# document

id: c5046e254f35476b12239b08e2892278
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 31971
content:
"[...] regions of India (Singh et al., 2014; Roxy et al., 2015; Juneng et al., \r 2016; Krishnan et al., 2016; Guhathakurta and Revadekar, 2017; Jin \r and Wang, 2017; Latif et al., 2017). A global modelling study with \r high resolution over South Asia (Sabin et  al., 2013) indicated that \r a juxtaposition of regional land-use changes, anthropogenic-aerosol \r forcing and the rapid warming signal of the Equatorial Indian Ocean \r was crucial to simulate the observed Indian summer monsoon \r weakening in recent decades (medium confidence)."


id: e3477e21b23aef9d172177f2eadb8fe9
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 37285
content:
"Whilst simulations of Indian summer monsoon rainfall (ISMR) have \r improved in CMIP5 compared to CMIP3 in terms of northward \r propagation, time for peak monsoon and withdrawal (Sperber et al., \r 2013), they fail to simulate the trends in monsoon rainfall and the \r post-1950 weakening of monsoon circulation (Saha et al., 2014). This \r is partially attributed to the failure of coarse-resolution CMIP5 models \r to simulate fine-resolution processes such as orographic effects or \r land surface feedback, and problems in cloud parametrization result \r in an overestimation of convective precipitation fraction (M.S. Singh \r et al., 2017). In CMIP6, a significant improvement is found in capturing \r the monsoon spatio-temporal patterns over India, particularly in \r the Western Ghats and north-eastern Himalayan foothills (Gusain \r et  al., 2020). Over Pakistan the CMIP6 models simulate surface \r temperature better in JJA than DJF (Karim et al., 2020). The CMIP6 \r [...]"


id: e1e39f650a66c4914e829305aa470682
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 37026
content:
"[...] ensemble underestimates annual mean temperature over all of \r South Asian with mixed results for precipitation (Almazroui et  al., \r 2020b). The CMIP6 GCMs have a large cold bias in both mean annual \r maximum and minimum temperatures in the complex Karakorum \r and Himalayan mountain ranges but exhibit warm biases in mean \r annual minimum temperature in most of the rest of South Asia."


id: ffe7b25d64c9e7eb0e089f2ec6fea9c9
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 42416
content:
"Regional climate model (RCM) downscaling of CMIP5 models as part \r of CORDEX South Asia uses higher resolution (50 km) and improved \r surface fields such as topography and coastlines to resolve better \r the complexities of the monsoon and other hydrological processes \r (Giorgi et  al., 2009). The added value of their simulations, relative \r to the driving GCMs, presents a  complex picture. CORDEX RCMs \r better represent spatial patterns of temperature (Sanjay et  al., \r 2017), the spatial features of precipitation distribution associated \r with the Indian summer monsoon (Choudhary and Dimri, 2018), \r and the simulation of monsoon active- and break-phase composite \r precipitation (Karmacharya et al., 2017b). The RCMs follow the driving \r GCMs in underestimating seasonal mean surface air temperature \r and overestimating spatial variability in precipitation. They amplify \r CMIP5 cold biases over almost the entire region, including over the \r [...]"


id: 682cb4d949ab9b460b2bd2ac4cd24d4
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 15593
content:
"[...] HKH region, Afghanistan and south-west Pakistan during winter \r (Iqbal et  al., 2017), and substantial cold biases of 6degC-10degC are \r found over the Himalayan watersheds of the Indus basin (Nengker \r et  al., 2018; Hasson et  al., 2019). Neither RCMs nor their driving \r CMIP5 GCMs reproduce well the region's precipitation climatology \r (Mishra, 2015). In addition, important characteristics of ISMR such \r as northward and eastward propagation, onset, seasonal rainfall \r patterns, intra-seasonal oscillations and patterns of extremes did \r not show consistent improvement (S. Singh et al., 2017). Also, these \r RCM simulations have not demonstrated added value in capturing \r the observed changes in ISMR characteristics over recent decades, \r though RegCM4 simulations at 25  km showed high accuracy in \r capturing monsoon precipitation characteristics and atmospheric \r dynamics in historical simulations (Ashfaq et al., 2021)."


id: 5f08c56f28cecdd7ee3f39a212b697e5
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 14019
content:
"With continued global warming and anticipated reductions in \r anthropogenic aerosol emissions in the future, CMIP5 models \r project an increase in the mean and variability of summer monsoon \r precipitation over India by the end of the 21st  century, together \r with substantial increases in daily precipitation extremes (medium \r confidence) (Krishnan et al., 2020), see also Section 8.4.2.4 on changes \r in the South Asian monsoon. The CMIP5 GCMs consistently project \r an increase in moisture transport over the Arabian Sea and Bay of \r Bengal towards the end of the 21st century, an increase in moisture \r convergence and consequent increases in monsoon rainfall over the \r Indo-Pakistan region which are higher under RCP8.5 than RCP4.5 \r (Srivastava and Delsole, 2014; Mei et  al., 2015; Latif et  al., 2018). \r Out of 20 CMIP5 GCMs, four showed an increase in magnitude and \r lengthening of the summer monsoon across India under RCP8.5. The \r [...]"


id: b2d4d8c493da5b8941c95ab0a5b54e08
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 28699
content:
"[...] intensity of both strong and weak monsoons is projected to increase \r during the period 2051-2099 (Srivastava and Delsole, 2014)."


id: b310e75ffde18133b9c73104f657719
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 28747
content:
"Evaluation of four global reanalysis products (ERA5 and ERA-Interim, \r JRA-55 and MERRA-2; Atlas.1.4.2) for snow depth and snow cover \r over TIB was performed against 33 in situ station observations, \r Interactive Multisensor Snow and Ice Mapping System (IMS) snow \r cover and a satellite microwave snow-depth dataset (Orsolini et al., \r 2019). Most of the reanalyses showed a systematic overestimation. \r Only ERA-Interim assimilated IMS snow cover at high altitudes, \r whereas ERA5 did not and the excessive snowfall, snow depth and \r snow cover in ERA5 was attributed to this difference. The analysis \r of annual maximum consecutive snow-covered days for the period \r 1980-2018 over TIB using JRA-55 and passive microwave satellite \r observations showed a decreasing trend in all time periods and in \r recent snow seasons for MERRA-2 (Bian et al., 2020). The uncertainty \r assessment of model physics in snow modelling over TIB using \r [...]"


id: 9b05fe000a5c956171e0e6a5254edb27
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 24524
content:
"[...] ground-based observations and high-resolution snow cover satellite \r products from the Moderate Resolution Imaging Spectroradiometer \r (MODIS) and FengYun-3B suggests that errors can be overcome by \r optimizing parametrizations of the snow cover fraction rather than \r optimizing physics-scheme options (Y. Jiang et al., 2020)."


id: ad5c49cab8288b8b2edd7422f5806262
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 27739
content:
"Within the last decade, there has been an increasing number of studies \r on climatic trends over South East Asia, carried out on a regional basis \r (Thirumalai et al., 2017; Cheong et al., 2018) or focused on specific \r countries (Cinco et al., 2014; Villafuerte et al., 2014; Mayowa et al., \r 2015; Villafuerte and Matsumoto, 2015; Guo et  al., 2017a; Supari \r et  al.,  2017; Sa'adi et  al., 2019; Tan et  al., 2021). They document \r virtually certain significant increases in mean as well as extreme \r temperature. The minimum temperature extremes very likely warmed \r faster compared to the maximum temperature. Temperatures, including \r extremes, are strongly influenced by ENSO in the region (Cinco et al., \r 2014; Thirumalai et al., 2017; Cheong et al., 2018). Over much of the \r region, extreme high temperatures occurred mostly in April and almost \r all April extreme temperatures occur in El Nino years (Thirumalai et al., \r [...]"


id: 55f1c32cbdb58e337185c72f2034fc87
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 12395
content:
"[...] 2017). In most of South East Asia (except for the north-eastern areas), \r there was likely an increase in the number of warm nights with El Nino \r episodes within the period 1972-2010 (Cheong et al., 2018)."


id: 7914bb1f3c08f7aed1413610bcf8f3c6
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 18488
content:
"physics (Manomaiphiboon et al., 2013; Kwan et al., 2014) and/or the \r biases in the SST forcing (Ngo-Duc et al., 2014). A few simulations \r revealed a  warm bias over some areas such as in the Maritime \r Continent (Cruz et  al., 2017) or Vietnam (Van Khiem et al., 2014). \r The biases for rainfall in GCMs and RCMs over South East Asia were \r found to be less systematic with wet or dry biases depending on \r the sub-regions (Manomaiphiboon et al., 2013; Kwan et al., 2014; \r Van Khiem et  al., 2014; Juneng et  al., 2016; Supari et  al., 2020; \r Tangang et al., 2020; Nguyen-Thi et al., 2021), although wet biases \r were more pronounced in RCMs (Kwan et al., 2014; Van Khiem et al., \r 2014; Kirono et  al., 2015; Juneng et  al., 2016; Supari et  al., 2020; \r Tangang et al., 2020). Some RCMs overestimated rainfall interannual \r variability (Juneng et al., 2016) while some others underestimated \r it (Kirono et al., 2015). Simulated rainfall amount is sensitive to the \r [...]"


id: 7e9f6f8bf08101f2823b265d62efbdc7
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 19434
content:
"[...] choice of convective scheme (Juneng et  al., 2016; Ngo-Duc et  al., \r 2017) and the choice of land surface scheme (Chung et al., 2018). \r Rainfall biases in current climate simulations can be greatly reduced \r if a  bias adjustment method such as quantile mapping is applied \r (Trinh-Tuan et al., 2018). The pattern of tropical cyclone numbers in \r the region were reasonable represented by RCM outputs (Van Khiem \r et al., 2014; Kieu-Thi et al., 2016; Herrmann et al., 2020)."


id: 1908e634b5acf7c905e1e0cf2d85d97c
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 1620
content:
"Mean temperature in South East Asia is projected to continue to rise \r through the 21st  century (virtually certain, very high confidence). \r Projections by multi-model regional climate simulations of \r CORDEX-SEA showed a temperature increment over land under RCP8.5 \r to range from 3degC-5degC by the end of the 21st century relative to the \r pre-1986-2005 period (Tangang et al., 2018). For the same periods, \r the average mean temperature increase over land projected by CMIP5 \r (CMIP6) varies, with 10th-90th percentile ranges, from 0.7degC to 1.3degC \r (0.7degC to 1.8degC) under RCP2.6 (SSP1-2.6) to 2.8degC to 4.4degC (2.6degC to \r 4.8degC) under RCP8.5 (SSP5-8.5) (Interactive Atlas). For all GWLs the \r land region is projected to warm by a slightly smaller amount than the \r global average, with 10th-90th percentile ranges for CMIP5 (CMIP6) \r of 1.2degC-1.6degC (1.2degC-1.5degC) for the 1.5degC GWL and of 3.3degC-4.0degC \r [...]"


id: ba159b5d49328f05d59b787c7929d19c
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 29961
content:
"[...] (3.3degC-3.9degC) for the 4degC GWL relative to the 1850-1900 baseline \r (calculated from RCP8.5 (SSP5-8.5) projections). Changes for other \r warming levels, periods and emissions pathways are shown in Figure \r Atlas.17 and can be explored in the Interactive Atlas."


id: 83a3f6bd4487c808f7e9861ad2fbfad6
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 20305
content:
"RCMs have been intensively used over the region in recent years in \r a series of single or multi-model experiments and there is medium \r confidence that they reproduce reasonably well seasonal climate \r patterns of temperature, precipitation and large-scale circulation \r over the different sub-regions of South East Asia with added values \r compared to their host GCMs (Kwan et  al., 2014; Ngo-Duc et  al., \r 2014, 2017; Van Khiem et  al., 2014; Juneng et  al., 2016; Katzfey \r et al., 2016; Loh et al., 2016; Raghavan et al., 2016; Cruz et al., 2017; \r Ratna et al., 2017; Trinh-Tuan et al., 2018; Nguyen-Thuy et al., 2021). \r RCM ensemble means tend to outperform the individual models in \r representing the climatological mean state (Ngo-Duc et  al., 2014; \r Trinh-Tuan et al., 2018; Nguyen-Thi et al., 2021). There is relatively \r high consistency among the simulations of historical climate over \r mainland South East Asia compared to those over the Maritime \r [...]"


id: c08e9ad49d9abe883ddddedf94fa114a
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 31172
content:
"[...] Continent for both seasonal and interannual variability (Ngo-Duc \r et al., 2017). The consistency in rainfall simulations was lower than \r for temperature simulations."


id: d70303d9958c2d332e6b3d9c5ec83612
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 35131
content:
"Projections of future rainfall changes are highly variable among \r sub-regions of South East Asia and among the models (high \r confidence). The CMIP5 and CMIP6 ensembles showed an increase \r in annual mean precipitation over most land areas by the mid- and \r late 21st century, although only with a strong model agreement for \r higher warming levels (Figure Atlas.17 and the Interactive Atlas), \r while CORDEX produces a general decrease in projected precipitation \r (Figure Atlas.17). Based on CORDEX South East Asia multi-model \r simulations, significant and robust increases of mean rainfall over \r Indochina and the Philippines were projected while there is a drying \r tendency over the Maritime Continent during DJF for the early, mid \r and end of the 21st century periods under both RCP4.5 and RCP8.5 \r (Figure Atlas.19; Tangang et al., 2020). At the end of the 21st century \r during DJF and under RCP8.5, an increase of 20% in mean rainfall \r [...]"


id: 66e98d4019793a15afa4780225bd6deb
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 15373
content:
"South West Asia includes the Arabian Peninsula (ARP) and West \r Central Asia (WCA) reference regions (Figure Atlas.17). ARP has \r a  semi-arid or arid desert climate with very low annual mean \r precipitation and very high temperature. Its temperature is influenced \r by SST variations over the tropical ocean (e.g., ENSO) and the NAO \r and AO (see Annex IV for these and subsequent modes of variability; \r Attada et al., 2019). Rainfall is influenced by the IOD and ENSO, with \r more rainfall during El Nino (Kang et al., 2015; Kumar et al., 2015; \r Abid et al., 2018; Kamil et al., 2019) and less during La Nina (Atif \r et  al., 2020). The wet season in ARP is mainly from November to \r April and the dry season is from June to August. Rainfall is confined \r mostly to the south-western part of the peninsula and contribution \r of extreme events to the total rainfall varies within 20-70% from \r region to region and season to season (Almazroui, 2020b; Almazroui \r [...]"


id: 578f6a3beacad05efbca9345f58fc1ac
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 12671
content:
"[...] and Saeed, 2020). WCA is separated from Eastern Europe by the \r Caucasus Mountains, is adjacent to ARP, with South Asia (SAS) to \r the south and West Siberia (WSB) to the north, and lies between \r the Mediterranean (MED), Tibetan Plateau (TIB) and East Central \r Asia (ECA) regions. WCA is heterogeneous in terrain with the Zagros \r Mountains and Iranian Plateau in the west and south-west, the \r Caspian Sea and lowland with deserts in the north and north-east. \r The regional climate of WCA is influenced by the NAO and ENSO \r and it is typically semi-arid or arid with a strong gradient in both \r precipitation and temperature from the mountains to the plains and \r from north to south."


id: 8bf171cb5ac30aa14259cf30bdc76da1
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 21790
content:
"The SR1.5 stated that even for 1.5degC and 2degC of global warming, South \r West Asia is among the regions with the strongest projected increase \r in hot extremes with more urban populations exposed to severe \r droughts in West Asia, while an increase of heavy precipitation events \r is projected in mountainous regions of Central Asia (Hoegh-Guldberg \r et al., 2018; IPCC, 2018c). Higher temperatures with less precipitation \r will likely result in higher risks of desertification, wildfires and dust \r storms exacerbated by land-use and land-cover changes in the region \r with consequent effects on human health. Further drying of the Aral \r Sea in Central Asia will likely have negative effects on the regional \r microclimate, adding to the growing wind erosion in adjacent deltaic \r areas and deserts that is already resulting in a  reduction of the \r vegetation productivity including croplands. There is also a projected \r increase of precipitation intensity in the Arabian Peninsula which is \r [...]"


id: af9fe729c499857337b4a81024a3db46
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 28136
content:
"[...] likely to lead to higher soil erosion particularly in winter and spring \r due to floods (Mirzabaev et al., 2019). WCA includes high mountains \r with enhanced warming above 500 m where, regardless of the \r emissions scenario, decreases in snow cover are projected due to \r increased winter snowmelt and more precipitation falling as rain \r (high confidence). A very strong interannual and decadal variability, \r as well as scarce in situ records for mountain snow cover, have \r prevented a quantification of recent trends in High Mountain Asia \r (Hock et al., 2019b)."


id: 92d253861e82c6a10819d80c66074ae2
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 23049
content:
"An overall increasing trend of annual precipitation (0.66 mm per \r decade) was found over Central Asia based on GPCC v7 data \r for the period 1901-2013 (Hu et  al., 2017), but annual trends \r were found not significant over the shorter period 1960-2013 \r (Figure Atlas.11 and Interactive Atlas). Winter precipitation saw \r a significant increase of 1.1 mm per decade (Song and Bai, 2016). \r These estimates have low to medium confidence since the satellite \r precipitation products have large systematic and random errors \r in mountainous regions. Moreover CMORPH and TRMM products \r fail to capture the precipitation events in the ice/snow covered \r regions in winter and show a  substantial false-alarm percentage \r in summer, but the gauge-corrected GSMAP performs better than \r other products (Song and Bai, 2016; Guo et al., 2017b; Hu et al., \r 2017; S. Chen et al., 2019). Over the elevated part of eastern WCA \r precipitation increases in the range of 1.3-4.8  mm per decade \r [...]"


id: b11bc042e0ce766a0b44dd726ba2c981
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 28386
content:
"[...] during 1960-2013 were observed (very high confidence) (Han \r and Yang, 2013; Li et al., 2013; Hu et al., 2014, 2017; Huang et al., \r 2014; Deng and Chen, 2017; Zhang et  al., 2017, 2019a; H.  Guo \r et  al., 2018; Haag et  al., 2019; Yu et  al., 2019). Reductions in \r spring precipitation and increases in winter have been reported \r for Uzbekistan over the period 1979-2013 based on station data \r but these are not significant (Khaydarov and Gerlitz, 2019). There \r is very low confidence of the impact of the Aral Sea shrinking on \r precipitation (Chen et al., 2011; Jin et al., 2017)."


id: fe2a01616467000d4f7ba23ba0361656
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 42079
content:
"In WCA, projections for different GWLs are consistent not only in \r annual and seasonal warming but in the ranges of the projections. \r Under RCP8.5, annual mean temperature will likely exceed 2degC by \r mid-century (compared with 1995-2014) and reach up to 4.8degC-6degC \r by the end of the century (Yang et al., 2017), with faster warming \r projected by the CMIP6 ensemble under SSP5-8.5. In individual \r county-level studies on GCM future climate projections, temperatures \r increased by up to 7degC by the end of the century, depending on \r season and emissions scenario (Allaberdiyev, 2010; MENRPG, 2015; \r MNP, 2015; Gevorgyan et al., 2016; Osborn et al., 2016; Aalto et al., \r 2017; IDOE, 2017; Salman et  al., 2017). Statistical downscaling of \r 18 CMIP5 GCMs projected an annual temperature increase of 0.37degC \r per decade (under RCP4.5) with the maximum in northern WCA \r and warming most conspicuous in summer (Luo et al., 2019). RCM \r [...]"


id: 1f17f4b07bad8775e9c302058a976a5f
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 2679
content:
"[...] downscaling of GCMs over Central Asia projected a larger increase of \r temperature under RCP8.5 for the 2071-2100 period, ranging from \r 5degC to 8degC (Ozturk et al., 2017)."


id: 1b645a3d997ab175175fdad398db5f31
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 1997
content:
"RCM simulations using the CORDEX-MENA domain reproduce \r the main features of the mean surface climatology over ARP with \r moderate biases (high confidence). RegCM4 driven by five GCMs \r (HadGEM2, GFDL, CNRM, CanESM2 and ECHAM6) showed an \r ensemble-mean cold bias of about -0.7degC and a dry bias of -13% \r over ARP (Almazroui, 2016) with a  cold (warm) bias over western \r (south-eastern) areas (Syed et  al., 2019). Temperature biases in \r 30-year historical simulations with WRF using three different radiation \r parametrizations were within +-2degC and mostly caused by surface \r long-wave radiation errors which affected nighttime minimum \r temperatures over 70% of the domain (Zittis and Hadjinicolaou, \r 2017). Mean absolute errors in COSMO-CLM driven by ERA-Interim \r were about 1.2degC for temperature, 15 mm per month for precipitation \r and 9% for total cloud cover, and with new parametrizations of \r albedo and aerosols optimized for the region the RCM simulated the \r [...]"


id: 79399c1546aa95310daf552fdd649503
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 18520
content:
"[...] main climate features of this very complex area (Bucchignani et al., \r 2016). RegCM4.4 also simulated the main features of the observed \r climatology (especially for dry regions) with temperature biases \r within +-3.0degC. Annual precipitation was overestimated with winter \r and spring underestimated (Ozturk et al., 2018)."


id: 4317ecd53c14bb28d1188b072b92a9f7
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 9009
content:
"Future projections of precipitation in South West Asia have large \r uncertainties and thus low confidence. There are few significant \r changes, little consensus on the sign and with a  tendency for \r reduction in CMIP5 being reversed in CMIP6 across all warming \r levels (Ozturk et  al., 2018). Statistical downscaling of 18 CMIP5 \r GCMs under RCP4.5 projected an increase in precipitation of \r 4.6 mm per decade in South West Asia during 2021-2060 relative \r to 1965-2004 (Luo et  al., 2019). CMIP5 simulations project \r a general decrease in precipitation over lowlands in Turkey, Iran, \r Afghanistan and Pakistan (Ozturk et  al., 2017), and an increase \r over high-mountain regions (Aalto et  al., 2017; Salman et  al., \r 2018). At a  4degC global warming level, the multi-model mean \r annual precipitation for Turkmenistan and parts of Tajikistan and \r Uzbekistan is projected to decrease by 20%, with somewhat \r stronger relative decreases in summer (Reyer et  al., 2017). Over \r [...]"


id: dd54355775232d9639f9cec53863c0b1
created_at: 2023-04-19 13:22:48
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content:
"[...] northern WCA, the CMIP5 ensemble mean projects increases of \r over 3 mm per decade under RCP2.6 and over 6 mm per decade \r under RCP4.5 and RCP8.5 over the 21st  century (Huang et  al., \r 2014). Mean annual precipitation is projected to rise by 5.2% at the \r end of the 21st century (2070-2099) under RCP8.5, compared to \r 1976-2005, while mean annual snowfall is projected to decrease "


id: b78bc02973b5d622d2a1be026f60a5f8
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
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index: climate_gpt_v2
vector_id: 29502
content:
"Australasia is divided into five regions for the Atlas (Figure Atlas.21), \r as follows: New Zealand (NZ), with a  varied climate with diverse \r landscapes, mainly maritime temperate with four distinct seasons; \r Northern Australia (NAU), which is mainly tropical with monsoonal \r summer-dominated rainfall (monsoon season December to March, \r see Annex V), but with a hot, semi-arid climate in the south of the \r region; Central Australia (CAU) with a predominantly hot, dry desert \r climate; Eastern Australia (EAU) with a temperate oceanic climate at \r the coast to semi-arid inland; and Southern Australia (SAU), which \r ranges from Mediterranean and semi-arid in the west to mainly cool \r temperate maritime climate in the south-east. Various remote drivers \r have notable teleconnections to regions within Australasia, including \r an effect of the El Nino-Southern Oscillation (ENSO) and the Indian \r Ocean Dipole (Table Atlas.1 and Annex IV). Much of southern NZ \r [...]"


id: 833b373fcf56ae109afa3a7d74931313
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
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index: climate_gpt_v2
vector_id: 20213
content:
"[...] and SAU are affected by systems within the westerly mid-latitude \r circulation, in turn affected by the Southern Annular Mode (SAM). \r The monsoon and the Madden-Julian Oscillation (MJO) affect rainfall \r variability in northern Australia."


id: f4bfa92166c730eb35eadc185d50f4bf
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 40431
content:
"The AR5 WGI and WGII reports (IPCC, 2013c; Stocker et al., 2013; \r Reisinger et al., 2014) give very high confidence that air and sea \r temperatures in the region have warmed; cool extremes have \r become rarer in Australia and New Zealand since 1950, while hot \r extremes have become more frequent and intense (e.g., it is very \r likely that the number of warm days and nights have increased). \r The AR5 reported that it is virtually certain that mean air and sea \r temperatures will continue to increase, with very high confidence\r that the greatest increase will be experienced by inland Australia \r and the smallest increase by coastal areas and New Zealand. \r The AR5 reported a range of different precipitation trends within \r the region. For example, while annual rainfall has been significantly \r increasing in north-western Australia since the 1950s (very high \r confidence), it has been decreasing in the north-east of the South \r [...]"


id: 1152c65bc5b9b016d595c72418da5a37
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 242
content:
"[...] Island of New Zealand over 1950-2004 (very high confidence) and \r over the south-west of the state of Western Australia. In line with \r these trends, AR5 reported it is likely that drought has decreased in \r north-west Australia. Future projections for precipitation extremes \r indicate an increase in most of Australia and New Zealand, in \r terms of rare daily rainfall extremes (i.e., current 20-year return "


id: d9ccabce4ca7f4d39a3fa58abf983735
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 35615
content:
"For a  longer-term perspective based on high-quality regional \r datasets, Figure Atlas.20 shows Australasia has warmed over the \r last century (very high confidence). Australian mean temperature \r has increased by 1.44degC +- 0.24degC during the period 1910-2019 \r using the updated observed temperature dataset ACORN-SATv2.1, \r with 2019 Australia's hottest year on record and nine out of 10 of \r the warmest years on record occurring since 2005 (Trewin et al., \r 2020). Much of the warming has occurred since 1960, there is \r clear anthropogenic attribution of this change and emergence of \r the signal from the1850-1900 climate (BOM and CSIRO, 2020; \r Hawkins et  al., 2020). Warming has been more rapid than the \r national average in central and eastern Australia, with a warming \r minimum and non-significant trends since the 1960s in the north\u0002west (CSIRO and BOM, 2015; BOM and CSIRO, 2020). The National \r Institute of Water and Atmospheric Research temperature record, \r [...]"


id: 5b90adf5a5c125d5cb39fe5e0721bb84
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 13356
content:
"[...] NIWA NZ, shows a warming of 1.13degC +- 0.27degC during the period \r 1909-2019, although several stations show non-significant trends \r since 1960 (Figure Atlas.20), including a warming minimum in the \r south-east at least partly due to a persistent shift in atmospheric \r circulation (Sturman and Quenol, 2013; MfE and Stats NZ, \r 2017, 2020)."


id: 3ad016c9695f496277279bc0a650ce81
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 7542
content:
"The AR5 reported mean sea levels have also increased in Australia \r and New Zealand at average rates of relative sea level rise of \r 1.4 +- 0.6 mm yr-1 from 1900 to 2011, and 1.7 +- 0.1 mm yr-1 from \r 1900 to 2009, respectively (very high confidence). The assessment \r found that the volume of ice in New Zealand has declined by 36-61% \r from the mid- to late 1800s to the late 1900s (high confidence), while \r late-season significant snow depth has also declined in three out of \r four Snowy Mountain sites in Australia between 1957 and 2002 \r (high confidence). As mean sea level rise is projected to continue \r for at least several more centuries, there is very high confidence that \r this will lead to large increases in the frequency of extreme sea level \r events in Australia and New Zealand. On the other hand, the volume \r of winter snow and the number of days with low-elevation snow \r cover in New Zealand are projected to decrease in the future (very \r [...]"


id: 30b6a7df19019f68c34dfd9d4b2fbfff
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
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index: climate_gpt_v2
vector_id: 5825
content:
"[...] high confidence), while both snow depth and area are projected to \r decline in Australia (very high confidence)."


id: 145cb9aa8b26d7efdc59b59de5668197
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 801
content:
"Since 1960, precipitation has increased in much of mainland Australia \r in austral summer and decreased in many regions of southern and \r eastern Australia in austral winter (Figure Atlas.20). A  detectable \r anthropogenic signal of increases in precipitation in Australia has \r been reported particularly for north central Australia and for a few \r regions along the south-central coast for the period 1901-2010 \r (Knutson and Zeng, 2018). Seasonally, there is a significant decline \r in winter rainfall in the south-west of the state of Western Australia \r (Figure Atlas.20), with an attributable human influence (high \r confidence, robust evidence, medium agreement) (Section 10.4 and \r references therein, e.g.,  Delworth and Zeng, 2014). Rainfall trends \r in the south-east are not significant since 1960 but have shown \r a notable reduction since the 1990s, and there is limited evidence\r for the attribution of this change to human influence (e.g., Rauniyar \r [...]"


id: 986f210dcc018467b415f639c417a8e3
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 24029
content:
"[...] and Power, 2020). In New Zealand between 1960 and 2019 in both \r summer and winter, rainfall increased in some stations in the South \r Island and decreased at many stations in the North Island, however \r most station trends are not statistically significant (Figure Atlas.20; \r MfE and Stats NZ, 2020). In JJA, Milford Sound (increasing) and \r Whangaparaoa (decreasing) trends are significant."


id: cd85c77db4e6a3deeddd82db319e0d36
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 33489
content:
"In line with recent trends, a  significant reduction in annual mean \r rainfall in south-west Australia is projected, with the greatest reduction \r in winter and spring (very likely, high confidence). There is more than \r 80% model agreement for projected mean annual rainfall decrease \r in the south-west of the state of Western Australia for both the mid- \r (2041-2060) and far (2081-2100) future, and for all warming levels \r (Interactive Atlas). Rainfall decreases, mainly in winter and spring, \r are also projected for other regions within southern Australia with \r only medium confidence (medium evidence, medium agreement). \r Almost all models project continued drying in SAU in winter (JJA) \r and also in spring (SON), but a few models show little change. CMIP5 \r and CMIP6 results are similar or with a slightly narrower range in the \r latter (Figure Atlas.21). CORDEX produces a similar range of change \r in winter rainfall change for SAU as a whole. Circulation change is \r [...]"


id: 26bbd6eb6a19de318e23799951dd44b
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 3970
content:
"[...] the dominant driver of these projected reductions, explaining the \r range of model results for southern Australia (CSIRO and BOM, 2015; \r Mindlin et al., 2020). Studies of winter rainfall change and circulation \r in southern Australia suggest the wettest changes in winter rainfall \r change may possibly be rejected (Grose et al., 2017, 2019a)."


id: 38a6a8dbd4a52b1d08fb3a43c5c6ae82
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 7171
content:
"The CORDEX Australasia simulations produce some regional detail \r in projected precipitation change associated with important features \r such as orography. Areas where there is coincident 'added value' in \r the simulation of the current climate and 'potential added value' as \r new information in the projected climate change signal (collectively \r termed 'realized added value') in Australia include the Australian \r Alps, Tasmania and parts of northern Australia (Di Virgilio et  al., \r 2020). There have been several studies of regional climate change for \r New Zealand and states within Australia at fine resolution (5-12 km) \r that have produced important insights. One is enhanced drying in \r cool seasons on the windward slopes of the southern Australian Alps \r (decreases of 20-30% compared to 10-15% in the driving models), \r and conversely a chance of enhanced rainfall increase on the peaks \r of mountains in summer (Grose et  al., 2019b), with the summer \r [...]"


id: 8f36e00a506265783331806365de3136
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 22397
content:
"The model mean projection of northern Australian wet-season \r precipitation (a period including DJF) is for little change under all \r SSPs and warming levels, with low confidence in the direction of \r change as the projections include both large and significant decrease \r and increases (Figure Atlas.21 and Interactive Atlas). Evidence \r from warming patterns suggests a  constraint on the dry end of \r projections (Brown et al., 2016), and the CMIP6 ensemble suggests \r that the projection follows the zonally averaged rainfall response in \r the Southern Hemisphere rather than changes in the western Pacific \r (Narsey et al., 2020). There is also evidence for a projected increase \r in rainfall variability in northern Australia in scales from days to \r decades (Brown et al., 2017). Liu et al. (2018) find that under 1.5degC \r warming, central and north-east Australia are projected to become \r wetter, however this projection has low confidence. There are similar \r [...]"


id: 4ed825bc282b084406f64d0f576f4fe8
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 11152
content:
"Pool and the Caribbean low-level jet (CLLJ), while the Atlantic ITCZ is \r responsible for the unimodal rainfall cycle of the central and southern \r Lesser Antilles (Martinez et  al., 2019). The CLLJ is a  persistent \r climatological feature of the low-level circulation in the Central \r Caribbean, with a characteristic semi-annual cycle with maxima in \r the summer (main) and winter (secondary) (Amador, 1998; Magana \r et al., 1999; Whyte et al., 2008). Temporal variability is influenced by \r several large-scale atmospheric modes (Annex IV and Table Atlas.1). \r A significant positive correlation between precipitation rates in CAR \r and the Atlantic Multi-decadal Variability (AMV) was found (Enfield \r et al., 2001). A similar result was found in southern Mexico (north of \r SCA) in the MSD region (see case-study discussion in Section 10.4.2.3; \r Mendez and Magana, 2010; Cavazos et al., 2020). On the other hand, \r ENSO favours wet conditions in NCA, but its effect is modulated by \r [...]"


id: 45cdf8e1cad5e489be8d3f1625f1967c
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
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index: climate_gpt_v2
vector_id: 9499
content:
"Precipitation in most of SCA is characterized by two maxima in \r June and September, an extended dry season from November to \r May, and a shorter relatively dry season between July and August \r known as the midsummer drought (MSD; Chapter 10; Magana et al., \r 1999; Perdigon-Morales et al., 2018). To some extent, precipitation \r seasonality is explained by the migration of the Inter-tropical \r Convergence Zone (ITCZ) (Taylor and Alfaro, 2005). The climate of \r NCA is temperate to the north of the Tropic of Cancer, with a marked \r difference between winter and summer, modulated by the North \r American Monsoon (NAmerM, Section  8.3.2.4.4). The CAR region \r has two main seasons, characterized by differences in temperature \r and precipitation. The wet or rainy season, with higher values of \r temperature and accumulated precipitation, occurs during the boreal \r summer and part of spring and autumn (Gouirand et  al., 2020). \r The MSD is also present in the Greater Antilles and the Bahamas \r [...]"


id: ea5afe8add6c96fb3a3828c43338e5a9
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 38588
content:
"[...] (Taylor and Alfaro, 2005), influenced by the oscillations of the North \r Atlantic Subtropical High (NASH), interacting with the Pacific and \r Atlantic branches of the ITCZ and modulated by the Atlantic Warm "


id: c160e900e6aad4eb94a10d4badb35183
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 31326
content:
"1.6degC (SCA). For the 3degC GWL, the corresponding projected ensemble \r mean regional warming values are 2.7degC (CAR), 3.5degC (NCA) and \r 3.1degC (SCA). CAR average annual warming is below the level of \r global warming, while the two continental reference regions are \r close to the global warming level with CMIP6 and CMIP5 showing \r very consistent results (Figure Atlas.22). However, when focusing on \r time slices instead of warming levels, the CMIP6 projections show \r systematically higher median values than CMIP5. CORDEX results \r are also consistent with the previous findings, though the subset of \r driving models spans a smaller range of uncertainty, particularly over \r CAR. Results have also been reported for this region based on CMIP5, \r CMIP6 and downscaled simulations over the CORDEX CAM domain or \r similar smaller domains (Taylor et al., 2013b; Nakaegawa et al., 2014; \r Imbach et al., 2018; Vichot-Llano et al., 2019; Almazroui et al., 2021). \r [...]"


id: f57e3aae5494bf96743e9da0d347396a
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index: climate_gpt_v2
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content:
"[...] Statistical downscaling methods have been also applied to CMIP5 \r projections to obtain bias-adjusted regional projections (Colorado\u0002Ruiz et al., 2018; Taylor et al., 2018; Vichot-Llano et al., 2019)."


id: 28d50d9f32d942eb020855e3a568da39
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 4400
content:
"Regarding precipitation, it is likely that the annual average \r precipitation changes for the 1.5degC GWL will be in the ranges of -11 \r to 0% in CAR, from -12 to 0% in SCA, and from -10 to +3% in \r NCA (Interactive Atlas). For the 3degC GWL, the corresponding annual \r average precipitation changes will be from -17 to -2% in CAR, from \r -16 to +2% in NCA, and from -23 to 0% in SCA. A  clear drying \r tendency is observed for the 3degC GWL relative to the 1.5degC GWL. \r Maloney et al. (2014) examined 21st-century climate projections of \r North American climate in CMIP5 models under RCP8.5, including \r Central America and the Caribbean. Summer drying was projected \r in CAR and SCA for most of the models, with good agreement. \r The strongest drying is projected to occur during July and August \r which are the months when the MSD occurs in many sub-regions \r (Figure  Atlas.22 and the Interactive Atlas). Intensification of the \r [...]"


id: 9e48eb1168252e9de00791309c84f53
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
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index: climate_gpt_v2
vector_id: 25077
content:
"[...] MSD in SCA was also projected by using the Rossby Centre Regional \r Climate Model (RCA4; Corrales-Suastegui et  al., 2020), but with \r a future decrease in area and frequency (Cross-Chapter Box Atlas.2). \r They also found a  projected intensification of CLLJ and drying for \r the future time slice of 2071-2095, relative to their baseline of \r 1981-2005. Decreased precipitation was also projected for SCA \r (Imbach et al., 2018) with the 8-km resolution Eta RCM during the \r rainy season, including an intensification of the MSD, although no \r significant change was projected for the CLLJ."


id: 8d34efc16512ec4815bddab7bc38f08b
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 22027
content:
"Regional climate models (RCMs) forced with reanalyses and \r atmosphere-only global climate models provide simulations with \r a  reasonably good performance over the core North American \r Monsoon region, mostly in NCA (high confidence) (Bukovsky \r et  al., 2013; Cerezo-Mota et  al., 2016). RCMs also reproduce the \r seasonal spatial patterns of temperature and the bimodal rainfall \r characteristics of the NCA, SCA and CAR (high confidence) (Karmalkar \r et al., 2013; Centella-Artola et al., 2015; Martinez-Castro et al., 2018; \r Cavazos et  al., 2020; Vichot-Llano et  al., 2021b), though in some \r sub-regions specific models overestimate and shift the month of the \r maxima. RCM simulations in the region do not necessarily improve \r with the size of the domain, as important features of the regional \r circulation and key rainfall climate features, such as the CLLJ and MSD, \r are well represented for a variety of domains of different sizes (high \r [...]"


id: 84bec13a98cd035626e1b68a2b23a859
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updated_at: 2023-04-19 13:22:48
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index: climate_gpt_v2
vector_id: 20500
content:
"[...] confidence) (Centella-Artola et al., 2015; Martinez-Castro et al., 2018; \r Cabos et al., 2019; Cavazos et al., 2020; Vichot-Llano et al., 2021b)."


id: e7d4cc8a7491775558cb2cd5bb322ab3
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 38095
content:
"RCP4.5 and RCP8.5 scenarios respectively. The greatest decrease in \r precipitation is projected during summer reaching 13%, especially \r in southern Mexico, Central America and the Caribbean. Dynamically \r downscaled simulations (Bukovsky et  al., 2015) also projected \r a decrease of precipitation for the middle of the century (2041-2069) \r relative to 1971-1999 for the north of Mexico, though despite good \r agreement amongst the models, these results must be considered of \r low confidence, because of their poor simulation of important monsoon \r physical processes. Vichot-Llano et al. (2021a) used a multi-parameter \r ensemble of RegCM4, driven by the CMIP5 global model HagGEM2-ES \r projections to conclude that, relative to the 1975-2004 baseline, in \r the near (2020-2049) and more prominently in the far (2070-2099) \r future, drier conditions will prevail at over the eastern Caribbean. The \r projected future warming trend was statistically significant at the 95% \r [...]"


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created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
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index: climate_gpt_v2
vector_id: 22880
content:
"[...] confidence level over CAR and SCA. Almazroui et al. (2021) used an \r ensemble of 31 CMIP6 models to estimate climate change signals of \r temperature and precipitation in six reference regions in North and \r Central America and the Caribbean, finding a decrease in precipitation \r (10-30%) over Central America and the Caribbean under three \r scenarios with regional and seasonal variations."


id: 5f2d8cf7c1e454131de17f4a20257b63
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 14050
content:
"The regional assessment in this section emphasizes the seven new \r South American reference regions (Figure Atlas.22; Iturbide et  al., \r 2020) that have a largely consistent climate and response to climate \r change, and can be used for analysis and impact studies (Solman \r et al., 2008; Neukom et al., 2010; Barros et al., 2015; Nobre et al., \r 2016). At the sub-regional scale, several phenomena drive climate \r variability. Brazil's north-east (North-Eastern South America; NES) \r is the most densely populated dryland globally and recurrently \r affected by climatic extremes. The climate variability, particularly \r the precipitation, is marked by strong interannual variability \r related to ENSO, the ITCZ, and the North Tropical Atlantic Ocean \r SSTs (Marengo et  al., 2018a). Northern (NSA) and North-Western \r South America (NWS) are part of the Amazonia region. Its most \r recognizable features are the high rainfall, high humidity and high \r [...]"


id: af150382d73ad5e8f05e665c132ac8
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updated_at: 2023-04-19 13:22:48
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vector_id: 28049
content:
"[...] temperatures that prevail in the region. Rainfall variability in these \r regions results from the interplay between regional atmospheric \r circulation, the SST variations in both the Pacific and Atlantic \r oceans, among other regional-to-local interactions (Marengo and \r Espinoza, 2016; Espinoza et al., 2020). The South American Monsoon \r (SAM) region has distinct wet (summer) and dry (winter) periods. \r Key drivers include the South Atlantic Convergence Zone (Marengo \r et  al., 2012), the Bolivian High, the 40- to 60-day intra-seasonal \r oscillation, and the forcing of the high Andes Mountains to the west \r (Almeida et  al., 2017). The geographic position of South-Western "


id: 6efffb53250109283c26b27f01076322
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
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index: climate_gpt_v2
vector_id: 16801
content:
"South America (SWS) results in very specific climatic characteristics \r since SWS contains subtropical climates as well as sub-Antarctic and \r Antarctic climates. The climate of SWS is driven by seasonal changes \r in the position of subtropical high-pressure air masses in the South \r Atlantic and South Pacific oceans, the Southern Annular Mode, the \r dynamics of the cold Humboldt ocean current, and icy cold fronts \r and mid-latitude westerlies (Valdes-Pineda et al., 2016). The densely \r populated, highly productive sub-region of South-Eastern South \r America (SES) has cool winters and hot summers typical of the \r temperate zone, and climatic conditions are strongly tied to ENSO, \r whose influence is moderated by local air-sea thermodynamics in the \r South Atlantic (Barreiro, 2010). Lastly, the climate of the southern \r tip of South America (SSA) is cold and dry, and is influenced by the \r Southern Annular Mode, and the interaction between the wetter \r [...]"


id: c1409dfb53cf4fd2a8cdcb1e732b0957
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
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index: climate_gpt_v2
vector_id: 31300
content:
"Regionally, analyses of temperatures point to an increased warming \r trend (high confidence) over Amazonia over the last 40 years, \r which reached approximately 0.6degC-0.7degC (Figure Atlas.11 and the \r Interactive Atlas) and with stronger warming during the dry season \r and over the south-east. The analyses also showed that 2016 was the \r warmest year since at least 1950 (Marengo et al., 2018b). Andean \r temperatures showed significant warming trends, especially at \r inland and higher-elevation sites, while trends are non-significant \r or negative at coastal sites (high confidence) (Vuille et  al., 2015; \r Burger et  al., 2018; Vicente-Serrano et  al., 2018; Pabon-Caicedo \r et al., 2020). Over central Chile, positive trends are largely restricted \r to austral spring, summer and autumn seasons for mean, maximum \r and minimum temperatures (Burger et  al., 2018; Vicente-Serrano \r et  al., 2018). Over Peru, trends of maximum air temperature were \r [...]"


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vector_id: 42194
content:
"[...] mainly amplified during the austral summer, but trends of cold\u0002season minimum air temperature showed an opposite pattern, with \r the strongest warming being recorded in the austral winter (Vicente\u0002Serrano et al., 2018)."


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vector_id: 24981
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"In general, the spatial patterns of observed trends in temperature \r are more consistent than for precipitation across the whole of South \r America (medium confidence) (Interactive Atlas; de Barros Soares \r et al., 2017). In south-east Brazil there is a region of highly significant \r decrease of rainfall in both wet and dry seasons recorded in the \r period 1979-2011 (Interactive Atlas; Rao et  al., 2016). The most \r consistent evidence of positive rainfall trend occurs in the southern \r part of the La Plata basin (high confidence) (southern Brazil, Uruguay, \r and north-eastern Argentina; de Barros Soares et  al., 2017). By \r contrast, there is high confidence that annual rainfall has decreased \r over north-east Brazil during the last decades (Carvalho et al., 2020). \r Contrary to temperature changes, trends in annual precipitation \r exhibit different signs across sectors in the Andes. For instance, \r annual precipitation trends in the north tropical (north of 8degS) and \r [...]"


id: a90c787c5d38547529a07a073498ac48
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
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index: climate_gpt_v2
vector_id: 26952
content:
"[...] south tropical (8degS-27degS) Andes do not show a  homogeneous \r pattern. Over the subtropical Andes, central Chile shows a  robust \r signal of declining precipitation since 1970 (high confidence) (Pabon\u0002Caicedo et al., 2020)."


id: ff75117044d474c3d3cb473e312a8cce
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 42349
content:
"Observational studies show that the dry-season length over southern \r Amazonia has increased significantly since 1979 (high confidence) \r (Fu et al., 2013; Alves, 2016). In the Peruvian Amazon-Andes basin, \r there is no trend in mean rainfall during the period 1965-2007 (Lavado \r Casimiro et  al., 2012) though statistically significant decreases in \r total annual rainfall in the central and southern Peruvian Andes from \r 1966 to 2010 were found (Heidinger et al., 2018). Despite that, recent \r analyses of Amazon hydrological and precipitation data suggest an \r intensification of the hydrological cycle over the past few decades \r (Gloor et al., 2015). In general, these changes are attributed mainly \r to decadal climate fluctuations (high confidence), ENSO, the Atlantic \r SST north-south gradient, feedbacks between fire and land-use \r change mainly across southern south-eastern Amazon, and changes \r in the frequency of organized deep convection (Fernandes et  al., \r [...]"


id: 62a3b372c9f6078eb558082d9fbdeca6
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 14646
content:
"the tropical Andes of NWS, CMIP5 models tend to underestimate \r precipitation which is associated with the misrepresentation of \r the Pacific ITCZ and local low-level jets (Sierra et al., 2015, 2018), \r whereas over the subtropical central Andes in SWS, the models are \r found to overestimate both mean temperature and precipitation \r values (limited evidence, high agreement) (Zazulie et  al., 2017; \r Rivera and Arnould, 2020; Diaz et  al., 2021). Most models show \r a dry bias over SES (Diaz and Vera, 2017; Barros and Doyle, 2018; \r Solman and Blazquez, 2019; Diaz et  al., 2021) associated with an \r underestimation of the northern flow that brings water vapour into \r the region (medium confidence) (Gulizia et al., 2013; Zazulie et al., \r 2017; Barros and Doyle, 2018). The biases in seasonal precipitation, \r annual precipitation and climate extremes over several regions of \r South America were reduced, including the Amazon, central South \r [...]"


id: 2babcd5a8492507b6356faa6f307dd7b
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 4944
content:
"[...] America, Bolivia, eastern Argentina and Uruguay, in the CMIP5 \r models when compared to those of CMIP3 (medium confidence) \r (Joetzjer et  al., 2013; Gulizia and Camilloni, 2015; Diaz and Vera, \r 2017). The evidence is still insufficient to determine whether \r CMIP6 biases are reduced when compared with CMIP5 simulations \r regarding precipitation and its variability in South America. The \r temperature and precipitation patterns of anomalies associated \r with ENSO in tropical South America (NWS, NSA and NES) are better \r captured by GCMs in tropical South America (NWS, NSA and NES) \r than in extratropical South America (SES), particularly during austral \r summer and autumn (limited evidence, high agreement) (Tedeschi \r and Collins, 2016; Perry et al., 2020)."


id: 6f2ab951b372dda3362f168bcbdb002e
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 16828
content:
"Based on regional simulations, studies showed that some RCMs \r improve the quality of the simulated climate when compared with \r the driving GCM (medium evidence, high agreement) (Llopart et al., \r 2014; Sanchez et al., 2015; Falco et al., 2019; Solman and Blazquez, \r 2019; Ciarlo` et al., 2021; Teichmann et al., 2021). Regional climate \r model (RCM) simulations over South America can reproduce the \r main features of temperature and precipitation in terms of both \r spatial distributions (Solman et  al., 2013; Falco et  al., 2019) and \r seasonal cycles over the different climate regimes, including the main \r SAmerM features (high confidence) (Jacob et al., 2012; Solman, 2013; \r Llopart et al., 2014; Reboita et al., 2014; de Jesus et al., 2016; Lyra \r et al., 2018; Bozkurt et al., 2019; Ashfaq et al., 2021). However, RCMs \r showed systematic biases such as precipitation overestimations and \r temperature underestimations along the Andes throughout the year \r [...]"


id: e5a7f3da39bab3373df03a7b5af09144
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 37709
content:
"[...] (high confidence), although these biases may be artificially amplified \r by the lack of a dense observational station network (Jacob et al., \r 2012; Solman et al., 2013; Bozkurt et al., 2019; Falco et al., 2019). \r RCMs tended to show dry biases over the Amazon and the northern \r part of the continent (SAM, NSA) during DJF and during the maximum \r precipitation associated with the ITCZ over NSA during JJA (medium \r evidence, high agreement) (Solman et al., 2013; Falco et al., 2019). \r Temperature overestimation and precipitation underestimation over \r La Plata basin (in SES) are also RCM common biases, with the warm \r bias amplified for austral summer and the dry bias amplified for the \r rainy season (high confidence) (Solman et al., 2013; Reboita et al., \r 2014; Solman, 2016; Falco et al., 2019). Despite their relevance, RCM \r simulations at very high resolution (less than 10  km) are still few \r in South America (high confidence) and are mainly designed for \r [...]"


id: 8b6b32346bfccba97a518422107b7f57
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 21688
content:
"[...] specific regions or purposes (Lyra et al., 2018; Bozkurt et al., 2019; \r Bettolli et al., 2021)."


id: d13c570183c3df5f2dba7b84784d983a
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 34164
content:
"(Figure Atlas.22 and the Interactive Atlas). In the medium and long \r term, under the high-emissions scenario, the CMIP5 multi-model \r ensemble projected an increase in precipitation (generally greater \r than 10%) in SES and NWS, and a decrease (less than 10%) in NSA \r across seasons (high confidence, robust evidence) (Solman, 2013; \r Chou et al., 2014; Coppola et al., 2014; Llopart et al., 2014, 2021; \r Reboita et al., 2014, 2021; Sanchez et al., 2015; Menendez et al., 2016; \r Ruscica et al., 2016; Bozkurt et al., 2018a; Zaninelli et al., 2019). Also, \r in parts of SWS, annual precipitation is projected to decrease (up to \r 30%) by the late 21st  century (Souvignet et  al., 2010; Palomino\u0002Lemus et al., 2017, 2018; Bozkurt et al., 2018a). Under high RCPs, the \r CMIP5 ensemble projects that all Brazilian regions will experience \r more rainfall variability in the future, so drier dry periods and wetter \r wet periods on daily, weekly, monthly and seasonal time scales, \r [...]"


id: de467c1cf9e28c8cbb8e31efe96cd03f
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 36366
content:
"[...] despite the future changes in mean rainfall being currently uncertain \r (medium confidence) (Alves et  al., 2021). Regarding the SAmerM, \r it is very likely that the monsoon will experience changes in its life \r cycle by the end of the 21st  century for both RCP4.5 and RCP8.5 \r emissions and, in particular, delayed onset. However there is low \r agreement on the projected changes in terms of extreme and total \r precipitation of the monsoon season in South America (Llopart et al., \r 2014; Ashfaq et al., 2021). Changes in the SAmerM are assessed in \r Section 8.3.2.4.5."


id: 4d3ad2518a45f17ef74564f79a0b385c
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 10868
content:
"The AR5 (Collins et al., 2013) reports that the ability of models to \r simulate the climate in Europe has improved in many important \r aspects. Particularly relevant for this region are increased model \r resolution and a better representation of the land surface processes \r in many of the models that participated in CMIP5. The spread in \r climate model projections is still substantial, partly due to pronounced \r internal variability in this region (particularly NAO and AMO). In the \r winter half year, NEU and WCE are likely to have increased mean \r precipitation associated with increased atmospheric moisture and \r moisture convergence, and intensification in extratropical cyclone \r activity. No change or a  moderate reduction is projected for \r MED. In the summer half year, it is likely that NEU and WCE mean \r precipitation will have only small changes with a notable reduction \r in MED. According to SR1.5 (Hoegh-Guldberg et  al., 2018), these \r [...]"


id: 47528dedc55b6c9161096e9935923ed6
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 9756
content:
"[...] precipitation changes are more pronounced at 2degC than at 1.5degC of \r global warming. For a 2degC global warming level, an increase in runoff \r is projected for north-eastern Europe while decreases are projected \r in the Mediterranean region, where runoff differences between 1.5degC \r and 2degC global warming will be most prominent (medium confidence). \r According to SROCC (Hock et al., 2019b) the RCP8.5 projections lead \r to a loss of more than 80% of the ice mass from small glaciers by the \r end of century in Central Europe (high confidence). Snow cover and \r glaciers are projected to decrease throughout the 21st century."


id: c54b824fe2a520f330fca7c0e39caf53
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
content_type: text
index: climate_gpt_v2
vector_id: 32015
content:
"To support climatological analyses and model evaluation, national \r meteorological and hydrological services are increasingly making \r available high spatial and temporal resolution gridded and in situ \r homogenized and quality-checked datasets (Deque and Somot, 2008; \r Vidal et al., 2010; Rauthe et al., 2013; Noel et al., 2015; Spinoni et al., \r 2015b; Ruti et  al., 2016; Fantini et  al., 2018; Lussana et  al., 2018; \r Herrera et al., 2019; Skrynyk et al., 2020). The inclusion of additional \r station data and data rescue activities lead to a better representation \r of extreme precipitation statistics than the global- or continental\u0002scale datasets (Atlas.1.4.1). Recent gridded products merging radar \r and station data allow higher spatial and temporal resolutions to be \r reached (Haiden et al., 2011; Tabary et al., 2012; Berg et al., 2016; \r Fumiere et al., 2020). A number of regional reanalysis products has \r become available for the European region (Bollmeyer et  al., 2015; \r [...]"


id: 489c2d318c2ef5803291ee7a0160345e
created_at: 2023-04-19 13:22:48
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index: climate_gpt_v2
vector_id: 9992
content:
"[...] Bach et  al., 2016; Dahlgren et  al., 2016; Landelius et  al., 2016). \r A  European ensemble of regional reanalyses from 1961 to 2019 "


id: 2a0b8bc60014d6c33135b27a7583e90c
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
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index: climate_gpt_v2
vector_id: 4613
content:
"The large-scale spatial patterns of the E-OBS annual mean \r precipitation trend between 1980 and 2015 shown in Figure Atlas.23 \r is broadly consistent with trends derived from CRU, GPCP and \r GPCC (Figure Atlas.11) but with more explicit spatial detail. Trends \r calculated for regional averages are sensitive to the selection of the \r time window: for 1980-2015 annual mean precipitation averaged \r over the regions shows a positive trend (not significant at p = 0.05), \r while for CRU and GPCC the trend calculated over 1901-2015 \r is positive for NEU, EEU and WCE, and non-significant for MED. \r Precipitation trends in the MED are significant only in selected areas \r (Lionello et al., 2012; MedECC, 2020). Also the NEU trends show large \r spatial variability and are subject to decadal variability related to \r NAO (Heikkila and Sorteberg, 2012), but are generally positive over \r the 20th century (Figure Atlas.23). There is medium confidence that \r [...]"


id: 687122df1a1cf7acee31bda335d0e1fe
created_at: 2023-04-19 13:22:48
updated_at: 2023-04-19 13:22:48
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index: climate_gpt_v2
vector_id: 15642
content:
"[...] annual mean precipitation in NEU, WCE and EEU has increased since \r the early 20th century. In the European Mediterranean, observed land \r precipitation trends show pronounced variability within the region, \r with magnitude and sign of trend in the past century depending on \r time period and exact study region (medium confidence)."


id: 588ff14ecd89e315bb2dfb56187a42d9
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 12826
content:
"In NEU continued warming has been observed, particularly during \r spring. An annual mean temperature increase of 0.4degC per decade \r was reported between 1970 and 2008 (Rutgersson et al., 2015). In \r WCE temperature increases since the mid-20th  century have been \r documented for Poland (Degirmendzic et  al., 2004) and Ukraine \r (Boychenko et  al., 2016; Balabukh and Malitskaya, 2017). Land\u0002only observations indicate a  rapid increase in summer (JJA) mean \r surface air temperature since the mid-1990s (Dong et al., 2017). In \r Eastern Europe no significant trend in winter mean air temperatures \r was found between 1881 and 2016 in Belarus (Loginov et al., 2018). \r In parts of the European area of the MED, spring and summer \r temperatures are reported to increase faster than in the other \r seasons (see the Mediterranean case study in Section  10.6.4 and \r Figure 10.18; Brunetti et al., 2006; Homar et al., 2009; Lionello et al., \r [...]"


id: f9b1ad42347dcb96b027c17b8a88ab06
created_at: 2023-04-19 13:22:49
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content:
"[...] 2012; Philandras et al., 2015; Gonzalez-Hidalgo et al., 2016; Vicente\u0002Serrano et al., 2017). Figure Atlas.23 shows that since 1980 in each \r European region all datasets show a consistent warming of annual \r mean temperature of 0.04degC yr -1 to 0.05degC yr -1. Trends in European \r land temperature  cannot be explained without accounting for \r anthropogenic warming offset by anthropogenic aerosol emissions \r (Section 3.3.1.1 and Figure 3.9). It is virtually certain that annual mean \r temperature continues to increase in each European subdomain."


id: 5e46dc12f25db22550f3dc714180e7be
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 13876
content:
"Trends in snowfall and snowmelt are related to seasonal changes \r in both temperature and precipitation. In EEU, melt onset dates \r have advanced by one to two weeks in the 1979-2012 period \r (Mioduszewski et  al., 2015). Over Eurasia, trends in spring and \r early summer snow cover extent increased over the 1971-2014 \r period (Hernandez-Henriquez et al., 2015). Between 1966 and 2012, \r averaged over entire Eurasia, monthly mean snow depth decreased \r in autumn and increased in winter and spring (Zhong et al., 2018), \r while the snow cover extent was reported to have decreased during \r the past 40 years (Bulygina et al., 2011). In NEU late winter and early \r spring snow depth and snow cover decreases since the early 1960s \r are reported over Finland (Luomaranta et al., 2019) and Norway (Rizzi \r et al., 2018) with a dependence on altitude (Skaugen et al., 2012), \r while winter snow depth increased in northern Sweden (Kohler et al., \r [...]"


id: b2a0d7a1034aee1281696f39e0046d08
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
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index: climate_gpt_v2
vector_id: 28665
content:
"[...] 2006). It is very likely that since the early 1980s in snow-dominant \r areas in NEU and EEU the length of the snowfall season is reduced \r with regional warming, and the melt onset dates have advanced."


id: 3c39e65d55e5cbd54c6223266ee9cab8
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 7789
content:
"Regional climate models (RCMs; Section  10.3.1.2) have been \r extensively evaluated for a  range of climate features over Europe \r (Casanueva et  al., 2016; Vaittinada Ayar et  al., 2016; Krakovska \r et  al., 2017; Terzago et  al., 2017; Cavicchia et  al., 2018; Drobinski \r et al., 2018; Fantini et al., 2018; Harzallah et al., 2018; Ivanov et al., \r 2018; Panthou et al., 2018a). Standard assessments of RCMs driven \r by reanalyses, typically run at 12-25 km spatial resolution, confirm \r that the Euro-CORDEX and Med-CORDEX ensembles are capable of \r reproducing the salient features of European climate (Kotlarski et al., \r 2014; Krakovska, 2018) and represent European circulation features \r realistically (Cardoso et al., 2016; Drobinski et al., 2018; Flaounas et al., \r 2018; Sanchez-Gomez and Somot, 2018). Seasonal and regionally \r averaged temperature biases generally do not exceed 1.5degC, while \r precipitation biases can be up to +-40% (Kotlarski et  al., 2014). \r [...]"


id: f39b42bb7419f374fedb2aa40606c9ff
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
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index: climate_gpt_v2
vector_id: 40231
content:
"[...] Extensive evaluation of a large collection of RCM-GCM combinations \r show a general wet, cold and windy bias compared to observations \r and reanalyses, but none of the models is systematically performing \r best or worst (Vautard et  al., 2021). Higher-resolution simulations \r do show improved performance in reproducing the spatial patterns \r and seasonal cycle of not only extreme precipitation but also mean \r precipitation over all European regions (see Sections  10.3.3.4 and \r 10.3.3.5 for an extensive evaluation of the added value of increased \r simulation resolution; Mayer et al., 2015; Fantini et al., 2018; Soares \r and Cardoso, 2018; Ciarlo` et al., 2021)."


id: 2a2dd127ffa2516842eb0c2904913625
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 4633
content:
"New, or updated, higher-resolution, coupled atmosphere-ocean-ice \r model systems have been found to improve simulations of observed \r climate features over the Baltic area compared to atmosphere-only \r model versions, including correlation between precipitation and SST, \r between surface heat-flux components and SST, and weather events \r like convective snow bands over the Baltic Sea (e.g., Tian et al., 2013; \r Van Pham et  al., 2014; Groger et  al., 2015; S. Wang et  al., 2015; \r Pham et al., 2017). Coupled atmosphere-land-river-ocean regional \r climate system models (RCSMs) from Med-CORDEX have similar skill \r as the ENSEMBLES and the Euro-CORDEX ensembles to represent \r decadal variability of Mediterranean climate and its extremes \r (Cavicchia et al., 2018; Dell'Aquila et al., 2018; Gaertner et al., 2018). \r Panthou et al. (2018a) showed that, over land, differences between \r atmosphere-only and coupled RCMs are confined to coastal areas \r [...]"


id: 5ae4e4096a571f356b1b7793cf287323
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 13239
content:
"[...] that are directly influenced by SST anomalies. In contrast, Van \r Pham et al. (2014) showed significant differences in seasonal mean \r temperature across a widespread continental domain."


id: fd3cbddb17ba2b1aa4a110cb866f0ba8
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 41904
content:
"SSP5-8.5 emissions in CMIP6 exceeds the warming projected by \r RCP8.5 emissions in CMIP5 (Section  4.3; Forster et  al., 2020). In \r selected regions in Europe CMIP6 also projects a  systematically \r higher mean temperature than CMIP5 (Seneviratne and Hauser, \r 2020). The annual mean projections from CMIP5, CMIP6 and 0.11deg \r resolution EURO-CORDEX contained in the Interactive Atlas are \r shown for the four European regions in Figure Atlas.24. For each \r region and season a  warming offset between the pre-industrial \r (1850-1900) and the recent past (1995-2014) baselines is also \r shown. The results confirm higher CMIP6 long-term annual mean \r warming rates for WCE, EEU and MED and a  larger inter-model \r spread for each region. For given GWLs, regional annual mean \r temperature change in CMIP5 and CMIP6 are largely consistent \r and higher than the global average, most prominently in EEU. \r For high warming levels the CMIP5 subset of eight GCMs used to \r [...]"


id: ee2223263ab7f6402a196d26a4067a96
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 39267
content:
"[...] drive the EURO-CORDEX simulations show a  lower annual mean \r temperature change than the full CMIP5 ensemble in each of the \r European sub-regions. This illustrates the large inter-model spread \r and implications for subsampling a relatively small subset from the \r full ensemble. Regional warming is strongest in continental EEU \r away from the Atlantic and in MED during summer (Lionello and \r Scarascia, 2018). The assessment of EURO-CORDEX projections for \r levels of global warming of 1.5degC and 2.0degC indicate enhanced local \r warming even at relatively low global warming levels, particularly \r towards the north in winter (Schaller et al., 2016; Dosio and Fischer, \r 2018; Kjellstrom et al., 2018; Teichmann et al., 2018)."


id: b6d7b88406baf2a435c8339313a210f9
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 29367
content:
"Regional warming is virtually certain to extend the observed \r downward trends in snow accumulation, snow water equivalent \r and length of the snow cover season in NEU and at low altitudes in \r mountainous areas in the Alps and Pyrenees (very high confidence). \r This is supported by regional and global multi-model and/or \r single-model ensemble projections including CMIP5, PRUDENCE, \r ENSEMBLES and EURO-CORDEX (Jylha et  al., 2008; Steger et  al., \r 2013; Mankin and Diffenbaugh, 2015; Schmucki et  al., 2015; \r Marty et al., 2017; Frei et al., 2018), and attributed to changes in \r the snowfall fraction of precipitation and to increased snowmelt. \r In mountain areas a  strong dependence of projected snow \r trends on altitude is shown, with most pronounced effects below \r 1500 m (Lopez-Moreno et al., 2009). Terzago et al. (2017) showed \r a  large positive bias in the amplitude of the annual snow cycle \r of EURO-CORDEX 0.11deg simulations driven by GCM projections, \r [...]"


id: ce54773ac41d0c050d0132cd8cc09311
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 33640
content:
"Some signatures of climate change projected by GCMs are modified \r by RCMs and CPRCMs. Projections of temperature, precipitation and \r wind in RCMs may deviate from GCM signals dependent on the \r dominant atmospheric circulation (Kjellstrom et al., 2018). In many \r areas RCMs produce lower warming rates and higher precipitation \r (less drying) in summer (Fernandez et al., 2019; Boe et al., 2020a). \r Also, for mean surface shortwave radiation, systematic differences \r between GCM and RCM outputs are found (Bartok et  al., 2017; \r Gutierrez et  al., 2020). Although RCMs generally have a  smaller \r bias for the present climate (Sorland et al., 2018) and better cloud \r representation (Bartok et  al., 2017), the representation of aerosol \r forcing (Boe et  al., 2020a; Gutierrez et  al., 2020), air-sea coupling \r (Boe et al., 2020a) or vegetation response to elevated atmospheric \r CO2 (Schwingshackl et  al., 2019) give rise to systematic biases in \r [...]"


id: e8f0a97bb39be8c9b12edce34af6e27e
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 38331
content:
"[...] RCM projections. The comparison between EURO-CORDEX and the \r CMIP5 subset shown in Figure Atlas.24 illustrates that the RCMs \r primarily modify the climate change warming signal from the driving \r GCMs for MED and WCE in summer (Boe et al., 2020a)."


id: 47bbfc961381343d1b79aa9615cb7ed8
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vector_id: 9834
content:
"The recent-past climate of North America is characterized by \r high spatial heterogeneity and by variability at diverse temporal \r scales. Considering the traditional Koppen-Geiger classification, \r North America covers all main climate types (see reference region \r descriptions below). Important geographical features influence \r local climates over various distances, like the Rocky Mountains \r through cyclogenesis (Grise et  al., 2013) and the Great Lakes \r through  lake-effect snowfall (Wright et  al., 2013). The cryosphere \r is an important component of the climate system in North America, \r with fundamental roles for sea ice cover, snow cover and permafrost. \r The ocean surrounding the continent also influences its climate, \r with water temperatures strongly influencing hurricane activity \r which impacts the coasts of eastern Mexico and south-eastern USA \r (Walsh et  al., 2010). Temporal variability is influenced by several \r [...]"


id: ddf58f35bdfddfa525c4215a56fdd3a5
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
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content:
"[...] large-scale atmospheric modes (Table Atlas.1 and Annex IV) with \r the North Atlantic Oscillation (NAO) affecting north-eastern USA and \r eastern Canada precipitation (Whan and Zwiers, 2017), and El Nino-\r Southern Oscillation (ENSO) affecting temperature and precipitation \r in California, although in a  complex and not yet fully understood \r manner (Yoon et al., 2015; Yeh et al., 2018)."


id: 2f5588b7ff371c2e700dbe6563d7168f
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
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index: climate_gpt_v2
vector_id: 5592
content:
"The observed trends in annual mean surface temperature \r (Figure Atlas.11 and the Interactive Atlas) across near-Arctic latitudes \r are exceptionally pronounced (>0.5degC per decade), significant \r and consistent across datasets except for far north-east Canada \r where trends are not significant in the CRU dataset. Significant \r positive trends are seen across the rest of North America during \r 1961-2015 (Figure Atlas.11) though over the shorter 1980-2015 \r period the regional dataset Daymet (Thornton et al., 2016) records \r non-significant changes over southern Alaska, western and south\u0002central Canada, and north-central USA (Interactive Atlas). An analysis \r of annual mean surface temperature in the Berkeley Earth dataset \r aggregated over the reference regions (Figure Atlas.11) demonstrates \r that a  temperature change signal has emerged over all regions \r of North America. There is a  detectable anthropogenic influence \r [...]"


id: d61e0390436ed3009689d91a6985221d
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
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index: climate_gpt_v2
vector_id: 34989
content:
"[...] (medium confidence) on the observed upward annual temperature \r trends in Western and northern North America (Vose et  al., 2017; \r Z. Wang et al., 2017; Smith et al., 2019)."


id: 9d79cd0b5b5d80c000d80a147469e5e7
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 24942
content:
"The reference regions defined for summarising North America climate \r change (Figure Atlas.26) include: North-Western North America (NWN), \r characterized by a sub-Arctic climate with cool summers and rainfall \r all year round; North-Eastern North America (NEN), which also has \r a sub-Arctic climate with sections of tundra climate in the far north \r (these two northern regions are also discussed in Section Atlas.11.2, \r Polar Arctic); Western North America (WNA), which has a  complex \r but mainly cold semi-arid climate; Central North America (CNA) with \r a mainly continental climate in the northern part of the region and \r a  humid subtropical climate in the southern portion; Eastern North \r America (ENA) with a humid continental climate in the northern half \r and a humid subtropical climate to the south; Northern Central America \r (northern Mexico; NCA), has a temperate climate to the north of the \r Tropic of Cancer, with marked differences between winter and summer, \r [...]"


id: 2f817cf4046fcafc2fd29f29bbff275
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 5618
content:
"There is evidence of a recent decline in the overall North American \r annual maximum snow mass, with a  trend for non-alpine regions \r above 40degN during 1980-2018 estimated from the bias-corrected \r GlobSnow 3.0 data (medium confidence) (Pulliainen et  al., 2020). \r This is despite technical challenges with in situ measurements and \r remote-sensing retrievals of snow variables (Larue et  al., 2017; \r Smith et al., 2017; X.L. Wang et al., 2017; Zeng et al., 2018), spatial \r heterogeneity and interpolation assumptions that affect gridded \r reference products, notably over alpine and forested areas (Mudryk \r et al., 2015; Dozier et al., 2016; Cantet et al., 2019), and breaks in \r instruments and procedures (Kunkel et  al., 2007; Mortimer et  al., \r 2020). Changes in snow cover have evolved in a complex way, with \r both positive and negative trends, and differing from one metric \r to another (Knowles, 2015; Brown et al., 2019). Evidence of snow \r [...]"


id: a011731ce9f71f20d8ba354c408be775
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 25374
content:
"[...] cover decline includes decreases in annual maximum snow depth \r and in snow water equivalent (Vincent et  al., 2015; Kunkel et  al., \r 2016; Mote et al., 2018), as well as a shortening of the snow-season \r duration (Knowles, 2015; Vincent et  al., 2015). However, reported \r snow-decline trends are statistically significant only for a fraction of \r the concerned areas or locations (low confidence) (Figure Atlas.25). \r See also Sections 2.3.2.2 and 9.5.3.1."


id: 23421d1566ed177b73082f8541ac9d04
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 3406
content:
"CMIP6 models have been evaluated in the literature, although these \r studies have not included the full set of CMIP6 simulations. Fan et al. \r (2020) established on a  continental basis for North America that \r temperature pattern correlations were quite accurate. Thorarinsdottir \r et al. (2020) compared maximum and minimum temperatures over \r Europe and North America with several observational datasets \r and found that the CMIP6 ensemble agreed better with ERA5 \r data than did CMIP5. Srivastava et  al. (2020) evaluated historical \r CMIP6 simulations for precipitation, comparing them with several \r observational datasets over the continental US. Most models show \r a  wet bias over the eastern half of the continental USA and the \r north-east region, while dry biases persist in the central part of \r the country (Akinsanola et  al., 2020a; Almazroui et  al., 2021). The \r spatial structure of biases is similar in CMIP5 and CMIP6, but with \r [...]"


id: 8baf0056364d63084f424a03a63af5e2
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
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index: climate_gpt_v2
vector_id: 21751
content:
"[...] lower magnitudes in CMIP6. Agel and Barlow (2020) examined \r 16 CMIP6 models over the north-eastern USA for precipitation and \r did not find a distinct improvement over CMIP5, although they did \r find the higher-resolution models tended to perform better. On the \r basis of the evidence so far, there is medium confidence that CMIP6 \r models are improved compared to CMIP5 in terms of biases in mean \r temperature and precipitation over North America."


id: 61343d35679d39b4b7dd5a584787acd1
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
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index: climate_gpt_v2
vector_id: 14400
content:
"Yoon et al., 2012), and NA-CORDEX (Bukovsky and Mearns, 2020). \r Therefore, much performance evaluation has been conducted with \r a focus on specific climate features in North America. For the North \r American Monsoon region, multi-model performance evaluation \r (Bukovsky et al., 2013; Tripathi and Dominguez, 2013; Cerezo-Mota \r et al., 2016) or a single-member performance (Lucas-Picher et al., \r 2013; Martynov et al., 2013; Separovic et al., 2013) demonstrated \r the added value of RCMs, particularly more recent CORDEX \r simulations, through improved simulation of summer precipitation \r and the climatological winter storm tracks across the western USA. \r NA-CORDEX simulations were more successful at reproducing \r weather types compared to a single model-based large perturbed\u0002physics ensemble (Prein et al., 2019). The application of a complex \r evaluation tool to the full suite of NA-CORDEX simulations found \r that the higher-resolution simulations (25 km compared with 50 km) \r [...]"


id: dff6bf9be975758b589ba7aa077ffec2
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 36645
content:
"CMIP5 and CMIP6 surface temperature and precipitation projections \r over the region are similar, with all regions warming more than the \r global average, most prominently those in the north (Figure Atlas.26). \r CMIP6 projects, for all scenarios and time periods, higher temperature \r changes (Chapter 4), with this contrast more accentuated in the long\u0002term future and at higher global warming levels. The higher warming \r in the north (Interactive Atlas) is clear when comparing NEN, with \r increases from 2degC to over 8.5degC on an annual basis for SSP5-8.5 \r (near term to long term compared to a 1995-2014 baseline), to NCA, \r where changes range from 1.5degC to 6degC across the same periods. \r Maps showing changes in temperature and precipitation, and their \r robustness, are available in the Interactive Atlas. The number of model \r results (i.e., ensemble size used to generate these figures) differs, and \r this sample size difference may affect the results, but the patterns \r [...]"


id: 13929e72547033d9daba041db5d28424
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updated_at: 2023-04-19 13:22:49
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index: climate_gpt_v2
vector_id: 657
content:
"[...] and magnitudes of change are generally consistent and thus it is very \r likely that temperatures will increase throughout the 21st century in \r all land areas, with stronger warming in the far north."


id: 58fea18eea4826f43b7ce080c88e1b4a
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
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index: climate_gpt_v2
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content:
"CMIP5 results have been analysed extensively (e.g., Maloney et al., \r 2014) and used in major climate change assessments. The most \r recent US National Climate Assessment analysis of CMIP5 focusing \r on RCP4.5 and RCP8.5 for two future time periods stated that the \r USA would continue to warm regardless of the scenario, but is likely\r to be higher with higher-emissions scenarios (e.g., RCP8.5). Projected \r changes in precipitation are somewhat complex, but increased \r precipitation dominates in winter and spring, whereas in summer \r changes are more variable and uncertain. Canada's Changing Climate \r Report (Bush and Lemmen, 2019) presents changes in temperature \r and precipitation, as well as other variables, such as snow, for \r future periods in Canada using results from CMIP5. It indicates that \r annual and winter precipitation is projected to increase everywhere \r in Canada over the 21st  century with larger percentage increases \r [...]"


id: ca2fab9df0d34bcb77e9848537dcd17
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 32911
content:
"[...] in the north. Temperature is also projected to increase, regardless of \r the scenario, and with larger changes occurring in the north."


id: 8f598a54af0a74ed7511c138a49189b9
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
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index: climate_gpt_v2
vector_id: 22433
content:
"To provide the basis for generating additional information compared to \r that derived from CMIP5 the NA-CORDEX experiments were designed \r to involve a  GCM-RCM matrix which included multiple  GCMs that \r sampled the full range of climate sensitivity, multiple RCMs, at \r two different spatial resolutions (25 and 50  km) and a  range of \r emissions scenarios (in most cases RCP4.5 and RCP8.5; Mearns et al., \r 2017). Karmalkar (2018) noted that the NA-CORDEX models cover \r sub-regional ranges of temperature change from the CMIP5 GCMs \r better than NARCCAP did for the CMIP3 models. This structural design \r shift provides greater confidence in the NA-CORDEX results in terms \r of sampling the uncertainty across the CMIP5 models (Figure Atlas.27; \r Bukovsky and Mearns, 2020). The pattern of warming is as seen in \r CMIP5 and CMIP6, which also builds confidence that the RCMs \r generate high-resolution results consistent with CMIP5 on large \r [...]"


id: f3d16a42d52f1cd8a197324a751a812a
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vector_id: 40272
content:
"[...] scales whilst providing added value over regions such as the complex \r topography of the Rocky Mountains in the western USA, which are not \r well resolved in the GCMs. There is high confidence that downscaling \r a subset of CMIP models that spans the range of climate sensitivities \r in the full ensemble is critical for producing a representative range of \r dynamically downscaled projections."


id: 71fc46a9bc215c9e604aa593daaea69c
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 17302
content:
"Recent studies have examined RCMs' simulation of SWE, a quantity \r of primary importance notably for hydrological modelling, though \r its ground measurements are restricted by relatively high time \r and monetary costs (Smith et  al., 2017; Odry et  al., 2020) which \r limit model assessment. Also, studies often emphasize that a false \r impression of model skill for SWE can be obtained by compensating \r temperature and precipitation biases. Assessment frameworks have \r dealt with these issues by considering observational uncertainty \r (Mccrary et  al., 2017) and by decomposing SWE biases into their \r contributing processes (Rhoades et al., 2018; Xu et al., 2019). SWE \r biases exceed observational uncertainty in several 50-km reanalysis\u0002driven NARCCAP simulations over several regions, for all cold \r months (Mccrary et al., 2017). Analyses of NA-CORDEX simulations \r show that refining spatial resolution from 50 to 12  km improves \r [...]"


id: 7bc6efc0958172cd67d7c65bcabe1c70
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
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index: climate_gpt_v2
vector_id: 18987
content:
"[...] certain (but not all) aspects of SWE, stemming from improved mean \r precipitation and topography-related temperature (Xu et al., 2019). \r Similarly an assessment of RCM simulations of freezing rain over \r eastern Canada found a mix of improved and deteriorated aspects \r from higher resolution (St-Pierre et al., 2019)."


id: 765ba3a46f06256080430150afdc1965
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 18047
content:
"The fraction of precipitation falling as snow is projected to decrease \r practically everywhere over North America, including over the \r western USA and south-western Canada (Mahoney et  al., 2021), \r and in the Great Lakes basin where lake-effect precipitation is \r important (Suriano and Leathers, 2016). In this basin, the frequency \r of heavy lake-effect snowstorms is expected to decrease during \r the 21st  century, except for a  possible temporary increase around \r Lake Superior by mid-century, if local air temperatures remain low \r enough (Notaro et  al., 2015). CMIP5 simulations of the periods \r 1981-2000 and 2081-2100 over the central and eastern USA \r suggest a  northward shift in the transition zone between rain\u0002dominated and snow-dominated areas, by about 2deg latitude under \r the RCP4.5 scenario and 4deg latitude under the RCP8.5 scenario \r (Ning and Bradley, 2015). Rain-on-snow event properties over North \r [...]"


id: 9855f6e3e5b6dc5773b3d08a3b8e1e12
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content:
"[...] America should also evolve during the 21st century, with non-trivial \r dependencies on the positioning relative to the freezing line (Jeong \r and Sushama, 2018) and on elevation (Musselman et al., 2018)."


id: 67f1a94cac5e370d8d1de057114b44f0
created_at: 2023-04-19 13:22:49
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index: climate_gpt_v2
vector_id: 15556
content:
"As discussed in Section 10.3.3.4, an important advance in regional \r modelling over the past decade or so is the use of convection\u0002permitting regional models (CPMs; Prein et al., 2015, 2017a). There \r have been a number of experiments using CPMs over North America \r (e.g.,  Rasmussen et  al., 2014; Prein et  al., 2015, 2019; Liu et  al., \r 2017; Komurcu et al., 2018). A CPM study over North America that \r investigated changes in Mesoscale Convective Systems projected \r that by the end of the century, assuming an RCP8.5 scenario, \r their frequency more than tripled and associated precipitation \r increased by 80% (Prein et al., 2017b). A multiple nesting of WRF \r over the north-eastern USA, downscaling to 3  km a  CESM GCM \r climate projection assuming an RCP8.5 scenario, found a different \r pattern of precipitation change of mixed increases and decreases \r compared to the GCM projection of increases every month (Komurcu \r [...]"


id: 8913cce8d570afa4071b529194477c64
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updated_at: 2023-04-19 13:22:49
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vector_id: 21283
content:
"[...] et  al., 2018). These investigations demonstrate the potential of \r very-high-resolution simulations to add important dimensions to our \r understanding of regional climate change, though not necessarily to \r reduce uncertainty (high confidence)."


id: e30ba41b8b4153123d31400bee10e885
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
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index: climate_gpt_v2
vector_id: 37241
content:
"Ocean warming rates have likely increased in recent decades with \r marine heatwaves increasing and very likely to have become longer\u0002lasting, more intense and extensive as a  result of anthropogenic \r warming. Open ocean oxygen levels have very likely decreased and \r oxygen minimum zones have likely increased in extent. There is very \r high confidence that global mean sea level rise has accelerated in \r recent decades which, combined with increases in tropical cyclone \r winds and rainfall and increases in extreme waves, has exacerbated \r extreme sea level events and coastal hazards (high confidence). It is \r virtually certain that during the 21st century, the ocean will transition \r to unprecedented conditions with further warming and acidification \r virtually certain, increased upper ocean stratification very likely and \r continued oxygen decline (medium confidence). There is very high \r confidence that marine heatwaves and medium confidence that \r [...]"


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vector_id: 19761
content:
"[...] extreme El Nino and La Nina events will become more frequent. It \r is very likely that these changes will be smaller under scenarios with \r low greenhouse gas emissions. Global mean sea level will continue \r to rise and there is high confidence that the consequent increases \r in extreme levels will result in local sea levels in most locations that \r historically occurred once per century occurring at least annually by \r the end of the century under all RCP scenarios (high confidence). In \r particular, many small islands are projected to experience historical \r centennial events at least annually by 2050 under RCP2.6 and higher \r emissions. The proportion of Category 4 and 5 tropical cyclones, and \r associated precipitation rates and storm surges, along with average \r tropical cyclone intensity are projected to increase with a 2degC global \r temperature rise, thereby exacerbating coastal hazards."


id: b1ebd58dfefe316f651b219b814700bf
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
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index: climate_gpt_v2
vector_id: 28526
content:
"Many small islands lie in tropical regions and their climate varies \r depending on a range of factors with location, extent and topography \r having major influences. In general, their climate is determined \r by that of the broader region in which they lie as they have little \r influence on the regional climate, although steep topography can \r induce higher rainfall totals locally. Temperature variability tends to \r be low due to the influence of the surrounding ocean, most marked \r in the tropics where oceanic temperature ranges are small. However, \r seasonal rainfall variability can often be significant, both through \r the annual cycle and also interannually through the influence of \r many modes of variability (Cross-Chapter Box Atlas.2:, Annex IV \r and Atlas.7.1 for the Caribbean). Many small islands are exposed to \r tropical cyclones and the associated hazards of high winds, storm \r surges and extreme rainfall, and many low-lying islands are exposed \r [...]"


id: cf5173e557b264c6ea5a5b6cf28203bb
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index: climate_gpt_v2
vector_id: 33826
content:
"[...] to regular flooding from natural high-tide and wave activity. In the \r Pacific, phases of the El Nino-Southern Oscillation result in periods \r of warmer or cooler than average temperatures following the upper \r ocean warming of El Nino events or cooling of La Nina events, and \r respectively weaker and stronger trade winds. El Nino conditions also \r lead to drought in Melanesian islands and increased tropical cyclones \r and storm surges in French Polynesia with La Nina conditions causing \r drought in Kiribati. Other islands experience increased rainfall during \r these periods."


id: 9e1a4d995d0ed6c9ccbbe42c50c1f35e
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index: climate_gpt_v2
vector_id: 25050
content:
"period and was strongly related to anthropogenic warming, which \r would have increased the severity of the event by 17% and its spatial \r extent by 7% (Herrera et al., 2018). However, a seasonal analysis \r of observations grouped into large sub-regions of the Caribbean \r revealed no significant long-term trends in rainfall over 1901-2012 \r but significant inter-decadal variability (P.D. Jones et  al., 2016b). \r Declines in summer rainfall (-4.4% per decade) and maximum \r five-day rainfall (-32.6  mm per decade) over 1960-2005 were \r reported for Jamaica (CSGM, 2012), and an insignificant decrease \r in summer precipitation was observed for Cuba for 1960-1995 \r (Naranjo-Diaz and Centella, 1998). Three of four stations examined \r for Puerto Rico exhibited declining JJA rainfall over 1955-2009 with \r the trend statistically significant at the 95% level for Canovana \r (Mendez-Lazaro et  al., 2014). In the Caribbean, positive regional \r [...]"


id: f3aeee7f62b1f96cdfc237489b008184
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vector_id: 40246
content:
"[...] trends in precipitation and trends in extremes during 1961-2010 \r were found to be not statistically significant (at the 5% level; \r Stephenson et  al., 2014; Beharry et  al., 2015). Positive trends in \r JJA rainfall over Cuba and Jamaica are seen in CRU, whereas they \r are negative over Cuba for GPCC; over eastern Hispaniola they are \r positive in CRU and negative in CHIRPS (Cavazos et al., 2020)."


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vector_id: 16832
content:
"(June-August) based on studies using GCMs (Rauscher et al., 2008; \r Karmalkar et  al., 2013; Karmacharya et  al., 2017a; Taylor et  al., \r 2018). The future summer drying over the Caribbean is associated \r with a projected future strengthening of the Caribbean low-level jet \r (Taylor et al., 2013a). Rauscher et al. (2008) hypothesized that the \r simulated 21st-century drying over Central America represents an \r early onset and intensification of the MSD. The westward expansion \r and intensification of the NASH associated with the MSD occurs \r earlier with stronger low-level easterlies. Rauscher et  al. (2008) \r further suggested that the eastern Pacific ITCZ is also located further \r southward and that there are some indications that these changes \r could be forced by ENSO-like warming of the tropical eastern Pacific \r and increased land-ocean heating contrasts over the North American \r continent. Other studies also suggest a future intensification of the \r [...]"


id: 901d35ad6590e3db506d4700d7b4edfa
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index: climate_gpt_v2
vector_id: 22566
content:
"[...] NASH due to changes in land-sea temperature contrast resulting \r from increased greenhouse-gas concentrations (W. Li et al., 2012)."


id: bb6d259db889e43cd940af78a6d1b8ae
created_at: 2023-04-19 13:22:49
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index: climate_gpt_v2
vector_id: 30193
content:
"The CMIP5 ensemble median projected precipitation decreases of \r up to -16% over the Caribbean, parts of the Atlantic and Indian \r oceans, and the southern subtropical and eastern Pacific Ocean, and \r increases of up to 10% over parts of the western Pacific and Southern \r oceans, and up to 55% in the equatorial Pacific Islands under RCP6.0 \r in the period 2081-2100 relative to 1986-2005 (Harter et al., 2015). \r A projected decrease in annual precipitation is also noted over the \r Lesser Antilles under the RCP4.5 and RCP8.5 scenarios (Cantet \r et al., 2014). Seasonal rainfall is projected to decrease in most areas \r in Hawaii, except for the climatically wet windward side of the \r mountains, which would increase the wet to dry gradient over the \r area (Timm et al., 2015). The average precipitation changes in Hawaii \r are estimated to be about -11% to -28% under RCP4.5 during \r the wet season, and about -4% to -28% under RCP4.5 during the \r [...]"


id: 7e57a9cfd3d605b5b73c78d49e19b08
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index: climate_gpt_v2
vector_id: 19392
content:
"[...] dry season in the period 2041-2071 relative to 1975-2005, with \r larger changes under RCP8.5 (Timm et  al., 2015). There are still \r uncertainties in the projected changes, which have been attributed to \r factors including insufficient model skill in representing topography \r in the small islands, and high variability in climate drivers. However, \r the broad-scale pattern of projected wetter conditions in the western \r and equatorial Pacific, and the north Indian and Southern oceans, and \r of drier conditions over the Caribbean, and in parts of the Atlantic, \r Indian and southern subtropical and eastern Pacific oceans are \r further strengthened in the CMIP6 ensemble (Figure Atlas.28), which \r are thus likely regional responses as the climate continues to warm."


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vector_id: 20831
content:
"Small Islands are predominantly located in the Pacific, Atlantic and Indian oceans, and in the Caribbean (Nurse et al., 2014; Shultz et al., \r 2019). They are characterized by their small physical size, being surrounded by large ocean expanses, vulnerability to natural disasters \r and extreme events, and relative isolation (Section 12.4.7, Atlas.10 and Glossary; Nurse et al., 2014). These and nearby larger islands \r (e.g., Madagascar and Cuba) are often water-scarce with low water volumes due to increasing demand (from population growth and \r tourism), aging and poorly designed infrastructure (Burns, 2002), and decreasing supply (from pollution, changes in precipitation \r patterns, drought, saltwater intrusion, regional sea level rise, inadequate water governance (Belmar et al., 2016; Mycoo, 2018) and \r competing and conflicting uses (Section 8.1.1.1; Cashman, 2014; Gheuens et al., 2019). In the Caribbean, groundwater is the main \r [...]"


id: 4a57445ebc997f4e623809eeebfb17e3
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
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index: climate_gpt_v2
vector_id: 10319
content:
"[...] freshwater source and depends strongly on rainfall variability (Post et al., 2018), while rain, ground or surface water are the primary \r sources for the Pacific Islands depending on island type (volcanic or atoll), size and quality of groundwater reserves (Burns, 2002). \r Groundwater pumping and increasing sea levels also affect water availability by increasing the salinity of the aquifer (e.g., Bailey \r et al., 2015, 2016), thus reinforcing negative drought effects from reduced rainfall and increased evaporative demand from higher \r temperatures. For example, in 54% of the Marshall Islands, groundwater is highly vulnerable to droughts (Barkey and Bailey, 2017)."


id: a23b71f4dbcd0acaf984ee006e0c97c0
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 25775
content:
"The climate of Small Islands and findings from previous IPCC assessments\r Intra-seasonal to interannual rainfall in the Caribbean and in the Indian and Pacific oceans is influenced by the trade winds, the passage \r of tropical cyclones (TCs), Madden-Julian Oscillation (MJO), easterly waves, migrations of the Inter-tropical Convergence Zone (ITCZ) \r and the North Atlantic Subtropical High (NASH) for the Caribbean; the South Pacific Convergence Zone (SPCZ) and western North \r Pacific summer monsoon for the Pacific; and the South Asian monsoons for the Indian Ocean. The relevant dominant modes of climate \r variability (Section 8.3.2.9 and Annex IV) are El Nino-Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) which have been \r associated with extreme events in the islands (Annex IV; Stephenson et al., 2014; Kruk et al., 2015; Frazier et al., 2018). The modes of \r [...]"


id: e01e1473334542dc33a879c9e4d6fc04
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 36681
content:
"[...] climate variability are modulated by Pacific Decadal Variability (PDV), Inter-decadal Pacific Oscillation (IPO) and Atlantic Multi-decadal \r Variability (AMV). These modes show no sustained trend since the late 19th century (high confidence) (Section 2.4)."


id: d6235169286da48dfff272f97e0979bb
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 34992
content:
"The AR5 WGI reports observed temperature increases of 0.1degC-0.2degC per decade in the Pacific Islands with these trends very likely to \r continue under high emissions, and projects a drier rainy season for many islands in the south-west Pacific (Christensen et al., 2013). \r The AR5 WGII reports rainfall reductions over the Caribbean, increases over the Seychelles, streamflow reductions over the Hawaiian \r Islands and saltwater intrusion into groundwater reserves in the Pacific Islands resulting from storm surges and high tides (Nurse et al., \r 2014). The SROCC (IPCC, 2019a) finds very high confidence that global mean sea level rise has accelerated in recent decades which \r has exacerbated extreme sea level events and flooding (high confidence). It will continue to rise with consequent increases in extreme \r levels so that the historical one-in-a-century extreme local sea level will become an annual event by the end of the century under all \r [...]"


id: 51729708d79ebbc1780303354225c2ea
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 11611
content:
"[...] RCP scenarios (high confidence). In particular, many Small Islands are projected to experience historical centennial events at least \r annually by 2050 under RCP2.6, RCP4.5 and RCP8.5 emissions. The proportion of Category 4 and 5 TCs and associated precipitation \r rates along with their average intensity are projected to increase with a 2degC global temperature rise which will further increase the \r magnitude of resultant storm surges and flooding. The SROCC Cross-Chapter Box on Low-lying Islands and Coasts (Magnan et al., \r 2019) focused on sea level rise and oceanic changes and their impacts, therefore the assessment presented here on climate changes \r relevant to water resources, including precipitation and temperature, is complementary."


id: cd033a835150cc2c3aa54061903a5cfc
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 33411
content:
"Rainfall is very likely to decline over the Caribbean, in the annual mean and especially in JJA, with a stronger and more coherent \r signal in CMIP6 compared to CMIP5 (Figure Atlas.28 and Interactive Atlas) and reductions of 20-30% by the end of the century \r under high future emissions (SSP5-8.5). This JJA drying has been linked to a  future strengthening of the Caribbean low level \r jet (CLLJ) (Taylor et al., 2013a), a westward expansion and intensification of the NASH, stronger low-level easterlies over the \r region, a southwardly-placed eastern Pacific ITCZ (Rauscher et al., 2008), and changing dynamics due to increased greenhouse \r gas concentrations (very high confidence) (W. Li et al., 2012). Projections from 15 GCM and two RCM experiments for 2080-2089 \r relative to 1970-1989 were for a generally drier Caribbean and a robust summer drying (Karmalkar et al., 2013). More recent \r [...]"


id: 549997cfcb63f686108808d8a1fe605d
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 12161
content:
"[...] downscaling studies (e.g., Taylor et  al., 2018; Vichot-Llano et  al., 2021a) also project a  drier Caribbean and longer dry spells \r (Van Meerbeeck, 2020)."


id: 735c7d4142a0590b28be372633d7f250
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 17548
content:
"Summary of information distilled from multiple lines of evidence\r It is very likely that most Small Islands have warmed over the period of instrumental records. The clearest precipitation trend is a likely\r decrease in JJA rainfall over the Caribbean since 1950. There is limited evidence and low agreement for the cause of the observed \r drying trend, whether it is mainly caused by decadal-scale internal variability or anthropogenic forcing, but it is likely that it will \r continue over coming decades. It is likely that drying has occurred since the mid-20th century in some parts of the Pacific poleward \r of 20deg latitude in both the Northern Hemisphere and the Southern Hemisphere and that these changes will continue over coming \r decades. Rainfall trends in most other Pacific Ocean and Indian Ocean Small Islands are mixed and largely non-significant. It is very \r [...]"


id: d0683fa109104d889bc4067424ab7bf9
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 34020
content:
"[...] likely that sea levels will continue to rise in all Small Island regions, and this will result in increased coastal flooding with the potential \r to increase saltwater intrusion into aquifers in Small Islands."


id: 196d5fb95b167d5592cdc0e2deab509c
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 1688
content:
"The assessment in this section focuses on changes in average \r temperature, precipitation (rainfall and snow) and surface mass \r balance over the polar regions, Antarctica and the Arctic, including \r the most recent years of observations, updates to observed datasets, \r the consideration of recent studies using CMIP5 simulations and \r those using CMIP6 and CORDEX simulations. Findings are presented \r for West Antarctica (WAN) and East Antarctica (EAN), and three \r Arctic regions: Arctic Ocean (ARO), Greenland/Iceland (GIC) and \r Russian Arctic (RAR; Figure Atlas.29) with some reference also to \r North-Eastern North America (NEN), North-Western North America \r (NWN) and Northern Europe (NEU), which are covered more \r extensively in Atlas.9 and Atlas.8 respectively. Sub-regional changes \r are discussed when relevant, for example the Antarctic Peninsula \r (AP) as a  sub-region of WAN. The Southern Ocean (SOO) region \r is assessed in Chapter  9 with changes in climatic impact-drivers \r [...]"


id: 949c0f23283628fa4d42c97ce33c0884
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 23350
content:
"[...] assessed in Chapter 12 (Section 12.4.9 and Table 12.11) and some \r extremes in Chapter 11 (Tables 11.7-9 for RAR). Chapter 9 provides "


id: c2d464d55722453e3a20f6a4dfd27858
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 31552
content:
"of snowfall and diamond dust, with sporadic coastal rainfall during \r the summer over the Antarctic Peninsula and sub-Antarctic islands. \r Drizzle events sometimes occur during warm air intrusions (Nicolas \r et  al., 2017) at relatively low temperatures (Silber et  al., 2019). \r Precipitation constitutes the largest component of the surface \r mass balance (SMB), which also includes sublimation (from the \r surface or drifting snow), meltwater runoff and redistribution by \r wind (Lenaerts et al., 2019). SMB can be considered as a proxy of \r precipitation if averaged over an annual cycle (Gorodetskaya et al., \r 2015; Bracegirdle et al., 2019). Precipitation and SMB exhibit spatial \r and temporal variability controlled by atmospheric large-scale low\u0002pressure systems and moisture advection from lower latitudes. SMB \r is an important component of the total ice-sheet mass balance \r (Section 9.4.2.1). The Antarctic contribution to sea level results from \r [...]"


id: ea541b8e3266665f1abb923c3e0fc9d0
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 38583
content:
"[...] the imbalance between net snow accumulation and ice discharge \r into the ocean (Box 9.1). Ice shelves buttress the ice sheet and are \r influenced by oceanic and atmospheric drivers (Box 9.1)."


id: 48fb32d85ecfb6f7dbf677ab1c8aa410
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 10059
content:
"Figure Atlas.30 (Antarctic map inset) shows near-surface air \r temperature trends for 1957-2016 and 1979-2016 at the stations \r where observations are available for at least 50 years and the \r detected trends have statistical significance of at least 90% according \r to the most recent (after SROCC) studies (Jones et al., 2019; Turner \r et al., 2020). It is very likely that the western and northern AP has \r been warming significantly since the 1950s (0.49degC +- 0.28degC per \r decade during 1957-2016 and 0.46degC +- 0.15degC during 1951-2018 \r at Faraday-Vernadsky station; 0.29degC +- 0.16degC per decade during \r 1957-2016 at Esperanza station), with no significant trends reported \r in the eastern AP during the same period (Gonzalez and Fortuny, \r 2018; Jones et al., 2019; Turner et al., 2020). Short-term cooling trends, \r strongest during austral summer, have been reported at AP stations \r during 1999-2016, but the absence of warming and cooling at some \r [...]"


id: 857e95813bab841c3ce6fdfc9b74b259
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 20627
content:
"[...] stations during 1999-2016 is consistent with natural variability, and \r there is no evidence of a shift in the overall warming trend observed \r since the 1950s (Turner et  al., 2016, 2020; Gonzalez and Fortuny, \r 2018; Jones et al., 2019; Bozkurt et al., 2020)."


id: b970a834df152ef5ae388cb03cc1e3d6
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 29838
content:
"While a trend towards a positive phase of the SAM since the 1970s \r likely explains a significant part of the warming at the northern AP, it \r had a cooling effect on continental WAN and EAN (particularly strong \r in DJF; Table Atlas.1). Warming in western AP and over WAN during \r 1957-2016 (Figure Atlas.30) and through to 2020 (Figure  2.11) is \r likely due to significant contribution of other factors, such as tropical \r Pacific forcing through PDV, ENSO, Amundsen Sea Low position/\r strength and also anthropogenic climate change (Jones et al., 2019; \r Scott et al., 2019; Wille et al., 2019; Donat-Magnin et al., 2020; Turner \r et al., 2020). Since SROCC, new studies confirmed the influence of \r foehn wind and cloud radiative forcing on Larsen C surface melt \r (Elvidge et al., 2020; Gilbert et al., 2020; Turton et al., 2020). In WAN, \r summer surface-melt occurrence over ice shelves may have increased \r since the late 2000s (Scott et  al., 2019). It is likely that increased \r [...]"


id: e816c9d5ba7fb80d991a9d852913e212
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 38151
content:
"[...] meltwater ponding and resulting hydrofracturing have been \r important mechanisms of the rapid disintegration of the Larsen B ice \r shelf (Banwell et al., 2013; MacAyeal and Sergienko, 2013; Robel and \r Banwell, 2019). Ice-shelf disintegration and relevant processes are \r discussed in Sections 9.4.2.1 and 9.4.2.3."


id: e8ba02d1fe3f0e3bd59a76390f5d4bd6
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 38290
content:
"The geographic pattern of accumulation changes since the 1950s \r bears a strong imprint of a trend towards a more positive phase of the \r SAM (e.g., Medley and Thomas, 2019), which could be linked to ozone \r depletion (Lenaerts et al., 2018) or large-scale atmospheric warming \r (Frieler et al., 2015; Medley and Thomas, 2019). More evidence has \r emerged showing the importance of the Pacific-South American \r pattern, ENSO and Pacific Ocean convection, and large-scale blocking \r causing warm-air intrusions and both extreme precipitation and \r melt events, responsible for large interannual SMB variability (high \r confidence) (Gorodetskaya et al., 2014; Bodart and Bingham, 2019; \r Scott et al., 2019; Turner et al., 2019; Wille et al., 2019; Adusumilli \r et al., 2021). This strengthens evidence for an important connection \r between Antarctic climate and tropical sea surface temperature \r stated by SROCC (Meredith et al., 2019). Section 3.4.3 and SROCC \r [...]"


id: 7af98a1b0f2249d3b6316c834ff32c68
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 18835
content:
"[...] (Meredith et al., 2019) provide a discussion of attribution of Antarctic \r ice-sheet changes."


id: 45ebe2071f18305e4efdb82276cae29
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 9523
content:
"Recent higher-resolution simulations covering the entire Antarctic Ice \r Sheet with a grid spacing of 12 to 50 km include five Polar-CORDEX \r RCMs  - RACMO2 (van Wessem et  al., 2018), MAR (Agosta et  al., \r 2019; Kittel et  al., 2021), COSMO-CLM2 (Souverijns et  al., 2019), \r HIRHAM5 (Lucas-Picher et  al., 2012) and MetUM (Walters et  al., \r 2017; Mottram et al., 2021) - and one stretched-grid GCM - ARPEGE \r (Beaumet et  al., 2019). RCM simulations forced by ERA-Interim \r agree well with automatic weather station temperatures, with \r high correlation (R2 > 0.9) and low bias (<1.5degC) except for high\u0002resolution HIRHAM5 (-2.1degC) and MetUM (-3.4degC), which are not \r internally nudged models (Mottram et  al., 2021). RCMs generally \r underestimate the observed SMB but with biases lower than \r 20%, except for COSMO-CLM2 at lower elevations (<1200 m) and \r HIRHAM5 and MetUM at higher elevations (>2200 m) (Mottram \r et  al., 2021). These RCM simulations lead to estimates of the \r [...]"


id: 322283fa427dcee2ef293ea548332d28
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 6078
content:
"[...] grounded Antarctic Ice Sheet SMB ranging from 2133 Gt yr-1 to \r 2328 Gt yr -1 when considering the four simulations compatible with \r the IMBIE2 Antarctic total mass budget (IMBIE team et  al., 2018; \r Mottram et al., 2021). However, the simulated spatial pattern of SMB \r differs widely between models, suggesting the importance of missing \r or under-represented processes in the models, such as drifting-snow \r transport and sublimation (Agosta et al., 2019), cloud-precipitation \r microphysical processes (van Wessem et  al., 2018) and snowpack \r modelling (Mottram et  al., 2021). Comparisons of integrated \r SMB estimates between models are also complicated by different \r resolutions and continental ice masks, with models showing large \r differences in the absolute SMB (Mottram et  al., 2021) but better \r agreement for SMB annual rates (Figure Atlas.30)."


id: e62982dd5076fc69393e39d9765a0a80
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 37794
content:
"There is high confidence that projected future surface air temperature \r increase over Antarctica will be accompanied by precipitation \r increase (Figure Atlas.29). CMIP6 models show a similar or larger but \r more constrained increase in precipitation (more models agreeing \r with larger precipitation increase) for the same GWLs compared to \r CMIP5. For example, over WAN during JJA for 3degC GWL, CMIP6 and \r CMIP5 models project a median 15% increase in precipitation with \r a 10th-90th percentile range of 7-25% in CMIP6 models and of 3-24% \r in CMIP5. Average precipitation changes relative to 1995-2014 over \r WAN and EAN are largely similar; they show projected increases for \r SSP2-4.5 (SSP5-8.5) of around 5% (5%) for 2021-2040, 7% (10%) \r for 2041-2060, and 12% (25%) for 2081-2100 with smaller \r increases projected for SSP1-2.6 emissions, reaching around 5% in \r 2081-2100. Regionally, the largest relative precipitation increase \r [...]"


id: 6e725e84230dd779281e1a728273d9e1
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 16694
content:
"[...] is projected (under all scenarios) for the eastern part of WAN, the \r western AP, large parts of the EAN plateau and over coastal EAN \r within 0degE-90degE longitudinal sector (Interactive Atlas). The largest \r increase in absolute precipitation amount is projected along the \r coastal regions, with the largest increase over coastal WAN and \r the western AP, and is projected to be largely driven by the increase \r in maximum five-day precipitation (Interactive Atlas), which is in line \r with the dominant contribution of extreme snowfall events to the \r total annual precipitation in the present Antarctic climate (Boening \r et al., 2012; Gorodetskaya et al., 2014; Turner et al., 2020). Under all \r emissions scenarios, the coastal precipitation increase corresponds to \r the snowfall increase, except for the northern and central part of the \r western AP, where snowfall is projected to decrease and rainfall to \r increase (similarly to the tendency towards increased precipitation, \r [...]"


id: 575098673c4910a574568a1e39988de
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 12618
content:
"The Antarctic region is very likely to experience a significant increase \r in annual mean temperature and precipitation by the end of this \r century under all emissions scenarios used in CMIP5 and CMIP6 \r (Figure Atlas.29; Bracegirdle et  al., 2015, 2020b; Frieler et  al., 2015; \r Lenaerts et al., 2016; Previdi and Polvani, 2016; Palerme et al., 2017). \r Ensemble means (and 10th-90th percentile ranges) of end-of-century \r (2081-2100) projected Antarctic surface air temperature change from \r 35 CMIP6 models and relative to 1995-2014 are 1.2degC (0.5degC-2.0degC) \r for the SSP1-2.6 emissions scenarios, 2.3degC (1.3degC-3.4degC) for SSP2-4.5, \r 3.5degC (2degC-5degC) for SSP3-7.0, and 4.4degC (2.8degC-6.4degC) for SSP5-8.5 \r (Interactive Atlas). Both temperature and precipitation projections are \r characterized by a relatively large multi-model range (Figure Atlas.29 \r and the Interactive Atlas). A strong regional variability is present with \r [...]"


id: 840fbe0b4c99e2094066393ddec23880
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 20388
content:
"[...] the projected changes over coastal Antarctica not scaling linearly \r with  global forcing. While continental mean temperatures are \r linearly  related to global mean temperatures in CMIP6 models, the \r relative increase in coastal temperatures are higher for low-emissions \r scenarios due to stronger relative Southern Ocean warming and \r relatively stronger effects of ozone recovery (Bracegirdle et al., 2020b). \r A  higher multi-model average increase in temperature is projected \r by CMIP6 models compared to CMIP5, with a  1.3degC higher mean \r Antarctic near-surface temperature at the end of the 21st  century \r (Kittel et  al., 2021). While similar median temperature changes are \r projected for WAN and EAN, the former shows larger spread and \r higher projected temperature range in both CMIP5 and CMIP6 models \r and for all scenarios (Figure Atlas.29). CORDEX-Antarctica simulations \r show a mean and range in the future temperature changes similar to \r [...]"


id: 2ec70112d7f13f18cee61a3bf0469e72
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 5507
content:
"[...] the subset of CMIP5 models used to drive them for the RCP8.5 scenario \r and 1.5degC, 2degC and 3degC GWLs (Figure Atlas.29)."


id: 96f44c826d584758e1287bd35361d634
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 23751
content:
"From 2000 to 2100, the grounded Antarctic SMB is projected to \r mitigate sea level rise for RCP4.5 (RCP8.5) by the following sea \r level equivalents (SLEs), 0.03 +- 0.02 m (0.08 +- 0.04 m SLE) from \r 30 CMIP5 models and for SSP2-4.5 (SSP5-8.5) by 0.03 +- 0.03  m \r SLE (0.07 +- 0.04 m SLE) from 24 CMIP6 models (Gorte et al., 2020). \r Subsets or downscaling of CMIP AOGCMs lead to 21st-century \r cumulative projections in the range of 0.05 +- 0.03 m SLE for \r CMIP5 RCP8.5 and 0.08 +- 0.04 m SLE for CMIP6 SSP5-8.5 (Gorte \r et al., 2020; Nowicki et al., 2020; Seroussi et al., 2020; Kittel et al., \r 2021). Use of model subsets reduces spread leading to either lower \r or higher climate sensitivity in the Antarctic depending on the \r selection method. For example, models selected by Gorte et al. (2020) \r based on SMB ice-core reconstruction from Medley and Thomas \r (2019) tend to underestimate strongly winter sea ice area (Agosta \r [...]"


id: f77b9de14a4269b2e5c1bbd82f654b03
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 40917
content:
"[...] et  al., 2015; Roach et  al., 2020) and show reduced 21st-century \r increase in Antarctic SMB compared to the full ensembles (Agosta \r et al., 2015; Bracegirdle et al., 2015). A different subset of models \r is used for ISMIP6 (Section 9.4.2.3) which gives a lower increase in \r Antarctic SMB than the full ensemble for CMIP5 but a larger increase \r for CMIP6."


id: 74edc6813f0e32929b812304d72ee003
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 17805
content:
"in Europe (NEU) and North America (NEN, NWN) (Figure Atlas.29). \r The region is one of the coldest and driest regions on Earth and \r plays a  key role influencing global and regional climates and the \r hydrological cycle. A  number of physical processes contribute to \r amplified Arctic temperature variations as compared to the global \r temperature, in particular thermodynamic changes that include \r the increase in surface absorption of solar radiation due to surface \r albedo feedbacks related with sea ice, ice, and snow cover retreat \r as well as poleward energy transports, water-vapour-radiation and \r cloud-radiation feedbacks (Screen and Simmonds, 2010; Serreze \r and Barry, 2011; Pithan and Mauritsen, 2014; Bintanja and Krikken, \r 2016; Graversen and Burtu, 2016; Franzke et  al., 2017; Stuecker \r et  al., 2018). Precipitation in the Arctic is dominated by snowfall, \r with rainfall present mostly during the summer period. Arctic \r [...]"


id: 10d02ffd60df3b7b1557282d3a335e7c
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 145
content:
"[...] climate is influenced by the North Atlantic Oscillation (NAO), the \r leading mode of atmospheric variability in the North Atlantic basin \r with a northward extension into the Arctic affecting temperature, \r precipitation and sea ice over the region, with ENSO and Atlantic \r Multi-decadal Variability (AMV) also affecting parts of the region \r (Annex IV). Further, the Greenland Ice Sheet contribution to sea \r level results from the imbalance between mass gain by net snow \r accumulation and mass loss by meltwater runoff and ice discharge \r into the ocean (IMBIE team, 2020), highlighting that the ice sheet is \r a major contributor to sea level changes."


id: 47d6614c22e6cdc6837e73d81c798564
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
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content:
"The Arctic has warmed at more than twice the global rate over the \r past 50 years with the greatest warming during the cold season \r (high confidence) (Davy et al., 2018; Box et al., 2019; Przybylak and \r Wyszynski, 2020; Xiao et al., 2020). This is based on various Arctic \r amplification processes, in particular the combined effect of several \r related feedback processes, including between various radiation \r components and (a) the albedo of sea ice and snow, (b) water vapour, \r and (c) clouds, as well as poleward energy transports. The annual \r average Arctic surface air temperature increased by 2.7degC from 1971 \r to 2017, with a  3.1degC increase in the cold season (October-May) \r and a 1.8degC increase in the warm season (June-September) (AMAP, \r 2019). Satellite-based data estimate the rate of annual warming for \r 1981-2012 over sea ice covered regions to be 0.47degC per decade, \r whereas the trend was significantly higher at 0.77degC per decade over \r [...]"


id: 5493d2a5255a6aa7a3343c3f42a31af
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content:
"[...] Greenland and amplified in the northern Barents and Kara seas \r (Comiso and Hall, 2014). The largest Arctic warming in 2003-2017 \r was reported over the Barents and Kara seas with trends larger than \r 2.5degC per decade (Susskind et  al., 2019), and Arctic temperatures \r from 2014 to 2018 have exceeded all previous records since 1900 \r (Blunden and Arndt, 2019)."


id: ebd31816f9995fe4f9b3641ad546aa29
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
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index: climate_gpt_v2
vector_id: 38857
content:
"Over the ARO, long-term temperature records are available from \r Spitsbergen (Svalbard Airport). For the period 1898-2018, the annual \r mean warming was 0.32degC per decade, about 3.5 times the global \r mean temperature for the same period and since 1991, it was 1.7degC \r per decade or about seven times the global average for the same \r period (Nordli et al., 2020). There is a positive trend in the annual \r temperature for all stations across Svalbard (Gjelten et  al., 2016; \r Hanssen-Bauer et al., 2019; Dahlke et al., 2020) of 0.64degC-1.01degC \r per decade for 1971-2017 (Hanssen-Bauer et al., 2019), co-varying \r with regional changes in sea ice conditions (Dahlke et al., 2020). The \r largest temperature trends very likely occur in winter, with Svalbard \r Airport warming at 0.43degC per decade during 1898-2018 and 3.19degC \r per decade during 1991-2018 (Nordli et al., 2020), and Isaksen et al. \r (2016) reporting on substantial warming in western Spitsbergen, \r [...]"


id: 81dc10c2da24a53cb9c975edb84c5a7c
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index: climate_gpt_v2
vector_id: 19968
content:
"The CMIP5 models reproduce the observed Arctic warming over the \r past  century (medium confidence) (Chylek et  al., 2016; Hao et  al., \r 2018; Huang et al., 2019). The simulated mean Arctic warming for \r 1900-2014 averaged over 40 CMIP5 models is 2.7degC compared \r to the observed values of 2.2degC (NASA GISS data smoothed using \r a  1200-km radius) or 1.7degC (using a  250-km smoothing radius) \r (Chylek et al., 2016). However, there are large inter-model differences \r in the simulated warming which ranges from 1.2degC to 5.0degC. Although \r the  CMIP5 models reproduce the spatially averaged observed \r warming over the past 50 to 100 years, the pattern is different \r from that of observations and reanalysis (Xie et al., 2016; Franzke \r et al., 2017; Hao et al., 2018). Zonal mean temperature trends in the \r CMIP5 models overestimate the warming in the cold season over \r high latitudes in the Northern Hemisphere (Xie et al., 2016). Overall, \r [...]"


id: e11090875f2c3bc38232edf8293186b0
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content:
"[...] the amplified Arctic warming in recent decades is overestimated by \r CMIP5 models (Huang et al., 2019). Possible reasons are modelled \r sea surface temperature biases and an overestimated temperature \r response to the Arctic sea ice decline. Furthermore, some models, "


id: 53b9e09c1ee3e54fa693833c8c347f2d
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content:
"runoff. Mouginot et al. (2019) stated that SMB played a strong role \r in the ice-sheet mass loss, where SMB dominated in the last two \r decades. Mottram et al. (2019) found that SMB processes dominate \r the ice-sheet mass budget over most of the interior, highlighting that \r the ice sheet is a contributor to global mean sea level rise between \r 1991 and 2015. More specifically, SMB models have improved \r (Fettweis et al., 2020; Hanna et al., 2021) due to increased availability \r and quality of remotely sensed (Koenig et al., 2016; Overly et al., 2016) \r and in situ observations (Machguth et al., 2016; Fausto et al., 2018; \r Vandecrux et al., 2019, 2020). Fettweis et al. (2020) showed that the \r models' ensemble mean provides the best estimate of the present\u0002day SMB relative to observations. This is the case for the patterns \r in all seven regions (regional division after Mouginot et al., 2019) \r apart from the SE accumulation zone where large discrepancies in \r [...]"


id: e2e1f0ed273918f5381b88fa22c76496
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content:
"[...] modelled snowfall accumulation occurred where the spread can reach \r 2-m water equivalent per year. Montgomery et al. (2020) confirmed \r this, highlighting that RCMs (MAR and RACMO) are underestimating \r accumulation in south-east Greenland and that models misrepresent \r spatial heterogeneity due to an orographically forced bias in snowfall \r near the coast. Further, for north-east Greenland, Karlsson et  al. \r (2020) found RCMs underestimate snow accumulation rates by up \r to 35%. The regional time series show that SMB has been gradually \r decreasing in all seven regions (1979-2017), although the trend \r is less strong in central-eastern and south-eastern regions. In the \r south-west, north-east and north-west, SMB turns negative or close \r to zero after 2000 and remains above zero in other regions (medium \r confidence) (Figure Atlas.30)."


id: 3c399502ea4dd53afea13577b81fd058
created_at: 2023-04-19 13:22:49
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index: climate_gpt_v2
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content:
"Overland et al. (2014) highlighted the difference between the near\u0002term 'adaptation timescale' and the long-term 'mitigation timescale' \r for the Arctic. Only in the latter half of the century do the projections \r under RCP4.5 and RCP8.5 noticeably separate. End-of-the-century \r warming is approximately twice as large under RCP8.5 demonstrating \r the impact of the lower emissions under RCP4.5 (high confidence) \r (AMAP, 2017). More specifically under the strong forcing scenario, \r annual mean surface air temperature in the Arctic is projected \r to increase by 8.5degC +- 2.1degC over the course of the 21st  century \r (Bintanja and Andry, 2017), and emerges as a 'new Arctic' climate \r being significantly different from that of the mid-20th  century \r (Landrum and Holland, 2020). The end-of-the-century warming \r (2080-2099 relative to 1980-1999, RCP8.5) can exceed 15degC in \r autumn and winter over the Arctic Ocean (Koenigk et  al., 2015). \r [...]"


id: f103f55eacdbbadef80b652a76eda5fc
created_at: 2023-04-19 13:22:49
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content:
"[...] Projections averaged over the four best-performing CMIP5 models \r show an Arctic annual warming of 4.1degC (RCP2.6), 5.7degC (RCP4.5) \r and 10.6degC (RCP8.5) by 2100 compared to 1951-1980 (Hao et al., \r 2018). Also, for neighbouring Arctic regions, for example NEU, WSB \r and ESB, temperature is projected to increase towards the end of the \r century under both RCP4.5 and RCP8.5 (Atlas.8 and Atlas.5.2)."


id: 18dd1d279361616b7344a1b4a4b7551b
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 1590
content:
"Mean precipitation in ARO, GIC and RAR is projected to rise in \r a  warming climate (Figure Atlas.29), with different rates for the \r different seasons and scenarios. For NWN and NEN, see Atlas.9. \r The CMIP5 multi-model mean projected precipitation increase in \r the Arctic is likely of the order of 50% under RCP8.5 by the end of \r the 21st century, which is among the highest globally (Bintanja and \r Selten, 2014). Over 70degN-90degN, the precipitation increase is likely \r 62 +- 20% and 56 +- 13% for RCP4.5 and RCP8.5 respectively. For \r ARO (Svalbard), the increase in annual precipitation by 2100 is \r estimated to be about 45% for RCP4.5 and 65% for RCP8.5 (CMIP5 \r ensemble median; Van der Bilt et  al., 2019). However, importantly \r the simulated Arctic precipitation increase varies by a factor of three \r to four between models (Bintanja and Selten, 2014). The projected \r increase is strongest in late autumn and winter (Vihma et al., 2016). \r [...]"


id: faa2712e2b7aa3641769a0a0c59ff1cc
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updated_at: 2023-04-19 13:22:49
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index: climate_gpt_v2
vector_id: 41469
content:
"[...] The interannual variability of Arctic precipitation will likely increase \r markedly (up to 40% over the 21st century), especially in summer \r (medium confidence) (Bintanja et al., 2020)."


id: eb63c940e261ceba791dd168846c07fe
created_at: 2023-04-19 13:22:49
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content:
"Detailed and structured information about climate models, \r simulations and their conformance to common experimental \r protocols is not only important for scientific interpretation but, \r under increased scrutiny from society, it is also demanded of climate \r science that purports to be mature, credible, open, transparent and \r reproducible (Guilyardi et al., 2013). Scientific publications remain an \r essential way of documenting models but remain largely scattered \r and not easily accessible by the growing community of users of model \r output. To address these challenges, the Earth System Documentation \r (ES-DOC) project offers an ecosystem of tools and services in support \r of Earth system modelling documentation creation, analysis and \r dissemination. ES-DOC is coordinated with other community efforts \r such as the Coupled Model Intercomparison Project (CMIP) via the \r World Climate Research Programme work group on Climate Modelling \r [...]"


id: e47dea61b50b380c760cc7de7ddcbba9
created_at: 2023-04-19 13:22:49
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"The key new model developments since AR5 are summarized in \r Chapter 1, Section 1.3.5, and model results are assessed in multiple \r chapters of this report. In this annex, Table AII.5 presents the main \r features of the CMIP6 coupled models, in a  format comparable \r with AR5 Table 9.A.1 for CMIP5 (Flato et al., 2013). At the date of \r March 2021, 136 models had registered for CMIP6, including the 23 \r CMIP6-endorsed MIPs (Chapter 1, Table 1.3). For conciseness, Table \r AII.5 documents only the coupled models used in the CMIP6 'DECK' \r experiments and ScenarioMIP, excluding atmosphere-only and \r ocean-only components (AMIP and OMIP), radiative transfer models \r (RFMIP), and other MIPs. Registered coupled models that have not \r submitted data in time to be used in this report are not included. The \r high-resolution models used for HighResMIP (Haarsma et al., 2016) \r are listed in Table AII.6, and ice-sheet models are documented in \r [...]"


id: d57135f4305753a50eb7656987d8d2f3
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vector_id: 34867
content:
"[...] Section AII.3. The citation information for all CMIP6 model datasets \r is compiled in Section AII.4, Table AII.10."


id: 8af164df6fe89c09a1a4510affd3cf6d
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content:
"The objective of CMIP is to better understand past, present and future \r global climate changes arising from natural, unforced variability or \r in response to changes in radiative forcing in a multi-model context \r (Chapter 1, Section 1.5.4). This understanding includes assessments \r of model performance during the historical period (Chapter 3) and \r quantifications of the causes of the spread in future projections \r (Chapters 4 and 7). Idealized experiments are also used to increase \r understanding of the model responses. In addition to these long \r time scale responses, experiments are performed to investigate the \r predictability of the climate system on various time and space scales, \r as well as making predictions from initialized climate states. The \r different activities (MIP) endorsed by CMIP6 are listed in Chapter 1, \r Table  1.3 (Eyring et  al., 2016). A set of common experiments, the \r DECK (Diagnostic, Evaluation and Characterization of Klima) and \r [...]"


id: 57c907f852c4619ce498c476dee0a909
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"[...] CMIP historical simulations (1850-2014), were introduced that will \r maintain continuity with previous CMIP phases and help document \r basic and evolving characteristics of models. ScenarioMIP is the \r framework for future climate projections (O'Neill et  al., 2016). \r The infrastructure panel of the World Climate Research Programme \r coordinates framework developments and defines data standards \r for CMIP. A key aspect is the dissemination of the data via the \r Earth System Grid Federation (ESGF; Williams et  al., 2016; Petrie \r et al., 2021)."


id: 21a619e88a055f784257ea974e819750
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index: climate_gpt_v2
vector_id: 3127
content:
"index time series, that is, principal component (PC), describing the \r temporal evolution tied to the spatial pattern. PCA/EOF is widely \r used with gridded datasets and model outputs. Correlation or \r regression maps between the climate index time series and other \r space-time variables, such as precipitation, temperature or wind, are \r then calculated to quantify the teleconnection patterns. Altogether, \r beyond their physical relevance, modes of climate variability and \r their teleconnection patterns provide a  simplified representation \r of climate variation affecting weather, from regional (i.e., oceanic \r or continental scale) to worldwide scales (i.e., for some specific \r phenomena like ENSO), having important impacts on human \r activities and ecosystems. Modes are often associated with climatic \r impact-drivers (CIDs, as defined in Chapter  12, Section  12.1) \r including droughts, heavy rains, storms, heatwaves and cold spells, \r [...]"


id: 8d433237efcb5d4c4de88114c6106e09
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content:
"[...] affecting agriculture, water resources, availability and consumption \r of energy, and fire risks."


id: 686e943838a3c179ce923e3c223f4782
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
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vector_id: 15640
content:
"This annex describes the fundamental features of the main modes \r of large-scale climate variability assessed across chapters in the AR6 \r WGI Report. Modes are defined as recurrent space-time structures \r of variability of the climate system with intrinsic spatial patterns, \r seasonality and time scales. They can arise through the dynamical \r characteristics of atmospheric circulation, but also through coupling \r between the ocean and the atmosphere, with some interactions with \r land surfaces and sea ice. The variability of the climate system at \r ocean- or continental-basin scales, and in particular, on seasonal\u0002to-multi-decadal time scales, can be described to a large extent by \r the occurrence and often combination of several modes of climate \r variability which lead to local impacts and remote responses through \r teleconnection processes on top of externally forced trends. More \r precisely, the concept of 'teleconnection' refers to the ability of modes \r [...]"


id: 5d529cac2bf18697f0a757d71f2cfde4
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content:
"[...] of variability to relate climate in remote regions through associated \r atmospheric or oceanic pathways. Regional climate variations \r are thus the complex outcome of local physical processes, such as \r thermodynamical and land-atmosphere feedback processes,  and \r non-local large-scale phenomena."


id: f900bc29ec26a688c76a6715f2d16177
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"Historically, modes have been identified based on evidence of \r correlations between regional climate variations at widely separated, \r mostly geographically fixed spatial locations. One of the oldest known \r space-time structured patterns of variability is the North Atlantic \r Oscillation (NAO; Section AIV.2.1), with some early description from \r seafaring activities available since several centuries ago (Stephenson \r et  al., 2003). Similarly, empirical, albeit remarkably precise, \r documentation of trans-Pacific coherent variability in atmospheric \r and oceanic conditions can be traced back to the 19th century for the \r El Nino-Southern Oscillation (ENSO; Section AIV.2.3). The Southern \r Oscillation was named in 1924 and the first use of the name 'El Nino' \r to describe a  climatic phenomenon appeared in South America in \r 1893 (see for instance Philander, 1983 for ENSO history). Since then, \r the increased spatial coverage of observations and the measurement \r [...]"


id: 652fbdf9b48862712bbbc4951f95e43
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content:
"[...] of new climatic parameters have improved significantly. These \r advancements, along with the development of new theoretical \r background and statistical methods for combining different sources \r of information, have allowed substantial improvements in our \r understanding of the characteristic spatial scale of the coherent \r structures of variability as well as of the characteristic temporal \r expression of the modes."


id: cff4ca81993603be2eea07168994815a
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content:
"Previous IPCC reports have described the observed and modelled \r behaviour of a  large number of modes of variability over the \r instrumental period and in a  paleoclimate context. The projected \r evolution of those modes has been also documented based on \r future climate scenarios. In AR5, large-scale modes of variability \r and teleconnections were assessed in depth in Chapter 14, setting \r the physical basis of future regional climate changes contingent on \r global mean temperature increase. Modes were defined through \r so-called 'indices of climate variability' in AR5 Chapter 2, dedicated to \r observed atmospheric and surface changes. Two boxes (AR5 Box 2.5 \r and AR5 Box 14.1) introduced the main concepts. AR4 had a similar \r structure, with a specific section on modes in the chapter dedicated \r to the observations (Chapter 3). In AR6, a different perspective has \r been adopted because the modes are discussed in various chapters \r [...]"


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"[...] and assessed with respect to the specific objectives of each of them. \r Space-time past changes of the modes using proxy records and \r instrumental data are assessed in Chapter  2. Human influence on \r modes is assessed in Chapter 3 over the instrumental period, while \r their future changes are addressed in Chapter 4. Their influence at \r regional scale through large-scale teleconnection is assessed in \r Chapters 10, 11 and 12, and in the Atlas, while the contribution \r of the modes in past and future hydrological changes and related \r uncertainties are assessed in Chapter 8. Chapter 9 focuses on oceanic \r expression of the modes. The aim of this annex is to set a common \r background for the assessment of the modes of climate variability \r and related regional climate anomalies for all the AR6 WG1 chapters, \r and to provide this information for the readers of the report. In doing \r so, the annex provides a more accessible summary of the main modes \r [...]"


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content:
"[...] of variability allowing a more coherent assessment of this important \r aspect of the climate system."


id: ba3a7e28216442b1ed809cadd43da643
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
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content:
"The NAO can be viewed as the regional expression of a hemispheric\u0002scale mode of atmospheric variability that was identified in the late \r 1990s known as the Arctic Oscillation (AO; Thompson and Wallace, \r 1998) or Northern Annular Mode (NAM; Thompson and \r Wallace,  2000; Ambaum et  al., 2001) to reflect the longitudinal \r scale that transcends the entire hemisphere. An additional centre \r of action is present for NAM over the North Pacific, albeit much \r weaker than its Atlantic counterpart, with a slackened Aleutian Low \r in the positive phase (Figure AIV.1b). The AO/NAM can be defined as \r the leading mode of variability across a range of atmospheric levels \r from the surface to the stratosphere (Baldwin and Dunkerton, 1999; \r Gerber et al., 2010). In the stratosphere, the AO/NAM corresponds \r to fluctuations in the strength of the boreal winter polar vortex over \r the Arctic. In the troposphere, the AO/NAM primarily corresponds to \r [...]"


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vector_id: 14411
content:
"[...] fluctuations in the position of the mid-latitude westerly jet stream. \r Interactions between the stratospheric and tropospheric annular \r modes have been extensively studied and are important for the \r variability and predictability of the mode at intra-seasonal time \r scales (i.e., see Domeisen, 2019)."


id: 8e4f0d82c7823c16e7d69fcb4f129c5a
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updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 22225
content:
"Despite the fact that NAM and NAO indices are strongly correlated \r (Figure AIV.1c), the debate on the relevance of the NAM as a mode of \r variability per se (Deser, 2000; Honda and Nakamura, 2001; Itoh, 2008) \r is not closed yet, despite progress in understanding the dynamical \r mechanisms linking the Pacific and Atlantic SLP cores on the basis \r of wave breaking theory that prevails at intra-seasonal time scales \r (e.g., Riviere and Drouard, 2015). Consequently, NAO and NAM are \r treated as indistinguishable in some studies (Feldstein and Franzke, \r 2006) while others still consider the NAO as a  stand-alone mode \r even if associated circumpolar patterns exist (Garcia-Serrano \r and Haarsma, 2017). In the latter case, the NAM is interpreted as \r a statistical artefact of two regional independent modes, namely the \r NAO and the Pacific-North American pattern (Ambaum et al., 2001; \r Douville et al., 2017). Throughout the report, NAO and NAM are often \r [...]"


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vector_id: 21175
content:
"[...] considered as the same entity because the NAO explains most of \r the variance on the zonally-averaged hemispheric circulation and \r because the associated time series are highly correlated."


id: 13fd9a02c7b4f85f963d5ad7235ff15a
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updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 734
content:
"The NAO/NAM arises as large-scale signals primarily driven by the \r internal atmospheric variability of mid-latitude dynamics (e.g., Lorenz \r and Hartmann, 2003). By nature, these signals correspond to \r barotropic phenomena being partly controlled by eddy-mean flow \r interaction (Feldstein and Franzke, 2017). Their fingerprint can be \r found along the entire atmospheric column with maximum amplitude \r in the winter season when storminess is the most pronounced. In \r addition, the tropospheric NAO/NAM variability has been shown \r in boreal winter to be linked to the strength of the stratospheric \r polar vortex (Baldwin and Dunkerton, 1999), including sudden \r stratospheric warmings (Domeisen, 2019). On intra-seasonal time \r scales, downward propagation of associated geopotential height \r anomalies occurs from the lower stratosphere to the troposphere \r through wave-mean flow interaction resulting in some predictability \r at the surface for CIDs, through temperature, precipitation and \r [...]"


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"The NAO is the main mode of climate variability over a broad North \r Atlantic-European region in all seasons (Hurrell and Deser, 2009) for \r intra-seasonal to multi-decadal time scales, albeit with seasonally \r dependent physical characteristics (fraction of explained variance, \r latitudinal shift and intensity of the related anomalous atmospheric \r circulation and SST fingerprint, etc.; Folland et al., 2009; Woollings \r et al., 2015). The phases of the NAO control a significant fraction of \r the variance of temperature and precipitation over the North Atlantic \r and surrounding continents, and of the prevailing westerly winds and \r the related storm tracks (Woollings et al., 2014). It is also strongly \r related to the occurrence and intensity of blocking conditions, \r especially over Greenland (Woollings et al., 2010; Davini et al., 2012), \r and controls a large part of the incidence of extremes (cold waves, \r very strong wind episodes related to explosive storminess, heavy \r [...]"


id: fe1fa67fcace3ba052254ea8cc39f318
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
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index: climate_gpt_v2
vector_id: 42075
content:
"[...] precipitation events, etc.) over Europe (Matthews et al., 2014; Yiou \r et al., 2017) and eastern North America (Durkee et al., 2008; Whan \r and Zwiers, 2017)."


id: b0f456b6b48e7ad33d36535def7f5dad
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
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index: climate_gpt_v2
vector_id: 28362
content:
"The NAO/NAM includes a  forced component arising from SST \r anomalies: key oceanic regions are identified but are dependent on the \r time scale of variability that is investigated (Baker et al., 2019). There \r is also growing evidence for the importance of cryosphere-atmosphere \r coupling to explain part of the observed variability of the NAO in \r response to recent Arctic sea ice decline (Cohen et al., 2014), even if \r discrepancies remain in model estimates (Screen et al., 2018) and may \r not be stationary in time (Blackport and Screen, 2020). Finally, part of \r the NAO/NAM variability has also been documented to be influenced \r by external forcing such as volcanic eruptions (Swingedouw et  al., \r 2017) and solar forcing (Ineson et al., 2011; Gray et al., 2016; Le Mouel \r et  al., 2019) as well as anthropogenic factors (Gillett et  al., 2003). \r However, internal variability is predominant and, because of signal\u0002to-noise issues, very long observational records and large ensembles \r [...]"


id: 20e2849ed0ad2e18a0e8eda9ec59326
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 3006
content:
"[...] of climate simulations are needed to clearly evaluate the fraction \r of the forced component in the observed NAO/NAM, as assessed in \r detail in Sections 2.4.1.1 and 3.7.1. Sea ice-NAO/NAM coupling is \r comprehensively assessed in Cross-Chapter Box 10.1."


id: b8bf63b18f2b4b681ca3eb83a4c46b41
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 29710
content:
"Large-scale changes in the position and strength of the North \r Atlantic jet stream and storm tracks associated with the NAO/NAM, \r as well as the related incidence in blocking episodes traditionally \r linked to extreme events, affect surface climate conditions over the \r entire North Atlantic and surrounding land masses from daily to \r multi-decadal time scales (Hurrell, 1995). Warmer and wetter weather \r over Northern Europe extending eastward in western Siberia with \r simultaneous cooler and drier conditions over a broad Mediterranean \r basin are distinct features of the positive boreal winter NAO/NAM \r (Figure AIV.1d,e). Over North America, regions around the Labrador \r Sea are significantly colder, with excess sea ice formation, while \r milder conditions dominate the Great Plains, forming altogether \r a  typical quadripolar anomaly pattern for temperature linked to \r the winter NAO/NAM. The latter structure is tightly related to the \r [...]"


id: c3c96169917cebcba6b3f207078d3bf7
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 31718
content:
"[...] latitudinal shift of the storm tracks that is particularly marked in \r precipitation over the ocean (Figure AIV.1e). More remotely, changes \r in North Atlantic storm track position and transient eddy activity \r associated with the positive (negative) phase of the NAO contribute \r downstream to negative (positive) SLP anomalies in north-eastern \r East Asia during boreal summer (Linderholm et al., 2011)."


id: 67ba5b63e9c90de00c7d63a12271b02
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 15517
content:
"An alternative and simpler estimate of the NAM/NAO uses differences \r in SLP between fixed locations. The NAM index can be calculated \r from the difference in normalized zonally averaged SLP anomalies \r between 35degN and 65degN (Li and Wang, 2003), which reflects the \r latitudinal fluctuation of the position and strength of the mid-latitude \r westerlies at the hemispheric scale. Similarly, for the NAO, a weather \r station-based index is traditionally calculated as the difference of \r normalized SLP anomalies located in the Azores (Ponta Delgada) or \r continental Iberia (Lisbon/Gibraltar) minus those along the northern \r centre of action around Iceland (Stykkisholmur/Reykjavik) (Jones \r et al., 1997). Cornes et al. (2013) alternatively uses the Paris-London \r difference because of data availability back to the 17th century. The \r station-based NAO index is simple to compute, extends the longest \r over the instrumental period, and can be used year round, but caveats \r [...]"


id: 444266f6fda729aafa5eff9105e02987
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 9244
content:
"[...] stand in its limitation to capture the seasonal latitudinal migration of \r the NAO variability pattern as well as potential shifts in the North \r Atlantic pressure centres of action at interannual to decadal time \r scales (Ulbrich and Christoph, 1999). Because it uses fixed locations, \r the index captures part of the variability that is not directly related to \r the latitudinal seesaw pressure balance and associated large-scale \r fluctuations of the zonal flow that characterize the NAO dynamics, \r and it also fails representing the spatial asymmetries between the \r positive and negative phases of the mode (Cassou et al., 2004)."


id: 8ffd988e98ea53650abbdd85757d960
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 22536
content:
"Related changes in both westerly winds at mid-latitudes and trade \r winds in the tropics over the North Atlantic result in anomalous \r fluxes of sensible and latent heat at sea surface, which create ocean \r temperature anomalies that extend down to the base of the deep mixed \r layer during boreal winter (Deser and Timlin, 1997; Seager et al., 2000). \r The SST imprint of the NAO has a tripolar shape with positive anomalies \r at mid-latitudes straddled by colder surface ocean in the subtropics \r and along the entire subpolar gyre (Figure AIV.1d). Weak feedbacks \r from SST on boreal winter NAO are found through the forced Rossby \r wave mechanism (Baker et al., 2019) and local phenomena such as \r oceanic re-emergence in the North Atlantic Nordic seas (Cassou et al., \r 2007). Negative NAO phases are characterized by a wavier jet stream \r and a southward shift of storm tracks, which favours cold outbreaks \r over Northern Europe (Cattiaux et al., 2010) and reinforced storminess \r [...]"


id: 8819c05f86bfb6b5436e12734b39cdd5
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
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index: climate_gpt_v2
vector_id: 21095
content:
"[...] along the Mediterranean basin leading to heavy precipitation events \r (Sanchez-Gomez et  al., 2008) and storm surges in southern Europe \r (Cid et  al., 2016). Those are also often related to a  collapse/split/\r displacement of the low-level stratospheric polar vortex and the \r occurrence of stratospheric sudden warming (Sections 2.3.1.4.5 and \r 3.3.3.4). In boreal winter, NAM and NAO teleconnections are very \r similar over the Atlantic-surrounding continents."


id: 26208ce5de96379f769f3d682825961d
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 3879
content:
"The Southern Annular Mode (SAM), sometimes called the Antarctic \r Oscillation (AAO), is the leading mode of large-scale atmospheric \r circulation variability in the Southern Hemisphere extratropics in \r all seasons and at intra-seasonal to multi-decadal time scales, \r albeit with season-dependent physical characteristics such as \r fraction of explained variance, latitudinal shift and intensity of the \r related anomalous atmospheric circulation and SST fingerprint. It is \r characterized by synchronous SLP or geopotential height anomalies \r of opposite signs between the mid- and high latitudes, modulating \r the mean westerly winds near 50degS. The SAM affects the frequency, \r strength and position of cold fronts, and extratropical synoptic to \r mesoscale weather systems (Thompson et al., 2000; Manatsa et al., \r 2016; Mariani and Fletcher, 2016; Cerrone and Fusco, 2018). It controls \r a  significant fraction of seasonal to multi-annual variance of the \r [...]"


id: 9ae21afe5e1b991c72670e4cda37cd38
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
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index: climate_gpt_v2
vector_id: 24493
content:
"[...] zonal mean precipitation in the Southern Hemisphere subtropics and \r extratropics (Kang et al., 2011). The SAM is also tightly linked to the \r fluctuations in the thermohaline properties of the Southern Ocean \r as well as its dynamics (Sen Gupta and England, 2006; Spence et al., \r 2014), and it modulates the Antarctic sea ice extent from interannual \r to multi-decadal time scales (Ferreira et al., 2015)."


id: 308ac9b39b6c9688160e8e26d4dbbd4c
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 5796
content:
"Sea level pressure records from Stykkisholmur, Akureyri, Reykjavik \r and from Gibraltar, Lisbon and Ponta Delgada weather stations, all \r located close to the NAO centres of action (i.e., the Icelandic Low \r and the Azores High), allowed the reconstruction of the NAO index \r back to the mid-19th Century (Cropper et al., 2015; Hernandez et al., \r 2020). The Paris-London sea level pressure difference used by Cornes \r et al. (2013) extends back to 1692, but it captures only part of the \r large-scale NAO phenomena because of the spatial proximity of \r the weather stations used. More reliable NAO reconstructions were \r recently developed based on the reconstruction of the sea level \r pressure field all over the North Atlantic and Europe (Delaygue et al., \r 2019; Mellado-Cano et  al., 2019). Other long-term reconstructions \r involve proxy-based information from documents (Barriopedro et al., \r 2014), tree rings (Cook et al., 2019), ice cores (Sjolte et al., 2018), \r [...]"


id: 350fb9327824c544b758398375b48330
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
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index: climate_gpt_v2
vector_id: 6579
content:
"[...] stalagmites (Baker et al., 2015), sediments (Faust et al., 2016) and \r a combination of multi-proxy information (Ortega et al., 2015), with \r large uncertainties due to the diverse methodological approaches \r used for reconstruction (Michel et al., 2020) and disparate correlations \r between proxies and associated temporal resolution (Hernandez \r et  al., 2020). An additional difficulty for reconstruction lies on the \r fact that the spatial structure of North Atlantic variability might be \r time-scale dependent (Woollings et al., 2015). It is shown that on the "


id: 4fb2d1a492dd9d31a9569351c4e855c9
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 11313
content:
"The regional influence of the SAM is recorded in tree rings (Holz \r et  al., 2017; Datwyler et  al., 2018), lake sediments (Fletcher et  al., \r 2018; Moreno et  al., 2018), ice cores (Goodwin et  al., 2004) and \r corals (Goodwin and Harvey, 2008) across the Southern Hemisphere \r extratropics. Multi-proxy-based reconstructions of the SAM have been \r developed using temperature-sensitive proxy records that cover the \r last millennium (Villalba et al., 2012; Abram et al., 2014; Datwyler et al., \r 2018). Villalba et al. (2012) used tree ring records to reconstruct the \r summer SAM since 1409, while Abram et al. (2014) combined ice-core \r records with tree ring data to derive a weighted composite-plus-scale \r reconstruction of the annual SAM index for the past millennium. \r Datwyler et al. (2018) further included proxies from corals, speleothems \r and lake sediments using different stationarity criteria for the proxy \r record selection to build an austral summer SAM reconstruction. Hessl \r [...]"


id: e989456347df70a25afc614b67330064
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 38441
content:
"[...] et al. (2017) compared three paleoclimatic reconstructions of the SAM, \r highlighting the need for additional proxy records over the Indian \r Ocean sector, to fully account for the annular structural properties of \r this mode of variability. Uncertainties also arise from the lack of proxy \r data related to cold season conditions, given that reconstructions \r relying on tree rings might be biased towards summer thus accounting \r only for SAM austral summer variability (Hernandez et  al., 2020). \r Several studies attempted to reconstruct the SAM behaviour during \r the Holocene, inferred through changes in the Southern Hemisphere \r westerly wind belt, but uncertainties remain large without consensus \r in the SAM phases for most of the periods at multi-decadal time scales \r (Hernandez et al., 2020). These SAM reconstructions are assessed in \r Section 2.4.1.2 and compared with simulations in Section 3.7.2."


id: b7b7c4813fc814630374792530ad9077
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 29542
content:
"The El Nino-Southern Oscillation (ENSO) refers to the large-scale \r alternation between anomalous warming and cooling of central/\r eastern equatorial Pacific SSTs that coincide with changes in the \r overlaying winds and precipitation (Philander, 1990; Neelin et  al., \r 1998; Wang, 2018). The main features of ENSO are displayed \r in Figure AIV.3. ENSO is the primary mode of tropical variability \r on interannual time scales and is considered as an internally \r occurring coupled ocean-atmosphere phenomenon. Whilst the \r ENSO phenomenon itself largely occurs in the tropical Pacific, it \r triggers climate teleconnections over the entire basin and in many \r other parts of the world (Figure AIV.3c,d). ENSO is consistently the \r main modulator of the global surface temperature at interannual \r time scales (Pan and Oort, 1983; Trenberth et  al., 2002). ENSO is \r the main source of climate predictability on seasonal to interannual \r [...]"


id: 49e4980d9ee8651542b2f2f4cf5e73b3
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index: climate_gpt_v2
vector_id: 10237
content:
"[...] time scales (Philander, 1990; Smith et  al., 2012) and its status is \r consequently closely monitored by meteorological institutes around \r the globe. El Nino events are typically identified by warmer than \r normal central/eastern equatorial Pacific SSTs, negative SLP anomaly \r difference between the eastern and western portions of the tropical \r Pacific Ocean, anomalous westerly surface winds, and increases in \r cloudiness and precipitation over the central and eastern equatorial \r Pacific and adjacent land areas. By contrast, La Nina events are \r typically identified by cooler than normal central/eastern equatorial \r Pacific SSTs, positive surface pressure anomaly difference between \r the eastern and western portions of the tropical Pacific Ocean \r and anomalous easterly surface winds reflecting altogether an \r intensification of the climatological east-west thermal gradient \r across the equatorial Pacific Ocean."


id: 623b7e0a58ef9644f4c3094fc6513c64
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
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index: climate_gpt_v2
vector_id: 14558
content:
"A particular aspect of ENSO diversity is whether the event is centred \r in the eastern Pacific (EP events) or central Pacific (CP or 'Modoki' \r events). This distinction is important as the type of event leads to \r different large-scale teleconnections and worldwide climatic impacts \r (e.g., Ashok et al., 2007; Ratnam et al., 2014; Capotondi et al., 2015; \r Timmermann et  al., 2018; Taschetto et  al., 2020). Differences can \r be significant at regional scale because of the displaced source \r of diabatic heating associated with the position of the main SST \r anomalies. For instance, stronger precipitation teleconnections occur \r over Australia during CP than during EP events (Wang and Hendon, \r 2007; Taschetto et al., 2009). It is noteworthy that central and eastern \r Pacific SST indices are more highly correlated with each other during \r La Nina than during El Nino events. This indicates that La Nina events \r have less geographic diversity than El Nino events (Kug and Ham, \r [...]"


id: c88eec1defaadfc7ca198089d540d898
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
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index: climate_gpt_v2
vector_id: 32594
content:
"[...] 2011) and that the CP/EP distinction is therefore more relevant \r for the latter. In the historical instrumental records, Banholzer and \r Donner (2014) found that CP events had a  weaker influence on \r global surface temperature than EP events did."


id: 2d7d7a5dc43fa82ee6a27ae5744ce621
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 5297
content:
"However, the distinction between CP and EP events is not \r necessarily clearly defined, as different methods yield an inconsistent \r classification of the events, which implies that no robust consensus \r on the classification of CP or EP events is possible (Wiedermann \r et  al., 2016). Furthermore, Giese and Ray (2011) found that the \r central longitude of peak SST anomalies associated with individual \r El Nino events had a distribution indistinguishable from a Gaussian \r distribution peaked at 140degW, as opposed to a bimodal distribution \r implied by the EP/CP paradigm. There are also occasional 'coastal \r Nino' events, such as those of 1925 (Takahashi and Martinez, 2019) \r or 2017 (Rodriguez-Morata et  al., 2019), where abnormally warm \r waters are confined to the South American coast. Even though these \r events are not conventionally classified as El Ninos, they have some \r El Nino-like teleconnections in South America. Instrumental data \r [...]"


id: e0f16eda9ca5969559bbfd09abec9a3e
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
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index: climate_gpt_v2
vector_id: 36838
content:
"[...] have historically been of limited value in assessing the classification \r of events before 1955, because of the almost total lack of SST \r measurements in the western half of the NINO3.4 region (L'Heureux \r et al., 2013). It is not yet known whether newer SST dataset versions, \r which benefit from improved data archives (Section 2.3.1.1), have \r reduced this uncertainty."


id: b2edbc5e5a6de8d5600369cedaee4820
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 28716
content:
"ENSO has been monitored through a  range of indices, which are \r based on SST or SLP anomalies during the instrumental period, \r and  various proxies on longer time scales. Differences between \r indices and thresholds used to define the occurrence of an El Nino or \r La Nina event imply that there is no commonly accepted list of events \r and associated magnitude. The three most prominent (Pepler, 2016; \r L'Heureux et al., 2017) indices used to monitor ENSO are the averaged \r SST anomalies over the so-called NINO3 (5degS-5degN, 150degW-90degW) \r or NINO3.4 (5degS-5degN, 170degW-120degW) regions and the Southern \r Oscillation Index (SOI) based on the normalized SLP difference \r between Darwin and Tahiti (Troup, 1965). Decomposition into EOFs \r of the tropical SST is also used in the literature. Those indices are \r highly correlated, as shown in Figure AIV.3b. The correlation between \r NINO3.4 SST and the EOF-SST index is about 0.97, and between \r [...]"


id: ea992716d184f7efa6ecfb84dc1b6a46
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
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vector_id: 38636
content:
"Figure AIV.3 | The boreal winter El Nino-Southern Oscillation (ENSO) mode defined by December-January-February (DJF) sea surface temperature (SST) \r anomalies averaged over the NINO3.4 region (5degS-5degN, 170degW-120degW, cyan box) or extracted as the leading EOF mode over the tropical Pacific Ocean \r (the region denoted by the black box in a) for 1958-2019 using ERSSTv5. (a) SST anomalies regressed onto the NINO3.4 time series, which is shown in (b) as red \r and blue bars. Black curve in (b) represents the standardized leading principal component and the purple curve stands for the SOI index. The percentage of explained variance of \r the leading EOF is given in the legend, as well as the correlation between all indices and the NINO3.4 time series. (c) Same as (a) but for land surface air temperature (shading; \r based on Berkeley Earth) and 10 m level wind (arrows; m s-1 based on JRA-55) anomalies over 1958-2018. (d) Same as (a) but for precipitation anomalies (shading based \r [...]"


id: 96322014b0715e913a738d9d3632675
created_at: 2023-04-19 13:22:49
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vector_id: 23619
content:
"[...] on GPCC for 1958-2016 and contours based on CMAP for 1979-2019 for every 0.5 mm day-1). Magenta dots in (c, d) stands for the Darwin and Tahiti weather station used \r for the SOI computation. On maps, no overlay indicates regions where the regressions are significant based on t-test accounting for false detection rates at 10% and crosses \r indicates no significance. Significance for CMAP precipitation is materialized by red contours in (d). All fields have been linearly detrended prior to computation."


id: b054bdad8cb98bcb7e796c2d5e678a04
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 28260
content:
"ENSO has a  broad range of teleconnections, both in and outside \r the Pacific region (Figure AIV.3c,d). Amongst the best-known \r teleconnections are the association of El Nino events with increased \r precipitation in the south-western and south-eastern USA, eastern \r Africa, coastal Ecuador and Peru, and northern Argentina, and \r with decreased precipitation in eastern Australia and the Maritime \r Continent, southern Africa, the Amazonian basin and much of the \r Indian subcontinent. A general reverse association is found during \r La Nina events although there are some asymmetries (Cai et al., 2010; \r Taschetto et al., 2020). ENSO also has a substantial impact on tropical \r cyclone occurrence (Wang and Chan, 2002; Chu, 2004; Kuleshov et al., \r 2008), with El Nino typically being associated with reduced tropical \r cyclone activity in the North Atlantic, and an eastward displacement \r of western North Pacific and South Pacific tropical cyclone genesis \r [...]"


id: 3e170abc56e7112aa5c0403660d14e7f
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
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index: climate_gpt_v2
vector_id: 8091
content:
"[...] away from the Philippine Sea and the Australian continent and into \r the central Pacific. ENSO has been shown to control part of the \r interannual variability of fire, including wildfire and human-triggered \r fires, in many regions of the world including all the continents \r surrounding the Pacific (see for instance the devastating extreme \r fire emissions in Indonesia in 2015; Chisholm et al., 2016) but also \r remotely, for example, over the Arctic (Monks et al., 2012)."


id: bdaa002edde2afa905590600431fcdc8
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 30628
content:
"Model evidence suggests that ENSO characteristics vary on decadal \r to centennial time scales (e.g.,  Lewis and LeGrande, 2015; Deser \r et  al., 2017), implying that a  minimum of several hundred years of \r observations, which is much longer than the instrumental record, \r are required to determine the full natural range of ENSO complexity \r (Wittenberg, 2009). Thus, one way to understand if anthropogenic \r forcing has modulated ENSO is to understand ENSO behaviour prior \r to the observational record. Paleoclimate archives can be used to fill \r this gap as they provide quantitative estimates of ENSO behaviour \r during the pre-industrial era, while also providing information of ENSO \r characteristics under a variety of past natural climate background states \r controlled by external forcings. Extensive proxy-based studies, using \r a range of ENSO-sensitive records, have been carried out to assess the \r ENSO changes on various paleoclimate time scales, specifically the last \r [...]"


id: 3c63d69496dd9daa76034f384c1122b6
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 7815
content:
"Tropical climate impacts during ENSO events are largely driven by \r a  reorganization of the zonal Walker circulation that results from \r anomalous diabatic heating associated with deep atmospheric \r convection changes (e.g., Gill, 1980; Klein et al., 1999; Chiang and Sobel, \r 2002; Choi et al., 2015). The latter are also responsible for atmospheric \r teleconnections to higher latitudes (Yeh et al., 2018), acting as a source \r of forced large-scale Rossby waves (Hoskins and Karoly, 1981). \r Specific phases of the Pacific-North American (PNA) pattern (Horel \r and Wallace, 1981) and Pacific-South American (PSA) pattern (Karoly, \r 1989) are then favoured in the Northern and Southern hemispheres, \r respectively, and are associated with shift and modulation of the mean \r storm tracks. These teleconnections influence temperature and rainfall \r around the globe, including extreme events (King et  al., 2014). For \r instance, mild winter conditions dominate Alaska/Canada, connected \r [...]"


id: 8426698c274c3fdbd86122f884dc868d
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vector_id: 20409
content:
"[...] to the south-eastward displacement and reinforcement of the Aleutian \r Low during El Nino, which generates anomalous southerlies and \r advects warmer air northward. By contrast, the anomalous cyclonic \r circulation favours cold outbreaks over the far-east Siberian region \r and Kamtchaka (Figure AIV.3c,d)."


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vector_id: 21722
content:
"Reconstructions of past ENSO behaviour traditionally merges \r information from multiple proxies that are considered sensitive \r to ENSO (Lu et al., 2018). This merging process may have multiple \r purposes, but it is generally used to extend the reconstruction \r length and also to increase its signal-to-noise ratio. The resulting \r reconstructions can be broadly placed into two main categories: \r (i) those that utilize proxies in the central and eastern equatorial \r Pacific itself; and (ii) those that utilize proxies from other regions \r impacted through teleconnections, along with those that mix both \r categories. ENSO reconstructions from category (i) are considered to \r be a more accurate measure of ENSO behaviour. However, proxies \r from the central and eastern equatorial Pacific are generally rarer \r than those outside the region, due to the small number of land/\r reefs, and they also tend to be of shorter duration. Thus, the ideal \r [...]"


id: 8ae5f930cccd274a5c7c7ae0c876c7df
created_at: 2023-04-19 13:22:49
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content:
"[...] case is a combination of both categories to get a robust quantitative \r estimate of ENSO behaviour in the past."


id: ecd9b887e58b7402ffeac484a26e35f1
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
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vector_id: 39038
content:
"Figure AIV.4 | The boreal spring Indian Ocean Basin (IOB) mode defined by March-April-May (MAM) SST anomalies averaged over the Indian Ocean (the \r region denoted by the cyan box in a) or extracted as the leading EOF mode over the same domain (black lines) for 1958-2019 using ERSSTv5. (a) SST \r anomalies regressed onto the IOB index time series shown in (b) in red and blue bars, while the black curve in (b) represents the leading principal component time series. \r Explained variance and correlation between indices are given in the legend in (b). (c) Same as (a) but for land surface air temperature (shading; based on Berkeley Earth) and \r 10 m level wind (arrows; m s-1 based on JRA-55) anomalies for 1958-2018. (d) Same as (a) but for precipitation anomalies (shading based on GPCC for 1958-2016 and \r contours based on CMAP for 1979-2019 for every 0.3 mm day-1). On maps, no overlay indicates regions where the regressions are significant based on t-test accounting \r [...]"


id: 93807453ffdc72322ddbeb0279cda4bc
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 23156
content:
"[...] for false detection rates at 10% and crosses indicates no significance. Significance for CMAP precipitation is materialized by red contours in (d). All fields have been linearly \r detrended prior to computation."


id: f0119f5db44259b21e65a930e8880c29
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 39616
content:
"ENSO's equatorial Pacific SST anomalies typically dissipate by boreal \r summer, when the lingering IOB then plays the leading role in exerting \r climate anomalies in Asia and the western North Pacific (the Indian \r Ocean capacitor effect; Xie et  al., 2009). The boreal summer IOB \r induces meridionally dipolar anomalies of precipitation and surface \r air temperature in South East and East Asia, with wetter and cooler \r summers in mid-latitude East Asia and drier and warmer summers in \r South East Asia during positive IOB events (Kosaka et al., 2013; Wang \r et al., 2013). These conditions affect the occurrence of extremes such as \r heavy rainfall (often caused through atmospheric rivers; Kamae et al., \r 2017) and heatwaves (Hu et al., 2012; Deng et al., 2019). The positive \r IOB suppresses summer tropical cyclone formation in the western \r North Pacific (Du et al., 2011; Ueda et al., 2018), leading to a delayed \r onset of the typhoon season (Zhao et  al., 2019). These regional \r [...]"


id: 4bc1cf353a02ebd7dab15988441a5437
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
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index: climate_gpt_v2
vector_id: 10588
content:
"[...] influences are already present in the spring season (Figure AIV.4c,d) \r but tend to persist and even get reinforced due to the persistence of \r the IOB, while following the seasonal climatological displacement of \r tropical dynamics (monsoon, etc.). In South Asia, tripolar precipitation \r anomalies form in boreal summer, with precipitation increase along \r the Western Ghats and in Bangladesh, and decrease around the \r Ganges Delta during the positive IOB (Chowdary et al., 2013, 2019)."


id: d88170315c682dcc573da4f1e0b542d3
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
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index: climate_gpt_v2
vector_id: 35383
content:
"The IOB and IOD are identified as the first and second EOF modes, \r respectively, of monthly SST variability in the tropical Indian Ocean \r (Weare, 1979; Saji et al., 1999). The IOD can be extracted also as the \r first EOF of SST variability in boreal autumn, although the pattern \r depends on the analysis period (Pillai et al., 2019). The IOD mode has \r a sub-surface signature and can be also calculated as the leading \r EOF mode of the 20degC isotherm depth of the Indian Ocean seawater \r temperature (Saji et al., 2006). Apart from the EOF definitions, the \r tropical Indian Ocean averaged SST (20degS-20degN, 40degE-100degE or \r 40degE-120degE) is often used as the IOB mode index (Figure AIV.4a). \r For March-April-May (MAM), its correlation with the leading PC \r of Indian Ocean SST (Figure AIV.4b) reaches 0.98. For the IOD, the \r difference between normalized SST anomalies between the western \r (10degS-10degN, 50degE-70degE) and eastern (10degS-0deg, 90degE-110degE) \r [...]"


id: 58f963c5532fb3f808ddb063928979e4
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 12892
content:
"[...] equatorial Indian Ocean is a  widely used index (Saji et  al., 1999; \r Figure AIV.5). It is highly correlated (at 0.76) with the leading principal \r component of the Indian Ocean September-October-November SST \r when assessed over 1958-2019 (Figure AIV.5b)."


id: 893a4762896739d11f78143591eef0e
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 21312
content:
"The positive phase of the IOD also decreases rainfall in western to \r south-eastern Australia (Ashok et al., 2003; Risbey et al., 2009), acting \r as preconditioning for fires (Cai et al., 2009). The rainfall anomalies \r cover the whole of Australia when combined with simultaneous ENSO \r with the same polarity (Risbey et al., 2009). The IOD influences South \r Asian summer monsoon rainfall either directly through moisture \r transport over the western Indian Ocean or modification of the local \r Hadley cell (e.g., Ashok et al., 2001; Gadgil et al., 2004; Ashok and Saji, \r 2007; Behera and Ratnam, 2018), or indirectly in the framework of \r the tropospheric biennial oscillation (e.g., Meehl et al., 2003; Li et al., \r 2006; Webster and Hoyos, 2010). IOD and ENSO often interfere in \r their influence on the South Asian summer monsoon (e.g.,  Ashok \r et al., 2001, 2004b; Pepler et al., 2014; Cretat et al., 2017). Opposing \r influences of IOD and ENSO have been found on surface temperature \r [...]"


id: cddc6abaeda311842e5cada4f611a767
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 33547
content:
"The AMM is thought to be primarily driven by air-sea heat fluxes \r and therefore thermodynamic in nature (Chang et al., 2000; Foltz and \r McPhaden, 2010), while various mechanisms for generating AZM \r events have been discussed. They mainly concern fluctuations in the \r wind field over the equatorial Atlantic. These wind-stress anomalies \r can be excited by local SST changes, as part of Bjerknes feedback \r (i.e., weakening of trade winds and thermocline slope adjustments \r via oceanic equatorial Kelvin wave propagation; Keenlyside and \r Latif, 2007; Deppenmeier et al., 2016). They can also be excited by \r meridional advection of temperature anomalies (Richter et al., 2013), \r generated remotely as a  response to ENSO (Latif and Grotzner, \r 2000; Tokinaga et  al., 2019) or to variations in the South Atlantic \r Subtropical High (Lubbecke et al., 2014), or occur as stochastic wind \r variability (Richter et al., 2014). In contrast to previous mechanisms \r [...]"


id: b38d143cb109e26d32c43af29391db30
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 28830
content:
"[...] involving coupled atmosphere-ocean dynamics, Bellomo et  al. \r (2015) and Nnamchi et al. (2015) suggested that a large part of the \r SST variability in the eastern equatorial Atlantic can be explained \r by thermodynamic feedbacks excited by stochastic atmospheric \r perturbations. A recent study based on observational and reanalysis \r products by Nnamchi et al. (2021) indicates that the seasonality of \r AZM is indeed largely controlled by the variability of the atmospheric \r diabatic heating. However, the balance between thermodynamic \r and dynamic processes determining SST variability in the equatorial \r Atlantic is subtle, and other studies (e.g., Jouanno et al., 2017; Dippe \r et al., 2018) conclude that, even if the thermodynamic component is \r not negligible, the dynamics is the dominant driver of the AZM."


id: 7c1150ac8f7834b8376ab389cbe81e90
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 19034
content:
"Corals record interannual and longer variability in the Indian \r Ocean (Zinke et  al., 2005, 2009). Several studies have developed \r IOD reconstructions based on oxygen isotope ratios from corals. \r These reconstructions include those based on samples from Kenya \r since 1887 (Kayanne et  al., 2006), from the Mentawai Islands in \r western Indonesia for the last 6500 years (Abram et  al., 2007), \r and in combination with samples from Bali in southern Indonesia \r and  the Seychelles since 1846 (Abram et  al., 2008). Abram et  al. \r (2020) developed a coral-based reconstruction of the IOD for the last \r millennium, albeit with some discontinuity. Niedermeyer et al. (2014) \r use isotope compositions in terrestrial plant wax to reconstruct \r rainfall changes over north-western Sumatra, which are highly \r correlated with the IOD. Compared to the IOD, proxy-based IOB \r reconstruction is limited. Yet Du et al. (2014) find that coral oxygen \r [...]"


id: 4adb87bb10a6e1bef043ce0ce1cef4b6
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 10411
content:
"[...] isotope records from the Seychelles are well correlated with the IOB, \r suggesting that they can be used as an IOB proxy. Evidence from \r other proxies that are potentially associated with the Indian Ocean \r variability is assessed in Section 2.4.3."


id: a5c7e2916d3f4a1e794d3f95999b1a4b
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
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index: climate_gpt_v2
vector_id: 26392
content:
"Figure AIV.6 | The boreal summer Atlantic Zonal Mode (AZM) defined by June-July-August (JJA) box-averaged sea surface temperature (SST) anomalies \r over the ATL3 region (3degS-3degN, 0deg-20degW, cyan box in a) or estimated as the leading empirical orthogonal function (EOF) over the tropical Atlantic Ocean \r (the region denoted by the black box in a) for 1958-2019 using ERSSTv5. (a) SST anomalies regressed onto the ATL3 time series, which is shown in (b) as red and \r blue bars, while the black curve represents the leading principal component time series. Explained variance and correlation between indices are given in the legend in (b). \r (c) Same as (a) but for land surface air temperature (shading; based on Berkeley Earth) and 10 m level wind (arrows; m s-1 based on JRA-55 for 1958-2019) anomalies. (d)\r Same as (a) but for precipitation anomalies (shading based on GPCC for 1958-2016 and contours based on CMAP for 1979-2019 for every 0.2 mm day-1). On maps, no overlay \r [...]"


id: 357caf6094f4cbe6bc0428840762f938
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 6659
content:
"[...] indicates regions where the regressions are significant based on t-test accounting for false detection rates at 10% and crosses indicates no significance. Significance for CMAP \r precipitation is materialized by red contours in (d). All fields have been linearly detrended prior to computation."


id: a6010380ace8136abeb7991db2a37df2
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 26426
content:
"The AZM affects the Indian summer monsoon (ISM), altering the \r ENSO-ISM connection (Kucharski et  al., 2007, 2008; Wang et  al., \r 2009; Barimalala et  al., 2012, 2013; Kucharski and Joshi, 2017). \r Specifically, warm SST in the equatorial Atlantic leads to decreased \r monsoon rainfall over central India, and the opposite occurs for \r negative Atlantic SST anomalies via strengthening of the Somali Jet \r and low-level convergence (Kucharski et al., 2007, 2008; Pottapinjara \r et al., 2016). This latter relationship opens up the possibility of using \r AZM phases to predict ISM rainfall (Sabeerali et al., 2019). In this \r framework, the AZM modulates SST variability in the Indian Ocean \r (Kajtar et al., 2017). The number of monsoon depressions over the \r Bay of Bengal increases during the cold phase of the AZM, which is \r remotely responsible for changes in low-level cyclonic vorticity and \r mid-tropospheric humidity (Pottapinjara et al., 2014). A relationship \r [...]"


id: bc9d328eda609d35543ad7187bb303fd
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 30430
content:
"[...] between AZM and the West African Monsoon is documented as \r well, with a  reduction of rainfall over the Sahel and an increase \r over Guinea (Losada et  al., 2010). But the relationship with the \r Sahel shows significant changes across the 20th century (Joly and \r Voldoire, 2010; Losada et al., 2012), which is strongly indicative for \r non-stationarity of the teleconnection."


id: 5e3505611c0250b4bde613cc71f96089
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 13863
content:
"A positive phase of the PDV is characterized by warm conditions in \r the central-eastern tropical Pacific from the dateline to the coasts of \r the Americas, encircled to the west in the mid-latitudes by negative \r SST anomalies forming a  so-called horseshoe pattern. The pattern \r has a  certain similarity with ENSO and related anomalous SST \r fingerprints through atmospheric teleconnections. However, the PDV \r features meridionally broader SST anomalies in the tropical Pacific \r extending to the subtropics compared to ENSO, and relatively stronger \r extratropical SST anomalies. Alternatively, the low-pass filtered \r SST regression on the PDO index, defined as the leading principal \r components of SST (Mantua et  al., 1997) or sea surface height \r (Di Lorenzo et al., 2008) over the extratropical North Pacific north \r of 20degN, are used in literature for PDV. Their corresponding spatial \r patterns are, by definition, weighted to the respective hemisphere of \r [...]"


id: a3ea4828efc2cf683e9ca1f3cce33613
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 26031
content:
"[...] the Pacific. Figure AIV.8b provides evidence that they are all highly \r correlated to each other. The correlation value between the TPI and \r the PDO index is equal to 0.76, and when low-pass filtered, it is \r greater than 0.9 for all the combination of indices."


id: 23adbfb677045245b246cb5ab6c0f47e
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 3473
content:
"Despite the PDO, SPDO and TPDV being considered to be physically \r distinct modes (Newman et al., 2016), they are highly correlated in \r observations (Chen and Wallace 2015) and they collectively constitute \r an equatorially symmetric pattern of basin-wide variability, often \r called the Inter-decadal Pacific Oscillation (IPO; Power et al., 1999; \r Folland et al., 2002; Henley et al., 2015). There is evidence that ENSO \r and the TPDV drive the PDO and SPDO in the North and South Pacific \r through tropical-extratropical teleconnections and the 'reddening' \r (i.e., selective retainment of decadal and longer variability) in the \r ocean, ensuring the synchronization of decadal variability in both \r hemispheres (Newman et al., 2016). Besides, positive feedbacks are \r suggested between the TPDV and the extratropical North Pacific \r through the Pacific Meridional Mode (Stuecker, 2018), which is the \r Pacific analogue of the AMM (Section AIV.2.5; Chiang and Vimont, \r [...]"


id: dd5ec8af742d9149fb6cc7f0088e04fc
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
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vector_id: 36213
content:
"[...] 2004), and modulations of the shallow subtropical cell of the Pacific \r ocean circulation (Farneti et al., 2014)."


id: 58fe6bc62d792be4b0cb7afba8abd4bc
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 12894
content:
"Since AR5, there has been considerable progress in the understanding \r of PDV itself, but also in its critical role in modulating the evolution \r of observed global surface temperature (Cross-Chapter Box 3.1) as \r well as regional climate through teleconnections. On interannual \r time scales, ENSO is the leading internal driver of global surface \r temperature variability (Section AIV.2.3). Since PDV encompasses \r decadal modulations of El Nino versus La Nina occurrences, PDV \r affects global surface temperature on decadal time scales, with \r its positive and negative phases tied to higher and lower global \r surface temperature, respectively. Indeed, studies identify PDV as the \r leading mode of variability associated with unforced decadal global \r surface temperature fluctuations in observations and a majority of \r CMIP5 models (Brown et al., 2015; Dai et al., 2015), with additional \r influence from AMV (Section AIV.2.7; Tung and Zhou, 2013). PDV \r [...]"


id: a68362192f0b6b29471d87f0eb6557d5
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 26511
content:
"[...] thus plays an important role in decadal acceleration and slowdown \r of global surface temperature trends (Cross-Chapter Box 3.1). So far, \r little predictability has been found for decadal PDV, despite its global \r relevance, and decadal prediction in the Pacific remains challenging \r with the current decadal forecast system (Section  4.4.3.5). Finally, \r there is an increasing body of evidence about the existence of \r a network of teleconnections related to decadal climate-variability \r phenomena (Cassou et al., 2018; Cai et al., 2019). For instance, AMV \r has been hypothesized to influence in part the phase of the PDV \r (McGregor et al., 2014; Ruprich-Robert et al., 2017), and consistently \r there is a  significant negative correlation between PDV and AMV. \r Other studies suggest that the PDV may be the main driver of the "


id: b43fb250d9a87378d02c8a459ff5c9af
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
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index: climate_gpt_v2
vector_id: 28946
content:
"PDV reconstructions have been developed with tree rings in North \r America and Asia (Biondi et al., 2001; D'Arrigo et al., 2001; Gedalof \r and Smith, 2001; MacDonald and Case, 2005; D'Arrigo and Wilson, \r 2006; D'Arrigo and Ummenhofer, 2015; Buckley et  al., 2019), \r corals in the Pacific (Linsley et al., 2015; Williams et al., 2017) and \r Indian Ocean (Crueger et al., 2009), historical documents in China \r (Shen et  al., 2006), ice cores (Vance et  al., 2015), and multi-proxy \r approaches (Verdon and Franks, 2006; Mann et al., 2009; McGregor \r et al., 2010) covering the past several centuries up to a millennium. \r Comparing 12 of these reconstructions, it has been found that \r respective correlations are generally low (Henley, 2017), indicating \r large uncertainty in the PDV temporal history before instrumental \r records. On longer time scales, a  variety of proxies consistently \r capture the variations of the Aleutian Low intensity throughout the \r [...]"


id: 37ecc34dc9d4257544cb0ddd3e20e5fb
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 7045
content:
"A positive phase of the PDV brings anomalously warm conditions \r in the north-western corner of North America, with maximum \r loading over Alaska linked to reinforced and eastward-displaced \r Aleutian Low and related anomalous southerlies (Figure AIV.8c). In \r contrast, concurrent cold conditions dominate far-eastern Siberia \r due to strengthened advection of Arctic air. The tail end of the storm \r track is strengthened leading to more precipitation along the Pacific \r coast of North America, especially in boreal winter. A positive PDV \r also induces a cooler and wetter climate in the southern part of the \r North American continent, with the wet anomalies being the most \r pronounced in boreal summer (Figure AIV.8d). The negative phase \r of the PDV is an important cause of droughts in the western and \r central USA (Dai, 2013). Besides the direct influence of atmospheric \r teleconnections from the tropical branch of the mode, extratropical \r [...]"


id: bf738784c722e011e608860e20aff8e2
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
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index: climate_gpt_v2
vector_id: 30948
content:
"[...] SST anomalies have been shown to modulate the amplitude of the \r precipitation anomalies in North America (Burgman and Jang, 2015)."


id: edfdbf255be2e3bbd79a34249b67a980
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 39244
content:
"The observed AMV is interpreted as the regional residual imprint in \r SST anomalies after removal of the externally forced signal. There \r is no unique and preferred way to remove the impact of external \r forcing in observations. Linear detrending is used in the traditional \r definition of the AMV index, which corresponds to the average over \r the entire North Atlantic basin of the linearly detrended SST yearly \r anomalies on which a 10-year running mean is applied, as proposed \r by Enfield et  al. (2001). To remove the residual nonlinear global\u0002scale signal from the North Atlantic mean SST, different approaches \r have been proposed either based on observations applying specific \r statistics (e.g., Trenberth and Shea, 2006; Frajka-Williams et al., 2017; \r Frankignoul et al., 2017; Sutton et al., 2018; Yan et al., 2019) or based \r on model estimates of the externally forced signal including both \r natural (solar and volcanoes) and anthropogenic (greenhouse gases \r [...]"


id: e5f62c8fe1397dc271db962da61c7996
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 37753
content:
"[...] and aerosols) influence assessed from historical simulations (Ting \r et al., 2009; Terray, 2012; Steinman et al., 2015; Tandon and Kushner, \r 2015). More specifically, in Trenberth and Shea (2006), the annual \r observed global-mean SST anomaly time series is subtracted from \r the observed annual North Atlantic spatially averaged time series to \r obtain the raw unfiltered AMV index. In Ting et al. (2009), a signal\u0002to-noise maximizing EOF analysis is applied to global annual mean \r SSTs derived from the CMIP5 multi-model ensemble to extract an \r estimation of the forced component, which is a priori removed before \r computation of the AMV index, then defined as a  low-frequency \r residual. The latter technique has been retained in the so-called \r CMIP6 DCPP-C sensitivity experiments (Boer et al., 2016) aimed at "


id: 72c252f3244b997489526b81873ca0c2
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 17434
content:
"Positive AMV is associated with much warmer conditions over \r the north-western corner of the Atlantic, with marked positive \r anomalies on land around the Labrador Sea (Figure AIV.9c). There \r is a strong seasonality in the AMV teleconnection and this pattern \r is mostly explained by boreal winter atmospheric dynamics and \r a  local reduction of sea ice formation. There is a  large-scale \r decrease of the winter mid-latitude westerlies and, concurrently, \r a weakening of the trade winds in the North Atlantic tropical basin, \r which is reminiscent of a  negative phase of the NAO (Peings and \r Magnusdottir, 2014; Gastineau and Frankignoul, 2015). Over Europe, \r AMV-related signals are weak on yearly average (Figure AIV.9c) but \r mask a  pronounced seasonality of the teleconnection as well as \r some interplay between dynamical (i.e., atmospheric circulation) \r versus thermodynamical changes controlled by the AMV (O'Reilly \r [...]"


id: 8389d631307426ddefab029b1714cc1
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content_type: text
index: climate_gpt_v2
vector_id: 20284
content:
"[...] et al., 2017; Ruprich-Robert et al., 2017, Qasmi et al. 2020). Summer \r tends to be warmer during positive AMV in Europe (Sutton and \r Dong, 2012; Nicoli et  al., 2020) with constructive contribution of \r both dynamical and thermodynamical responses while destructive \r interference prevails in boreal winter leading to colder conditions "


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"In terms of precipitation (Figure AIV.9d), positive AMV is associated \r with a northward shift of the Atlantic ITCZ in response to changes in \r the meridional SST gradient with respect to the equator (Martin and \r Thorncroft, 2014; Friedman et al., 2017; Green et al., 2017). The AMV \r influence is the strongest in the boreal summer affecting the West \r African Monsoon, leading to reinforced Sahel rainfall, and wetter \r conditions in the Caribbean basin through the modulation of the \r occurrence of hurricanes (Dunstone et al., 2011; Hermanson et al., \r 2014). By contrast, dry conditions prevail in north-eastern Brazil \r and in the south-eastern corner of South America as well as in the \r Great Plains in North America. The signal of the positive AMV lead \r to intensified precipitation and river runoff over northern Eurasia \r (Nicoli et al., 2020) and controls the multi-decadal variations in river \r flows over much of Europe (Boe and Habets, 2014; Hodgkins et al., \r [...]"


id: 30a9b75fb62238cf56a78914b983eec1
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
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index: climate_gpt_v2
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content:
"[...] 2017). Because of global teleconnections, especially in the tropics, \r the AMV also affects the other monsoon systems with signals found \r over India, South East Asia and the Maritime Continent and, more \r broadly, over the entire tropical Pacific (Chylek et al., 2014; Kucharski \r et al., 2016; Ruprich-Robert et al., 2017)."


id: 6f35d858a9f2bb27403b81f358cd3e50
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
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index: climate_gpt_v2
vector_id: 16833
content:
"The Madden-Julian Oscillation (MJO) is the leading mode of tropical \r intra-seasonal variability with 20-90 days' time scale (Madden \r and Julian, 1994), and it is an important source of regional climate \r variability and predictability across the globe at sub-seasonal time \r scales from a week to months (Madden and Julian, 1994; Zhang, 2005; \r Vitart, 2017; Wheeler et al., 2017; Kim et al., 2018). It is characterized \r by planetary-scale disturbances (zonal wavenumber 1-3) of pressure, \r wind, clouds and rainfall moving predominantly eastward along the \r equator at average speed of 5 m s-1 (Roxy et al., 2019). More precisely, \r the MJO is characterized by alternating regions of suppressed and \r enhanced convection coupled to an anomalous zonal overturning \r circulation of the atmosphere (Zhang, 2005). The MJO is driven by \r internal ocean-atmosphere processes that occur all year round but \r it exhibits prominent seasonality with more pronounced signals in \r [...]"


id: 6484429a796ece6bcece390435eb20cc
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
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index: climate_gpt_v2
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content:
"[...] boreal winter. During boreal summer, centres of convective activity \r associated with the MJO are shifted away from the equator to \r 10degN-20degN and propagate northward in the Asian monsoon region \r (Yasunari, 1980; Madden, 1986) in addition to eastward propagation, \r often called as the boreal summer intra-seasonal oscillation (BSISO; \r Madden, 1986; Kikuchi et al., 2012). Whereas the MJO is considered \r present in all seasons, albeit with weaker amplitude in boreal summer, \r the BSISO tends to prevail during boreal summer, with influences on \r monsoons, tropical cyclones and other water cycle aspects (Kikuchi \r et al., 2012; Lee et al., 2013)."


id: 458713c58bf5d33c6ec0795a62ebb31d
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 9449
content:
"Paleoclimate reconstructions of AMV-related signals have been \r developed using terrestrial, marine and ice-core proxies. The AMV \r reconstruction in the last 1200 years by J. Wang et al. (2017), using \r terrestrial proxy records, shows a  clear broad band of enhanced \r spectral power at multi-decadal time scales but no evidence of \r a  distinct multi-decadal or centennial spectral peak (Singh et  al., \r 2018). This is largely consistent with the record of the instrumental \r period and with previous proxy-based studies (Gray et al., 2004; Mann \r et al., 2009) in the overlapping periods. Studies focusing on marine \r proxies also show persistent multi-decadal variability over the last \r three centuries (Kilbourne et al., 2014; Svendsen et al., 2014; Moore \r et al., 2017). Multi-decadal Arctic temperature variability related to \r the AMV is also evident in ice-core records over the last millennium \r (Chylek et al., 2011; Zhou et al., 2016) and beyond (Knudsen et al., \r [...]"


id: d6b73988704343480233fe14baac9077
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 35081
content:
"[...] 2011). High-resolution sedimentary records from the Arctic were \r recently analysed to reconstruct the AMV over the last 2900 years \r (Lapointe et  al., 2020). However, this new reconstruction refers to "


id: 2d78cb3edba36f4974c8b5dfbc45a5be
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 5290
content:
"The real-time multivariate MJO (RMM) index has been widely used \r to detect the MJO, based on the first two combined EOF modes of \r daily outgoing longwave radiation (OLR) and zonal winds at 850 and \r 200 hPa averaged over 15degN-15degS (Wheeler and Hendon, 2004). The \r OLR represents convective activity and zonal winds at the lower and \r upper troposphere depict the overturning zonal circulation along the \r equator. The composite maps of convective activity and atmospheric \r circulation (Figure AIV.10a) and the phase diagram (Figure AIV.10b) \r based on eight phases constructed by the two PCs time series provide \r effective ways to understand and monitor spatial and temporal \r evolution of the MJO event. In general, the MJO disturbances are \r initiated in the equatorial western Indian Ocean and travel eastward \r mainly over the Indo-Pacific warm pool region. However dynamical \r fields in composite maps indicate that the MJO also propagates over \r [...]"


id: fe4df28351707dd27c9060349b099dc9
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
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index: climate_gpt_v2
vector_id: 42109
content:
"[...] climatological non-convective regions (e.g., the eastern Pacific, part of \r the tropical Atlantic; Figure AIV.10a), then manifesting the planetary \r nature of the tropical intra-seasonal oscillation (Cassou,  2008). "


id: a18ea8b86c8ea8b5a60fcd9de2cfc32c
created_at: 2023-04-19 13:22:49
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vector_id: 25636
content:
"Figure AIV.10 | (a) The spatio-temporal properties of the Madden-Julian Oscillation (MJO). Daily composites of outgoing longwave radiation (OLR, W m-2, shaded) \r anomalies and stream-function anomalies at 300 hPa (STF, contours every 0.5 x 106 m2 s-1) for the eight MJO phases in boreal winter (1 December to 31 March) based on \r NOAA interpolated satellite products and NCEP-NCAR reanalysis over 1979-2019, respectively. Greenish (brownish) colours indicate enhanced (reduced) convective activity \r associated with wet (dry) conditions. Solid (dashed) contours show for positive (negative) STF anomalies which stand for anomalous anticyclonic circulation in the Northern \r (Southern) Hemisphere. (b) Phase diagram of the MJO constructed from the two leading principal components drawn from empirical orthogonal decomposition of combined \r [...]"


id: 31308ec8a131e954cd417def69f49e40
created_at: 2023-04-19 13:22:49
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index: climate_gpt_v2
vector_id: 5905
content:
"[...] wind at 300 hPa + OLR fields following Wheeler and Hendon (2004) for winter 2007-2008 chosen as an example of active MJO years. (c) Schematic representation of \r MJO-related tropical-Northern Hemisphere interactions and teleconnections inferred from observational and numerical studies (adapted from Stan et al., 2017). Because the \r interactions and teleconnections of the Southern Hemisphere are not as well understood except for the Southern Annular Mode, they are not included in the current schematic."


id: 4924d7c0a826b095ee395a52ee5315fc
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 10094
content:
"The schematic in Figure AIV.10c shows the main regions of \r influence of the MJO. During boreal winter, the MJO fingerprint \r is mostly confined to the deep tropics with a  zonal alternation of \r wet and dry zones depending on the MJO phase. For instance, in \r phase 4, enhanced rainfall is found over the Maritime Continent \r while suppression of convection is present over the central Pacific, \r northeast Brazil and equatorial Africa (Figure AIV.10a). The opposite \r is found during phase 8. Anomalous upper-level divergence in \r the deep tropics associated with altered convection and diabatic \r heating leads to anomalous vorticity in the subtropics (shown by \r stream-function dipoles straddling the equator in Figure AIV.10a) \r acting as an efficient and primary source of teleconnections outside \r the tropics (L'Heureux and Higgins, 2008; Lin et  al., 2009; Lukens \r et al., 2017). The extratropical influence of the MJO may occur via \r [...]"


id: 1dd5dca17da8faf2d7e795990d45eeb7
created_at: 2023-04-19 13:22:49
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index: climate_gpt_v2
vector_id: 2436
content:
"[...] the excitation of forced tropospheric Rossby waves (Ferranti et al., \r 1990; Matthews et al., 2004; Cassou, 2008; Stan et al., 2017) that \r affect the climate variability over the North Pacific (Ferranti et  al., \r 1990; Higgins and Mo, 1997), the North Atlantic (Ferranti et al., 1990; \r Cassou, 2008) and the Arctic (Zhou and Miller, 2005). For example, \r tropical convective heating associated with MJO phase 5 contributes \r to Arctic warming, while the opposite occurs for MJO phase 1 (Yoo \r et  al., 2011, 2012). Phase 2-3 of the MJO is clearly associated \r with the prevalence of the positive phase of the NAO with a 10- to \r 15-day lag after the MJO forcing. Stratospheric pathways are also \r reported to support tropical-extratropical teleconnections though \r the modified occurrence of sudden stratospheric warming (Liu et al., \r 2014; Garfinkel and Schwartz, 2017) with 15- to 20-day delay leading \r to sub-seasonal predictability."


id: 6afba169ca0f3da9e0711cf9caaf9fcb
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 16115
content:
"The evaluation of the MJO in climate models is done through \r phenomenological (Waliser et  al., 2009) and process-oriented \r (Xavier, 2012) diagnostics. MJO simulation remains a  challenge in \r both CMIP3 and CMIP5 models, even though overall improvements \r have been reported in the most recent model versions (Waliser et al., \r 2003; Lin et al., 2006; Sperber and Annamalai, 2008; Kim et al., 2009; \r Xavier et al., 2010; Jiang et al., 2016). Poor MJO simulation in coupled \r climate models has been attributed to many factors, including \r an insufficient buildup of atmospheric moisture for large-scale \r organized convection to occur (Kim et al., 2012; Mizuta et al., 2012), \r biases in models' mean state (Inness et al., 2003; Hannah et al., 2015; \r Kim, 2017), coupling frequency with the ocean and representation \r of air-sea interactions (Bernie et  al., 2008; DeMott et  al., 2015). \r In CMIP5 simulations, the eastward propagation of boreal winter \r [...]"


id: 80923fe2be36aaeb5ef7afa5db00626
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
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index: climate_gpt_v2
vector_id: 19748
content:
"[...] MJO convection from the Indian Ocean into the western Pacific \r (Hung et  al., 2013) and the northward propagation during boreal \r summer (Sperber et al., 2013) are slightly better represented, even \r if major biases persist and still affect the monsoon systems. These \r improvements have been attributed to advances in parametrizing \r moist turbulence and convection (Hohenegger and Bretherton, 2011; \r Mapes and Neale, 2011; Del Genio et  al., 2012; Kim et  al., 2012; \r Hourdin et al., 2013), leading to greater MJO predictive skill of up \r to four weeks in operational sub-seasonal prediction centres (Vitart, \r 2014). Super-parametrized models (Benedict and Randall, 2009), \r global cloud system-resolving models (Miyakawa et al., 2014) and \r high-resolution global climate models with an improved seasonal \r cycle (Mizuta et  al., 2012) have been shown to produce a  more \r realistic simulation of the MJO. Progresses in the representation of \r [...]"


id: 798294cb51a334ec5a4b0e11dcafc52d
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 18582
content:
"During boreal summer, the MJO/BSISO convective activities and \r related impacts propagate northward/north-eastward over the Indian \r Ocean-South Asia region (Yasunari, 1980; Annamalai and Sperber, \r 2005), and northward/north-westward over the western North \r Pacific-East Asia region (Kemball-Cook and Wang, 2001; Lee et al., \r 2013). The MJO/BSISO significantly modulate occurrences of extreme \r rainfall and heatwaves over Asia (Hsu et al., 2016, 2017; Chen and \r Zhai, 2017; Diao et al., 2018) and North America (Moon et al., 2013; Lee \r and Grotjahn, 2019). They also regulate tropical cyclone activities in \r the Indian (Frank and Roundy, 2006), Pacific (Maloney and Hartmann, \r 2000; Diamond and Renwick, 2015; Zhao et al., 2015) and Atlantic \r (Klotzbach and Oliver, 2015) tropical oceans. They contribute to the \r intra-seasonal fluctuations of monsoons, including timing of onset \r and termination during summer (Maloney and Shaman, 2008; Wheeler \r [...]"


id: e26556a5a40b02e7b9a0691de8c64a06
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 37122
content:
"[...] et al., 2009; Lee et al., 2013; Grimm, 2019), and active/break phases \r (Pohl and Camberlin, 2006; Maloney and Shaman, 2008; Joseph et al., \r 2009; Lee et al., 2017)."


id: f9b8884d04ebd1bfd9d41e5587d2f42d
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 41297
content:
"The definition of the regional monsoons has been slightly modified \r in AR6 with respect to AR5, starting from the consideration that \r some of the continental areas identified using the global metric have \r a seasonality in precipitation that is not necessarily of monsoon origin. \r In particular, the dotted regions in Figure AV.1 located over South \r Africa, Central America and equatorial South America have a strong \r seasonality in precipitation but their qualification as monsoons is \r a subject of discussion. In the assessment of the regional monsoons \r in Sections 8.3.2.4 and 8.4.2.4, these regions are not considered as \r distinct regional monsoons, but they are discussed in Box 8.2, which \r is dedicated to changes in water cycle seasonality. The domains \r of the regional monsoons in AR6 are defined based on published \r literature and expert judgement, and accounting for the fact that \r the climatological summer monsoon rainy season varies across the \r [...]"


id: 6ebe70ebf041d9c66dcf1ba6cfa1cbda
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 16749
content:
"[...] individual monsoon regions. As shown in Figure AV.1, the AR6 regional \r monsoons are: South and South East Asian (Section AV.4.1), East \r Asian (Section AV.4.2), West African (Section AV.4.3), North American \r (Section AV.4.4), South American (Section AV.4.5) and Australian-\r Maritime Continent (Section AV.4.6) monsoons. For each region, the \r definition, regional justification and key features are provided, along \r with cross references to the main areas of assessment in AR6."


id: befcad9be3859ded2e5da035212dec80
created_at: 2023-04-19 13:22:49
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content_type: text
index: climate_gpt_v2
vector_id: 30870
content:
"The SAsiaM rainy season from June to September contributes to more \r than 75% of the annual rainfall over much of the region, including the \r southern slopes of the central and eastern Himalayas (Krishnan et al., \r 2019b). Considering the spatial domain of the SAsiaM, monsoon \r precipitation maxima are located over the west coast, north-east and \r central/north India as well as Myanmar and Bangladesh, whereas \r minima are located over north-west and south-eastern India, western \r Pakistan, and south-eastern and northern Sri Lanka (Pant and Rupa \r Kumar, 1997; Gadgil et al., 2010). Prior to the SAsiaM rainy season, \r areas in the north-western Himalaya receive precipitation during \r winter and early spring from so-called 'western disturbances', which \r are extratropical synoptic systems originating over the Mediterranean \r region and propagating eastward along the subtropical westerly jet \r (Madhura et al., 2014; Cannon et al., 2015; Dimri et al., 2015; Hunt \r [...]"


id: f2224e8d53c8fe44a702f041bacfd298
created_at: 2023-04-19 13:22:49
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index: climate_gpt_v2
vector_id: 39995
content:
"Intra-seasonal variability of the EAsiaM has been mostly related to \r the Madden-Julian Oscillation (MJO) (Yasunari, 1979; Zhang et al., \r 2009; Chen et  al., 2015), to the phase of the Pacific-Japan mode \r (Nitta, 1987) or the Indian summer monsoon (Li et al., 2018) and to \r the BSISO (Kikuchi et al., 2012; Chen et al., 2015), particularly for its \r onset (J.-Y. Lee et al., 2013). At interannual time scales, changes in \r East Asian summer monsoon intensity lead to a position shift of the \r monsoon rain belt under the influence of ENSO (Wang et al., 2000) \r and the western North Pacific subtropical high (Kosaka et al., 2013; \r Wang et al., 2013), Arctic sea ice (Guo et al., 2014), and solar and \r volcanic forcing (Peng et al., 2010; Man et al., 2012; Cui et al., 2014). \r Variability at inter-decadal time scales is more prominent in the \r second half of the 20th century (Jiang, 2005; Ding et al., 2008; Wang \r et al., 2018), and a specific assessment about this aspect is provided \r [...]"


id: 899ef65c177dbcd73b10d13c084fcc88
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 21388
content:
"The basic features and interannual variations of the East Asian \r summer monsoon are well reproduced in climate models. For \r example, the climatological circulation structure is well reproduced \r in both atmospheric and coupled GCMs (Song and Zhou, 2013; Song \r et al., 2013; Jiang et al., 2016, 2020), and the relationship between \r the monsoon and ENSO is well represented (Sperber et  al., 2013; \r Fu and Lu, 2017). In CMIP5 models, the main shortcomings relate \r to missing rainfall bands around 30degN and the northward shift of \r the western North Pacific subtropical high (Huang et al., 2013). Most \r coupled models show an inadequate strength of monsoon circulation \r over southern East Asia, and with little change in model performance \r from the Third Assessment Report (TAR) to AR6 (Jiang et al., 2016, \r 2020). In coupled model simulations, air-sea coupling helps improve \r the climatology and interannual variability of rainfall over the East \r [...]"


id: 5a8824536c11ccce84488760e894e8e
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 13183
content:
"The West African monsoon (WAfriM) is a seasonal reversal in wind \r and precipitation extending over a vast and contrasted geographical \r region, from the equator to the margins of the Sahara, and from the \r Atlantic coast inland. The WAfriM domain includes Togo, Guinea \r Bissau, Gambia, Senegal, Mauritania, Guinea, Sierra Leone, Liberia, \r Mali, Ivory Coast, Burkina Faso, Ghana, Benin, Chad, Cape Verde, \r northern Cameroon, Niger, Nigeria and the northern Central African \r Republic. It is roughly bounded by 18degW-30degE, 5degN-18degN, and is \r shaded grey in Figure AV.1 (e.g., Adedokun, 1978). The West African \r monsoon and Sahel are sometimes considered interchangeably. \r However, the Sahel lies in the northern part of the WAfriM region, \r often limited to the west and central north Africa (e.g.,  Nicholson \r et al. (2018) considered the domain to be 20degW-30degE, 10degN-20degN) \r or sometimes extended to the east (e.g.,  Giannini and Kaplan \r [...]"


id: 944c79b4d4891f105a83cf7838ee4015
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vector_id: 23298
content:
"[...] (2019) considered the domain to be 20degW-40degE, 10degN-20degN). The \r East African region with the Greater Horn of Africa, which includes \r Ethiopia, Sudan and South Sudan, lies on the fringes of both the West \r African and Indian monsoons (Nicholson, 2017)."


id: 4799f8a9e15536a7a7a26a05dec0f1fc
created_at: 2023-04-19 13:22:49
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index: climate_gpt_v2
vector_id: 9818
content:
"The WAfriM is characterized by the northward progression from \r May to September of moist low-level south-westerlies from the Gulf \r of Guinea, meeting the dry north-easterlies (Harmattan) from the \r Saharan Heat Low at the inter-tropical discontinuity (e.g., Hamilton \r et  al., 1945; Omotosho, 1988). In May and June, rainfall remains \r essentially along the Guinean coast with a  maximum occurring \r near 5degN, then the rainfall maximum jumps suddenly over the \r Sudan-Sahel zone near 12degN, followed by a  'little dry season' \r over the Guinea coast (Adejuwon and Odekunle, 2006). This \r apparent shift is known as the West African monsoon jump and it \r is concomitant with the monsoon onset over the Sahel (Sultan and \r Janicot, 2003; Cook, 2015). Rainfall continues to progress towards \r the north up to about 18degN-20degN. The rainfall maximum occurs in \r the Sahel in August/September, followed by a rapid retreat of rainfall \r [...]"


id: 3f8b8c5ebb6514a25fdee660e370a17e
created_at: 2023-04-19 13:22:49
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index: climate_gpt_v2
vector_id: 8369
content:
"At interannual time scales, tropical oceans (Atlantic, Pacific and Indian) \r appear to be the major drivers with their SST anomalies leading to \r variations in the accumulated seasonal rainfall (e.g.,  Lamb, 1978; \r Diakhate et al. 2019). At intra-seasonal time scales, equatorial waves \r (e.g., Mekonnen et al., 2008; Janicot et al., 2010) and interactions \r with mid-latitudes and the Mediterranean (e.g.,  Vizy and  Cook, \r 2009; Roehrig et al., 2011) have an effect on WAfriM activity. African \r Easterly Waves (AEWs) and mesoscale convective systems (MCSs), \r including squall lines, are the prominent weather synoptic scale \r aspects of the WAfriM, supplying almost all rainfall in the Sahel. The \r strong coupling between AEWs and MCSs has been investigated \r in depth, as well as their interactions with the Saharan Heat Low, \r the moisture supply from East Africa and the Mediterranean region \r or from near the equator (e.g., Diongue et al., 2002; Brammer and \r [...]"


id: 198468f7b7f2b23625bc7d13e8f49553
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index: climate_gpt_v2
vector_id: 1700
content:
"[...] Thorncroft, 2017; Lafore et  al., 2017). Land surface processes are \r known to influence WAfriM precipitation (Boone et al., 2016)."


id: 5907709f26a0518fffee14473908d91e
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 12905
content:
"Simulation of West African climate, including the monsoon, has \r received specific attention under coordinated programmes in the \r recent past: the African Multidisciplinary Monsoon Analysis Model \r Intercomparison Project (AMMA-MIP), the AMMA Land-surface \r Model Intercomparison Project (ALMIP), the West African Monsoon \r Modelling and Evaluation (WAMME) project and the Coordinated \r Regional Downscaling Experiment (CORDEX; Raj et al., 2019). CMIP5 \r and CMIP6 struggle to reproduce the amplitude of observed decadal \r variability in the 20th century and to represent the mean climatology \r including the northward propagation of the monsoon (Roehrig et al., \r 2013; Monerie et  al., 2020; Sow et  al., 2020). A  higher horizontal \r resolution improves the representation of the intensity and spatial \r distribution of WAfriM rainfall and related circulation, because of the \r effects of vegetation, orography and coastlines (Hourdin et al., 2010; \r [...]"


id: fe93c13216bc76d3a9c68e0685217e6c
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 42160
content:
"The region is challenging for climate modelling for several \r reasons, including complex topography, the importance of \r Mesoscale Convective Systems (MCSs), and sensitivity to SST bias \r (e.g., Pascale et al., 2019). Many CMIP3 and CMIP5 models with \r resolutions coarser than 100 km are unable to realistically resolve \r the topography of the NAmerM region, thus inducing biases in \r simulating the monsoon (Geil et al., 2013). Among other factors, \r these biases are due to deficiencies in the simulation of the Gulf of \r California summer low-level flow (Kim et al., 2008; Pascale et al., \r 2017) and to failures in representing properly the diurnal cycle \r (Risanto et al., 2019) and the decay (Bukovsky et al., 2015) of the \r NAmerM precipitation. Simulations at higher horizontal resolution \r (i.e., with at least 0.25deg grid) exhibit an improved representation \r of the regional topography, which provides a better representation \r [...]"


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vector_id: 5364
content:
"[...] of the regional circulation and therefore of the NAmerM (Varuolo\u0002Clarke et al., 2019). "


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content:
"The SAmerM displays considerable variability on time scales raging \r from intra-seasonal to decadal (Vera et  al., 2006; Marengo et  al., \r 2012; Vuille et  al., 2012; Novello et  al., 2017). The Madden-Julian \r Oscillation (MJO, Section AIV.2.8) influences the SACZ via changes \r in mid-latitude synoptic disturbances (Jones and Carvalho, 2002; \r Liebmann et  al., 2004). At interannual time scales, ENSO explains \r most of the SAmerM variability (e.g., Paegle and Mo, 2002; Marengo \r et al., 2012). Tropical Atlantic temperatures also affect the SAmerM, \r with reduced atmospheric moisture transport to feed the monsoon \r under warmer tropical North Atlantic conditions (e.g., Marengo et al., \r 2008; Zeng et al., 2008). In addition to SSTs, interannual variability of \r the SAmerM is linked to changes in land surface processes, cold-air \r incursions, the latitudinal location of the subtropical jet, and the \r Southern Annular Mode (Section AIV.2.2; e.g., Silvestri and Vera, 2003; \r [...]"


id: 1df653febdb5ec04c58670f6b16368ed
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
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index: climate_gpt_v2
vector_id: 2463
content:
"[...] Li and Fu, 2004; Collini et al., 2008; Yin et al., 2014). At inter-decadal \r time scales, the SAmerM is influenced by important modes of climate \r variability (e.g., Robertson and Mechoso, 2000; Paegle and Mo, 2002; \r Chiessi et al., 2009; Silvestri and Vera, 2009)."


id: 5f202d09ab1622c5d489bd42ba5fc137
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
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vector_id: 14039
content:
"Indices are, in principle, computable from observations, reanalyses \r or model simulations, although it is important to consider scale in \r comparing across datasets. For example, an extreme precipitation \r event has a lower magnitude across a large grid cell than it would at \r a single station within that grid cell. In many cases, CIDs are simply \r characterized by the exceedance of a  threshold for an Essential \r Climate Variable (ECV). For instance, the probability of crop failure \r dramatically increases as temperature rises above certain thresholds, \r which may differ from one species to another (Hatfield and Prueger, \r 2015; Grotjahn, 2021). To assess the effect of climate change on \r threshold-based indices (e.g., the change in the number of days with \r maximum temperature above 35degC), a  bias adjustment of model \r outputs should be considered where sensible as model simulations \r can have biases compared to observations and reanalyses \r [...]"


id: 9a3df89aec9be5aa96d260ffcf484ec0
created_at: 2023-04-19 13:22:49
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content_type: text
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vector_id: 24373
content:
"In the climate science literature, a  number of indices are used to \r characterize and quantify one or several aspects of climate phenomena \r occurring due to natural variability or due to long-term changes in \r the system. There is an extremely large number of examples. One can \r cite mean global climate indices, such as global mean sea level rise \r or global surface temperature, which characterize the state of the \r climate system and act as a shifting baseline for regional changes. \r One can also examine mean regional trends, for example in mean \r spring precipitation, which reflect large-scale patterns and alter the \r background conditions within which episodic hazards may occur. \r One can also calculate indices of extremes characterizing episodic \r events within the tail of the distributions of specific variables within \r their variability range, for instance the annual maximal temperature \r at a given location or the 100-year return value of river discharge \r [...]"


id: ca11d622237a765e09ab0ca223f6654e
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 32879
content:
"[...] characterizing extreme floods. Such extreme indices have been the \r subject of a number of studies and have been used to characterize \r how climate change modifies extreme values of climate variables \r and subsequent impacts in the IPCC Special Report on 'Managing \r the risks of Extreme Events and Disasters to Advance Climate Change \r Adaptation' (IPCC, 2012), as well other recent IPCC reports."


id: 444dfd38a9150e8e047ceb09b9281692
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 9256
content:
"In Chapter  11, extreme weather and climate events (collectively \r referred to as extremes) are assessed and the main focus is on extreme \r events over land. Since the analysis of extremes often involves the \r examination of the tails of the statistical distributions, a parametric \r or non-parametric approach can be used to define extremes. The \r non-parametric approach is largely adopted in most of the literature \r to characterize moderate temperature and precipitation extremes \r with shorter return periods. The Expert Team on Climate Change \r Detection and Indices (ETCCDI; https://www.wcrp-climate.org/etccdi) \r defined 27 indices to characterize different aspects of moderate \r temperature and precipitation extremes, which are described by Frich \r et al. (2002), Alexander et al. (2006), Zhang et al. (2011), Donat et al. \r (2013), and Sillmann et al. (2013), and were also extensively used \r in previous IPCC reports. In Chapter 11, a subset of these indices is \r [...]"


id: 1ee40428ea9819b31d42a49b595c8c58
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 2637
content:
"[...] assessed in detail (Sections 11.3 and 11.4). For events with longer \r return periods (e.g., events that occur once in 20 years or even more \r rarely), the parametric approach based on Extreme Value Theory (EVT; \r Coles, 2001) is used and adopted in the literature (e.g., Kharin and \r Zwiers, 2000; Brown et al., 2008; Kharin et al., 2013). These events are \r also assessed throughout the chapter. Aside from temperature and \r precipitation, the chapter also assesses indices used to characterize \r droughts. Table AVI.1 lists the indices used."


id: 16363df82a7143fc1f502faaecf18b26
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 1120
content:
"To quantify heat stress, yearly numbers of daily HI-threshold \r exceedances are calculated using a  threshold of 41degC, which \r corresponds to conditions that the US National Weather Service \r classifies into the category of 'Danger' (Blazejczyk et  al., 2012). \r Bias-adjusted model simulations are used for calculating threshold \r exceedances of HI, employing the quantile delta mapping (QDM) \r approach as described by Cannon et al. (2015). The QDM approach \r adjusts the model data in the application period to fit the reference \r data in the reference period (using quantile mapping). Subsequently, \r the climate change signal is added for each quantile by considering \r the change between the model's reference and application periods. \r QDM is directly applied to the HI data using WFDE5 as the reference \r dataset and 1981-2010 as the reference period. WFDE5 HI data are \r conservatively remapped to each model's grid before bias adjustment \r [...]"


id: 6307c368be86cce2e7e19e65240bf740
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updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 14717
content:
"Aerosol A suspension of airborne solid or liquid particles, with \r typical particle size in the range of a few nanometres to several \r tens of micrometres and atmospheric lifetimes of up to several \r days in the troposphere and up to years in the stratosphere. The \r term aerosol, which includes both the particles and the suspending \r gas, is often used in this report in its plural form to mean 'aerosol \r particles'. Aerosols may be of either natural or anthropogenic origin \r in the troposphere; stratospheric aerosols mostly stem from volcanic \r eruptions. Aerosols can cause an effective radiative forcing directly \r through scattering and absorbing radiation (aerosol-radiation \r interaction), and indirectly by acting as cloud condensation nuclei or \r ice nucleating particles that affect the properties of clouds (aerosol-\r cloud interaction), and upon deposition on snow- or ice-covered \r surfaces. Atmospheric aerosols may be either emitted as primary \r [...]"


id: f5abf0e32c662f0b040f44cbd94adea6
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content:
"[...] particulate matter or formed within the atmosphere from gaseous \r precursors (secondary production). Aerosols may be composed of \r sea salt, organic carbon, black carbon (BC), mineral species (mainly \r desert dust), sulphate, nitrate and ammonium or their mixtures. See \r also Short-lived climate forcers (SLCFs). "


id: 124f3df7d3c654d4fe2b54fc0ade8c3e
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 419
content:
"A see-saw latitudinal fluctuation in Northern Hemisphere sea level \r pressure or geopotential height between the Arctic and the mid\u0002latitudes. The NAM has some links with the stratospheric polar \r vortex and is related to the fluctuation in strength and latitude of the \r mean westerlies. Its variance is maximum in winter and its pattern \r has a strong regional expression in the North Atlantic, being strongly \r correlated with the North Atlantic Oscillation index. The NAM is also \r known as the Arctic Oscillation (AO). In its positive phase, the NAM is \r characterized by anomalously low pressure over the Arctic and high \r pressure over the mid-latitudes/subtropics, with a strengthening of \r the zonally averaged westerly winds on their polar flank that confines \r colder air across the Arctic. The negative NAM phase is characterized \r by a more distorted wind pattern and jet meanders that increase \r storminess in the mid-latitude regions. See Section AIV.2.1 in Annex \r [...]"


id: c965c3648b5ec9e82c1caca42dbeab3
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vector_id: 32759
content:
"Anthropocene A proposed new geological epoch resulting from \r significant human-driven changes to the structure and functioning of \r the Earth System, including the climate system. Originally proposed \r in the Earth system science community in 2000, the proposed new \r epoch is undergoing a formalization process within the geological \r community based on the stratigraphic evidence that human activities \r have changed the Earth system to the extent of forming geological \r deposits with a signature that is distinct from those of the Holocene, \r and which will remain in the geological record. Both the stratigraphic \r and Earth system approaches to defining the Anthropocene consider \r the mid-20th century to be the most appropriate starting date \r (Steffen et al., 2016), although others have been proposed and \r continue to be discussed. The Anthropocene concept has already \r been informally adopted by diverse disciplines and the public to \r [...]"


id: 365a3b595ac72d18b6c28501f2e1f636
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
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vector_id: 6795
content:
"Anthropogenic removals The withdrawal of greenhouse gases \r (GHGs) from the atmosphere as a result of deliberate human \r activities. These include enhancing biological sinks of CO2 and using \r chemical engineering to achieve long-term removal and storage. \r Carbon dioxide capture and storage (CCS), which alone does not \r remove CO2 from the atmosphere, can help reduce atmospheric CO2\r from industrial and energy-related sources if it is combined with \r bioenergy production (BECCS), or if CO2 is captured from the air \r directly and stored (DACCS). [Note: In the 2006 IPCC Guidelines for \r national GHG Inventories (IPCC, 2006), which are used in reporting of \r emissions to the UNFCCC, 'anthropogenic' land-related GHG fluxes \r are defined as all those occurring on 'managed land', i.e., 'where \r human interventions and practices have been applied to perform \r production, ecological or social functions'. However, some removals \r [...]"


id: f54bcaa63ba7abb847f14a35fb93e19e
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index: climate_gpt_v2
vector_id: 40526
content:
"[...] (e.g., removals associated with CO2 fertilization and N  deposition) \r are not considered as 'anthropogenic', or are referred to as 'indirect' \r anthropogenic effects, in some of the scientific literature assessed "


id: 3006841cf051bffa5afab9c9a8b772cf
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updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 5711
content:
"Atmosphere The gaseous envelope surrounding the Earth, \r divided into five layers - the troposphere which contains half of \r the Earth's atmosphere, the stratosphere, the mesosphere, the \r thermosphere and the exosphere, which is the outer limit of the \r atmosphere. The dry atmosphere consists almost entirely of nitrogen \r (78.1% volume mixing ratio) and oxygen (20.9% volume mixing \r ratio), together with a number of trace gases, such as argon (0.93% \r volume mixing ratio), helium and radiatively active greenhouse gases \r (GHGs) such as carbon dioxide (CO2) (0.04% volume mixing ratio), \r methane (CH4 ), nitrous oxide (N2O) and ozone (O3). In addition, \r the atmosphere contains the GHG water vapour (H2O), whose \r concentrations are highly variable (0-5% volume mixing ratio) as the \r sources (evapotranspiration) and sinks (precipitation) of water vapour \r show large spatio-temporal variations, and atmospheric temperature \r exerts a strong constraint on the amount of water vapour an air \r [...]"


id: 5c2a55ee178926b3d41500428a6390bc
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updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 13469
content:
"[...] parcel can hold. The atmosphere also contains clouds and aerosols. \r See also Hydrological cycle, Stratosphere and Troposphere."


id: ab796c377b7de8267adc8fad82cc6a4c
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 27398
content:
"Climate change A change in the state of the climate that can be \r identified (e.g., by using statistical tests) by changes in the mean and/\r or the variability of its properties and that persists for an extended \r period, typically decades or longer. Climate change may be due to \r natural internal processes or external forcings such as modulations \r of the solar cycles, volcanic eruptions and persistent anthropogenic\r changes in the composition of the atmosphere or in land use. Note \r that the United Nations Framework Convention on Climate Change \r (UNFCCC), in its Article 1, defines climate change as: 'a change of \r climate which is attributed directly or indirectly to human activity \r that alters the composition of the global atmosphere and which is \r in addition to natural climate variability observed over comparable \r time periods'. The UNFCCC thus makes a distinction between climate \r change attributable to human activities altering the atmospheric \r [...]"


id: 81825ac0c82fa9a7b72cc360dbecc2de
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updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 19910
content:
"[...] composition and climate variability attributable to natural causes. See \r also Climate variability, Detection and attribution, Global warming\r and Ocean acidification (OA)."


id: 5c3a9f9e5bc38542b0f0ed623f7187a9
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 13482
content:
"Climate model A qualitative or quantitative representation of \r the climate system based on the physical, chemical and biological \r properties of its components, their interactions and feedback \r processes and accounting for some of its known properties. The \r climate system can be represented by models of varying complexity; \r that is, for any one component or combination of components, \r a spectrum or hierarchy of models can be identified, differing in such \r aspects as the number of spatial dimensions, the extent to which \r physical, chemical or biological processes are explicitly represented, \r or the level at which empirical parametrizations are involved. There is \r an evolution towards more complex models with interactive chemistry \r and biology. Climate models are applied as a research tool to study \r and simulate the climate and for operational purposes, including \r monthly, seasonal and interannual climate predictions. See also Earth \r [...]"


id: d21f0d6022e021b41dc3d19085b6276f
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
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index: climate_gpt_v2
vector_id: 34321
content:
"[...] system model (ESM), Earth system model of intermediate complexity \r (EMIC), Energy balance model (EBM), Simple climate model (SCM), \r Regional climate model (RCM), Dynamic global vegetation model \r (DGVM), General circulation model (GCM) and Emulators."


id: 4eea557394a40e65c74295813fcca519
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
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index: climate_gpt_v2
vector_id: 11167
content:
"CO2 equivalent (CO2-eq) emission The amount of carbon \r dioxide (CO2) emission that would have an equivalent effect on \r a  specified key measure of climate change, over a specified time \r horizon, as an emitted amount of another greenhouse gas (GHG) or \r a mixture of other GHGs. For a mix of GHGs, it is obtained by summing \r the CO2-equivalent emissions of each gas. There are various ways and \r time horizons to compute such equivalent emissions (see greenhouse \r gas emission metric). CO2-equivalent emissions are commonly used \r to compare emissions of different GHGs, but should not be taken \r to imply that these emissions have an equivalent effect across all \r key measures of climate change. [Note: Under the Paris Rulebook \r (Decision 18/CMA.1, annex, paragraph 37), parties have agreed to \r use GWP-100 values from the IPCC AR5 or GWP-100 values from a \r subsequent IPCC Assessment Report to report aggregate emissions \r and removals of GHGs. In addition, parties may use other metrics \r [...]"


id: 27851202092b6c33ffec0dbe2ddab8b9
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 4128
content:
"El Nino-Southern Oscillation (ENSO) The term El Nino was \r initially used to describe a warm-water current that periodically flows \r along the coast of Ecuador and Peru, disrupting the local fishery. It has \r since become identified with warming of the tropical Pacific Ocean \r east of the dateline. This oceanic event is associated with a fluctuation \r of a global-scale tropical and subtropical surface pressure pattern \r called the Southern Oscillation. This coupled atmosphere-ocean\r phenomenon, with preferred time scales of two to about seven years, \r is known as the El Nino-Southern Oscillation (ENSO). The warm and \r cold phases of ENSO are called El Nino and La Nina, respectively. ENSO \r is often measured by the surface pressure anomaly difference between \r Tahiti and Darwin and/or the sea surface temperatures in the central \r and eastern equatorial Pacific. This phenomenon has a great impact \r on the wind, sea surface temperature and precipitation patterns in the \r [...]"


id: fb1560ae737b7dd648cc330e0375f6b5
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updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 41550
content:
"[...] tropical Pacific. It has climatic effects throughout the Pacific region \r and in many other parts of the world through global teleconnections. \r See Section AIV.2.3 in Annex IV of the AR6 WGI report."


id: 740ede4b08c5d7f44a2a0a881265e4c1
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 17660
content:
"Energy budget (of the Earth) The Earth is a physical system \r with an energy budget that includes all gains of incoming energy and \r all losses of outgoing energy. The Earth's energy budget is determined \r by measuring how much energy comes into the Earth system from \r the Sun, how much energy is lost to space, and accounting for \r the remainder on Earth and its atmosphere. Solar radiation is the \r dominant source of energy into the Earth system. Incoming solar \r energy may be scattered and reflected by clouds and aerosols or \r absorbed in the atmosphere. The transmitted radiation is then either \r absorbed or reflected at the Earth's surface. The average albedo of \r the Earth is about 0.3, which means that 30% of the incident solar \r energy is reflected into space, while 70% is absorbed by the Earth. \r Radiant solar or shortwave energy is transformed into sensible heat, \r latent energy (involving different water states), potential energy, and \r [...]"


id: 2d6f5d7062058f2908271422f1258420
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updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 5282
content:
"[...] kinetic energy before being emitted as infrared radiation. With the \r average surface temperature of the Earth of about 15degC (288 K), \r the main outgoing energy flux is in the infrared part of the spectrum. \r See also Sensible heat flux and Latent heat flux."


id: 82cbea6cbe03c26e924765de1996f8d
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 20139
content:
"Glacial or glaciation A period characterized by the establishment \r of expanded ice sheets and glaciers, and associated with global mean \r sea level (GMSL) substantially lower than present; generally coincides \r with even-numbered marine isotope stages. Glacial intervals were \r interrupted by interglacial intervals. The Last Glacial Maximum (LGM) \r is a specific interval within the most recent glaciation, when ice sheets \r were near their global maximum volume (Clark et al., 2009; Gowan \r et al., 2021) and GMSL was nearly at its lowest level (Lambeck et al., \r 2014; Yokoyama et al., 2018). Local or regional glacial maxima may \r be diachronous, for example ranging from about 29,000 years ago \r and 16,000 years ago. For purposes of global synthesis, IPCC AR6 \r adopts a practical chronostratigraphic definition of LGM of 23,000-\r 19,000 years BP (before 1950; chronozone level 1 of Mix et al., 2001). \r For modelling purposes, LGM is defined by the model time step \r [...]"


id: 8ea6afbadd9a6991c4e08abfe7f063f9
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updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 22283
content:
"[...] nearest to the centre of this interval, 21,000 years ago (Kageyama \r et al., 2017). See also Deglacial or deglaciation or glacial termination, \r Glacial-interglacial cycles, Ice age and Interglacial or interglaciation."


id: 27fa98f2d0a0bb06686c5706db6712b7
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 4231
content:
"The East Asian monsoon (EAsiaM) is the seasonal reversal in wind \r and precipitation occurring over East Asia, including eastern China, \r Japan and the Korean peninsula. In contrast to the other monsoons \r it extends quite far north, out of the tropical belt, and it is largely \r influenced by subtropical systems and by disturbances from the mid\u0002latitudes. The EAsiaM manifests during boreal summer with warm \r and wet southerly winds, but also during boreal winter with cold \r and dry northerly winds. In late April/early May, rainfall onsets in \r the central Indochina Peninsula, and in mid-June the rainy season \r arrives over East Asia with the formation of the Meiyu front along \r the Yangtze River valley, Changma in Korea and Baiu in Japan. In \r July, the monsoon advances up to North China, the Korean peninsula \r and central Japan. During boreal winter, strong north-westerlies \r manifest over north and north-east China, Korea and Japan, while \r [...]"


id: c8ccf95f1a2d0f3f0fe6db15d5ec59a9
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updated_at: 2023-04-19 13:22:49
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index: climate_gpt_v2
vector_id: 32653
content:
"[...] strong north-easterlies arrive along the coast of East Asia. Further \r details on how EAsiaM is defined and used throughout the Report \r are provided in Annex V."


id: 2b3fd3a7e24b1952968e511aca681347
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 4854
content:
"Greenhouse gas emission metric A simplified relationship \r used to quantify the effect of emitting a unit mass of a given \r greenhouse gas on a specified key measure of climate change. \r A relative GHG emission metric expresses the effect from one gas \r relative to the effect of emitting a unit mass of a reference GHG on \r the same measure of climate change. There are multiple emission \r metrics, and the most appropriate metric depends on the application. \r GHG emission metrics may differ with respect to (i) the key measure \r of climate change they consider, (ii) whether they consider climate \r outcomes for a specified point in time or integrated over a specified \r time horizon, (iii) the time horizon over which the metric is applied, \r (iv) whether they apply to a single emission pulse, emissions sustained \r over a period of time, or a combination of both, and (v)  whether \r they consider the climate effect from an emission compared to the \r [...]"


id: d2069ed67d84357f0628e8e3f6f8ae86
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updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 34301
content:
"The West African monsoon (WAfriM) is a seasonal reversal in wind \r and precipitation whose domain includes Benin, Burkina-Faso, \r northern Cameroon, Cape Verde, northern Central African Republic, \r Chad, Gambia, Ghana, Guinea, Guinea Bissau, Ivory Coast, Liberia, \r Mali, Mauritania, Niger, Nigeria, Senegal, Sierra Leone and Togo. The \r WAfriM is characterized by the northward progression from May to \r September of moist low-level south-westerlies from the Gulf of Guinea. \r In May and June, rainfall essentially remains along the Guinean coast \r with a maximum occurring near 5degN, followed by a sudden decrease \r of rainfall, marking the 'short dry season' in the Guinean coast and \r the monsoon onset in the Sahel. Then rainfall continues to progress \r northward up to about 18-20degN, with a maximum near 12degN in late \r August/September, until it retreats starting from October towards \r the Guinean coast for a second maximum. Further details on how \r [...]"


id: a5f65386402266c1e901868121db0c65
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 26420
content:
"Holocene The current interglacial geological epoch, the second \r of two epochs within the Quaternary Period, the preceding being \r the Pleistocene. The International Commission on Stratigraphy (ICS) \r defines the start of the Holocene Epoch at 11,700 years before 2000 \r (Walker et al., 2019) spanning the interval from 11,700 yr to the \r present day. Together with the subadjacent Pleistocene, it comprises \r the Quaternary System/Period. The Holocene record contains diverse \r geomorphological, biological, climatological and archaeological \r evidence, within sequences that are often continuous and extremely \r well-preserved at decadal, annual and even seasonal resolution. \r As a consequence, the Holocene is perhaps the most intensively \r studied series/epoch within the entire Geological Time Scale. Yet \r until recently little attention had been paid to a formal subdivision of \r the Holocene. Here we describe an initiative by the Subcommission \r [...]"


id: ffb9279d47488c7deaa2ebcbc75ffb48
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 42381
content:
"[...] on Quaternary Stratigraphy (SQS. It encompasses the mid-Holocene \r (MH), the 1000-year-long interval centred at 6000 years before 1950; \r a period of long-standing focus for climate modelling, with enhanced \r seasonality in the Northern Hemisphere and decreased seasonality in \r the Southern Hemisphere. The early part of the Holocene is marked by \r the late stages of deglaciation of Pleistocene land ice, sea level rise, \r and the occurrence of warm phases that affected different regions at \r different times, often referred to as the 'Holocene Thermal Maximum'. \r In addition, the epoch includes the post-glacial interval, which began \r approximately 7000 years ago when the fundamental features of the \r modern climate system were essentially in place, as the influence of \r remnant Pleistocene ice sheets waned. See also Anthropocene."


id: 87feafb8dd427914500f300d85c61067
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 21080
content:
"Integrated assessment model (IAM) Models that integrate \r knowledge from two or more domains into a single framework. They \r are one of the main tools for undertaking integrated assessments. \r One class of IAM used with respect to climate change mitigation may \r include representations of: multiple sectors of the economy, such as \r energy, land use and land-use change; interactions between sectors; \r the economy as a whole; associated greenhouse gas (GHG) emissions \r and sinks; and reduced representations of the climate system. This \r class of model is used to assess linkages between economic, social \r and technological development and the evolution of the climate \r system. Another class of IAM additionally includes representations of \r the costs associated with climate change impacts, but includes less \r detailed representations of economic systems. These can be used to \r assess impacts and mitigation in a cost-benefit framework and have \r [...]"


id: 24cd66caf787943d1933b193990287d8
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 3671
content:
"Modes of climate variability Recurrent space-time structures \r of natural variability of the climate system with intrinsic spatial \r patterns, seasonality and time scales. Modes can arise through the \r dynamical characteristics of the atmospheric circulation but also \r through coupling between the ocean and the atmosphere, with some \r interactions with land surfaces and sea ice. Many modes of variability \r are driven by internal climate processes and are a critical potential \r source of climate predictability on sub-seasonal to decadal time \r scales. See Annex IV of the AR6 WGI report. See also Annular modes, \r Tropical Atlantic Variability (TAV), Indian Ocean Dipole (IOD), Indian \r Ocean Basin (IOB) mode, Pacific Decadal Variability (PDV), Pacific \r Decadal Oscillation (PDO) (under Pacific Decadal Variability (PDV)), \r El Nino-Southern Oscillation (ENSO), North Atlantic Oscillation \r (NAO), Northern Annular Mode (NAM) (under Annular modes), \r [...]"


id: a58ad50560b3b856c9bde9f5485eca1b
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 26359
content:
"[...] Southern Annular Mode (SAM) (under Annular modes), Atlantic \r Meridional Mode (AMM) (under Tropical Atlantic Variability (TAV)), \r Atlantic Zonal Mode (AZM) (under Tropical Atlantic Variability (TAV)), \r Madden-Julian Oscillation (MJO), Atlantic Multi-decadal Variability \r (AMV) and Inter-decadal Pacific Oscillation (IPO) (under Pacific \r Decadal Variability (PDV))."


id: d692c2c6a73cd4972db59b8833ddee4b
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 35057
content:
"Pacific Decadal Variability (PDV) Coupled decadal-to-inter\u0002decadal variability of the atmospheric circulation and underlying \r ocean that is typically observed over the entire Pacific Basin \r beyond the El Nino-Southern Oscillation (ENSO) time scale. In the \r AR6 WGI report, PDV encapsulates the Pacific Decadal Oscillation \r (PDO), the South Pacific Decadal Oscillation (SPDO), tropical Pacific \r decadal variability (also called decadal ENSO), and the Inter-decadal \r Pacific Oscillation (IPO). Typically, the positive phase of the PDV is \r characterized by anomalously high sea surface temperatures in \r the central-eastern tropical Pacific that extend to the extratropical \r North and South Pacific along the American coasts, encircled to the \r west by cold sea surface anomalies in the mid-latitude North and \r South Pacific. The negative phase is accompanied by sea surface \r temperature anomalies of the opposite sign. Those sea surface \r [...]"


id: d950392b09e8819b7b4929bc3a5110cc
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 35533
content:
"[...] temperature anomalies are linked to anomalies in atmospheric and \r oceanic circulation throughout the whole Pacific Basin. The PDV is \r associated with decadal modulations in the relative occurrence of \r El Nino and La Nina. See Section AIV.2.6 in Annex IV of the AR6 \r WGI report."


id: bed4cff6cf91d21f1314e77bfe3fbfce
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 30842
content:
"y()\r Shared Socio-economic Pathways (SSPs) have been developed \r to complement the Representative Concentration Pathways \r (RCPs). By design, the RCP emission and concentration pathways \r were stripped of their association with a certain socio-economic \r development. Different levels of emissions and climate change\r along the dimension of the RCPs can hence be explored against \r the backdrop of different socio-economic development pathways \r (SSPs) on the other dimension in a matrix. This integrative SSP-RCP \r framework is now widely used in the climate impact and policy \r analysis literature, where climate projections obtained under the \r RCP scenarios are analysed against the backdrop of various SSPs. \r As several emissions updates were due, a new set of emissions \r scenarios was developed in conjunction with the SSPs. Hence, the \r abbreviation SSP is now used for two things: On the one hand \r SSP1, SSP2, ..., SSP5 are used to denote the five socio-economic \r [...]"


id: 24c314f29d50bd998b9661f068409fb0
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 3665
content:
"[...] scenario families. On the other hand, the abbreviations SSP1-1.9, \r SSP1-2.6, ..., SSP5-8.5 are used to denote the newly developed \r emissions scenarios that are the result of an SSP implementation "


id: ca1e402d0ed15e40ee4449ada571a10a
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 32894
content:
"Sea level change (sea level rise/sea level fall) Change to \r the height of sea level, both globally and locally (relative sea level\r change) at seasonal, annual, or longer time scales due to (i) a change \r in ocean volume as a result of a change in the mass of water in the \r ocean (e.g., due to melt of glaciers and ice sheets), (ii) changes in \r ocean volume as a result of changes in ocean water density (e.g., \r expansion under warmer conditions), (iii) changes in the shape of the \r ocean basins and changes in the Earth's gravitational and rotational \r fields, and (iv) local subsidence or uplift of the land. Global mean sea \r level (GMSL) change resulting from change in the mass of the ocean \r is called barystatic. The amount of barystatic sea level change due \r to the addition or removal of a mass of water is called its sea level \r equivalent (SLE). Sea level changes, both globally and locally, resulting \r from changes in water density are called steric. Density changes \r [...]"


id: 772f39082994b956027153bebed40ef5
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 18172
content:
"[...] induced by temperature changes only are called thermosteric, while \r density changes induced by salinity changes are called halosteric. \r Barystatic and steric sea level changes do not include the effect of \r changes in the shape of ocean basins induced by the change in the \r ocean mass and its distribution. See also Vertical land motion (VLM), \r Land water storage, Glacial isostatic adjustment (GIA), Extreme sea \r level (ESL) and Storm surge."


id: 7de85e705bbb51d54e1cb28caf283a0a
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 19331
content:
"Changes in Earth gravity, Earth rotation and viscoelastic solid Earth \r deformation (GRD) result from the redistribution of mass between \r terrestrial ice and water reservoirs and the ocean. Contemporary \r terrestrial mass loss leads to elastic solid Earth uplift and a nearby \r relative sea level fall (for a single source of terrestrial mass loss this \r is within ~2000 km, for multiple sources the distance depends on \r the interaction of the different relative sea level patterns). Farther \r away (more than ~7000 km for a single source of terrestrial mass \r loss), relative sea level rises more than the global average, due (to \r first order) to gravitational effects. Earth deformation associated \r with adding water to the oceans and a shift of the Earth's rotation \r axis towards the source of terrestrial mass loss leads to second-order \r effects that increase spatial variability of the pattern globally. GRD \r effects due to the redistribution of ocean water within the ocean \r [...]"


id: 525e905d175f2176379537b8a77159b9
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 11770
content:
"Deforestation*\r biogenic volatile organic compound emissions, 831\r for CO2 removal, 621, 698, 762\r for negative emissions, 1528\r freshwater requirements of, 699\r in temperate regions, 310\r precipitation effects in tropics, 698\r regional water cycle alteration with, 1076\r surface albedo effects of, 956\r rica\r 2degC warming effects on, 135\r air pollution sources in, 869\r assessment of projections, 1969-1970\r Atlas, 1967-1971\r attribution for trends in, 1968-1969\r carbon monoxide burden in, 841\r climate features and previous findings, 1967\r climatic features in variability in, 1967\r climatic impact-driver change in, 1791-1797\r climatic impact-driver indices change, 1792\r coastal and ocean related hazards, 1796\r coastal erosion changes, 1796\r confidence in climatic impact-driver changes, 1797\r drought changes and human contribution, 1624\r drought duration projections, 1120\r extreme daily precipitation in, 1558\r findings in AR5 and SR1.5 assessments, 1968-1969\r [...]"


id: 13dd7903945ed3615478b722fd1fc669
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 707
content:
"[...] glacier, snow and ice changes in, 135\r global runoff projections, 1119\r groundwater projections in South-western Africa, \r 1123\r heat and cold changes in, 1791-1792\r heat stress threshold projections, 1793\r heavy precipitation and flooding in, 135\r heavy precipitation changes by warming level, \r 1619\r heavy precipitation projections, 1565\r hot and cold extreme projections, 1556\r model performance assessment, 1969\r oceanic influences on climate, 1971\r particulate matter emission sources, 821\r precipitation and temperature change \r (1961-2015), 1971\r precipitation projections for, 1795\r projections for warming, 613\r rainfall projections through 21st century, 1971\r regional precipitation changes for, 135\r regional solar radiation projections, 1791\r regional wind projections, 135\r sea level changes in, 135\r snow and ice projections for, 1795\r summary of changes, 122, 135, 1971\r surface ozone and particulate matter projections, \r 882"


id: 8f0bb37031892f5528d3c2f14d83c3cd
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 22364
content:
"implications of COVID-19 for, 875-878, 876\r links to climate change limitation, 896\r meteorology change effects on, 861\r precursor emissions driving of, 820\r projections for effects of, 103\r regional source attribution for, 869\r response to mitigation, 864\r response to short-lived climate forcers in SSPs, 103\r scenarios for air quality control, 820\r short-lived climate forcer patterns, 829\r sulphate aerosol effects on, 845\r Air-sea CO2 flux\r atmospheric CO2 effects on (1960-2018), 691\r ocean circulation shaping of, 745\r predictability of, 742\r Air-sea fluxes\r assessment approach for, 692\r biases in modelling of, 1225\r observations and inference of, 1224\r ocean circulation effects on, 745\r regional ocean storage for CO2, 693\r air-sea heat flux\r Atlantic Meridional Mode driving by, 2168\r effects in subantarctic mode water, 1235\r estimates of net air-sea flux, 1224-1225\r patterns of, 1224\r Albedo*\r carbon dioxide removal effects on, 100\r enhancement of surface albedo, 624\r [...]"


id: 5c5c743600e6e6d5e5994a564f13379c
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 13503
content:
"[...] feedback contribution to Greenland Ice Sheet \r melt, 1257\r land cover change effects on, 188, 310\r surface-albedo feedback, 971\r surface-based solar radiation modification, 629\r Alcohols, emissions of, 831\r Aldehydes, emissions of, 831\r Aleutian Low\r Arctic Oscillation and, 2156\r El Nino-Southern Oscillation effects on, 2164\r Pacific Decadal Oscillation and, 2171\r Pacific Decadal Variability effects and, 2174\r proxy reconstructions of, 2174\r Alkanes, C2-C3 and C2-C5 trends, 841\r Alkenes, emissions of, 831\r Alkenone d13C proxies, 299\r Aluminium oxide (Al2O3), stratospheric \r injection*, 627\r Amazon region\r climate features of, 1994\r deforestation and drying in, 1149\r dry-season length trends, 1995\r fire weather projections for, 1817\r impact of forest dieback, 740\r meteorological drought projections, 1119\r Pacific Decadal Variability effects on, 2174\r seasonal change of water cycle in, 1058\r warming projections for basin, 1997\r warming trend in, 1995\r Ammonia (NH3)\r [...]"


id: 59e6117351975c4a16d6b0120ba55077
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 13062
content:
"[...] estimated radiative forcing from, 854\r indirect climate forcing by, 823\r sources and trends in atmosphere, 842"


id: b0e2359c060d0f8844af8ed8307e90ba
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 28349
content:
"temperature and heat extremes in, 135\r temperature and precipitation changes, 1970\r temperature and precipitation changes for, \r 1967-1973\r temperature changes in, 1971\r temperature extremes in, 1548\r temperature increase projections, 1971\r tropospheric NO2 levels over, 839\r warming level projections for temperature, 1613\r wet and dry conditions in, 1793-1794\r wind changes in, 135\r wind speed, severe wind and dust storm \r changes, 1795-1797\r African Easterly Waves (AEWs), 2198\r African Multidisciplinary Monsoon Analysis \r Model Intercomparison Project \r (AMMA-MIP), 2198\r Agricultural and ecological drought*\r African changes, 1624, 1794\r Asian changes, 1644, 1801\r attribution and projections for, 1518\r Australasian projections, 1662, 1809\r Central and South American changes, 1675, 1816\r European changes, 1686, 1823\r human contribution to, 8\r indices used for, 1783\r North American changes, 141, 1701, 1831\r projections for drying regions, 18\r regional observations of, 1575\r [...]"


id: addc9913c987883cef656e2f584dde5f
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 27830
content:
"[...] Small Islands changes, 1838\r Agriculture\r atmospheric ammonia from, 828, 842\r greenhouse gas emissions from, 866\r heat and cold impact on, 1780\r heavy precipitation impact on, 1783\r methane production from, 821\r nitrogen cycle perturbation by, 708\r soil carbon loss with, 763\r solar radiation modification effects, 768\r surface effects of atmospheric CO2, 1787\r Agriculture, Forestry and Other Land Use \r (AFOLU), emissions from, 188, 866\r Airborne fraction of CO2* (AF)\r constraint of carbon-climate feedbacks, 681\r in CO2 removal, 760\r ocean and land sinks determination of, 744\r perturbation of, 760\r Air pollution*. See Air quality-climate \r interaction\r Air quality-climate interaction\r anthropogenically driven climatic impact-driver \r effects on, 1787\r AR5 and AR6 assessment of, 41, 825\r assessment of future air pollution change, 892\r capture by CMIP6 simulations, 835\r climate response to policies on, 872\r cooking and heating emissions in, 866\r decarbonization strategies, 875\r [...]"


id: 95acb24382c4b53629b0f1e1f5a0e36c
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 23525
content:
"[...] decarbonization strategies for, 822\r dedicated policy for air quality, 889, 890, 891\r effects on short-lived climate forcer warming, 892\r extreme pollution criteria, 863\r future emissions policy for control of, 1790"


id: f92c8b176a5407a41b2314588f581c28
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 41202
content:
"ventilation in last deglacial transition, 715\r warming projections for, 1230, 1233\r Antarctic Circumpolar Current (ACC)\r sensitivity to winds, 1239\r Southern Hemisphere atmospheric jet effects, \r 1240\r Southern Ocean circulation and, 485, 1235\r Antarctic Ice Sheet* (AIS)\r anthropogenic effects on, 472, 1266\r carbon-climate feedback information from, 684\r changes in extent of, 468\r CMIP5 and CMIP6 modelling of, 467\r confidence level for projections, 1252, 1253\r contribution to sea level change, 1220, 1270, \r 1271, 1270-1273, 1298\r cumulative emissions effects on, 21\r drivers of future change, 1267-1272\r during Last Interglacial period, 1294\r evaluation of model simulations, 1266-1268\r feedbacks associated with, 977\r historical development and retreat of, 294, 1269\r historical record and CMIP projections, 1252\r ice-sheet instability, 1269\r ice-shelf disintegration, 1268\r ice-shelf melt rates and projections for, 1267\r low confidence ice-sheet projections, 1299\r [...]"


id: c790fa09cc008254a9a111b70fd049c6
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 32421
content:
"[...] mass change and change rate for, 1255\r mass change and sea level contribution, 1264\r mass loss (1992-2020), 1215\r mass loss and change rate for, 77, 187, 188\r projections to 2100 and beyond, 1272-1273\r recent observed changes, 1263-1265\r response in emissions scenarios, 1270\r snowfall and glacier flow in mass change, 1264\r Southern Annular Mode modulation of, 2159\r sub-shelf melting, 1268\r sulphate records from, 298\r trends and interannual variability, 1215\r uncertainties in sea level and cryosphere change, \r 1314\r Antarctic intermediate water (AAIW)"


id: fa2bfb5f458b424f2075b64a80036623
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 41383
content:
"invasion of depth, 717\r land and ocean uptake scenarios, 20\r ocean absorption of, 693, 714\r ocean acidification effects, 608\r ocean and land sink driving by, 681\r removal for net zero emissions, 29\r sea-air CO2 flux and storage, 693\r sources of, 687-689\r thropogenic forcing\r agents contributing to, 956\r atmospheric aerosol concentration changes, 676\r attribution of, 438-445\r CO atmospheric burden, 841\r cooking and heating emissions, 866\r cooling trend in Northern Hemisphere, 438\r estimates for 1850-1900, 193\r evapotranspiration projections, 1117\r global and regional trends (1850-2000), 827\r greenhouse gases in, 712, 1414\r in emerging signal detection, 1853\r in hydrological deficits, 1578\r land-based transportation contribution to, 868\r land water storage changes, 1288\r large-scale precipitation changes, 456, 1563\r local effects on temperature variability, 438\r ocean effects on Earth system, 1841\r ocean heat content gains (since 1970s), 1228\r ocean heat content increase from, 714\r [...]"


id: 9b9b848787f494eef2b130ac757a4b2c
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 24633
content:
"[...] of atmospheric modes of variability, 1374\r of regional temperature extremes, 1553\r over industrial era (1750-2019), 926\r ozone-depleting substances in, 445\r regional response to warming level, 1543\r regional temperature change (since 1950), 1423\r short-lived climate forcers in, 827, 829\r signal emergence in oceanic regions, 1855\r since pre-industrial era, 948\r soil moisture drying effects of, 1578\r solar radiation modification effects on, 679\r sulphur dioxide transport to stratosphere, 845\r surface melting in Greenland from, 1258\r surface warming trend (1998-2012), 425\r top-of-atmosphere budget imbalance, 933\r tropical cyclone effects of, 1588\r warming contribution in scenarios, 13\r warming over inhabited continents, 441\r water cycle changes from, 1057\r western boundary currents and gyre shifts, 1241\r wind stress changes with, 1214\r thropogenic signal emergence\r at regional scale, 1423, 1424\r internal variability delay of emergence, 1427\r [...]"


id: 47b7c7b103f2bed88d841bc2e1b53627
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 9832
content:
"[...] in the Combined Extreme Index (CEI), 1552\r time of emergence for, 1853\r proximate Partial Radiation Perturbation \r technique, quantification of ERFari, 950\r bian basins, tropical cyclone activity in, 1589\r bian Peninsula (ARP), trends and projections \r for, 1983-1986\r gonite saturation state (Oarag) change\r in Arctic Ocean and Southern Ocean, 720\r in Atlantic Ocean, 721\r in Pacific Ocean, 717, 721"


id: d699965cf7a949573c880a5663f4e688
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 35060
content:
"sub-continental areas of assessment, 1971\r sulphate aerosol concentrations in, 846\r sulphur dioxide trends in, 843\r summary of changes, 123, 138, 1986\r surface air temperature and precipitation \r changes, 1989\r surface air temperature projections, 1978\r surface air temperature simulations for, 1978\r temperature extreme trends and projections for \r warming level, 1631\r warm/cool days trends in, 1978\r Atlantic Equatorial Mode*, 501. See also Atlantic \r Zonal Mode* (AZM)\r Atlantic Inter-tropical Convergence Zone, 1967\r Atlantic Meridional Mode* (AMM)\r air-sea heat fluxes driving of, 2168\r AR5 findings for, 375\r boreal summertime, 2170\r climate variability and, 2156\r CMIP simulations of, 501\r proxy-based reconstruction of, 2171\r sustained changes to variability, 375\r tropical Atlantic climate variability, 2168\r Atlantic Meridional Overturning Circulation* \r (AMOC)\r 21st century projections for, 1214\r change relative to 1850-1900, 1237\r deep ocean ventilation in last deglacial \r [...]"


id: 5d72a3ce9a1238a12118319d8d498452
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 13729
content:
"[...] transition, 715\r Gulf stream and, 1321\r heat and carbon storage in North Atlantic, 745\r heat transport in climate system, 483, 1232\r in ice ages, 160\r in South and North Atlantic oceans, 1236-1240\r likelihood of weakening and abrupt collapse, 427\r links to Atlantic Multi-decadal Variability, 2174\r mean state, variability and long-term trends, 484\r North Atlantic Oscillation fluctuations and, 2159\r ocean heat content change, 1232\r precipitation response to collapse, 1149\r projections for weakening with CMIP \r simulations, 576\r RAPID Array measurements, 1238\r response to anthropogenic forcing, 485\r response to stabilization of warming, 106\r strength and sensitivity to resolution and forcing, \r 1238\r water cycle response to collapse of, 1148\r water cycle shifts in paleoclimate records, 1059\r weakening and abrupt collapse likelihood, 611, \r 1149, 1239\r Atlantic Multi-decadal Oscillation* (AMO)\r definition of, 2176\r influence in Russian Far East climate, 1976\r [...]"


id: aa32b1950b71cb9f0cfc245cbc1b8fd6
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 27168
content:
"[...] influence on European climate, 1998\r Atlantic Multi-decadal Variability* (AMV)\r aerosol contribution to, 427, 1237\r Atlantic Inter-tropical Tropical Convergence \r Zone shift and, 2177\r Atlantic Ninos modulation by, 589\r based on index (1900-2014), 2175\r changes in indices, 376\r climate driver for South America, 1994"


id: 58184e7aa0a81f2093a2fb4b29679799
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 12753
content:
"changes at surface, 1791\r changes in deep past and next 300 years, 44\r changes in remote locations, 689\r characteristics of last 50 Myr, 684\r concentration by reference period, 299\r during last deglacial transition, 715\r evolution through last 450 million years, 300\r geological carbon sources for, 686\r geological versus current comparison, 300\r glacial-interglacial fluctuations, 300\r human activities role in growth, 689\r increase in all SSPs, 135\r in paleoclimate reconstructions, 159\r isotope proxy reconstructions of, 298\r land and ocean sinks regulation of, 680\r land carbon storage effects of, 677\r last millennium (1000-1750), 299\r leaf-level photosynthesis effects, 697, 722\r levels in recent times, 8\r mid-Pliocene and Early Eocene, 683\r ocean and land carbon sink effectiveness with \r accumulation, 19-20\r paleoclimate mixing ratios, 299\r paleo period reference ranges, 1292\r past growth rates and projections, 683\r photosynthetic response to, 486\r plant growth effects of, 722, 1787\r [...]"


id: 1ccb8a7b08bff6b4a085689fed9ca227
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 2248
content:
"[...] pre-industrial regulation of, 684\r present-day concentrations of, 290\r reconstruction over past 800 kyr, 160\r response to CO2 emissions and removals, 761\r seasonal cycle of, 427, 487\r seawater pH changes with, 716\r simulated changes in climate indices, 617\r solar radiation modification effects, 679\r terrestrial cycle changes and, 486\r threshold for regional ice-sheet development, \r 684\r Atmospheric concentrations (abundances)\r carbonaceous aerosols reliance on models, 848\r carbon cycle and indirect contributions, 1013\r carbon from peat soils, 725\r CO2, CH4 and N2O in ice cores, 685\r CO2, CH4 and N2O projections in SSPs, 2144"


id: f16ca76cb8a8758b7363689df0aa060f
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 39861
content:
"Baltic Sea\r hypoxia in, 722\r projections for freshening in, 2002\r Barents-Kara Sea, 1976\r Barotropic stream function simulation, 1242\r Barystatic* sea level change, 1220\r Baseline and reference periods*\r alternative recent baselines (1986-2005), 192\r approximate pre-industrial (1850-1900), 299\r AR5 20-year baseline (1986-2005), 1935\r AR6 20-year baseline period (1995-2014), 1935\r climate normal period (1981-2010), 1935\r comparing observations and model simulations, \r 190\r Early Eocene Climatic Optimum, 299\r for future time slice and global warming levels, \r 1935\r future periods (AR6), 192\r historical (1961-1990), 1935\r last 100 years (1919-2019), 299\r Last Deglacial Transition, 299\r Last Glacial Maximum, 299\r Last Interglacial, 299\r last millennium (1000-1750), 299\r long-term (2081-2100) projections, 1936\r Mid-Holocene, 299\r mid-Pliocene Warm Period (KM5c), 299\r mid-term (2041-2060) climate, 1936\r modern (1995-2014), 299\r near-term (2021-2040) projections, 1936\r [...]"


id: b77778a57d917b0a38f1597ad580e9fb
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 29491
content:
"[...] Paleocene-Eocene Thermal Maximum, 299\r paleoclimate, 45, 295\r paleoclimate and recent periods, 45"


id: e7f5b878ff89dba8e38edcf0e12bfed7
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 38123
content:
"pre-industrial (1750) greenhouse gases, 676\r pre-industrial (1851-1900), 1936\r recent decades (2010-2020), 1936\r recent past (1986-2005) for AR5, 1935\r recent past (1995-2014), 1935\r since 2014 (last historical simulations), 1935\r surface temperature, CO2 concentration, and sea \r level ranges, 1292\r warming comparisons (1850-1900), 612, 1935\r World Meteorological Organization 30-year \r climate normal, 1935\r Baseline Surface Radiation Network, 939\r Bayesian model averaging, 430\r Bay of Bengal (BOB), 1978\r Bering Sea/Strait, 1243, 2174\r Bias adjustments\r at top-of-atmosphere for climate drivers, 943\r climate model simulations, 1945\r cold bias in some Regional Climate Models, 1982\r definition used in AR6, 941\r dynamical methods for, 1415\r for downscaling, 1412\r for snow water equivalent uncertainty, 1284\r in regional climate modelling, 1391\r model biases with dynamic models, 1394\r observational uncertainty and internal variability, \r 1413\r rainfall biases in climate simulation, 1982\r [...]"


id: 80c8ff8a02ad5416efe19ed80e9ac770
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 19791
content:
"[...] relevance and limitations of, 1411-1415\r systematic biases identified, 428\r to stratosphere aerosol optical depth, 958\r to top-of-atmosphere CO2 forcing, 945\r tropospheric effects on effective radiative \r forcing, 925\r Bifurcation tipping*, 202\r Biochar production, 763\r Biodiversity* hotspots\r AR6 definition of, 1848\r deserts and semi-arid areas, 1849\r hotspot change with 2degC warming, 143\r tropical forests, 1850\r Bioenergy with carbon capture and storage* \r (BECCS), 698, 763, 1528\r Biogenic/microbial processes\r anaerobic ammonium oxidation, 719\r methane uptake by soil, 704\r nitrification/denitrification of soil, 830\r Biogenic nitrification processes, 830\r Biogenic volatile organic compounds* (BVOCs)\r contribution to land use forcing, 956\r emissions from forests, 825\r environmental change effects on, 831\r plant emission rates, 831\r production of, 859\r sources and effects of, 825\r temperature effect on emissions, 863\r vegetation emissions of, 831\r [...]"


id: 25dc8ec96adb8208d9c7d5bb79408645
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 3841
content:
"[...] Biogeochemical/biogeophysical feedbacks\r assessment for emissions reduction, 857\r changes of surface temperature in, 967\r CO2 and non-CO2, 739\r developments for future assessments, 769\r from CH4 and N2O sources and sinks, 737"


id: fea278687cc5517871c9bfd2eee121e9
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 42184
content:
"Biomass* burning emissions. See also Wildfires/\r biomass burning\r biochar effects on soil carbon, 763\r carbonaceous aerosols from, 825\r contribution to air pollution, 869\r emissions from tropical forest fires, 832\r patterns of carbon monoxide from, 841\r percentages of global emissions, 832\r regional ammonia in, 842\r short-lived climate forcer emissions (1850-\r 2100), 879\r trends in emissions from, 832\r uncertainty in effects of, 833\r Biosphere*\r global changes in terrestrial, 292\r human influence on, 427, 485-489\r in physical climate system, 157\r large-scale indicator of change, 314\r large-scale warming in, 82\r observational capability improvements, 210\r Bipolar seesaw*, 160\r Bjerknes feedback, 476, 589, 2167, 2168\r Black carbon* (BC)\r changes in Northern Hemisphere, 819\r contribution to aerosol-cloud interactions, 949\r deposition on snow, 893, 957\r diesel versus gasoline vehicle emissions, 868\r effective radiative forcing by, 847\r emissions trends for, 828\r [...]"


id: 18f6a5fd31c8470187e60f1278e776c0
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 1607
content:
"[...] emissions with fossil fuel use, 829\r in regional warming patterns, 1373\r interaction with aviation cirrus, 867\r lower atmosphere effects of, 1077\r model simulations of burdens, 848\r reduction effects on warming, 825\r Blocking*. See Storm tracks* and blocking*\r Bolling/Allerod (B/A) warm interval, 685"


id: 4b38873d716faa2263e722e8180de739
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 10482
content:
"calcite (CaCO3) for stratospheric injection, 627\r Calibrated language. 169 See also Uncertainty*\r Calibrated simple climate models comparisons, \r 965\r CanESM2 large ensembles, 222\r Cape Town drought (case study)\r anthropogenic and natural drivers, 1441\r global simulations, 1442\r historical and projections for rainfall, 1440\r historical simulation and attribution, 1442\r information distilled from multiple lines of \r evidence, 1443\r motivation and regional context, 1439\r observational issues, 1441\r regional climate information construction, 1439\r regional downscaling, 1442\r storyline approaches, 1442\r Carbonaceous aerosols\r atmospheric distribution of, 848\r black carbon and organic aerosols, 825\r components of, 847-848\r effective radiative forcing from, 847\r emissions trends for, 828\r impact from residential burning, 866\r trends at background observation sites, 847\r Carbonate chemistry (in seawater)\r basis for the carbon-heat nexus, 746\r biological processes and drivers, 720\r [...]"


id: 2cfbe5dbfa8d533286e05d7cf935e55e
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 5185
content:
"[...] CO2 spread into ocean interior, 717\r excess CO2 effects on, 723\r processes involved in, 714\r projections for changes in, 745\r shoaling effects on, 717\r Carbon budget*. See also Remaining carbon \r budget*\r CMIP scenario simulations for uptake and \r storage, 587"


id: 7f1f0b3ac9003456566eca58663de332
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 19516
content:
"Carbon dioxide (CO2) sink\r anomalies of net land sink and air temperature \r (1980-2019), 696\r drought effects on terrestrial, 697\r ocean and land transition to source, 677\r restoration of peatlands for sequestration, 763\r Carbon dioxide removal* (CDR)\r afforestation/reforestation for, 698\r anthropogenic activities, 755\r assessments of feasibility for, 770\r biogeochemical implications of, 678\r biogeochemical responses to, 755\r characteristics of methods for, 756\r chemical methods for, 765\r climate response to mitigation, 619, 621\r decadal mean for the global ocean sink, 692\r delayed climate response to net-negative \r emissions, 623\r effectiveness of, 760\r effects of land-based biological removal \r methods, 762\r effects on land and ocean stores of CO2, 678\r effects on water cycle, 698\r for net zero CO2 emissions, 99-100\r geochemical methods for, 764\r impact for climate mitigation, 681\r methods and carbon storage time scale, 766\r net negative CO2 emissions from, 622\r [...]"


id: 86278e306cd93715a99d666fb41fbca1
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 20752
content:
"[...] ocean-based biological methods for, 764\r overshoot effects, 618\r perturbation airborne fraction metric for, 760\r ramp-up, ramp-down simulations for, 633\r removal effectiveness, 761\r removal of anthropogenic CO2, 29\r reversal of ocean acidification by, 720\r side-effects of methods for, 678\r Carbon Dioxide Removal Model \r Intercomparison Project (CDRMIP), 633, 757\r Carbon fluxes\r 10-, 20- and 30-year trends (2021-2040), 587\r Air-sea fluxes and storage, 691\r Atmosphere-land CO2 fluxes, 486\r from land-use change, 688\r land-atmosphere CO2 exchange, 694\r ocean flux mitigation and driving effects, 587\r Carboniferous periods, CO2 proxy estimates for,\r 299\r Carbon monoxide (CO) budget\r changes in abundance of, 819\r current trends since 2000, 841\r global mean burden of, 841\r global trends, 840\r indirect climate forcing by, 823\r model simulation comparisons, 841\r sources of atmospheric burden of, 841\r trends in burden of (1850-2000), 841\r Carbon sinks*\r [...]"


id: a6f4f868a059827723c6662d63894af4
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 26581
content:
"[...] anthropogenic CO2 emissions effects on, 681\r anthropogenic warming effects on, 726\r growth of oceanic and terrestrial, 677\r imbalance of sources and sinks, 303\r increased CO2 emissions effects, 19, 42\r land and ocean change with overshoot, 677"


id: 818342e17b19e44c1aae421644b429b3
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 19912
content:
"relationship, 624\r direct effect on land carbon uptake, 722\r direct effects on ocean carbon uptake, 723\r during 450 Ma to 800 ka, 298\r during Carboniferous and Permian periods, 299\r early Holocene period concentrations, 687\r effective radiative forcing and, 312\r effective radiative forcing by, 944\r effective radiative forcing estimates, 853\r effective radiative forcing for 2xCO2, 945\r effects oceanic heat budgets, 1233\r effects of net negative CO2 emissions, 242\r equilibrium warming pattern under forcing, 989\r forcing of polar amplification, 987\r fraction of well-mixed greenhouse gases, 303\r from human activities, 180, 676\r future abundances for SSPs (2020-2500), 2144\r global anthropogenic emissions, 688\r historical abundances and radiative forcing, \r 2141\r historical and future concentrations, 682\r historical emissions and remaining carbon \r budget, 29\r indirect and direct carbon dioxide forcing, 857\r initiation of carbon dioxide removal, 621\r land use, land-use change and forestry \r [...]"


id: 9309d424fde99d4e2d43952c0027d02f
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 23080
content:
"[...] emissions, 689\r last glacial and deglacial transition, 300\r limiting warming to below 2degC, 189\r long-term temperature effects, 821\r ocean acidification by absorption, 714\r ocean storage trends and variability, 1573\r partial pressure in detection and attribution, 489\r permafrost thawing, 677\r polar amplifications with, 985\r probability distribution of effective radiative \r forcing with doubling, 994\r proxy records for estimates of, 299\r quantifying past accumulation rate, 683\r radiative efficiency change, 947\r remineralized CO2 storage in ocean, 685\r seasonal cycles in high-latitude regions, 726\r sensitivity to climate, 686\r solar radiation modification effects on sinks, 679\r stratospheric-temperature-adjusted radiative \r forcing revision, 944\r surface CO2 mixing ratio trends, 303\r temperature change with cessation, 618\r trends, variability and budget, 687\r tropospheric adjustments effect on effective \r radiative forcing, 925\r variability in uptake, 487\r [...]"


id: 1977d4e8e7cceb6614d6ba9c9ef4d407
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 1695
content:
"[...] rbon dioxide (CO2) budget\r estimates of flux components in, 699\r industrial era accumulation, 699\r proxy estimates accumulation rates, 299\r sources and sinks, 699, 700\r rbon dioxide (CO2) fertilization*\r nutrient availability limitation of, 723\r photosynthesis effects of, 768\r projections for land carbon uptake (1869-2100), \r 723"


id: ff2bcabc593a9f5b5e5df394436b826b
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 42297
content:
"biological carbon pump effects, 729\r carbon reservoirs in paleoclimate, 47\r climate change effects of feedback from, 677\r CMIP6 Earth system model properties, 730\r CO2 removal processes in, 756\r constraints in Earth system models, 736\r Earth system model projections, 730\r feedback interactions in, 682\r fluxes in climate system, 758\r historical simulations of (1850-2014), 486\r human-induced changes in, 79-83\r interaction with water cycle, 697\r land-use change influence on, 485\r long-term extension scenarios, 741\r long-term response of, 741\r outgassing of CO2 from sinks, 757\r ozone-vegetation interactions in, 857\r processes and projections for, 81\r removal and emission relationships, 678\r response to CO2 removal, 758, 759\r response to emissions and removal, 761\r response to indirect contributions, 1013\r response to instantaneous CO2 removal, 759\r scenario-dependent feedbacks, 95\r shifts during Cenozoic period, 294\r short-lived climate forcers effects, 820, 857\r [...]"


id: b2c12a18870b971dd520e7231f5da610
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 28690
content:
"[...] sink response with net CO2 removal, 760\r solar radiation modification effects, 768\r terrestrial cycle change indicators, 485-487\r trajectory dynamics beyond 2100, 741\r tropospheric aerosol effect on climate, 857\r rbon-cycle feedbacks\r physical and biogeochemical processes in \r feedback681-698"


id: 1db1a148b1d4f51ad8d7e4866ffd34a7
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 2408
content:
"climatic impact-driver changes, 1812\r climatic impact-driver changes with 2degC \r warming, 140\r drought duration expected in, 1119\r drought trends and warming level projections, \r 1675\r extreme precipitation changes and projections, \r 1559, 1565\r heatwave projections for, 1994\r heavy precipitation trends and warming level \r projections, 1672\r hot and cold extreme projections, 1556\r model performance assessment, 1993\r observations, trends and attribution, 1991\r observed hot extremes in, 1549\r precipitation changes with warming, 1993\r precipitation projections for, 1994\r precipitation regional and seasonal trends, 1991\r previous IPCC assessments, 1991\r regional climate features in, 1991\r regional precipitation and drought projections, \r 140\r regional relative sea level projections, 141\r regional surface air temperature and \r precipitation, 1992\r regional temperature projections with warming, \r 1993\r relative sea level rise in, 1820\r runoff projections for, 1119\r [...]"


id: d1cd50415065341cc0219c55b750ef13
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 34264
content:
"[...] scenario-based warming projections, 1993\r summer rainfall projections for, 2012\r temperature extreme trends and warming level \r projections, 1665\r temperature projections for, 1991\r temperature trends over, 1991\r warming projections for, 1994\r warming trends in, 1994\r ntral and South AmericaSee also Latin "


id: f5dce36a4fe475df684f56e3514e47da
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 40615
content:
"Caribbean (CAR) region\r anthropogenic influence in (1950-2016), 2010\r areas included in, 1991\r assessment and projections, 1993\r Atlantic Multi-decadal Variability effects in, 2177\r Caribbean low-level jet effects in, 1991\r climate change assessment for, 2010\r climate features of, 1991\r drought projections for, 1994\r drying projections for, 1993\r model performance assessment, 1993, 2010\r observations, trends and attribution, 1991\r precipitation trends in, 2009\r previous IPCC assessments, 1991\r rainfall projections for, 2012, 2015\r regional climate model simulations, 2010\r regional warming 1.5degC in, 1991\r temperature projections for, 1993\r temperature trends for, 1993\r warming trends and projections, 1994, 2009\r Caribbean low-level jet (CLLJ), 1991, 2010, 2012\r Cenozoic period\r CO2 estimates by proxies, 299\r Earth's mean temperature during, 294\r proxy CO2 record, 683\r Central Africa (CAF)\r greenhouse gas emissions for, 713\r precipitation changes in, 1968\r [...]"


id: f9f883ae8a5a2995dcfbd8dffc60708e
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 41343
content:
"[...] regional climate features, 1967\r Central America\r climate extreme projections, 1994\r climate variability and extreme events in, 1991"


id: e7b83dfdfb592e1acd7e61195a76820d
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 38077
content:
"Climate change*. See also Committed climate \r change; Abrupt climate change*\r abrupt and irreversible potentials, 633\r air-quality interactions with, 820, 895\r altered climatic impact-driver profiles, 1770\r assessment at regional scale, 1435\r assessment of distillation of information, 1433\r barriers to distillation of information, 1432\r black carbon and methane reduction effects, 825\r carbon feedbacks effects, 677\r cause-effect chain, 53\r changes across global system, 63\r characteristics of assessment, 39-40\r communication of scientific information, 151\r current status of climate, 4-13, 157\r cycles before industrialization, 160\r dimensions of integration for, 227\r effects on surface ozone, 861\r effects without mitigation, 621\r emergence of signals for, 157, 193, 194\r following zero emissions commitment, 630\r global and regional signs of, 246\r global, regional, and local scale of, 50\r historical estimates of regional short-lived \r climate forcing, 851\r human influence, 41, 430-552\r [...]"


id: cb06d583f7b30d3777d89b05fc1f81f7
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 33062
content:
"[...] impact on extreme pollution, 863\r impact on particulate matter, 863\r information construction for, 1431\r information for decision-making, 1366, 1431\r information for risk assessment, 1774\r key indicators of change, 160\r knowledge synthesis, 227-230\r large-scale indicators for, 312, 313\r large-scale indicators of, 486\r lightning nitrogen oxide emissions \r perturbation by, 830\r limiting future change, 27-31\r low-likelihood, high-impact events with, 1520\r media coverage of, 173\r milestones in science, 174\r motivated reasoning in acceptance of, 1430\r multivariate attribution of, 506\r natural change, 150\r observations of, 187\r overshoot effects in scenarios, 741\r possible futures of, 12-26, 88, 182-184\r regional impact and risk assessment, 1770, \r 1767-1926\r regional manifestations of global change, 1366\r representation of change in projections, 565\r risk assessment and regional adaptation, 23-26\r scenarios for future assessment, 52-56\r scenarios, models and projections for, 12-16, \r [...]"


id: c9c6846e0936f505d6081df01a1fbe45
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 32831
content:
"[...] 52-56, 118, 562\r scientific evidence and projections for, 43\r short-lived climate forcers effect on, 819\r SRCCL assessment of drivers, 188\r storylines with impact of concern, 1434\r synthesis of projections for changes in extremes, \r 1533\r temperature and precipitation signals for, 1944\r uncertainty and calibrated language for, 169"


id: 9be1ce13841bd6f86f7c370785e79cd5
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 24677
content:
"associated with tropical cyclones, 1784\r changes in climatic impact-drivers and, 132\r climate impacts and consequences, 1773\r coastal/oceanic related for 2degC global warming, \r 2009\r compound events and multiple hazards, 1314\r extremes in Tasmania (case study), 1987\r hazard profile components, 1875\r infrastructure hazards with permafrost thaw, \r 1785\r magnitude or intensity of, 1875\r of sea level changes, 1786\r regional hazard profile, 1876\r regional impact of warming, 152\r risk from landslides, 1783\r risks and reasons for concern by warming level, \r 1857\r sand and dust storm impact, 1784\r summary of climatic impact-driver hazards, 1858\r tropical cyclones in Central/South America, 1991\r vulnerable zones and hotspots, 1847-1856\r with extreme water levels, 1786\r Climate index time series*, 2155\r Climate indicators*\r key set of, 312\r recent changes in, 157\r simulated changes in, 194\r Climate indices\r derivation of, 2207\r extreme indices selection, 2207, 2208\r [...]"


id: 1fde68164ce3e4db0a260e612096ebdf
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 2812
content:
"[...] for characterization and quantification, 2207\r Climate-methane lifetime feedback, 859\r Climate metrics* use, 931 See also Emissions \r metrics\r Climate model emulators, 962\r approximation of large-scale climate responses, \r 962\r comparison of RCPs and SSPs, 619\r constrained ensemble use, 1956\r emulator value compared to best estimate966"


id: 18b2ed5d164ef42363445a00d2578a98
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 1561
content:
"assessment methods, 967-968\r assessment of parameters for (ax), 968\r biogeochemical feedback, 737\r biogeophysical and non-CO2 biogeochemical \r feedbacks, 975, 976\r climate-biogenic volatile organic compound \r feedback, 859\r climate-fire, 859\r climate-lightning-NOx feedback, 859\r climate-ozone feedback, 858\r components of, 967\r defined, 967\r dependence on climate mean state, 979\r dimethyl sulphide, 858\r direct and indirect influence of sea spray, 858\r dust aerosol feedback in abrupt climate events, \r 1151\r effective radiative forcing probability with CO2\r doubling, 994\r effects rate of greenhouse gas accumulation on, \r 680\r equilibrium climate sensitivity to, 926\r forcing-feedback framework, 932\r for non-CO2 biogeochemical processes, 858\r from CH4 and N2O, 737\r from change in atmospheric composition, 857\r from galactic cosmic rays, 297\r from thawing of permafrost, 677\r global carbon and biogeochemical cycles, 683\r in abrupt 4xCO2 simulations, 979\r in Earth system models, 978\r [...]"


id: 9c884206f76013aa1d13058a625fbfa
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 24790
content:
"[...] methane lifetime feedback, 859\r mineral dust, 858\r net cloud feedback, 974-975\r net feedback parameter estimate confidence, 981"


id: 1e2cdd2020b1d18f7e581f70a681ca2
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 2506
content:
"fronts, 1400\r glacier mass projection evaluations, 1278-1279\r global monsoon domain, intensity, and \r circulation, 462\r historical carbon cycle simulations \r (concentration-driven), 732\r historical period, 2100\r hydrological deficits, 1576\r improvements in CMIP6, 48-49\r in AR5 and AR6, 154\r mapping between global warming level- and \r scenario-based responses, 1544\r methods for, 429\r mid-to-high latitude atmospheric variability \r phenomena, 1396\r Model Intercomparison Projects, 561\r model response uncertainty, 197\r mountain wind systems, 1399\r multivariate model evaluation, 508\r multivariate models, 508-512\r North America, 2004-2008\r of Atlantic Multi-decadal Variability, 505\r of El Nino--Southern Oscillation teleconnections, \r 498\r of North Annular Mode, North Atlantic \r Oscillation and Southern Annular Mode in boreal \r winter, 491\r of Pacific Decadal Variability, 503\r of permafrost in climate models, 1281-1283\r paleoclimate records for, 449\r perfect prognosis methods, 1402\r [...]"


id: 4206864238beb4f85b244e24c0ee7930
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 8817
content:
"[...] performance evaluation for regional climate \r projections, 1366\r performance of weather generators, 1404\r Polar regions, 2016-2025\r precipitation and temperature trends for North \r Asia, 1977\r precipitation deficits, 1575\r process representation in model classes, \r 512-514\r relative space-time root-mean-square deviation \r for CMIP simulations, 509\r seasonal snow in, 1285\r severe convective storms, 1595\r simulating historical regional climate changes, \r 1404\r simulating regional drivers of climate, 1401\r simulating regional feedbacks, 1400\r simulating regional phenomena and processes, \r 1398\r simulation of current trends, 433\r simulation of heavy precipitation extremes, \r 1561-1562\r Small Islands, 2009-2012\r snow cover simulation by, 470\r soil moisture deficits, 1575\r statistical downscaling, bias adjustment and \r weather generation, 1402\r statistical methods for representing local \r weather, 1403\r temperature extreme reproduction by, 1552\r tropical cyclone, 1587-1590\r [...]"


id: a1ecdb881137347e26eb514db01af009
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 25721
content:
"updated observation comparison with \r simulations, 446\r user-defined or user-relevant diagnostics for, \r 1395\r mate modelling techniques\r added value of increased resolution, 1139\r advantages of multi-model use, 561\r aerosol and cloud size distributions, 851\r aerosol-cloud interactions evidence, 953\r aerosol effects on clouds and precipitation, 1138\r Antarctic model intercomparison studies, 2110\r atmospheric blocking, 607\r atmospheric sulphur loading (1990-2015) \r estimates, 846\r bias adjustments for, 1945\r biomass burning emissions estimates, 832\r centres contributing to CMIP and CORDEX, 218\r changes in ocean N2O emissions, 719\r cloud seeding, 860\r CMIP5 models use in downscaling, 2098\r CMIP6 structures, 223, 2111\r common types of model ensembles, 222\r dedicated climate change assessment \r programmes, 1957\r developments since AR5, 2100\r emergent constraints in, 225\r ensemble modelling technique, 221\r European climates, 1998\r fine-mode nitrate trends, 846\r [...]"


id: 68d6b7d2ce85e2120b1625586745e62
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 15709
content:
"[...] fitness-for-purpose, 221, 566\r forecast quality assessment, 564\r GlacierMIP2 model intercomparison, 2111\r global ammonium burden, 846\r global spatial distribution of carbon monoxide, \r 841\r global to regional models, 218\r high-resolution and variable resolution models, \r 1389\r high-resolution global climate models, 1140\r high resolution improvements, 1315\r ice-sheet and glacier intercomparison studies, \r 2108\r improvements since AR5, 151\r initialized versus uninitialized systems for \r predictions, 564\r irradiance models, 297\r land surface processes, 1138\r model evaluation tools, 224\r model grids and resolution improvements, 215\r model performance evaluations for skill, \r 563-564, 564\r models of lower complexity, 219-220\r models participating in HighResMIP, 2108\r multi-model mean and pattern-scaled response, \r 637\r observational constraints for projections, 555\r observation-constrained methods, 563\r ocean dynamic/thermosteric response \r (1961-2005), 1290\r ocean simulation in, 473\r [...]"


id: e28a1cfc512480934f23ff1a8cecac42
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 37158
content:
"[...] of forced changes in climate system, 151\r paleoclimate models and reconstructions, 45\r performance assessments221-224"


id: 926e3d6201633a3a630a8bd15a96909d
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 22981
content:
"equilibrium climate sensitivity and transient \r response emulation, 994\r estimates of equilibrium climate sensitivity and \r transient climate response based on, 997\r global surface air temperature attribution from, \r 962\r idealized energy balance models, 1011\r non-CO2 emissions impact on remaining carbon \r budget, 751\r performance compared to physical climate \r assessment, 936\r relationship of ocean heat content and \r thermosteric sea level, 1245\r scenario classification in WGIII, 964\r simple climate model versus, 219\r understanding generations of scenarios, 618\r use in AR6, 963\r use with ISMIP6 and GlacierMIP projections, \r 1263\r mate model evaluation\r Africa, 1967-1971\r Asia, 1971-1986\r atmospheric-based drought indices, 1576\r atmospheric convection simulation, 1137\r atmospheric evaporative demand, 1575\r Australasia, 1989-1990\r bias adjustment methods, 1403\r bias corrections for, 563\r biases in simulations, 1395\r carbon cycle in concentration-driven runs, 730\r [...]"


id: a70759c3c85ea7dcd3d93326da212a35
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 26593
content:
"[...] Central and South America, 1990-1997\r centred pattern correlations between models \r and observations (1980-1999), 510\r changes in for mean austral summer \r precipitation, 1409\r changes in Hadley and Walker circulations, 460\r CMIP5, CMIP6 and Earth system models for \r benchmarks/datasets, 731\r CMIP model simulation warming biases, 444\r CMIP reproduction of surface temperature \r patterns, 438\r coastal winds and lake effects, 1399\r control simulations, 1393\r convection including tropical cyclones, 1398\r correlations patterns between models and \r variables, 520\r detection and attribution of surface \r temperatures, 431-438\r diagnostics for, 1395\r El Nino-Southern Oscillation teleconnections, \r 1397\r ensemble evaluation and weighting, 568\r Europe, 1998-2003\r evaluation of downscaling methods, 1393\r extratropical cyclones, 1592\r extratropical cyclones and storm track \r simulations, 463\r fitness-for-purpose and poorly constrained key \r processes, 1137-1139\r [...]"


id: fb9c253f97c676b742961c76bf4a406a
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 41636
content:
"[...] flooding even simulations, 1568\r for drought simulations, 1577\r for regional climate projections, 1393\r for simulated temperature extremes, 1550-1552"


id: 75ee27e39cac7617af67e75cbc322d7e
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 17973
content:
"main modes assessed in AR6, 2156\r natural changes in, 160\r ocean basin scale processes driving, 312\r regional teleconnections and, 1134\r regional water cycle consequences of, 1134\r sea surface temperature (1854-2019), 376\r simulations of indicators for change, 570\r Climate zone shifts, 82. See also Seasonal \r change\r Climatic impact-drivers* (CIDs)\r African changes, 1971-1799\r Asian changes, 1799-1805\r additional factors interacting with, 1777\r air pollution weather, 1787\r assessment method and tables, 1788\r association with modes of variability, 2155\r atmospheric carbon dioxide at surface, 1787\r Australasian changes, 1805-1812\r categories and associated risks, 1775\r categories of, 1776\r Central and South American changes, \r 1812-1820\r changes for polar regions, 1846\r changes from oceanic drivers, 1848\r characterization by indices, 2207\r coastal sea level hazards, 1786\r common regional changes, 132-135\r concurrent changes and variables with, 135\r confidence for changes in, 122-127\r [...]"


id: 5ca88bbddb1f93de2e6d87fb8b3ce3d0
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 13564
content:
"[...] confidence for projected direction and mitigation \r efforts, 1771\r crop response to temperature thresholds, 1874\r dangerous hazard combinations of, 1788\r defined and used in AR6, 201, 1773\r defined in AR6, 1871\r ecosystems and society effects of, 1872\r effects on biodiversity hotspots, 1848\r emergence across time and scenarios, \r 1853-1854\r European changes, 1820-1828\r evolution of effective radiative forcing, 311\r extreme conditions as, 1521\r extremes, 2205-2214\r geographical distribution of change, 131\r global scale change in, 1851-1854\r graduating thresholds affecting hazards, 1782\r heat and cold, 1780\r heat and cold hazard for natural systems, 1782\r impact of the residential sector, 866\r impact on human or ecological systems, 40\r index definitions and projections, 1869\r indices and thresholds for vulnerability, 1773\r low greenhouse gas emissions scenarios, 31\r main types and assessment of impacts, \r 1774-1776\r North American changes, 1828-1836\r [...]"


id: 1b981ff5e2f90fbd228abef650a8f953
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 2040
content:
"[...] oceanic changes and impacts, 1786\r open and deep ocean changes, 1841-1844\r polar terrestrial region changes, 1844-1847\r projected regional changes in, 26\r projections for changes by 2050 (2041-2060), \r 1852\r projections for changes for selected indices, 1789"


id: 8a5b9fa8821de01494f52d37aa8c8263
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 21517
content:
"reversibility of climate response, 106\r zero emissions commitment, 630\r imate system response*\r change in drivers of, 67-68\r changes in modes of variability, 292\r changes in short-lived climate forcers, 884\r changes occurring in, 158\r changing state of, 287-422\r continuous warming of, 425\r drivers of Earth's energy balance, 290\r effects of inertia on modifications of drivers, 865\r emergence with mitigation, 619\r forcing due to aerosols, 851\r human-forced air temperature change \r (1750-2019), 927\r human influences on, 39, 423-552\r implications for limiting warming, 90-98\r international climate governance efforts, 157\r key indicators of change in, 290\r ocean role in, 473\r ozone-vegetation interaction influence on, 857\r physical changes since AR5, 157\r reduction of uncertainty in AR6, 41\r scale of recent change in, 8\r scientific understanding of processes in, 174\r short-lived climate forcers interaction with, 823\r temporal coverage of, 176\r to COVID-19 restrictions, 877\r [...]"


id: bedee6a4cd8484b1355a3f32aceaa351
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 30847
content:
"[...] to past air quality policies, 872\r to solar radiation modification, 629\r to strong mitigation, 557\r to termination of solar radiation modification, \r 629\r uncertainty in, 197\r understanding features of, 150\r warming effects on, 15\r imate (topological) regions\r Africa, 1967\r AR6 WGI reference regions, 1937\r Asia, 1799\r Australasia, 1805\r Central and South America, 1812\r Europe, 1820\r land grid boxes AR6, CMIP5 and CMIP6, 1938\r North American sub-regions, 1828\r polar region subdivisions, 2016\r principles used in definition of, 1936\r Small Island regions, 1836\r spatial domains and socio-economic regions, \r 1938\r subcontinental regions defined in SREX, 1936\r typological and socio-economic, 1939\r used in AR5 and AR6, 206-208, 1937\r imate variability*\r Atlantic Multi-decadal Variability index, 2176\r documentation and description of, 2155\r driver interactions with regional changes, 117, \r 117-118\r extratropical modes, 1135\r from sea surface temperature (1950-2019), 374\r [...]"


id: 27765c6da907944dace92d8152519e91
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 4121
content:
"[...] indices defined by AR5, 2155\r influence on trends over short periods, 193\r Madden-Julian Oscillation index, 2177"


id: f1a8f35201aaeda0d2da55f0366f0f8c
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 39902
content:
"Clouds and the Earth's Radiant Energy System \r (CERES), 935, 1225\r Cloud-top temperature and aerosol optical \r depth/AI, 952\r Coarse-mode nitrate, in global nitrate budget, \r 846\r Coastal and oceanic changes\r changes in climatic impact-drivers, 134\r changes with 2degC global warming for polar \r regions, 142\r climatic impact-driver changes for Africa, 1796\r climatic impact-driver changes for Asia, 1803\r estimated total water level around Australia, 140\r polar terrestrial changes in, 1847\r regional responses to acidification, 721\r Coastal cities and settlements, 1848\r Coastal erosion, 1820\r African changes, 1796\r Arctic changes, 1847\r Asian changes, 1805\r Australasian changes, 1811\r Central and South American changes, 1818\r ecosystem and infrastructure impacts, 1786\r European changes, 1826\r index, derivation of, 2211\r North American changes, 1836\r polar terrestrial changes, 1847\r projections for shoreline retreat, 1853\r Small Islands changes, 1841\r Coastal flooding\r [...]"


id: 2ad1db148db279f3209ceafb7e9de91e
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 4763
content:
"[...] 1-in-100-year extreme total water level \r as index for, 2211\r African changes, 1796\r Asian changes, 1803\r Arctic changes, 1847\r Australasia changes, 1811\r Central and South American changes, 1818\r European changes, 1826\r North American changes, 1834\r polar terrestrial changes in, 1847\r Small Islands changes, 1839\r Coastal systems and marginal seas, 1244, 1786\r Cold extremes\r agricultural, ecosystem and health relevance, \r 1781\r human influence on probability of, 1553\r projections for, 1518, 1851\r Cold spells and frost\r African changes, 1793\r agricultural impact, 1781\r Asian changes, 1799\r Australasian changes, 1807\r Central and South American changes, 1815\r European changes, 1822\r frost impact, 1781\r index for climatic impact-drivers, 2210\r magnitude of cold spells, 1781\r North American changes in, 1830\r Cold tongue estimates, 989\r Combined Extreme Index (CEI), 1552\r Combined global temperature change potential \r (CGTP), metrics for, 927\r C\r C\r C\r C\r C\r C\r C\r C\r C\r C\r C\r C\r C\r C\r [...]"


id: 14964fea0ecb9d4e2a8b603f7454bc9e
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 850
content:
"near-term Southern Annular Mode trend, 588\r North America, 1829, 1978, 2006-2007\r Northern Annular Mode/North Atlantic \r Oscillation, 489-492, 578, 609\r Northern Hemisphere blocking, 1397\r Northern Hemisphere snow cover extent, 471\r North Pacific storm track simulations, 464\r observed natural variability estimates in, 444\r ocean acidification, 608\r ocean carbon and heat storage, 745\r ocean dynamic sea level projections, 1247\r ocean heat content, 1232\r ocean mixed layer projections, 1226\r ocean salinity detection and attribution, 479\r ocean temperature biases in, 473, 474\r output availability, 2089\r ozone concentration simulations, 836, 838\r Pacific Decadal Variability simulations, 502-504\r precipitation anomalies (1995-2014), 574\r precipitation change at 2degC warming, 1544\r precipitation increase with warming, 616\r precipitation over land by scenario, 574\r projections for deoxygenation rate, 718\r regional seasonal precipitation, 601\r reproduction of precipitation changes in paleo \r [...]"


id: cd9a8fcea433887c977f1c0a15e465ed
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 33507
content:
"[...] reference periods, 451\r reproductions of overturning circulations \r representation in, 427\r scenario sets used in, 236, 618, 2090\r sea level change evaluation, 481\r sea surface temperature biases in, 1223\r sea surface temperature pattern representation, \r 1223\r Shared Socio-economic Pathways used, 555\r short-lived climate forcer emissions, 591, 827\r simulated and observed snow cover extent, 1286\r simulation of global monsoon variability, 452\r simulation of rainfall patterns, 452\r Small Islands, 2012\r snow cover extent biases in, 470\r sources of uncertainty in, 567\r Southern and Northern Annular Mode \r reproduction comparisons, 427\r Southern Annular Mode projections, 609\r Southern Ocean circulation characteristics, 1239\r Southern Ocean fresh bias in, 479\r sudden stratospheric warmings/vortex splits \r biases, 466\r surface air temperature anomalies, 572\r temperature and salinity biases, 475\r temperature bias distribution in, 431\r temperature estimations for Europe, 2001\r [...]"


id: cb31a1b128eda5c1c7017babfec10baf
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 33086
content:
"[...] temporal simulation of ice-sheet changes, 471\r top-of-atmosphere flux anomalies in, 936\r total ozone column change, 838\r tropical Atlantic Ocean evaluation, 476\r Tropical Atlantic Variability changes, 590\r tropical circulation projection, 1124\r tropical Indian Ocean evaluation, 476\r tropical Pacific Ocean evaluation, 476\r tropical volcanic eruptions effect on, 452\r troposphere warming simulations, 599\r upper ocean salinity biases in, 479"


id: 52e8bb9579acb67934b5058d3c5b831e
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 11853
content:
"biogenic volatile organic compound projections, \r 831\r biogeochemical model performance, 488\r boreal wintertime Annular Mode indices, 578\r cascade of uncertainties in projections, 198\r Central and South America, 1814, 1991-1992\r change in Hadley circulation, 459\r climate change signals, 1945\r CMIP3 studies assessed in AR5, 1989\r CMIP5 and CMIP6 global model (Atlas), \r 1941-1942\r concentration-driven experiments in, 561\r concentration-driven projections, 681\r concentration-driven simulations (1850-2014), \r 732\r control simulations with external forcings, 1393\r coupled models participating in CMIP6 DECK, \r 2101\r cyclone and blocking simulations, 463\r datasets used in report, 2111\r DECK for assessment and projections in AR6, \r 224\r derived global monsoon changes from, 586\r detection and attribution of warming, 425, 428\r El Nino-Southern Oscillation, 495-499, 610\r equatorial sea surface temperature biases, 474\r equilibrium climate sensitivity and transient \r [...]"


id: aa72689489452e41e0b7d77bcf1a3cdd
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 27212
content:
"[...] response estimates, 12, 93, 927, 1008, 1009\r estimates of Arctic warming in the mid\u0002Holocene, 431\r Europe, 1821, 2001-2003\r feedbacks in CMIP5 and CMIP6, 979\r fractional changes with warming, 1527\r global land monsoon precipitation, 603\r global mean sea level rise, 575, 1245\r global surface air temperature, 555\r global surface-albedo feedback (aA), 970\r Hadley circulation changes, 1125\r heat index calculation for, 2210\r heavy precipitation simulation, 1561\r historical and scenario precipitation changes, \r 574\r historical carbon uptake and scenarios, 577\r historical simulations, 12, 447, 462, 477, 570, \r 1234, 1562\r improvements in simulations, 48\r Indian Ocean Basin and Dipole modes \r simulations, 499-500\r Indian summer monsoon simulations, 1979\r Indo-Pacific climate assessment, 1989\r intercomparisons of chemistry-climate models, \r 835\r Inter-tropical Convergence Zone shift detection, \r 452\r large-scale indicators of climate change, 425\r long-term surface warming scenarios, 572\r [...]"


id: 217acc89f9f029df44715877c2c64d8f
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 3098
content:
"[...] lower stratospheric temperatures, 445\r Madden-Julian Oscillation simulations, 2179\r model intercomparisons used for AR6, 151, 223\r models for regional simulations, 2098\r models used for simulations, 2089\r near-surface air temperatures anomalies, 441\r near-surface humidity projections, 601"


id: a57948a28348df9dc9c34cbfa54624c6
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 26342
content:
"Coordinated Regional Downscaling Experiment \r (CORDEX). See CORDEX (Coordinated \r Regional Downscaling Experiment)\r Copernicus Atmosphere Monitoring Service \r (CAMS), 213, 732\r Copernicus Climate Change Service (C3S), 1382, \r 1867\r CORDEX (Coordinated Regional Downscaling \r Experiment)\r Africa, 1792, 1970\r Asia, 1798, 1972\r Australasia, 1806, 1989\r Central and South America, 1814, 1992\r climate modelling/simulation, 224\r CORDEX-CA, 1977\r CORDEX-CORE, 1944\r CORDEX-EA, 1974, 1975, 1977\r curvilinear domain boundaries in, 1942\r domains and resolutions, 2089\r domains used in Interactive Atlas, 1943\r Europe, 1821, 2003\r extreme rainfall event capture, 1562\r Flagship Pilot Study (FPS), 1393\r heat index calculation and bias adjustment, \r 2210\r high-resolution historical and future climate \r projections, 1390\r high-resolution regional projections, 1945\r historical reference (1970-2005) in Atlas, 1944\r inter-model comparison experiment, 151\r NA-CORDEX, 2006\r North America, 1829, 2007\r [...]"


id: a4567ded24be719fe2c6edcc728008d0
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 26117
content:
"[...] regional downscaling activities, 2089\r regional simulations, 2099\r regional simulations by, 1942\r simulation of regional extremes, 1551\r Coupled atmosphere-land-river-ocean models, \r 2001"


id: eeead498557b14f9c53688e23b9932ed
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 39402
content:
"forest dieback effects, 740\r in tropical regions, 171\r precipitation changes from, 1076\r surface albedo effects of, 956\r Denitrification/nitrification processes in soil, \r 830\r Desert and semi-arid areas, 143. See also \r Aridity* and desertification\r Detection and attribution*\r AR5 assessment of regional-scale, 206\r attributable regional climate change, 109\r changes in ocean circulation, 483-492\r current warming attribution, 60\r drought conditions, 1577-1581, 1606\r extratropical cyclones, 1593\r extreme changes and contribution to, 1525\r extreme storm events, 1588, 1596\r for extratropical modes, 1105\r for mitigation response, 559\r for water cycle changes, 1152\r global study of streamflow trends, 1086\r heavy precipitation events, 1562\r historical model reproductions for, 425\r human-induced change, 8\r human influence on ocean, 473-478\r human influence on surges and waves, 1310\r internal variability effects on, 449\r methods for attribution, 429-430\r [...]"


id: cf00e28172de4bef6bee6e8ed863d5ff
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 33761
content:
"[...] methods used for near-term projections, 563\r of temperature extremes, 1552-1553\r optimal fingerprint regression-based methods, \r 1414\r precipitation changes with global monsoons, \r 463\r regional climate attribution examples, \r 1416-1422\r regional climate change, 11, 1414\r regional patterns of biogeochemical change, \r 488-490\r regional-scale extremes, 1540\r regional sea level change, 1290\r regional temperature change, 1367\r removing model biases for, 1416\r simulation and observed change integration, 429\r streamflow use for terrestrial water cycle, 456\r studies on flood events, 1569\r studies on Greenland Ice Sheet, 472\r studies on ocean heat content, 478\r Detection and Attribution Model \r Intercomparison Project (DAMIP), 435\r Developing Pacific region, surface O3 and \r particulate matter projections, 882\r Diabatic heating, 2164\r Diagnostic, Evaluation and Characterization \r of Klima (DECK), 224, 2100\r Dichloromethane (CH2Cl2), 843\r Dimensions of integration*\r [...]"


id: 11c5fc381bc29e281e3048d65cfabb48
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 317
content:
"[...] across Working Groups, 227\r comparison of CO2 and greenhouse gases, 241\r cumulative CO2 emissions, 240\r global warming levels as, 239, 1543\r history of scenarios in IPCC, 237\r reference scenarios and storylines, 238"


id: 8ab72036bfdfa158f6369937265f7af3
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 21576
content:
"scenario generation process, 236\r Dimethyl sulphide (DMS), 832, 858\r Dimming (solar radiation). See also Solar \r radiation modification* (SRM)\r decadal variations in past, 1791\r observational records, 925\r over Northern Hemisphere, 1077\r solar radiation decline (1950s-1980s), 938\r Direct air carbon capture with carbon storage \r (DACCS), 757, 765\r Dissolved inorganic carbon* (DIC), 723, 742\r Dissolved oxygen, 1787, 1842. See also Ocean \r deoxygenation*\r Distillation of climate information, 40, 1375\r Droughts* See also Agricultural and Ecological \r Drought and Hydrological Drought\r agricultural and ecological indices for, 1783\r agricultural and hydrological characteristics, \r 1851\r amplification by dust, 1150\r assessed variables and metrics for, 1572\r assessment for drought projections, 1606\r atmospheric-based indices for, 493, 1572\r atmospheric evaporative demand, 1571\r attribution of types of, 1579\r Australasian trends, 1987\r carbon and water cycle interactions, 697-699\r [...]"


id: 80f9d8edd21af3c2541cef74a755245e
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 19830
content:
"[...] climatic drivers of, 1074\r common metrics, types, and indices, 1760\r concurrent heatwaves and, 1600\r consecutive dry days (1960-2018) trend, 1574\r consecutive dry days, soil moisture with warming \r levels, 1584\r continental atlases for, 330\r definition and types of, 1518, 1570\r detection and attribution of, 1577, 1577-1580\r drivers of, 1073\r effects of warming over land on, 1057\r elevated CO2 effects on, 697\r emergence of signals for, 1854\r hydrological deficits, 1572, 1574\r impact in semi-arid regions, 1849\r intensity and frequency with warming levels, \r 1581\r map of drought areas, 1158\r mechanisms and drivers, 1570-1573\r meteorological changes, 1119\r model evaluation for, 1575-1577\r observed trends, 1573-1575\r precipitation deficits, 1570\r processes affecting, 1527-1529\r projections for, 1579-1584\r regional atmospheric-based drought indices, \r 1574\r regional drought projections, 1583, 1605\r regional occurrence of, 1575\r risk projection for southern Australia, 1987\r [...]"


id: 191ac458eabcb26bce288dc7eaaf9253
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 1632
content:
"[...] soil moisture deficits, 1571\r terrestrial CO2 sink effects of, 697\r water management use of indices, 1783\r Dry deposition\r CO removal from atmosphere, 841\r of sulphur dioxide, 845"


id: 6a7ab79baec29ddce2a217415257a45c
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 16014
content:
"aerosol trends over, 310\r drying in recent decades, 449\r extreme total water levels, 135\r precipitation projections for, 1971\r rainfall changes, 1968-1969\r rainfall projections, 1971\r snow and glacier projections, 1795\r temperature changes, 1968\r tropical cyclone projections, 135\r warming projections for, 1969\r East Antarctica (EAN), 2016\r East Antarctic Ice Sheet*\r mass loss in, 1263\r new projections for, 1270\r tipping/critical thresholds for, 1272\r East Asia (EAS)\r aerosol effective radiative forcing, 852\r air pollution in, 869\r atmospheric sulphate changes, 846\r atmospheric sulphur dioxide levels, 842\r distribution of short-lived climate forcers, 819\r global warming level 2degC, 1974\r greenhouse gas emissions, 713\r non-methane volatile organic compound \r emissions, 828\r observations, trends and attribution, 1973\r particulate matter emissions sources, 821\r precipitation and temperature projections, 1974\r precipitation projections for, 1973\r precipitation trends over, 1973\r [...]"


id: 18998127a66c803d80fffa8460664857
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 1541
content:
"[...] regional climate features and AR5 findings, 1973\r regional snowfall changes, 139\r short-lived climate forcers, CO2, and NO2\r emissions, 821\r short-lived climate forcer effects, 888\r summer monsoon projections, 1973\r surface ozone and particulate matter, 869, 882\r temperature projections for, 1973\r trends and projections summary, 1975\r warming trends in, 1973\r East Asian monsoon* (EAsiaM)\r AR5 findings for summer and winter circulations, \r 1973\r boreal summer manifestation, 2196\r decadal variations in winter, 1973\r projections for, 1096, 1973, 1975\r seasonality and large-scale features, 2197\r Eastern Africa see East Africa\r Eastern Asia see East Asia\r Eastern Australia (EAU), climatic features of,\r 1986\r Eastern boundary upwelling systems* (EBUS)\r large-scale wind intensification in, 1243\r productivity in food chain, 1243-1244\r sea surface temperature trends, 1223\r Eastern Europe (EEU)\r climatic features of, 1998\r winter temperature trends in, 1999\r Eastern North America (ENA)\r [...]"


id: 833d89aadb394cfc6b0ba74e32153233
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 20216
content:
"[...] climatic features of, 2004\r precipitation projections for, 2007\r East Siberia (ESB)\r aridification trend (1976-2016), 1976"


id: 5a8255cd72015d01ef64428e59f1ba10
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 13178
content:
"impacts of carbon dioxide removal methods, 764\r multivariate attribution of change in \r components, 506\r multivariate model evaluation, 508-511\r ocean circulation role in, 483-487\r perturbation effects on, 857\r response to solar radiation modification, \r 104-105\r rotation effects in, 1221\r total energy accumulation in, 929\r understanding of components and processes, 154\r Earth System Documentation (ES-DOC) project, \r 2100\r Earth System Grid Federation (ESGF)\r distributed data platform use, 224\r metadata for simulations, 1944\r regional climate data dissemination, 2089\r shared model datasets, 2100\r Earth System Model Evaluation Tool \r (ESMValTool), 224, 429\r Earth system models* (ESM)\r aerosol-cloud interactions in, 851\r aerosol-radiation interactions estimates, 949\r biogeochemistry representation in, 217\r bottom-up models for methane life assessment, \r 835\r carbon cycle projections, 730\r climate feedback in, 978\r climate forcer data sets for SSPs, 234\r climate projections, 2100\r [...]"


id: e55d891e940dd028a8d77ae1092ab148
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 37655
content:
"[...] CO2 composition experiments, 943\r drought variables, 1577\r emissions or concentration scenarios, 234\r emissions- versus concentration-driven runs, 730\r evolution of ocean carbon sink, 427\r feedback parameter (a) quantification, 858\r increased complexity of, 681\r instantaneous removal of anthropogenic or fossil \r fuel-related emissions, 865\r interactive ozone-vegetation damage in, 857\r land and ocean carbon sinks simulation, 485\r land carbon sinks simulation, 487\r methane lifetime estimates, 835\r non-CO2 biogeochemical feedback estimation, \r 820\r ocean acidification projections, 719\r ocean deoxygenation projections, 677\r participating in the CMIP6 DECK, 2101\r physical and chemical processes representation, \r 216\r precipitation deficits and trend identification, 1575\r regional climate modelling, 1389\r removal-caused net negative CO2 emissions, 622\r reproduction of dimming and brightening, 939\r scenario classification and integration, 962\r [...]"


id: ff936944a8cec301380ef92757b17daa
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 42366
content:
"[...] short-lived climate forcer future concentration \r estimates, 887\r tipping point detection, 202\r top-of-atmosphere flux estimates, 935\r types of, 215\r zero emissions commitment, 631\r East Africa\r aerosol optical depth, 290"


id: e8181212066bb99a020c5aabf1b74d2a
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 38153
content:
"E\r Early Eocene Climatic Optimum* (EECO)\r atmospheric CO2 estimates during, 299\r global mean surface temperature estimate for, \r 292\r polar amplifications in, 986\r reference period, 295\r Earth gravity, changes in, 1221\r Earth models of intermediate complexity* \r (EMIC), 219, 686\r Earth's energy budget*\r aerosol emissions in, 948\r changes for surface energy budget, 938-939\r changes in, 935\r changes in inventory, 937\r changes through time, 933\r constraints on total aerosol effective radiative \r forcing, 954\r cryospheric component assessment, 937\r direct aerosol-radiation interactions, 948\r sea level budget, 1291-1296\r Earth's surface energy budget, 938\r effective radiative forcing in, 925\r estimated energy imbalance for, 935\r forcing and response, 931\r forcing-feedback framework for, 932\r global energy inventory, 938\r global energy inventory changes, 937\r global energy inventory increase, 925\r global mean with/without cloud effects, 934\r global water cycle change, 1057\r [...]"


id: 502dc1a1aa627ae52994999423cfadd2
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 11394
content:
"[...] heat redistribution in climate system, 447\r imbalance from perturbations, 931\r imbalance in 2000s, 447\r increase in global energy inventory, 925\r internal energy flow in, 933\r latent and sensible heat fluxes, 939\r measure of global climate change, 925\r net cumulative energy change (1971-2018), 90\r observed energy increase in the Earth system \r (1971-2018), 925\r ocean heat content change contribution to, 937\r ocean heat storage in, 476\r ocean uptake of imbalance, 313\r ocean warming effects on (1971-2010), 1228\r present-day (early 21st century), 933\r present state of knowledge of, 925\r quantification of energy imbalance in, 93\r surface radiation components in, 935\r the imbalance in, 39, 937\r Earth system\r accumulation of energy in, 925"


id: 1200abc912fb2a6df3b5888528c10c79
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 354
content:
"net effect from climate drivers, 312\r net energy flux of the Earth system, 931\r of assessed climate drivers, 310\r of black carbon, 847\r of climate drivers, 290\r quantification and representation in models, \r 941-942\r quantification following perturbation, 941-953\r quantification of perturbations, 925\r short-lived gases association with, 290\r solar radiation modification by aerosols, 860\r specific forcing agents, 942-944\r stratospheric aerosol injection effects, 860\r summary by time period and driver, 960\r temporal evolution of, 311\r time evolution of 20-year mean averages, 853\r time series for SSP1-1.9, 2145\r time series for SSP1-2.6, 2146\r time series for SSP2-4.5, 2146\r time series for SSP3-7.0, 2147\r time series for SSP5-8.5, 2147\r time series of halogen components, 2148\r total solar irradiance change, 297\r versus stratospheric-temperature-adjusted \r radiative forcing, 944\r Ekman pumping, 1227\r El Chichon (1982) eruption, 327\r Elevation-dependent warming (EDW), 1849\r [...]"


id: b0e69da982b4ca618a8b5adbf33b94a6
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 28352
content:
"[...] El Nino-Southern Oscillation* (ENSO)\r 2015-2016 boreal spring and summer, 1601\r activity on multi-decadal to centennial time \r scales, 371\r amplitude changes in, 1104\r AR5 conclusions on, 371\r Australasia teleconnections, 1986\r Australian-Maritime Continent monsoon, 2199\r boreal wintertime mode, 2163\r Central America and Caribbean teleconnections, \r 1991\r changes in amplitude of variability, 580\r climate variability and, 2156\r CMIP projections for, 589\r CMIP simulations of, 495-499\r decadal variability in, 2172\r diagnostics for feedbacks in, 1395\r dominant mode of interannual variability, 610\r East Asian winter monsoon effects, 1973\r eastern and central Pacific events with, 2162\r eastern and southern African effects, 1967\r effect on interannual ocean heat transport \r variability, 1231\r effects on Aleutian Low, 2164\r effects on surface moisture levels (2015-2016), \r 330\r events related to 2015-2016 extreme, 1602\r Indian Ocean Basin mode, 2167\r [...]"


id: 730651c8a63ab3d4dbca65b9713acc19
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 17489
content:
"[...] Indian summer monsoon connection, 2171\r indices for monitoring, 2162\r influences on extreme still water levels, 1309\r in interannual variability projections, 557\r interannual time scale of variability, 579\r links to modes of variability, 2164\r model evaluation of teleconnections, 498"


id: 84f1f10410612f6ec097c70560ba0edd
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 20530
content:
"climate features of, 1975\r warming trends in, 1976, 1978\r wildfire risk in, 1976\r East Southern Africa (ESAF)\r climatic features, 1967\r tropical cyclone projections, 135, 1795\r Ecological drought. See Agricultural and \r ecological droughts*\r Economic sectors/stakeholders\r climate services for, 1433\r physical and cultural diversity of, 1461\r prioritizing metrics for use by, 172\r spatial and temporal scales of, 1431\r transdisciplinary approaches to interactions, \r 1432\r useful regional information, 1461\r Ecosystems*, large-scale indicator of change, \r 314\r Eddy\r exchanges between gyres, 1234, 2156\r large eddy simulation, 972\r transient activity of, 2158\r Effective feedback parameter, 933\r Effective radiative forcing* (ERF). See Aerosol \r effective radiative forcing* (ERFari+aci); \r aerosol-radiation interactions* (ERFari)\r 2100 level in scenarios, 555\r aerosol changes (1850 and recent-past), 852\r aerosol-radiation interaction effects on, 948, \r 950\r aerosols in historical period, 819\r [...]"


id: 7b18d6f98668e94e43b495a76be9fdc7
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 18858
content:
"[...] altitude of ozone changes, 306\r assessment in AR5, 941\r assessment summary, 959\r attributable to emissions versus short-lived \r climate forcers, 853\r aviation contribution to, 867\r changes with COVID-19, 876\r climate forcers (1750-2019), 2144\r climate forcers assessed in AR6, 2140\r climate outcomes with overshoot, 617\r cloud seeding effects on, 860\r component emissions and global surface \r temperature, 92\r contribution of CO2, N2O, and halogenated, 713\r distribution of net aerosol forcing (1850-2014), \r 819\r emissions- versus abundance-based, 853-855\r estimates from biophysical processes, 310\r evidence for net aerosol forcing, 955\r forcing agents (1750 to 2019), 959\r for CO2 doubling, 925\r for industrial era (1750-2019), 926\r for stratospheric water vapour, 947\r from component emissions (1750-2019), 854\r from solar activity (since late 19th century), 298\r historical timeseries of (1750-2019), 962\r instantaneous and cloud water content \r adjustments, 948\r [...]"


id: db878f8f5aaa23327d2e434c2f9b7393
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 35903
content:
"[...] land-use change effects, 290, 310\r linear and cirrus contrails, 867\r natural versus anthropogenic drivers (since \r 1750), 290"


id: af0662aaf6c0bdbb53d36fe4c7a6fe46
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 28039
content:
"snow and ice trends for, 1802, 1999\r surface ozone concentration changes, 882\r asian Ice Sheet, 1294\r ope\r aerosol effective radiative forcing, 852\r aerosol forcing of climate in, 2001\r agricultural and ecological droughts, 1823\r air pollution in, 869\r ammonia trends, 842\r anthropogenic aerosol effects, 2000\r anthropogenic aerosol trends in, 2003\r AR5 assessment, 1998\r assessment and synthesis of projections, 2001\r Atlantic Multi-decadal Variability effects, 2176\r Atlantic storm track effects on, 1998\r Atlantic Zonal Mode effects, 2171\r atmospheric sulphur dioxide reduction \r (1990-2000), 843\r black carbon model estimates, 848\r carbonaceous aerosol emissions in, 828\r climatic impact-driver change projections, 140, \r 1821, 1827\r climatic reference regions, 1998\r coastal and ocean climatic impact-driver \r changes, 141, 1826, 1828\r compound events observations and projections, \r 1828\r drought observed trends and warming level \r projections, 1686\r extratropical cyclones projections, 1998\r [...]"


id: b819f07ad1b10f30cf5a6a0fcc0a62a2
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 29605
content:
"[...] extreme precipitation increase (since 1950s), \r 1559\r extreme precipitation projections, 1566\r fossil fuel emissions, 829\r glacier mass change projections, 1825\r glacier mass loss estimates for Alps, 1275\r glacier mass loss projections, 1998\r greenhouse gas emissions for, 713\r heat and cold changes, 141, 1820\r heavy precipitation trends and warming level \r projections, 1684\r hot and cold extremes projections, 1556\r large-scale patterns of precipitation, 1999\r maximum temperature and heatwave changes, \r 1549\r model estimates of temperature trends, 2001\r model performance assessment, 2001\r NO2 decreases, 839\r observations, trends and attribution synthesis, 1998\r ozone-induced gross-primary productivity losses, \r 857\r particulate matter emissions sources in, 821\r present day 1-in-100-year extreme total water \r level and projections, 1826\r recent precipitation trends in, 1999\r regional climate and main hazard assessment, \r 1820-1829\r regional climate features, 1998\r [...]"


id: 40261b279f8b199742126182b6f29119
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 8475
content:
"[...] regional model performance for, 2001\r regional precipitation trends, 1999, 2003\r regional solar radiation projections, 1791\r regional surface air temperature change over \r land, 2003"


id: d25279315557e7b27a0a9a26d860faa6
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 34357
content:
"regional temperature and rainfall changes, 1998\r regional temperature extreme studies for, 1552\r regional temperature projections for, 2003\r regional wind projections, 141\r river floods changes, 1824\r seasonal signal and precipitation gradient, 2002\r short-lived climate forcer effects in, 888\r short-lived climate forcers distribution, 819\r snow and ice changes, 141, 1825\r snow cover extent projections, 1825, 1998\r snow depth and snow water equivalent changes, \r 1825\r snowfall/snow melt trends and projections, \r 1999, 2003\r snow projections for, 2002\r solar radiation changes in, 938\r sulphate aerosol concentrations, 846\r sulphate aerosols in, 845\r sulphur dioxide emissions over, 844\r sulphur dioxide reductions in, 842\r sulphur trends (1990-2015), 846\r summary of changes, 126, 140, 2003\r surface air temperature trends (1980-2015), 2000\r surface ozone concentration changes, 882\r surface shortwave radiation trends, 1999\r temperature and precipitation projections, 2003\r [...]"


id: 1da80a6c096c19c154751d9c2b79cd4f
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 2403
content:
"[...] temperature and precipitation trends \r (1980-2015), 1999\r temperature extreme trends and warming level \r projections, 1680\r temperature trends in subdomains, 1999\r warming level projections, 1998\r wet and dry climate impact-driver changes, 1822\r wind changes, 1824\r wind speed changes, 1824\r European Centre for Medium-Range Weather \r Forecasts (ECMWF), 431\r European Space Agency Climate Change \r Initiative (ESA CCI), 1382\r Eutrophication*\r anthropogenic mechanisms, 721\r carbonate chemistry effects, 720\r exacerbation of coastal ocean acidification, 721\r from inadvertent fertilization, 857\r in Baltic Sea, 722\r in costal tropical waters, 721\r Evaluation diagnostics (for models), 1395\r Evaporation* minus precipitation (E-P) \r patterns, 1225\r Evapotranspiration* (ET)\r atmospheric water demand effects, 1058\r change in terrestrial (since 1980), 1084\r changes in, 1084\r drivers of change in, 1116\r in regional droughts, 1575\r linear trends in annual means, 1085\r projections for, 1116\r [...]"


id: 5384cbf1d605c3cf3d913dec57fc370
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 11969
content:
"[...] quantification of, 1571\r Exposure* in risk assessment, 201\r External forcing, effects on variability and \r teleconnections, 2155\r Extratropical cloud optical depth feedback, 974"


id: 91966883c896310916905d6ef7aa6656
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 22840
content:
"atmospheric CO2 effect on land storage, 677\r mineral nitrogen application, 828\r perturbation of nitrogen cycle by, 708\r reactive nitrogen deposition in forests, 857\r seasonal cycle of atmospheric CO2, 427\r fth Assessment Report (AR5)\r 20-year baseline (1986-2005), 1935\r advances in assessment on uncertainty since, \r 559\r air quality-climate interaction, 825\r anthropogenic effects on Arctic sea ice, 466-467\r Arctic sea ice coverage projections with RCPs, \r 574, 586\r assessed magnitude of aerosol-cloud \r interaction, 926\r assessment of warming potential of human \r emissions, 180\r assessment on climate system warming, 157\r carbon-cycle response for non-CO2 gases, 1017\r carbon-cycle response in 21st century, 741\r changes in circulation features, 1101\r changes in tropospheric and stratospheric \r temperatures, 327\r climate change effects on land carbon uptake, \r 724\r climate-DMS feedback parameter estimation, \r 858\r climate driver changes over industrial period, \r 310\r [...]"


id: 2ea50a406de75c07d8386879b19c7c62
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 5485
content:
"[...] climate-fire feedback assessment, 859\r cloud microphysical properties in, 951\r CMIP5 models of sea surface temperature \r biases, 1223\r confidence of model reproductions of \r temperature, 433\r contribution of anthropogenic forcings to \r warming, 443\r contribution of internal variability to warming \r (1951-2010), 443\r correlation between Northern Hemisphere snow \r cover extent (SCE), 470\r cumulative emissions of CO2 and warming, 742\r decadal climate predictions from observed \r climate state, 563\r deep ocean warming, 1228\r determining carbon budgets, 749\r Earth system components susceptible to abrupt \r change/tipping, 634\r effective radiative forcing estimates, 925\r effective radiative forcing introduction, 931\r effects of air pollution reduction on warming, \r 591\r effects of ozone on plant physiology and carbon \r storage, 857\r El Nino-Southern Oscillation climate effects, 589\r equilibrium climate sensitivity range, 1006\r evidence for human influence, 425\r [...]"


id: a29b23c1beb2a8386ed34f1c9a6670db
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 25826
content:
"[...] factors in marine aerosol formation, 832\r feedback of cloud regimes in, 972\r glacier change assessment, 1273\r global energy inventory increase, 937\r global warming potential and global \r temperature change potential estimatesof927"


id: b96c1e7603430a40af4eb979e654ae5f
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 29836
content:
"tidal constituent effects on, 1309\r tides, surges and waves, 1309\r Extreme still water level* (ESWL)\r causes of changes in, 1309\r historical occurrences of extreme, 1311\r in sea level change, 1221\r observed trends in, 1309\r projections of amplification factors, 1313\r Extreme storms\r quantifying effects of climate change, \r 1583-1599\r rain rates with, 1519\r severe wind storms, 1784\r tropical cyclones, 1519\r Extreme total water level* (ETWL)\r components of, 1310\r defined, 1221\r frequency projections, 1852\r hazards associated with, 1786\r index for episodic coastal flooding, 2211\r Extreme weather events*\r AR6 use of \"extreme\", 201\r as climatic impact-drivers, 201, 1521\r attribution of, 1523\r attribution to human influence in AR6, 42\r confidence in changes, 1517\r definition of, 1522\r incremental warming effects on, 15\r indices for, 2208\r indices selection for, 2207\r maps for extreme conditions, 1609\r modes of variability influence on, 1962\r Extreme winds\r climatic impact-drivers, 1784\r [...]"


id: f306e81298acaed9064e79e52b123425
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 40143
content:
"[...] definition of, 1597\r in extreme storm characteristics, 1598\r observed intensity changes in, 1598"


id: 7013211e48e56d2dd22dea4cb248003
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 16970
content:
"Extratropical cyclones* (ETCs)\r annual anomalies in numbers, 1102\r changes in intensity, 1101\r detection and attribution, 1593\r extreme sea level events, 1310\r future projections for, 606\r model evaluation, 1396, 1592\r observed trends, 1592\r precipitation changes with warming, 1058\r precipitation with, 1132\r rain rate changes, 1519\r uncertainties in projections, 1133\r wind speed changes, 1519\r Extratropical jets* and cyclone tracks\r effects of, 463\r shift in tracks of, 464\r storm tracks, and blocking, 338\r Extratropical modes, 1105\r Extreme Coastal Water Level* (ECWL)\r contribution to sea level change, 1221\r impacts of, 1786\r swash contribution to extreme sea level, 1310\r Extreme heat\r African regional changes, 1791\r Asian regional changes, 1799\r Australasian changes, 1807\r Central and South American changes, 1815\r European changes for, 1820\r global projections for, 1851\r health and agricultural impact and risks, 1780\r hot temperature threshold changes, 1793\r [...]"


id: e424d69af8fdbfbe9255647285111120
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 37440
content:
"[...] impact in polar regions, 1844\r North American changes, 1828\r projections for, 1518, 1555\r scenarios in arid and semi-arid regions, 1849\r signal emergence in land regions, 1854\r Small Islands changes, 1837\r societies, health, and habitability impact, 1849\r thresholds crossing projections for, 1851\r Extreme precipitation. See also Heavy \r Precipitation\r attribution of anthropogenic influence, 457, \r 1563\r changes with warming levels, 1564\r from orographic effects in mountains, 1850\r human influence attribution, 457\r increase in (since 1950s), 1558\r observed and simulated changes, 458\r projections with warming levels, 1531\r temperature increase effects on, 1526\r thermodynamic and dynamic processes, 1557\r Extreme river flow trends, 1569\r Extreme sea level* (ESL)\r defined, 1221\r factors contributing to, 2211\r localized storm surge processes, 1310\r methods and projections for, 1312\r modelled trends in, 1309\r past changes in, 1309\r regional projections, 1853\r [...]"


id: 5ab73ff49565672f8095fb6fbcffa8cc
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 13213
content:
"327\r Freezing Level Height (FLH), elevation-warming \r and, 1850\r Frequently Asked Questions (FAQs)\r Are climate models improving?, 519\r At a given level of global warming, what are the \r spatial patterns of climate change?, 644\r Atlantic Meridional Overturning Circulation, 1321\r Can continued melting of the Greenland and \r Antarctic ice sheets be reversed?, 1316\r Can thawing permafrost substantially increase \r global warming?, 773, 774\r Clouds - What is the role in a warming climate?, \r 1022, 1023\r Could climate change be reversed by removing \r carbon dioxide from the atmosphere?, 775\r Did climate change cause that recent extreme \r event in my country?, 1610, 1611\r Do we understand climate change better now \r compared to when the IPCC started?, 244\r How can we provide useful climate information \r for regional stakeholders?, 1460\r How do changes in climate extremes compare \r with changes in climate averages?, 1608\r How does land use change alter the water \r cycle?, 1153, 1154\r [...]"


id: 83735787eb63a3770c7f7c7bffa8073c
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 20262
content:
"[...] How do we know humans are responsible for \r climate change?, 515\r How is the current warming any different?, 378\r How much will sea level rise in the next few \r decades?, 1318\r How quickly would we see the effects of \r reducing carbon dioxide emissions?, 642\r How will climate change affect the regional \r characteristics of a climate hazard?, 1875\r How will the climate change over the next \r twenty years?, 640\r Is the natural removal of carbon from the \r atmosphere weakening?, 771\r The Earth's temperature has varied before. How \r is the current warming any different?, 378\r What are carbon budgets?, 777\r What are climatic thresholds and why are they \r important?, 1873\r What are short-lived climate forcers and how do \r they affect the climate?, 893\r What are the links between limiting climate \r change and improving air quality?, 895\r What can past climate teach us about the \r future?, 248, 249\r What causes droughts, and will climate change \r make them worse?, 1157\r [...]"


id: d6c0b4065cc65b9cb370f26b2ad000f6
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 35091
content:
"human influence on, 5\r in Northern Hemisphere systems, 471\r in the Andes Cordillera, 140\r meltwater dispersion from, 1220\r New Zealand, 1987\r North American regional changes, 1833\r polar and high mountain ecosystems, 1785\r South Asia, 1978, 1979\r GlacierMIP Project\r contribution to sea level rise, 1263\r emulations used for projections, 1299\r loss projected in South America, 1818\r model intercomparisons, 2108, 2111\r modelling for retreat, 471\r Glacier response time, 1220\r Glaciers* and ice sheets*\r anthropogenic influence on, 471\r attribution of large-scale changes, 471-474\r bathymetry requirements for projections, 1257\r committed changes in, 21\r drivers of change in, 1276\r extent and mass change, 1273-1274\r GlacierMIP projections, 1299\r global and regional mass change rate \r (1960-2019), 1274\r global and regional mass evolution \r (1901-2100), 1277\r global changes in, 1273, 1275\r global mass loss projections, 1122, 1215, 1852\r ice loss projections for, 42\r mass loss of, 158\r [...]"


id: d5f8b8c3bf3d5c48bc861a110e461abf
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 34963
content:
"[...] model evaluation, 1278\r model intercomparison studies, 2108\r New Zealand projections, 1811\r ocean temperatures and calving rate, 1257\r polar region changes, 1847\r polar terrestrial changes, 1845\r projections for, 1122\r projections for global glacier mass, 1278\r regional changes in, 1275-1277\r sea level change contribution, 482, 1089, 1220, \r 1279\r simulations of mass changes, 471\r West Antarctic Ice Sheet, 1265\r Global budgets of organic aerosols, 831\r Global Carbon Project (GCP), 695, 699\r Global climate change projections\r assessment of air temperature projections, \r 580-581\r atmospheric blocking, 607\r attribution and projections for extremes, 1529\r climate policy implications, 612\r CMIP6 Annular Mode index change, 610\r CMIP6 global surface air temperature \r simulations, 570\r CMIP scenario simulations for precipitation, 584\r commitment and change (beyond 2100), 557, \r 630\r confidence in climatic impact-drivers change, \r 1853\r cryosphere, ocean, and biosphere, 574, 586\r [...]"


id: e1ebeca76095f90e31af6a56072acfe0
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 37031
content:
"global mean sea level projections (2081-2100), \r 576\r global monsoon projections, 585, 603\r historical land-cover change in, 310\r historical simulations, 570\r Indian Ocean Basin and Dipole Modes, 611\r indicators of change, 22\r indicators of change from simulations, 571\r initialized predictions (2019-2028), 569\r key indicators for, 290\r large-scale circulation and modes of variability, \r 556\r linking global to regional change, 1363-1365\r mid- to long-term change, 595-614\r mitigation response, 557\r model agreement and spread display, 567\r models used for, 561\r modes of variability, 609\r modes of variability in assessment, 578-579\r near-surface relative humidity, 600\r near-term changes, 583\r near-term information sources, 563\r Northern and Southern Annular Modes, 609\r ocean, 608\r ocean and cryosphere, 557\r ocean and land carbon flux projections, 587\r ocean and land carbon uptake (RCP/SSP), 576\r Pacific Decadal Variability, 611\r past regional evaluations, 186\r pattern scaling, 565\r [...]"


id: d52d2ab268ed780d404a5189b17558ea
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 34827
content:
"[...] precipitation projections, 556, 601\r projected global climate indices changes (21st \r century), 570\r projections specific warming levels, 612\r response to mitigation, 619\r sea ice extent and external drivers, 575\r seasonal warming patterns, 597-601\r simulations for two indicators (1995-2040), 641\r solar radiation modification response, 619\r SSP scenarios, 232, 555\r temperature patterns for warming levels, 614\r temperature variability projections, 597\r time-shift method for representation, 565\r uncertainty quantification, 566\r Global climate models (GCMs)\r atmospheric global climate models, 1389\r climate change projections for Europe, 2002\r equilibrium climate sensitivity and transient \r climate response in, 1007\r feedback parameter in estimations, 968\r regional climate models, 1389\r water cycle projections, 1058\r Global Climate Observing System (GCOS) \r program, 312, 1382\r Global energy budget. See Earth's energy budget*\r Global energy budget and inventory\r [...]"


id: d4e02473ca22643b169e82ab6256269c
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 34764
content:
"[...] and sea level budget, 1291-1293\r assessed changes in, 937, 938\r change in, 937\r components of, 938\r energy change (1971-2018), 939, 940\r equilibrium climate sensitivity estimates, \r 995-1004"


id: e65b6b69deb372d04d03bd0467306214
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 37832
content:
"What is equilibrium climate sensitivity and how \r does it relate to future warming?, 1024, 1025\r What is natural variability and how has it \r influenced recent climate changes?, 517\r What is the Earth's energy budget, and what \r does it tell us about climate change?, 1020, \r 1021\r What is the evidence for climate change?, 380, \r 381\r What is the role of clouds in a warming climate? \r 1022\r Where is climate change most apparent?, 246, 247\r Why are cities hotspots of global warming?, \r 1462, 1463\r Will floods become more severe or more \r frequent as a result of climate change?, 1155\r Will the Gulf Stream shut down?, 1320\r Will unprecedented extremes occur as a result of \r human-induced climate change?, 1610\r Freshening\r in Baltic, 2002\r Pacific and Southern Ocean projections, 143\r Southern Hemisphere subtropics, 1235\r Freshwater reservoirs\r changes in, 1089-1092\r Greenland and Antarctic ice sheets as, 1220\r groundwater, 1123\r in sea level change, 1220\r methane from wetlands, 704\r [...]"


id: b1fcbd3f648073342358f278ee0f05e4
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 28537
content:
"[...] terrestrial water storage trends, 1092\r wetlands and lakes, 1123\r Frost, 1793. See also Cold spells and frost\r Frost days (FD) index, 2210\r Future Resilience for African CiTies And Lands \r (FRACTAL) project, 1867"


id: 609c09eb46a11007d103a6a845b627d5
created_at: 2023-04-19 13:22:49
updated_at: 2023-04-19 13:22:49
content_type: text
index: climate_gpt_v2
vector_id: 14295
content:
"SSP-based projections, 1302-1304\r thermosteric and halosteric changes, 1220\r threshold exceedance projections, 1304, 1305\r time scales and scenario changes, 78\r Global mean surface temperature* (GMST)\r anomalies in simulations and observations, 1009\r anthropogenic influence attribution, 442\r attributable to human influence (1951-2010), \r 428\r climate system warming indicator, 157\r compared to global surface air temperature, 291\r cumulative CO2 emissions and remaining \r carbon budget, 97\r current interglacial period estimates, 317\r effects on sea level, 160\r five-year trend (2016-2020), 426\r history and key findings, 316\r Holocene prior to industrialization, 315\r human activity effects (2006-2015), 187\r improved estimation of, 325\r in situ data products considered in AR6, 325\r Last Glacial Maximum reconstructions, 315\r long-term context of anthropogenic effects, 159\r long-term effects of CO2 emissions, 160\r observed increase in, 326\r paleoclimate reference periods, 294-296\r [...]"


id: 130d3e16f60bd6ec45a2abd8953bd4e0
created_at: 2023-04-19 13:22:50
updated_at: 2023-04-19 13:22:50
content_type: text
index: climate_gpt_v2
vector_id: 566
content:
"[...] past 60 million years, 296\r rate of increase, 290\r rate of increase (1998-2012), 447\r rate of increase (1998-2012 versus 1951-2012), \r 425\r reconstruction (past 800 kyr), 160\r reconstructions for last millennium, 317\r response to mitigation, 621\r updates to observational dataset, 446\r Global mean thermosteric sea level (GMTSL)\r anthropogenic forcings, 481\r change in, 1220\r from ocean heat content increase, 1244\r Global monsoon* (GM) See also Regional \r Monsoon\r AR5 report on, 337, 1094\r CMIP projections for, 585\r global and regional areas of, 2195\r global and regional trends and projections, 119, \r 1094, 1127\r history of concept, 2195\r human influence on, 461\r identification of, 2195\r land-ocean heat contrast effects on, 1528\r land precipitation changes and projections, \r 118-121\r land precipitation changes in, 9\r land precipitation projections, 1127\r mean water cycle projections for mid- and long\u0002term, 1128\r onset and precipitation projections, 19\r [...]"


id: af6af0807a0860b1e8c4daa19d045908
created_at: 2023-04-19 13:22:50
updated_at: 2023-04-19 13:22:50
content_type: text
index: climate_gpt_v2
vector_id: 28091
content:
"[...] paleoclimate records and projections, 118\r rationale and definition of regional, 2195\r summer precipitation changes in, 462\r volcanic eruption effects on, 24\r wet season changes with warming, 1114"


id: e6b7cdd1bafc0bede16104692d1f043a
created_at: 2023-04-19 13:22:50
updated_at: 2023-04-19 13:22:50
content_type: text
index: climate_gpt_v2
vector_id: 37889
content:
"projections with CMIP scenario simulations, \r 580-582\r quantification of surface temperature change, \r 929\r rate of increase (1998-2012), 447\r recent increases in, 5\r regional emissions of short-lived climate forcers, \r 887\r relationship to carbon dioxide emissions, 678\r relation to global mean surface temperature, 291\r response at 10 and 100 years after pulse, 870\r response to abrupt emissions reduction, 865\r response to mitigation, 621, 865\r response to perturbations, 931\r response to pulse of current emissions, 868\r response to short-lived climate forcers, 820\r scenario-based projections for, 571\r scenario-dependence of, 632\r seasonal contrast and regional differences, 556\r short-lived climate forcers and \r hydrofluorocarbons, 886\r simulated internal variability versus observed \r change, 437\r spatial patterns of surface warming, 583\r threshold crossing time for, 582\r total human-forced change (1750-2019), 927\r variance and skewness changes, 597\r [...]"


id: febd8a552186e13cffc81cfbc084e5b2
created_at: 2023-04-19 13:22:50
updated_at: 2023-04-19 13:22:50
content_type: text
index: climate_gpt_v2
vector_id: 42206
content:
"[...] warming attributable to human influence, 442\r Global surface temperature*\r 10 and 100 years after pulse, 102\r 15-year trends, 448\r 20-year averaged change, 63\r annual, decadal and multi-decadal variations, \r 518\r anthropogenic factors in warming, 150\r AR5 limitation of datasets, 324\r AR6-assessed range and model-simulated, 45\r assessment of global surface air temperature* \r (GSAT) and global mean surface temperature \r (GMST), 317\r confidence in change, 430\r deep past (65 Ma to 8 ka), 314\r during the instrumental period, 323\r effects on regional water cycle, 1071\r estimated from proxy records and models, 46\r future changes, 60-62\r historical changes in, 60\r history and future with key findings, 61\r human influence on, 430-443\r human influence on current warming, 60\r land domain, 324\r marine domain, 323\r mid- and long-term change, 595\r model evaluation, 431-438\r near-term change in, 584\r past, current and future changes, 59-64\r post-glacial period (past 7000 years), 315\r [...]"


id: 6ee1aa8e6e01b4d5152c5e614d541d04
created_at: 2023-04-19 13:22:50
updated_at: 2023-04-19 13:22:50
content_type: text
index: climate_gpt_v2
vector_id: 16781
content:
"[...] rate of increase since 1970, 8\r response for forcers with short lifetimes, 819\r scenario projections for, 14\r seasonal warming pattern changes, 597\r Global surface warming (early 21st century), \r 445"


id: 6fc4c5686bbf1dd8f92554fe6bd6448a
created_at: 2023-04-19 13:22:50
updated_at: 2023-04-19 13:22:50
content_type: text
index: climate_gpt_v2
vector_id: 16925
content:
"regional effects of increase in, 24-25\r remaining carbon budget and, 751\r sectors contributing most to, 821\r short-lived climate forcers contribution to, 885\r surface warming for land and ocean, 556\r temperature signal emergence over land, 150\r temporary slowing (1998-2012), 447\r warming since mid-20th century, 438\r water vapour and lapse rate feedback, 95\r Global warming level 1.5degC\r Atlas presentation of, 1936\r austral-winter rainfall projections, 615\r greenhouse gas emissions levels, 15\r precipitation projections, 573\r threshold-crossing time, 42, 582\r Global warming level 2degC\r Atlas presentation of, 1936\r biodiversity hotspots change, 143\r climatic impact-driver changes, 24-25\r confidence in changes and projections, 1529\r greenhouse gas scenarios, 14\r precipitation changes with, 1544\r projection with high emissions, 582\r regional change projections, 135\r tropical cyclone projections for, 2009\r Global warming levels (GWLs)\r 20-year periods at specific warming levels, 2210\r [...]"


id: ccf70bf81526eae203a780c47c83a7d0
created_at: 2023-04-19 13:22:50
updated_at: 2023-04-19 13:22:50
content_type: text
index: climate_gpt_v2
vector_id: 33403
content:
"[...] agriculture contribution to, 866\r analysis framework for, 967\r as dimension of integration, 55\r assessing past projections, 185\r assessments based on warming levels, 1546\r basin-scale runoff with increases in, 1147\r calculation of, 1936\r climate extremes projections, 1541\r CMIP model scenario simulations for global \r surface air temperature, 572\r constraints on projections, 1008\r contrast of wet and dry seasons with, 1114\r cryosphere and sea level rise (1.5degC and 2degC \r warming), 1217\r cryospheric change (2degC, 3degC and 5degC), 1217\r drought frequency, 1519\r effects of first versus second 2 degrees on \r precipitation, 1145\r emissions scenarios, regional climate response, \r and impacts, 1543\r expectations with CO2 emissions levels, 618\r extended scenarios (to 2300), 558\r extreme change projections, 1533\r extreme changes with, 1522\r extreme precipitation projections, 1531\r for information integration, 227\r hazard assessment for, 1857\r heavy precipitation changes with, 1566\r [...]"


id: 966f994c01a1e4abceed2b064af93a48
created_at: 2023-04-19 13:22:50
updated_at: 2023-04-19 13:22:50
content_type: text
index: climate_gpt_v2
vector_id: 23668
content:
"[...] hot and cold extremes with, 1518\r human-caused warming (to 2017), 187\r information for end users, 1542\r land-related changes as function of level, 83\r land versus ocean and latitude differences, 613\r levels used in report, 1936\r low-likelihood, high-impact extreme changes, \r 1535"


id: 527937fa3e9e98a1b84c086ef82b1fbf
created_at: 2023-04-19 13:22:50
updated_at: 2023-04-19 13:22:50
content_type: text
index: climate_gpt_v2
vector_id: 11794
content:
"Global temperature change potential (GTP), \r 220, 927\r Global Terrestrial Network for Glaciers, 210\r Global Terrestrial Network for Permafrost, 210\r Global Tide and Surge Reanalysis (GTSR), 1309\r Global total column water vapour content \r change, 291\r Global warming*\r 21st century projections, 1931\r air pollution control in mitigation, 821\r amplification of greenhouse gas-induced \r changes, 1059\r assessed human influence on, 440\r atmospheric moisture and precipitation effects, \r 1057\r attribution of, 7, 438\r changes in regional climate variability, 1960\r changes in year-to-year variability, 598\r climatic impact-driver evolution with warming, \r 1777\r cumulative emissions and mean warming, 240\r defined, 39\r effective radiative forcing values with, 290\r effects of CO2 and short-lived climate forcers, \r 821\r effects of methane emissions, 821\r emissions pathways to limit warming, 189\r equator-to-pole amplification of, 1554\r estimated warming to date, 323\r estimate of human-induced, 425\r [...]"


id: 8cb271e808e9620e456c715754a30a3
created_at: 2023-04-19 13:22:50
updated_at: 2023-04-19 13:22:50
content_type: text
index: climate_gpt_v2
vector_id: 21939
content:
"[...] first versus second 2degC of warming, 1145\r history and temperature change, 6\r impact on climatic impact-drivers, 1771\r incremental warming effects, 15\r Kigali Amendment enforcement, 873\r land-use change effects, 310\r loss of frozen water stores, 1072\r low oxygen zone changes with, 1842\r mid-19th century beginning, 161\r modes of variability in projections, 1557\r N2O response to, 677\r Nationally Determined Contribution submissions, \r 150\r net zero anthropogenic emissions to halt, 678\r new estimates and implications, 317\r nitrogen fixation in non-agricultural ecosystems, \r 830\r observed warming (1850-2019), 516\r observed warming to date, 41\r ocean warming contribution, 938\r offset by cessation of emissions, 630\r overshooting global warming levels, 558\r over the instrumental period, 191\r precipitation and evaporation increase with, \r 1057\r precipitation changes with, 616\r projected transient warming, 1010\r projections and observations on, 150\r [...]"


id: 1712fc2dbe04e78c1060f185d8baf784
created_at: 2023-04-19 13:22:50
updated_at: 2023-04-19 13:22:50
content_type: text
index: climate_gpt_v2
vector_id: 1279
content:
"[...] projections for short-lived climate forcer \r emissions, 820\r rate in early 21st century, 447"


id: 8e477c9249121828b0baf4f7b4b66848
created_at: 2023-04-19 13:22:50
updated_at: 2023-04-19 13:22:50
content_type: text
index: climate_gpt_v2
vector_id: 22223
content:
"land monsoon precipitation changes, 9\r land precipitation scenario projections, 19\r lifetimes, radiative efficiencies and metrics for, 833\r likelihood of 2degC warming, 14\r long-lived versus short-lived, 1014\r natural emissions in ocean-climate feedback, \r 719\r net zero emissions for, 30, 101, 928\r paleoclimate concentration measurement, 684\r projections for concentrations, 2140\r radiative perturbations due to, 944\r regional distribution of CO2, CH4, N2O (net \r fluxes), 713\r relation to cumulative CO2 emissions, 240\r release from permafrost thaw (21st century), 740\r scenarios for projections, 12\r simulated temperature response, 445\r temperature change with low emissions, 15\r Greenhouse Gases Observing Satellite (GOSAT), \r 209, 689\r Greening\r CMIP model simulations of, 1150\r CO2 radiative forcing of, 1150\r evapotranspiration trends and, 1084\r land surface and precipitation feedbacks, 1150\r of the Sahara and the Sahel region, 1150\r Greenland and Iceland (GIC)\r [...]"


id: db5dea1f48917dc20bddfc4a2efddf1b
created_at: 2023-04-19 13:22:50
updated_at: 2023-04-19 13:22:50
content_type: text
index: climate_gpt_v2
vector_id: 35866
content:
"[...] contribution to sea level (in 2300), 1260\r glacier mass loss estimates, 1275\r ice core data on atmospheric sulphur, 846\r in Arctic region, 2022\r in polar climate regions, 2016\r models used for intercomparison studies, 2108\r Greenland Blocking Index, 1257\r Greenland Ice Sheet* (GrIS)\r anthropogenic contribution to melting, 472-552\r contribution to sea level (in 2100), 1259-1261\r contribution to sea level rise, 1220\r cumulative emissions effects, 21\r feedback associated with, 977\r high-end warming scenarios, 1308\r human influence on melting, 5\r Last Interglacial period response, 1294\r low confidence projections, 1299\r mass change and change rate for, 1255\r mass change and sea level contribution, 1256\r mass loss (1992-2020), 1215\r mass loss from, 187, 1260\r mass loss (since 2000), 291\r mass loss through 21st century, 188\r model evaluation for, 1258-1259\r projected contribution to sea level (by 2100), \r 1260\r projections (beyond 2100), 1260-1261\r recent observed changes, 1254-1257\r [...]"


id: 7e63df96ca150456f94b61a34b754227
created_at: 2023-04-19 13:22:50
updated_at: 2023-04-19 13:22:50
content_type: text
index: climate_gpt_v2
vector_id: 19400
content:
"[...] response to stabilization of warming, 106\r sea level change drivers, 1298\r sulphate records from, 298\r surface mass balance loss in, 1254\r thresholds and tipping points, 1261\r time series of annual surface mass balance rate, \r 2020\r uncertainties in future change, 1314"


id: f0231096906f544b13f120b826ee24c
created_at: 2023-04-19 13:22:50
updated_at: 2023-04-19 13:22:50
content_type: text
index: climate_gpt_v2
vector_id: 39631
content:
"Hadley circulation* (HC)\r AR5 report on trends in strength, 335\r effects on tropical cyclones, 1528\r extent and intensity trends, 336\r human influence on, 459, 461\r Northern and Southern hemisphere changes, 459\r poleward expansion of, 426, 606, 1528\r poleward shift of dryland areas, 1849\r stratospheric ozone depletion effects \r (1981-2000), 459, 1124, 1373\r strength and widening, 1093\r Hail \r Asian changes, 1803\r European changes, 1825\r frequency and distribution changes, 1771, 1785\r North American change, 1834\r Halogenated greenhouse gases\r effective radiative efficiencies, 946\r effective radiative forcing increase, 947\r natural sources, 304\r ozone-depleting substances in, 304\r summary of changes in, 305\r types and current status of, 304\r Halons, historical abundances forcing, 843, 2143\r Halosteric sea level change* defined, 1220\r Hazards*. See also Climate hazards and \r consequences\r characteristics of, 1875\r defined for AR6, 201\r Heat extremes. See Extreme heat\r Heat index* (HI)\r [...]"


id: 21f7300ce9c885ef41a5f045657cbb5d
created_at: 2023-04-19 13:22:50
updated_at: 2023-04-19 13:22:50
content_type: text
index: climate_gpt_v2
vector_id: 3202
content:
"[...] calculation of, 2209\r definition of, 2207\r mapping threshold exceedances, 2210\r threshold relation to emissions levels, 132\r Heating degree days (HD), 2210\r Heat stress* and Heatwaves*\r African regional changes, 1613, 1791\r Asian regional changes, 1631, 1799\r Australasian changes, 1655, 1807\r Central and South American changes, 1665, \r 1815\r European changes for, 1680, 1820\r expected increase in, 1851\r extreme ozone and pollution effects, 864\r in Antarctica, 1844"


