waters (STMW) ventilate the main thermocline of \r\nthe ocean at mid- to low-latitudes and have circulation time scales \r\naway from the surface of the order of years to decades. The SROCC \r\n(Bindoff et al., 2019) reported that warming in the subtropical gyres \r\npenetrates deeper than in other gyres, following the density surfaces \r\nin these gyres. Consistently, we assess that STMW have deepened \r\nworldwide, with greatest deepening in the Southern Hemisphere \r\n(high confidence) (Hakkinen et al., 2016; Desbruyeres et al., 2017). \r\nSubsurface warming in the Northern Hemisphere STMW is larger \r\nthan at the surface (Sugimoto et al., 2017) because they are formed in \r\nwinter western boundary current extensions, where surface warming \r\nis larger than the global average (Section  9.2.1.1). Variability \r\nin STMW thickness or temperature has a  large imprint on OHC \r\n(Section 9.2.2.1; Kolodziejczyk et al., 2019)"


id: dba173894d44f98dc32d94aff5dbdcea
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 32073
content:
" STMW are observed to be \r\nfreshening in the North Pacific and associated with increased salinity \r\nin the North Atlantic (Oka et al., 2017; Silvy et al., 2020), with large \r\ndecadal variability (Oka et al., 2019; Wu et al., 2020). Anthropogenic \r\ntemperature and salinity changes in the STMW layer are projected \r\nto intensify in the future, with emergence from natural variability \r\naround 2020 to 2040 (Silvy et al., 2020)."


id: 508f68fd6631d33f27f01168fc3dbe02
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 10119
content:
"Subantarctic mode water (SAMW) and Antarctic intermediate water \r\n(AAIW) form at the Southern Ocean surface directly north of the \r\nAntarctic Circumpolar Current and ventilate the upper 1000 m of \r\nthe Southern Hemisphere subtropics. The SROCC (Meredith et  al., \r\n2019) reported a freshening of these water masses between 1950 \r\nand 2018, and they are projected to have the largest subsurface \r\ntemperature increase of the Southern Hemisphere oceans, along with \r\na continued freshening, in the 21st century. The SROCC connected \r\nSAMW and AAIW to Southern Ocean temperature changes as the \r\nlarge Southern Ocean surface heat uptake is circulated and mixed \r\nalong with these water masses (high confidence). Close to its \r\nformation region, SAMW is predominantly affected by air-sea flux \r\nchanges, while further northward it is influenced by wind-forced \r\nchanges (Meredith et al., 2019)"


id: 7bf4f0b4e8aff47caf7243d17907db65
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 17104
content:
" New evidence shows that a change \r\nin SAMW heat content over the last decade is primarily attributable \r\nto its thickening (Kolodziejczyk et al., 2019). Over the past decade, the \r\nSAMW and AAIW volumes have changed by thickening of the lighter \r\nand thinning of the denser parts of SAMW and AAIW, leading to \r\nlightening of these ventilated ocean layers overall (Hong et al., 2020; \r\nPortela et al., 2020). Over the last decade, there is limited evidence\r\nof increased subduction of SAMW due to deepening mixed layers \r\nin the SAMW formation region (Section  9.2.1.3; Qu et  al.,  2020). "


id: c44c581675abee370bbc732b46a3bdfa
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 28452
content:
"Ocean and a higher rate of freshening in the Indian Sector from the \r\n2000s to 2010s than from the 1990s to 2000s (low confidence). \r\nSince SROCC, freshening of Indian Ocean AABW from 1974 to 2016 \r\nhas been revealed (Aoki et  al., 2020). Additionally, interannual to \r\ndecadal variability in AABW has been quantified to be larger than \r\npreviously thought in terms of temperature, salinity and thickness, \r\nand in volume transport (Abrahamsen et  al., 2019; Purkey et  al., \r\n2019; Gordon et  al., 2020; Silvano et  al., 2020). Multi-decadal to \r\ncentennial modes of variability could have driven the observed trends \r\nof the lower cell over the past decades via the opening of a Weddell \r\nSea Polynya (L. Zhang et  al., 2019), although other studies find it \r\ncontributed minimally to the observed abyssal warming (Zanowski \r\net al., 2015; Zanowski and Hallberg, 2017)"


id: b7d74b407fb44a0a5a955607926dfb8a
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 26462
content:
" Therefore, there is limited \r\nevidence and low agreement in the role of open ocean polynyas in \r\ndriving past decadal observed trends of AABW. Beyond variability, all \r\nobservational, theoretical, and numerical evidence supports SROCC \r\nassessment that formation and export of AABW will continue to \r\ndecrease due to warming and freshening of surface source waters \r\nnear the Antarctic continent. Consistent with Section  9.2.3.2, \r\nconfidence in this assessment is increased to medium confidence\r\ncompared to SROCC."


id: d9803f422faa2a02be36da4695664549
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 31738
content:
"Circumpolar Deep Water (CDW) lies in the Southern Ocean and forms \r\nby the mixing of NADW and AABW (Talley, 2013). The SROCC (Meredith \r\net  al., 2019) assessed with low confidence that mean southward \r\nand upward CDW transport is linked to decadal wind variability \r\n(Section 9.2.3.2), and that CDW has warmed south of the Antarctic \r\nCircumpolar Current (ACC) in the past decades. New  evidence \r\nreinforces SROCC assessment: changes in Southern Ocean wind stress \r\nhave been confirmed to drive variability and increase the large-scale \r\nsouthward CDW transport (Waugh et al., 2019). In addition, growing \r\nevidence suggests that the upper-ocean stratification increase in \r\nthe subpolar Southern Ocean since the 1970s (Section 9.2.1.3) has \r\nreduced the volume of CDW that is mixed to the surface, causing \r\nsubsurface CDW warming (Bronselaer et al., 2020; Haumann et al., \r\n2020; Jeong et al., 2020; Moorman et al., 2020)"


id: ba340a7f20a2bcb866720e3b595b53f4
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 26837
content:
" Large regions of the \r\nAntarctic shelves are currently isolated from warm CDW (Thompson \r\net al., 2018; Jourdain et al., 2020). The SROCC (Meredith et al., 2019) \r\nassessed that subsurface warming extends close to Antarctica and has \r\nco-occurred with shoaling of the CDW since the 1980s, influencing \r\nthe continental shelf most in the Amundsen-Bellingshausen Seas, \r\nWilkes Land, and the Antarctic Peninsula. New evidence since SROCC \r\nreinforces confidence in the importance of the role of winds in \r\ntransporting heat associated with CDW to continental shelves and \r\nice cavities in the Amundsen-Bellingshausen Seas (Dotto et al., 2019) \r\nand via variable small-scale undercurrents to the Shirase Glacier \r\nTongue in East Antarctica (Hirano et al., 2020; Kusahara et al., 2021)"


id: a4bbd06c839fd3ef04d03ab9210d643f
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 23490
content:
" \r\nThere is limited evidence that increased greenhouse gas forcing has \r\ncaused a slight mean change of the local winds from 1920 to 2018, \r\nfacilitating CDW heat intrusion onto the Amundsen-Bellingshausen \r\ncontinental shelf and ice shelf melt (Holland et al., 2019). Multiple \r\nlines of observational, numerical, theoretical, and paleo evidence \r\nprovide high confidence that changes in wind pattern (Spence et al., \r\n2014; Dotto et  al., 2019;  Holland et  al., 2019), increased ice-shelf \r\nmelt (Golledge et al., 2019; Moorman et al., 2020), reduction in sea \r\nice production (Timmermann and Hellmer, 2013; Obase et al., 2017), \r\nand eddies (Stewart and Thompson, 2015; Thompson et al., 2018) can "


id: b376d9d258163437a1e42ceedbe9d09b
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 25742
content:
"The AR5 (Flato et  al., 2013) found that the mean AMOC strength \r\nin CMIP5 models ranges from 15 to 30 Sv for the historical period. \r\nThe multi-model mean overturning at 26degN in CMIP5 and CMIP6 is \r\ncomparable to the RAPID array measurements (Reintges et al., 2017), \r\nbut the inter-model spread in CMIP6 is as large (10-31 Sv) as in \r\nCMIP5 (Section 3.5.4; Weijer et al., 2020). Biases in simulations of the \r\npresent-day AMOC and associated deep convection in the subpolar \r\ngyre and Nordic Seas were large in CMIP5 models, with many models \r\nexhibiting ocean convection that is too deep, over too large an area, \r\ntoo far south, and occurring too frequently (Section  9.2.1.3 and \r\nFigure 9.5; Heuze, 2017) related to biases in sea ice extent, overflows, \r\nand freshwater forcing (Deshayes et al., 2014; H. Wang et al., 2015). \r\nAs a result, the AMOC in CMIP5 was nearly always too shallow, with too \r\nweak a temperature contrast between the northward and southward \r\nflowing branches"


id: 4c40ef970ef4e91c4a5c04dd36a78fcc
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 9424
content:
" Deep convection errors are still large in CMIP6, and \r\nthe shallow bias in AMOC persists (Weijer et al., 2020; Heuze, 2021). \r\nSince AR5, there is emerging evidence that enhancing horizontal \r\nresolution can reduce long-standing climate model biases in AMOC \r\nstrength, where the magnitude and profile of northward heat transport \r\nat 26degN become more comparable to observations (Chassignet et al., \r\n2020; Roberts et  al., 2020). The sensitivity of the AMOC to ocean \r\nresolution, however, is model-dependent and can be positive as well as \r\nnegative (Roberts et al., 2020). An increase in AMOC strength at 26degN, \r\nwith higher resolution in the ocean component, has been associated \r\nwith too strong (deep) convection in the subpolar gyre and too deep \r\nwinter mixed layers (L.C. Jackson et al., 2020), which occurs in most \r\nCMIP6 models that are unable to overflow deep water formed in the "


id: 95390893f8c7dd25f13b65c218c10e16
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 20976
content:
"The AMOC is a potential driver of Atlantic Multi-decadal Variability \r\n(AMV), but there is new evidence that anthropogenic aerosol \r\nchanges have contributed to observed AMV changes, and that \r\nunderestimation of the magnitude and duration of AMV changes \r\nin CMIP5 is tempered in CMIP6 (Section  3.7.7 and Annex IV.2.7). \r\nComparison of observed AMOC variability at the RAPID section with \r\nmodelled variability reveals that CMIP5 models appear to largely \r\nunderestimate the interannual and decadal time scale variability \r\n(Roberts et al., 2014; Yan et al., 2018), and similar results are found \r\nwhen analysing CMIP6 models (Section 3.5.4.1). By underestimating \r\nthe multi-decadal AMOC-AMV link and other low-frequency AMOC \r\nvariability, climate models also underestimate internal variability \r\nin subpolar SSTs that feed back on the North Atlantic Oscillation \r\n(NAO). This causes the NAO to lack variability on multi-decadal time \r\nscales (Kim et al., 2018)"


id: d773ef31ca945f1b71eb5148b6fde3f5
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 31420
content:
" Despite the role of the AMOC in generating \r\nAMV through subsurface temperatures in antiphase with SST and \r\ndownward heat fluxes into the ocean that anticorrelate with SSTs \r\n(R. Zhang et al., 2019), it is generally accepted that AMOC forcing \r\nof SST variability exists alongside stochastic wind forcing and \r\nexternal  forcing by aerosols (Bellomo et  al., 2018; Haustein et  al., \r\n2019; O'Reilly et al., 2019; Wills et al., 2019)."


id: 68ee95f11171f67abb87f64b63572e6a
created_at: 2023-03-16 14:02:05
updated_at: 2023-03-16 14:02:05
content_type: text
index: climate_gpt_only_giec
vector_id: 14100
content:
"Nordic Seas across the Greenland-Iceland-Scotland Ridge. Models \r\nwith a correct AMOC strength may do so by compensating a lack of \r\ndeep-water outflow from the Nordic Seas through too much deep \r\nconvection and deep-water formation in the Labrador and Irminger \r\nSeas (Heuze, 2021).\r\nModels and paleoreconstructions have often assumed a close relation \r\nbetween the AMOC and deep convection in the Labrador Sea; the \r\nLabrador Sea convection variability has been interpreted as connecting \r\nto AMOC variability. Observational studies have been inconclusive on \r\nwhether this relation exists (Buckley and Marshall, 2016). New insight \r\nfrom observed overturning in the eastern and western subpolar \r\ngyre in the North Atlantic in OSNAP (Lozier et al., 2019; Petit et al., \r\n2020) reveals that 15.6 +- 3.1Sv takes place north of the OSNAP array \r\nbetween Greenland and Scotland, with only 2.1 +- 0"


